.
diff --git a/README.md b/README.md
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+---
+title: Flux Illusion Diffusion
+emoji: 🚀
+colorFrom: indigo
+colorTo: gray
+sdk: gradio
+sdk_version: 5.7.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: Optical illusions and style transfer with FLUX
+---
+
+
+# ComfyUI
+**The most powerful and modular diffusion model GUI and backend.**
+
+
+[![Website][website-shield]][website-url]
+[![Dynamic JSON Badge][discord-shield]][discord-url]
+[![Matrix][matrix-shield]][matrix-url]
+
+[![][github-release-shield]][github-release-link]
+[![][github-release-date-shield]][github-release-link]
+[![][github-downloads-shield]][github-downloads-link]
+[![][github-downloads-latest-shield]][github-downloads-link]
+
+[matrix-shield]: https://img.shields.io/badge/Matrix-000000?style=flat&logo=matrix&logoColor=white
+[matrix-url]: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
+[website-shield]: https://img.shields.io/badge/ComfyOrg-4285F4?style=flat
+[website-url]: https://www.comfy.org/
+
+[discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total
+[discord-url]: https://www.comfy.org/discord
+
+[github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver
+[github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases
+[github-release-date-shield]: https://img.shields.io/github/release-date/comfyanonymous/ComfyUI?style=flat
+[github-downloads-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/total?style=flat
+[github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
+[github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
+
+![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
+
+
+This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
+### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
+
+### [Installing ComfyUI](#installing)
+
+## Features
+- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
+- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
+- [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
+- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
+- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
+- Asynchronous Queue system
+- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
+- Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
+- Works even if you don't have a GPU with: ```--cpu``` (slow)
+- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
+- Embeddings/Textual inversion
+- [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
+- [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
+- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
+- Saving/Loading workflows as Json files.
+- Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.
+- [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/)
+- [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.
+- [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)
+- [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)
+- [unCLIP Models](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)
+- [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
+- [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
+- [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
+- [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
+- [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
+- [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
+- Latent previews with [TAESD](#how-to-show-high-quality-previews)
+- Starts up very fast.
+- Works fully offline: will never download anything.
+- [Config file](extra_model_paths.yaml.example) to set the search paths for models.
+
+Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
+
+## Shortcuts
+
+| Keybind | Explanation |
+|------------------------------------|--------------------------------------------------------------------------------------------------------------------|
+| `Ctrl` + `Enter` | Queue up current graph for generation |
+| `Ctrl` + `Shift` + `Enter` | Queue up current graph as first for generation |
+| `Ctrl` + `Alt` + `Enter` | Cancel current generation |
+| `Ctrl` + `Z`/`Ctrl` + `Y` | Undo/Redo |
+| `Ctrl` + `S` | Save workflow |
+| `Ctrl` + `O` | Load workflow |
+| `Ctrl` + `A` | Select all nodes |
+| `Alt `+ `C` | Collapse/uncollapse selected nodes |
+| `Ctrl` + `M` | Mute/unmute selected nodes |
+| `Ctrl` + `B` | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) |
+| `Delete`/`Backspace` | Delete selected nodes |
+| `Ctrl` + `Backspace` | Delete the current graph |
+| `Space` | Move the canvas around when held and moving the cursor |
+| `Ctrl`/`Shift` + `Click` | Add clicked node to selection |
+| `Ctrl` + `C`/`Ctrl` + `V` | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) |
+| `Ctrl` + `C`/`Ctrl` + `Shift` + `V` | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
+| `Shift` + `Drag` | Move multiple selected nodes at the same time |
+| `Ctrl` + `D` | Load default graph |
+| `Alt` + `+` | Canvas Zoom in |
+| `Alt` + `-` | Canvas Zoom out |
+| `Ctrl` + `Shift` + LMB + Vertical drag | Canvas Zoom in/out |
+| `P` | Pin/Unpin selected nodes |
+| `Ctrl` + `G` | Group selected nodes |
+| `Q` | Toggle visibility of the queue |
+| `H` | Toggle visibility of history |
+| `R` | Refresh graph |
+| Double-Click LMB | Open node quick search palette |
+| `Shift` + Drag | Move multiple wires at once |
+| `Ctrl` + `Alt` + LMB | Disconnect all wires from clicked slot |
+
+`Ctrl` can also be replaced with `Cmd` instead for macOS users
+
+# Installing
+
+## Windows
+
+There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).
+
+### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)
+
+Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
+
+If you have trouble extracting it, right click the file -> properties -> unblock
+
+#### How do I share models between another UI and ComfyUI?
+
+See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
+
+## Jupyter Notebook
+
+To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb)
+
+## Manual Install (Windows, Linux)
+
+Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.
+
+Git clone this repo.
+
+Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
+
+Put your VAE in: models/vae
+
+
+### AMD GPUs (Linux only)
+AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
+
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2```
+
+This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
+
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2```
+
+### NVIDIA
+
+Nvidia users should install stable pytorch using this command:
+
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124```
+
+This is the command to install pytorch nightly instead which might have performance improvements:
+
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124```
+
+#### Troubleshooting
+
+If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with:
+
+```pip uninstall torch```
+
+And install it again with the command above.
+
+### Dependencies
+
+Install the dependencies by opening your terminal inside the ComfyUI folder and:
+
+```pip install -r requirements.txt```
+
+After this you should have everything installed and can proceed to running ComfyUI.
+
+### Others:
+
+#### Intel GPUs
+
+Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) page. Choose your platform and method of install and follow the instructions. The steps are as follows:
+
+1. Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed.
+1. Follow the instructions to install [Intel's oneAPI Basekit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for your platform.
+1. Install the packages for IPEX using the instructions provided in the Installation page for your platform.
+1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed.
+
+Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
+
+#### Apple Mac silicon
+
+You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
+
+1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).
+1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
+1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies).
+1. Launch ComfyUI by running `python main.py`
+
+> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
+
+#### DirectML (AMD Cards on Windows)
+
+```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
+
+# Running
+
+```python main.py```
+
+### For AMD cards not officially supported by ROCm
+
+Try running it with this command if you have issues:
+
+For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py```
+
+For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py```
+
+### AMD ROCm Tips
+
+You can enable experimental memory efficient attention on pytorch 2.5 in ComfyUI on RDNA3 and potentially other AMD GPUs using this command:
+
+```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
+
+# Notes
+
+Only parts of the graph that have an output with all the correct inputs will be executed.
+
+Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed.
+
+Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.
+
+You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\).
+
+You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}.
+
+Dynamic prompts also support C-style comments, like `// comment` or `/* comment */`.
+
+To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):
+
+```embedding:embedding_filename.pt```
+
+
+## How to show high-quality previews?
+
+Use ```--preview-method auto``` to enable previews.
+
+The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews.
+
+## How to use TLS/SSL?
+Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"`
+
+Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.
+
+> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.
+
If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.
+
+## Support and dev channel
+
+[Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
+
+See also: [https://www.comfy.org/](https://www.comfy.org/)
+
+## Frontend Development
+
+As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory.
+
+### Reporting Issues and Requesting Features
+
+For any bugs, issues, or feature requests related to the frontend, please use the [ComfyUI Frontend repository](https://github.com/Comfy-Org/ComfyUI_frontend). This will help us manage and address frontend-specific concerns more efficiently.
+
+### Using the Latest Frontend
+
+The new frontend is now the default for ComfyUI. However, please note:
+
+1. The frontend in the main ComfyUI repository is updated weekly.
+2. Daily releases are available in the separate frontend repository.
+
+To use the most up-to-date frontend version:
+
+1. For the latest daily release, launch ComfyUI with this command line argument:
+
+ ```
+ --front-end-version Comfy-Org/ComfyUI_frontend@latest
+ ```
+
+2. For a specific version, replace `latest` with the desired version number:
+
+ ```
+ --front-end-version Comfy-Org/ComfyUI_frontend@1.2.2
+ ```
+
+This approach allows you to easily switch between the stable weekly release and the cutting-edge daily updates, or even specific versions for testing purposes.
+
+### Accessing the Legacy Frontend
+
+If you need to use the legacy frontend for any reason, you can access it using the following command line argument:
+
+```
+--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest
+```
+
+This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend).
+
+# QA
+
+### Which GPU should I buy for this?
+
+[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
+
diff --git a/api_server/__init__.py b/api_server/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/api_server/routes/__init__.py b/api_server/routes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/api_server/routes/internal/README.md b/api_server/routes/internal/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..35330c36f83962385b4afe4653c5967f2bdb73c1
--- /dev/null
+++ b/api_server/routes/internal/README.md
@@ -0,0 +1,3 @@
+# ComfyUI Internal Routes
+
+All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice.
diff --git a/api_server/routes/internal/__init__.py b/api_server/routes/internal/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/api_server/routes/internal/internal_routes.py b/api_server/routes/internal/internal_routes.py
new file mode 100644
index 0000000000000000000000000000000000000000..aaefa9335f534f93990e773109efd58b0494a34f
--- /dev/null
+++ b/api_server/routes/internal/internal_routes.py
@@ -0,0 +1,75 @@
+from aiohttp import web
+from typing import Optional
+from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
+from api_server.services.file_service import FileService
+from api_server.services.terminal_service import TerminalService
+import app.logger
+
+class InternalRoutes:
+ '''
+ The top level web router for internal routes: /internal/*
+ The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only.
+ Check README.md for more information.
+ '''
+
+ def __init__(self, prompt_server):
+ self.routes: web.RouteTableDef = web.RouteTableDef()
+ self._app: Optional[web.Application] = None
+ self.file_service = FileService({
+ "models": models_dir,
+ "user": user_directory,
+ "output": output_directory
+ })
+ self.prompt_server = prompt_server
+ self.terminal_service = TerminalService(prompt_server)
+
+ def setup_routes(self):
+ @self.routes.get('/files')
+ async def list_files(request):
+ directory_key = request.query.get('directory', '')
+ try:
+ file_list = self.file_service.list_files(directory_key)
+ return web.json_response({"files": file_list})
+ except ValueError as e:
+ return web.json_response({"error": str(e)}, status=400)
+ except Exception as e:
+ return web.json_response({"error": str(e)}, status=500)
+
+ @self.routes.get('/logs')
+ async def get_logs(request):
+ return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()]))
+
+ @self.routes.get('/logs/raw')
+ async def get_logs(request):
+ self.terminal_service.update_size()
+ return web.json_response({
+ "entries": list(app.logger.get_logs()),
+ "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows}
+ })
+
+ @self.routes.patch('/logs/subscribe')
+ async def subscribe_logs(request):
+ json_data = await request.json()
+ client_id = json_data["clientId"]
+ enabled = json_data["enabled"]
+ if enabled:
+ self.terminal_service.subscribe(client_id)
+ else:
+ self.terminal_service.unsubscribe(client_id)
+
+ return web.Response(status=200)
+
+
+ @self.routes.get('/folder_paths')
+ async def get_folder_paths(request):
+ response = {}
+ for key in folder_names_and_paths:
+ response[key] = folder_names_and_paths[key][0]
+ return web.json_response(response)
+
+ def get_app(self):
+ if self._app is None:
+ self._app = web.Application()
+ self.setup_routes()
+ self._app.add_routes(self.routes)
+ return self._app
diff --git a/api_server/services/__init__.py b/api_server/services/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/api_server/services/file_service.py b/api_server/services/file_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..394571084e97909f9aa2544500c6ebb7697c962c
--- /dev/null
+++ b/api_server/services/file_service.py
@@ -0,0 +1,13 @@
+from typing import Dict, List, Optional
+from api_server.utils.file_operations import FileSystemOperations, FileSystemItem
+
+class FileService:
+ def __init__(self, allowed_directories: Dict[str, str], file_system_ops: Optional[FileSystemOperations] = None):
+ self.allowed_directories: Dict[str, str] = allowed_directories
+ self.file_system_ops: FileSystemOperations = file_system_ops or FileSystemOperations()
+
+ def list_files(self, directory_key: str) -> List[FileSystemItem]:
+ if directory_key not in self.allowed_directories:
+ raise ValueError("Invalid directory key")
+ directory_path: str = self.allowed_directories[directory_key]
+ return self.file_system_ops.walk_directory(directory_path)
\ No newline at end of file
diff --git a/api_server/services/terminal_service.py b/api_server/services/terminal_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed788d9a4ab005600f65c08ed6f9e52ccb50cc5f
--- /dev/null
+++ b/api_server/services/terminal_service.py
@@ -0,0 +1,60 @@
+from app.logger import on_flush
+import os
+import shutil
+
+
+class TerminalService:
+ def __init__(self, server):
+ self.server = server
+ self.cols = None
+ self.rows = None
+ self.subscriptions = set()
+ on_flush(self.send_messages)
+
+ def get_terminal_size(self):
+ try:
+ size = os.get_terminal_size()
+ return (size.columns, size.lines)
+ except OSError:
+ try:
+ size = shutil.get_terminal_size()
+ return (size.columns, size.lines)
+ except OSError:
+ return (80, 24) # fallback to 80x24
+
+ def update_size(self):
+ columns, lines = self.get_terminal_size()
+ changed = False
+
+ if columns != self.cols:
+ self.cols = columns
+ changed = True
+
+ if lines != self.rows:
+ self.rows = lines
+ changed = True
+
+ if changed:
+ return {"cols": self.cols, "rows": self.rows}
+
+ return None
+
+ def subscribe(self, client_id):
+ self.subscriptions.add(client_id)
+
+ def unsubscribe(self, client_id):
+ self.subscriptions.discard(client_id)
+
+ def send_messages(self, entries):
+ if not len(entries) or not len(self.subscriptions):
+ return
+
+ new_size = self.update_size()
+
+ for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration
+ if client_id not in self.server.sockets:
+ # Automatically unsub if the socket has disconnected
+ self.unsubscribe(client_id)
+ continue
+
+ self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id)
diff --git a/api_server/utils/file_operations.py b/api_server/utils/file_operations.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef1bf999e52871ad2d338cde8bdb8b48d4747b00
--- /dev/null
+++ b/api_server/utils/file_operations.py
@@ -0,0 +1,42 @@
+import os
+from typing import List, Union, TypedDict, Literal
+from typing_extensions import TypeGuard
+class FileInfo(TypedDict):
+ name: str
+ path: str
+ type: Literal["file"]
+ size: int
+
+class DirectoryInfo(TypedDict):
+ name: str
+ path: str
+ type: Literal["directory"]
+
+FileSystemItem = Union[FileInfo, DirectoryInfo]
+
+def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]:
+ return item["type"] == "file"
+
+class FileSystemOperations:
+ @staticmethod
+ def walk_directory(directory: str) -> List[FileSystemItem]:
+ file_list: List[FileSystemItem] = []
+ for root, dirs, files in os.walk(directory):
+ for name in files:
+ file_path = os.path.join(root, name)
+ relative_path = os.path.relpath(file_path, directory)
+ file_list.append({
+ "name": name,
+ "path": relative_path,
+ "type": "file",
+ "size": os.path.getsize(file_path)
+ })
+ for name in dirs:
+ dir_path = os.path.join(root, name)
+ relative_path = os.path.relpath(dir_path, directory)
+ file_list.append({
+ "name": name,
+ "path": relative_path,
+ "type": "directory"
+ })
+ return file_list
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..87875bf17f41d574d61c3ae5b12d5f7fc43ce71b
--- /dev/null
+++ b/app.py
@@ -0,0 +1,305 @@
+import os
+import random
+import sys
+from typing import Sequence, Mapping, Any, Union
+import torch
+import gradio as gr
+from PIL import Image
+
+# Import all the necessary functions from the original script
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+ try:
+ return obj[index]
+ except KeyError:
+ return obj["result"][index]
+
+# Add all the necessary setup functions from the original script
+def find_path(name: str, path: str = None) -> str:
+ if path is None:
+ path = os.getcwd()
+ if name in os.listdir(path):
+ path_name = os.path.join(path, name)
+ print(f"{name} found: {path_name}")
+ return path_name
+ parent_directory = os.path.dirname(path)
+ if parent_directory == path:
+ return None
+ return find_path(name, parent_directory)
+
+def add_comfyui_directory_to_sys_path() -> None:
+ comfyui_path = find_path("ComfyUI")
+ if comfyui_path is not None and os.path.isdir(comfyui_path):
+ sys.path.append(comfyui_path)
+ print(f"'{comfyui_path}' added to sys.path")
+
+def add_extra_model_paths() -> None:
+ try:
+ from main import load_extra_path_config
+ except ImportError:
+ from utils.extra_config import load_extra_path_config
+ extra_model_paths = find_path("extra_model_paths.yaml")
+ if extra_model_paths is not None:
+ load_extra_path_config(extra_model_paths)
+ else:
+ print("Could not find the extra_model_paths config file.")
+
+# Initialize paths
+add_comfyui_directory_to_sys_path()
+add_extra_model_paths()
+
+def import_custom_nodes() -> None:
+ import asyncio
+ import execution
+ from nodes import init_extra_nodes
+ import server
+ loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(loop)
+ server_instance = server.PromptServer(loop)
+ execution.PromptQueue(server_instance)
+ init_extra_nodes()
+
+# Import all necessary nodes
+from nodes import (
+ StyleModelLoader,
+ VAEEncode,
+ NODE_CLASS_MAPPINGS,
+ LoadImage,
+ CLIPVisionLoader,
+ SaveImage,
+ VAELoader,
+ CLIPVisionEncode,
+ DualCLIPLoader,
+ EmptyLatentImage,
+ VAEDecode,
+ UNETLoader,
+ CLIPTextEncode,
+)
+
+# Initialize all constant nodes and models in global context
+import_custom_nodes()
+
+# Global variables for preloaded models and constants
+with torch.inference_mode():
+ # Initialize constants
+ intconstant = NODE_CLASS_MAPPINGS["INTConstant"]()
+ CONST_1024 = intconstant.get_value(value=1024)
+
+ # Load CLIP
+ dualcliploader = DualCLIPLoader()
+ CLIP_MODEL = dualcliploader.load_clip(
+ clip_name1="t5/t5xxl_fp16.safetensors",
+ clip_name2="clip_l.safetensors",
+ type="flux",
+ )
+
+ # Load VAE
+ vaeloader = VAELoader()
+ VAE_MODEL = vaeloader.load_vae(vae_name="FLUX1/ae.safetensors")
+
+ # Load UNET
+ unetloader = UNETLoader()
+ UNET_MODEL = unetloader.load_unet(
+ unet_name="flux1-depth-dev.safetensors", weight_dtype="default"
+ )
+
+ # Load CLIP Vision
+ clipvisionloader = CLIPVisionLoader()
+ CLIP_VISION_MODEL = clipvisionloader.load_clip(
+ clip_name="sigclip_vision_patch14_384.safetensors"
+ )
+
+ # Load Style Model
+ stylemodelloader = StyleModelLoader()
+ STYLE_MODEL = stylemodelloader.load_style_model(
+ style_model_name="flux1-redux-dev.safetensors"
+ )
+
+ # Initialize samplers
+ ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
+ SAMPLER = ksamplerselect.get_sampler(sampler_name="euler")
+
+ # Initialize depth model
+ cr_clip_input_switch = NODE_CLASS_MAPPINGS["CR Clip Input Switch"]()
+ downloadandloaddepthanythingv2model = NODE_CLASS_MAPPINGS["DownloadAndLoadDepthAnythingV2Model"]()
+ DEPTH_MODEL = downloadandloaddepthanythingv2model.loadmodel(
+ model="depth_anything_v2_vitl_fp32.safetensors"
+ )
+ cliptextencode = CLIPTextEncode()
+ loadimage = LoadImage()
+ vaeencode = VAEEncode()
+ fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
+ instructpixtopixconditioning = NODE_CLASS_MAPPINGS["InstructPixToPixConditioning"]()
+ clipvisionencode = CLIPVisionEncode()
+ stylemodelapplyadvanced = NODE_CLASS_MAPPINGS["StyleModelApplyAdvanced"]()
+ emptylatentimage = EmptyLatentImage()
+ basicguider = NODE_CLASS_MAPPINGS["BasicGuider"]()
+ basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()
+ randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
+ samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
+ vaedecode = VAEDecode()
+ cr_text = NODE_CLASS_MAPPINGS["CR Text"]()
+ saveimage = SaveImage()
+ getimagesizeandcount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
+ depthanything_v2 = NODE_CLASS_MAPPINGS["DepthAnything_V2"]()
+ imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]()
+def generate_image(prompt: str, structure_image: str, depth_strength: float, style_image: str, style_strength: float, progress=gr.Progress(track_tqdm=True)) -> str:
+ """Main generation function that processes inputs and returns the path to the generated image."""
+
+ with torch.inference_mode():
+ # Set up CLIP
+ clip_switch = cr_clip_input_switch.switch(
+ Input=1,
+ clip1=get_value_at_index(CLIP_MODEL, 0),
+ clip2=get_value_at_index(CLIP_MODEL, 0),
+ )
+
+ # Encode text
+ text_encoded = cliptextencode.encode(
+ text=prompt,
+ clip=get_value_at_index(clip_switch, 0),
+ )
+ empty_text = cliptextencode.encode(
+ text="",
+ clip=get_value_at_index(clip_switch, 0),
+ )
+
+ # Process structure image
+ structure_img = loadimage.load_image(image=structure_image)
+
+ # Resize image
+ resized_img = imageresize.execute(
+ width=get_value_at_index(CONST_1024, 0),
+ height=get_value_at_index(CONST_1024, 0),
+ interpolation="bicubic",
+ method="keep proportion",
+ condition="always",
+ multiple_of=16,
+ image=get_value_at_index(structure_img, 0),
+ )
+
+ # Get image size
+ size_info = getimagesizeandcount.getsize(
+ image=get_value_at_index(resized_img, 0)
+ )
+
+ # Encode VAE
+ vae_encoded = vaeencode.encode(
+ pixels=get_value_at_index(size_info, 0),
+ vae=get_value_at_index(VAE_MODEL, 0),
+ )
+
+ # Process depth
+ depth_processed = depthanything_v2.process(
+ da_model=get_value_at_index(DEPTH_MODEL, 0),
+ images=get_value_at_index(size_info, 0),
+ )
+
+ # Apply Flux guidance
+ flux_guided = fluxguidance.append(
+ guidance=depth_strength,
+ conditioning=get_value_at_index(text_encoded, 0),
+ )
+
+ # Process style image
+ style_img = loadimage.load_image(image=style_image)
+
+ # Encode style with CLIP Vision
+ style_encoded = clipvisionencode.encode(
+ crop="center",
+ clip_vision=get_value_at_index(CLIP_VISION_MODEL, 0),
+ image=get_value_at_index(style_img, 0),
+ )
+
+ # Set up conditioning
+ conditioning = instructpixtopixconditioning.encode(
+ positive=get_value_at_index(flux_guided, 0),
+ negative=get_value_at_index(empty_text, 0),
+ vae=get_value_at_index(VAE_MODEL, 0),
+ pixels=get_value_at_index(depth_processed, 0),
+ )
+
+ # Apply style
+ style_applied = stylemodelapplyadvanced.apply_stylemodel(
+ strength=style_strength,
+ conditioning=get_value_at_index(conditioning, 0),
+ style_model=get_value_at_index(STYLE_MODEL, 0),
+ clip_vision_output=get_value_at_index(style_encoded, 0),
+ )
+
+ # Set up empty latent
+ empty_latent = emptylatentimage.generate(
+ width=get_value_at_index(resized_img, 1),
+ height=get_value_at_index(resized_img, 2),
+ batch_size=1,
+ )
+
+ # Set up guidance
+ guided = basicguider.get_guider(
+ model=get_value_at_index(UNET_MODEL, 0),
+ conditioning=get_value_at_index(style_applied, 0),
+ )
+
+ # Set up scheduler
+ schedule = basicscheduler.get_sigmas(
+ scheduler="simple",
+ steps=28,
+ denoise=1,
+ model=get_value_at_index(UNET_MODEL, 0),
+ )
+
+ # Generate random noise
+ noise = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))
+
+ # Sample
+ sampled = samplercustomadvanced.sample(
+ noise=get_value_at_index(noise, 0),
+ guider=get_value_at_index(guided, 0),
+ sampler=get_value_at_index(SAMPLER, 0),
+ sigmas=get_value_at_index(schedule, 0),
+ latent_image=get_value_at_index(empty_latent, 0),
+ )
+
+ # Decode VAE
+ decoded = vaedecode.decode(
+ samples=get_value_at_index(sampled, 0),
+ vae=get_value_at_index(VAE_MODEL, 0),
+ )
+
+ # Save image
+ prefix = cr_text.text_multiline(text="Flux_BFL_Depth_Redux")
+
+ saved = saveimage.save_images(
+ filename_prefix=get_value_at_index(prefix, 0),
+ images=get_value_at_index(decoded, 0),
+ )
+ saved_path = f"output/{saved['ui']['images'][0]['filename']}"
+ print(saved_path)
+ return saved_path
+
+# Create Gradio interface
+with gr.Blocks() as app:
+ gr.Markdown("# Image Generation with Style Transfer")
+
+ with gr.Row():
+ with gr.Column():
+ prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
+ with gr.Row():
+ with gr.Group():
+ structure_image = gr.Image(label="Structure Image", type="filepath")
+ depth_strength = gr.Slider(minimum=0, maximum=50, value=15, label="Depth Strength")
+ with gr.Group():
+ style_image = gr.Image(label="Style Image", type="filepath")
+ style_strength = gr.Slider(minimum=0, maximum=1, value=0.5, label="Style Strength")
+ generate_btn = gr.Button("Generate")
+
+ with gr.Column():
+ output_image = gr.Image(label="Generated Image")
+
+ generate_btn.click(
+ fn=generate_image,
+ inputs=[prompt_input, structure_image, depth_strength, style_image, style_strength],
+ outputs=[output_image]
+ )
+
+if __name__ == "__main__":
+ app.launch(share=True)
\ No newline at end of file
diff --git a/app/__init__.py b/app/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/app/app_settings.py b/app/app_settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c6edc56c1d68cca481e7c78487454e278f6b326
--- /dev/null
+++ b/app/app_settings.py
@@ -0,0 +1,54 @@
+import os
+import json
+from aiohttp import web
+
+
+class AppSettings():
+ def __init__(self, user_manager):
+ self.user_manager = user_manager
+
+ def get_settings(self, request):
+ file = self.user_manager.get_request_user_filepath(
+ request, "comfy.settings.json")
+ if os.path.isfile(file):
+ with open(file) as f:
+ return json.load(f)
+ else:
+ return {}
+
+ def save_settings(self, request, settings):
+ file = self.user_manager.get_request_user_filepath(
+ request, "comfy.settings.json")
+ with open(file, "w") as f:
+ f.write(json.dumps(settings, indent=4))
+
+ def add_routes(self, routes):
+ @routes.get("/settings")
+ async def get_settings(request):
+ return web.json_response(self.get_settings(request))
+
+ @routes.get("/settings/{id}")
+ async def get_setting(request):
+ value = None
+ settings = self.get_settings(request)
+ setting_id = request.match_info.get("id", None)
+ if setting_id and setting_id in settings:
+ value = settings[setting_id]
+ return web.json_response(value)
+
+ @routes.post("/settings")
+ async def post_settings(request):
+ settings = self.get_settings(request)
+ new_settings = await request.json()
+ self.save_settings(request, {**settings, **new_settings})
+ return web.Response(status=200)
+
+ @routes.post("/settings/{id}")
+ async def post_setting(request):
+ setting_id = request.match_info.get("id", None)
+ if not setting_id:
+ return web.Response(status=400)
+ settings = self.get_settings(request)
+ settings[setting_id] = await request.json()
+ self.save_settings(request, settings)
+ return web.Response(status=200)
\ No newline at end of file
diff --git a/app/frontend_management.py b/app/frontend_management.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f20e439c306fe4717f33b6dc66296e090dbcd57
--- /dev/null
+++ b/app/frontend_management.py
@@ -0,0 +1,204 @@
+from __future__ import annotations
+import argparse
+import logging
+import os
+import re
+import tempfile
+import zipfile
+from dataclasses import dataclass
+from functools import cached_property
+from pathlib import Path
+from typing import TypedDict, Optional
+
+import requests
+from typing_extensions import NotRequired
+from comfy.cli_args import DEFAULT_VERSION_STRING
+
+
+REQUEST_TIMEOUT = 10 # seconds
+
+
+class Asset(TypedDict):
+ url: str
+
+
+class Release(TypedDict):
+ id: int
+ tag_name: str
+ name: str
+ prerelease: bool
+ created_at: str
+ published_at: str
+ body: str
+ assets: NotRequired[list[Asset]]
+
+
+@dataclass
+class FrontEndProvider:
+ owner: str
+ repo: str
+
+ @property
+ def folder_name(self) -> str:
+ return f"{self.owner}_{self.repo}"
+
+ @property
+ def release_url(self) -> str:
+ return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases"
+
+ @cached_property
+ def all_releases(self) -> list[Release]:
+ releases = []
+ api_url = self.release_url
+ while api_url:
+ response = requests.get(api_url, timeout=REQUEST_TIMEOUT)
+ response.raise_for_status() # Raises an HTTPError if the response was an error
+ releases.extend(response.json())
+ # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly.
+ if "next" in response.links:
+ api_url = response.links["next"]["url"]
+ else:
+ api_url = None
+ return releases
+
+ @cached_property
+ def latest_release(self) -> Release:
+ latest_release_url = f"{self.release_url}/latest"
+ response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT)
+ response.raise_for_status() # Raises an HTTPError if the response was an error
+ return response.json()
+
+ def get_release(self, version: str) -> Release:
+ if version == "latest":
+ return self.latest_release
+ else:
+ for release in self.all_releases:
+ if release["tag_name"] in [version, f"v{version}"]:
+ return release
+ raise ValueError(f"Version {version} not found in releases")
+
+
+def download_release_asset_zip(release: Release, destination_path: str) -> None:
+ """Download dist.zip from github release."""
+ asset_url = None
+ for asset in release.get("assets", []):
+ if asset["name"] == "dist.zip":
+ asset_url = asset["url"]
+ break
+
+ if not asset_url:
+ raise ValueError("dist.zip not found in the release assets")
+
+ # Use a temporary file to download the zip content
+ with tempfile.TemporaryFile() as tmp_file:
+ headers = {"Accept": "application/octet-stream"}
+ response = requests.get(
+ asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT
+ )
+ response.raise_for_status() # Ensure we got a successful response
+
+ # Write the content to the temporary file
+ tmp_file.write(response.content)
+
+ # Go back to the beginning of the temporary file
+ tmp_file.seek(0)
+
+ # Extract the zip file content to the destination path
+ with zipfile.ZipFile(tmp_file, "r") as zip_ref:
+ zip_ref.extractall(destination_path)
+
+
+class FrontendManager:
+ DEFAULT_FRONTEND_PATH = str(Path(__file__).parents[1] / "web")
+ CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
+
+ @classmethod
+ def parse_version_string(cls, value: str) -> tuple[str, str, str]:
+ """
+ Args:
+ value (str): The version string to parse.
+
+ Returns:
+ tuple[str, str]: A tuple containing provider name and version.
+
+ Raises:
+ argparse.ArgumentTypeError: If the version string is invalid.
+ """
+ VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+|latest)$"
+ match_result = re.match(VERSION_PATTERN, value)
+ if match_result is None:
+ raise argparse.ArgumentTypeError(f"Invalid version string: {value}")
+
+ return match_result.group(1), match_result.group(2), match_result.group(3)
+
+ @classmethod
+ def init_frontend_unsafe(cls, version_string: str, provider: Optional[FrontEndProvider] = None) -> str:
+ """
+ Initializes the frontend for the specified version.
+
+ Args:
+ version_string (str): The version string.
+ provider (FrontEndProvider, optional): The provider to use. Defaults to None.
+
+ Returns:
+ str: The path to the initialized frontend.
+
+ Raises:
+ Exception: If there is an error during the initialization process.
+ main error source might be request timeout or invalid URL.
+ """
+ if version_string == DEFAULT_VERSION_STRING:
+ return cls.DEFAULT_FRONTEND_PATH
+
+ repo_owner, repo_name, version = cls.parse_version_string(version_string)
+
+ if version.startswith("v"):
+ expected_path = str(Path(cls.CUSTOM_FRONTENDS_ROOT) / f"{repo_owner}_{repo_name}" / version.lstrip("v"))
+ if os.path.exists(expected_path):
+ logging.info(f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}")
+ return expected_path
+
+ logging.info(f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub...")
+
+ provider = provider or FrontEndProvider(repo_owner, repo_name)
+ release = provider.get_release(version)
+
+ semantic_version = release["tag_name"].lstrip("v")
+ web_root = str(
+ Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version
+ )
+ if not os.path.exists(web_root):
+ try:
+ os.makedirs(web_root, exist_ok=True)
+ logging.info(
+ "Downloading frontend(%s) version(%s) to (%s)",
+ provider.folder_name,
+ semantic_version,
+ web_root,
+ )
+ logging.debug(release)
+ download_release_asset_zip(release, destination_path=web_root)
+ finally:
+ # Clean up the directory if it is empty, i.e. the download failed
+ if not os.listdir(web_root):
+ os.rmdir(web_root)
+
+ return web_root
+
+ @classmethod
+ def init_frontend(cls, version_string: str) -> str:
+ """
+ Initializes the frontend with the specified version string.
+
+ Args:
+ version_string (str): The version string to initialize the frontend with.
+
+ Returns:
+ str: The path of the initialized frontend.
+ """
+ try:
+ return cls.init_frontend_unsafe(version_string)
+ except Exception as e:
+ logging.error("Failed to initialize frontend: %s", e)
+ logging.info("Falling back to the default frontend.")
+ return cls.DEFAULT_FRONTEND_PATH
diff --git a/app/logger.py b/app/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..527be9fe71f9e103ae19cdb3d52cd5eb36aab316
--- /dev/null
+++ b/app/logger.py
@@ -0,0 +1,73 @@
+from collections import deque
+from datetime import datetime
+import io
+import logging
+import sys
+import threading
+
+logs = None
+stdout_interceptor = None
+stderr_interceptor = None
+
+
+class LogInterceptor(io.TextIOWrapper):
+ def __init__(self, stream, *args, **kwargs):
+ buffer = stream.buffer
+ encoding = stream.encoding
+ super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering)
+ self._lock = threading.Lock()
+ self._flush_callbacks = []
+ self._logs_since_flush = []
+
+ def write(self, data):
+ entry = {"t": datetime.now().isoformat(), "m": data}
+ with self._lock:
+ self._logs_since_flush.append(entry)
+
+ # Simple handling for cr to overwrite the last output if it isnt a full line
+ # else logs just get full of progress messages
+ if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"):
+ logs.pop()
+ logs.append(entry)
+ super().write(data)
+
+ def flush(self):
+ super().flush()
+ for cb in self._flush_callbacks:
+ cb(self._logs_since_flush)
+ self._logs_since_flush = []
+
+ def on_flush(self, callback):
+ self._flush_callbacks.append(callback)
+
+
+def get_logs():
+ return logs
+
+
+def on_flush(callback):
+ if stdout_interceptor is not None:
+ stdout_interceptor.on_flush(callback)
+ if stderr_interceptor is not None:
+ stderr_interceptor.on_flush(callback)
+
+def setup_logger(log_level: str = 'INFO', capacity: int = 300):
+ global logs
+ if logs:
+ return
+
+ # Override output streams and log to buffer
+ logs = deque(maxlen=capacity)
+
+ global stdout_interceptor
+ global stderr_interceptor
+ stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout)
+ stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr)
+
+ # Setup default global logger
+ logger = logging.getLogger()
+ logger.setLevel(log_level)
+
+ stream_handler = logging.StreamHandler()
+ stream_handler.setFormatter(logging.Formatter("%(message)s"))
+ logger.addHandler(stream_handler)
diff --git a/app/user_manager.py b/app/user_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..e863b93dd2969f8663503d9068cee73e6833de80
--- /dev/null
+++ b/app/user_manager.py
@@ -0,0 +1,330 @@
+from __future__ import annotations
+import json
+import os
+import re
+import uuid
+import glob
+import shutil
+import logging
+from aiohttp import web
+from urllib import parse
+from comfy.cli_args import args
+import folder_paths
+from .app_settings import AppSettings
+from typing import TypedDict
+
+default_user = "default"
+
+
+class FileInfo(TypedDict):
+ path: str
+ size: int
+ modified: int
+
+
+def get_file_info(path: str, relative_to: str) -> FileInfo:
+ return {
+ "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
+ "size": os.path.getsize(path),
+ "modified": os.path.getmtime(path)
+ }
+
+
+class UserManager():
+ def __init__(self):
+ user_directory = folder_paths.get_user_directory()
+
+ self.settings = AppSettings(self)
+ if not os.path.exists(user_directory):
+ os.makedirs(user_directory, exist_ok=True)
+ if not args.multi_user:
+ print("****** User settings have been changed to be stored on the server instead of browser storage. ******")
+ print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
+
+ if args.multi_user:
+ if os.path.isfile(self.get_users_file()):
+ with open(self.get_users_file()) as f:
+ self.users = json.load(f)
+ else:
+ self.users = {}
+ else:
+ self.users = {"default": "default"}
+
+ def get_users_file(self):
+ return os.path.join(folder_paths.get_user_directory(), "users.json")
+
+ def get_request_user_id(self, request):
+ user = "default"
+ if args.multi_user and "comfy-user" in request.headers:
+ user = request.headers["comfy-user"]
+
+ if user not in self.users:
+ raise KeyError("Unknown user: " + user)
+
+ return user
+
+ def get_request_user_filepath(self, request, file, type="userdata", create_dir=True):
+ user_directory = folder_paths.get_user_directory()
+
+ if type == "userdata":
+ root_dir = user_directory
+ else:
+ raise KeyError("Unknown filepath type:" + type)
+
+ user = self.get_request_user_id(request)
+ path = user_root = os.path.abspath(os.path.join(root_dir, user))
+
+ # prevent leaving /{type}
+ if os.path.commonpath((root_dir, user_root)) != root_dir:
+ return None
+
+ if file is not None:
+ # Check if filename is url encoded
+ if "%" in file:
+ file = parse.unquote(file)
+
+ # prevent leaving /{type}/{user}
+ path = os.path.abspath(os.path.join(user_root, file))
+ if os.path.commonpath((user_root, path)) != user_root:
+ return None
+
+ parent = os.path.split(path)[0]
+
+ if create_dir and not os.path.exists(parent):
+ os.makedirs(parent, exist_ok=True)
+
+ return path
+
+ def add_user(self, name):
+ name = name.strip()
+ if not name:
+ raise ValueError("username not provided")
+ user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name)
+ user_id = user_id + "_" + str(uuid.uuid4())
+
+ self.users[user_id] = name
+
+ with open(self.get_users_file(), "w") as f:
+ json.dump(self.users, f)
+
+ return user_id
+
+ def add_routes(self, routes):
+ self.settings.add_routes(routes)
+
+ @routes.get("/users")
+ async def get_users(request):
+ if args.multi_user:
+ return web.json_response({"storage": "server", "users": self.users})
+ else:
+ user_dir = self.get_request_user_filepath(request, None, create_dir=False)
+ return web.json_response({
+ "storage": "server",
+ "migrated": os.path.exists(user_dir)
+ })
+
+ @routes.post("/users")
+ async def post_users(request):
+ body = await request.json()
+ username = body["username"]
+ if username in self.users.values():
+ return web.json_response({"error": "Duplicate username."}, status=400)
+
+ user_id = self.add_user(username)
+ return web.json_response(user_id)
+
+ @routes.get("/userdata")
+ async def listuserdata(request):
+ """
+ List user data files in a specified directory.
+
+ This endpoint allows listing files in a user's data directory, with options for recursion,
+ full file information, and path splitting.
+
+ Query Parameters:
+ - dir (required): The directory to list files from.
+ - recurse (optional): If "true", recursively list files in subdirectories.
+ - full_info (optional): If "true", return detailed file information (path, size, modified time).
+ - split (optional): If "true", split file paths into components (only applies when full_info is false).
+
+ Returns:
+ - 400: If 'dir' parameter is missing.
+ - 403: If the requested path is not allowed.
+ - 404: If the requested directory does not exist.
+ - 200: JSON response with the list of files or file information.
+
+ The response format depends on the query parameters:
+ - Default: List of relative file paths.
+ - full_info=true: List of dictionaries with file details.
+ - split=true (and full_info=false): List of lists, each containing path components.
+ """
+ directory = request.rel_url.query.get('dir', '')
+ if not directory:
+ return web.Response(status=400, text="Directory not provided")
+
+ path = self.get_request_user_filepath(request, directory)
+ if not path:
+ return web.Response(status=403, text="Invalid directory")
+
+ if not os.path.exists(path):
+ return web.Response(status=404, text="Directory not found")
+
+ recurse = request.rel_url.query.get('recurse', '').lower() == "true"
+ full_info = request.rel_url.query.get('full_info', '').lower() == "true"
+ split_path = request.rel_url.query.get('split', '').lower() == "true"
+
+ # Use different patterns based on whether we're recursing or not
+ if recurse:
+ pattern = os.path.join(glob.escape(path), '**', '*')
+ else:
+ pattern = os.path.join(glob.escape(path), '*')
+
+ def process_full_path(full_path: str) -> FileInfo | str | list[str]:
+ if full_info:
+ return get_file_info(full_path, path)
+
+ rel_path = os.path.relpath(full_path, path).replace(os.sep, '/')
+ if split_path:
+ return [rel_path] + rel_path.split('/')
+
+ return rel_path
+
+ results = [
+ process_full_path(full_path)
+ for full_path in glob.glob(pattern, recursive=recurse)
+ if os.path.isfile(full_path)
+ ]
+
+ return web.json_response(results)
+
+ def get_user_data_path(request, check_exists = False, param = "file"):
+ file = request.match_info.get(param, None)
+ if not file:
+ return web.Response(status=400)
+
+ path = self.get_request_user_filepath(request, file)
+ if not path:
+ return web.Response(status=403)
+
+ if check_exists and not os.path.exists(path):
+ return web.Response(status=404)
+
+ return path
+
+ @routes.get("/userdata/{file}")
+ async def getuserdata(request):
+ path = get_user_data_path(request, check_exists=True)
+ if not isinstance(path, str):
+ return path
+
+ return web.FileResponse(path)
+
+ @routes.post("/userdata/{file}")
+ async def post_userdata(request):
+ """
+ Upload or update a user data file.
+
+ This endpoint handles file uploads to a user's data directory, with options for
+ controlling overwrite behavior and response format.
+
+ Query Parameters:
+ - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
+ - full_info (optional): If "true", returns detailed file information (path, size, modified time).
+ If "false", returns only the relative file path.
+
+ Path Parameters:
+ - file: The target file path (URL encoded if necessary).
+
+ Returns:
+ - 400: If 'file' parameter is missing.
+ - 403: If the requested path is not allowed.
+ - 409: If overwrite=false and the file already exists.
+ - 200: JSON response with either:
+ - Full file information (if full_info=true)
+ - Relative file path (if full_info=false)
+
+ The request body should contain the raw file content to be written.
+ """
+ path = get_user_data_path(request)
+ if not isinstance(path, str):
+ return path
+
+ overwrite = request.query.get("overwrite", 'true') != "false"
+ full_info = request.query.get('full_info', 'false').lower() == "true"
+
+ if not overwrite and os.path.exists(path):
+ return web.Response(status=409, text="File already exists")
+
+ body = await request.read()
+
+ with open(path, "wb") as f:
+ f.write(body)
+
+ user_path = self.get_request_user_filepath(request, None)
+ if full_info:
+ resp = get_file_info(path, user_path)
+ else:
+ resp = os.path.relpath(path, user_path)
+
+ return web.json_response(resp)
+
+ @routes.delete("/userdata/{file}")
+ async def delete_userdata(request):
+ path = get_user_data_path(request, check_exists=True)
+ if not isinstance(path, str):
+ return path
+
+ os.remove(path)
+
+ return web.Response(status=204)
+
+ @routes.post("/userdata/{file}/move/{dest}")
+ async def move_userdata(request):
+ """
+ Move or rename a user data file.
+
+ This endpoint handles moving or renaming files within a user's data directory, with options for
+ controlling overwrite behavior and response format.
+
+ Path Parameters:
+ - file: The source file path (URL encoded if necessary)
+ - dest: The destination file path (URL encoded if necessary)
+
+ Query Parameters:
+ - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
+ - full_info (optional): If "true", returns detailed file information (path, size, modified time).
+ If "false", returns only the relative file path.
+
+ Returns:
+ - 400: If either 'file' or 'dest' parameter is missing
+ - 403: If either requested path is not allowed
+ - 404: If the source file does not exist
+ - 409: If overwrite=false and the destination file already exists
+ - 200: JSON response with either:
+ - Full file information (if full_info=true)
+ - Relative file path (if full_info=false)
+ """
+ source = get_user_data_path(request, check_exists=True)
+ if not isinstance(source, str):
+ return source
+
+ dest = get_user_data_path(request, check_exists=False, param="dest")
+ if not isinstance(source, str):
+ return dest
+
+ overwrite = request.query.get("overwrite", 'true') != "false"
+ full_info = request.query.get('full_info', 'false').lower() == "true"
+
+ if not overwrite and os.path.exists(dest):
+ return web.Response(status=409, text="File already exists")
+
+ logging.info(f"moving '{source}' -> '{dest}'")
+ shutil.move(source, dest)
+
+ user_path = self.get_request_user_filepath(request, None)
+ if full_info:
+ resp = get_file_info(dest, user_path)
+ else:
+ resp = os.path.relpath(dest, user_path)
+
+ return web.json_response(resp)
diff --git a/comfy/checkpoint_pickle.py b/comfy/checkpoint_pickle.py
new file mode 100644
index 0000000000000000000000000000000000000000..206551d3c1cf0d654c907534629a800196ba138b
--- /dev/null
+++ b/comfy/checkpoint_pickle.py
@@ -0,0 +1,13 @@
+import pickle
+
+load = pickle.load
+
+class Empty:
+ pass
+
+class Unpickler(pickle.Unpickler):
+ def find_class(self, module, name):
+ #TODO: safe unpickle
+ if module.startswith("pytorch_lightning"):
+ return Empty
+ return super().find_class(module, name)
diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ec64a22751d10718af7483c3370e0dc4ce578a7
--- /dev/null
+++ b/comfy/cldm/cldm.py
@@ -0,0 +1,437 @@
+#taken from: https://github.com/lllyasviel/ControlNet
+#and modified
+
+import torch
+import torch as th
+import torch.nn as nn
+
+from ..ldm.modules.diffusionmodules.util import (
+ zero_module,
+ timestep_embedding,
+)
+
+from ..ldm.modules.attention import SpatialTransformer
+from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
+from ..ldm.util import exists
+from .control_types import UNION_CONTROLNET_TYPES
+from collections import OrderedDict
+import comfy.ops
+from comfy.ldm.modules.attention import optimized_attention
+
+class OptimizedAttention(nn.Module):
+ def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.heads = nhead
+ self.c = c
+
+ self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device)
+ self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+
+ def forward(self, x):
+ x = self.in_proj(x)
+ q, k, v = x.split(self.c, dim=2)
+ out = optimized_attention(q, k, v, self.heads)
+ return self.out_proj(out)
+
+class QuickGELU(nn.Module):
+ def forward(self, x: torch.Tensor):
+ return x * torch.sigmoid(1.702 * x)
+
+class ResBlockUnionControlnet(nn.Module):
+ def __init__(self, dim, nhead, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations)
+ self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device)
+ self.mlp = nn.Sequential(
+ OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()),
+ ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))]))
+ self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device)
+
+ def attention(self, x: torch.Tensor):
+ return self.attn(x)
+
+ def forward(self, x: torch.Tensor):
+ x = x + self.attention(self.ln_1(x))
+ x = x + self.mlp(self.ln_2(x))
+ return x
+
+class ControlledUnetModel(UNetModel):
+ #implemented in the ldm unet
+ pass
+
+class ControlNet(nn.Module):
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ hint_channels,
+ num_res_blocks,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ num_classes=None,
+ use_checkpoint=False,
+ dtype=torch.float32,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ disable_self_attentions=None,
+ num_attention_blocks=None,
+ disable_middle_self_attn=False,
+ use_linear_in_transformer=False,
+ adm_in_channels=None,
+ transformer_depth_middle=None,
+ transformer_depth_output=None,
+ attn_precision=None,
+ union_controlnet_num_control_type=None,
+ device=None,
+ operations=comfy.ops.disable_weight_init,
+ **kwargs,
+ ):
+ super().__init__()
+ assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
+ if use_spatial_transformer:
+ assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ # from omegaconf.listconfig import ListConfig
+ # if type(context_dim) == ListConfig:
+ # context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.dims = dims
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+
+ if isinstance(num_res_blocks, int):
+ self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+ else:
+ if len(num_res_blocks) != len(channel_mult):
+ raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+ "as a list/tuple (per-level) with the same length as channel_mult")
+ self.num_res_blocks = num_res_blocks
+
+ if disable_self_attentions is not None:
+ # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+ assert len(disable_self_attentions) == len(channel_mult)
+ if num_attention_blocks is not None:
+ assert len(num_attention_blocks) == len(self.num_res_blocks)
+ assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+
+ transformer_depth = transformer_depth[:]
+
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.num_classes = num_classes
+ self.use_checkpoint = use_checkpoint
+ self.dtype = dtype
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
+ )
+
+ if self.num_classes is not None:
+ if isinstance(self.num_classes, int):
+ self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+ elif self.num_classes == "continuous":
+ print("setting up linear c_adm embedding layer")
+ self.label_emb = nn.Linear(1, time_embed_dim)
+ elif self.num_classes == "sequential":
+ assert adm_in_channels is not None
+ self.label_emb = nn.Sequential(
+ nn.Sequential(
+ operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
+ )
+ )
+ else:
+ raise ValueError()
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
+ )
+ ]
+ )
+ self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)])
+
+ self.input_hint_block = TimestepEmbedSequential(
+ operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device)
+ )
+
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for nr in range(self.num_res_blocks[level]):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ dtype=self.dtype,
+ device=device,
+ operations=operations,
+ )
+ ]
+ ch = mult * model_channels
+ num_transformers = transformer_depth.pop(0)
+ if num_transformers > 0:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+ layers.append(
+ SpatialTransformer(
+ ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ dtype=self.dtype,
+ device=device,
+ operations=operations
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device))
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ mid_block = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ dtype=self.dtype,
+ device=device,
+ operations=operations
+ )]
+ if transformer_depth_middle >= 0:
+ mid_block += [SpatialTransformer( # always uses a self-attn
+ ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
+ disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
+ ),
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ dtype=self.dtype,
+ device=device,
+ operations=operations
+ )]
+ self.middle_block = TimestepEmbedSequential(*mid_block)
+ self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
+ self._feature_size += ch
+
+ if union_controlnet_num_control_type is not None:
+ self.num_control_type = union_controlnet_num_control_type
+ num_trans_channel = 320
+ num_trans_head = 8
+ num_trans_layer = 1
+ num_proj_channel = 320
+ # task_scale_factor = num_trans_channel ** 0.5
+ self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device))
+
+ self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)])
+ self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device)
+ #-----------------------------------------------------------------------------------------------------
+
+ control_add_embed_dim = 256
+ class ControlAddEmbedding(nn.Module):
+ def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.num_control_type = num_control_type
+ self.in_dim = in_dim
+ self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device)
+ self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device)
+ def forward(self, control_type, dtype, device):
+ c_type = torch.zeros((self.num_control_type,), device=device)
+ c_type[control_type] = 1.0
+ c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim))
+ return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type)))
+
+ self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations)
+ else:
+ self.task_embedding = None
+ self.control_add_embedding = None
+
+ def union_controlnet_merge(self, hint, control_type, emb, context):
+ # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main
+ inputs = []
+ condition_list = []
+
+ for idx in range(min(1, len(control_type))):
+ controlnet_cond = self.input_hint_block(hint[idx], emb, context)
+ feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
+ if idx < len(control_type):
+ feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device)
+
+ inputs.append(feat_seq.unsqueeze(1))
+ condition_list.append(controlnet_cond)
+
+ x = torch.cat(inputs, dim=1)
+ x = self.transformer_layes(x)
+ controlnet_cond_fuser = None
+ for idx in range(len(control_type)):
+ alpha = self.spatial_ch_projs(x[:, idx])
+ alpha = alpha.unsqueeze(-1).unsqueeze(-1)
+ o = condition_list[idx] + alpha
+ if controlnet_cond_fuser is None:
+ controlnet_cond_fuser = o
+ else:
+ controlnet_cond_fuser += o
+ return controlnet_cond_fuser
+
+ def make_zero_conv(self, channels, operations=None, dtype=None, device=None):
+ return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))
+
+ def forward(self, x, hint, timesteps, context, y=None, **kwargs):
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
+ emb = self.time_embed(t_emb)
+
+ guided_hint = None
+ if self.control_add_embedding is not None: #Union Controlnet
+ control_type = kwargs.get("control_type", [])
+
+ if any([c >= self.num_control_type for c in control_type]):
+ max_type = max(control_type)
+ max_type_name = {
+ v: k for k, v in UNION_CONTROLNET_TYPES.items()
+ }[max_type]
+ raise ValueError(
+ f"Control type {max_type_name}({max_type}) is out of range for the number of control types" +
+ f"({self.num_control_type}) supported.\n" +
+ "Please consider using the ProMax ControlNet Union model.\n" +
+ "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main"
+ )
+
+ emb += self.control_add_embedding(control_type, emb.dtype, emb.device)
+ if len(control_type) > 0:
+ if len(hint.shape) < 5:
+ hint = hint.unsqueeze(dim=0)
+ guided_hint = self.union_controlnet_merge(hint, control_type, emb, context)
+
+ if guided_hint is None:
+ guided_hint = self.input_hint_block(hint, emb, context)
+
+ out_output = []
+ out_middle = []
+
+ hs = []
+ if self.num_classes is not None:
+ assert y.shape[0] == x.shape[0]
+ emb = emb + self.label_emb(y)
+
+ h = x
+ for module, zero_conv in zip(self.input_blocks, self.zero_convs):
+ if guided_hint is not None:
+ h = module(h, emb, context)
+ h += guided_hint
+ guided_hint = None
+ else:
+ h = module(h, emb, context)
+ out_output.append(zero_conv(h, emb, context))
+
+ h = self.middle_block(h, emb, context)
+ out_middle.append(self.middle_block_out(h, emb, context))
+
+ return {"middle": out_middle, "output": out_output}
+
diff --git a/comfy/cldm/control_types.py b/comfy/cldm/control_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..4128631a305a13d65c3c37ced17179d23fbbdcff
--- /dev/null
+++ b/comfy/cldm/control_types.py
@@ -0,0 +1,10 @@
+UNION_CONTROLNET_TYPES = {
+ "openpose": 0,
+ "depth": 1,
+ "hed/pidi/scribble/ted": 2,
+ "canny/lineart/anime_lineart/mlsd": 3,
+ "normal": 4,
+ "segment": 5,
+ "tile": 6,
+ "repaint": 7,
+}
diff --git a/comfy/cldm/dit_embedder.py b/comfy/cldm/dit_embedder.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9cdd49910bab42613f90f1f31fb9690237e0610
--- /dev/null
+++ b/comfy/cldm/dit_embedder.py
@@ -0,0 +1,122 @@
+import math
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from torch import Tensor
+
+from comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch
+
+
+class ControlNetEmbedder(nn.Module):
+
+ def __init__(
+ self,
+ img_size: int,
+ patch_size: int,
+ in_chans: int,
+ attention_head_dim: int,
+ num_attention_heads: int,
+ adm_in_channels: int,
+ num_layers: int,
+ main_model_double: int,
+ double_y_emb: bool,
+ device: torch.device,
+ dtype: torch.dtype,
+ pos_embed_max_size: Optional[int] = None,
+ operations = None,
+ ):
+ super().__init__()
+ self.main_model_double = main_model_double
+ self.dtype = dtype
+ self.hidden_size = num_attention_heads * attention_head_dim
+ self.patch_size = patch_size
+ self.x_embedder = PatchEmbed(
+ img_size=img_size,
+ patch_size=patch_size,
+ in_chans=in_chans,
+ embed_dim=self.hidden_size,
+ strict_img_size=pos_embed_max_size is None,
+ device=device,
+ dtype=dtype,
+ operations=operations,
+ )
+
+ self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+ self.double_y_emb = double_y_emb
+ if self.double_y_emb:
+ self.orig_y_embedder = VectorEmbedder(
+ adm_in_channels, self.hidden_size, dtype, device, operations=operations
+ )
+ self.y_embedder = VectorEmbedder(
+ self.hidden_size, self.hidden_size, dtype, device, operations=operations
+ )
+ else:
+ self.y_embedder = VectorEmbedder(
+ adm_in_channels, self.hidden_size, dtype, device, operations=operations
+ )
+
+ self.transformer_blocks = nn.ModuleList(
+ DismantledBlock(
+ hidden_size=self.hidden_size, num_heads=num_attention_heads, qkv_bias=True,
+ dtype=dtype, device=device, operations=operations
+ )
+ for _ in range(num_layers)
+ )
+
+ # self.use_y_embedder = pooled_projection_dim != self.time_text_embed.text_embedder.linear_1.in_features
+ # TODO double check this logic when 8b
+ self.use_y_embedder = True
+
+ self.controlnet_blocks = nn.ModuleList([])
+ for _ in range(len(self.transformer_blocks)):
+ controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+ self.controlnet_blocks.append(controlnet_block)
+
+ self.pos_embed_input = PatchEmbed(
+ img_size=img_size,
+ patch_size=patch_size,
+ in_chans=in_chans,
+ embed_dim=self.hidden_size,
+ strict_img_size=False,
+ device=device,
+ dtype=dtype,
+ operations=operations,
+ )
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ timesteps: torch.Tensor,
+ y: Optional[torch.Tensor] = None,
+ context: Optional[torch.Tensor] = None,
+ hint = None,
+ ) -> Tuple[Tensor, List[Tensor]]:
+ x_shape = list(x.shape)
+ x = self.x_embedder(x)
+ if not self.double_y_emb:
+ h = (x_shape[-2] + 1) // self.patch_size
+ w = (x_shape[-1] + 1) // self.patch_size
+ x += get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=x.device)
+ c = self.t_embedder(timesteps, dtype=x.dtype)
+ if y is not None and self.y_embedder is not None:
+ if self.double_y_emb:
+ y = self.orig_y_embedder(y)
+ y = self.y_embedder(y)
+ c = c + y
+
+ x = x + self.pos_embed_input(hint)
+
+ block_out = ()
+
+ repeat = math.ceil(self.main_model_double / len(self.transformer_blocks))
+ for i in range(len(self.transformer_blocks)):
+ out = self.transformer_blocks[i](x, c)
+ if not self.double_y_emb:
+ x = out
+ block_out += (self.controlnet_blocks[i](out),) * repeat
+
+ return {"output": block_out}
diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py
new file mode 100644
index 0000000000000000000000000000000000000000..54a58ab835a30d20a7b6a2a34572a66fbc1c95c0
--- /dev/null
+++ b/comfy/cldm/mmdit.py
@@ -0,0 +1,81 @@
+import torch
+from typing import Dict, Optional
+import comfy.ldm.modules.diffusionmodules.mmdit
+
+class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
+ def __init__(
+ self,
+ num_blocks = None,
+ control_latent_channels = None,
+ dtype = None,
+ device = None,
+ operations = None,
+ **kwargs,
+ ):
+ super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs)
+ # controlnet_blocks
+ self.controlnet_blocks = torch.nn.ModuleList([])
+ for _ in range(len(self.joint_blocks)):
+ self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
+
+ if control_latent_channels is None:
+ control_latent_channels = self.in_channels
+
+ self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
+ None,
+ self.patch_size,
+ control_latent_channels,
+ self.hidden_size,
+ bias=True,
+ strict_img_size=False,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ timesteps: torch.Tensor,
+ y: Optional[torch.Tensor] = None,
+ context: Optional[torch.Tensor] = None,
+ hint = None,
+ ) -> torch.Tensor:
+
+ #weird sd3 controlnet specific stuff
+ y = torch.zeros_like(y)
+
+ if self.context_processor is not None:
+ context = self.context_processor(context)
+
+ hw = x.shape[-2:]
+ x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
+ x += self.pos_embed_input(hint)
+
+ c = self.t_embedder(timesteps, dtype=x.dtype)
+ if y is not None and self.y_embedder is not None:
+ y = self.y_embedder(y)
+ c = c + y
+
+ if context is not None:
+ context = self.context_embedder(context)
+
+ output = []
+
+ blocks = len(self.joint_blocks)
+ for i in range(blocks):
+ context, x = self.joint_blocks[i](
+ context,
+ x,
+ c=c,
+ use_checkpoint=self.use_checkpoint,
+ )
+
+ out = self.controlnet_blocks[i](x)
+ count = self.depth // blocks
+ if i == blocks - 1:
+ count -= 1
+ for j in range(count):
+ output.append(out)
+
+ return {"output": output}
diff --git a/comfy/cli_args.py b/comfy/cli_args.py
new file mode 100644
index 0000000000000000000000000000000000000000..847f35abdf2582517e83b521c665bab8d7318ee7
--- /dev/null
+++ b/comfy/cli_args.py
@@ -0,0 +1,187 @@
+import argparse
+import enum
+import os
+from typing import Optional
+import comfy.options
+
+
+class EnumAction(argparse.Action):
+ """
+ Argparse action for handling Enums
+ """
+ def __init__(self, **kwargs):
+ # Pop off the type value
+ enum_type = kwargs.pop("type", None)
+
+ # Ensure an Enum subclass is provided
+ if enum_type is None:
+ raise ValueError("type must be assigned an Enum when using EnumAction")
+ if not issubclass(enum_type, enum.Enum):
+ raise TypeError("type must be an Enum when using EnumAction")
+
+ # Generate choices from the Enum
+ choices = tuple(e.value for e in enum_type)
+ kwargs.setdefault("choices", choices)
+ kwargs.setdefault("metavar", f"[{','.join(list(choices))}]")
+
+ super(EnumAction, self).__init__(**kwargs)
+
+ self._enum = enum_type
+
+ def __call__(self, parser, namespace, values, option_string=None):
+ # Convert value back into an Enum
+ value = self._enum(values)
+ setattr(namespace, self.dest, value)
+
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0,::", help="Specify the IP address to listen on (default: 127.0.0.1). You can give a list of ip addresses by separating them with a comma like: 127.2.2.2,127.3.3.3 If --listen is provided without an argument, it defaults to 0.0.0.0,:: (listens on all ipv4 and ipv6)")
+parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
+parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function")
+parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function")
+parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
+parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
+
+parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
+parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
+parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).")
+parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory.")
+parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
+parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
+parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
+cm_group = parser.add_mutually_exclusive_group()
+cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
+cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
+
+
+fp_group = parser.add_mutually_exclusive_group()
+fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
+fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
+
+fpunet_group = parser.add_mutually_exclusive_group()
+fpunet_group.add_argument("--fp32-unet", action="store_true", help="Run the diffusion model in fp32.")
+fpunet_group.add_argument("--fp64-unet", action="store_true", help="Run the diffusion model in fp64.")
+fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diffusion model in bf16.")
+fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16")
+fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.")
+fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.")
+
+fpvae_group = parser.add_mutually_exclusive_group()
+fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.")
+fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.")
+fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.")
+
+parser.add_argument("--cpu-vae", action="store_true", help="Run the VAE on the CPU.")
+
+fpte_group = parser.add_mutually_exclusive_group()
+fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).")
+fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).")
+fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.")
+fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
+
+parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
+
+parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
+
+parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.")
+
+class LatentPreviewMethod(enum.Enum):
+ NoPreviews = "none"
+ Auto = "auto"
+ Latent2RGB = "latent2rgb"
+ TAESD = "taesd"
+
+parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
+
+parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
+
+cache_group = parser.add_mutually_exclusive_group()
+cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
+cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")
+
+attn_group = parser.add_mutually_exclusive_group()
+attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.")
+attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.")
+attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.")
+
+parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
+
+upcast = parser.add_mutually_exclusive_group()
+upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.")
+upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
+
+
+vram_group = parser.add_mutually_exclusive_group()
+vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
+vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
+vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
+vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
+vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
+vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
+
+parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reverved depending on your OS.")
+
+
+parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.")
+
+parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.")
+parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.")
+parser.add_argument("--fast", action="store_true", help="Enable some untested and potentially quality deteriorating optimizations.")
+
+parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
+parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
+parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
+
+parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
+parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.")
+
+parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
+
+parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
+
+# The default built-in provider hosted under web/
+DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
+
+parser.add_argument(
+ "--front-end-version",
+ type=str,
+ default=DEFAULT_VERSION_STRING,
+ help="""
+ Specifies the version of the frontend to be used. This command needs internet connectivity to query and
+ download available frontend implementations from GitHub releases.
+
+ The version string should be in the format of:
+ [repoOwner]/[repoName]@[version]
+ where version is one of: "latest" or a valid version number (e.g. "1.0.0")
+ """,
+)
+
+def is_valid_directory(path: Optional[str]) -> Optional[str]:
+ """Validate if the given path is a directory."""
+ if path is None:
+ return None
+
+ if not os.path.isdir(path):
+ raise argparse.ArgumentTypeError(f"{path} is not a valid directory.")
+ return path
+
+parser.add_argument(
+ "--front-end-root",
+ type=is_valid_directory,
+ default=None,
+ help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
+)
+
+parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path.")
+
+if comfy.options.args_parsing:
+ args = parser.parse_args()
+else:
+ args = parser.parse_args([])
+
+if args.windows_standalone_build:
+ args.auto_launch = True
+
+if args.disable_auto_launch:
+ args.auto_launch = False
diff --git a/comfy/clip_config_bigg.json b/comfy/clip_config_bigg.json
new file mode 100644
index 0000000000000000000000000000000000000000..35261deef14a68fcc6c5b1fc32914b5c102781a9
--- /dev/null
+++ b/comfy/clip_config_bigg.json
@@ -0,0 +1,23 @@
+{
+ "architectures": [
+ "CLIPTextModel"
+ ],
+ "attention_dropout": 0.0,
+ "bos_token_id": 0,
+ "dropout": 0.0,
+ "eos_token_id": 49407,
+ "hidden_act": "gelu",
+ "hidden_size": 1280,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 5120,
+ "layer_norm_eps": 1e-05,
+ "max_position_embeddings": 77,
+ "model_type": "clip_text_model",
+ "num_attention_heads": 20,
+ "num_hidden_layers": 32,
+ "pad_token_id": 1,
+ "projection_dim": 1280,
+ "torch_dtype": "float32",
+ "vocab_size": 49408
+}
diff --git a/comfy/clip_model.py b/comfy/clip_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..23ddea9c02991b7b82c5525a315bb47023b64143
--- /dev/null
+++ b/comfy/clip_model.py
@@ -0,0 +1,218 @@
+import torch
+from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
+
+class CLIPAttention(torch.nn.Module):
+ def __init__(self, embed_dim, heads, dtype, device, operations):
+ super().__init__()
+
+ self.heads = heads
+ self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+ self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+ self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+
+ self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+
+ def forward(self, x, mask=None, optimized_attention=None):
+ q = self.q_proj(x)
+ k = self.k_proj(x)
+ v = self.v_proj(x)
+
+ out = optimized_attention(q, k, v, self.heads, mask)
+ return self.out_proj(out)
+
+ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a),
+ "gelu": torch.nn.functional.gelu,
+ "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
+}
+
+class CLIPMLP(torch.nn.Module):
+ def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations):
+ super().__init__()
+ self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device)
+ self.activation = ACTIVATIONS[activation]
+ self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.activation(x)
+ x = self.fc2(x)
+ return x
+
+class CLIPLayer(torch.nn.Module):
+ def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):
+ super().__init__()
+ self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
+ self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations)
+ self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
+ self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations)
+
+ def forward(self, x, mask=None, optimized_attention=None):
+ x += self.self_attn(self.layer_norm1(x), mask, optimized_attention)
+ x += self.mlp(self.layer_norm2(x))
+ return x
+
+
+class CLIPEncoder(torch.nn.Module):
+ def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations):
+ super().__init__()
+ self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)])
+
+ def forward(self, x, mask=None, intermediate_output=None):
+ optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
+
+ if intermediate_output is not None:
+ if intermediate_output < 0:
+ intermediate_output = len(self.layers) + intermediate_output
+
+ intermediate = None
+ for i, l in enumerate(self.layers):
+ x = l(x, mask, optimized_attention)
+ if i == intermediate_output:
+ intermediate = x.clone()
+ return x, intermediate
+
+class CLIPEmbeddings(torch.nn.Module):
+ def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
+ self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+
+ def forward(self, input_tokens, dtype=torch.float32):
+ return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device)
+
+
+class CLIPTextModel_(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ num_layers = config_dict["num_hidden_layers"]
+ embed_dim = config_dict["hidden_size"]
+ heads = config_dict["num_attention_heads"]
+ intermediate_size = config_dict["intermediate_size"]
+ intermediate_activation = config_dict["hidden_act"]
+ num_positions = config_dict["max_position_embeddings"]
+ self.eos_token_id = config_dict["eos_token_id"]
+
+ super().__init__()
+ self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, dtype=dtype, device=device, operations=operations)
+ self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
+ self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
+
+ def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32):
+ x = self.embeddings(input_tokens, dtype=dtype)
+ mask = None
+ if attention_mask is not None:
+ mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+ mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+
+ causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
+ if mask is not None:
+ mask += causal_mask
+ else:
+ mask = causal_mask
+
+ x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output)
+ x = self.final_layer_norm(x)
+ if i is not None and final_layer_norm_intermediate:
+ i = self.final_layer_norm(i)
+
+ pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),]
+ return x, i, pooled_output
+
+class CLIPTextModel(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ self.num_layers = config_dict["num_hidden_layers"]
+ self.text_model = CLIPTextModel_(config_dict, dtype, device, operations)
+ embed_dim = config_dict["hidden_size"]
+ self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
+ self.dtype = dtype
+
+ def get_input_embeddings(self):
+ return self.text_model.embeddings.token_embedding
+
+ def set_input_embeddings(self, embeddings):
+ self.text_model.embeddings.token_embedding = embeddings
+
+ def forward(self, *args, **kwargs):
+ x = self.text_model(*args, **kwargs)
+ out = self.text_projection(x[2])
+ return (x[0], x[1], out, x[2])
+
+
+class CLIPVisionEmbeddings(torch.nn.Module):
+ def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, model_type="", dtype=None, device=None, operations=None):
+ super().__init__()
+
+ num_patches = (image_size // patch_size) ** 2
+ if model_type == "siglip_vision_model":
+ self.class_embedding = None
+ patch_bias = True
+ else:
+ num_patches = num_patches + 1
+ self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device))
+ patch_bias = False
+
+ self.patch_embedding = operations.Conv2d(
+ in_channels=num_channels,
+ out_channels=embed_dim,
+ kernel_size=patch_size,
+ stride=patch_size,
+ bias=patch_bias,
+ dtype=dtype,
+ device=device
+ )
+
+ self.position_embedding = operations.Embedding(num_patches, embed_dim, dtype=dtype, device=device)
+
+ def forward(self, pixel_values):
+ embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
+ if self.class_embedding is not None:
+ embeds = torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1)
+ return embeds + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)
+
+
+class CLIPVision(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ num_layers = config_dict["num_hidden_layers"]
+ embed_dim = config_dict["hidden_size"]
+ heads = config_dict["num_attention_heads"]
+ intermediate_size = config_dict["intermediate_size"]
+ intermediate_activation = config_dict["hidden_act"]
+ model_type = config_dict["model_type"]
+
+ self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], model_type=model_type, dtype=dtype, device=device, operations=operations)
+ if model_type == "siglip_vision_model":
+ self.pre_layrnorm = lambda a: a
+ self.output_layernorm = True
+ else:
+ self.pre_layrnorm = operations.LayerNorm(embed_dim)
+ self.output_layernorm = False
+ self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
+ self.post_layernorm = operations.LayerNorm(embed_dim)
+
+ def forward(self, pixel_values, attention_mask=None, intermediate_output=None):
+ x = self.embeddings(pixel_values)
+ x = self.pre_layrnorm(x)
+ #TODO: attention_mask?
+ x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output)
+ if self.output_layernorm:
+ x = self.post_layernorm(x)
+ pooled_output = x
+ else:
+ pooled_output = self.post_layernorm(x[:, 0, :])
+ return x, i, pooled_output
+
+class CLIPVisionModelProjection(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ self.vision_model = CLIPVision(config_dict, dtype, device, operations)
+ if "projection_dim" in config_dict:
+ self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False)
+ else:
+ self.visual_projection = lambda a: a
+
+ def forward(self, *args, **kwargs):
+ x = self.vision_model(*args, **kwargs)
+ out = self.visual_projection(x[2])
+ return (x[0], x[1], out)
diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9c82e9ade0ecfc6587d527889969e28b4cd39af
--- /dev/null
+++ b/comfy/clip_vision.py
@@ -0,0 +1,129 @@
+from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace
+import os
+import torch
+import json
+import logging
+
+import comfy.ops
+import comfy.model_patcher
+import comfy.model_management
+import comfy.utils
+import comfy.clip_model
+
+class Output:
+ def __getitem__(self, key):
+ return getattr(self, key)
+ def __setitem__(self, key, item):
+ setattr(self, key, item)
+
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
+ mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
+ std = torch.tensor(std, device=image.device, dtype=image.dtype)
+ image = image.movedim(-1, 1)
+ if not (image.shape[2] == size and image.shape[3] == size):
+ if crop:
+ scale = (size / min(image.shape[2], image.shape[3]))
+ scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
+ else:
+ scale_size = (size, size)
+
+ image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
+ h = (image.shape[2] - size)//2
+ w = (image.shape[3] - size)//2
+ image = image[:,:,h:h+size,w:w+size]
+ image = torch.clip((255. * image), 0, 255).round() / 255.0
+ return (image - mean.view([3,1,1])) / std.view([3,1,1])
+
+class ClipVisionModel():
+ def __init__(self, json_config):
+ with open(json_config) as f:
+ config = json.load(f)
+
+ self.image_size = config.get("image_size", 224)
+ self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
+ self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
+ self.load_device = comfy.model_management.text_encoder_device()
+ offload_device = comfy.model_management.text_encoder_offload_device()
+ self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
+ self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.manual_cast)
+ self.model.eval()
+
+ self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+
+ def load_sd(self, sd):
+ return self.model.load_state_dict(sd, strict=False)
+
+ def get_sd(self):
+ return self.model.state_dict()
+
+ def encode_image(self, image, crop=True):
+ comfy.model_management.load_model_gpu(self.patcher)
+ pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
+ out = self.model(pixel_values=pixel_values, intermediate_output=-2)
+
+ outputs = Output()
+ outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
+ outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device())
+ outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
+ return outputs
+
+def convert_to_transformers(sd, prefix):
+ sd_k = sd.keys()
+ if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k:
+ keys_to_replace = {
+ "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding",
+ "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight",
+ "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight",
+ "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias",
+ "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight",
+ "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias",
+ "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight",
+ }
+
+ for x in keys_to_replace:
+ if x in sd_k:
+ sd[keys_to_replace[x]] = sd.pop(x)
+
+ if "{}proj".format(prefix) in sd_k:
+ sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1)
+
+ sd = transformers_convert(sd, prefix, "vision_model.", 48)
+ else:
+ replace_prefix = {prefix: ""}
+ sd = state_dict_prefix_replace(sd, replace_prefix)
+ return sd
+
+def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
+ if convert_keys:
+ sd = convert_to_transformers(sd, prefix)
+ if "vision_model.encoder.layers.47.layer_norm1.weight" in sd:
+ json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json")
+ elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
+ json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
+ elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
+ if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152:
+ json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json")
+ elif sd["vision_model.embeddings.position_embedding.weight"].shape[0] == 577:
+ json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
+ else:
+ json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
+ else:
+ return None
+
+ clip = ClipVisionModel(json_config)
+ m, u = clip.load_sd(sd)
+ if len(m) > 0:
+ logging.warning("missing clip vision: {}".format(m))
+ u = set(u)
+ keys = list(sd.keys())
+ for k in keys:
+ if k not in u:
+ sd.pop(k)
+ return clip
+
+def load(ckpt_path):
+ sd = load_torch_file(ckpt_path)
+ if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd:
+ return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True)
+ else:
+ return load_clipvision_from_sd(sd)
diff --git a/comfy/clip_vision_config_g.json b/comfy/clip_vision_config_g.json
new file mode 100644
index 0000000000000000000000000000000000000000..708e7e21ac3513a719d6a49e88e756f5ef7e2c8d
--- /dev/null
+++ b/comfy/clip_vision_config_g.json
@@ -0,0 +1,18 @@
+{
+ "attention_dropout": 0.0,
+ "dropout": 0.0,
+ "hidden_act": "gelu",
+ "hidden_size": 1664,
+ "image_size": 224,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 8192,
+ "layer_norm_eps": 1e-05,
+ "model_type": "clip_vision_model",
+ "num_attention_heads": 16,
+ "num_channels": 3,
+ "num_hidden_layers": 48,
+ "patch_size": 14,
+ "projection_dim": 1280,
+ "torch_dtype": "float32"
+}
diff --git a/comfy/clip_vision_config_h.json b/comfy/clip_vision_config_h.json
new file mode 100644
index 0000000000000000000000000000000000000000..bb71be419a4be0ad5c8c157850de032a65593cb9
--- /dev/null
+++ b/comfy/clip_vision_config_h.json
@@ -0,0 +1,18 @@
+{
+ "attention_dropout": 0.0,
+ "dropout": 0.0,
+ "hidden_act": "gelu",
+ "hidden_size": 1280,
+ "image_size": 224,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 5120,
+ "layer_norm_eps": 1e-05,
+ "model_type": "clip_vision_model",
+ "num_attention_heads": 16,
+ "num_channels": 3,
+ "num_hidden_layers": 32,
+ "patch_size": 14,
+ "projection_dim": 1024,
+ "torch_dtype": "float32"
+}
diff --git a/comfy/clip_vision_config_vitl.json b/comfy/clip_vision_config_vitl.json
new file mode 100644
index 0000000000000000000000000000000000000000..c59b8ed5a4c1f41fbcc9e6811d2c7dfe44273de7
--- /dev/null
+++ b/comfy/clip_vision_config_vitl.json
@@ -0,0 +1,18 @@
+{
+ "attention_dropout": 0.0,
+ "dropout": 0.0,
+ "hidden_act": "quick_gelu",
+ "hidden_size": 1024,
+ "image_size": 224,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-05,
+ "model_type": "clip_vision_model",
+ "num_attention_heads": 16,
+ "num_channels": 3,
+ "num_hidden_layers": 24,
+ "patch_size": 14,
+ "projection_dim": 768,
+ "torch_dtype": "float32"
+}
diff --git a/comfy/clip_vision_config_vitl_336.json b/comfy/clip_vision_config_vitl_336.json
new file mode 100644
index 0000000000000000000000000000000000000000..f26945273d99e88f207d64dcec78feee63b4b625
--- /dev/null
+++ b/comfy/clip_vision_config_vitl_336.json
@@ -0,0 +1,18 @@
+{
+ "attention_dropout": 0.0,
+ "dropout": 0.0,
+ "hidden_act": "quick_gelu",
+ "hidden_size": 1024,
+ "image_size": 336,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-5,
+ "model_type": "clip_vision_model",
+ "num_attention_heads": 16,
+ "num_channels": 3,
+ "num_hidden_layers": 24,
+ "patch_size": 14,
+ "projection_dim": 768,
+ "torch_dtype": "float32"
+}
diff --git a/comfy/clip_vision_siglip_384.json b/comfy/clip_vision_siglip_384.json
new file mode 100644
index 0000000000000000000000000000000000000000..532e03ac181d8849a7202445d42565f01441177b
--- /dev/null
+++ b/comfy/clip_vision_siglip_384.json
@@ -0,0 +1,13 @@
+{
+ "num_channels": 3,
+ "hidden_act": "gelu_pytorch_tanh",
+ "hidden_size": 1152,
+ "image_size": 384,
+ "intermediate_size": 4304,
+ "model_type": "siglip_vision_model",
+ "num_attention_heads": 16,
+ "num_hidden_layers": 27,
+ "patch_size": 14,
+ "image_mean": [0.5, 0.5, 0.5],
+ "image_std": [0.5, 0.5, 0.5]
+}
diff --git a/comfy/comfy_types.py b/comfy/comfy_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..70cf4b158e5f969192c0c11d9bd461964aaea5b5
--- /dev/null
+++ b/comfy/comfy_types.py
@@ -0,0 +1,32 @@
+import torch
+from typing import Callable, Protocol, TypedDict, Optional, List
+
+
+class UnetApplyFunction(Protocol):
+ """Function signature protocol on comfy.model_base.BaseModel.apply_model"""
+
+ def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor:
+ pass
+
+
+class UnetApplyConds(TypedDict):
+ """Optional conditions for unet apply function."""
+
+ c_concat: Optional[torch.Tensor]
+ c_crossattn: Optional[torch.Tensor]
+ control: Optional[torch.Tensor]
+ transformer_options: Optional[dict]
+
+
+class UnetParams(TypedDict):
+ # Tensor of shape [B, C, H, W]
+ input: torch.Tensor
+ # Tensor of shape [B]
+ timestep: torch.Tensor
+ c: UnetApplyConds
+ # List of [0, 1], [0], [1], ...
+ # 0 means conditional, 1 means conditional unconditional
+ cond_or_uncond: List[int]
+
+
+UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor]
diff --git a/comfy/conds.py b/comfy/conds.py
new file mode 100644
index 0000000000000000000000000000000000000000..660690af8425209e6cc8d8b3e17185065e269a47
--- /dev/null
+++ b/comfy/conds.py
@@ -0,0 +1,83 @@
+import torch
+import math
+import comfy.utils
+
+
+def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
+ return abs(a*b) // math.gcd(a, b)
+
+class CONDRegular:
+ def __init__(self, cond):
+ self.cond = cond
+
+ def _copy_with(self, cond):
+ return self.__class__(cond)
+
+ def process_cond(self, batch_size, device, **kwargs):
+ return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
+
+ def can_concat(self, other):
+ if self.cond.shape != other.cond.shape:
+ return False
+ return True
+
+ def concat(self, others):
+ conds = [self.cond]
+ for x in others:
+ conds.append(x.cond)
+ return torch.cat(conds)
+
+class CONDNoiseShape(CONDRegular):
+ def process_cond(self, batch_size, device, area, **kwargs):
+ data = self.cond
+ if area is not None:
+ dims = len(area) // 2
+ for i in range(dims):
+ data = data.narrow(i + 2, area[i + dims], area[i])
+
+ return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
+
+
+class CONDCrossAttn(CONDRegular):
+ def can_concat(self, other):
+ s1 = self.cond.shape
+ s2 = other.cond.shape
+ if s1 != s2:
+ if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
+ return False
+
+ mult_min = lcm(s1[1], s2[1])
+ diff = mult_min // min(s1[1], s2[1])
+ if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
+ return False
+ return True
+
+ def concat(self, others):
+ conds = [self.cond]
+ crossattn_max_len = self.cond.shape[1]
+ for x in others:
+ c = x.cond
+ crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
+ conds.append(c)
+
+ out = []
+ for c in conds:
+ if c.shape[1] < crossattn_max_len:
+ c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
+ out.append(c)
+ return torch.cat(out)
+
+class CONDConstant(CONDRegular):
+ def __init__(self, cond):
+ self.cond = cond
+
+ def process_cond(self, batch_size, device, **kwargs):
+ return self._copy_with(self.cond)
+
+ def can_concat(self, other):
+ if self.cond != other.cond:
+ return False
+ return True
+
+ def concat(self, others):
+ return self.cond
diff --git a/comfy/controlnet.py b/comfy/controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..a44f3725e807fb1fe889ec18ce6ff2fd339dd4f2
--- /dev/null
+++ b/comfy/controlnet.py
@@ -0,0 +1,850 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Comfy
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+
+import torch
+from enum import Enum
+import math
+import os
+import logging
+import comfy.utils
+import comfy.model_management
+import comfy.model_detection
+import comfy.model_patcher
+import comfy.ops
+import comfy.latent_formats
+
+import comfy.cldm.cldm
+import comfy.t2i_adapter.adapter
+import comfy.ldm.cascade.controlnet
+import comfy.cldm.mmdit
+import comfy.ldm.hydit.controlnet
+import comfy.ldm.flux.controlnet
+import comfy.cldm.dit_embedder
+
+def broadcast_image_to(tensor, target_batch_size, batched_number):
+ current_batch_size = tensor.shape[0]
+ #print(current_batch_size, target_batch_size)
+ if current_batch_size == 1:
+ return tensor
+
+ per_batch = target_batch_size // batched_number
+ tensor = tensor[:per_batch]
+
+ if per_batch > tensor.shape[0]:
+ tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0)
+
+ current_batch_size = tensor.shape[0]
+ if current_batch_size == target_batch_size:
+ return tensor
+ else:
+ return torch.cat([tensor] * batched_number, dim=0)
+
+class StrengthType(Enum):
+ CONSTANT = 1
+ LINEAR_UP = 2
+
+class ControlBase:
+ def __init__(self):
+ self.cond_hint_original = None
+ self.cond_hint = None
+ self.strength = 1.0
+ self.timestep_percent_range = (0.0, 1.0)
+ self.latent_format = None
+ self.vae = None
+ self.global_average_pooling = False
+ self.timestep_range = None
+ self.compression_ratio = 8
+ self.upscale_algorithm = 'nearest-exact'
+ self.extra_args = {}
+ self.previous_controlnet = None
+ self.extra_conds = []
+ self.strength_type = StrengthType.CONSTANT
+ self.concat_mask = False
+ self.extra_concat_orig = []
+ self.extra_concat = None
+ self.preprocess_image = lambda a: a
+
+ def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
+ self.cond_hint_original = cond_hint
+ self.strength = strength
+ self.timestep_percent_range = timestep_percent_range
+ if self.latent_format is not None:
+ if vae is None:
+ logging.warning("WARNING: no VAE provided to the controlnet apply node when this controlnet requires one.")
+ self.vae = vae
+ self.extra_concat_orig = extra_concat.copy()
+ if self.concat_mask and len(self.extra_concat_orig) == 0:
+ self.extra_concat_orig.append(torch.tensor([[[[1.0]]]]))
+ return self
+
+ def pre_run(self, model, percent_to_timestep_function):
+ self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1]))
+ if self.previous_controlnet is not None:
+ self.previous_controlnet.pre_run(model, percent_to_timestep_function)
+
+ def set_previous_controlnet(self, controlnet):
+ self.previous_controlnet = controlnet
+ return self
+
+ def cleanup(self):
+ if self.previous_controlnet is not None:
+ self.previous_controlnet.cleanup()
+
+ self.cond_hint = None
+ self.extra_concat = None
+ self.timestep_range = None
+
+ def get_models(self):
+ out = []
+ if self.previous_controlnet is not None:
+ out += self.previous_controlnet.get_models()
+ return out
+
+ def copy_to(self, c):
+ c.cond_hint_original = self.cond_hint_original
+ c.strength = self.strength
+ c.timestep_percent_range = self.timestep_percent_range
+ c.global_average_pooling = self.global_average_pooling
+ c.compression_ratio = self.compression_ratio
+ c.upscale_algorithm = self.upscale_algorithm
+ c.latent_format = self.latent_format
+ c.extra_args = self.extra_args.copy()
+ c.vae = self.vae
+ c.extra_conds = self.extra_conds.copy()
+ c.strength_type = self.strength_type
+ c.concat_mask = self.concat_mask
+ c.extra_concat_orig = self.extra_concat_orig.copy()
+ c.preprocess_image = self.preprocess_image
+
+ def inference_memory_requirements(self, dtype):
+ if self.previous_controlnet is not None:
+ return self.previous_controlnet.inference_memory_requirements(dtype)
+ return 0
+
+ def control_merge(self, control, control_prev, output_dtype):
+ out = {'input':[], 'middle':[], 'output': []}
+
+ for key in control:
+ control_output = control[key]
+ applied_to = set()
+ for i in range(len(control_output)):
+ x = control_output[i]
+ if x is not None:
+ if self.global_average_pooling:
+ x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
+
+ if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once
+ applied_to.add(x)
+ if self.strength_type == StrengthType.CONSTANT:
+ x *= self.strength
+ elif self.strength_type == StrengthType.LINEAR_UP:
+ x *= (self.strength ** float(len(control_output) - i))
+
+ if output_dtype is not None and x.dtype != output_dtype:
+ x = x.to(output_dtype)
+
+ out[key].append(x)
+
+ if control_prev is not None:
+ for x in ['input', 'middle', 'output']:
+ o = out[x]
+ for i in range(len(control_prev[x])):
+ prev_val = control_prev[x][i]
+ if i >= len(o):
+ o.append(prev_val)
+ elif prev_val is not None:
+ if o[i] is None:
+ o[i] = prev_val
+ else:
+ if o[i].shape[0] < prev_val.shape[0]:
+ o[i] = prev_val + o[i]
+ else:
+ o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue
+ return out
+
+ def set_extra_arg(self, argument, value=None):
+ self.extra_args[argument] = value
+
+
+class ControlNet(ControlBase):
+ def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False, preprocess_image=lambda a: a):
+ super().__init__()
+ self.control_model = control_model
+ self.load_device = load_device
+ if control_model is not None:
+ self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
+
+ self.compression_ratio = compression_ratio
+ self.global_average_pooling = global_average_pooling
+ self.model_sampling_current = None
+ self.manual_cast_dtype = manual_cast_dtype
+ self.latent_format = latent_format
+ self.extra_conds += extra_conds
+ self.strength_type = strength_type
+ self.concat_mask = concat_mask
+ self.preprocess_image = preprocess_image
+
+ def get_control(self, x_noisy, t, cond, batched_number):
+ control_prev = None
+ if self.previous_controlnet is not None:
+ control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
+
+ if self.timestep_range is not None:
+ if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
+ if control_prev is not None:
+ return control_prev
+ else:
+ return None
+
+ dtype = self.control_model.dtype
+ if self.manual_cast_dtype is not None:
+ dtype = self.manual_cast_dtype
+
+ if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
+ if self.cond_hint is not None:
+ del self.cond_hint
+ self.cond_hint = None
+ compression_ratio = self.compression_ratio
+ if self.vae is not None:
+ compression_ratio *= self.vae.downscale_ratio
+ else:
+ if self.latent_format is not None:
+ raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.")
+ self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
+ self.cond_hint = self.preprocess_image(self.cond_hint)
+ if self.vae is not None:
+ loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+ self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))
+ comfy.model_management.load_models_gpu(loaded_models)
+ if self.latent_format is not None:
+ self.cond_hint = self.latent_format.process_in(self.cond_hint)
+ if len(self.extra_concat_orig) > 0:
+ to_concat = []
+ for c in self.extra_concat_orig:
+ c = c.to(self.cond_hint.device)
+ c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center")
+ to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0]))
+ self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1)
+
+ self.cond_hint = self.cond_hint.to(device=x_noisy.device, dtype=dtype)
+ if x_noisy.shape[0] != self.cond_hint.shape[0]:
+ self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
+
+ context = cond.get('crossattn_controlnet', cond['c_crossattn'])
+ extra = self.extra_args.copy()
+ for c in self.extra_conds:
+ temp = cond.get(c, None)
+ if temp is not None:
+ extra[c] = temp.to(dtype)
+
+ timestep = self.model_sampling_current.timestep(t)
+ x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
+
+ control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra)
+ return self.control_merge(control, control_prev, output_dtype=None)
+
+ def copy(self):
+ c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
+ c.control_model = self.control_model
+ c.control_model_wrapped = self.control_model_wrapped
+ self.copy_to(c)
+ return c
+
+ def get_models(self):
+ out = super().get_models()
+ out.append(self.control_model_wrapped)
+ return out
+
+ def pre_run(self, model, percent_to_timestep_function):
+ super().pre_run(model, percent_to_timestep_function)
+ self.model_sampling_current = model.model_sampling
+
+ def cleanup(self):
+ self.model_sampling_current = None
+ super().cleanup()
+
+class ControlLoraOps:
+ class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
+ def __init__(self, in_features: int, out_features: int, bias: bool = True,
+ device=None, dtype=None) -> None:
+ factory_kwargs = {'device': device, 'dtype': dtype}
+ super().__init__()
+ self.in_features = in_features
+ self.out_features = out_features
+ self.weight = None
+ self.up = None
+ self.down = None
+ self.bias = None
+
+ def forward(self, input):
+ weight, bias = comfy.ops.cast_bias_weight(self, input)
+ if self.up is not None:
+ return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
+ else:
+ return torch.nn.functional.linear(input, weight, bias)
+
+ class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=1,
+ padding=0,
+ dilation=1,
+ groups=1,
+ bias=True,
+ padding_mode='zeros',
+ device=None,
+ dtype=None
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.kernel_size = kernel_size
+ self.stride = stride
+ self.padding = padding
+ self.dilation = dilation
+ self.transposed = False
+ self.output_padding = 0
+ self.groups = groups
+ self.padding_mode = padding_mode
+
+ self.weight = None
+ self.bias = None
+ self.up = None
+ self.down = None
+
+
+ def forward(self, input):
+ weight, bias = comfy.ops.cast_bias_weight(self, input)
+ if self.up is not None:
+ return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
+ else:
+ return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
+
+
+class ControlLora(ControlNet):
+ def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options
+ ControlBase.__init__(self)
+ self.control_weights = control_weights
+ self.global_average_pooling = global_average_pooling
+ self.extra_conds += ["y"]
+
+ def pre_run(self, model, percent_to_timestep_function):
+ super().pre_run(model, percent_to_timestep_function)
+ controlnet_config = model.model_config.unet_config.copy()
+ controlnet_config.pop("out_channels")
+ controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
+ self.manual_cast_dtype = model.manual_cast_dtype
+ dtype = model.get_dtype()
+ if self.manual_cast_dtype is None:
+ class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init):
+ pass
+ else:
+ class control_lora_ops(ControlLoraOps, comfy.ops.manual_cast):
+ pass
+ dtype = self.manual_cast_dtype
+
+ controlnet_config["operations"] = control_lora_ops
+ controlnet_config["dtype"] = dtype
+ self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
+ self.control_model.to(comfy.model_management.get_torch_device())
+ diffusion_model = model.diffusion_model
+ sd = diffusion_model.state_dict()
+ cm = self.control_model.state_dict()
+
+ for k in sd:
+ weight = sd[k]
+ try:
+ comfy.utils.set_attr_param(self.control_model, k, weight)
+ except:
+ pass
+
+ for k in self.control_weights:
+ if k not in {"lora_controlnet"}:
+ comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
+
+ def copy(self):
+ c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
+ self.copy_to(c)
+ return c
+
+ def cleanup(self):
+ del self.control_model
+ self.control_model = None
+ super().cleanup()
+
+ def get_models(self):
+ out = ControlBase.get_models(self)
+ return out
+
+ def inference_memory_requirements(self, dtype):
+ return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
+
+def controlnet_config(sd, model_options={}):
+ model_config = comfy.model_detection.model_config_from_unet(sd, "", True)
+
+ unet_dtype = model_options.get("dtype", None)
+ if unet_dtype is None:
+ weight_dtype = comfy.utils.weight_dtype(sd)
+
+ supported_inference_dtypes = list(model_config.supported_inference_dtypes)
+ if weight_dtype is not None:
+ supported_inference_dtypes.append(weight_dtype)
+
+ unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
+
+ load_device = comfy.model_management.get_torch_device()
+ manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+
+ operations = model_options.get("custom_operations", None)
+ if operations is None:
+ operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)
+
+ offload_device = comfy.model_management.unet_offload_device()
+ return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device
+
+def controlnet_load_state_dict(control_model, sd):
+ missing, unexpected = control_model.load_state_dict(sd, strict=False)
+
+ if len(missing) > 0:
+ logging.warning("missing controlnet keys: {}".format(missing))
+
+ if len(unexpected) > 0:
+ logging.debug("unexpected controlnet keys: {}".format(unexpected))
+ return control_model
+
+
+def load_controlnet_mmdit(sd, model_options={}):
+ new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
+ model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
+ num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
+ for k in sd:
+ new_sd[k] = sd[k]
+
+ concat_mask = False
+ control_latent_channels = new_sd.get("pos_embed_input.proj.weight").shape[1]
+ if control_latent_channels == 17: #inpaint controlnet
+ concat_mask = True
+
+ control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+ control_model = controlnet_load_state_dict(control_model, new_sd)
+
+ latent_format = comfy.latent_formats.SD3()
+ latent_format.shift_factor = 0 #SD3 controlnet weirdness
+ control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+ return control
+
+
+class ControlNetSD35(ControlNet):
+ def pre_run(self, model, percent_to_timestep_function):
+ if self.control_model.double_y_emb:
+ missing, unexpected = self.control_model.orig_y_embedder.load_state_dict(model.diffusion_model.y_embedder.state_dict(), strict=False)
+ else:
+ missing, unexpected = self.control_model.x_embedder.load_state_dict(model.diffusion_model.x_embedder.state_dict(), strict=False)
+ super().pre_run(model, percent_to_timestep_function)
+
+ def copy(self):
+ c = ControlNetSD35(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
+ c.control_model = self.control_model
+ c.control_model_wrapped = self.control_model_wrapped
+ self.copy_to(c)
+ return c
+
+def load_controlnet_sd35(sd, model_options={}):
+ control_type = -1
+ if "control_type" in sd:
+ control_type = round(sd.pop("control_type").item())
+
+ # blur_cnet = control_type == 0
+ canny_cnet = control_type == 1
+ depth_cnet = control_type == 2
+
+ new_sd = {}
+ for k in comfy.utils.MMDIT_MAP_BASIC:
+ if k[1] in sd:
+ new_sd[k[0]] = sd.pop(k[1])
+ for k in sd:
+ new_sd[k] = sd[k]
+ sd = new_sd
+
+ y_emb_shape = sd["y_embedder.mlp.0.weight"].shape
+ depth = y_emb_shape[0] // 64
+ hidden_size = 64 * depth
+ num_heads = depth
+ head_dim = hidden_size // num_heads
+ num_blocks = comfy.model_detection.count_blocks(new_sd, 'transformer_blocks.{}.')
+
+ load_device = comfy.model_management.get_torch_device()
+ offload_device = comfy.model_management.unet_offload_device()
+ unet_dtype = comfy.model_management.unet_dtype(model_params=-1)
+
+ manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+
+ operations = model_options.get("custom_operations", None)
+ if operations is None:
+ operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)
+
+ control_model = comfy.cldm.dit_embedder.ControlNetEmbedder(img_size=None,
+ patch_size=2,
+ in_chans=16,
+ num_layers=num_blocks,
+ main_model_double=depth,
+ double_y_emb=y_emb_shape[0] == y_emb_shape[1],
+ attention_head_dim=head_dim,
+ num_attention_heads=num_heads,
+ adm_in_channels=2048,
+ device=offload_device,
+ dtype=unet_dtype,
+ operations=operations)
+
+ control_model = controlnet_load_state_dict(control_model, sd)
+
+ latent_format = comfy.latent_formats.SD3()
+ preprocess_image = lambda a: a
+ if canny_cnet:
+ preprocess_image = lambda a: (a * 255 * 0.5 + 0.5)
+ elif depth_cnet:
+ preprocess_image = lambda a: 1.0 - a
+
+ control = ControlNetSD35(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, preprocess_image=preprocess_image)
+ return control
+
+
+
+def load_controlnet_hunyuandit(controlnet_data, model_options={}):
+ model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options)
+
+ control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype)
+ control_model = controlnet_load_state_dict(control_model, controlnet_data)
+
+ latent_format = comfy.latent_formats.SDXL()
+ extra_conds = ['text_embedding_mask', 'encoder_hidden_states_t5', 'text_embedding_mask_t5', 'image_meta_size', 'style', 'cos_cis_img', 'sin_cis_img']
+ control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT)
+ return control
+
+def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_options={}):
+ model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options)
+ control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+ control_model = controlnet_load_state_dict(control_model, sd)
+ extra_conds = ['y', 'guidance']
+ control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
+ return control
+
+def load_controlnet_flux_instantx(sd, model_options={}):
+ new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
+ model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
+ for k in sd:
+ new_sd[k] = sd[k]
+
+ num_union_modes = 0
+ union_cnet = "controlnet_mode_embedder.weight"
+ if union_cnet in new_sd:
+ num_union_modes = new_sd[union_cnet].shape[0]
+
+ control_latent_channels = new_sd.get("pos_embed_input.weight").shape[1] // 4
+ concat_mask = False
+ if control_latent_channels == 17:
+ concat_mask = True
+
+ control_model = comfy.ldm.flux.controlnet.ControlNetFlux(latent_input=True, num_union_modes=num_union_modes, control_latent_channels=control_latent_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+ control_model = controlnet_load_state_dict(control_model, new_sd)
+
+ latent_format = comfy.latent_formats.Flux()
+ extra_conds = ['y', 'guidance']
+ control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
+ return control
+
+def convert_mistoline(sd):
+ return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."})
+
+
+def load_controlnet_state_dict(state_dict, model=None, model_options={}):
+ controlnet_data = state_dict
+ if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT
+ return load_controlnet_hunyuandit(controlnet_data, model_options=model_options)
+
+ if "lora_controlnet" in controlnet_data:
+ return ControlLora(controlnet_data, model_options=model_options)
+
+ controlnet_config = None
+ supported_inference_dtypes = None
+
+ if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
+ controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data)
+ diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
+ diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
+ diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
+
+ count = 0
+ loop = True
+ while loop:
+ suffix = [".weight", ".bias"]
+ for s in suffix:
+ k_in = "controlnet_down_blocks.{}{}".format(count, s)
+ k_out = "zero_convs.{}.0{}".format(count, s)
+ if k_in not in controlnet_data:
+ loop = False
+ break
+ diffusers_keys[k_in] = k_out
+ count += 1
+
+ count = 0
+ loop = True
+ while loop:
+ suffix = [".weight", ".bias"]
+ for s in suffix:
+ if count == 0:
+ k_in = "controlnet_cond_embedding.conv_in{}".format(s)
+ else:
+ k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s)
+ k_out = "input_hint_block.{}{}".format(count * 2, s)
+ if k_in not in controlnet_data:
+ k_in = "controlnet_cond_embedding.conv_out{}".format(s)
+ loop = False
+ diffusers_keys[k_in] = k_out
+ count += 1
+
+ new_sd = {}
+ for k in diffusers_keys:
+ if k in controlnet_data:
+ new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
+
+ if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet
+ controlnet_config["union_controlnet_num_control_type"] = controlnet_data["task_embedding"].shape[0]
+ for k in list(controlnet_data.keys()):
+ new_k = k.replace('.attn.in_proj_', '.attn.in_proj.')
+ new_sd[new_k] = controlnet_data.pop(k)
+
+ leftover_keys = controlnet_data.keys()
+ if len(leftover_keys) > 0:
+ logging.warning("leftover keys: {}".format(leftover_keys))
+ controlnet_data = new_sd
+ elif "controlnet_blocks.0.weight" in controlnet_data:
+ if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data:
+ return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options)
+ elif "pos_embed_input.proj.weight" in controlnet_data:
+ if "transformer_blocks.0.adaLN_modulation.1.bias" in controlnet_data:
+ return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format
+ else:
+ return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet
+ elif "controlnet_x_embedder.weight" in controlnet_data:
+ return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)
+ elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux
+ return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options)
+
+ pth_key = 'control_model.zero_convs.0.0.weight'
+ pth = False
+ key = 'zero_convs.0.0.weight'
+ if pth_key in controlnet_data:
+ pth = True
+ key = pth_key
+ prefix = "control_model."
+ elif key in controlnet_data:
+ prefix = ""
+ else:
+ net = load_t2i_adapter(controlnet_data, model_options=model_options)
+ if net is None:
+ logging.error("error could not detect control model type.")
+ return net
+
+ if controlnet_config is None:
+ model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True)
+ supported_inference_dtypes = list(model_config.supported_inference_dtypes)
+ controlnet_config = model_config.unet_config
+
+ unet_dtype = model_options.get("dtype", None)
+ if unet_dtype is None:
+ weight_dtype = comfy.utils.weight_dtype(controlnet_data)
+
+ if supported_inference_dtypes is None:
+ supported_inference_dtypes = [comfy.model_management.unet_dtype()]
+
+ if weight_dtype is not None:
+ supported_inference_dtypes.append(weight_dtype)
+
+ unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
+
+ load_device = comfy.model_management.get_torch_device()
+
+ manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+ operations = model_options.get("custom_operations", None)
+ if operations is None:
+ operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype)
+
+ controlnet_config["operations"] = operations
+ controlnet_config["dtype"] = unet_dtype
+ controlnet_config["device"] = comfy.model_management.unet_offload_device()
+ controlnet_config.pop("out_channels")
+ controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
+ control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
+
+ if pth:
+ if 'difference' in controlnet_data:
+ if model is not None:
+ comfy.model_management.load_models_gpu([model])
+ model_sd = model.model_state_dict()
+ for x in controlnet_data:
+ c_m = "control_model."
+ if x.startswith(c_m):
+ sd_key = "diffusion_model.{}".format(x[len(c_m):])
+ if sd_key in model_sd:
+ cd = controlnet_data[x]
+ cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
+ else:
+ logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
+
+ class WeightsLoader(torch.nn.Module):
+ pass
+ w = WeightsLoader()
+ w.control_model = control_model
+ missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
+ else:
+ missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
+
+ if len(missing) > 0:
+ logging.warning("missing controlnet keys: {}".format(missing))
+
+ if len(unexpected) > 0:
+ logging.debug("unexpected controlnet keys: {}".format(unexpected))
+
+ global_average_pooling = model_options.get("global_average_pooling", False)
+ control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+ return control
+
+def load_controlnet(ckpt_path, model=None, model_options={}):
+ if "global_average_pooling" not in model_options:
+ filename = os.path.splitext(ckpt_path)[0]
+ if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling
+ model_options["global_average_pooling"] = True
+
+ cnet = load_controlnet_state_dict(comfy.utils.load_torch_file(ckpt_path, safe_load=True), model=model, model_options=model_options)
+ if cnet is None:
+ logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
+ return cnet
+
+class T2IAdapter(ControlBase):
+ def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None):
+ super().__init__()
+ self.t2i_model = t2i_model
+ self.channels_in = channels_in
+ self.control_input = None
+ self.compression_ratio = compression_ratio
+ self.upscale_algorithm = upscale_algorithm
+ if device is None:
+ device = comfy.model_management.get_torch_device()
+ self.device = device
+
+ def scale_image_to(self, width, height):
+ unshuffle_amount = self.t2i_model.unshuffle_amount
+ width = math.ceil(width / unshuffle_amount) * unshuffle_amount
+ height = math.ceil(height / unshuffle_amount) * unshuffle_amount
+ return width, height
+
+ def get_control(self, x_noisy, t, cond, batched_number):
+ control_prev = None
+ if self.previous_controlnet is not None:
+ control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
+
+ if self.timestep_range is not None:
+ if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
+ if control_prev is not None:
+ return control_prev
+ else:
+ return None
+
+ if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
+ if self.cond_hint is not None:
+ del self.cond_hint
+ self.control_input = None
+ self.cond_hint = None
+ width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio)
+ self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device)
+ if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
+ self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
+ if x_noisy.shape[0] != self.cond_hint.shape[0]:
+ self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
+ if self.control_input is None:
+ self.t2i_model.to(x_noisy.dtype)
+ self.t2i_model.to(self.device)
+ self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
+ self.t2i_model.cpu()
+
+ control_input = {}
+ for k in self.control_input:
+ control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k]))
+
+ return self.control_merge(control_input, control_prev, x_noisy.dtype)
+
+ def copy(self):
+ c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm)
+ self.copy_to(c)
+ return c
+
+def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options
+ compression_ratio = 8
+ upscale_algorithm = 'nearest-exact'
+
+ if 'adapter' in t2i_data:
+ t2i_data = t2i_data['adapter']
+ if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format
+ prefix_replace = {}
+ for i in range(4):
+ for j in range(2):
+ prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j)
+ prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2)
+ prefix_replace["adapter."] = ""
+ t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace)
+ keys = t2i_data.keys()
+
+ if "body.0.in_conv.weight" in keys:
+ cin = t2i_data['body.0.in_conv.weight'].shape[1]
+ model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
+ elif 'conv_in.weight' in keys:
+ cin = t2i_data['conv_in.weight'].shape[1]
+ channel = t2i_data['conv_in.weight'].shape[0]
+ ksize = t2i_data['body.0.block2.weight'].shape[2]
+ use_conv = False
+ down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys))
+ if len(down_opts) > 0:
+ use_conv = True
+ xl = False
+ if cin == 256 or cin == 768:
+ xl = True
+ model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
+ elif "backbone.0.0.weight" in keys:
+ model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
+ compression_ratio = 32
+ upscale_algorithm = 'bilinear'
+ elif "backbone.10.blocks.0.weight" in keys:
+ model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
+ compression_ratio = 1
+ upscale_algorithm = 'nearest-exact'
+ else:
+ return None
+
+ missing, unexpected = model_ad.load_state_dict(t2i_data)
+ if len(missing) > 0:
+ logging.warning("t2i missing {}".format(missing))
+
+ if len(unexpected) > 0:
+ logging.debug("t2i unexpected {}".format(unexpected))
+
+ return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm)
diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed2a45fea586284c7b881a2a7ab46983cd4baafb
--- /dev/null
+++ b/comfy/diffusers_convert.py
@@ -0,0 +1,281 @@
+import re
+import torch
+import logging
+
+# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
+
+# =================#
+# UNet Conversion #
+# =================#
+
+unet_conversion_map = [
+ # (stable-diffusion, HF Diffusers)
+ ("time_embed.0.weight", "time_embedding.linear_1.weight"),
+ ("time_embed.0.bias", "time_embedding.linear_1.bias"),
+ ("time_embed.2.weight", "time_embedding.linear_2.weight"),
+ ("time_embed.2.bias", "time_embedding.linear_2.bias"),
+ ("input_blocks.0.0.weight", "conv_in.weight"),
+ ("input_blocks.0.0.bias", "conv_in.bias"),
+ ("out.0.weight", "conv_norm_out.weight"),
+ ("out.0.bias", "conv_norm_out.bias"),
+ ("out.2.weight", "conv_out.weight"),
+ ("out.2.bias", "conv_out.bias"),
+]
+
+unet_conversion_map_resnet = [
+ # (stable-diffusion, HF Diffusers)
+ ("in_layers.0", "norm1"),
+ ("in_layers.2", "conv1"),
+ ("out_layers.0", "norm2"),
+ ("out_layers.3", "conv2"),
+ ("emb_layers.1", "time_emb_proj"),
+ ("skip_connection", "conv_shortcut"),
+]
+
+unet_conversion_map_layer = []
+# hardcoded number of downblocks and resnets/attentions...
+# would need smarter logic for other networks.
+for i in range(4):
+ # loop over downblocks/upblocks
+
+ for j in range(2):
+ # loop over resnets/attentions for downblocks
+ hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
+ sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
+ unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
+
+ if i < 3:
+ # no attention layers in down_blocks.3
+ hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
+ sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
+ unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
+
+ for j in range(3):
+ # loop over resnets/attentions for upblocks
+ hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
+ sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
+ unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
+
+ if i > 0:
+ # no attention layers in up_blocks.0
+ hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
+ sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
+ unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
+
+ if i < 3:
+ # no downsample in down_blocks.3
+ hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
+ sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
+ unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
+
+ # no upsample in up_blocks.3
+ hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+ sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
+ unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
+
+hf_mid_atn_prefix = "mid_block.attentions.0."
+sd_mid_atn_prefix = "middle_block.1."
+unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
+
+for j in range(2):
+ hf_mid_res_prefix = f"mid_block.resnets.{j}."
+ sd_mid_res_prefix = f"middle_block.{2 * j}."
+ unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+
+def convert_unet_state_dict(unet_state_dict):
+ # buyer beware: this is a *brittle* function,
+ # and correct output requires that all of these pieces interact in
+ # the exact order in which I have arranged them.
+ mapping = {k: k for k in unet_state_dict.keys()}
+ for sd_name, hf_name in unet_conversion_map:
+ mapping[hf_name] = sd_name
+ for k, v in mapping.items():
+ if "resnets" in k:
+ for sd_part, hf_part in unet_conversion_map_resnet:
+ v = v.replace(hf_part, sd_part)
+ mapping[k] = v
+ for k, v in mapping.items():
+ for sd_part, hf_part in unet_conversion_map_layer:
+ v = v.replace(hf_part, sd_part)
+ mapping[k] = v
+ new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
+ return new_state_dict
+
+
+# ================#
+# VAE Conversion #
+# ================#
+
+vae_conversion_map = [
+ # (stable-diffusion, HF Diffusers)
+ ("nin_shortcut", "conv_shortcut"),
+ ("norm_out", "conv_norm_out"),
+ ("mid.attn_1.", "mid_block.attentions.0."),
+]
+
+for i in range(4):
+ # down_blocks have two resnets
+ for j in range(2):
+ hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
+ sd_down_prefix = f"encoder.down.{i}.block.{j}."
+ vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
+
+ if i < 3:
+ hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
+ sd_downsample_prefix = f"down.{i}.downsample."
+ vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
+
+ hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
+ sd_upsample_prefix = f"up.{3 - i}.upsample."
+ vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
+
+ # up_blocks have three resnets
+ # also, up blocks in hf are numbered in reverse from sd
+ for j in range(3):
+ hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
+ sd_up_prefix = f"decoder.up.{3 - i}.block.{j}."
+ vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
+
+# this part accounts for mid blocks in both the encoder and the decoder
+for i in range(2):
+ hf_mid_res_prefix = f"mid_block.resnets.{i}."
+ sd_mid_res_prefix = f"mid.block_{i + 1}."
+ vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
+
+vae_conversion_map_attn = [
+ # (stable-diffusion, HF Diffusers)
+ ("norm.", "group_norm."),
+ ("q.", "query."),
+ ("k.", "key."),
+ ("v.", "value."),
+ ("q.", "to_q."),
+ ("k.", "to_k."),
+ ("v.", "to_v."),
+ ("proj_out.", "to_out.0."),
+ ("proj_out.", "proj_attn."),
+]
+
+
+def reshape_weight_for_sd(w):
+ # convert HF linear weights to SD conv2d weights
+ return w.reshape(*w.shape, 1, 1)
+
+
+def convert_vae_state_dict(vae_state_dict):
+ mapping = {k: k for k in vae_state_dict.keys()}
+ for k, v in mapping.items():
+ for sd_part, hf_part in vae_conversion_map:
+ v = v.replace(hf_part, sd_part)
+ mapping[k] = v
+ for k, v in mapping.items():
+ if "attentions" in k:
+ for sd_part, hf_part in vae_conversion_map_attn:
+ v = v.replace(hf_part, sd_part)
+ mapping[k] = v
+ new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
+ weights_to_convert = ["q", "k", "v", "proj_out"]
+ for k, v in new_state_dict.items():
+ for weight_name in weights_to_convert:
+ if f"mid.attn_1.{weight_name}.weight" in k:
+ logging.debug(f"Reshaping {k} for SD format")
+ new_state_dict[k] = reshape_weight_for_sd(v)
+ return new_state_dict
+
+
+# =========================#
+# Text Encoder Conversion #
+# =========================#
+
+
+textenc_conversion_lst = [
+ # (stable-diffusion, HF Diffusers)
+ ("resblocks.", "text_model.encoder.layers."),
+ ("ln_1", "layer_norm1"),
+ ("ln_2", "layer_norm2"),
+ (".c_fc.", ".fc1."),
+ (".c_proj.", ".fc2."),
+ (".attn", ".self_attn"),
+ ("ln_final.", "transformer.text_model.final_layer_norm."),
+ ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
+ ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
+]
+protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
+textenc_pattern = re.compile("|".join(protected.keys()))
+
+# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
+code2idx = {"q": 0, "k": 1, "v": 2}
+
+# This function exists because at the time of writing torch.cat can't do fp8 with cuda
+def cat_tensors(tensors):
+ x = 0
+ for t in tensors:
+ x += t.shape[0]
+
+ shape = [x] + list(tensors[0].shape)[1:]
+ out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype)
+
+ x = 0
+ for t in tensors:
+ out[x:x + t.shape[0]] = t
+ x += t.shape[0]
+
+ return out
+
+def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
+ new_state_dict = {}
+ capture_qkv_weight = {}
+ capture_qkv_bias = {}
+ for k, v in text_enc_dict.items():
+ if not k.startswith(prefix):
+ continue
+ if (
+ k.endswith(".self_attn.q_proj.weight")
+ or k.endswith(".self_attn.k_proj.weight")
+ or k.endswith(".self_attn.v_proj.weight")
+ ):
+ k_pre = k[: -len(".q_proj.weight")]
+ k_code = k[-len("q_proj.weight")]
+ if k_pre not in capture_qkv_weight:
+ capture_qkv_weight[k_pre] = [None, None, None]
+ capture_qkv_weight[k_pre][code2idx[k_code]] = v
+ continue
+
+ if (
+ k.endswith(".self_attn.q_proj.bias")
+ or k.endswith(".self_attn.k_proj.bias")
+ or k.endswith(".self_attn.v_proj.bias")
+ ):
+ k_pre = k[: -len(".q_proj.bias")]
+ k_code = k[-len("q_proj.bias")]
+ if k_pre not in capture_qkv_bias:
+ capture_qkv_bias[k_pre] = [None, None, None]
+ capture_qkv_bias[k_pre][code2idx[k_code]] = v
+ continue
+
+ text_proj = "transformer.text_projection.weight"
+ if k.endswith(text_proj):
+ new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous()
+ else:
+ relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
+ new_state_dict[relabelled_key] = v
+
+ for k_pre, tensors in capture_qkv_weight.items():
+ if None in tensors:
+ raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
+ relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
+ new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors)
+
+ for k_pre, tensors in capture_qkv_bias.items():
+ if None in tensors:
+ raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
+ relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
+ new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors)
+
+ return new_state_dict
+
+
+def convert_text_enc_state_dict(text_enc_dict):
+ return text_enc_dict
+
+
diff --git a/comfy/diffusers_load.py b/comfy/diffusers_load.py
new file mode 100644
index 0000000000000000000000000000000000000000..56e63a7565f083eb4e3bc484a3a9f90103306a2f
--- /dev/null
+++ b/comfy/diffusers_load.py
@@ -0,0 +1,36 @@
+import os
+
+import comfy.sd
+
+def first_file(path, filenames):
+ for f in filenames:
+ p = os.path.join(path, f)
+ if os.path.exists(p):
+ return p
+ return None
+
+def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None):
+ diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"]
+ unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names)
+ vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names)
+
+ text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"]
+ text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names)
+ text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names)
+
+ text_encoder_paths = [text_encoder1_path]
+ if text_encoder2_path is not None:
+ text_encoder_paths.append(text_encoder2_path)
+
+ unet = comfy.sd.load_diffusion_model(unet_path)
+
+ clip = None
+ if output_clip:
+ clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory)
+
+ vae = None
+ if output_vae:
+ sd = comfy.utils.load_torch_file(vae_path)
+ vae = comfy.sd.VAE(sd=sd)
+
+ return (unet, clip, vae)
diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ab42c6a940f2639b3ccbfefc8e0721fd85a456b
--- /dev/null
+++ b/comfy/extra_samplers/uni_pc.py
@@ -0,0 +1,875 @@
+#code taken from: https://github.com/wl-zhao/UniPC and modified
+
+import torch
+import torch.nn.functional as F
+import math
+
+from tqdm.auto import trange, tqdm
+
+
+class NoiseScheduleVP:
+ def __init__(
+ self,
+ schedule='discrete',
+ betas=None,
+ alphas_cumprod=None,
+ continuous_beta_0=0.1,
+ continuous_beta_1=20.,
+ ):
+ r"""Create a wrapper class for the forward SDE (VP type).
+
+ ***
+ Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
+ We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
+ ***
+
+ The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
+ We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
+ Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
+
+ log_alpha_t = self.marginal_log_mean_coeff(t)
+ sigma_t = self.marginal_std(t)
+ lambda_t = self.marginal_lambda(t)
+
+ Moreover, as lambda(t) is an invertible function, we also support its inverse function:
+
+ t = self.inverse_lambda(lambda_t)
+
+ ===============================================================
+
+ We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
+
+ 1. For discrete-time DPMs:
+
+ For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
+ t_i = (i + 1) / N
+ e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
+ We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
+
+ Args:
+ betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
+ alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
+
+ Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
+
+ **Important**: Please pay special attention for the args for `alphas_cumprod`:
+ The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
+ q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
+ Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
+ alpha_{t_n} = \sqrt{\hat{alpha_n}},
+ and
+ log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
+
+
+ 2. For continuous-time DPMs:
+
+ We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
+ schedule are the default settings in DDPM and improved-DDPM:
+
+ Args:
+ beta_min: A `float` number. The smallest beta for the linear schedule.
+ beta_max: A `float` number. The largest beta for the linear schedule.
+ cosine_s: A `float` number. The hyperparameter in the cosine schedule.
+ cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
+ T: A `float` number. The ending time of the forward process.
+
+ ===============================================================
+
+ Args:
+ schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
+ 'linear' or 'cosine' for continuous-time DPMs.
+ Returns:
+ A wrapper object of the forward SDE (VP type).
+
+ ===============================================================
+
+ Example:
+
+ # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
+ >>> ns = NoiseScheduleVP('discrete', betas=betas)
+
+ # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
+ >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+
+ # For continuous-time DPMs (VPSDE), linear schedule:
+ >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
+
+ """
+
+ if schedule not in ['discrete', 'linear', 'cosine']:
+ raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
+
+ self.schedule = schedule
+ if schedule == 'discrete':
+ if betas is not None:
+ log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
+ else:
+ assert alphas_cumprod is not None
+ log_alphas = 0.5 * torch.log(alphas_cumprod)
+ self.total_N = len(log_alphas)
+ self.T = 1.
+ self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
+ self.log_alpha_array = log_alphas.reshape((1, -1,))
+ else:
+ self.total_N = 1000
+ self.beta_0 = continuous_beta_0
+ self.beta_1 = continuous_beta_1
+ self.cosine_s = 0.008
+ self.cosine_beta_max = 999.
+ self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+ self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
+ self.schedule = schedule
+ if schedule == 'cosine':
+ # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
+ # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
+ self.T = 0.9946
+ else:
+ self.T = 1.
+
+ def marginal_log_mean_coeff(self, t):
+ """
+ Compute log(alpha_t) of a given continuous-time label t in [0, T].
+ """
+ if self.schedule == 'discrete':
+ return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
+ elif self.schedule == 'linear':
+ return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+ elif self.schedule == 'cosine':
+ log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
+ log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
+ return log_alpha_t
+
+ def marginal_alpha(self, t):
+ """
+ Compute alpha_t of a given continuous-time label t in [0, T].
+ """
+ return torch.exp(self.marginal_log_mean_coeff(t))
+
+ def marginal_std(self, t):
+ """
+ Compute sigma_t of a given continuous-time label t in [0, T].
+ """
+ return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+ def marginal_lambda(self, t):
+ """
+ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+ """
+ log_mean_coeff = self.marginal_log_mean_coeff(t)
+ log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+ return log_mean_coeff - log_std
+
+ def inverse_lambda(self, lamb):
+ """
+ Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
+ """
+ if self.schedule == 'linear':
+ tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+ Delta = self.beta_0**2 + tmp
+ return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
+ elif self.schedule == 'discrete':
+ log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
+ t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
+ return t.reshape((-1,))
+ else:
+ log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+ t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+ t = t_fn(log_alpha)
+ return t
+
+
+def model_wrapper(
+ model,
+ noise_schedule,
+ model_type="noise",
+ model_kwargs={},
+ guidance_type="uncond",
+ condition=None,
+ unconditional_condition=None,
+ guidance_scale=1.,
+ classifier_fn=None,
+ classifier_kwargs={},
+):
+ """Create a wrapper function for the noise prediction model.
+
+ DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
+ firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
+
+ We support four types of the diffusion model by setting `model_type`:
+
+ 1. "noise": noise prediction model. (Trained by predicting noise).
+
+ 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
+
+ 3. "v": velocity prediction model. (Trained by predicting the velocity).
+ The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
+
+ [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
+ arXiv preprint arXiv:2202.00512 (2022).
+ [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
+ arXiv preprint arXiv:2210.02303 (2022).
+
+ 4. "score": marginal score function. (Trained by denoising score matching).
+ Note that the score function and the noise prediction model follows a simple relationship:
+ ```
+ noise(x_t, t) = -sigma_t * score(x_t, t)
+ ```
+
+ We support three types of guided sampling by DPMs by setting `guidance_type`:
+ 1. "uncond": unconditional sampling by DPMs.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+ ``
+
+ 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+ ``
+
+ The input `classifier_fn` has the following format:
+ ``
+ classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
+ ``
+
+ [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
+ in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
+
+ 3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
+ ``
+ And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
+
+ [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
+ arXiv preprint arXiv:2207.12598 (2022).
+
+
+ The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
+ or continuous-time labels (i.e. epsilon to T).
+
+ We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
+ ``
+ def model_fn(x, t_continuous) -> noise:
+ t_input = get_model_input_time(t_continuous)
+ return noise_pred(model, x, t_input, **model_kwargs)
+ ``
+ where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
+
+ ===============================================================
+
+ Args:
+ model: A diffusion model with the corresponding format described above.
+ noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+ model_type: A `str`. The parameterization type of the diffusion model.
+ "noise" or "x_start" or "v" or "score".
+ model_kwargs: A `dict`. A dict for the other inputs of the model function.
+ guidance_type: A `str`. The type of the guidance for sampling.
+ "uncond" or "classifier" or "classifier-free".
+ condition: A pytorch tensor. The condition for the guided sampling.
+ Only used for "classifier" or "classifier-free" guidance type.
+ unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
+ Only used for "classifier-free" guidance type.
+ guidance_scale: A `float`. The scale for the guided sampling.
+ classifier_fn: A classifier function. Only used for the classifier guidance.
+ classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
+ Returns:
+ A noise prediction model that accepts the noised data and the continuous time as the inputs.
+ """
+
+ def get_model_input_time(t_continuous):
+ """
+ Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
+ For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
+ For continuous-time DPMs, we just use `t_continuous`.
+ """
+ if noise_schedule.schedule == 'discrete':
+ return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+ else:
+ return t_continuous
+
+ def noise_pred_fn(x, t_continuous, cond=None):
+ if t_continuous.reshape((-1,)).shape[0] == 1:
+ t_continuous = t_continuous.expand((x.shape[0]))
+ t_input = get_model_input_time(t_continuous)
+ output = model(x, t_input, **model_kwargs)
+ if model_type == "noise":
+ return output
+ elif model_type == "x_start":
+ alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
+ elif model_type == "v":
+ alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+ elif model_type == "score":
+ sigma_t = noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return -expand_dims(sigma_t, dims) * output
+
+ def cond_grad_fn(x, t_input):
+ """
+ Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
+ """
+ with torch.enable_grad():
+ x_in = x.detach().requires_grad_(True)
+ log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
+ return torch.autograd.grad(log_prob.sum(), x_in)[0]
+
+ def model_fn(x, t_continuous):
+ """
+ The noise predicition model function that is used for DPM-Solver.
+ """
+ if t_continuous.reshape((-1,)).shape[0] == 1:
+ t_continuous = t_continuous.expand((x.shape[0]))
+ if guidance_type == "uncond":
+ return noise_pred_fn(x, t_continuous)
+ elif guidance_type == "classifier":
+ assert classifier_fn is not None
+ t_input = get_model_input_time(t_continuous)
+ cond_grad = cond_grad_fn(x, t_input)
+ sigma_t = noise_schedule.marginal_std(t_continuous)
+ noise = noise_pred_fn(x, t_continuous)
+ return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
+ elif guidance_type == "classifier-free":
+ if guidance_scale == 1. or unconditional_condition is None:
+ return noise_pred_fn(x, t_continuous, cond=condition)
+ else:
+ x_in = torch.cat([x] * 2)
+ t_in = torch.cat([t_continuous] * 2)
+ c_in = torch.cat([unconditional_condition, condition])
+ noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+ return noise_uncond + guidance_scale * (noise - noise_uncond)
+
+ assert model_type in ["noise", "x_start", "v"]
+ assert guidance_type in ["uncond", "classifier", "classifier-free"]
+ return model_fn
+
+
+class UniPC:
+ def __init__(
+ self,
+ model_fn,
+ noise_schedule,
+ predict_x0=True,
+ thresholding=False,
+ max_val=1.,
+ variant='bh1',
+ ):
+ """Construct a UniPC.
+
+ We support both data_prediction and noise_prediction.
+ """
+ self.model = model_fn
+ self.noise_schedule = noise_schedule
+ self.variant = variant
+ self.predict_x0 = predict_x0
+ self.thresholding = thresholding
+ self.max_val = max_val
+
+ def dynamic_thresholding_fn(self, x0, t=None):
+ """
+ The dynamic thresholding method.
+ """
+ dims = x0.dim()
+ p = self.dynamic_thresholding_ratio
+ s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+ s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims)
+ x0 = torch.clamp(x0, -s, s) / s
+ return x0
+
+ def noise_prediction_fn(self, x, t):
+ """
+ Return the noise prediction model.
+ """
+ return self.model(x, t)
+
+ def data_prediction_fn(self, x, t):
+ """
+ Return the data prediction model (with thresholding).
+ """
+ noise = self.noise_prediction_fn(x, t)
+ dims = x.dim()
+ alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
+ x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
+ if self.thresholding:
+ p = 0.995 # A hyperparameter in the paper of "Imagen" [1].
+ s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+ s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
+ x0 = torch.clamp(x0, -s, s) / s
+ return x0
+
+ def model_fn(self, x, t):
+ """
+ Convert the model to the noise prediction model or the data prediction model.
+ """
+ if self.predict_x0:
+ return self.data_prediction_fn(x, t)
+ else:
+ return self.noise_prediction_fn(x, t)
+
+ def get_time_steps(self, skip_type, t_T, t_0, N, device):
+ """Compute the intermediate time steps for sampling.
+ """
+ if skip_type == 'logSNR':
+ lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
+ lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
+ logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
+ return self.noise_schedule.inverse_lambda(logSNR_steps)
+ elif skip_type == 'time_uniform':
+ return torch.linspace(t_T, t_0, N + 1).to(device)
+ elif skip_type == 'time_quadratic':
+ t_order = 2
+ t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
+ return t
+ else:
+ raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+
+ def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
+ """
+ Get the order of each step for sampling by the singlestep DPM-Solver.
+ """
+ if order == 3:
+ K = steps // 3 + 1
+ if steps % 3 == 0:
+ orders = [3,] * (K - 2) + [2, 1]
+ elif steps % 3 == 1:
+ orders = [3,] * (K - 1) + [1]
+ else:
+ orders = [3,] * (K - 1) + [2]
+ elif order == 2:
+ if steps % 2 == 0:
+ K = steps // 2
+ orders = [2,] * K
+ else:
+ K = steps // 2 + 1
+ orders = [2,] * (K - 1) + [1]
+ elif order == 1:
+ K = steps
+ orders = [1,] * steps
+ else:
+ raise ValueError("'order' must be '1' or '2' or '3'.")
+ if skip_type == 'logSNR':
+ # To reproduce the results in DPM-Solver paper
+ timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
+ else:
+ timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)]
+ return timesteps_outer, orders
+
+ def denoise_to_zero_fn(self, x, s):
+ """
+ Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
+ """
+ return self.data_prediction_fn(x, s)
+
+ def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs):
+ if len(t.shape) == 0:
+ t = t.view(-1)
+ if 'bh' in self.variant:
+ return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
+ else:
+ assert self.variant == 'vary_coeff'
+ return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
+
+ def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
+ print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
+ ns = self.noise_schedule
+ assert order <= len(model_prev_list)
+
+ # first compute rks
+ t_prev_0 = t_prev_list[-1]
+ lambda_prev_0 = ns.marginal_lambda(t_prev_0)
+ lambda_t = ns.marginal_lambda(t)
+ model_prev_0 = model_prev_list[-1]
+ sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+ log_alpha_t = ns.marginal_log_mean_coeff(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ h = lambda_t - lambda_prev_0
+
+ rks = []
+ D1s = []
+ for i in range(1, order):
+ t_prev_i = t_prev_list[-(i + 1)]
+ model_prev_i = model_prev_list[-(i + 1)]
+ lambda_prev_i = ns.marginal_lambda(t_prev_i)
+ rk = (lambda_prev_i - lambda_prev_0) / h
+ rks.append(rk)
+ D1s.append((model_prev_i - model_prev_0) / rk)
+
+ rks.append(1.)
+ rks = torch.tensor(rks, device=x.device)
+
+ K = len(rks)
+ # build C matrix
+ C = []
+
+ col = torch.ones_like(rks)
+ for k in range(1, K + 1):
+ C.append(col)
+ col = col * rks / (k + 1)
+ C = torch.stack(C, dim=1)
+
+ if len(D1s) > 0:
+ D1s = torch.stack(D1s, dim=1) # (B, K)
+ C_inv_p = torch.linalg.inv(C[:-1, :-1])
+ A_p = C_inv_p
+
+ if use_corrector:
+ print('using corrector')
+ C_inv = torch.linalg.inv(C)
+ A_c = C_inv
+
+ hh = -h if self.predict_x0 else h
+ h_phi_1 = torch.expm1(hh)
+ h_phi_ks = []
+ factorial_k = 1
+ h_phi_k = h_phi_1
+ for k in range(1, K + 2):
+ h_phi_ks.append(h_phi_k)
+ h_phi_k = h_phi_k / hh - 1 / factorial_k
+ factorial_k *= (k + 1)
+
+ model_t = None
+ if self.predict_x0:
+ x_t_ = (
+ sigma_t / sigma_prev_0 * x
+ - alpha_t * h_phi_1 * model_prev_0
+ )
+ # now predictor
+ x_t = x_t_
+ if len(D1s) > 0:
+ # compute the residuals for predictor
+ for k in range(K - 1):
+ x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
+ # now corrector
+ if use_corrector:
+ model_t = self.model_fn(x_t, t)
+ D1_t = (model_t - model_prev_0)
+ x_t = x_t_
+ k = 0
+ for k in range(K - 1):
+ x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
+ x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
+ else:
+ log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+ x_t_ = (
+ (torch.exp(log_alpha_t - log_alpha_prev_0)) * x
+ - (sigma_t * h_phi_1) * model_prev_0
+ )
+ # now predictor
+ x_t = x_t_
+ if len(D1s) > 0:
+ # compute the residuals for predictor
+ for k in range(K - 1):
+ x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
+ # now corrector
+ if use_corrector:
+ model_t = self.model_fn(x_t, t)
+ D1_t = (model_t - model_prev_0)
+ x_t = x_t_
+ k = 0
+ for k in range(K - 1):
+ x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
+ x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
+ return x_t, model_t
+
+ def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True):
+ # print(f'using unified predictor-corrector with order {order} (solver type: B(h))')
+ ns = self.noise_schedule
+ assert order <= len(model_prev_list)
+ dims = x.dim()
+
+ # first compute rks
+ t_prev_0 = t_prev_list[-1]
+ lambda_prev_0 = ns.marginal_lambda(t_prev_0)
+ lambda_t = ns.marginal_lambda(t)
+ model_prev_0 = model_prev_list[-1]
+ sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+ log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ h = lambda_t - lambda_prev_0
+
+ rks = []
+ D1s = []
+ for i in range(1, order):
+ t_prev_i = t_prev_list[-(i + 1)]
+ model_prev_i = model_prev_list[-(i + 1)]
+ lambda_prev_i = ns.marginal_lambda(t_prev_i)
+ rk = ((lambda_prev_i - lambda_prev_0) / h)[0]
+ rks.append(rk)
+ D1s.append((model_prev_i - model_prev_0) / rk)
+
+ rks.append(1.)
+ rks = torch.tensor(rks, device=x.device)
+
+ R = []
+ b = []
+
+ hh = -h[0] if self.predict_x0 else h[0]
+ h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
+ h_phi_k = h_phi_1 / hh - 1
+
+ factorial_i = 1
+
+ if self.variant == 'bh1':
+ B_h = hh
+ elif self.variant == 'bh2':
+ B_h = torch.expm1(hh)
+ else:
+ raise NotImplementedError()
+
+ for i in range(1, order + 1):
+ R.append(torch.pow(rks, i - 1))
+ b.append(h_phi_k * factorial_i / B_h)
+ factorial_i *= (i + 1)
+ h_phi_k = h_phi_k / hh - 1 / factorial_i
+
+ R = torch.stack(R)
+ b = torch.tensor(b, device=x.device)
+
+ # now predictor
+ use_predictor = len(D1s) > 0 and x_t is None
+ if len(D1s) > 0:
+ D1s = torch.stack(D1s, dim=1) # (B, K)
+ if x_t is None:
+ # for order 2, we use a simplified version
+ if order == 2:
+ rhos_p = torch.tensor([0.5], device=b.device)
+ else:
+ rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
+ else:
+ D1s = None
+
+ if use_corrector:
+ # print('using corrector')
+ # for order 1, we use a simplified version
+ if order == 1:
+ rhos_c = torch.tensor([0.5], device=b.device)
+ else:
+ rhos_c = torch.linalg.solve(R, b)
+
+ model_t = None
+ if self.predict_x0:
+ x_t_ = (
+ expand_dims(sigma_t / sigma_prev_0, dims) * x
+ - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0
+ )
+
+ if x_t is None:
+ if use_predictor:
+ pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+ else:
+ pred_res = 0
+ x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res
+
+ if use_corrector:
+ model_t = self.model_fn(x_t, t)
+ if D1s is not None:
+ corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+ else:
+ corr_res = 0
+ D1_t = (model_t - model_prev_0)
+ x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
+ else:
+ x_t_ = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+ - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0
+ )
+ if x_t is None:
+ if use_predictor:
+ pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+ else:
+ pred_res = 0
+ x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res
+
+ if use_corrector:
+ model_t = self.model_fn(x_t, t)
+ if D1s is not None:
+ corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+ else:
+ corr_res = 0
+ D1_t = (model_t - model_prev_0)
+ x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
+ return x_t, model_t
+
+
+ def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform',
+ method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
+ atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False
+ ):
+ # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
+ # t_T = self.noise_schedule.T if t_start is None else t_start
+ device = x.device
+ steps = len(timesteps) - 1
+ if method == 'multistep':
+ assert steps >= order
+ # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+ assert timesteps.shape[0] - 1 == steps
+ # with torch.no_grad():
+ for step_index in trange(steps, disable=disable_pbar):
+ if step_index == 0:
+ vec_t = timesteps[0].expand((x.shape[0]))
+ model_prev_list = [self.model_fn(x, vec_t)]
+ t_prev_list = [vec_t]
+ elif step_index < order:
+ init_order = step_index
+ # Init the first `order` values by lower order multistep DPM-Solver.
+ # for init_order in range(1, order):
+ vec_t = timesteps[init_order].expand(x.shape[0])
+ x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
+ if model_x is None:
+ model_x = self.model_fn(x, vec_t)
+ model_prev_list.append(model_x)
+ t_prev_list.append(vec_t)
+ else:
+ extra_final_step = 0
+ if step_index == (steps - 1):
+ extra_final_step = 1
+ for step in range(step_index, step_index + 1 + extra_final_step):
+ vec_t = timesteps[step].expand(x.shape[0])
+ if lower_order_final:
+ step_order = min(order, steps + 1 - step)
+ else:
+ step_order = order
+ # print('this step order:', step_order)
+ if step == steps:
+ # print('do not run corrector at the last step')
+ use_corrector = False
+ else:
+ use_corrector = True
+ x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
+ for i in range(order - 1):
+ t_prev_list[i] = t_prev_list[i + 1]
+ model_prev_list[i] = model_prev_list[i + 1]
+ t_prev_list[-1] = vec_t
+ # We do not need to evaluate the final model value.
+ if step < steps:
+ if model_x is None:
+ model_x = self.model_fn(x, vec_t)
+ model_prev_list[-1] = model_x
+ if callback is not None:
+ callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]})
+ else:
+ raise NotImplementedError()
+ # if denoise_to_zero:
+ # x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+ return x
+
+
+#############################################################
+# other utility functions
+#############################################################
+
+def interpolate_fn(x, xp, yp):
+ """
+ A piecewise linear function y = f(x), using xp and yp as keypoints.
+ We implement f(x) in a differentiable way (i.e. applicable for autograd).
+ The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
+
+ Args:
+ x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
+ xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
+ yp: PyTorch tensor with shape [C, K].
+ Returns:
+ The function values f(x), with shape [N, C].
+ """
+ N, K = x.shape[0], xp.shape[1]
+ all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
+ sorted_all_x, x_indices = torch.sort(all_x, dim=2)
+ x_idx = torch.argmin(x_indices, dim=2)
+ cand_start_idx = x_idx - 1
+ start_idx = torch.where(
+ torch.eq(x_idx, 0),
+ torch.tensor(1, device=x.device),
+ torch.where(
+ torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+ ),
+ )
+ end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
+ start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
+ end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
+ start_idx2 = torch.where(
+ torch.eq(x_idx, 0),
+ torch.tensor(0, device=x.device),
+ torch.where(
+ torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+ ),
+ )
+ y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
+ start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
+ end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
+ cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
+ return cand
+
+
+def expand_dims(v, dims):
+ """
+ Expand the tensor `v` to the dim `dims`.
+
+ Args:
+ `v`: a PyTorch tensor with shape [N].
+ `dim`: a `int`.
+ Returns:
+ a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
+ """
+ return v[(...,) + (None,)*(dims - 1)]
+
+
+class SigmaConvert:
+ schedule = ""
+ def marginal_log_mean_coeff(self, sigma):
+ return 0.5 * torch.log(1 / ((sigma * sigma) + 1))
+
+ def marginal_alpha(self, t):
+ return torch.exp(self.marginal_log_mean_coeff(t))
+
+ def marginal_std(self, t):
+ return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+ def marginal_lambda(self, t):
+ """
+ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+ """
+ log_mean_coeff = self.marginal_log_mean_coeff(t)
+ log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+ return log_mean_coeff - log_std
+
+def predict_eps_sigma(model, input, sigma_in, **kwargs):
+ sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1))
+ input = input * ((sigma ** 2 + 1.0) ** 0.5)
+ return (input - model(input, sigma_in, **kwargs)) / sigma
+
+
+def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'):
+ timesteps = sigmas.clone()
+ if sigmas[-1] == 0:
+ timesteps = sigmas[:]
+ timesteps[-1] = 0.001
+ else:
+ timesteps = sigmas.clone()
+ ns = SigmaConvert()
+
+ noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0)
+ model_type = "noise"
+
+ model_fn = model_wrapper(
+ lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs),
+ ns,
+ model_type=model_type,
+ guidance_type="uncond",
+ model_kwargs=extra_args,
+ )
+
+ order = min(3, len(timesteps) - 2)
+ uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant)
+ x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable)
+ x /= ns.marginal_alpha(timesteps[-1])
+ return x
+
+def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False):
+ return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')
\ No newline at end of file
diff --git a/comfy/float.py b/comfy/float.py
new file mode 100644
index 0000000000000000000000000000000000000000..521316fd2facaab90583da8487029a365aefd9e7
--- /dev/null
+++ b/comfy/float.py
@@ -0,0 +1,67 @@
+import torch
+
+def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=None):
+ mantissa_scaled = torch.where(
+ normal_mask,
+ (abs_x / (2.0 ** (exponent - EXPONENT_BIAS)) - 1.0) * (2**MANTISSA_BITS),
+ (abs_x / (2.0 ** (-EXPONENT_BIAS + 1 - MANTISSA_BITS)))
+ )
+
+ mantissa_scaled += torch.rand(mantissa_scaled.size(), dtype=mantissa_scaled.dtype, layout=mantissa_scaled.layout, device=mantissa_scaled.device, generator=generator)
+ return mantissa_scaled.floor() / (2**MANTISSA_BITS)
+
+#Not 100% sure about this
+def manual_stochastic_round_to_float8(x, dtype, generator=None):
+ if dtype == torch.float8_e4m3fn:
+ EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 4, 3, 7
+ elif dtype == torch.float8_e5m2:
+ EXPONENT_BITS, MANTISSA_BITS, EXPONENT_BIAS = 5, 2, 15
+ else:
+ raise ValueError("Unsupported dtype")
+
+ x = x.half()
+ sign = torch.sign(x)
+ abs_x = x.abs()
+ sign = torch.where(abs_x == 0, 0, sign)
+
+ # Combine exponent calculation and clamping
+ exponent = torch.clamp(
+ torch.floor(torch.log2(abs_x)) + EXPONENT_BIAS,
+ 0, 2**EXPONENT_BITS - 1
+ )
+
+ # Combine mantissa calculation and rounding
+ normal_mask = ~(exponent == 0)
+
+ abs_x[:] = calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_BIAS, generator=generator)
+
+ sign *= torch.where(
+ normal_mask,
+ (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x),
+ (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x
+ )
+
+ inf = torch.finfo(dtype)
+ torch.clamp(sign, min=inf.min, max=inf.max, out=sign)
+ return sign
+
+
+
+def stochastic_rounding(value, dtype, seed=0):
+ if dtype == torch.float32:
+ return value.to(dtype=torch.float32)
+ if dtype == torch.float16:
+ return value.to(dtype=torch.float16)
+ if dtype == torch.bfloat16:
+ return value.to(dtype=torch.bfloat16)
+ if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2:
+ generator = torch.Generator(device=value.device)
+ generator.manual_seed(seed)
+ output = torch.empty_like(value, dtype=dtype)
+ num_slices = max(1, (value.numel() / (4096 * 4096)))
+ slice_size = max(1, round(value.shape[0] / num_slices))
+ for i in range(0, value.shape[0], slice_size):
+ output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype, generator=generator))
+ return output
+
+ return value.to(dtype=dtype)
diff --git a/comfy/gligen.py b/comfy/gligen.py
new file mode 100644
index 0000000000000000000000000000000000000000..592522767e98bbe11b6e5e9411b1f734cbf92b9b
--- /dev/null
+++ b/comfy/gligen.py
@@ -0,0 +1,343 @@
+import torch
+from torch import nn
+from .ldm.modules.attention import CrossAttention
+from inspect import isfunction
+import comfy.ops
+ops = comfy.ops.manual_cast
+
+def exists(val):
+ return val is not None
+
+
+def uniq(arr):
+ return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+# feedforward
+class GEGLU(nn.Module):
+ def __init__(self, dim_in, dim_out):
+ super().__init__()
+ self.proj = ops.Linear(dim_in, dim_out * 2)
+
+ def forward(self, x):
+ x, gate = self.proj(x).chunk(2, dim=-1)
+ return x * torch.nn.functional.gelu(gate)
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = default(dim_out, dim)
+ project_in = nn.Sequential(
+ ops.Linear(dim, inner_dim),
+ nn.GELU()
+ ) if not glu else GEGLU(dim, inner_dim)
+
+ self.net = nn.Sequential(
+ project_in,
+ nn.Dropout(dropout),
+ ops.Linear(inner_dim, dim_out)
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+class GatedCrossAttentionDense(nn.Module):
+ def __init__(self, query_dim, context_dim, n_heads, d_head):
+ super().__init__()
+
+ self.attn = CrossAttention(
+ query_dim=query_dim,
+ context_dim=context_dim,
+ heads=n_heads,
+ dim_head=d_head,
+ operations=ops)
+ self.ff = FeedForward(query_dim, glu=True)
+
+ self.norm1 = ops.LayerNorm(query_dim)
+ self.norm2 = ops.LayerNorm(query_dim)
+
+ self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
+ self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
+
+ # this can be useful: we can externally change magnitude of tanh(alpha)
+ # for example, when it is set to 0, then the entire model is same as
+ # original one
+ self.scale = 1
+
+ def forward(self, x, objs):
+
+ x = x + self.scale * \
+ torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs)
+ x = x + self.scale * \
+ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
+
+ return x
+
+
+class GatedSelfAttentionDense(nn.Module):
+ def __init__(self, query_dim, context_dim, n_heads, d_head):
+ super().__init__()
+
+ # we need a linear projection since we need cat visual feature and obj
+ # feature
+ self.linear = ops.Linear(context_dim, query_dim)
+
+ self.attn = CrossAttention(
+ query_dim=query_dim,
+ context_dim=query_dim,
+ heads=n_heads,
+ dim_head=d_head,
+ operations=ops)
+ self.ff = FeedForward(query_dim, glu=True)
+
+ self.norm1 = ops.LayerNorm(query_dim)
+ self.norm2 = ops.LayerNorm(query_dim)
+
+ self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
+ self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
+
+ # this can be useful: we can externally change magnitude of tanh(alpha)
+ # for example, when it is set to 0, then the entire model is same as
+ # original one
+ self.scale = 1
+
+ def forward(self, x, objs):
+
+ N_visual = x.shape[1]
+ objs = self.linear(objs)
+
+ x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn(
+ self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :]
+ x = x + self.scale * \
+ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
+
+ return x
+
+
+class GatedSelfAttentionDense2(nn.Module):
+ def __init__(self, query_dim, context_dim, n_heads, d_head):
+ super().__init__()
+
+ # we need a linear projection since we need cat visual feature and obj
+ # feature
+ self.linear = ops.Linear(context_dim, query_dim)
+
+ self.attn = CrossAttention(
+ query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops)
+ self.ff = FeedForward(query_dim, glu=True)
+
+ self.norm1 = ops.LayerNorm(query_dim)
+ self.norm2 = ops.LayerNorm(query_dim)
+
+ self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
+ self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
+
+ # this can be useful: we can externally change magnitude of tanh(alpha)
+ # for example, when it is set to 0, then the entire model is same as
+ # original one
+ self.scale = 1
+
+ def forward(self, x, objs):
+
+ B, N_visual, _ = x.shape
+ B, N_ground, _ = objs.shape
+
+ objs = self.linear(objs)
+
+ # sanity check
+ size_v = math.sqrt(N_visual)
+ size_g = math.sqrt(N_ground)
+ assert int(size_v) == size_v, "Visual tokens must be square rootable"
+ assert int(size_g) == size_g, "Grounding tokens must be square rootable"
+ size_v = int(size_v)
+ size_g = int(size_g)
+
+ # select grounding token and resize it to visual token size as residual
+ out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[
+ :, N_visual:, :]
+ out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g)
+ out = torch.nn.functional.interpolate(
+ out, (size_v, size_v), mode='bicubic')
+ residual = out.reshape(B, -1, N_visual).permute(0, 2, 1)
+
+ # add residual to visual feature
+ x = x + self.scale * torch.tanh(self.alpha_attn) * residual
+ x = x + self.scale * \
+ torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
+
+ return x
+
+
+class FourierEmbedder():
+ def __init__(self, num_freqs=64, temperature=100):
+
+ self.num_freqs = num_freqs
+ self.temperature = temperature
+ self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)
+
+ @torch.no_grad()
+ def __call__(self, x, cat_dim=-1):
+ "x: arbitrary shape of tensor. dim: cat dim"
+ out = []
+ for freq in self.freq_bands:
+ out.append(torch.sin(freq * x))
+ out.append(torch.cos(freq * x))
+ return torch.cat(out, cat_dim)
+
+
+class PositionNet(nn.Module):
+ def __init__(self, in_dim, out_dim, fourier_freqs=8):
+ super().__init__()
+ self.in_dim = in_dim
+ self.out_dim = out_dim
+
+ self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
+ self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy
+
+ self.linears = nn.Sequential(
+ ops.Linear(self.in_dim + self.position_dim, 512),
+ nn.SiLU(),
+ ops.Linear(512, 512),
+ nn.SiLU(),
+ ops.Linear(512, out_dim),
+ )
+
+ self.null_positive_feature = torch.nn.Parameter(
+ torch.zeros([self.in_dim]))
+ self.null_position_feature = torch.nn.Parameter(
+ torch.zeros([self.position_dim]))
+
+ def forward(self, boxes, masks, positive_embeddings):
+ B, N, _ = boxes.shape
+ masks = masks.unsqueeze(-1)
+ positive_embeddings = positive_embeddings
+
+ # embedding position (it may includes padding as placeholder)
+ xyxy_embedding = self.fourier_embedder(boxes) # B*N*4 --> B*N*C
+
+ # learnable null embedding
+ positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1)
+ xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1)
+
+ # replace padding with learnable null embedding
+ positive_embeddings = positive_embeddings * \
+ masks + (1 - masks) * positive_null
+ xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null
+
+ objs = self.linears(
+ torch.cat([positive_embeddings, xyxy_embedding], dim=-1))
+ assert objs.shape == torch.Size([B, N, self.out_dim])
+ return objs
+
+
+class Gligen(nn.Module):
+ def __init__(self, modules, position_net, key_dim):
+ super().__init__()
+ self.module_list = nn.ModuleList(modules)
+ self.position_net = position_net
+ self.key_dim = key_dim
+ self.max_objs = 30
+ self.current_device = torch.device("cpu")
+
+ def _set_position(self, boxes, masks, positive_embeddings):
+ objs = self.position_net(boxes, masks, positive_embeddings)
+ def func(x, extra_options):
+ key = extra_options["transformer_index"]
+ module = self.module_list[key]
+ return module(x, objs.to(device=x.device, dtype=x.dtype))
+ return func
+
+ def set_position(self, latent_image_shape, position_params, device):
+ batch, c, h, w = latent_image_shape
+ masks = torch.zeros([self.max_objs], device="cpu")
+ boxes = []
+ positive_embeddings = []
+ for p in position_params:
+ x1 = (p[4]) / w
+ y1 = (p[3]) / h
+ x2 = (p[4] + p[2]) / w
+ y2 = (p[3] + p[1]) / h
+ masks[len(boxes)] = 1.0
+ boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)]
+ positive_embeddings += [p[0]]
+ append_boxes = []
+ append_conds = []
+ if len(boxes) < self.max_objs:
+ append_boxes = [torch.zeros(
+ [self.max_objs - len(boxes), 4], device="cpu")]
+ append_conds = [torch.zeros(
+ [self.max_objs - len(boxes), self.key_dim], device="cpu")]
+
+ box_out = torch.cat(
+ boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1)
+ masks = masks.unsqueeze(0).repeat(batch, 1)
+ conds = torch.cat(positive_embeddings +
+ append_conds).unsqueeze(0).repeat(batch, 1, 1)
+ return self._set_position(
+ box_out.to(device),
+ masks.to(device),
+ conds.to(device))
+
+ def set_empty(self, latent_image_shape, device):
+ batch, c, h, w = latent_image_shape
+ masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1)
+ box_out = torch.zeros([self.max_objs, 4],
+ device="cpu").repeat(batch, 1, 1)
+ conds = torch.zeros([self.max_objs, self.key_dim],
+ device="cpu").repeat(batch, 1, 1)
+ return self._set_position(
+ box_out.to(device),
+ masks.to(device),
+ conds.to(device))
+
+
+def load_gligen(sd):
+ sd_k = sd.keys()
+ output_list = []
+ key_dim = 768
+ for a in ["input_blocks", "middle_block", "output_blocks"]:
+ for b in range(20):
+ k_temp = filter(lambda k: "{}.{}.".format(a, b)
+ in k and ".fuser." in k, sd_k)
+ k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp)
+
+ n_sd = {}
+ for k in k_temp:
+ n_sd[k[1]] = sd[k[0]]
+ if len(n_sd) > 0:
+ query_dim = n_sd["linear.weight"].shape[0]
+ key_dim = n_sd["linear.weight"].shape[1]
+
+ if key_dim == 768: # SD1.x
+ n_heads = 8
+ d_head = query_dim // n_heads
+ else:
+ d_head = 64
+ n_heads = query_dim // d_head
+
+ gated = GatedSelfAttentionDense(
+ query_dim, key_dim, n_heads, d_head)
+ gated.load_state_dict(n_sd, strict=False)
+ output_list.append(gated)
+
+ if "position_net.null_positive_feature" in sd_k:
+ in_dim = sd["position_net.null_positive_feature"].shape[0]
+ out_dim = sd["position_net.linears.4.weight"].shape[0]
+
+ class WeightsLoader(torch.nn.Module):
+ pass
+ w = WeightsLoader()
+ w.position_net = PositionNet(in_dim, out_dim)
+ w.load_state_dict(sd, strict=False)
+
+ gligen = Gligen(output_list, w.position_net, key_dim)
+ return gligen
diff --git a/comfy/k_diffusion/deis.py b/comfy/k_diffusion/deis.py
new file mode 100644
index 0000000000000000000000000000000000000000..6074106566288434bdfc06e34e77732903c4b81b
--- /dev/null
+++ b/comfy/k_diffusion/deis.py
@@ -0,0 +1,121 @@
+#Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py
+#under Apache 2 license
+import torch
+import numpy as np
+
+# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis).
+#############################
+### Utils for DEIS solver ###
+#############################
+#----------------------------------------------------------------------------
+# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS.
+
+def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80):
+ vp_sigma = lambda beta_d, beta_min: lambda t: (np.e ** (0.5 * beta_d * (t ** 2) + beta_min * t) - 1) ** 0.5
+ vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d
+ vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1)
+ vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d
+ t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu())
+ return t_steps, vp_beta_min, vp_beta_d + vp_beta_min
+
+#----------------------------------------------------------------------------
+
+def cal_poly(prev_t, j, taus):
+ poly = 1
+ for k in range(prev_t.shape[0]):
+ if k == j:
+ continue
+ poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k])
+ return poly
+
+#----------------------------------------------------------------------------
+# Transfer from t to alpha_t.
+
+def t2alpha_fn(beta_0, beta_1, t):
+ return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0)
+
+#----------------------------------------------------------------------------
+
+def cal_intergrand(beta_0, beta_1, taus):
+ with torch.inference_mode(mode=False):
+ taus = taus.clone()
+ beta_0 = beta_0.clone()
+ beta_1 = beta_1.clone()
+ with torch.enable_grad():
+ taus.requires_grad_(True)
+ alpha = t2alpha_fn(beta_0, beta_1, taus)
+ log_alpha = alpha.log()
+ log_alpha.sum().backward()
+ d_log_alpha_dtau = taus.grad
+ integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha))
+ return integrand
+
+#----------------------------------------------------------------------------
+
+def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'):
+ """
+ Get the coefficient list for DEIS sampling.
+
+ Args:
+ t_steps: A pytorch tensor. The time steps for sampling.
+ max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4
+ N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'.
+ deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS.
+ Returns:
+ A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True.
+ """
+ if deis_mode == 'tab':
+ t_steps, beta_0, beta_1 = edm2t(t_steps)
+ C = []
+ for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+ order = min(i+1, max_order)
+ if order == 1:
+ C.append([])
+ else:
+ taus = torch.linspace(t_cur, t_next, N) # split the interval for integral appximation
+ dtau = (t_next - t_cur) / N
+ prev_t = t_steps[[i - k for k in range(order)]]
+ coeff_temp = []
+ integrand = cal_intergrand(beta_0, beta_1, taus)
+ for j in range(order):
+ poly = cal_poly(prev_t, j, taus)
+ coeff_temp.append(torch.sum(integrand * poly) * dtau)
+ C.append(coeff_temp)
+
+ elif deis_mode == 'rhoab':
+ # Analytical solution, second order
+ def get_def_intergral_2(a, b, start, end, c):
+ coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b
+ return coeff / ((c - a) * (c - b))
+
+ # Analytical solution, third order
+ def get_def_intergral_3(a, b, c, start, end, d):
+ coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \
+ + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c
+ return coeff / ((d - a) * (d - b) * (d - c))
+
+ C = []
+ for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+ order = min(i, max_order)
+ if order == 0:
+ C.append([])
+ else:
+ prev_t = t_steps[[i - k for k in range(order+1)]]
+ if order == 1:
+ coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1]))
+ coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur))
+ coeff_temp = [coeff_cur, coeff_prev1]
+ elif order == 2:
+ coeff_cur = get_def_intergral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur)
+ coeff_prev1 = get_def_intergral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1])
+ coeff_prev2 = get_def_intergral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2])
+ coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2]
+ elif order == 3:
+ coeff_cur = get_def_intergral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur)
+ coeff_prev1 = get_def_intergral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1])
+ coeff_prev2 = get_def_intergral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2])
+ coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3])
+ coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3]
+ C.append(coeff_temp)
+ return C
+
diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..a829316273d382d0600305a8bd8de1b7261d127d
--- /dev/null
+++ b/comfy/k_diffusion/sampling.py
@@ -0,0 +1,1247 @@
+import math
+
+from scipy import integrate
+import torch
+from torch import nn
+import torchsde
+from tqdm.auto import trange, tqdm
+
+from . import utils
+from . import deis
+import comfy.model_patcher
+import comfy.model_sampling
+
+def append_zero(x):
+ return torch.cat([x, x.new_zeros([1])])
+
+
+def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
+ """Constructs the noise schedule of Karras et al. (2022)."""
+ ramp = torch.linspace(0, 1, n, device=device)
+ min_inv_rho = sigma_min ** (1 / rho)
+ max_inv_rho = sigma_max ** (1 / rho)
+ sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+ return append_zero(sigmas).to(device)
+
+
+def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
+ """Constructs an exponential noise schedule."""
+ sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
+ return append_zero(sigmas)
+
+
+def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
+ """Constructs an polynomial in log sigma noise schedule."""
+ ramp = torch.linspace(1, 0, n, device=device) ** rho
+ sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min))
+ return append_zero(sigmas)
+
+
+def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
+ """Constructs a continuous VP noise schedule."""
+ t = torch.linspace(1, eps_s, n, device=device)
+ sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
+ return append_zero(sigmas)
+
+
+def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'):
+ """Constructs the noise schedule proposed by Tiankai et al. (2024). """
+ epsilon = 1e-5 # avoid log(0)
+ x = torch.linspace(0, 1, n, device=device)
+ clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max)
+ lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon)
+ sigmas = clamp(torch.exp(lmb))
+ return sigmas
+
+
+
+def to_d(x, sigma, denoised):
+ """Converts a denoiser output to a Karras ODE derivative."""
+ return (x - denoised) / utils.append_dims(sigma, x.ndim)
+
+
+def get_ancestral_step(sigma_from, sigma_to, eta=1.):
+ """Calculates the noise level (sigma_down) to step down to and the amount
+ of noise to add (sigma_up) when doing an ancestral sampling step."""
+ if not eta:
+ return sigma_to, 0.
+ sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)
+ sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
+ return sigma_down, sigma_up
+
+
+def default_noise_sampler(x):
+ return lambda sigma, sigma_next: torch.randn_like(x)
+
+
+class BatchedBrownianTree:
+ """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
+
+ def __init__(self, x, t0, t1, seed=None, **kwargs):
+ self.cpu_tree = True
+ if "cpu" in kwargs:
+ self.cpu_tree = kwargs.pop("cpu")
+ t0, t1, self.sign = self.sort(t0, t1)
+ w0 = kwargs.get('w0', torch.zeros_like(x))
+ if seed is None:
+ seed = torch.randint(0, 2 ** 63 - 1, []).item()
+ self.batched = True
+ try:
+ assert len(seed) == x.shape[0]
+ w0 = w0[0]
+ except TypeError:
+ seed = [seed]
+ self.batched = False
+ if self.cpu_tree:
+ self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
+ else:
+ self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
+
+ @staticmethod
+ def sort(a, b):
+ return (a, b, 1) if a < b else (b, a, -1)
+
+ def __call__(self, t0, t1):
+ t0, t1, sign = self.sort(t0, t1)
+ if self.cpu_tree:
+ w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
+ else:
+ w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+
+ return w if self.batched else w[0]
+
+
+class BrownianTreeNoiseSampler:
+ """A noise sampler backed by a torchsde.BrownianTree.
+
+ Args:
+ x (Tensor): The tensor whose shape, device and dtype to use to generate
+ random samples.
+ sigma_min (float): The low end of the valid interval.
+ sigma_max (float): The high end of the valid interval.
+ seed (int or List[int]): The random seed. If a list of seeds is
+ supplied instead of a single integer, then the noise sampler will
+ use one BrownianTree per batch item, each with its own seed.
+ transform (callable): A function that maps sigma to the sampler's
+ internal timestep.
+ """
+
+ def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
+ self.transform = transform
+ t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
+ self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
+
+ def __call__(self, sigma, sigma_next):
+ t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
+ return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
+
+
+@torch.no_grad()
+def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+ """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ if s_churn > 0:
+ gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+ sigma_hat = sigmas[i] * (gamma + 1)
+ else:
+ gamma = 0
+ sigma_hat = sigmas[i]
+
+ if gamma > 0:
+ eps = torch.randn_like(x) * s_noise
+ x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x, sigma_hat, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ dt = sigmas[i + 1] - sigma_hat
+ # Euler method
+ x = x + d * dt
+ return x
+
+
+@torch.no_grad()
+def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
+ return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
+ """Ancestral sampling with Euler method steps."""
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ d = to_d(x, sigmas[i], denoised)
+ # Euler method
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ if sigmas[i + 1] > 0:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+
+@torch.no_grad()
+def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None):
+ """Ancestral sampling with Euler method steps."""
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
+ sigma_down = sigmas[i+1] * downstep_ratio
+ alpha_ip1 = 1 - sigmas[i+1]
+ alpha_down = 1 - sigma_down
+ renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ # Euler method
+ sigma_down_i_ratio = sigma_down / sigmas[i]
+ x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised
+ if sigmas[i + 1] > 0 and eta > 0:
+ x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
+ return x
+
+@torch.no_grad()
+def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+ """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ if s_churn > 0:
+ gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+ sigma_hat = sigmas[i] * (gamma + 1)
+ else:
+ gamma = 0
+ sigma_hat = sigmas[i]
+
+ sigma_hat = sigmas[i] * (gamma + 1)
+ if gamma > 0:
+ eps = torch.randn_like(x) * s_noise
+ x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x, sigma_hat, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ dt = sigmas[i + 1] - sigma_hat
+ if sigmas[i + 1] == 0:
+ # Euler method
+ x = x + d * dt
+ else:
+ # Heun's method
+ x_2 = x + d * dt
+ denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+ d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+ d_prime = (d + d_2) / 2
+ x = x + d_prime * dt
+ return x
+
+
+@torch.no_grad()
+def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+ """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ if s_churn > 0:
+ gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+ sigma_hat = sigmas[i] * (gamma + 1)
+ else:
+ gamma = 0
+ sigma_hat = sigmas[i]
+
+ if gamma > 0:
+ eps = torch.randn_like(x) * s_noise
+ x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x, sigma_hat, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Euler method
+ dt = sigmas[i + 1] - sigma_hat
+ x = x + d * dt
+ else:
+ # DPM-Solver-2
+ sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp()
+ dt_1 = sigma_mid - sigma_hat
+ dt_2 = sigmas[i + 1] - sigma_hat
+ x_2 = x + d * dt_1
+ denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
+ d_2 = to_d(x_2, sigma_mid, denoised_2)
+ x = x + d_2 * dt_2
+ return x
+
+
+@torch.no_grad()
+def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
+ return sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
+
+ """Ancestral sampling with DPM-Solver second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ d = to_d(x, sigmas[i], denoised)
+ if sigma_down == 0:
+ # Euler method
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ else:
+ # DPM-Solver-2
+ sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()
+ dt_1 = sigma_mid - sigmas[i]
+ dt_2 = sigma_down - sigmas[i]
+ x_2 = x + d * dt_1
+ denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
+ d_2 = to_d(x_2, sigma_mid, denoised_2)
+ x = x + d_2 * dt_2
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+
+@torch.no_grad()
+def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """Ancestral sampling with DPM-Solver second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
+ sigma_down = sigmas[i+1] * downstep_ratio
+ alpha_ip1 = 1 - sigmas[i+1]
+ alpha_down = 1 - sigma_down
+ renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
+
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ d = to_d(x, sigmas[i], denoised)
+ if sigma_down == 0:
+ # Euler method
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ else:
+ # DPM-Solver-2
+ sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()
+ dt_1 = sigma_mid - sigmas[i]
+ dt_2 = sigma_down - sigmas[i]
+ x_2 = x + d * dt_1
+ denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
+ d_2 = to_d(x_2, sigma_mid, denoised_2)
+ x = x + d_2 * dt_2
+ x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
+ return x
+
+def linear_multistep_coeff(order, t, i, j):
+ if order - 1 > i:
+ raise ValueError(f'Order {order} too high for step {i}')
+ def fn(tau):
+ prod = 1.
+ for k in range(order):
+ if j == k:
+ continue
+ prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
+ return prod
+ return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
+
+
+@torch.no_grad()
+def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4):
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ sigmas_cpu = sigmas.detach().cpu().numpy()
+ ds = []
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ d = to_d(x, sigmas[i], denoised)
+ ds.append(d)
+ if len(ds) > order:
+ ds.pop(0)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ cur_order = min(i + 1, order)
+ coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
+ x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
+ return x
+
+
+class PIDStepSizeController:
+ """A PID controller for ODE adaptive step size control."""
+ def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
+ self.h = h
+ self.b1 = (pcoeff + icoeff + dcoeff) / order
+ self.b2 = -(pcoeff + 2 * dcoeff) / order
+ self.b3 = dcoeff / order
+ self.accept_safety = accept_safety
+ self.eps = eps
+ self.errs = []
+
+ def limiter(self, x):
+ return 1 + math.atan(x - 1)
+
+ def propose_step(self, error):
+ inv_error = 1 / (float(error) + self.eps)
+ if not self.errs:
+ self.errs = [inv_error, inv_error, inv_error]
+ self.errs[0] = inv_error
+ factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3
+ factor = self.limiter(factor)
+ accept = factor >= self.accept_safety
+ if accept:
+ self.errs[2] = self.errs[1]
+ self.errs[1] = self.errs[0]
+ self.h *= factor
+ return accept
+
+
+class DPMSolver(nn.Module):
+ """DPM-Solver. See https://arxiv.org/abs/2206.00927."""
+
+ def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None):
+ super().__init__()
+ self.model = model
+ self.extra_args = {} if extra_args is None else extra_args
+ self.eps_callback = eps_callback
+ self.info_callback = info_callback
+
+ def t(self, sigma):
+ return -sigma.log()
+
+ def sigma(self, t):
+ return t.neg().exp()
+
+ def eps(self, eps_cache, key, x, t, *args, **kwargs):
+ if key in eps_cache:
+ return eps_cache[key], eps_cache
+ sigma = self.sigma(t) * x.new_ones([x.shape[0]])
+ eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t)
+ if self.eps_callback is not None:
+ self.eps_callback()
+ return eps, {key: eps, **eps_cache}
+
+ def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):
+ eps_cache = {} if eps_cache is None else eps_cache
+ h = t_next - t
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+ x_1 = x - self.sigma(t_next) * h.expm1() * eps
+ return x_1, eps_cache
+
+ def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):
+ eps_cache = {} if eps_cache is None else eps_cache
+ h = t_next - t
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+ s1 = t + r1 * h
+ u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
+ eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
+ x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps)
+ return x_2, eps_cache
+
+ def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None):
+ eps_cache = {} if eps_cache is None else eps_cache
+ h = t_next - t
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+ s1 = t + r1 * h
+ s2 = t + r2 * h
+ u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
+ eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
+ u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps)
+ eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2)
+ x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps)
+ return x_3, eps_cache
+
+ def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ if not t_end > t_start and eta:
+ raise ValueError('eta must be 0 for reverse sampling')
+
+ m = math.floor(nfe / 3) + 1
+ ts = torch.linspace(t_start, t_end, m + 1, device=x.device)
+
+ if nfe % 3 == 0:
+ orders = [3] * (m - 2) + [2, 1]
+ else:
+ orders = [3] * (m - 1) + [nfe % 3]
+
+ for i in range(len(orders)):
+ eps_cache = {}
+ t, t_next = ts[i], ts[i + 1]
+ if eta:
+ sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta)
+ t_next_ = torch.minimum(t_end, self.t(sd))
+ su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5
+ else:
+ t_next_, su = t_next, 0.
+
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+ denoised = x - self.sigma(t) * eps
+ if self.info_callback is not None:
+ self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised})
+
+ if orders[i] == 1:
+ x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache)
+ elif orders[i] == 2:
+ x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache)
+ else:
+ x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache)
+
+ x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next))
+
+ return x
+
+ def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ if order not in {2, 3}:
+ raise ValueError('order should be 2 or 3')
+ forward = t_end > t_start
+ if not forward and eta:
+ raise ValueError('eta must be 0 for reverse sampling')
+ h_init = abs(h_init) * (1 if forward else -1)
+ atol = torch.tensor(atol)
+ rtol = torch.tensor(rtol)
+ s = t_start
+ x_prev = x
+ accept = True
+ pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety)
+ info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0}
+
+ while s < t_end - 1e-5 if forward else s > t_end + 1e-5:
+ eps_cache = {}
+ t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h)
+ if eta:
+ sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta)
+ t_ = torch.minimum(t_end, self.t(sd))
+ su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5
+ else:
+ t_, su = t, 0.
+
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, s)
+ denoised = x - self.sigma(s) * eps
+
+ if order == 2:
+ x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache)
+ x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache)
+ else:
+ x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache)
+ x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache)
+ delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs()))
+ error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5
+ accept = pid.propose_step(error)
+ if accept:
+ x_prev = x_low
+ x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t))
+ s = t
+ info['n_accept'] += 1
+ else:
+ info['n_reject'] += 1
+ info['nfe'] += order
+ info['steps'] += 1
+
+ if self.info_callback is not None:
+ self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info})
+
+ return x, info
+
+
+@torch.no_grad()
+def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None):
+ """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927."""
+ if sigma_min <= 0 or sigma_max <= 0:
+ raise ValueError('sigma_min and sigma_max must not be 0')
+ with tqdm(total=n, disable=disable) as pbar:
+ dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
+ if callback is not None:
+ dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
+ return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler)
+
+
+@torch.no_grad()
+def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False):
+ """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927."""
+ if sigma_min <= 0 or sigma_max <= 0:
+ raise ValueError('sigma_min and sigma_max must not be 0')
+ with tqdm(disable=disable) as pbar:
+ dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
+ if callback is not None:
+ dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
+ x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler)
+ if return_info:
+ return x, info
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
+ return sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
+
+ """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda t: t.neg().exp()
+ t_fn = lambda sigma: sigma.log().neg()
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigma_down == 0:
+ # Euler method
+ d = to_d(x, sigmas[i], denoised)
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ else:
+ # DPM-Solver++(2S)
+ t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
+ r = 1 / 2
+ h = t_next - t
+ s = t + r * h
+ x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised
+ denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+ x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2
+ # Noise addition
+ if sigmas[i + 1] > 0:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
+ lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
+
+ # logged_x = x.unsqueeze(0)
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
+ sigma_down = sigmas[i+1] * downstep_ratio
+ alpha_ip1 = 1 - sigmas[i+1]
+ alpha_down = 1 - sigma_down
+ renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
+ # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Euler method
+ d = to_d(x, sigmas[i], denoised)
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ else:
+ # DPM-Solver++(2S)
+ if sigmas[i] == 1.0:
+ sigma_s = 0.9999
+ else:
+ t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down)
+ r = 1 / 2
+ h = t_down - t_i
+ s = t_i + r * h
+ sigma_s = sigma_fn(s)
+ # sigma_s = sigmas[i+1]
+ sigma_s_i_ratio = sigma_s / sigmas[i]
+ u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised
+ D_i = model(u, sigma_s * s_in, **extra_args)
+ sigma_down_i_ratio = sigma_down / sigmas[i]
+ x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i
+ # print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff)
+ # Noise addition
+ if sigmas[i + 1] > 0 and eta > 0:
+ x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
+ # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0)
+ return x
+
+@torch.no_grad()
+def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
+ """DPM-Solver++ (stochastic)."""
+ if len(sigmas) <= 1:
+ return x
+
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ seed = extra_args.get("seed", None)
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda t: t.neg().exp()
+ t_fn = lambda sigma: sigma.log().neg()
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Euler method
+ d = to_d(x, sigmas[i], denoised)
+ dt = sigmas[i + 1] - sigmas[i]
+ x = x + d * dt
+ else:
+ # DPM-Solver++
+ t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+ h = t_next - t
+ s = t + h * r
+ fac = 1 / (2 * r)
+
+ # Step 1
+ sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta)
+ s_ = t_fn(sd)
+ x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised
+ x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su
+ denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+
+ # Step 2
+ sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta)
+ t_next_ = t_fn(sd)
+ denoised_d = (1 - fac) * denoised + fac * denoised_2
+ x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d
+ x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+ """DPM-Solver++(2M)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda t: t.neg().exp()
+ t_fn = lambda sigma: sigma.log().neg()
+ old_denoised = None
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+ h = t_next - t
+ if old_denoised is None or sigmas[i + 1] == 0:
+ x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
+ else:
+ h_last = t - t_fn(sigmas[i - 1])
+ r = h_last / h
+ denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
+ x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
+ old_denoised = denoised
+ return x
+
+@torch.no_grad()
+def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+ """DPM-Solver++(2M) SDE."""
+ if len(sigmas) <= 1:
+ return x
+
+ if solver_type not in {'heun', 'midpoint'}:
+ raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
+
+ seed = extra_args.get("seed", None)
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+
+ old_denoised = None
+ h_last = None
+ h = None
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Denoising step
+ x = denoised
+ else:
+ # DPM-Solver++(2M) SDE
+ t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+ h = s - t
+ eta_h = eta * h
+
+ x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
+
+ if old_denoised is not None:
+ r = h_last / h
+ if solver_type == 'heun':
+ x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
+ elif solver_type == 'midpoint':
+ x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
+
+ if eta:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
+
+ old_denoised = denoised
+ h_last = h
+ return x
+
+@torch.no_grad()
+def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """DPM-Solver++(3M) SDE."""
+
+ if len(sigmas) <= 1:
+ return x
+
+ seed = extra_args.get("seed", None)
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+
+ denoised_1, denoised_2 = None, None
+ h, h_1, h_2 = None, None, None
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Denoising step
+ x = denoised
+ else:
+ t, s = -sigmas[i].log(), -sigmas[i + 1].log()
+ h = s - t
+ h_eta = h * (eta + 1)
+
+ x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
+
+ if h_2 is not None:
+ r0 = h_1 / h
+ r1 = h_2 / h
+ d1_0 = (denoised - denoised_1) / r0
+ d1_1 = (denoised_1 - denoised_2) / r1
+ d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
+ d2 = (d1_0 - d1_1) / (r0 + r1)
+ phi_2 = h_eta.neg().expm1() / h_eta + 1
+ phi_3 = phi_2 / h_eta - 0.5
+ x = x + phi_2 * d1 - phi_3 * d2
+ elif h_1 is not None:
+ r = h_1 / h
+ d = (denoised - denoised_1) / r
+ phi_2 = h_eta.neg().expm1() / h_eta + 1
+ x = x + phi_2 * d
+
+ if eta:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
+
+ denoised_1, denoised_2 = denoised, denoised_1
+ h_1, h_2 = h, h_1
+ return x
+
+@torch.no_grad()
+def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ if len(sigmas) <= 1:
+ return x
+
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
+ return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
+
+@torch.no_grad()
+def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+ if len(sigmas) <= 1:
+ return x
+
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
+ return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
+
+@torch.no_grad()
+def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
+ if len(sigmas) <= 1:
+ return x
+
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
+ return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
+
+
+def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
+ alpha_cumprod = 1 / ((sigma * sigma) + 1)
+ alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1)
+ alpha = (alpha_cumprod / alpha_cumprod_prev)
+
+ mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt())
+ if sigma_prev > 0:
+ mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev)
+ return mu
+
+def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None):
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler)
+ if sigmas[i + 1] != 0:
+ x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0)
+ return x
+
+
+@torch.no_grad()
+def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
+ return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
+
+@torch.no_grad()
+def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ x = denoised
+ if sigmas[i + 1] > 0:
+ x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
+ return x
+
+
+
+@torch.no_grad()
+def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+ # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ s_end = sigmas[-1]
+ for i in trange(len(sigmas) - 1, disable=disable):
+ gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+ eps = torch.randn_like(x) * s_noise
+ sigma_hat = sigmas[i] * (gamma + 1)
+ if gamma > 0:
+ x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x, sigma_hat, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ dt = sigmas[i + 1] - sigma_hat
+ if sigmas[i + 1] == s_end:
+ # Euler method
+ x = x + d * dt
+ elif sigmas[i + 2] == s_end:
+
+ # Heun's method
+ x_2 = x + d * dt
+ denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+ d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+
+ w = 2 * sigmas[0]
+ w2 = sigmas[i+1]/w
+ w1 = 1 - w2
+
+ d_prime = d * w1 + d_2 * w2
+
+
+ x = x + d_prime * dt
+
+ else:
+ # Heun++
+ x_2 = x + d * dt
+ denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+ d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+ dt_2 = sigmas[i + 2] - sigmas[i + 1]
+
+ x_3 = x_2 + d_2 * dt_2
+ denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args)
+ d_3 = to_d(x_3, sigmas[i + 2], denoised_3)
+
+ w = 3 * sigmas[0]
+ w2 = sigmas[i + 1] / w
+ w3 = sigmas[i + 2] / w
+ w1 = 1 - w2 - w3
+
+ d_prime = w1 * d + w2 * d_2 + w3 * d_3
+ x = x + d_prime * dt
+ return x
+
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+
+ x_next = x
+
+ buffer_model = []
+ for i in trange(len(sigmas) - 1, disable=disable):
+ t_cur = sigmas[i]
+ t_next = sigmas[i + 1]
+
+ x_cur = x_next
+
+ denoised = model(x_cur, t_cur * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ d_cur = (x_cur - denoised) / t_cur
+
+ order = min(max_order, i+1)
+ if order == 1: # First Euler step.
+ x_next = x_cur + (t_next - t_cur) * d_cur
+ elif order == 2: # Use one history point.
+ x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
+ elif order == 3: # Use two history points.
+ x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12
+ elif order == 4: # Use three history points.
+ x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24
+
+ if len(buffer_model) == max_order - 1:
+ for k in range(max_order - 2):
+ buffer_model[k] = buffer_model[k+1]
+ buffer_model[-1] = d_cur
+ else:
+ buffer_model.append(d_cur)
+
+ return x_next
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+
+ x_next = x
+ t_steps = sigmas
+
+ buffer_model = []
+ for i in trange(len(sigmas) - 1, disable=disable):
+ t_cur = sigmas[i]
+ t_next = sigmas[i + 1]
+
+ x_cur = x_next
+
+ denoised = model(x_cur, t_cur * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ d_cur = (x_cur - denoised) / t_cur
+
+ order = min(max_order, i+1)
+ if order == 1: # First Euler step.
+ x_next = x_cur + (t_next - t_cur) * d_cur
+ elif order == 2: # Use one history point.
+ h_n = (t_next - t_cur)
+ h_n_1 = (t_cur - t_steps[i-1])
+ coeff1 = (2 + (h_n / h_n_1)) / 2
+ coeff2 = -(h_n / h_n_1) / 2
+ x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1])
+ elif order == 3: # Use two history points.
+ h_n = (t_next - t_cur)
+ h_n_1 = (t_cur - t_steps[i-1])
+ h_n_2 = (t_steps[i-1] - t_steps[i-2])
+ temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
+ coeff1 = (2 + (h_n / h_n_1)) / 2 + temp
+ coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp
+ coeff3 = temp * h_n_1 / h_n_2
+ x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2])
+ elif order == 4: # Use three history points.
+ h_n = (t_next - t_cur)
+ h_n_1 = (t_cur - t_steps[i-1])
+ h_n_2 = (t_steps[i-1] - t_steps[i-2])
+ h_n_3 = (t_steps[i-2] - t_steps[i-3])
+ temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
+ temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \
+ * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))
+ coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2
+ coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2
+ coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2
+ coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2
+ x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3])
+
+ if len(buffer_model) == max_order - 1:
+ for k in range(max_order - 2):
+ buffer_model[k] = buffer_model[k+1]
+ buffer_model[-1] = d_cur.detach()
+ else:
+ buffer_model.append(d_cur.detach())
+
+ return x_next
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+@torch.no_grad()
+def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'):
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+
+ x_next = x
+ t_steps = sigmas
+
+ coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode)
+
+ buffer_model = []
+ for i in trange(len(sigmas) - 1, disable=disable):
+ t_cur = sigmas[i]
+ t_next = sigmas[i + 1]
+
+ x_cur = x_next
+
+ denoised = model(x_cur, t_cur * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ d_cur = (x_cur - denoised) / t_cur
+
+ order = min(max_order, i+1)
+ if t_next <= 0:
+ order = 1
+
+ if order == 1: # First Euler step.
+ x_next = x_cur + (t_next - t_cur) * d_cur
+ elif order == 2: # Use one history point.
+ coeff_cur, coeff_prev1 = coeff_list[i]
+ x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1]
+ elif order == 3: # Use two history points.
+ coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i]
+ x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2]
+ elif order == 4: # Use three history points.
+ coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i]
+ x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3]
+
+ if len(buffer_model) == max_order - 1:
+ for k in range(max_order - 2):
+ buffer_model[k] = buffer_model[k+1]
+ buffer_model[-1] = d_cur.detach()
+ else:
+ buffer_model.append(d_cur.detach())
+
+ return x_next
+
+@torch.no_grad()
+def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+ extra_args = {} if extra_args is None else extra_args
+
+ temp = [0]
+ def post_cfg_function(args):
+ temp[0] = args["uncond_denoised"]
+ return args["denoised"]
+
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ sigma_hat = sigmas[i]
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x, sigma_hat, temp[0])
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ # Euler method
+ x = denoised + d * sigmas[i + 1]
+ return x
+
+@torch.no_grad()
+def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """Ancestral sampling with Euler method steps."""
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+
+ temp = [0]
+ def post_cfg_function(args):
+ temp[0] = args["uncond_denoised"]
+ return args["denoised"]
+
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ d = to_d(x, sigmas[i], temp[0])
+ # Euler method
+ x = denoised + d * sigma_down
+ if sigmas[i + 1] > 0:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+
+ temp = [0]
+ def post_cfg_function(args):
+ temp[0] = args["uncond_denoised"]
+ return args["denoised"]
+
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda t: t.neg().exp()
+ t_fn = lambda sigma: sigma.log().neg()
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigma_down == 0:
+ # Euler method
+ d = to_d(x, sigmas[i], temp[0])
+ x = denoised + d * sigma_down
+ else:
+ # DPM-Solver++(2S)
+ t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
+ # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird
+ r = 1 / 2
+ h = t_next - t
+ s = t + r * h
+ x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised
+ denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+ x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2
+ # Noise addition
+ if sigmas[i + 1] > 0:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+
+@torch.no_grad()
+def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+ """DPM-Solver++(2M)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ t_fn = lambda sigma: sigma.log().neg()
+
+ old_uncond_denoised = None
+ uncond_denoised = None
+ def post_cfg_function(args):
+ nonlocal uncond_denoised
+ uncond_denoised = args["uncond_denoised"]
+ return args["denoised"]
+
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+ h = t_next - t
+ if old_uncond_denoised is None or sigmas[i + 1] == 0:
+ denoised_mix = -torch.exp(-h) * uncond_denoised
+ else:
+ h_last = t - t_fn(sigmas[i - 1])
+ r = h_last / h
+ denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised)
+ x = denoised + denoised_mix + torch.exp(-h) * x
+ old_uncond_denoised = uncond_denoised
+ return x
diff --git a/comfy/k_diffusion/utils.py b/comfy/k_diffusion/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a644df2f3cf82b32ac6e9bf2cb7bfc70c95e05f9
--- /dev/null
+++ b/comfy/k_diffusion/utils.py
@@ -0,0 +1,313 @@
+from contextlib import contextmanager
+import hashlib
+import math
+from pathlib import Path
+import shutil
+import urllib
+import warnings
+
+from PIL import Image
+import torch
+from torch import nn, optim
+from torch.utils import data
+
+
+def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'):
+ """Apply passed in transforms for HuggingFace Datasets."""
+ images = [transform(image.convert(mode)) for image in examples[image_key]]
+ return {image_key: images}
+
+
+def append_dims(x, target_dims):
+ """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+ dims_to_append = target_dims - x.ndim
+ if dims_to_append < 0:
+ raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+ expanded = x[(...,) + (None,) * dims_to_append]
+ # MPS will get inf values if it tries to index into the new axes, but detaching fixes this.
+ # https://github.com/pytorch/pytorch/issues/84364
+ return expanded.detach().clone() if expanded.device.type == 'mps' else expanded
+
+
+def n_params(module):
+ """Returns the number of trainable parameters in a module."""
+ return sum(p.numel() for p in module.parameters())
+
+
+def download_file(path, url, digest=None):
+ """Downloads a file if it does not exist, optionally checking its SHA-256 hash."""
+ path = Path(path)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ if not path.exists():
+ with urllib.request.urlopen(url) as response, open(path, 'wb') as f:
+ shutil.copyfileobj(response, f)
+ if digest is not None:
+ file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest()
+ if digest != file_digest:
+ raise OSError(f'hash of {path} (url: {url}) failed to validate')
+ return path
+
+
+@contextmanager
+def train_mode(model, mode=True):
+ """A context manager that places a model into training mode and restores
+ the previous mode on exit."""
+ modes = [module.training for module in model.modules()]
+ try:
+ yield model.train(mode)
+ finally:
+ for i, module in enumerate(model.modules()):
+ module.training = modes[i]
+
+
+def eval_mode(model):
+ """A context manager that places a model into evaluation mode and restores
+ the previous mode on exit."""
+ return train_mode(model, False)
+
+
+@torch.no_grad()
+def ema_update(model, averaged_model, decay):
+ """Incorporates updated model parameters into an exponential moving averaged
+ version of a model. It should be called after each optimizer step."""
+ model_params = dict(model.named_parameters())
+ averaged_params = dict(averaged_model.named_parameters())
+ assert model_params.keys() == averaged_params.keys()
+
+ for name, param in model_params.items():
+ averaged_params[name].mul_(decay).add_(param, alpha=1 - decay)
+
+ model_buffers = dict(model.named_buffers())
+ averaged_buffers = dict(averaged_model.named_buffers())
+ assert model_buffers.keys() == averaged_buffers.keys()
+
+ for name, buf in model_buffers.items():
+ averaged_buffers[name].copy_(buf)
+
+
+class EMAWarmup:
+ """Implements an EMA warmup using an inverse decay schedule.
+ If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are
+ good values for models you plan to train for a million or more steps (reaches decay
+ factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models
+ you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at
+ 215.4k steps).
+ Args:
+ inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
+ power (float): Exponential factor of EMA warmup. Default: 1.
+ min_value (float): The minimum EMA decay rate. Default: 0.
+ max_value (float): The maximum EMA decay rate. Default: 1.
+ start_at (int): The epoch to start averaging at. Default: 0.
+ last_epoch (int): The index of last epoch. Default: 0.
+ """
+
+ def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0,
+ last_epoch=0):
+ self.inv_gamma = inv_gamma
+ self.power = power
+ self.min_value = min_value
+ self.max_value = max_value
+ self.start_at = start_at
+ self.last_epoch = last_epoch
+
+ def state_dict(self):
+ """Returns the state of the class as a :class:`dict`."""
+ return dict(self.__dict__.items())
+
+ def load_state_dict(self, state_dict):
+ """Loads the class's state.
+ Args:
+ state_dict (dict): scaler state. Should be an object returned
+ from a call to :meth:`state_dict`.
+ """
+ self.__dict__.update(state_dict)
+
+ def get_value(self):
+ """Gets the current EMA decay rate."""
+ epoch = max(0, self.last_epoch - self.start_at)
+ value = 1 - (1 + epoch / self.inv_gamma) ** -self.power
+ return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value))
+
+ def step(self):
+ """Updates the step count."""
+ self.last_epoch += 1
+
+
+class InverseLR(optim.lr_scheduler._LRScheduler):
+ """Implements an inverse decay learning rate schedule with an optional exponential
+ warmup. When last_epoch=-1, sets initial lr as lr.
+ inv_gamma is the number of steps/epochs required for the learning rate to decay to
+ (1 / 2)**power of its original value.
+ Args:
+ optimizer (Optimizer): Wrapped optimizer.
+ inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1.
+ power (float): Exponential factor of learning rate decay. Default: 1.
+ warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)
+ Default: 0.
+ min_lr (float): The minimum learning rate. Default: 0.
+ last_epoch (int): The index of last epoch. Default: -1.
+ verbose (bool): If ``True``, prints a message to stdout for
+ each update. Default: ``False``.
+ """
+
+ def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0.,
+ last_epoch=-1, verbose=False):
+ self.inv_gamma = inv_gamma
+ self.power = power
+ if not 0. <= warmup < 1:
+ raise ValueError('Invalid value for warmup')
+ self.warmup = warmup
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch, verbose)
+
+ def get_lr(self):
+ if not self._get_lr_called_within_step:
+ warnings.warn("To get the last learning rate computed by the scheduler, "
+ "please use `get_last_lr()`.")
+
+ return self._get_closed_form_lr()
+
+ def _get_closed_form_lr(self):
+ warmup = 1 - self.warmup ** (self.last_epoch + 1)
+ lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power
+ return [warmup * max(self.min_lr, base_lr * lr_mult)
+ for base_lr in self.base_lrs]
+
+
+class ExponentialLR(optim.lr_scheduler._LRScheduler):
+ """Implements an exponential learning rate schedule with an optional exponential
+ warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate
+ continuously by decay (default 0.5) every num_steps steps.
+ Args:
+ optimizer (Optimizer): Wrapped optimizer.
+ num_steps (float): The number of steps to decay the learning rate by decay in.
+ decay (float): The factor by which to decay the learning rate every num_steps
+ steps. Default: 0.5.
+ warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)
+ Default: 0.
+ min_lr (float): The minimum learning rate. Default: 0.
+ last_epoch (int): The index of last epoch. Default: -1.
+ verbose (bool): If ``True``, prints a message to stdout for
+ each update. Default: ``False``.
+ """
+
+ def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0.,
+ last_epoch=-1, verbose=False):
+ self.num_steps = num_steps
+ self.decay = decay
+ if not 0. <= warmup < 1:
+ raise ValueError('Invalid value for warmup')
+ self.warmup = warmup
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch, verbose)
+
+ def get_lr(self):
+ if not self._get_lr_called_within_step:
+ warnings.warn("To get the last learning rate computed by the scheduler, "
+ "please use `get_last_lr()`.")
+
+ return self._get_closed_form_lr()
+
+ def _get_closed_form_lr(self):
+ warmup = 1 - self.warmup ** (self.last_epoch + 1)
+ lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch
+ return [warmup * max(self.min_lr, base_lr * lr_mult)
+ for base_lr in self.base_lrs]
+
+
+def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32):
+ """Draws samples from an lognormal distribution."""
+ return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp()
+
+
+def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):
+ """Draws samples from an optionally truncated log-logistic distribution."""
+ min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64)
+ max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64)
+ min_cdf = min_value.log().sub(loc).div(scale).sigmoid()
+ max_cdf = max_value.log().sub(loc).div(scale).sigmoid()
+ u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf
+ return u.logit().mul(scale).add(loc).exp().to(dtype)
+
+
+def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32):
+ """Draws samples from an log-uniform distribution."""
+ min_value = math.log(min_value)
+ max_value = math.log(max_value)
+ return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp()
+
+
+def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):
+ """Draws samples from a truncated v-diffusion training timestep distribution."""
+ min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi
+ max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi
+ u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf
+ return torch.tan(u * math.pi / 2) * sigma_data
+
+
+def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32):
+ """Draws samples from a split lognormal distribution."""
+ n = torch.randn(shape, device=device, dtype=dtype).abs()
+ u = torch.rand(shape, device=device, dtype=dtype)
+ n_left = n * -scale_1 + loc
+ n_right = n * scale_2 + loc
+ ratio = scale_1 / (scale_1 + scale_2)
+ return torch.where(u < ratio, n_left, n_right).exp()
+
+
+class FolderOfImages(data.Dataset):
+ """Recursively finds all images in a directory. It does not support
+ classes/targets."""
+
+ IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'}
+
+ def __init__(self, root, transform=None):
+ super().__init__()
+ self.root = Path(root)
+ self.transform = nn.Identity() if transform is None else transform
+ self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS)
+
+ def __repr__(self):
+ return f'FolderOfImages(root="{self.root}", len: {len(self)})'
+
+ def __len__(self):
+ return len(self.paths)
+
+ def __getitem__(self, key):
+ path = self.paths[key]
+ with open(path, 'rb') as f:
+ image = Image.open(f).convert('RGB')
+ image = self.transform(image)
+ return image,
+
+
+class CSVLogger:
+ def __init__(self, filename, columns):
+ self.filename = Path(filename)
+ self.columns = columns
+ if self.filename.exists():
+ self.file = open(self.filename, 'a')
+ else:
+ self.file = open(self.filename, 'w')
+ self.write(*self.columns)
+
+ def write(self, *args):
+ print(*args, sep=',', file=self.file, flush=True)
+
+
+@contextmanager
+def tf32_mode(cudnn=None, matmul=None):
+ """A context manager that sets whether TF32 is allowed on cuDNN or matmul."""
+ cudnn_old = torch.backends.cudnn.allow_tf32
+ matmul_old = torch.backends.cuda.matmul.allow_tf32
+ try:
+ if cudnn is not None:
+ torch.backends.cudnn.allow_tf32 = cudnn
+ if matmul is not None:
+ torch.backends.cuda.matmul.allow_tf32 = matmul
+ yield
+ finally:
+ if cudnn is not None:
+ torch.backends.cudnn.allow_tf32 = cudnn_old
+ if matmul is not None:
+ torch.backends.cuda.matmul.allow_tf32 = matmul_old
diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
new file mode 100644
index 0000000000000000000000000000000000000000..1afd391cf5806d34c562641c82be341c020c36a2
--- /dev/null
+++ b/comfy/latent_formats.py
@@ -0,0 +1,354 @@
+import torch
+
+class LatentFormat:
+ scale_factor = 1.0
+ latent_channels = 4
+ latent_rgb_factors = None
+ latent_rgb_factors_bias = None
+ taesd_decoder_name = None
+
+ def process_in(self, latent):
+ return latent * self.scale_factor
+
+ def process_out(self, latent):
+ return latent / self.scale_factor
+
+class SD15(LatentFormat):
+ def __init__(self, scale_factor=0.18215):
+ self.scale_factor = scale_factor
+ self.latent_rgb_factors = [
+ # R G B
+ [ 0.3512, 0.2297, 0.3227],
+ [ 0.3250, 0.4974, 0.2350],
+ [-0.2829, 0.1762, 0.2721],
+ [-0.2120, -0.2616, -0.7177]
+ ]
+ self.taesd_decoder_name = "taesd_decoder"
+
+class SDXL(LatentFormat):
+ scale_factor = 0.13025
+
+ def __init__(self):
+ self.latent_rgb_factors = [
+ # R G B
+ [ 0.3651, 0.4232, 0.4341],
+ [-0.2533, -0.0042, 0.1068],
+ [ 0.1076, 0.1111, -0.0362],
+ [-0.3165, -0.2492, -0.2188]
+ ]
+ self.latent_rgb_factors_bias = [ 0.1084, -0.0175, -0.0011]
+
+ self.taesd_decoder_name = "taesdxl_decoder"
+
+class SDXL_Playground_2_5(LatentFormat):
+ def __init__(self):
+ self.scale_factor = 0.5
+ self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
+ self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)
+
+ self.latent_rgb_factors = [
+ # R G B
+ [ 0.3920, 0.4054, 0.4549],
+ [-0.2634, -0.0196, 0.0653],
+ [ 0.0568, 0.1687, -0.0755],
+ [-0.3112, -0.2359, -0.2076]
+ ]
+ self.taesd_decoder_name = "taesdxl_decoder"
+
+ def process_in(self, latent):
+ latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+ latents_std = self.latents_std.to(latent.device, latent.dtype)
+ return (latent - latents_mean) * self.scale_factor / latents_std
+
+ def process_out(self, latent):
+ latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+ latents_std = self.latents_std.to(latent.device, latent.dtype)
+ return latent * latents_std / self.scale_factor + latents_mean
+
+
+class SD_X4(LatentFormat):
+ def __init__(self):
+ self.scale_factor = 0.08333
+ self.latent_rgb_factors = [
+ [-0.2340, -0.3863, -0.3257],
+ [ 0.0994, 0.0885, -0.0908],
+ [-0.2833, -0.2349, -0.3741],
+ [ 0.2523, -0.0055, -0.1651]
+ ]
+
+class SC_Prior(LatentFormat):
+ latent_channels = 16
+ def __init__(self):
+ self.scale_factor = 1.0
+ self.latent_rgb_factors = [
+ [-0.0326, -0.0204, -0.0127],
+ [-0.1592, -0.0427, 0.0216],
+ [ 0.0873, 0.0638, -0.0020],
+ [-0.0602, 0.0442, 0.1304],
+ [ 0.0800, -0.0313, -0.1796],
+ [-0.0810, -0.0638, -0.1581],
+ [ 0.1791, 0.1180, 0.0967],
+ [ 0.0740, 0.1416, 0.0432],
+ [-0.1745, -0.1888, -0.1373],
+ [ 0.2412, 0.1577, 0.0928],
+ [ 0.1908, 0.0998, 0.0682],
+ [ 0.0209, 0.0365, -0.0092],
+ [ 0.0448, -0.0650, -0.1728],
+ [-0.1658, -0.1045, -0.1308],
+ [ 0.0542, 0.1545, 0.1325],
+ [-0.0352, -0.1672, -0.2541]
+ ]
+
+class SC_B(LatentFormat):
+ def __init__(self):
+ self.scale_factor = 1.0 / 0.43
+ self.latent_rgb_factors = [
+ [ 0.1121, 0.2006, 0.1023],
+ [-0.2093, -0.0222, -0.0195],
+ [-0.3087, -0.1535, 0.0366],
+ [ 0.0290, -0.1574, -0.4078]
+ ]
+
+class SD3(LatentFormat):
+ latent_channels = 16
+ def __init__(self):
+ self.scale_factor = 1.5305
+ self.shift_factor = 0.0609
+ self.latent_rgb_factors = [
+ [-0.0922, -0.0175, 0.0749],
+ [ 0.0311, 0.0633, 0.0954],
+ [ 0.1994, 0.0927, 0.0458],
+ [ 0.0856, 0.0339, 0.0902],
+ [ 0.0587, 0.0272, -0.0496],
+ [-0.0006, 0.1104, 0.0309],
+ [ 0.0978, 0.0306, 0.0427],
+ [-0.0042, 0.1038, 0.1358],
+ [-0.0194, 0.0020, 0.0669],
+ [-0.0488, 0.0130, -0.0268],
+ [ 0.0922, 0.0988, 0.0951],
+ [-0.0278, 0.0524, -0.0542],
+ [ 0.0332, 0.0456, 0.0895],
+ [-0.0069, -0.0030, -0.0810],
+ [-0.0596, -0.0465, -0.0293],
+ [-0.1448, -0.1463, -0.1189]
+ ]
+ self.latent_rgb_factors_bias = [0.2394, 0.2135, 0.1925]
+ self.taesd_decoder_name = "taesd3_decoder"
+
+ def process_in(self, latent):
+ return (latent - self.shift_factor) * self.scale_factor
+
+ def process_out(self, latent):
+ return (latent / self.scale_factor) + self.shift_factor
+
+class StableAudio1(LatentFormat):
+ latent_channels = 64
+
+class Flux(SD3):
+ latent_channels = 16
+ def __init__(self):
+ self.scale_factor = 0.3611
+ self.shift_factor = 0.1159
+ self.latent_rgb_factors =[
+ [-0.0346, 0.0244, 0.0681],
+ [ 0.0034, 0.0210, 0.0687],
+ [ 0.0275, -0.0668, -0.0433],
+ [-0.0174, 0.0160, 0.0617],
+ [ 0.0859, 0.0721, 0.0329],
+ [ 0.0004, 0.0383, 0.0115],
+ [ 0.0405, 0.0861, 0.0915],
+ [-0.0236, -0.0185, -0.0259],
+ [-0.0245, 0.0250, 0.1180],
+ [ 0.1008, 0.0755, -0.0421],
+ [-0.0515, 0.0201, 0.0011],
+ [ 0.0428, -0.0012, -0.0036],
+ [ 0.0817, 0.0765, 0.0749],
+ [-0.1264, -0.0522, -0.1103],
+ [-0.0280, -0.0881, -0.0499],
+ [-0.1262, -0.0982, -0.0778]
+ ]
+ self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851]
+ self.taesd_decoder_name = "taef1_decoder"
+
+ def process_in(self, latent):
+ return (latent - self.shift_factor) * self.scale_factor
+
+ def process_out(self, latent):
+ return (latent / self.scale_factor) + self.shift_factor
+
+class Mochi(LatentFormat):
+ latent_channels = 12
+
+ def __init__(self):
+ self.scale_factor = 1.0
+ self.latents_mean = torch.tensor([-0.06730895953510081, -0.038011381506090416, -0.07477820912866141,
+ -0.05565264470995561, 0.012767231469026969, -0.04703542746246419,
+ 0.043896967884726704, -0.09346305707025976, -0.09918314763016893,
+ -0.008729793427399178, -0.011931556316503654, -0.0321993391887285]).view(1, self.latent_channels, 1, 1, 1)
+ self.latents_std = torch.tensor([0.9263795028493863, 0.9248894543193766, 0.9393059390890617,
+ 0.959253732819592, 0.8244560132752793, 0.917259975397747,
+ 0.9294154431013696, 1.3720942357788521, 0.881393668867029,
+ 0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1)
+
+ self.latent_rgb_factors =[
+ [-0.0069, -0.0045, 0.0018],
+ [ 0.0154, -0.0692, -0.0274],
+ [ 0.0333, 0.0019, 0.0206],
+ [-0.1390, 0.0628, 0.1678],
+ [-0.0725, 0.0134, -0.1898],
+ [ 0.0074, -0.0270, -0.0209],
+ [-0.0176, -0.0277, -0.0221],
+ [ 0.5294, 0.5204, 0.3852],
+ [-0.0326, -0.0446, -0.0143],
+ [-0.0659, 0.0153, -0.0153],
+ [ 0.0185, -0.0217, 0.0014],
+ [-0.0396, -0.0495, -0.0281]
+ ]
+ self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453]
+ self.taesd_decoder_name = None #TODO
+
+ def process_in(self, latent):
+ latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+ latents_std = self.latents_std.to(latent.device, latent.dtype)
+ return (latent - latents_mean) * self.scale_factor / latents_std
+
+ def process_out(self, latent):
+ latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+ latents_std = self.latents_std.to(latent.device, latent.dtype)
+ return latent * latents_std / self.scale_factor + latents_mean
+
+class LTXV(LatentFormat):
+ latent_channels = 128
+ def __init__(self):
+ self.latent_rgb_factors = [
+ [ 1.1202e-02, -6.3815e-04, -1.0021e-02],
+ [ 8.6031e-02, 6.5813e-02, 9.5409e-04],
+ [-1.2576e-02, -7.5734e-03, -4.0528e-03],
+ [ 9.4063e-03, -2.1688e-03, 2.6093e-03],
+ [ 3.7636e-03, 1.2765e-02, 9.1548e-03],
+ [ 2.1024e-02, -5.2973e-03, 3.4373e-03],
+ [-8.8896e-03, -1.9703e-02, -1.8761e-02],
+ [-1.3160e-02, -1.0523e-02, 1.9709e-03],
+ [-1.5152e-03, -6.9891e-03, -7.5810e-03],
+ [-1.7247e-03, 4.6560e-04, -3.3839e-03],
+ [ 1.3617e-02, 4.7077e-03, -2.0045e-03],
+ [ 1.0256e-02, 7.7318e-03, 1.3948e-02],
+ [-1.6108e-02, -6.2151e-03, 1.1561e-03],
+ [ 7.3407e-03, 1.5628e-02, 4.4865e-04],
+ [ 9.5357e-04, -2.9518e-03, -1.4760e-02],
+ [ 1.9143e-02, 1.0868e-02, 1.2264e-02],
+ [ 4.4575e-03, 3.6682e-05, -6.8508e-03],
+ [-4.5681e-04, 3.2570e-03, 7.7929e-03],
+ [ 3.3902e-02, 3.3405e-02, 3.7454e-02],
+ [-2.3001e-02, -2.4877e-03, -3.1033e-03],
+ [ 5.0265e-02, 3.8841e-02, 3.3539e-02],
+ [-4.1018e-03, -1.1095e-03, 1.5859e-03],
+ [-1.2689e-01, -1.3107e-01, -2.1005e-01],
+ [ 2.6276e-02, 1.4189e-02, -3.5963e-03],
+ [-4.8679e-03, 8.8486e-03, 7.8029e-03],
+ [-1.6610e-03, -4.8597e-03, -5.2060e-03],
+ [-2.1010e-03, 2.3610e-03, 9.3796e-03],
+ [-2.2482e-02, -2.1305e-02, -1.5087e-02],
+ [-1.5753e-02, -1.0646e-02, -6.5083e-03],
+ [-4.6975e-03, 5.0288e-03, -6.7390e-03],
+ [ 1.1951e-02, 2.0712e-02, 1.6191e-02],
+ [-6.3704e-03, -8.4827e-03, -9.5483e-03],
+ [ 7.2610e-03, -9.9326e-03, -2.2978e-02],
+ [-9.1904e-04, 6.2882e-03, 9.5720e-03],
+ [-3.7178e-02, -3.7123e-02, -5.6713e-02],
+ [-1.3373e-01, -1.0720e-01, -5.3801e-02],
+ [-5.3702e-03, 8.1256e-03, 8.8397e-03],
+ [-1.5247e-01, -2.1437e-01, -2.1843e-01],
+ [ 3.1441e-02, 7.0335e-03, -9.7541e-03],
+ [ 2.1528e-03, -8.9817e-03, -2.1023e-02],
+ [ 3.8461e-03, -5.8957e-03, -1.5014e-02],
+ [-4.3470e-03, -1.2940e-02, -1.5972e-02],
+ [-5.4781e-03, -1.0842e-02, -3.0204e-03],
+ [-6.5347e-03, 3.0806e-03, -1.0163e-02],
+ [-5.0414e-03, -7.1503e-03, -8.9686e-04],
+ [-8.5851e-03, -2.4351e-03, 1.0674e-03],
+ [-9.0016e-03, -9.6493e-03, 1.5692e-03],
+ [ 5.0914e-03, 1.2099e-02, 1.9968e-02],
+ [ 1.3758e-02, 1.1669e-02, 8.1958e-03],
+ [-1.0518e-02, -1.1575e-02, -4.1307e-03],
+ [-2.8410e-02, -3.1266e-02, -2.2149e-02],
+ [ 2.9336e-03, 3.6511e-02, 1.8717e-02],
+ [-1.6703e-02, -1.6696e-02, -4.4529e-03],
+ [ 4.8818e-02, 4.0063e-02, 8.7410e-03],
+ [-1.5066e-02, -5.7328e-04, 2.9785e-03],
+ [-1.7613e-02, -8.1034e-03, 1.3086e-02],
+ [-9.2633e-03, 1.0803e-02, -6.3489e-03],
+ [ 3.0851e-03, 4.7750e-04, 1.2347e-02],
+ [-2.2785e-02, -2.3043e-02, -2.6005e-02],
+ [-2.4787e-02, -1.5389e-02, -2.2104e-02],
+ [-2.3572e-02, 1.0544e-03, 1.2361e-02],
+ [-7.8915e-03, -1.2271e-03, -6.0968e-03],
+ [-1.1478e-02, -1.2543e-03, 6.2679e-03],
+ [-5.4229e-02, 2.6644e-02, 6.3394e-03],
+ [ 4.4216e-03, -7.3338e-03, -1.0464e-02],
+ [-4.5013e-03, 1.6082e-03, 1.4420e-02],
+ [ 1.3673e-02, 8.8877e-03, 4.1253e-03],
+ [-1.0145e-02, 9.0072e-03, 1.5695e-02],
+ [-5.6234e-03, 1.1847e-03, 8.1261e-03],
+ [-3.7171e-03, -5.3538e-03, 1.2590e-03],
+ [ 2.9476e-02, 2.1424e-02, 3.0424e-02],
+ [-3.4925e-02, -2.4340e-02, -2.5316e-02],
+ [-3.4127e-02, -2.2406e-02, -1.0589e-02],
+ [-1.7342e-02, -1.3249e-02, -1.0719e-02],
+ [-2.1478e-03, -8.6051e-03, -2.9878e-03],
+ [ 1.2089e-03, -4.2391e-03, -6.8569e-03],
+ [ 9.0411e-04, -6.6886e-03, -6.7547e-05],
+ [ 1.6048e-02, -1.0057e-02, -2.8929e-02],
+ [ 1.2290e-03, 1.0163e-02, 1.8861e-02],
+ [ 1.7264e-02, 2.7257e-04, 1.3785e-02],
+ [-1.3482e-02, -3.6427e-03, 6.7481e-04],
+ [ 4.6782e-03, -5.2423e-03, 2.4467e-03],
+ [-5.9113e-03, -6.2244e-03, -1.8162e-03],
+ [ 1.5496e-02, 1.4582e-02, 1.9514e-03],
+ [ 7.4958e-03, 1.5886e-03, -8.2305e-03],
+ [ 1.9086e-02, 1.6360e-03, -3.9674e-03],
+ [-5.7021e-03, -2.7307e-03, -4.1066e-03],
+ [ 1.7450e-03, 1.4602e-02, 2.5794e-02],
+ [-8.2788e-04, 2.2902e-03, 4.5161e-03],
+ [ 1.1632e-02, 8.9193e-03, -7.2813e-03],
+ [ 7.5721e-03, 2.6784e-03, 1.1393e-02],
+ [ 5.1939e-03, 3.6903e-03, 1.4049e-02],
+ [-1.8383e-02, -2.2529e-02, -2.4477e-02],
+ [ 5.8842e-04, -5.7874e-03, -1.4770e-02],
+ [-1.6125e-02, -8.6101e-03, -1.4533e-02],
+ [ 2.0540e-02, 2.0729e-02, 6.4338e-03],
+ [ 3.3587e-03, -1.1226e-02, -1.6444e-02],
+ [-1.4742e-03, -1.0489e-02, 1.7097e-03],
+ [ 2.8130e-02, 2.3546e-02, 3.2791e-02],
+ [-1.8532e-02, -1.2842e-02, -8.7756e-03],
+ [-8.0533e-03, -1.0771e-02, -1.7536e-02],
+ [-3.9009e-03, 1.6150e-02, 3.3359e-02],
+ [-7.4554e-03, -1.4154e-02, -6.1910e-03],
+ [ 3.4734e-03, -1.1370e-02, -1.0581e-02],
+ [ 1.1476e-02, 3.9281e-03, 2.8231e-03],
+ [ 7.1639e-03, -1.4741e-03, -3.8066e-03],
+ [ 2.2250e-03, -8.7552e-03, -9.5719e-03],
+ [ 2.4146e-02, 2.1696e-02, 2.8056e-02],
+ [-5.4365e-03, -2.4291e-02, -1.7802e-02],
+ [ 7.4263e-03, 1.0510e-02, 1.2705e-02],
+ [ 6.2669e-03, 6.2658e-03, 1.9211e-02],
+ [ 1.6378e-02, 9.4933e-03, 6.6971e-03],
+ [ 1.7173e-02, 2.3601e-02, 2.3296e-02],
+ [-1.4568e-02, -9.8279e-03, -1.1556e-02],
+ [ 1.4431e-02, 1.4430e-02, 6.6362e-03],
+ [-6.8230e-03, 1.8863e-02, 1.4555e-02],
+ [ 6.1156e-03, 3.4700e-03, -2.6662e-03],
+ [-2.6983e-03, -5.9402e-03, -9.2276e-03],
+ [ 1.0235e-02, 7.4173e-03, -7.6243e-03],
+ [-1.3255e-02, 1.9322e-02, -9.2153e-04],
+ [ 2.4222e-03, -4.8039e-03, -1.5759e-02],
+ [ 2.6244e-02, 2.5951e-02, 2.0249e-02],
+ [ 1.5711e-02, 1.8498e-02, 2.7407e-03],
+ [-2.1714e-03, 4.7214e-03, -2.2443e-02],
+ [-7.4747e-03, 7.4166e-03, 1.4430e-02],
+ [-8.3906e-03, -7.9776e-03, 9.7927e-03],
+ [ 3.8321e-02, 9.6622e-03, -1.9268e-02],
+ [-1.4605e-02, -6.7032e-03, 3.9675e-03]
+ ]
+
+ self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]
diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..8123e66a50074d63bea45591f48e44723dbe5ebf
--- /dev/null
+++ b/comfy/ldm/audio/autoencoder.py
@@ -0,0 +1,282 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+import torch
+from torch import nn
+from typing import Literal, Dict, Any
+import math
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def vae_sample(mean, scale):
+ stdev = nn.functional.softplus(scale) + 1e-4
+ var = stdev * stdev
+ logvar = torch.log(var)
+ latents = torch.randn_like(mean) * stdev + mean
+
+ kl = (mean * mean + var - logvar - 1).sum(1).mean()
+
+ return latents, kl
+
+class VAEBottleneck(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.is_discrete = False
+
+ def encode(self, x, return_info=False, **kwargs):
+ info = {}
+
+ mean, scale = x.chunk(2, dim=1)
+
+ x, kl = vae_sample(mean, scale)
+
+ info["kl"] = kl
+
+ if return_info:
+ return x, info
+ else:
+ return x
+
+ def decode(self, x):
+ return x
+
+
+def snake_beta(x, alpha, beta):
+ return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2)
+
+# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license
+class SnakeBeta(nn.Module):
+
+ def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
+ super(SnakeBeta, self).__init__()
+ self.in_features = in_features
+
+ # initialize alpha
+ self.alpha_logscale = alpha_logscale
+ if self.alpha_logscale: # log scale alphas initialized to zeros
+ self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
+ self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
+ else: # linear scale alphas initialized to ones
+ self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
+ self.beta = nn.Parameter(torch.ones(in_features) * alpha)
+
+ # self.alpha.requires_grad = alpha_trainable
+ # self.beta.requires_grad = alpha_trainable
+
+ self.no_div_by_zero = 0.000000001
+
+ def forward(self, x):
+ alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T]
+ beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device)
+ if self.alpha_logscale:
+ alpha = torch.exp(alpha)
+ beta = torch.exp(beta)
+ x = snake_beta(x, alpha, beta)
+
+ return x
+
+def WNConv1d(*args, **kwargs):
+ try:
+ return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs))
+ except:
+ return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) #support pytorch 2.1 and older
+
+def WNConvTranspose1d(*args, **kwargs):
+ try:
+ return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs))
+ except:
+ return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) #support pytorch 2.1 and older
+
+def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
+ if activation == "elu":
+ act = torch.nn.ELU()
+ elif activation == "snake":
+ act = SnakeBeta(channels)
+ elif activation == "none":
+ act = torch.nn.Identity()
+ else:
+ raise ValueError(f"Unknown activation {activation}")
+
+ if antialias:
+ act = Activation1d(act)
+
+ return act
+
+
+class ResidualUnit(nn.Module):
+ def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False):
+ super().__init__()
+
+ self.dilation = dilation
+
+ padding = (dilation * (7-1)) // 2
+
+ self.layers = nn.Sequential(
+ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+ WNConv1d(in_channels=in_channels, out_channels=out_channels,
+ kernel_size=7, dilation=dilation, padding=padding),
+ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+ WNConv1d(in_channels=out_channels, out_channels=out_channels,
+ kernel_size=1)
+ )
+
+ def forward(self, x):
+ res = x
+
+ #x = checkpoint(self.layers, x)
+ x = self.layers(x)
+
+ return x + res
+
+class EncoderBlock(nn.Module):
+ def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):
+ super().__init__()
+
+ self.layers = nn.Sequential(
+ ResidualUnit(in_channels=in_channels,
+ out_channels=in_channels, dilation=1, use_snake=use_snake),
+ ResidualUnit(in_channels=in_channels,
+ out_channels=in_channels, dilation=3, use_snake=use_snake),
+ ResidualUnit(in_channels=in_channels,
+ out_channels=in_channels, dilation=9, use_snake=use_snake),
+ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+ WNConv1d(in_channels=in_channels, out_channels=out_channels,
+ kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)),
+ )
+
+ def forward(self, x):
+ return self.layers(x)
+
+class DecoderBlock(nn.Module):
+ def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False):
+ super().__init__()
+
+ if use_nearest_upsample:
+ upsample_layer = nn.Sequential(
+ nn.Upsample(scale_factor=stride, mode="nearest"),
+ WNConv1d(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=2*stride,
+ stride=1,
+ bias=False,
+ padding='same')
+ )
+ else:
+ upsample_layer = WNConvTranspose1d(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2))
+
+ self.layers = nn.Sequential(
+ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+ upsample_layer,
+ ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+ dilation=1, use_snake=use_snake),
+ ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+ dilation=3, use_snake=use_snake),
+ ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+ dilation=9, use_snake=use_snake),
+ )
+
+ def forward(self, x):
+ return self.layers(x)
+
+class OobleckEncoder(nn.Module):
+ def __init__(self,
+ in_channels=2,
+ channels=128,
+ latent_dim=32,
+ c_mults = [1, 2, 4, 8],
+ strides = [2, 4, 8, 8],
+ use_snake=False,
+ antialias_activation=False
+ ):
+ super().__init__()
+
+ c_mults = [1] + c_mults
+
+ self.depth = len(c_mults)
+
+ layers = [
+ WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)
+ ]
+
+ for i in range(self.depth-1):
+ layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]
+
+ layers += [
+ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels),
+ WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)
+ ]
+
+ self.layers = nn.Sequential(*layers)
+
+ def forward(self, x):
+ return self.layers(x)
+
+
+class OobleckDecoder(nn.Module):
+ def __init__(self,
+ out_channels=2,
+ channels=128,
+ latent_dim=32,
+ c_mults = [1, 2, 4, 8],
+ strides = [2, 4, 8, 8],
+ use_snake=False,
+ antialias_activation=False,
+ use_nearest_upsample=False,
+ final_tanh=True):
+ super().__init__()
+
+ c_mults = [1] + c_mults
+
+ self.depth = len(c_mults)
+
+ layers = [
+ WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),
+ ]
+
+ for i in range(self.depth-1, 0, -1):
+ layers += [DecoderBlock(
+ in_channels=c_mults[i]*channels,
+ out_channels=c_mults[i-1]*channels,
+ stride=strides[i-1],
+ use_snake=use_snake,
+ antialias_activation=antialias_activation,
+ use_nearest_upsample=use_nearest_upsample
+ )
+ ]
+
+ layers += [
+ get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels),
+ WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),
+ nn.Tanh() if final_tanh else nn.Identity()
+ ]
+
+ self.layers = nn.Sequential(*layers)
+
+ def forward(self, x):
+ return self.layers(x)
+
+
+class AudioOobleckVAE(nn.Module):
+ def __init__(self,
+ in_channels=2,
+ channels=128,
+ latent_dim=64,
+ c_mults = [1, 2, 4, 8, 16],
+ strides = [2, 4, 4, 8, 8],
+ use_snake=True,
+ antialias_activation=False,
+ use_nearest_upsample=False,
+ final_tanh=False):
+ super().__init__()
+ self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation)
+ self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation,
+ use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh)
+ self.bottleneck = VAEBottleneck()
+
+ def encode(self, x):
+ return self.bottleneck.encode(self.encoder(x))
+
+ def decode(self, x):
+ return self.decoder(self.bottleneck.decode(x))
+
diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b3f498f7db49ee747a561a0d1cac92ee318bb82
--- /dev/null
+++ b/comfy/ldm/audio/dit.py
@@ -0,0 +1,902 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+from comfy.ldm.modules.attention import optimized_attention
+import typing as tp
+
+import torch
+
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+import math
+import comfy.ops
+
+class FourierFeatures(nn.Module):
+ def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
+ super().__init__()
+ assert out_features % 2 == 0
+ self.weight = nn.Parameter(torch.empty(
+ [out_features // 2, in_features], dtype=dtype, device=device))
+
+ def forward(self, input):
+ f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input)
+ return torch.cat([f.cos(), f.sin()], dim=-1)
+
+# norms
+class LayerNorm(nn.Module):
+ def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):
+ """
+ bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
+ """
+ super().__init__()
+
+ self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+
+ if bias:
+ self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+ else:
+ self.beta = None
+
+ def forward(self, x):
+ beta = self.beta
+ if beta is not None:
+ beta = comfy.ops.cast_to_input(beta, x)
+ return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta)
+
+class GLU(nn.Module):
+ def __init__(
+ self,
+ dim_in,
+ dim_out,
+ activation,
+ use_conv = False,
+ conv_kernel_size = 3,
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.act = activation
+ self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device)
+ self.use_conv = use_conv
+
+ def forward(self, x):
+ if self.use_conv:
+ x = rearrange(x, 'b n d -> b d n')
+ x = self.proj(x)
+ x = rearrange(x, 'b d n -> b n d')
+ else:
+ x = self.proj(x)
+
+ x, gate = x.chunk(2, dim = -1)
+ return x * self.act(gate)
+
+class AbsolutePositionalEmbedding(nn.Module):
+ def __init__(self, dim, max_seq_len):
+ super().__init__()
+ self.scale = dim ** -0.5
+ self.max_seq_len = max_seq_len
+ self.emb = nn.Embedding(max_seq_len, dim)
+
+ def forward(self, x, pos = None, seq_start_pos = None):
+ seq_len, device = x.shape[1], x.device
+ assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
+
+ if pos is None:
+ pos = torch.arange(seq_len, device = device)
+
+ if seq_start_pos is not None:
+ pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
+
+ pos_emb = self.emb(pos)
+ pos_emb = pos_emb * self.scale
+ return pos_emb
+
+class ScaledSinusoidalEmbedding(nn.Module):
+ def __init__(self, dim, theta = 10000):
+ super().__init__()
+ assert (dim % 2) == 0, 'dimension must be divisible by 2'
+ self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
+
+ half_dim = dim // 2
+ freq_seq = torch.arange(half_dim).float() / half_dim
+ inv_freq = theta ** -freq_seq
+ self.register_buffer('inv_freq', inv_freq, persistent = False)
+
+ def forward(self, x, pos = None, seq_start_pos = None):
+ seq_len, device = x.shape[1], x.device
+
+ if pos is None:
+ pos = torch.arange(seq_len, device = device)
+
+ if seq_start_pos is not None:
+ pos = pos - seq_start_pos[..., None]
+
+ emb = torch.einsum('i, j -> i j', pos, self.inv_freq)
+ emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
+ return emb * self.scale
+
+class RotaryEmbedding(nn.Module):
+ def __init__(
+ self,
+ dim,
+ use_xpos = False,
+ scale_base = 512,
+ interpolation_factor = 1.,
+ base = 10000,
+ base_rescale_factor = 1.,
+ dtype=None,
+ device=None,
+ ):
+ super().__init__()
+ # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+ # has some connection to NTK literature
+ # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+ base *= base_rescale_factor ** (dim / (dim - 2))
+
+ # inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
+ self.register_buffer('inv_freq', torch.empty((dim // 2,), device=device, dtype=dtype))
+
+ assert interpolation_factor >= 1.
+ self.interpolation_factor = interpolation_factor
+
+ if not use_xpos:
+ self.register_buffer('scale', None)
+ return
+
+ scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
+
+ self.scale_base = scale_base
+ self.register_buffer('scale', scale)
+
+ def forward_from_seq_len(self, seq_len, device, dtype):
+ # device = self.inv_freq.device
+
+ t = torch.arange(seq_len, device=device, dtype=dtype)
+ return self.forward(t)
+
+ def forward(self, t):
+ # device = self.inv_freq.device
+ device = t.device
+ dtype = t.dtype
+
+ # t = t.to(torch.float32)
+
+ t = t / self.interpolation_factor
+
+ freqs = torch.einsum('i , j -> i j', t, comfy.ops.cast_to_input(self.inv_freq, t))
+ freqs = torch.cat((freqs, freqs), dim = -1)
+
+ if self.scale is None:
+ return freqs, 1.
+
+ power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
+ scale = comfy.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1')
+ scale = torch.cat((scale, scale), dim = -1)
+
+ return freqs, scale
+
+def rotate_half(x):
+ x = rearrange(x, '... (j d) -> ... j d', j = 2)
+ x1, x2 = x.unbind(dim = -2)
+ return torch.cat((-x2, x1), dim = -1)
+
+def apply_rotary_pos_emb(t, freqs, scale = 1):
+ out_dtype = t.dtype
+
+ # cast to float32 if necessary for numerical stability
+ dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))
+ rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
+ freqs, t = freqs.to(dtype), t.to(dtype)
+ freqs = freqs[-seq_len:, :]
+
+ if t.ndim == 4 and freqs.ndim == 3:
+ freqs = rearrange(freqs, 'b n d -> b 1 n d')
+
+ # partial rotary embeddings, Wang et al. GPT-J
+ t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
+ t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
+
+ t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)
+
+ return torch.cat((t, t_unrotated), dim = -1)
+
+class FeedForward(nn.Module):
+ def __init__(
+ self,
+ dim,
+ dim_out = None,
+ mult = 4,
+ no_bias = False,
+ glu = True,
+ use_conv = False,
+ conv_kernel_size = 3,
+ zero_init_output = True,
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ inner_dim = int(dim * mult)
+
+ # Default to SwiGLU
+
+ activation = nn.SiLU()
+
+ dim_out = dim if dim_out is None else dim_out
+
+ if glu:
+ linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations)
+ else:
+ linear_in = nn.Sequential(
+ Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+ operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device),
+ Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+ activation
+ )
+
+ linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)
+
+ # # init last linear layer to 0
+ # if zero_init_output:
+ # nn.init.zeros_(linear_out.weight)
+ # if not no_bias:
+ # nn.init.zeros_(linear_out.bias)
+
+
+ self.ff = nn.Sequential(
+ linear_in,
+ Rearrange('b d n -> b n d') if use_conv else nn.Identity(),
+ linear_out,
+ Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+ )
+
+ def forward(self, x):
+ return self.ff(x)
+
+class Attention(nn.Module):
+ def __init__(
+ self,
+ dim,
+ dim_heads = 64,
+ dim_context = None,
+ causal = False,
+ zero_init_output=True,
+ qk_norm = False,
+ natten_kernel_size = None,
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.dim_heads = dim_heads
+ self.causal = causal
+
+ dim_kv = dim_context if dim_context is not None else dim
+
+ self.num_heads = dim // dim_heads
+ self.kv_heads = dim_kv // dim_heads
+
+ if dim_context is not None:
+ self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
+ else:
+ self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
+
+ self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+ # if zero_init_output:
+ # nn.init.zeros_(self.to_out.weight)
+
+ self.qk_norm = qk_norm
+
+
+ def forward(
+ self,
+ x,
+ context = None,
+ mask = None,
+ context_mask = None,
+ rotary_pos_emb = None,
+ causal = None
+ ):
+ h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
+
+ kv_input = context if has_context else x
+
+ if hasattr(self, 'to_q'):
+ # Use separate linear projections for q and k/v
+ q = self.to_q(x)
+ q = rearrange(q, 'b n (h d) -> b h n d', h = h)
+
+ k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+ k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
+ else:
+ # Use fused linear projection
+ q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+
+ # Normalize q and k for cosine sim attention
+ if self.qk_norm:
+ q = F.normalize(q, dim=-1)
+ k = F.normalize(k, dim=-1)
+
+ if rotary_pos_emb is not None and not has_context:
+ freqs, _ = rotary_pos_emb
+
+ q_dtype = q.dtype
+ k_dtype = k.dtype
+
+ q = q.to(torch.float32)
+ k = k.to(torch.float32)
+ freqs = freqs.to(torch.float32)
+
+ q = apply_rotary_pos_emb(q, freqs)
+ k = apply_rotary_pos_emb(k, freqs)
+
+ q = q.to(q_dtype)
+ k = k.to(k_dtype)
+
+ input_mask = context_mask
+
+ if input_mask is None and not has_context:
+ input_mask = mask
+
+ # determine masking
+ masks = []
+ final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account
+
+ if input_mask is not None:
+ input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
+ masks.append(~input_mask)
+
+ # Other masks will be added here later
+
+ if len(masks) > 0:
+ final_attn_mask = ~or_reduce(masks)
+
+ n, device = q.shape[-2], q.device
+
+ causal = self.causal if causal is None else causal
+
+ if n == 1 and causal:
+ causal = False
+
+ if h != kv_h:
+ # Repeat interleave kv_heads to match q_heads
+ heads_per_kv_head = h // kv_h
+ k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+
+ out = optimized_attention(q, k, v, h, skip_reshape=True)
+ out = self.to_out(out)
+
+ if mask is not None:
+ mask = rearrange(mask, 'b n -> b n 1')
+ out = out.masked_fill(~mask, 0.)
+
+ return out
+
+class ConformerModule(nn.Module):
+ def __init__(
+ self,
+ dim,
+ norm_kwargs = {},
+ ):
+
+ super().__init__()
+
+ self.dim = dim
+
+ self.in_norm = LayerNorm(dim, **norm_kwargs)
+ self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+ self.glu = GLU(dim, dim, nn.SiLU())
+ self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)
+ self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm
+ self.swish = nn.SiLU()
+ self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+
+ def forward(self, x):
+ x = self.in_norm(x)
+ x = rearrange(x, 'b n d -> b d n')
+ x = self.pointwise_conv(x)
+ x = rearrange(x, 'b d n -> b n d')
+ x = self.glu(x)
+ x = rearrange(x, 'b n d -> b d n')
+ x = self.depthwise_conv(x)
+ x = rearrange(x, 'b d n -> b n d')
+ x = self.mid_norm(x)
+ x = self.swish(x)
+ x = rearrange(x, 'b n d -> b d n')
+ x = self.pointwise_conv_2(x)
+ x = rearrange(x, 'b d n -> b n d')
+
+ return x
+
+class TransformerBlock(nn.Module):
+ def __init__(
+ self,
+ dim,
+ dim_heads = 64,
+ cross_attend = False,
+ dim_context = None,
+ global_cond_dim = None,
+ causal = False,
+ zero_init_branch_outputs = True,
+ conformer = False,
+ layer_ix = -1,
+ remove_norms = False,
+ attn_kwargs = {},
+ ff_kwargs = {},
+ norm_kwargs = {},
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+
+ super().__init__()
+ self.dim = dim
+ self.dim_heads = dim_heads
+ self.cross_attend = cross_attend
+ self.dim_context = dim_context
+ self.causal = causal
+
+ self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+
+ self.self_attn = Attention(
+ dim,
+ dim_heads = dim_heads,
+ causal = causal,
+ zero_init_output=zero_init_branch_outputs,
+ dtype=dtype,
+ device=device,
+ operations=operations,
+ **attn_kwargs
+ )
+
+ if cross_attend:
+ self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+ self.cross_attn = Attention(
+ dim,
+ dim_heads = dim_heads,
+ dim_context=dim_context,
+ causal = causal,
+ zero_init_output=zero_init_branch_outputs,
+ dtype=dtype,
+ device=device,
+ operations=operations,
+ **attn_kwargs
+ )
+
+ self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+ self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)
+
+ self.layer_ix = layer_ix
+
+ self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
+
+ self.global_cond_dim = global_cond_dim
+
+ if global_cond_dim is not None:
+ self.to_scale_shift_gate = nn.Sequential(
+ nn.SiLU(),
+ nn.Linear(global_cond_dim, dim * 6, bias=False)
+ )
+
+ nn.init.zeros_(self.to_scale_shift_gate[1].weight)
+ #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
+
+ def forward(
+ self,
+ x,
+ context = None,
+ global_cond=None,
+ mask = None,
+ context_mask = None,
+ rotary_pos_emb = None
+ ):
+ if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
+
+ scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
+
+ # self-attention with adaLN
+ residual = x
+ x = self.pre_norm(x)
+ x = x * (1 + scale_self) + shift_self
+ x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
+ x = x * torch.sigmoid(1 - gate_self)
+ x = x + residual
+
+ if context is not None:
+ x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+
+ if self.conformer is not None:
+ x = x + self.conformer(x)
+
+ # feedforward with adaLN
+ residual = x
+ x = self.ff_norm(x)
+ x = x * (1 + scale_ff) + shift_ff
+ x = self.ff(x)
+ x = x * torch.sigmoid(1 - gate_ff)
+ x = x + residual
+
+ else:
+ x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
+
+ if context is not None:
+ x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+
+ if self.conformer is not None:
+ x = x + self.conformer(x)
+
+ x = x + self.ff(self.ff_norm(x))
+
+ return x
+
+class ContinuousTransformer(nn.Module):
+ def __init__(
+ self,
+ dim,
+ depth,
+ *,
+ dim_in = None,
+ dim_out = None,
+ dim_heads = 64,
+ cross_attend=False,
+ cond_token_dim=None,
+ global_cond_dim=None,
+ causal=False,
+ rotary_pos_emb=True,
+ zero_init_branch_outputs=True,
+ conformer=False,
+ use_sinusoidal_emb=False,
+ use_abs_pos_emb=False,
+ abs_pos_emb_max_length=10000,
+ dtype=None,
+ device=None,
+ operations=None,
+ **kwargs
+ ):
+
+ super().__init__()
+
+ self.dim = dim
+ self.depth = depth
+ self.causal = causal
+ self.layers = nn.ModuleList([])
+
+ self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()
+ self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
+
+ if rotary_pos_emb:
+ self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32), device=device, dtype=dtype)
+ else:
+ self.rotary_pos_emb = None
+
+ self.use_sinusoidal_emb = use_sinusoidal_emb
+ if use_sinusoidal_emb:
+ self.pos_emb = ScaledSinusoidalEmbedding(dim)
+
+ self.use_abs_pos_emb = use_abs_pos_emb
+ if use_abs_pos_emb:
+ self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
+
+ for i in range(depth):
+ self.layers.append(
+ TransformerBlock(
+ dim,
+ dim_heads = dim_heads,
+ cross_attend = cross_attend,
+ dim_context = cond_token_dim,
+ global_cond_dim = global_cond_dim,
+ causal = causal,
+ zero_init_branch_outputs = zero_init_branch_outputs,
+ conformer=conformer,
+ layer_ix=i,
+ dtype=dtype,
+ device=device,
+ operations=operations,
+ **kwargs
+ )
+ )
+
+ def forward(
+ self,
+ x,
+ mask = None,
+ prepend_embeds = None,
+ prepend_mask = None,
+ global_cond = None,
+ return_info = False,
+ **kwargs
+ ):
+ patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {})
+ batch, seq, device = *x.shape[:2], x.device
+ context = kwargs["context"]
+
+ info = {
+ "hidden_states": [],
+ }
+
+ x = self.project_in(x)
+
+ if prepend_embeds is not None:
+ prepend_length, prepend_dim = prepend_embeds.shape[1:]
+
+ assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'
+
+ x = torch.cat((prepend_embeds, x), dim = -2)
+
+ if prepend_mask is not None or mask is not None:
+ mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool)
+ prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool)
+
+ mask = torch.cat((prepend_mask, mask), dim = -1)
+
+ # Attention layers
+
+ if self.rotary_pos_emb is not None:
+ rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device)
+ else:
+ rotary_pos_emb = None
+
+ if self.use_sinusoidal_emb or self.use_abs_pos_emb:
+ x = x + self.pos_emb(x)
+
+ blocks_replace = patches_replace.get("dit", {})
+ # Iterate over the transformer layers
+ for i, layer in enumerate(self.layers):
+ if ("double_block", i) in blocks_replace:
+ def block_wrap(args):
+ out = {}
+ out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"])
+ return out
+
+ out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap})
+ x = out["img"]
+ else:
+ x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context)
+ # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+
+ if return_info:
+ info["hidden_states"].append(x)
+
+ x = self.project_out(x)
+
+ if return_info:
+ return x, info
+
+ return x
+
+class AudioDiffusionTransformer(nn.Module):
+ def __init__(self,
+ io_channels=64,
+ patch_size=1,
+ embed_dim=1536,
+ cond_token_dim=768,
+ project_cond_tokens=False,
+ global_cond_dim=1536,
+ project_global_cond=True,
+ input_concat_dim=0,
+ prepend_cond_dim=0,
+ depth=24,
+ num_heads=24,
+ transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
+ global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
+ audio_model="",
+ dtype=None,
+ device=None,
+ operations=None,
+ **kwargs):
+
+ super().__init__()
+
+ self.dtype = dtype
+ self.cond_token_dim = cond_token_dim
+
+ # Timestep embeddings
+ timestep_features_dim = 256
+
+ self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
+
+ self.to_timestep_embed = nn.Sequential(
+ operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device),
+ )
+
+ if cond_token_dim > 0:
+ # Conditioning tokens
+
+ cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
+ self.to_cond_embed = nn.Sequential(
+ operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device)
+ )
+ else:
+ cond_embed_dim = 0
+
+ if global_cond_dim > 0:
+ # Global conditioning
+ global_embed_dim = global_cond_dim if not project_global_cond else embed_dim
+ self.to_global_embed = nn.Sequential(
+ operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device)
+ )
+
+ if prepend_cond_dim > 0:
+ # Prepend conditioning
+ self.to_prepend_embed = nn.Sequential(
+ operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
+ )
+
+ self.input_concat_dim = input_concat_dim
+
+ dim_in = io_channels + self.input_concat_dim
+
+ self.patch_size = patch_size
+
+ # Transformer
+
+ self.transformer_type = transformer_type
+
+ self.global_cond_type = global_cond_type
+
+ if self.transformer_type == "continuous_transformer":
+
+ global_dim = None
+
+ if self.global_cond_type == "adaLN":
+ # The global conditioning is projected to the embed_dim already at this point
+ global_dim = embed_dim
+
+ self.transformer = ContinuousTransformer(
+ dim=embed_dim,
+ depth=depth,
+ dim_heads=embed_dim // num_heads,
+ dim_in=dim_in * patch_size,
+ dim_out=io_channels * patch_size,
+ cross_attend = cond_token_dim > 0,
+ cond_token_dim = cond_embed_dim,
+ global_cond_dim=global_dim,
+ dtype=dtype,
+ device=device,
+ operations=operations,
+ **kwargs
+ )
+ else:
+ raise ValueError(f"Unknown transformer type: {self.transformer_type}")
+
+ self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device)
+ self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device)
+
+ def _forward(
+ self,
+ x,
+ t,
+ mask=None,
+ cross_attn_cond=None,
+ cross_attn_cond_mask=None,
+ input_concat_cond=None,
+ global_embed=None,
+ prepend_cond=None,
+ prepend_cond_mask=None,
+ return_info=False,
+ **kwargs):
+
+ if cross_attn_cond is not None:
+ cross_attn_cond = self.to_cond_embed(cross_attn_cond)
+
+ if global_embed is not None:
+ # Project the global conditioning to the embedding dimension
+ global_embed = self.to_global_embed(global_embed)
+
+ prepend_inputs = None
+ prepend_mask = None
+ prepend_length = 0
+ if prepend_cond is not None:
+ # Project the prepend conditioning to the embedding dimension
+ prepend_cond = self.to_prepend_embed(prepend_cond)
+
+ prepend_inputs = prepend_cond
+ if prepend_cond_mask is not None:
+ prepend_mask = prepend_cond_mask
+
+ if input_concat_cond is not None:
+
+ # Interpolate input_concat_cond to the same length as x
+ if input_concat_cond.shape[2] != x.shape[2]:
+ input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
+
+ x = torch.cat([x, input_concat_cond], dim=1)
+
+ # Get the batch of timestep embeddings
+ timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim)
+
+ # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
+ if global_embed is not None:
+ global_embed = global_embed + timestep_embed
+ else:
+ global_embed = timestep_embed
+
+ # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
+ if self.global_cond_type == "prepend":
+ if prepend_inputs is None:
+ # Prepend inputs are just the global embed, and the mask is all ones
+ prepend_inputs = global_embed.unsqueeze(1)
+ prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
+ else:
+ # Prepend inputs are the prepend conditioning + the global embed
+ prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
+ prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)
+
+ prepend_length = prepend_inputs.shape[1]
+
+ x = self.preprocess_conv(x) + x
+
+ x = rearrange(x, "b c t -> b t c")
+
+ extra_args = {}
+
+ if self.global_cond_type == "adaLN":
+ extra_args["global_cond"] = global_embed
+
+ if self.patch_size > 1:
+ x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
+
+ if self.transformer_type == "x-transformers":
+ output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
+ elif self.transformer_type == "continuous_transformer":
+ output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
+
+ if return_info:
+ output, info = output
+ elif self.transformer_type == "mm_transformer":
+ output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs)
+
+ output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:]
+
+ if self.patch_size > 1:
+ output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)
+
+ output = self.postprocess_conv(output) + output
+
+ if return_info:
+ return output, info
+
+ return output
+
+ def forward(
+ self,
+ x,
+ timestep,
+ context=None,
+ context_mask=None,
+ input_concat_cond=None,
+ global_embed=None,
+ negative_global_embed=None,
+ prepend_cond=None,
+ prepend_cond_mask=None,
+ mask=None,
+ return_info=False,
+ control=None,
+ **kwargs):
+ return self._forward(
+ x,
+ timestep,
+ cross_attn_cond=context,
+ cross_attn_cond_mask=context_mask,
+ input_concat_cond=input_concat_cond,
+ global_embed=global_embed,
+ prepend_cond=prepend_cond,
+ prepend_cond_mask=prepend_cond_mask,
+ mask=mask,
+ return_info=return_info,
+ **kwargs
+ )
diff --git a/comfy/ldm/audio/embedders.py b/comfy/ldm/audio/embedders.py
new file mode 100644
index 0000000000000000000000000000000000000000..82a3210c60de10b4294335cd0001cb3e72b68bd6
--- /dev/null
+++ b/comfy/ldm/audio/embedders.py
@@ -0,0 +1,108 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+import torch
+import torch.nn as nn
+from torch import Tensor, einsum
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
+from einops import rearrange
+import math
+import comfy.ops
+
+class LearnedPositionalEmbedding(nn.Module):
+ """Used for continuous time"""
+
+ def __init__(self, dim: int):
+ super().__init__()
+ assert (dim % 2) == 0
+ half_dim = dim // 2
+ self.weights = nn.Parameter(torch.empty(half_dim))
+
+ def forward(self, x: Tensor) -> Tensor:
+ x = rearrange(x, "b -> b 1")
+ freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi
+ fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1)
+ fouriered = torch.cat((x, fouriered), dim=-1)
+ return fouriered
+
+def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
+ return nn.Sequential(
+ LearnedPositionalEmbedding(dim),
+ comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features),
+ )
+
+
+class NumberEmbedder(nn.Module):
+ def __init__(
+ self,
+ features: int,
+ dim: int = 256,
+ ):
+ super().__init__()
+ self.features = features
+ self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
+
+ def forward(self, x: Union[List[float], Tensor]) -> Tensor:
+ if not torch.is_tensor(x):
+ device = next(self.embedding.parameters()).device
+ x = torch.tensor(x, device=device)
+ assert isinstance(x, Tensor)
+ shape = x.shape
+ x = rearrange(x, "... -> (...)")
+ embedding = self.embedding(x)
+ x = embedding.view(*shape, self.features)
+ return x # type: ignore
+
+
+class Conditioner(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ output_dim: int,
+ project_out: bool = False
+ ):
+
+ super().__init__()
+
+ self.dim = dim
+ self.output_dim = output_dim
+ self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity()
+
+ def forward(self, x):
+ raise NotImplementedError()
+
+class NumberConditioner(Conditioner):
+ '''
+ Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings
+ '''
+ def __init__(self,
+ output_dim: int,
+ min_val: float=0,
+ max_val: float=1
+ ):
+ super().__init__(output_dim, output_dim)
+
+ self.min_val = min_val
+ self.max_val = max_val
+
+ self.embedder = NumberEmbedder(features=output_dim)
+
+ def forward(self, floats, device=None):
+ # Cast the inputs to floats
+ floats = [float(x) for x in floats]
+
+ if device is None:
+ device = next(self.embedder.parameters()).device
+
+ floats = torch.tensor(floats).to(device)
+
+ floats = floats.clamp(self.min_val, self.max_val)
+
+ normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val)
+
+ # Cast floats to same type as embedder
+ embedder_dtype = next(self.embedder.parameters()).dtype
+ normalized_floats = normalized_floats.to(embedder_dtype)
+
+ float_embeds = self.embedder(normalized_floats).unsqueeze(1)
+
+ return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)]
diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py
new file mode 100644
index 0000000000000000000000000000000000000000..77090372e3c91ef0d04c9f4a1ab12a1f063bbe08
--- /dev/null
+++ b/comfy/ldm/aura/mmdit.py
@@ -0,0 +1,500 @@
+#AuraFlow MMDiT
+#Originally written by the AuraFlow Authors
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from comfy.ldm.modules.attention import optimized_attention
+import comfy.ops
+import comfy.ldm.common_dit
+
+def modulate(x, shift, scale):
+ return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def find_multiple(n: int, k: int) -> int:
+ if n % k == 0:
+ return n
+ return n + k - (n % k)
+
+
+class MLP(nn.Module):
+ def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None:
+ super().__init__()
+ if hidden_dim is None:
+ hidden_dim = 4 * dim
+
+ n_hidden = int(2 * hidden_dim / 3)
+ n_hidden = find_multiple(n_hidden, 256)
+
+ self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+ self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+ self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+ x = self.c_proj(x)
+ return x
+
+
+class MultiHeadLayerNorm(nn.Module):
+ def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
+ # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78
+
+ super().__init__()
+ self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ mean = hidden_states.mean(-1, keepdim=True)
+ variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
+ hidden_states = (hidden_states - mean) * torch.rsqrt(
+ variance + self.variance_epsilon
+ )
+ hidden_states = self.weight.to(torch.float32) * hidden_states
+ return hidden_states.to(input_dtype)
+
+class SingleAttention(nn.Module):
+ def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+ super().__init__()
+
+ self.n_heads = n_heads
+ self.head_dim = dim // n_heads
+
+ # this is for cond
+ self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+ self.q_norm1 = (
+ MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+ if mh_qknorm
+ else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+ )
+ self.k_norm1 = (
+ MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+ if mh_qknorm
+ else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+ )
+
+ #@torch.compile()
+ def forward(self, c):
+
+ bsz, seqlen1, _ = c.shape
+
+ q, k, v = self.w1q(c), self.w1k(c), self.w1v(c)
+ q = q.view(bsz, seqlen1, self.n_heads, self.head_dim)
+ k = k.view(bsz, seqlen1, self.n_heads, self.head_dim)
+ v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
+ q, k = self.q_norm1(q), self.k_norm1(k)
+
+ output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
+ c = self.w1o(output)
+ return c
+
+
+
+class DoubleAttention(nn.Module):
+ def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+ super().__init__()
+
+ self.n_heads = n_heads
+ self.head_dim = dim // n_heads
+
+ # this is for cond
+ self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+ # this is for x
+ self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+ self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+ self.q_norm1 = (
+ MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+ if mh_qknorm
+ else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+ )
+ self.k_norm1 = (
+ MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+ if mh_qknorm
+ else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+ )
+
+ self.q_norm2 = (
+ MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+ if mh_qknorm
+ else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+ )
+ self.k_norm2 = (
+ MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+ if mh_qknorm
+ else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+ )
+
+
+ #@torch.compile()
+ def forward(self, c, x):
+
+ bsz, seqlen1, _ = c.shape
+ bsz, seqlen2, _ = x.shape
+ seqlen = seqlen1 + seqlen2
+
+ cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c)
+ cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim)
+ ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim)
+ cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim)
+ cq, ck = self.q_norm1(cq), self.k_norm1(ck)
+
+ xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x)
+ xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim)
+ xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim)
+ xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim)
+ xq, xk = self.q_norm2(xq), self.k_norm2(xk)
+
+ # concat all
+ q, k, v = (
+ torch.cat([cq, xq], dim=1),
+ torch.cat([ck, xk], dim=1),
+ torch.cat([cv, xv], dim=1),
+ )
+
+ output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
+
+ c, x = output.split([seqlen1, seqlen2], dim=1)
+ c = self.w1o(c)
+ x = self.w2o(x)
+
+ return c, x
+
+
+class MMDiTBlock(nn.Module):
+ def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None):
+ super().__init__()
+
+ self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+ self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+ if not is_last:
+ self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+ self.modC = nn.Sequential(
+ nn.SiLU(),
+ operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+ )
+ else:
+ self.modC = nn.Sequential(
+ nn.SiLU(),
+ operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+ )
+
+ self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+ self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+ self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+ self.modX = nn.Sequential(
+ nn.SiLU(),
+ operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+ )
+
+ self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+ self.is_last = is_last
+
+ #@torch.compile()
+ def forward(self, c, x, global_cond, **kwargs):
+
+ cres, xres = c, x
+
+ cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = (
+ self.modC(global_cond).chunk(6, dim=1)
+ )
+
+ c = modulate(self.normC1(c), cshift_msa, cscale_msa)
+
+ # xpath
+ xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = (
+ self.modX(global_cond).chunk(6, dim=1)
+ )
+
+ x = modulate(self.normX1(x), xshift_msa, xscale_msa)
+
+ # attention
+ c, x = self.attn(c, x)
+
+
+ c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
+ c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp))
+ c = cres + c
+
+ x = self.normX2(xres + xgate_msa.unsqueeze(1) * x)
+ x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp))
+ x = xres + x
+
+ return c, x
+
+class DiTBlock(nn.Module):
+ # like MMDiTBlock, but it only has X
+ def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None):
+ super().__init__()
+
+ self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+ self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+
+ self.modCX = nn.Sequential(
+ nn.SiLU(),
+ operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+ )
+
+ self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+ self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+
+ #@torch.compile()
+ def forward(self, cx, global_cond, **kwargs):
+ cxres = cx
+ shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
+ global_cond
+ ).chunk(6, dim=1)
+ cx = modulate(self.norm1(cx), shift_msa, scale_msa)
+ cx = self.attn(cx)
+ cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
+ mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
+ cx = gate_mlp.unsqueeze(1) * mlpout
+
+ cx = cxres + cx
+
+ return cx
+
+
+
+class TimestepEmbedder(nn.Module):
+ def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.mlp = nn.Sequential(
+ operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device),
+ )
+ self.frequency_embedding_size = frequency_embedding_size
+
+ @staticmethod
+ def timestep_embedding(t, dim, max_period=10000):
+ half = dim // 2
+ freqs = 1000 * torch.exp(
+ -math.log(max_period) * torch.arange(start=0, end=half) / half
+ ).to(t.device)
+ args = t[:, None] * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat(
+ [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+ )
+ return embedding
+
+ #@torch.compile()
+ def forward(self, t, dtype):
+ t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+ t_emb = self.mlp(t_freq)
+ return t_emb
+
+
+class MMDiT(nn.Module):
+ def __init__(
+ self,
+ in_channels=4,
+ out_channels=4,
+ patch_size=2,
+ dim=3072,
+ n_layers=36,
+ n_double_layers=4,
+ n_heads=12,
+ global_conddim=3072,
+ cond_seq_dim=2048,
+ max_seq=32 * 32,
+ device=None,
+ dtype=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.dtype = dtype
+
+ self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations)
+
+ self.cond_seq_linear = operations.Linear(
+ cond_seq_dim, dim, bias=False, dtype=dtype, device=device
+ ) # linear for something like text sequence.
+ self.init_x_linear = operations.Linear(
+ patch_size * patch_size * in_channels, dim, dtype=dtype, device=device
+ ) # init linear for patchified image.
+
+ self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device))
+ self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device))
+
+ self.double_layers = nn.ModuleList([])
+ self.single_layers = nn.ModuleList([])
+
+
+ for idx in range(n_double_layers):
+ self.double_layers.append(
+ MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations)
+ )
+
+ for idx in range(n_double_layers, n_layers):
+ self.single_layers.append(
+ DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations)
+ )
+
+
+ self.final_linear = operations.Linear(
+ dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device
+ )
+
+ self.modF = nn.Sequential(
+ nn.SiLU(),
+ operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+ )
+
+ self.out_channels = out_channels
+ self.patch_size = patch_size
+ self.n_double_layers = n_double_layers
+ self.n_layers = n_layers
+
+ self.h_max = round(max_seq**0.5)
+ self.w_max = round(max_seq**0.5)
+
+ @torch.no_grad()
+ def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
+ # extend pe
+ pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]]
+
+ pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1)
+
+ # now we need to extend this to target_dim. for this we will use interpolation.
+ # we will use torch.nn.functional.interpolate
+ pe_as_2d = F.interpolate(
+ pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear"
+ )
+ pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
+ self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
+ self.h_max, self.w_max = target_dim
+ print("PE extended to", target_dim)
+
+ def pe_selection_index_based_on_dim(self, h, w):
+ h_p, w_p = h // self.patch_size, w // self.patch_size
+ original_pe_indexes = torch.arange(self.positional_encoding.shape[1])
+ original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max)
+ starth = self.h_max // 2 - h_p // 2
+ endh =starth + h_p
+ startw = self.w_max // 2 - w_p // 2
+ endw = startw + w_p
+ original_pe_indexes = original_pe_indexes[
+ starth:endh, startw:endw
+ ]
+ return original_pe_indexes.flatten()
+
+ def unpatchify(self, x, h, w):
+ c = self.out_channels
+ p = self.patch_size
+
+ x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+ x = torch.einsum("nhwpqc->nchpwq", x)
+ imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+ return imgs
+
+ def patchify(self, x):
+ B, C, H, W = x.size()
+ x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+ x = x.view(
+ B,
+ C,
+ (H + 1) // self.patch_size,
+ self.patch_size,
+ (W + 1) // self.patch_size,
+ self.patch_size,
+ )
+ x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
+ return x
+
+ def apply_pos_embeds(self, x, h, w):
+ h = (h + 1) // self.patch_size
+ w = (w + 1) // self.patch_size
+ max_dim = max(h, w)
+
+ cur_dim = self.h_max
+ pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x)
+
+ if max_dim > cur_dim:
+ pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1)
+ cur_dim = max_dim
+
+ from_h = (cur_dim - h) // 2
+ from_w = (cur_dim - w) // 2
+ pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
+ return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
+
+ def forward(self, x, timestep, context, transformer_options={}, **kwargs):
+ patches_replace = transformer_options.get("patches_replace", {})
+ # patchify x, add PE
+ b, c, h, w = x.shape
+
+ # pe_indexes = self.pe_selection_index_based_on_dim(h, w)
+ # print(pe_indexes, pe_indexes.shape)
+
+ x = self.init_x_linear(self.patchify(x)) # B, T_x, D
+ x = self.apply_pos_embeds(x, h, w)
+ # x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype)
+ # x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype)
+
+ # process conditions for MMDiT Blocks
+ c_seq = context # B, T_c, D_c
+ t = timestep
+
+ c = self.cond_seq_linear(c_seq) # B, T_c, D
+ c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1)
+
+ global_cond = self.t_embedder(t, x.dtype) # B, D
+
+ blocks_replace = patches_replace.get("dit", {})
+ if len(self.double_layers) > 0:
+ for i, layer in enumerate(self.double_layers):
+ if ("double_block", i) in blocks_replace:
+ def block_wrap(args):
+ out = {}
+ out["txt"], out["img"] = layer(args["txt"],
+ args["img"],
+ args["vec"])
+ return out
+ out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
+ c = out["txt"]
+ x = out["img"]
+ else:
+ c, x = layer(c, x, global_cond, **kwargs)
+
+ if len(self.single_layers) > 0:
+ c_len = c.size(1)
+ cx = torch.cat([c, x], dim=1)
+ for i, layer in enumerate(self.single_layers):
+ if ("single_block", i) in blocks_replace:
+ def block_wrap(args):
+ out = {}
+ out["img"] = layer(args["img"], args["vec"])
+ return out
+
+ out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
+ cx = out["img"]
+ else:
+ cx = layer(cx, global_cond, **kwargs)
+
+ x = cx[:, c_len:]
+
+ fshift, fscale = self.modF(global_cond).chunk(2, dim=1)
+
+ x = modulate(x, fshift, fscale)
+ x = self.final_linear(x)
+ x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]
+ return x
diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..3eaa0c821cccddbe891ac8a705d702c509c85582
--- /dev/null
+++ b/comfy/ldm/cascade/common.py
@@ -0,0 +1,154 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Stability AI
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+import torch.nn as nn
+from comfy.ldm.modules.attention import optimized_attention
+import comfy.ops
+
+class OptimizedAttention(nn.Module):
+ def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.heads = nhead
+
+ self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+ self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+ self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+
+ self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+
+ def forward(self, q, k, v):
+ q = self.to_q(q)
+ k = self.to_k(k)
+ v = self.to_v(v)
+
+ out = optimized_attention(q, k, v, self.heads)
+
+ return self.out_proj(out)
+
+class Attention2D(nn.Module):
+ def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations)
+ # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device)
+
+ def forward(self, x, kv, self_attn=False):
+ orig_shape = x.shape
+ x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4
+ if self_attn:
+ kv = torch.cat([x, kv], dim=1)
+ # x = self.attn(x, kv, kv, need_weights=False)[0]
+ x = self.attn(x, kv, kv)
+ x = x.permute(0, 2, 1).view(*orig_shape)
+ return x
+
+
+def LayerNorm2d_op(operations):
+ class LayerNorm2d(operations.LayerNorm):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ def forward(self, x):
+ return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+ return LayerNorm2d
+
+class GlobalResponseNorm(nn.Module):
+ "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105"
+ def __init__(self, dim, dtype=None, device=None):
+ super().__init__()
+ self.gamma = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device))
+ self.beta = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device))
+
+ def forward(self, x):
+ Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
+ Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+ return comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + x
+
+
+class ResBlock(nn.Module):
+ def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None): # , num_heads=4, expansion=2):
+ super().__init__()
+ self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device)
+ # self.depthwise = SAMBlock(c, num_heads, expansion)
+ self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.channelwise = nn.Sequential(
+ operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device),
+ nn.GELU(),
+ GlobalResponseNorm(c * 4, dtype=dtype, device=device),
+ nn.Dropout(dropout),
+ operations.Linear(c * 4, c, dtype=dtype, device=device)
+ )
+
+ def forward(self, x, x_skip=None):
+ x_res = x
+ x = self.norm(self.depthwise(x))
+ if x_skip is not None:
+ x = torch.cat([x, x_skip], dim=1)
+ x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+ return x + x_res
+
+
+class AttnBlock(nn.Module):
+ def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.self_attn = self_attn
+ self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations)
+ self.kv_mapper = nn.Sequential(
+ nn.SiLU(),
+ operations.Linear(c_cond, c, dtype=dtype, device=device)
+ )
+
+ def forward(self, x, kv):
+ kv = self.kv_mapper(kv)
+ x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn)
+ return x
+
+
+class FeedForwardBlock(nn.Module):
+ def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.channelwise = nn.Sequential(
+ operations.Linear(c, c * 4, dtype=dtype, device=device),
+ nn.GELU(),
+ GlobalResponseNorm(c * 4, dtype=dtype, device=device),
+ nn.Dropout(dropout),
+ operations.Linear(c * 4, c, dtype=dtype, device=device)
+ )
+
+ def forward(self, x):
+ x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+ return x
+
+
+class TimestepBlock(nn.Module):
+ def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None):
+ super().__init__()
+ self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)
+ self.conds = conds
+ for cname in conds:
+ setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device))
+
+ def forward(self, x, t):
+ t = t.chunk(len(self.conds) + 1, dim=1)
+ a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1)
+ for i, c in enumerate(self.conds):
+ ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1)
+ a, b = a + ac, b + bc
+ return x * (1 + a) + b
diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a52c3c263f96008e7e2b0ca56e3214784c57eb3
--- /dev/null
+++ b/comfy/ldm/cascade/controlnet.py
@@ -0,0 +1,93 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Stability AI
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+import torchvision
+from torch import nn
+from .common import LayerNorm2d_op
+
+
+class CNetResBlock(nn.Module):
+ def __init__(self, c, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.blocks = nn.Sequential(
+ LayerNorm2d_op(operations)(c, dtype=dtype, device=device),
+ nn.GELU(),
+ operations.Conv2d(c, c, kernel_size=3, padding=1),
+ LayerNorm2d_op(operations)(c, dtype=dtype, device=device),
+ nn.GELU(),
+ operations.Conv2d(c, c, kernel_size=3, padding=1),
+ )
+
+ def forward(self, x):
+ return x + self.blocks(x)
+
+
+class ControlNet(nn.Module):
+ def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn):
+ super().__init__()
+ if bottleneck_mode is None:
+ bottleneck_mode = 'effnet'
+ self.proj_blocks = proj_blocks
+ if bottleneck_mode == 'effnet':
+ embd_channels = 1280
+ self.backbone = torchvision.models.efficientnet_v2_s().features.eval()
+ if c_in != 3:
+ in_weights = self.backbone[0][0].weight.data
+ self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device)
+ if c_in > 3:
+ # nn.init.constant_(self.backbone[0][0].weight, 0)
+ self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone()
+ else:
+ self.backbone[0][0].weight.data = in_weights[:, :c_in].clone()
+ elif bottleneck_mode == 'simple':
+ embd_channels = c_in
+ self.backbone = nn.Sequential(
+ operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device),
+ nn.LeakyReLU(0.2, inplace=True),
+ operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device),
+ )
+ elif bottleneck_mode == 'large':
+ self.backbone = nn.Sequential(
+ operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device),
+ nn.LeakyReLU(0.2, inplace=True),
+ operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device),
+ *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)],
+ operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device),
+ )
+ embd_channels = 1280
+ else:
+ raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}')
+ self.projections = nn.ModuleList()
+ for _ in range(len(proj_blocks)):
+ self.projections.append(nn.Sequential(
+ operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device),
+ nn.LeakyReLU(0.2, inplace=True),
+ operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device),
+ ))
+ # nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection
+ self.xl = False
+ self.input_channels = c_in
+ self.unshuffle_amount = 8
+
+ def forward(self, x):
+ x = self.backbone(x)
+ proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)]
+ for i, idx in enumerate(self.proj_blocks):
+ proj_outputs[idx] = self.projections[i](x)
+ return {"input": proj_outputs[::-1]}
diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca8867eaf35cbc57eb5d925082b7e2bb7b36932d
--- /dev/null
+++ b/comfy/ldm/cascade/stage_a.py
@@ -0,0 +1,255 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Stability AI
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+from torch import nn
+from torch.autograd import Function
+
+class vector_quantize(Function):
+ @staticmethod
+ def forward(ctx, x, codebook):
+ with torch.no_grad():
+ codebook_sqr = torch.sum(codebook ** 2, dim=1)
+ x_sqr = torch.sum(x ** 2, dim=1, keepdim=True)
+
+ dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0)
+ _, indices = dist.min(dim=1)
+
+ ctx.save_for_backward(indices, codebook)
+ ctx.mark_non_differentiable(indices)
+
+ nn = torch.index_select(codebook, 0, indices)
+ return nn, indices
+
+ @staticmethod
+ def backward(ctx, grad_output, grad_indices):
+ grad_inputs, grad_codebook = None, None
+
+ if ctx.needs_input_grad[0]:
+ grad_inputs = grad_output.clone()
+ if ctx.needs_input_grad[1]:
+ # Gradient wrt. the codebook
+ indices, codebook = ctx.saved_tensors
+
+ grad_codebook = torch.zeros_like(codebook)
+ grad_codebook.index_add_(0, indices, grad_output)
+
+ return (grad_inputs, grad_codebook)
+
+
+class VectorQuantize(nn.Module):
+ def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False):
+ """
+ Takes an input of variable size (as long as the last dimension matches the embedding size).
+ Returns one tensor containing the nearest neigbour embeddings to each of the inputs,
+ with the same size as the input, vq and commitment components for the loss as a touple
+ in the second output and the indices of the quantized vectors in the third:
+ quantized, (vq_loss, commit_loss), indices
+ """
+ super(VectorQuantize, self).__init__()
+
+ self.codebook = nn.Embedding(k, embedding_size)
+ self.codebook.weight.data.uniform_(-1./k, 1./k)
+ self.vq = vector_quantize.apply
+
+ self.ema_decay = ema_decay
+ self.ema_loss = ema_loss
+ if ema_loss:
+ self.register_buffer('ema_element_count', torch.ones(k))
+ self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight))
+
+ def _laplace_smoothing(self, x, epsilon):
+ n = torch.sum(x)
+ return ((x + epsilon) / (n + x.size(0) * epsilon) * n)
+
+ def _updateEMA(self, z_e_x, indices):
+ mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float()
+ elem_count = mask.sum(dim=0)
+ weight_sum = torch.mm(mask.t(), z_e_x)
+
+ self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count)
+ self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5)
+ self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum)
+
+ self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1)
+
+ def idx2vq(self, idx, dim=-1):
+ q_idx = self.codebook(idx)
+ if dim != -1:
+ q_idx = q_idx.movedim(-1, dim)
+ return q_idx
+
+ def forward(self, x, get_losses=True, dim=-1):
+ if dim != -1:
+ x = x.movedim(dim, -1)
+ z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x
+ z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach())
+ vq_loss, commit_loss = None, None
+ if self.ema_loss and self.training:
+ self._updateEMA(z_e_x.detach(), indices.detach())
+ # pick the graded embeddings after updating the codebook in order to have a more accurate commitment loss
+ z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices)
+ if get_losses:
+ vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean()
+ commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean()
+
+ z_q_x = z_q_x.view(x.shape)
+ if dim != -1:
+ z_q_x = z_q_x.movedim(-1, dim)
+ return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1])
+
+
+class ResBlock(nn.Module):
+ def __init__(self, c, c_hidden):
+ super().__init__()
+ # depthwise/attention
+ self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6)
+ self.depthwise = nn.Sequential(
+ nn.ReplicationPad2d(1),
+ nn.Conv2d(c, c, kernel_size=3, groups=c)
+ )
+
+ # channelwise
+ self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6)
+ self.channelwise = nn.Sequential(
+ nn.Linear(c, c_hidden),
+ nn.GELU(),
+ nn.Linear(c_hidden, c),
+ )
+
+ self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True)
+
+ # Init weights
+ def _basic_init(module):
+ if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+ torch.nn.init.xavier_uniform_(module.weight)
+ if module.bias is not None:
+ nn.init.constant_(module.bias, 0)
+
+ self.apply(_basic_init)
+
+ def _norm(self, x, norm):
+ return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+
+ def forward(self, x):
+ mods = self.gammas
+
+ x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1]
+ try:
+ x = x + self.depthwise(x_temp) * mods[2]
+ except: #operation not implemented for bf16
+ x_temp = self.depthwise[0](x_temp.float()).to(x.dtype)
+ x = x + self.depthwise[1](x_temp) * mods[2]
+
+ x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4]
+ x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5]
+
+ return x
+
+
+class StageA(nn.Module):
+ def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192):
+ super().__init__()
+ self.c_latent = c_latent
+ c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))]
+
+ # Encoder blocks
+ self.in_block = nn.Sequential(
+ nn.PixelUnshuffle(2),
+ nn.Conv2d(3 * 4, c_levels[0], kernel_size=1)
+ )
+ down_blocks = []
+ for i in range(levels):
+ if i > 0:
+ down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1))
+ block = ResBlock(c_levels[i], c_levels[i] * 4)
+ down_blocks.append(block)
+ down_blocks.append(nn.Sequential(
+ nn.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False),
+ nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1
+ ))
+ self.down_blocks = nn.Sequential(*down_blocks)
+ self.down_blocks[0]
+
+ self.codebook_size = codebook_size
+ self.vquantizer = VectorQuantize(c_latent, k=codebook_size)
+
+ # Decoder blocks
+ up_blocks = [nn.Sequential(
+ nn.Conv2d(c_latent, c_levels[-1], kernel_size=1)
+ )]
+ for i in range(levels):
+ for j in range(bottleneck_blocks if i == 0 else 1):
+ block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4)
+ up_blocks.append(block)
+ if i < levels - 1:
+ up_blocks.append(
+ nn.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2,
+ padding=1))
+ self.up_blocks = nn.Sequential(*up_blocks)
+ self.out_block = nn.Sequential(
+ nn.Conv2d(c_levels[0], 3 * 4, kernel_size=1),
+ nn.PixelShuffle(2),
+ )
+
+ def encode(self, x, quantize=False):
+ x = self.in_block(x)
+ x = self.down_blocks(x)
+ if quantize:
+ qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1)
+ return qe, x, indices, vq_loss + commit_loss * 0.25
+ else:
+ return x
+
+ def decode(self, x):
+ x = self.up_blocks(x)
+ x = self.out_block(x)
+ return x
+
+ def forward(self, x, quantize=False):
+ qe, x, _, vq_loss = self.encode(x, quantize)
+ x = self.decode(qe)
+ return x, vq_loss
+
+
+class Discriminator(nn.Module):
+ def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6):
+ super().__init__()
+ d = max(depth - 3, 3)
+ layers = [
+ nn.utils.spectral_norm(nn.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)),
+ nn.LeakyReLU(0.2),
+ ]
+ for i in range(depth - 1):
+ c_in = c_hidden // (2 ** max((d - i), 0))
+ c_out = c_hidden // (2 ** max((d - 1 - i), 0))
+ layers.append(nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1)))
+ layers.append(nn.InstanceNorm2d(c_out))
+ layers.append(nn.LeakyReLU(0.2))
+ self.encoder = nn.Sequential(*layers)
+ self.shuffle = nn.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1)
+ self.logits = nn.Sigmoid()
+
+ def forward(self, x, cond=None):
+ x = self.encoder(x)
+ if cond is not None:
+ cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1))
+ x = torch.cat([x, cond], dim=1)
+ x = self.shuffle(x)
+ x = self.logits(x)
+ return x
diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c3d8feabd826accc702b6e6e598b61b4a739194
--- /dev/null
+++ b/comfy/ldm/cascade/stage_b.py
@@ -0,0 +1,256 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Stability AI
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import math
+import torch
+from torch import nn
+from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock
+
+class StageB(nn.Module):
+ def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280],
+ nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]],
+ block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280,
+ c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True,
+ t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.dtype = dtype
+ self.c_r = c_r
+ self.t_conds = t_conds
+ self.c_clip_seq = c_clip_seq
+ if not isinstance(dropout, list):
+ dropout = [dropout] * len(c_hidden)
+ if not isinstance(self_attn, list):
+ self_attn = [self_attn] * len(c_hidden)
+
+ # CONDITIONING
+ self.effnet_mapper = nn.Sequential(
+ operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device),
+ nn.GELU(),
+ operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device),
+ LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ )
+ self.pixels_mapper = nn.Sequential(
+ operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device),
+ nn.GELU(),
+ operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device),
+ LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ )
+ self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device)
+ self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+ self.embedding = nn.Sequential(
+ nn.PixelUnshuffle(patch_size),
+ operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device),
+ LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ )
+
+ def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
+ if block_type == 'C':
+ return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations)
+ elif block_type == 'A':
+ return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations)
+ elif block_type == 'F':
+ return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations)
+ elif block_type == 'T':
+ return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations)
+ else:
+ raise Exception(f'Block type {block_type} not supported')
+
+ # BLOCKS
+ # -- down blocks
+ self.down_blocks = nn.ModuleList()
+ self.down_downscalers = nn.ModuleList()
+ self.down_repeat_mappers = nn.ModuleList()
+ for i in range(len(c_hidden)):
+ if i > 0:
+ self.down_downscalers.append(nn.Sequential(
+ LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
+ operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device),
+ ))
+ else:
+ self.down_downscalers.append(nn.Identity())
+ down_block = nn.ModuleList()
+ for _ in range(blocks[0][i]):
+ for block_type in level_config[i]:
+ block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i])
+ down_block.append(block)
+ self.down_blocks.append(down_block)
+ if block_repeat is not None:
+ block_repeat_mappers = nn.ModuleList()
+ for _ in range(block_repeat[0][i] - 1):
+ block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+ self.down_repeat_mappers.append(block_repeat_mappers)
+
+ # -- up blocks
+ self.up_blocks = nn.ModuleList()
+ self.up_upscalers = nn.ModuleList()
+ self.up_repeat_mappers = nn.ModuleList()
+ for i in reversed(range(len(c_hidden))):
+ if i > 0:
+ self.up_upscalers.append(nn.Sequential(
+ LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
+ operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device),
+ ))
+ else:
+ self.up_upscalers.append(nn.Identity())
+ up_block = nn.ModuleList()
+ for j in range(blocks[1][::-1][i]):
+ for k, block_type in enumerate(level_config[i]):
+ c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0
+ block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i],
+ self_attn=self_attn[i])
+ up_block.append(block)
+ self.up_blocks.append(up_block)
+ if block_repeat is not None:
+ block_repeat_mappers = nn.ModuleList()
+ for _ in range(block_repeat[1][::-1][i] - 1):
+ block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+ self.up_repeat_mappers.append(block_repeat_mappers)
+
+ # OUTPUT
+ self.clf = nn.Sequential(
+ LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
+ operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device),
+ nn.PixelShuffle(patch_size),
+ )
+
+ # --- WEIGHT INIT ---
+ # self.apply(self._init_weights) # General init
+ # nn.init.normal_(self.clip_mapper.weight, std=0.02) # conditionings
+ # nn.init.normal_(self.effnet_mapper[0].weight, std=0.02) # conditionings
+ # nn.init.normal_(self.effnet_mapper[2].weight, std=0.02) # conditionings
+ # nn.init.normal_(self.pixels_mapper[0].weight, std=0.02) # conditionings
+ # nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings
+ # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs
+ # nn.init.constant_(self.clf[1].weight, 0) # outputs
+ #
+ # # blocks
+ # for level_block in self.down_blocks + self.up_blocks:
+ # for block in level_block:
+ # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock):
+ # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0]))
+ # elif isinstance(block, TimestepBlock):
+ # for layer in block.modules():
+ # if isinstance(layer, nn.Linear):
+ # nn.init.constant_(layer.weight, 0)
+ #
+ # def _init_weights(self, m):
+ # if isinstance(m, (nn.Conv2d, nn.Linear)):
+ # torch.nn.init.xavier_uniform_(m.weight)
+ # if m.bias is not None:
+ # nn.init.constant_(m.bias, 0)
+
+ def gen_r_embedding(self, r, max_positions=10000):
+ r = r * max_positions
+ half_dim = self.c_r // 2
+ emb = math.log(max_positions) / (half_dim - 1)
+ emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
+ emb = r[:, None] * emb[None, :]
+ emb = torch.cat([emb.sin(), emb.cos()], dim=1)
+ if self.c_r % 2 == 1: # zero pad
+ emb = nn.functional.pad(emb, (0, 1), mode='constant')
+ return emb
+
+ def gen_c_embeddings(self, clip):
+ if len(clip.shape) == 2:
+ clip = clip.unsqueeze(1)
+ clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1)
+ clip = self.clip_norm(clip)
+ return clip
+
+ def _down_encode(self, x, r_embed, clip):
+ level_outputs = []
+ block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
+ for down_block, downscaler, repmap in block_group:
+ x = downscaler(x)
+ for i in range(len(repmap) + 1):
+ for block in down_block:
+ if isinstance(block, ResBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ ResBlock)):
+ x = block(x)
+ elif isinstance(block, AttnBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ AttnBlock)):
+ x = block(x, clip)
+ elif isinstance(block, TimestepBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ TimestepBlock)):
+ x = block(x, r_embed)
+ else:
+ x = block(x)
+ if i < len(repmap):
+ x = repmap[i](x)
+ level_outputs.insert(0, x)
+ return level_outputs
+
+ def _up_decode(self, level_outputs, r_embed, clip):
+ x = level_outputs[0]
+ block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
+ for i, (up_block, upscaler, repmap) in enumerate(block_group):
+ for j in range(len(repmap) + 1):
+ for k, block in enumerate(up_block):
+ if isinstance(block, ResBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ ResBlock)):
+ skip = level_outputs[i] if k == 0 and i > 0 else None
+ if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
+ x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear',
+ align_corners=True)
+ x = block(x, skip)
+ elif isinstance(block, AttnBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ AttnBlock)):
+ x = block(x, clip)
+ elif isinstance(block, TimestepBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ TimestepBlock)):
+ x = block(x, r_embed)
+ else:
+ x = block(x)
+ if j < len(repmap):
+ x = repmap[j](x)
+ x = upscaler(x)
+ return x
+
+ def forward(self, x, r, effnet, clip, pixels=None, **kwargs):
+ if pixels is None:
+ pixels = x.new_zeros(x.size(0), 3, 8, 8)
+
+ # Process the conditioning embeddings
+ r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
+ for c in self.t_conds:
+ t_cond = kwargs.get(c, torch.zeros_like(r))
+ r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1)
+ clip = self.gen_c_embeddings(clip)
+
+ # Model Blocks
+ x = self.embedding(x)
+ x = x + self.effnet_mapper(
+ nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True))
+ x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear',
+ align_corners=True)
+ level_outputs = self._down_encode(x, r_embed, clip)
+ x = self._up_decode(level_outputs, r_embed, clip)
+ return self.clf(x)
+
+ def update_weights_ema(self, src_model, beta=0.999):
+ for self_params, src_params in zip(self.parameters(), src_model.parameters()):
+ self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta)
+ for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()):
+ self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)
diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py
new file mode 100644
index 0000000000000000000000000000000000000000..c85da1f01c1d862de5906e73fc746fc92eb51304
--- /dev/null
+++ b/comfy/ldm/cascade/stage_c.py
@@ -0,0 +1,273 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Stability AI
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+from torch import nn
+import math
+from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock
+# from .controlnet import ControlNetDeliverer
+
+class UpDownBlock2d(nn.Module):
+ def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None):
+ super().__init__()
+ assert mode in ['up', 'down']
+ interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear',
+ align_corners=True) if enabled else nn.Identity()
+ mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device)
+ self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation])
+
+ def forward(self, x):
+ for block in self.blocks:
+ x = block(x)
+ return x
+
+
+class StageC(nn.Module):
+ def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32],
+ blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'],
+ c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3,
+ dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None,
+ dtype=None, device=None, operations=None):
+ super().__init__()
+ self.dtype = dtype
+ self.c_r = c_r
+ self.t_conds = t_conds
+ self.c_clip_seq = c_clip_seq
+ if not isinstance(dropout, list):
+ dropout = [dropout] * len(c_hidden)
+ if not isinstance(self_attn, list):
+ self_attn = [self_attn] * len(c_hidden)
+
+ # CONDITIONING
+ self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device)
+ self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device)
+ self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device)
+ self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+ self.embedding = nn.Sequential(
+ nn.PixelUnshuffle(patch_size),
+ operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device),
+ LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6)
+ )
+
+ def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
+ if block_type == 'C':
+ return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations)
+ elif block_type == 'A':
+ return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations)
+ elif block_type == 'F':
+ return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations)
+ elif block_type == 'T':
+ return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations)
+ else:
+ raise Exception(f'Block type {block_type} not supported')
+
+ # BLOCKS
+ # -- down blocks
+ self.down_blocks = nn.ModuleList()
+ self.down_downscalers = nn.ModuleList()
+ self.down_repeat_mappers = nn.ModuleList()
+ for i in range(len(c_hidden)):
+ if i > 0:
+ self.down_downscalers.append(nn.Sequential(
+ LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6),
+ UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)
+ ))
+ else:
+ self.down_downscalers.append(nn.Identity())
+ down_block = nn.ModuleList()
+ for _ in range(blocks[0][i]):
+ for block_type in level_config[i]:
+ block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i])
+ down_block.append(block)
+ self.down_blocks.append(down_block)
+ if block_repeat is not None:
+ block_repeat_mappers = nn.ModuleList()
+ for _ in range(block_repeat[0][i] - 1):
+ block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+ self.down_repeat_mappers.append(block_repeat_mappers)
+
+ # -- up blocks
+ self.up_blocks = nn.ModuleList()
+ self.up_upscalers = nn.ModuleList()
+ self.up_repeat_mappers = nn.ModuleList()
+ for i in reversed(range(len(c_hidden))):
+ if i > 0:
+ self.up_upscalers.append(nn.Sequential(
+ LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6),
+ UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)
+ ))
+ else:
+ self.up_upscalers.append(nn.Identity())
+ up_block = nn.ModuleList()
+ for j in range(blocks[1][::-1][i]):
+ for k, block_type in enumerate(level_config[i]):
+ c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0
+ block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i],
+ self_attn=self_attn[i])
+ up_block.append(block)
+ self.up_blocks.append(up_block)
+ if block_repeat is not None:
+ block_repeat_mappers = nn.ModuleList()
+ for _ in range(block_repeat[1][::-1][i] - 1):
+ block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+ self.up_repeat_mappers.append(block_repeat_mappers)
+
+ # OUTPUT
+ self.clf = nn.Sequential(
+ LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
+ operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device),
+ nn.PixelShuffle(patch_size),
+ )
+
+ # --- WEIGHT INIT ---
+ # self.apply(self._init_weights) # General init
+ # nn.init.normal_(self.clip_txt_mapper.weight, std=0.02) # conditionings
+ # nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02) # conditionings
+ # nn.init.normal_(self.clip_img_mapper.weight, std=0.02) # conditionings
+ # torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs
+ # nn.init.constant_(self.clf[1].weight, 0) # outputs
+ #
+ # # blocks
+ # for level_block in self.down_blocks + self.up_blocks:
+ # for block in level_block:
+ # if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock):
+ # block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0]))
+ # elif isinstance(block, TimestepBlock):
+ # for layer in block.modules():
+ # if isinstance(layer, nn.Linear):
+ # nn.init.constant_(layer.weight, 0)
+ #
+ # def _init_weights(self, m):
+ # if isinstance(m, (nn.Conv2d, nn.Linear)):
+ # torch.nn.init.xavier_uniform_(m.weight)
+ # if m.bias is not None:
+ # nn.init.constant_(m.bias, 0)
+
+ def gen_r_embedding(self, r, max_positions=10000):
+ r = r * max_positions
+ half_dim = self.c_r // 2
+ emb = math.log(max_positions) / (half_dim - 1)
+ emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
+ emb = r[:, None] * emb[None, :]
+ emb = torch.cat([emb.sin(), emb.cos()], dim=1)
+ if self.c_r % 2 == 1: # zero pad
+ emb = nn.functional.pad(emb, (0, 1), mode='constant')
+ return emb
+
+ def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img):
+ clip_txt = self.clip_txt_mapper(clip_txt)
+ if len(clip_txt_pooled.shape) == 2:
+ clip_txt_pooled = clip_txt_pooled.unsqueeze(1)
+ if len(clip_img.shape) == 2:
+ clip_img = clip_img.unsqueeze(1)
+ clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1)
+ clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1)
+ clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1)
+ clip = self.clip_norm(clip)
+ return clip
+
+ def _down_encode(self, x, r_embed, clip, cnet=None):
+ level_outputs = []
+ block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
+ for down_block, downscaler, repmap in block_group:
+ x = downscaler(x)
+ for i in range(len(repmap) + 1):
+ for block in down_block:
+ if isinstance(block, ResBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ ResBlock)):
+ if cnet is not None:
+ next_cnet = cnet.pop()
+ if next_cnet is not None:
+ x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',
+ align_corners=True).to(x.dtype)
+ x = block(x)
+ elif isinstance(block, AttnBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ AttnBlock)):
+ x = block(x, clip)
+ elif isinstance(block, TimestepBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ TimestepBlock)):
+ x = block(x, r_embed)
+ else:
+ x = block(x)
+ if i < len(repmap):
+ x = repmap[i](x)
+ level_outputs.insert(0, x)
+ return level_outputs
+
+ def _up_decode(self, level_outputs, r_embed, clip, cnet=None):
+ x = level_outputs[0]
+ block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
+ for i, (up_block, upscaler, repmap) in enumerate(block_group):
+ for j in range(len(repmap) + 1):
+ for k, block in enumerate(up_block):
+ if isinstance(block, ResBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ ResBlock)):
+ skip = level_outputs[i] if k == 0 and i > 0 else None
+ if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
+ x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear',
+ align_corners=True)
+ if cnet is not None:
+ next_cnet = cnet.pop()
+ if next_cnet is not None:
+ x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',
+ align_corners=True).to(x.dtype)
+ x = block(x, skip)
+ elif isinstance(block, AttnBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ AttnBlock)):
+ x = block(x, clip)
+ elif isinstance(block, TimestepBlock) or (
+ hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+ TimestepBlock)):
+ x = block(x, r_embed)
+ else:
+ x = block(x)
+ if j < len(repmap):
+ x = repmap[j](x)
+ x = upscaler(x)
+ return x
+
+ def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs):
+ # Process the conditioning embeddings
+ r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
+ for c in self.t_conds:
+ t_cond = kwargs.get(c, torch.zeros_like(r))
+ r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1)
+ clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img)
+
+ if control is not None:
+ cnet = control.get("input")
+ else:
+ cnet = None
+
+ # Model Blocks
+ x = self.embedding(x)
+ level_outputs = self._down_encode(x, r_embed, clip, cnet)
+ x = self._up_decode(level_outputs, r_embed, clip, cnet)
+ return self.clf(x)
+
+ def update_weights_ema(self, src_model, beta=0.999):
+ for self_params, src_params in zip(self.parameters(), src_model.parameters()):
+ self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta)
+ for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()):
+ self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)
diff --git a/comfy/ldm/cascade/stage_c_coder.py b/comfy/ldm/cascade/stage_c_coder.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cb7c49fc90c434553954772cbf522e1f4a88955
--- /dev/null
+++ b/comfy/ldm/cascade/stage_c_coder.py
@@ -0,0 +1,95 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Stability AI
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+import torch
+import torchvision
+from torch import nn
+
+
+# EfficientNet
+class EfficientNetEncoder(nn.Module):
+ def __init__(self, c_latent=16):
+ super().__init__()
+ self.backbone = torchvision.models.efficientnet_v2_s().features.eval()
+ self.mapper = nn.Sequential(
+ nn.Conv2d(1280, c_latent, kernel_size=1, bias=False),
+ nn.BatchNorm2d(c_latent, affine=False), # then normalize them to have mean 0 and std 1
+ )
+ self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406]))
+ self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225]))
+
+ def forward(self, x):
+ x = x * 0.5 + 0.5
+ x = (x - self.mean.view([3,1,1])) / self.std.view([3,1,1])
+ o = self.mapper(self.backbone(x))
+ return o
+
+
+# Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192
+class Previewer(nn.Module):
+ def __init__(self, c_in=16, c_hidden=512, c_out=3):
+ super().__init__()
+ self.blocks = nn.Sequential(
+ nn.Conv2d(c_in, c_hidden, kernel_size=1), # 16 channels to 512 channels
+ nn.GELU(),
+ nn.BatchNorm2d(c_hidden),
+
+ nn.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1),
+ nn.GELU(),
+ nn.BatchNorm2d(c_hidden),
+
+ nn.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2), # 16 -> 32
+ nn.GELU(),
+ nn.BatchNorm2d(c_hidden // 2),
+
+ nn.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1),
+ nn.GELU(),
+ nn.BatchNorm2d(c_hidden // 2),
+
+ nn.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2), # 32 -> 64
+ nn.GELU(),
+ nn.BatchNorm2d(c_hidden // 4),
+
+ nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),
+ nn.GELU(),
+ nn.BatchNorm2d(c_hidden // 4),
+
+ nn.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2), # 64 -> 128
+ nn.GELU(),
+ nn.BatchNorm2d(c_hidden // 4),
+
+ nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),
+ nn.GELU(),
+ nn.BatchNorm2d(c_hidden // 4),
+
+ nn.Conv2d(c_hidden // 4, c_out, kernel_size=1),
+ )
+
+ def forward(self, x):
+ return (self.blocks(x) - 0.5) * 2.0
+
+class StageC_coder(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.previewer = Previewer()
+ self.encoder = EfficientNetEncoder()
+
+ def encode(self, x):
+ return self.encoder(x)
+
+ def decode(self, x):
+ return self.previewer(x)
diff --git a/comfy/ldm/common_dit.py b/comfy/ldm/common_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9139696f1d3d1e010f52d4376917b5bba5e16cd
--- /dev/null
+++ b/comfy/ldm/common_dit.py
@@ -0,0 +1,27 @@
+import torch
+import comfy.ops
+
+def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):
+ if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()):
+ padding_mode = "reflect"
+ pad_h = (patch_size[0] - img.shape[-2] % patch_size[0]) % patch_size[0]
+ pad_w = (patch_size[1] - img.shape[-1] % patch_size[1]) % patch_size[1]
+ return torch.nn.functional.pad(img, (0, pad_w, 0, pad_h), mode=padding_mode)
+
+try:
+ rms_norm_torch = torch.nn.functional.rms_norm
+except:
+ rms_norm_torch = None
+
+def rms_norm(x, weight=None, eps=1e-6):
+ if rms_norm_torch is not None and not (torch.jit.is_tracing() or torch.jit.is_scripting()):
+ if weight is None:
+ return rms_norm_torch(x, (x.shape[-1],), eps=eps)
+ else:
+ return rms_norm_torch(x, weight.shape, weight=comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps)
+ else:
+ r = x * torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps)
+ if weight is None:
+ return r
+ else:
+ return r * comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device)
diff --git a/comfy/ldm/flux/controlnet.py b/comfy/ldm/flux/controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..c033dea52f2e677c57797c17677cb9072fbb04e5
--- /dev/null
+++ b/comfy/ldm/flux/controlnet.py
@@ -0,0 +1,205 @@
+#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
+#modified to support different types of flux controlnets
+
+import torch
+import math
+from torch import Tensor, nn
+from einops import rearrange, repeat
+
+from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
+ MLPEmbedder, SingleStreamBlock,
+ timestep_embedding)
+
+from .model import Flux
+import comfy.ldm.common_dit
+
+class MistolineCondDownsamplBlock(nn.Module):
+ def __init__(self, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.encoder = nn.Sequential(
+ operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+ )
+
+ def forward(self, x):
+ return self.encoder(x)
+
+class MistolineControlnetBlock(nn.Module):
+ def __init__(self, hidden_size, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device)
+ self.act = nn.SiLU()
+
+ def forward(self, x):
+ return self.act(self.linear(x))
+
+
+class ControlNetFlux(Flux):
+ def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, control_latent_channels=None, image_model=None, dtype=None, device=None, operations=None, **kwargs):
+ super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
+
+ self.main_model_double = 19
+ self.main_model_single = 38
+
+ self.mistoline = mistoline
+ # add ControlNet blocks
+ if self.mistoline:
+ control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations)
+ else:
+ control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+
+ self.controlnet_blocks = nn.ModuleList([])
+ for _ in range(self.params.depth):
+ self.controlnet_blocks.append(control_block())
+
+ self.controlnet_single_blocks = nn.ModuleList([])
+ for _ in range(self.params.depth_single_blocks):
+ self.controlnet_single_blocks.append(control_block())
+
+ self.num_union_modes = num_union_modes
+ self.controlnet_mode_embedder = None
+ if self.num_union_modes > 0:
+ self.controlnet_mode_embedder = operations.Embedding(self.num_union_modes, self.hidden_size, dtype=dtype, device=device)
+
+ self.gradient_checkpointing = False
+ self.latent_input = latent_input
+ if control_latent_channels is None:
+ control_latent_channels = self.in_channels
+ else:
+ control_latent_channels *= 2 * 2 #patch size
+
+ self.pos_embed_input = operations.Linear(control_latent_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+ if not self.latent_input:
+ if self.mistoline:
+ self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations)
+ else:
+ self.input_hint_block = nn.Sequential(
+ operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+ )
+
+ def forward_orig(
+ self,
+ img: Tensor,
+ img_ids: Tensor,
+ controlnet_cond: Tensor,
+ txt: Tensor,
+ txt_ids: Tensor,
+ timesteps: Tensor,
+ y: Tensor,
+ guidance: Tensor = None,
+ control_type: Tensor = None,
+ ) -> Tensor:
+ if img.ndim != 3 or txt.ndim != 3:
+ raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+ # running on sequences img
+ img = self.img_in(img)
+
+ controlnet_cond = self.pos_embed_input(controlnet_cond)
+ img = img + controlnet_cond
+ vec = self.time_in(timestep_embedding(timesteps, 256))
+ if self.params.guidance_embed:
+ vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+ vec = vec + self.vector_in(y)
+ txt = self.txt_in(txt)
+
+ if self.controlnet_mode_embedder is not None and len(control_type) > 0:
+ control_cond = self.controlnet_mode_embedder(torch.tensor(control_type, device=img.device), out_dtype=img.dtype).unsqueeze(0).repeat((txt.shape[0], 1, 1))
+ txt = torch.cat([control_cond, txt], dim=1)
+ txt_ids = torch.cat([txt_ids[:,:1], txt_ids], dim=1)
+
+ ids = torch.cat((txt_ids, img_ids), dim=1)
+ pe = self.pe_embedder(ids)
+
+ controlnet_double = ()
+
+ for i in range(len(self.double_blocks)):
+ img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe)
+ controlnet_double = controlnet_double + (self.controlnet_blocks[i](img),)
+
+ img = torch.cat((txt, img), 1)
+
+ controlnet_single = ()
+
+ for i in range(len(self.single_blocks)):
+ img = self.single_blocks[i](img, vec=vec, pe=pe)
+ controlnet_single = controlnet_single + (self.controlnet_single_blocks[i](img[:, txt.shape[1] :, ...]),)
+
+ repeat = math.ceil(self.main_model_double / len(controlnet_double))
+ if self.latent_input:
+ out_input = ()
+ for x in controlnet_double:
+ out_input += (x,) * repeat
+ else:
+ out_input = (controlnet_double * repeat)
+
+ out = {"input": out_input[:self.main_model_double]}
+ if len(controlnet_single) > 0:
+ repeat = math.ceil(self.main_model_single / len(controlnet_single))
+ out_output = ()
+ if self.latent_input:
+ for x in controlnet_single:
+ out_output += (x,) * repeat
+ else:
+ out_output = (controlnet_single * repeat)
+ out["output"] = out_output[:self.main_model_single]
+ return out
+
+ def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
+ patch_size = 2
+ if self.latent_input:
+ hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))
+ elif self.mistoline:
+ hint = hint * 2.0 - 1.0
+ hint = self.input_cond_block(hint)
+ else:
+ hint = hint * 2.0 - 1.0
+ hint = self.input_hint_block(hint)
+
+ hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+ bs, c, h, w = x.shape
+ x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
+
+ img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+ h_len = ((h + (patch_size // 2)) // patch_size)
+ w_len = ((w + (patch_size // 2)) // patch_size)
+ img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+ img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
+ img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
+ img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+ txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+ return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance, control_type=kwargs.get("control_type", []))
diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..dabab3e33023308785b3594a96dc9796e777dcdb
--- /dev/null
+++ b/comfy/ldm/flux/layers.py
@@ -0,0 +1,249 @@
+import math
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+
+from .math import attention, rope
+import comfy.ops
+import comfy.ldm.common_dit
+
+
+class EmbedND(nn.Module):
+ def __init__(self, dim: int, theta: int, axes_dim: list):
+ super().__init__()
+ self.dim = dim
+ self.theta = theta
+ self.axes_dim = axes_dim
+
+ def forward(self, ids: Tensor) -> Tensor:
+ n_axes = ids.shape[-1]
+ emb = torch.cat(
+ [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+ dim=-3,
+ )
+
+ return emb.unsqueeze(1)
+
+
+def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
+ """
+ Create sinusoidal timestep embeddings.
+ :param t: a 1-D Tensor of N indices, one per batch element.
+ These may be fractional.
+ :param dim: the dimension of the output.
+ :param max_period: controls the minimum frequency of the embeddings.
+ :return: an (N, D) Tensor of positional embeddings.
+ """
+ t = time_factor * t
+ half = dim // 2
+ freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)
+
+ args = t[:, None].float() * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ if torch.is_floating_point(t):
+ embedding = embedding.to(t)
+ return embedding
+
+class MLPEmbedder(nn.Module):
+ def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+ self.silu = nn.SiLU()
+ self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+
+ def forward(self, x: Tensor) -> Tensor:
+ return self.out_layer(self.silu(self.in_layer(x)))
+
+
+class RMSNorm(torch.nn.Module):
+ def __init__(self, dim: int, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device))
+
+ def forward(self, x: Tensor):
+ return comfy.ldm.common_dit.rms_norm(x, self.scale, 1e-6)
+
+
+class QKNorm(torch.nn.Module):
+ def __init__(self, dim: int, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+ self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+
+ def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
+ q = self.query_norm(q)
+ k = self.key_norm(k)
+ return q.to(v), k.to(v)
+
+
+class SelfAttention(nn.Module):
+ def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+
+ self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+ self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+ self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+
+
+@dataclass
+class ModulationOut:
+ shift: Tensor
+ scale: Tensor
+ gate: Tensor
+
+
+class Modulation(nn.Module):
+ def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.is_double = double
+ self.multiplier = 6 if double else 3
+ self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
+
+ def forward(self, vec: Tensor) -> tuple:
+ out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
+
+ return (
+ ModulationOut(*out[:3]),
+ ModulationOut(*out[3:]) if self.is_double else None,
+ )
+
+
+class DoubleStreamBlock(nn.Module):
+ def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, dtype=None, device=None, operations=None):
+ super().__init__()
+
+ mlp_hidden_dim = int(hidden_size * mlp_ratio)
+ self.num_heads = num_heads
+ self.hidden_size = hidden_size
+ self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+ self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+ self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.img_mlp = nn.Sequential(
+ operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+ nn.GELU(approximate="tanh"),
+ operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+ )
+
+ self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+ self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+ self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.txt_mlp = nn.Sequential(
+ operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+ nn.GELU(approximate="tanh"),
+ operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+ )
+
+ def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor):
+ img_mod1, img_mod2 = self.img_mod(vec)
+ txt_mod1, txt_mod2 = self.txt_mod(vec)
+
+ # prepare image for attention
+ img_modulated = self.img_norm1(img)
+ img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+ img_qkv = self.img_attn.qkv(img_modulated)
+ img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+ img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+ # prepare txt for attention
+ txt_modulated = self.txt_norm1(txt)
+ txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+ txt_qkv = self.txt_attn.qkv(txt_modulated)
+ txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+ txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+ # run actual attention
+ attn = attention(torch.cat((txt_q, img_q), dim=2),
+ torch.cat((txt_k, img_k), dim=2),
+ torch.cat((txt_v, img_v), dim=2), pe=pe)
+
+ txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+ # calculate the img bloks
+ img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+ img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+
+ # calculate the txt bloks
+ txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
+ txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+
+ if txt.dtype == torch.float16:
+ txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
+
+ return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+ """
+ A DiT block with parallel linear layers as described in
+ https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+ """
+
+ def __init__(
+ self,
+ hidden_size: int,
+ num_heads: int,
+ mlp_ratio: float = 4.0,
+ qk_scale: float = None,
+ dtype=None,
+ device=None,
+ operations=None
+ ):
+ super().__init__()
+ self.hidden_dim = hidden_size
+ self.num_heads = num_heads
+ head_dim = hidden_size // num_heads
+ self.scale = qk_scale or head_dim**-0.5
+
+ self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+ # qkv and mlp_in
+ self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
+ # proj and mlp_out
+ self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
+
+ self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+
+ self.hidden_size = hidden_size
+ self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+ self.mlp_act = nn.GELU(approximate="tanh")
+ self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
+
+ def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
+ mod, _ = self.modulation(vec)
+ x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+ qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+ q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+ q, k = self.norm(q, k, v)
+
+ # compute attention
+ attn = attention(q, k, v, pe=pe)
+ # compute activation in mlp stream, cat again and run second linear layer
+ output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+ x += mod.gate * output
+ if x.dtype == torch.float16:
+ x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
+ return x
+
+
+class LastLayer(nn.Module):
+ def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+ self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
+
+ def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+ shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+ x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+ x = self.linear(x)
+ return x
diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
new file mode 100644
index 0000000000000000000000000000000000000000..136ce2aa83cf6f80713a37d6f7ad5ba62ce5b186
--- /dev/null
+++ b/comfy/ldm/flux/math.py
@@ -0,0 +1,35 @@
+import torch
+from einops import rearrange
+from torch import Tensor
+from comfy.ldm.modules.attention import optimized_attention
+import comfy.model_management
+
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
+ q, k = apply_rope(q, k, pe)
+
+ heads = q.shape[1]
+ x = optimized_attention(q, k, v, heads, skip_reshape=True)
+ return x
+
+
+def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
+ assert dim % 2 == 0
+ if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu():
+ device = torch.device("cpu")
+ else:
+ device = pos.device
+
+ scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
+ omega = 1.0 / (theta**scale)
+ out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega)
+ out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
+ out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+ return out.to(dtype=torch.float32, device=pos.device)
+
+
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
+ xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+ xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+ xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+ xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+ return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..97ad8ffeaf63cb511fedf876a9826cf3ee1331c3
--- /dev/null
+++ b/comfy/ldm/flux/model.py
@@ -0,0 +1,185 @@
+#Original code can be found on: https://github.com/black-forest-labs/flux
+
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+
+from .layers import (
+ DoubleStreamBlock,
+ EmbedND,
+ LastLayer,
+ MLPEmbedder,
+ SingleStreamBlock,
+ timestep_embedding,
+)
+
+from einops import rearrange, repeat
+import comfy.ldm.common_dit
+
+@dataclass
+class FluxParams:
+ in_channels: int
+ out_channels: int
+ vec_in_dim: int
+ context_in_dim: int
+ hidden_size: int
+ mlp_ratio: float
+ num_heads: int
+ depth: int
+ depth_single_blocks: int
+ axes_dim: list
+ theta: int
+ patch_size: int
+ qkv_bias: bool
+ guidance_embed: bool
+
+
+class Flux(nn.Module):
+ """
+ Transformer model for flow matching on sequences.
+ """
+
+ def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
+ super().__init__()
+ self.dtype = dtype
+ params = FluxParams(**kwargs)
+ self.params = params
+ self.patch_size = params.patch_size
+ self.in_channels = params.in_channels * params.patch_size * params.patch_size
+ self.out_channels = params.out_channels * params.patch_size * params.patch_size
+ if params.hidden_size % params.num_heads != 0:
+ raise ValueError(
+ f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
+ )
+ pe_dim = params.hidden_size // params.num_heads
+ if sum(params.axes_dim) != pe_dim:
+ raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+ self.hidden_size = params.hidden_size
+ self.num_heads = params.num_heads
+ self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+ self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+ self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
+ self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
+ self.guidance_in = (
+ MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
+ )
+ self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
+
+ self.double_blocks = nn.ModuleList(
+ [
+ DoubleStreamBlock(
+ self.hidden_size,
+ self.num_heads,
+ mlp_ratio=params.mlp_ratio,
+ qkv_bias=params.qkv_bias,
+ dtype=dtype, device=device, operations=operations
+ )
+ for _ in range(params.depth)
+ ]
+ )
+
+ self.single_blocks = nn.ModuleList(
+ [
+ SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
+ for _ in range(params.depth_single_blocks)
+ ]
+ )
+
+ if final_layer:
+ self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
+
+ def forward_orig(
+ self,
+ img: Tensor,
+ img_ids: Tensor,
+ txt: Tensor,
+ txt_ids: Tensor,
+ timesteps: Tensor,
+ y: Tensor,
+ guidance: Tensor = None,
+ control=None,
+ transformer_options={},
+ ) -> Tensor:
+ patches_replace = transformer_options.get("patches_replace", {})
+ if img.ndim != 3 or txt.ndim != 3:
+ raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+ # running on sequences img
+ img = self.img_in(img)
+ vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
+ if self.params.guidance_embed:
+ if guidance is None:
+ raise ValueError("Didn't get guidance strength for guidance distilled model.")
+ vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
+
+ vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
+ txt = self.txt_in(txt)
+
+ ids = torch.cat((txt_ids, img_ids), dim=1)
+ pe = self.pe_embedder(ids)
+
+ blocks_replace = patches_replace.get("dit", {})
+ for i, block in enumerate(self.double_blocks):
+ if ("double_block", i) in blocks_replace:
+ def block_wrap(args):
+ out = {}
+ out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"])
+ return out
+
+ out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe}, {"original_block": block_wrap})
+ txt = out["txt"]
+ img = out["img"]
+ else:
+ img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+
+ if control is not None: # Controlnet
+ control_i = control.get("input")
+ if i < len(control_i):
+ add = control_i[i]
+ if add is not None:
+ img += add
+
+ img = torch.cat((txt, img), 1)
+
+ for i, block in enumerate(self.single_blocks):
+ if ("single_block", i) in blocks_replace:
+ def block_wrap(args):
+ out = {}
+ out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"])
+ return out
+
+ out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe}, {"original_block": block_wrap})
+ img = out["img"]
+ else:
+ img = block(img, vec=vec, pe=pe)
+
+ if control is not None: # Controlnet
+ control_o = control.get("output")
+ if i < len(control_o):
+ add = control_o[i]
+ if add is not None:
+ img[:, txt.shape[1] :, ...] += add
+
+ img = img[:, txt.shape[1] :, ...]
+
+ img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
+ return img
+
+ def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs):
+ bs, c, h, w = x.shape
+ patch_size = self.patch_size
+ x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
+
+ img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+ h_len = ((h + (patch_size // 2)) // patch_size)
+ w_len = ((w + (patch_size // 2)) // patch_size)
+ img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+ img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+ img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
+ img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+ txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+ out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options)
+ return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
diff --git a/comfy/ldm/flux/redux.py b/comfy/ldm/flux/redux.py
new file mode 100644
index 0000000000000000000000000000000000000000..527e83164ea2fd1bdf7431f3791ebede00895051
--- /dev/null
+++ b/comfy/ldm/flux/redux.py
@@ -0,0 +1,25 @@
+import torch
+import comfy.ops
+
+ops = comfy.ops.manual_cast
+
+class ReduxImageEncoder(torch.nn.Module):
+ def __init__(
+ self,
+ redux_dim: int = 1152,
+ txt_in_features: int = 4096,
+ device=None,
+ dtype=None,
+ ) -> None:
+ super().__init__()
+
+ self.redux_dim = redux_dim
+ self.device = device
+ self.dtype = dtype
+
+ self.redux_up = ops.Linear(redux_dim, txt_in_features * 3, dtype=dtype)
+ self.redux_down = ops.Linear(txt_in_features * 3, txt_in_features, dtype=dtype)
+
+ def forward(self, sigclip_embeds) -> torch.Tensor:
+ projected_x = self.redux_down(torch.nn.functional.silu(self.redux_up(sigclip_embeds)))
+ return projected_x
diff --git a/comfy/ldm/genmo/joint_model/asymm_models_joint.py b/comfy/ldm/genmo/joint_model/asymm_models_joint.py
new file mode 100644
index 0000000000000000000000000000000000000000..45c93896680c328f5a6645782420d98278cbe508
--- /dev/null
+++ b/comfy/ldm/genmo/joint_model/asymm_models_joint.py
@@ -0,0 +1,559 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+#adapted to ComfyUI
+
+from typing import Dict, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+# from flash_attn import flash_attn_varlen_qkvpacked_func
+from comfy.ldm.modules.attention import optimized_attention
+
+from .layers import (
+ FeedForward,
+ PatchEmbed,
+ RMSNorm,
+ TimestepEmbedder,
+)
+
+from .rope_mixed import (
+ compute_mixed_rotation,
+ create_position_matrix,
+)
+from .temporal_rope import apply_rotary_emb_qk_real
+from .utils import (
+ AttentionPool,
+ modulate,
+)
+
+import comfy.ldm.common_dit
+import comfy.ops
+
+
+def modulated_rmsnorm(x, scale, eps=1e-6):
+ # Normalize and modulate
+ x_normed = comfy.ldm.common_dit.rms_norm(x, eps=eps)
+ x_modulated = x_normed * (1 + scale.unsqueeze(1))
+
+ return x_modulated
+
+
+def residual_tanh_gated_rmsnorm(x, x_res, gate, eps=1e-6):
+ # Apply tanh to gate
+ tanh_gate = torch.tanh(gate).unsqueeze(1)
+
+ # Normalize and apply gated scaling
+ x_normed = comfy.ldm.common_dit.rms_norm(x_res, eps=eps) * tanh_gate
+
+ # Apply residual connection
+ output = x + x_normed
+
+ return output
+
+class AsymmetricAttention(nn.Module):
+ def __init__(
+ self,
+ dim_x: int,
+ dim_y: int,
+ num_heads: int = 8,
+ qkv_bias: bool = True,
+ qk_norm: bool = False,
+ attn_drop: float = 0.0,
+ update_y: bool = True,
+ out_bias: bool = True,
+ attend_to_padding: bool = False,
+ softmax_scale: Optional[float] = None,
+ device: Optional[torch.device] = None,
+ dtype=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.dim_x = dim_x
+ self.dim_y = dim_y
+ self.num_heads = num_heads
+ self.head_dim = dim_x // num_heads
+ self.attn_drop = attn_drop
+ self.update_y = update_y
+ self.attend_to_padding = attend_to_padding
+ self.softmax_scale = softmax_scale
+ if dim_x % num_heads != 0:
+ raise ValueError(
+ f"dim_x={dim_x} should be divisible by num_heads={num_heads}"
+ )
+
+ # Input layers.
+ self.qkv_bias = qkv_bias
+ self.qkv_x = operations.Linear(dim_x, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype)
+ # Project text features to match visual features (dim_y -> dim_x)
+ self.qkv_y = operations.Linear(dim_y, 3 * dim_x, bias=qkv_bias, device=device, dtype=dtype)
+
+ # Query and key normalization for stability.
+ assert qk_norm
+ self.q_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype)
+ self.k_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype)
+ self.q_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype)
+ self.k_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype)
+
+ # Output layers. y features go back down from dim_x -> dim_y.
+ self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype)
+ self.proj_y = (
+ operations.Linear(dim_x, dim_y, bias=out_bias, device=device, dtype=dtype)
+ if update_y
+ else nn.Identity()
+ )
+
+ def forward(
+ self,
+ x: torch.Tensor, # (B, N, dim_x)
+ y: torch.Tensor, # (B, L, dim_y)
+ scale_x: torch.Tensor, # (B, dim_x), modulation for pre-RMSNorm.
+ scale_y: torch.Tensor, # (B, dim_y), modulation for pre-RMSNorm.
+ crop_y,
+ **rope_rotation,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ rope_cos = rope_rotation.get("rope_cos")
+ rope_sin = rope_rotation.get("rope_sin")
+ # Pre-norm for visual features
+ x = modulated_rmsnorm(x, scale_x) # (B, M, dim_x) where M = N / cp_group_size
+
+ # Process visual features
+ # qkv_x = self.qkv_x(x) # (B, M, 3 * dim_x)
+ # assert qkv_x.dtype == torch.bfloat16
+ # qkv_x = all_to_all_collect_tokens(
+ # qkv_x, self.num_heads
+ # ) # (3, B, N, local_h, head_dim)
+
+ # Process text features
+ y = modulated_rmsnorm(y, scale_y) # (B, L, dim_y)
+ q_y, k_y, v_y = self.qkv_y(y).view(y.shape[0], y.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim)
+
+ q_y = self.q_norm_y(q_y)
+ k_y = self.k_norm_y(k_y)
+
+ # Split qkv_x into q, k, v
+ q_x, k_x, v_x = self.qkv_x(x).view(x.shape[0], x.shape[1], 3, self.num_heads, -1).unbind(2) # (B, N, local_h, head_dim)
+ q_x = self.q_norm_x(q_x)
+ q_x = apply_rotary_emb_qk_real(q_x, rope_cos, rope_sin)
+ k_x = self.k_norm_x(k_x)
+ k_x = apply_rotary_emb_qk_real(k_x, rope_cos, rope_sin)
+
+ q = torch.cat([q_x, q_y[:, :crop_y]], dim=1).transpose(1, 2)
+ k = torch.cat([k_x, k_y[:, :crop_y]], dim=1).transpose(1, 2)
+ v = torch.cat([v_x, v_y[:, :crop_y]], dim=1).transpose(1, 2)
+
+ xy = optimized_attention(q,
+ k,
+ v, self.num_heads, skip_reshape=True)
+
+ x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1)
+ x = self.proj_x(x)
+ o = torch.zeros(y.shape[0], q_y.shape[1], y.shape[-1], device=y.device, dtype=y.dtype)
+ o[:, :y.shape[1]] = y
+
+ y = self.proj_y(o)
+ # print("ox", x)
+ # print("oy", y)
+ return x, y
+
+
+class AsymmetricJointBlock(nn.Module):
+ def __init__(
+ self,
+ hidden_size_x: int,
+ hidden_size_y: int,
+ num_heads: int,
+ *,
+ mlp_ratio_x: float = 8.0, # Ratio of hidden size to d_model for MLP for visual tokens.
+ mlp_ratio_y: float = 4.0, # Ratio of hidden size to d_model for MLP for text tokens.
+ update_y: bool = True, # Whether to update text tokens in this block.
+ device: Optional[torch.device] = None,
+ dtype=None,
+ operations=None,
+ **block_kwargs,
+ ):
+ super().__init__()
+ self.update_y = update_y
+ self.hidden_size_x = hidden_size_x
+ self.hidden_size_y = hidden_size_y
+ self.mod_x = operations.Linear(hidden_size_x, 4 * hidden_size_x, device=device, dtype=dtype)
+ if self.update_y:
+ self.mod_y = operations.Linear(hidden_size_x, 4 * hidden_size_y, device=device, dtype=dtype)
+ else:
+ self.mod_y = operations.Linear(hidden_size_x, hidden_size_y, device=device, dtype=dtype)
+
+ # Self-attention:
+ self.attn = AsymmetricAttention(
+ hidden_size_x,
+ hidden_size_y,
+ num_heads=num_heads,
+ update_y=update_y,
+ device=device,
+ dtype=dtype,
+ operations=operations,
+ **block_kwargs,
+ )
+
+ # MLP.
+ mlp_hidden_dim_x = int(hidden_size_x * mlp_ratio_x)
+ assert mlp_hidden_dim_x == int(1536 * 8)
+ self.mlp_x = FeedForward(
+ in_features=hidden_size_x,
+ hidden_size=mlp_hidden_dim_x,
+ multiple_of=256,
+ ffn_dim_multiplier=None,
+ device=device,
+ dtype=dtype,
+ operations=operations,
+ )
+
+ # MLP for text not needed in last block.
+ if self.update_y:
+ mlp_hidden_dim_y = int(hidden_size_y * mlp_ratio_y)
+ self.mlp_y = FeedForward(
+ in_features=hidden_size_y,
+ hidden_size=mlp_hidden_dim_y,
+ multiple_of=256,
+ ffn_dim_multiplier=None,
+ device=device,
+ dtype=dtype,
+ operations=operations,
+ )
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ c: torch.Tensor,
+ y: torch.Tensor,
+ **attn_kwargs,
+ ):
+ """Forward pass of a block.
+
+ Args:
+ x: (B, N, dim) tensor of visual tokens
+ c: (B, dim) tensor of conditioned features
+ y: (B, L, dim) tensor of text tokens
+ num_frames: Number of frames in the video. N = num_frames * num_spatial_tokens
+
+ Returns:
+ x: (B, N, dim) tensor of visual tokens after block
+ y: (B, L, dim) tensor of text tokens after block
+ """
+ N = x.size(1)
+
+ c = F.silu(c)
+ mod_x = self.mod_x(c)
+ scale_msa_x, gate_msa_x, scale_mlp_x, gate_mlp_x = mod_x.chunk(4, dim=1)
+
+ mod_y = self.mod_y(c)
+ if self.update_y:
+ scale_msa_y, gate_msa_y, scale_mlp_y, gate_mlp_y = mod_y.chunk(4, dim=1)
+ else:
+ scale_msa_y = mod_y
+
+ # Self-attention block.
+ x_attn, y_attn = self.attn(
+ x,
+ y,
+ scale_x=scale_msa_x,
+ scale_y=scale_msa_y,
+ **attn_kwargs,
+ )
+
+ assert x_attn.size(1) == N
+ x = residual_tanh_gated_rmsnorm(x, x_attn, gate_msa_x)
+ if self.update_y:
+ y = residual_tanh_gated_rmsnorm(y, y_attn, gate_msa_y)
+
+ # MLP block.
+ x = self.ff_block_x(x, scale_mlp_x, gate_mlp_x)
+ if self.update_y:
+ y = self.ff_block_y(y, scale_mlp_y, gate_mlp_y)
+
+ return x, y
+
+ def ff_block_x(self, x, scale_x, gate_x):
+ x_mod = modulated_rmsnorm(x, scale_x)
+ x_res = self.mlp_x(x_mod)
+ x = residual_tanh_gated_rmsnorm(x, x_res, gate_x) # Sandwich norm
+ return x
+
+ def ff_block_y(self, y, scale_y, gate_y):
+ y_mod = modulated_rmsnorm(y, scale_y)
+ y_res = self.mlp_y(y_mod)
+ y = residual_tanh_gated_rmsnorm(y, y_res, gate_y) # Sandwich norm
+ return y
+
+
+class FinalLayer(nn.Module):
+ """
+ The final layer of DiT.
+ """
+
+ def __init__(
+ self,
+ hidden_size,
+ patch_size,
+ out_channels,
+ device: Optional[torch.device] = None,
+ dtype=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.norm_final = operations.LayerNorm(
+ hidden_size, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype
+ )
+ self.mod = operations.Linear(hidden_size, 2 * hidden_size, device=device, dtype=dtype)
+ self.linear = operations.Linear(
+ hidden_size, patch_size * patch_size * out_channels, device=device, dtype=dtype
+ )
+
+ def forward(self, x, c):
+ c = F.silu(c)
+ shift, scale = self.mod(c).chunk(2, dim=1)
+ x = modulate(self.norm_final(x), shift, scale)
+ x = self.linear(x)
+ return x
+
+
+class AsymmDiTJoint(nn.Module):
+ """
+ Diffusion model with a Transformer backbone.
+
+ Ingests text embeddings instead of a label.
+ """
+
+ def __init__(
+ self,
+ *,
+ patch_size=2,
+ in_channels=4,
+ hidden_size_x=1152,
+ hidden_size_y=1152,
+ depth=48,
+ num_heads=16,
+ mlp_ratio_x=8.0,
+ mlp_ratio_y=4.0,
+ use_t5: bool = False,
+ t5_feat_dim: int = 4096,
+ t5_token_length: int = 256,
+ learn_sigma=True,
+ patch_embed_bias: bool = True,
+ timestep_mlp_bias: bool = True,
+ attend_to_padding: bool = False,
+ timestep_scale: Optional[float] = None,
+ use_extended_posenc: bool = False,
+ posenc_preserve_area: bool = False,
+ rope_theta: float = 10000.0,
+ image_model=None,
+ device: Optional[torch.device] = None,
+ dtype=None,
+ operations=None,
+ **block_kwargs,
+ ):
+ super().__init__()
+
+ self.dtype = dtype
+ self.learn_sigma = learn_sigma
+ self.in_channels = in_channels
+ self.out_channels = in_channels * 2 if learn_sigma else in_channels
+ self.patch_size = patch_size
+ self.num_heads = num_heads
+ self.hidden_size_x = hidden_size_x
+ self.hidden_size_y = hidden_size_y
+ self.head_dim = (
+ hidden_size_x // num_heads
+ ) # Head dimension and count is determined by visual.
+ self.attend_to_padding = attend_to_padding
+ self.use_extended_posenc = use_extended_posenc
+ self.posenc_preserve_area = posenc_preserve_area
+ self.use_t5 = use_t5
+ self.t5_token_length = t5_token_length
+ self.t5_feat_dim = t5_feat_dim
+ self.rope_theta = (
+ rope_theta # Scaling factor for frequency computation for temporal RoPE.
+ )
+
+ self.x_embedder = PatchEmbed(
+ patch_size=patch_size,
+ in_chans=in_channels,
+ embed_dim=hidden_size_x,
+ bias=patch_embed_bias,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+ # Conditionings
+ # Timestep
+ self.t_embedder = TimestepEmbedder(
+ hidden_size_x, bias=timestep_mlp_bias, timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations
+ )
+
+ if self.use_t5:
+ # Caption Pooling (T5)
+ self.t5_y_embedder = AttentionPool(
+ t5_feat_dim, num_heads=8, output_dim=hidden_size_x, dtype=dtype, device=device, operations=operations
+ )
+
+ # Dense Embedding Projection (T5)
+ self.t5_yproj = operations.Linear(
+ t5_feat_dim, hidden_size_y, bias=True, dtype=dtype, device=device
+ )
+
+ # Initialize pos_frequencies as an empty parameter.
+ self.pos_frequencies = nn.Parameter(
+ torch.empty(3, self.num_heads, self.head_dim // 2, dtype=dtype, device=device)
+ )
+
+ assert not self.attend_to_padding
+
+ # for depth 48:
+ # b = 0: AsymmetricJointBlock, update_y=True
+ # b = 1: AsymmetricJointBlock, update_y=True
+ # ...
+ # b = 46: AsymmetricJointBlock, update_y=True
+ # b = 47: AsymmetricJointBlock, update_y=False. No need to update text features.
+ blocks = []
+ for b in range(depth):
+ # Joint multi-modal block
+ update_y = b < depth - 1
+ block = AsymmetricJointBlock(
+ hidden_size_x,
+ hidden_size_y,
+ num_heads,
+ mlp_ratio_x=mlp_ratio_x,
+ mlp_ratio_y=mlp_ratio_y,
+ update_y=update_y,
+ attend_to_padding=attend_to_padding,
+ device=device,
+ dtype=dtype,
+ operations=operations,
+ **block_kwargs,
+ )
+
+ blocks.append(block)
+ self.blocks = nn.ModuleList(blocks)
+
+ self.final_layer = FinalLayer(
+ hidden_size_x, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
+ )
+
+ def embed_x(self, x: torch.Tensor) -> torch.Tensor:
+ """
+ Args:
+ x: (B, C=12, T, H, W) tensor of visual tokens
+
+ Returns:
+ x: (B, C=3072, N) tensor of visual tokens with positional embedding.
+ """
+ return self.x_embedder(x) # Convert BcTHW to BCN
+
+ def prepare(
+ self,
+ x: torch.Tensor,
+ sigma: torch.Tensor,
+ t5_feat: torch.Tensor,
+ t5_mask: torch.Tensor,
+ ):
+ """Prepare input and conditioning embeddings."""
+ # Visual patch embeddings with positional encoding.
+ T, H, W = x.shape[-3:]
+ pH, pW = H // self.patch_size, W // self.patch_size
+ x = self.embed_x(x) # (B, N, D), where N = T * H * W / patch_size ** 2
+ assert x.ndim == 3
+ B = x.size(0)
+
+
+ pH, pW = H // self.patch_size, W // self.patch_size
+ N = T * pH * pW
+ assert x.size(1) == N
+ pos = create_position_matrix(
+ T, pH=pH, pW=pW, device=x.device, dtype=torch.float32
+ ) # (N, 3)
+ rope_cos, rope_sin = compute_mixed_rotation(
+ freqs=comfy.ops.cast_to(self.pos_frequencies, dtype=x.dtype, device=x.device), pos=pos
+ ) # Each are (N, num_heads, dim // 2)
+
+ c_t = self.t_embedder(1 - sigma, out_dtype=x.dtype) # (B, D)
+
+ t5_y_pool = self.t5_y_embedder(t5_feat, t5_mask) # (B, D)
+
+ c = c_t + t5_y_pool
+
+ y_feat = self.t5_yproj(t5_feat) # (B, L, t5_feat_dim) --> (B, L, D)
+
+ return x, c, y_feat, rope_cos, rope_sin
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ timestep: torch.Tensor,
+ context: List[torch.Tensor],
+ attention_mask: List[torch.Tensor],
+ num_tokens=256,
+ packed_indices: Dict[str, torch.Tensor] = None,
+ rope_cos: torch.Tensor = None,
+ rope_sin: torch.Tensor = None,
+ control=None, transformer_options={}, **kwargs
+ ):
+ patches_replace = transformer_options.get("patches_replace", {})
+ y_feat = context
+ y_mask = attention_mask
+ sigma = timestep
+ """Forward pass of DiT.
+
+ Args:
+ x: (B, C, T, H, W) tensor of spatial inputs (images or latent representations of images)
+ sigma: (B,) tensor of noise standard deviations
+ y_feat: List((B, L, y_feat_dim) tensor of caption token features. For SDXL text encoders: L=77, y_feat_dim=2048)
+ y_mask: List((B, L) boolean tensor indicating which tokens are not padding)
+ packed_indices: Dict with keys for Flash Attention. Result of compute_packed_indices.
+ """
+ B, _, T, H, W = x.shape
+
+ x, c, y_feat, rope_cos, rope_sin = self.prepare(
+ x, sigma, y_feat, y_mask
+ )
+ del y_mask
+
+ blocks_replace = patches_replace.get("dit", {})
+ for i, block in enumerate(self.blocks):
+ if ("double_block", i) in blocks_replace:
+ def block_wrap(args):
+ out = {}
+ out["img"], out["txt"] = block(
+ args["img"],
+ args["vec"],
+ args["txt"],
+ rope_cos=args["rope_cos"],
+ rope_sin=args["rope_sin"],
+ crop_y=args["num_tokens"]
+ )
+ return out
+ out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap})
+ y_feat = out["txt"]
+ x = out["img"]
+ else:
+ x, y_feat = block(
+ x,
+ c,
+ y_feat,
+ rope_cos=rope_cos,
+ rope_sin=rope_sin,
+ crop_y=num_tokens,
+ ) # (B, M, D), (B, L, D)
+ del y_feat # Final layers don't use dense text features.
+
+ x = self.final_layer(x, c) # (B, M, patch_size ** 2 * out_channels)
+ x = rearrange(
+ x,
+ "B (T hp wp) (p1 p2 c) -> B c T (hp p1) (wp p2)",
+ T=T,
+ hp=H // self.patch_size,
+ wp=W // self.patch_size,
+ p1=self.patch_size,
+ p2=self.patch_size,
+ c=self.out_channels,
+ )
+
+ return -x
diff --git a/comfy/ldm/genmo/joint_model/layers.py b/comfy/ldm/genmo/joint_model/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..51d979559ed574ca97bd4c86d52576ea9bd33826
--- /dev/null
+++ b/comfy/ldm/genmo/joint_model/layers.py
@@ -0,0 +1,164 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+#adapted to ComfyUI
+
+import collections.abc
+import math
+from itertools import repeat
+from typing import Callable, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+import comfy.ldm.common_dit
+
+
+# From PyTorch internals
+def _ntuple(n):
+ def parse(x):
+ if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+ return tuple(x)
+ return tuple(repeat(x, n))
+
+ return parse
+
+
+to_2tuple = _ntuple(2)
+
+
+class TimestepEmbedder(nn.Module):
+ def __init__(
+ self,
+ hidden_size: int,
+ frequency_embedding_size: int = 256,
+ *,
+ bias: bool = True,
+ timestep_scale: Optional[float] = None,
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.mlp = nn.Sequential(
+ operations.Linear(frequency_embedding_size, hidden_size, bias=bias, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(hidden_size, hidden_size, bias=bias, dtype=dtype, device=device),
+ )
+ self.frequency_embedding_size = frequency_embedding_size
+ self.timestep_scale = timestep_scale
+
+ @staticmethod
+ def timestep_embedding(t, dim, max_period=10000):
+ half = dim // 2
+ freqs = torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
+ freqs.mul_(-math.log(max_period) / half).exp_()
+ args = t[:, None].float() * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat(
+ [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+ )
+ return embedding
+
+ def forward(self, t, out_dtype):
+ if self.timestep_scale is not None:
+ t = t * self.timestep_scale
+ t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype=out_dtype)
+ t_emb = self.mlp(t_freq)
+ return t_emb
+
+
+class FeedForward(nn.Module):
+ def __init__(
+ self,
+ in_features: int,
+ hidden_size: int,
+ multiple_of: int,
+ ffn_dim_multiplier: Optional[float],
+ device: Optional[torch.device] = None,
+ dtype=None,
+ operations=None,
+ ):
+ super().__init__()
+ # keep parameter count and computation constant compared to standard FFN
+ hidden_size = int(2 * hidden_size / 3)
+ # custom dim factor multiplier
+ if ffn_dim_multiplier is not None:
+ hidden_size = int(ffn_dim_multiplier * hidden_size)
+ hidden_size = multiple_of * ((hidden_size + multiple_of - 1) // multiple_of)
+
+ self.hidden_dim = hidden_size
+ self.w1 = operations.Linear(in_features, 2 * hidden_size, bias=False, device=device, dtype=dtype)
+ self.w2 = operations.Linear(hidden_size, in_features, bias=False, device=device, dtype=dtype)
+
+ def forward(self, x):
+ x, gate = self.w1(x).chunk(2, dim=-1)
+ x = self.w2(F.silu(x) * gate)
+ return x
+
+
+class PatchEmbed(nn.Module):
+ def __init__(
+ self,
+ patch_size: int = 16,
+ in_chans: int = 3,
+ embed_dim: int = 768,
+ norm_layer: Optional[Callable] = None,
+ flatten: bool = True,
+ bias: bool = True,
+ dynamic_img_pad: bool = False,
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.patch_size = to_2tuple(patch_size)
+ self.flatten = flatten
+ self.dynamic_img_pad = dynamic_img_pad
+
+ self.proj = operations.Conv2d(
+ in_chans,
+ embed_dim,
+ kernel_size=patch_size,
+ stride=patch_size,
+ bias=bias,
+ device=device,
+ dtype=dtype,
+ )
+ assert norm_layer is None
+ self.norm = (
+ norm_layer(embed_dim, device=device) if norm_layer else nn.Identity()
+ )
+
+ def forward(self, x):
+ B, _C, T, H, W = x.shape
+ if not self.dynamic_img_pad:
+ assert H % self.patch_size[0] == 0, f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
+ assert W % self.patch_size[1] == 0, f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
+ else:
+ pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
+ pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
+ x = F.pad(x, (0, pad_w, 0, pad_h))
+
+ x = rearrange(x, "B C T H W -> (B T) C H W", B=B, T=T)
+ x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode='circular')
+ x = self.proj(x)
+
+ # Flatten temporal and spatial dimensions.
+ if not self.flatten:
+ raise NotImplementedError("Must flatten output.")
+ x = rearrange(x, "(B T) C H W -> B (T H W) C", B=B, T=T)
+
+ x = self.norm(x)
+ return x
+
+
+class RMSNorm(torch.nn.Module):
+ def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None):
+ super().__init__()
+ self.eps = eps
+ self.weight = torch.nn.Parameter(torch.empty(hidden_size, device=device, dtype=dtype))
+ self.register_parameter("bias", None)
+
+ def forward(self, x):
+ return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)
diff --git a/comfy/ldm/genmo/joint_model/rope_mixed.py b/comfy/ldm/genmo/joint_model/rope_mixed.py
new file mode 100644
index 0000000000000000000000000000000000000000..dee3fa21f5318a610321fc9372553d618462f773
--- /dev/null
+++ b/comfy/ldm/genmo/joint_model/rope_mixed.py
@@ -0,0 +1,88 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+
+# import functools
+import math
+
+import torch
+
+
+def centers(start: float, stop, num, dtype=None, device=None):
+ """linspace through bin centers.
+
+ Args:
+ start (float): Start of the range.
+ stop (float): End of the range.
+ num (int): Number of points.
+ dtype (torch.dtype): Data type of the points.
+ device (torch.device): Device of the points.
+
+ Returns:
+ centers (Tensor): Centers of the bins. Shape: (num,).
+ """
+ edges = torch.linspace(start, stop, num + 1, dtype=dtype, device=device)
+ return (edges[:-1] + edges[1:]) / 2
+
+
+# @functools.lru_cache(maxsize=1)
+def create_position_matrix(
+ T: int,
+ pH: int,
+ pW: int,
+ device: torch.device,
+ dtype: torch.dtype,
+ *,
+ target_area: float = 36864,
+):
+ """
+ Args:
+ T: int - Temporal dimension
+ pH: int - Height dimension after patchify
+ pW: int - Width dimension after patchify
+
+ Returns:
+ pos: [T * pH * pW, 3] - position matrix
+ """
+ # Create 1D tensors for each dimension
+ t = torch.arange(T, dtype=dtype)
+
+ # Positionally interpolate to area 36864.
+ # (3072x3072 frame with 16x16 patches = 192x192 latents).
+ # This automatically scales rope positions when the resolution changes.
+ # We use a large target area so the model is more sensitive
+ # to changes in the learned pos_frequencies matrix.
+ scale = math.sqrt(target_area / (pW * pH))
+ w = centers(-pW * scale / 2, pW * scale / 2, pW)
+ h = centers(-pH * scale / 2, pH * scale / 2, pH)
+
+ # Use meshgrid to create 3D grids
+ grid_t, grid_h, grid_w = torch.meshgrid(t, h, w, indexing="ij")
+
+ # Stack and reshape the grids.
+ pos = torch.stack([grid_t, grid_h, grid_w], dim=-1) # [T, pH, pW, 3]
+ pos = pos.view(-1, 3) # [T * pH * pW, 3]
+ pos = pos.to(dtype=dtype, device=device)
+
+ return pos
+
+
+def compute_mixed_rotation(
+ freqs: torch.Tensor,
+ pos: torch.Tensor,
+):
+ """
+ Project each 3-dim position into per-head, per-head-dim 1D frequencies.
+
+ Args:
+ freqs: [3, num_heads, num_freqs] - learned rotation frequency (for t, row, col) for each head position
+ pos: [N, 3] - position of each token
+ num_heads: int
+
+ Returns:
+ freqs_cos: [N, num_heads, num_freqs] - cosine components
+ freqs_sin: [N, num_heads, num_freqs] - sine components
+ """
+ assert freqs.ndim == 3
+ freqs_sum = torch.einsum("Nd,dhf->Nhf", pos.to(freqs), freqs)
+ freqs_cos = torch.cos(freqs_sum)
+ freqs_sin = torch.sin(freqs_sum)
+ return freqs_cos, freqs_sin
diff --git a/comfy/ldm/genmo/joint_model/temporal_rope.py b/comfy/ldm/genmo/joint_model/temporal_rope.py
new file mode 100644
index 0000000000000000000000000000000000000000..88f5d6d26151db0c8ad0a89fcf748c45d4b89bc0
--- /dev/null
+++ b/comfy/ldm/genmo/joint_model/temporal_rope.py
@@ -0,0 +1,34 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+
+# Based on Llama3 Implementation.
+import torch
+
+
+def apply_rotary_emb_qk_real(
+ xqk: torch.Tensor,
+ freqs_cos: torch.Tensor,
+ freqs_sin: torch.Tensor,
+) -> torch.Tensor:
+ """
+ Apply rotary embeddings to input tensors using the given frequency tensor without complex numbers.
+
+ Args:
+ xqk (torch.Tensor): Query and/or Key tensors to apply rotary embeddings. Shape: (B, S, *, num_heads, D)
+ Can be either just query or just key, or both stacked along some batch or * dim.
+ freqs_cos (torch.Tensor): Precomputed cosine frequency tensor.
+ freqs_sin (torch.Tensor): Precomputed sine frequency tensor.
+
+ Returns:
+ torch.Tensor: The input tensor with rotary embeddings applied.
+ """
+ # Split the last dimension into even and odd parts
+ xqk_even = xqk[..., 0::2]
+ xqk_odd = xqk[..., 1::2]
+
+ # Apply rotation
+ cos_part = (xqk_even * freqs_cos - xqk_odd * freqs_sin).type_as(xqk)
+ sin_part = (xqk_even * freqs_sin + xqk_odd * freqs_cos).type_as(xqk)
+
+ # Interleave the results back into the original shape
+ out = torch.stack([cos_part, sin_part], dim=-1).flatten(-2)
+ return out
diff --git a/comfy/ldm/genmo/joint_model/utils.py b/comfy/ldm/genmo/joint_model/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..411902423b41a09808e208137639919aa75b0311
--- /dev/null
+++ b/comfy/ldm/genmo/joint_model/utils.py
@@ -0,0 +1,102 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+#adapted to ComfyUI
+
+from typing import Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def modulate(x, shift, scale):
+ return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -> torch.Tensor:
+ """
+ Pool tokens in x using mask.
+
+ NOTE: We assume x does not require gradients.
+
+ Args:
+ x: (B, L, D) tensor of tokens.
+ mask: (B, L) boolean tensor indicating which tokens are not padding.
+
+ Returns:
+ pooled: (B, D) tensor of pooled tokens.
+ """
+ assert x.size(1) == mask.size(1) # Expected mask to have same length as tokens.
+ assert x.size(0) == mask.size(0) # Expected mask to have same batch size as tokens.
+ mask = mask[:, :, None].to(dtype=x.dtype)
+ mask = mask / mask.sum(dim=1, keepdim=True).clamp(min=1)
+ pooled = (x * mask).sum(dim=1, keepdim=keepdim)
+ return pooled
+
+
+class AttentionPool(nn.Module):
+ def __init__(
+ self,
+ embed_dim: int,
+ num_heads: int,
+ output_dim: int = None,
+ device: Optional[torch.device] = None,
+ dtype=None,
+ operations=None,
+ ):
+ """
+ Args:
+ spatial_dim (int): Number of tokens in sequence length.
+ embed_dim (int): Dimensionality of input tokens.
+ num_heads (int): Number of attention heads.
+ output_dim (int): Dimensionality of output tokens. Defaults to embed_dim.
+ """
+ super().__init__()
+ self.num_heads = num_heads
+ self.to_kv = operations.Linear(embed_dim, 2 * embed_dim, device=device, dtype=dtype)
+ self.to_q = operations.Linear(embed_dim, embed_dim, device=device, dtype=dtype)
+ self.to_out = operations.Linear(embed_dim, output_dim or embed_dim, device=device, dtype=dtype)
+
+ def forward(self, x, mask):
+ """
+ Args:
+ x (torch.Tensor): (B, L, D) tensor of input tokens.
+ mask (torch.Tensor): (B, L) boolean tensor indicating which tokens are not padding.
+
+ NOTE: We assume x does not require gradients.
+
+ Returns:
+ x (torch.Tensor): (B, D) tensor of pooled tokens.
+ """
+ D = x.size(2)
+
+ # Construct attention mask, shape: (B, 1, num_queries=1, num_keys=1+L).
+ attn_mask = mask[:, None, None, :].bool() # (B, 1, 1, L).
+ attn_mask = F.pad(attn_mask, (1, 0), value=True) # (B, 1, 1, 1+L).
+
+ # Average non-padding token features. These will be used as the query.
+ x_pool = pool_tokens(x, mask, keepdim=True) # (B, 1, D)
+
+ # Concat pooled features to input sequence.
+ x = torch.cat([x_pool, x], dim=1) # (B, L+1, D)
+
+ # Compute queries, keys, values. Only the mean token is used to create a query.
+ kv = self.to_kv(x) # (B, L+1, 2 * D)
+ q = self.to_q(x[:, 0]) # (B, D)
+
+ # Extract heads.
+ head_dim = D // self.num_heads
+ kv = kv.unflatten(2, (2, self.num_heads, head_dim)) # (B, 1+L, 2, H, head_dim)
+ kv = kv.transpose(1, 3) # (B, H, 2, 1+L, head_dim)
+ k, v = kv.unbind(2) # (B, H, 1+L, head_dim)
+ q = q.unflatten(1, (self.num_heads, head_dim)) # (B, H, head_dim)
+ q = q.unsqueeze(2) # (B, H, 1, head_dim)
+
+ # Compute attention.
+ x = F.scaled_dot_product_attention(
+ q, k, v, attn_mask=attn_mask, dropout_p=0.0
+ ) # (B, H, 1, head_dim)
+
+ # Concatenate heads and run output.
+ x = x.squeeze(2).flatten(1, 2) # (B, D = H * head_dim)
+ x = self.to_out(x)
+ return x
diff --git a/comfy/ldm/genmo/vae/model.py b/comfy/ldm/genmo/vae/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b68d48ae5d7cf005e95a982af27e7cfa6e8fa40d
--- /dev/null
+++ b/comfy/ldm/genmo/vae/model.py
@@ -0,0 +1,711 @@
+#original code from https://github.com/genmoai/models under apache 2.0 license
+#adapted to ComfyUI
+
+from typing import Callable, List, Optional, Tuple, Union
+from functools import partial
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+from comfy.ldm.modules.attention import optimized_attention
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+# import mochi_preview.dit.joint_model.context_parallel as cp
+# from mochi_preview.vae.cp_conv import cp_pass_frames, gather_all_frames
+
+
+def cast_tuple(t, length=1):
+ return t if isinstance(t, tuple) else ((t,) * length)
+
+
+class GroupNormSpatial(ops.GroupNorm):
+ """
+ GroupNorm applied per-frame.
+ """
+
+ def forward(self, x: torch.Tensor, *, chunk_size: int = 8):
+ B, C, T, H, W = x.shape
+ x = rearrange(x, "B C T H W -> (B T) C H W")
+ # Run group norm in chunks.
+ output = torch.empty_like(x)
+ for b in range(0, B * T, chunk_size):
+ output[b : b + chunk_size] = super().forward(x[b : b + chunk_size])
+ return rearrange(output, "(B T) C H W -> B C T H W", B=B, T=T)
+
+class PConv3d(ops.Conv3d):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size: Union[int, Tuple[int, int, int]],
+ stride: Union[int, Tuple[int, int, int]],
+ causal: bool = True,
+ context_parallel: bool = True,
+ **kwargs,
+ ):
+ self.causal = causal
+ self.context_parallel = context_parallel
+ kernel_size = cast_tuple(kernel_size, 3)
+ stride = cast_tuple(stride, 3)
+ height_pad = (kernel_size[1] - 1) // 2
+ width_pad = (kernel_size[2] - 1) // 2
+
+ super().__init__(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dilation=(1, 1, 1),
+ padding=(0, height_pad, width_pad),
+ **kwargs,
+ )
+
+ def forward(self, x: torch.Tensor):
+ # Compute padding amounts.
+ context_size = self.kernel_size[0] - 1
+ if self.causal:
+ pad_front = context_size
+ pad_back = 0
+ else:
+ pad_front = context_size // 2
+ pad_back = context_size - pad_front
+
+ # Apply padding.
+ assert self.padding_mode == "replicate" # DEBUG
+ mode = "constant" if self.padding_mode == "zeros" else self.padding_mode
+ x = F.pad(x, (0, 0, 0, 0, pad_front, pad_back), mode=mode)
+ return super().forward(x)
+
+
+class Conv1x1(ops.Linear):
+ """*1x1 Conv implemented with a linear layer."""
+
+ def __init__(self, in_features: int, out_features: int, *args, **kwargs):
+ super().__init__(in_features, out_features, *args, **kwargs)
+
+ def forward(self, x: torch.Tensor):
+ """Forward pass.
+
+ Args:
+ x: Input tensor. Shape: [B, C, *] or [B, *, C].
+
+ Returns:
+ x: Output tensor. Shape: [B, C', *] or [B, *, C'].
+ """
+ x = x.movedim(1, -1)
+ x = super().forward(x)
+ x = x.movedim(-1, 1)
+ return x
+
+
+class DepthToSpaceTime(nn.Module):
+ def __init__(
+ self,
+ temporal_expansion: int,
+ spatial_expansion: int,
+ ):
+ super().__init__()
+ self.temporal_expansion = temporal_expansion
+ self.spatial_expansion = spatial_expansion
+
+ # When printed, this module should show the temporal and spatial expansion factors.
+ def extra_repr(self):
+ return f"texp={self.temporal_expansion}, sexp={self.spatial_expansion}"
+
+ def forward(self, x: torch.Tensor):
+ """Forward pass.
+
+ Args:
+ x: Input tensor. Shape: [B, C, T, H, W].
+
+ Returns:
+ x: Rearranged tensor. Shape: [B, C/(st*s*s), T*st, H*s, W*s].
+ """
+ x = rearrange(
+ x,
+ "B (C st sh sw) T H W -> B C (T st) (H sh) (W sw)",
+ st=self.temporal_expansion,
+ sh=self.spatial_expansion,
+ sw=self.spatial_expansion,
+ )
+
+ # cp_rank, _ = cp.get_cp_rank_size()
+ if self.temporal_expansion > 1: # and cp_rank == 0:
+ # Drop the first self.temporal_expansion - 1 frames.
+ # This is because we always want the 3x3x3 conv filter to only apply
+ # to the first frame, and the first frame doesn't need to be repeated.
+ assert all(x.shape)
+ x = x[:, :, self.temporal_expansion - 1 :]
+ assert all(x.shape)
+
+ return x
+
+
+def norm_fn(
+ in_channels: int,
+ affine: bool = True,
+):
+ return GroupNormSpatial(affine=affine, num_groups=32, num_channels=in_channels)
+
+
+class ResBlock(nn.Module):
+ """Residual block that preserves the spatial dimensions."""
+
+ def __init__(
+ self,
+ channels: int,
+ *,
+ affine: bool = True,
+ attn_block: Optional[nn.Module] = None,
+ causal: bool = True,
+ prune_bottleneck: bool = False,
+ padding_mode: str,
+ bias: bool = True,
+ ):
+ super().__init__()
+ self.channels = channels
+
+ assert causal
+ self.stack = nn.Sequential(
+ norm_fn(channels, affine=affine),
+ nn.SiLU(inplace=True),
+ PConv3d(
+ in_channels=channels,
+ out_channels=channels // 2 if prune_bottleneck else channels,
+ kernel_size=(3, 3, 3),
+ stride=(1, 1, 1),
+ padding_mode=padding_mode,
+ bias=bias,
+ causal=causal,
+ ),
+ norm_fn(channels, affine=affine),
+ nn.SiLU(inplace=True),
+ PConv3d(
+ in_channels=channels // 2 if prune_bottleneck else channels,
+ out_channels=channels,
+ kernel_size=(3, 3, 3),
+ stride=(1, 1, 1),
+ padding_mode=padding_mode,
+ bias=bias,
+ causal=causal,
+ ),
+ )
+
+ self.attn_block = attn_block if attn_block else nn.Identity()
+
+ def forward(self, x: torch.Tensor):
+ """Forward pass.
+
+ Args:
+ x: Input tensor. Shape: [B, C, T, H, W].
+ """
+ residual = x
+ x = self.stack(x)
+ x = x + residual
+ del residual
+
+ return self.attn_block(x)
+
+
+class Attention(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ head_dim: int = 32,
+ qkv_bias: bool = False,
+ out_bias: bool = True,
+ qk_norm: bool = True,
+ ) -> None:
+ super().__init__()
+ self.head_dim = head_dim
+ self.num_heads = dim // head_dim
+ self.qk_norm = qk_norm
+
+ self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias)
+ self.out = nn.Linear(dim, dim, bias=out_bias)
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ ) -> torch.Tensor:
+ """Compute temporal self-attention.
+
+ Args:
+ x: Input tensor. Shape: [B, C, T, H, W].
+ chunk_size: Chunk size for large tensors.
+
+ Returns:
+ x: Output tensor. Shape: [B, C, T, H, W].
+ """
+ B, _, T, H, W = x.shape
+
+ if T == 1:
+ # No attention for single frame.
+ x = x.movedim(1, -1) # [B, C, T, H, W] -> [B, T, H, W, C]
+ qkv = self.qkv(x)
+ _, _, x = qkv.chunk(3, dim=-1) # Throw away queries and keys.
+ x = self.out(x)
+ return x.movedim(-1, 1) # [B, T, H, W, C] -> [B, C, T, H, W]
+
+ # 1D temporal attention.
+ x = rearrange(x, "B C t h w -> (B h w) t C")
+ qkv = self.qkv(x)
+
+ # Input: qkv with shape [B, t, 3 * num_heads * head_dim]
+ # Output: x with shape [B, num_heads, t, head_dim]
+ q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2)
+
+ if self.qk_norm:
+ q = F.normalize(q, p=2, dim=-1)
+ k = F.normalize(k, p=2, dim=-1)
+
+ x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True)
+
+ assert x.size(0) == q.size(0)
+
+ x = self.out(x)
+ x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W)
+ return x
+
+
+class AttentionBlock(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ **attn_kwargs,
+ ) -> None:
+ super().__init__()
+ self.norm = norm_fn(dim)
+ self.attn = Attention(dim, **attn_kwargs)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ return x + self.attn(self.norm(x))
+
+
+class CausalUpsampleBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ num_res_blocks: int,
+ *,
+ temporal_expansion: int = 2,
+ spatial_expansion: int = 2,
+ **block_kwargs,
+ ):
+ super().__init__()
+
+ blocks = []
+ for _ in range(num_res_blocks):
+ blocks.append(block_fn(in_channels, **block_kwargs))
+ self.blocks = nn.Sequential(*blocks)
+
+ self.temporal_expansion = temporal_expansion
+ self.spatial_expansion = spatial_expansion
+
+ # Change channels in the final convolution layer.
+ self.proj = Conv1x1(
+ in_channels,
+ out_channels * temporal_expansion * (spatial_expansion**2),
+ )
+
+ self.d2st = DepthToSpaceTime(
+ temporal_expansion=temporal_expansion, spatial_expansion=spatial_expansion
+ )
+
+ def forward(self, x):
+ x = self.blocks(x)
+ x = self.proj(x)
+ x = self.d2st(x)
+ return x
+
+
+def block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs):
+ attn_block = AttentionBlock(channels) if has_attention else None
+ return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs)
+
+
+class DownsampleBlock(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ num_res_blocks,
+ *,
+ temporal_reduction=2,
+ spatial_reduction=2,
+ **block_kwargs,
+ ):
+ """
+ Downsample block for the VAE encoder.
+
+ Args:
+ in_channels: Number of input channels.
+ out_channels: Number of output channels.
+ num_res_blocks: Number of residual blocks.
+ temporal_reduction: Temporal reduction factor.
+ spatial_reduction: Spatial reduction factor.
+ """
+ super().__init__()
+ layers = []
+
+ # Change the channel count in the strided convolution.
+ # This lets the ResBlock have uniform channel count,
+ # as in ConvNeXt.
+ assert in_channels != out_channels
+ layers.append(
+ PConv3d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction),
+ stride=(temporal_reduction, spatial_reduction, spatial_reduction),
+ # First layer in each block always uses replicate padding
+ padding_mode="replicate",
+ bias=block_kwargs["bias"],
+ )
+ )
+
+ for _ in range(num_res_blocks):
+ layers.append(block_fn(out_channels, **block_kwargs))
+
+ self.layers = nn.Sequential(*layers)
+
+ def forward(self, x):
+ return self.layers(x)
+
+
+def add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1):
+ num_freqs = (stop - start) // step
+ assert inputs.ndim == 5
+ C = inputs.size(1)
+
+ # Create Base 2 Fourier features.
+ freqs = torch.arange(start, stop, step, dtype=inputs.dtype, device=inputs.device)
+ assert num_freqs == len(freqs)
+ w = torch.pow(2.0, freqs) * (2 * torch.pi) # [num_freqs]
+ C = inputs.shape[1]
+ w = w.repeat(C)[None, :, None, None, None] # [1, C * num_freqs, 1, 1, 1]
+
+ # Interleaved repeat of input channels to match w.
+ h = inputs.repeat_interleave(num_freqs, dim=1) # [B, C * num_freqs, T, H, W]
+ # Scale channels by frequency.
+ h = w * h
+
+ return torch.cat(
+ [
+ inputs,
+ torch.sin(h),
+ torch.cos(h),
+ ],
+ dim=1,
+ )
+
+
+class FourierFeatures(nn.Module):
+ def __init__(self, start: int = 6, stop: int = 8, step: int = 1):
+ super().__init__()
+ self.start = start
+ self.stop = stop
+ self.step = step
+
+ def forward(self, inputs):
+ """Add Fourier features to inputs.
+
+ Args:
+ inputs: Input tensor. Shape: [B, C, T, H, W]
+
+ Returns:
+ h: Output tensor. Shape: [B, (1 + 2 * num_freqs) * C, T, H, W]
+ """
+ return add_fourier_features(inputs, self.start, self.stop, self.step)
+
+
+class Decoder(nn.Module):
+ def __init__(
+ self,
+ *,
+ out_channels: int = 3,
+ latent_dim: int,
+ base_channels: int,
+ channel_multipliers: List[int],
+ num_res_blocks: List[int],
+ temporal_expansions: Optional[List[int]] = None,
+ spatial_expansions: Optional[List[int]] = None,
+ has_attention: List[bool],
+ output_norm: bool = True,
+ nonlinearity: str = "silu",
+ output_nonlinearity: str = "silu",
+ causal: bool = True,
+ **block_kwargs,
+ ):
+ super().__init__()
+ self.input_channels = latent_dim
+ self.base_channels = base_channels
+ self.channel_multipliers = channel_multipliers
+ self.num_res_blocks = num_res_blocks
+ self.output_nonlinearity = output_nonlinearity
+ assert nonlinearity == "silu"
+ assert causal
+
+ ch = [mult * base_channels for mult in channel_multipliers]
+ self.num_up_blocks = len(ch) - 1
+ assert len(num_res_blocks) == self.num_up_blocks + 2
+
+ blocks = []
+
+ first_block = [
+ ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))
+ ] # Input layer.
+ # First set of blocks preserve channel count.
+ for _ in range(num_res_blocks[-1]):
+ first_block.append(
+ block_fn(
+ ch[-1],
+ has_attention=has_attention[-1],
+ causal=causal,
+ **block_kwargs,
+ )
+ )
+ blocks.append(nn.Sequential(*first_block))
+
+ assert len(temporal_expansions) == len(spatial_expansions) == self.num_up_blocks
+ assert len(num_res_blocks) == len(has_attention) == self.num_up_blocks + 2
+
+ upsample_block_fn = CausalUpsampleBlock
+
+ for i in range(self.num_up_blocks):
+ block = upsample_block_fn(
+ ch[-i - 1],
+ ch[-i - 2],
+ num_res_blocks=num_res_blocks[-i - 2],
+ has_attention=has_attention[-i - 2],
+ temporal_expansion=temporal_expansions[-i - 1],
+ spatial_expansion=spatial_expansions[-i - 1],
+ causal=causal,
+ **block_kwargs,
+ )
+ blocks.append(block)
+
+ assert not output_norm
+
+ # Last block. Preserve channel count.
+ last_block = []
+ for _ in range(num_res_blocks[0]):
+ last_block.append(
+ block_fn(
+ ch[0], has_attention=has_attention[0], causal=causal, **block_kwargs
+ )
+ )
+ blocks.append(nn.Sequential(*last_block))
+
+ self.blocks = nn.ModuleList(blocks)
+ self.output_proj = Conv1x1(ch[0], out_channels)
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x: Latent tensor. Shape: [B, input_channels, t, h, w]. Scaled [-1, 1].
+
+ Returns:
+ x: Reconstructed video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1].
+ T + 1 = (t - 1) * 4.
+ H = h * 16, W = w * 16.
+ """
+ for block in self.blocks:
+ x = block(x)
+
+ if self.output_nonlinearity == "silu":
+ x = F.silu(x, inplace=not self.training)
+ else:
+ assert (
+ not self.output_nonlinearity
+ ) # StyleGAN3 omits the to-RGB nonlinearity.
+
+ return self.output_proj(x).contiguous()
+
+class LatentDistribution:
+ def __init__(self, mean: torch.Tensor, logvar: torch.Tensor):
+ """Initialize latent distribution.
+
+ Args:
+ mean: Mean of the distribution. Shape: [B, C, T, H, W].
+ logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W].
+ """
+ assert mean.shape == logvar.shape
+ self.mean = mean
+ self.logvar = logvar
+
+ def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None):
+ if temperature == 0.0:
+ return self.mean
+
+ if noise is None:
+ noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator)
+ else:
+ assert noise.device == self.mean.device
+ noise = noise.to(self.mean.dtype)
+
+ if temperature != 1.0:
+ raise NotImplementedError(f"Temperature {temperature} is not supported.")
+
+ # Just Gaussian sample with no scaling of variance.
+ return noise * torch.exp(self.logvar * 0.5) + self.mean
+
+ def mode(self):
+ return self.mean
+
+class Encoder(nn.Module):
+ def __init__(
+ self,
+ *,
+ in_channels: int,
+ base_channels: int,
+ channel_multipliers: List[int],
+ num_res_blocks: List[int],
+ latent_dim: int,
+ temporal_reductions: List[int],
+ spatial_reductions: List[int],
+ prune_bottlenecks: List[bool],
+ has_attentions: List[bool],
+ affine: bool = True,
+ bias: bool = True,
+ input_is_conv_1x1: bool = False,
+ padding_mode: str,
+ ):
+ super().__init__()
+ self.temporal_reductions = temporal_reductions
+ self.spatial_reductions = spatial_reductions
+ self.base_channels = base_channels
+ self.channel_multipliers = channel_multipliers
+ self.num_res_blocks = num_res_blocks
+ self.latent_dim = latent_dim
+
+ self.fourier_features = FourierFeatures()
+ ch = [mult * base_channels for mult in channel_multipliers]
+ num_down_blocks = len(ch) - 1
+ assert len(num_res_blocks) == num_down_blocks + 2
+
+ layers = (
+ [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)]
+ if not input_is_conv_1x1
+ else [Conv1x1(in_channels, ch[0])]
+ )
+
+ assert len(prune_bottlenecks) == num_down_blocks + 2
+ assert len(has_attentions) == num_down_blocks + 2
+ block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias)
+
+ for _ in range(num_res_blocks[0]):
+ layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0]))
+ prune_bottlenecks = prune_bottlenecks[1:]
+ has_attentions = has_attentions[1:]
+
+ assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1
+ for i in range(num_down_blocks):
+ layer = DownsampleBlock(
+ ch[i],
+ ch[i + 1],
+ num_res_blocks=num_res_blocks[i + 1],
+ temporal_reduction=temporal_reductions[i],
+ spatial_reduction=spatial_reductions[i],
+ prune_bottleneck=prune_bottlenecks[i],
+ has_attention=has_attentions[i],
+ affine=affine,
+ bias=bias,
+ padding_mode=padding_mode,
+ )
+
+ layers.append(layer)
+
+ # Additional blocks.
+ for _ in range(num_res_blocks[-1]):
+ layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1]))
+
+ self.layers = nn.Sequential(*layers)
+
+ # Output layers.
+ self.output_norm = norm_fn(ch[-1])
+ self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False)
+
+ @property
+ def temporal_downsample(self):
+ return math.prod(self.temporal_reductions)
+
+ @property
+ def spatial_downsample(self):
+ return math.prod(self.spatial_reductions)
+
+ def forward(self, x) -> LatentDistribution:
+ """Forward pass.
+
+ Args:
+ x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]
+
+ Returns:
+ means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1].
+ h = H // 8, w = W // 8, t - 1 = (T - 1) // 6
+ logvar: Shape: [B, latent_dim, t, h, w].
+ """
+ assert x.ndim == 5, f"Expected 5D input, got {x.shape}"
+ x = self.fourier_features(x)
+
+ x = self.layers(x)
+
+ x = self.output_norm(x)
+ x = F.silu(x, inplace=True)
+ x = self.output_proj(x)
+
+ means, logvar = torch.chunk(x, 2, dim=1)
+
+ assert means.ndim == 5
+ assert logvar.shape == means.shape
+ assert means.size(1) == self.latent_dim
+
+ return LatentDistribution(means, logvar)
+
+
+class VideoVAE(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.encoder = Encoder(
+ in_channels=15,
+ base_channels=64,
+ channel_multipliers=[1, 2, 4, 6],
+ num_res_blocks=[3, 3, 4, 6, 3],
+ latent_dim=12,
+ temporal_reductions=[1, 2, 3],
+ spatial_reductions=[2, 2, 2],
+ prune_bottlenecks=[False, False, False, False, False],
+ has_attentions=[False, True, True, True, True],
+ affine=True,
+ bias=True,
+ input_is_conv_1x1=True,
+ padding_mode="replicate"
+ )
+ self.decoder = Decoder(
+ out_channels=3,
+ base_channels=128,
+ channel_multipliers=[1, 2, 4, 6],
+ temporal_expansions=[1, 2, 3],
+ spatial_expansions=[2, 2, 2],
+ num_res_blocks=[3, 3, 4, 6, 3],
+ latent_dim=12,
+ has_attention=[False, False, False, False, False],
+ padding_mode="replicate",
+ output_norm=False,
+ nonlinearity="silu",
+ output_nonlinearity="silu",
+ causal=True,
+ )
+
+ def encode(self, x):
+ return self.encoder(x).mode()
+
+ def decode(self, x):
+ return self.decoder(x)
diff --git a/comfy/ldm/hydit/attn_layers.py b/comfy/ldm/hydit/attn_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2801f714956d89bfd8938fd2f5010387b49de77
--- /dev/null
+++ b/comfy/ldm/hydit/attn_layers.py
@@ -0,0 +1,218 @@
+import torch
+import torch.nn as nn
+from typing import Tuple, Union, Optional
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False):
+ """
+ Reshape frequency tensor for broadcasting it with another tensor.
+
+ This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
+ for the purpose of broadcasting the frequency tensor during element-wise operations.
+
+ Args:
+ freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped.
+ x (torch.Tensor): Target tensor for broadcasting compatibility.
+ head_first (bool): head dimension first (except batch dim) or not.
+
+ Returns:
+ torch.Tensor: Reshaped frequency tensor.
+
+ Raises:
+ AssertionError: If the frequency tensor doesn't match the expected shape.
+ AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions.
+ """
+ ndim = x.ndim
+ assert 0 <= 1 < ndim
+
+ if isinstance(freqs_cis, tuple):
+ # freqs_cis: (cos, sin) in real space
+ if head_first:
+ assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
+ shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+ else:
+ assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
+ shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+ return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
+ else:
+ # freqs_cis: values in complex space
+ if head_first:
+ assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
+ shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+ else:
+ assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
+ shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+ return freqs_cis.view(*shape)
+
+
+def rotate_half(x):
+ x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2]
+ return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+
+
+def apply_rotary_emb(
+ xq: torch.Tensor,
+ xk: Optional[torch.Tensor],
+ freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
+ head_first: bool = False,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ Apply rotary embeddings to input tensors using the given frequency tensor.
+
+ This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided
+ frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor
+ is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are
+ returned as real tensors.
+
+ Args:
+ xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D]
+ xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D]
+ freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials.
+ head_first (bool): head dimension first (except batch dim) or not.
+
+ Returns:
+ Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
+
+ """
+ xk_out = None
+ if isinstance(freqs_cis, tuple):
+ cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D]
+ xq_out = (xq * cos + rotate_half(xq) * sin)
+ if xk is not None:
+ xk_out = (xk * cos + rotate_half(xk) * sin)
+ else:
+ xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2]
+ freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2]
+ xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
+ if xk is not None:
+ xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2]
+ xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
+
+ return xq_out, xk_out
+
+
+
+class CrossAttention(nn.Module):
+ """
+ Use QK Normalization.
+ """
+ def __init__(self,
+ qdim,
+ kdim,
+ num_heads,
+ qkv_bias=True,
+ qk_norm=False,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ attn_precision=None,
+ device=None,
+ dtype=None,
+ operations=None,
+ ):
+ factory_kwargs = {'device': device, 'dtype': dtype}
+ super().__init__()
+ self.attn_precision = attn_precision
+ self.qdim = qdim
+ self.kdim = kdim
+ self.num_heads = num_heads
+ assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads"
+ self.head_dim = self.qdim // num_heads
+ assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
+ self.scale = self.head_dim ** -0.5
+
+ self.q_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
+ self.kv_proj = operations.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs)
+
+ # TODO: eps should be 1 / 65530 if using fp16
+ self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+ self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.out_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ def forward(self, x, y, freqs_cis_img=None):
+ """
+ Parameters
+ ----------
+ x: torch.Tensor
+ (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim)
+ y: torch.Tensor
+ (batch, seqlen2, hidden_dim2)
+ freqs_cis_img: torch.Tensor
+ (batch, hidden_dim // 2), RoPE for image
+ """
+ b, s1, c = x.shape # [b, s1, D]
+ _, s2, c = y.shape # [b, s2, 1024]
+
+ q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d]
+ kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d]
+ k, v = kv.unbind(dim=2) # [b, s, h, d]
+ q = self.q_norm(q)
+ k = self.k_norm(k)
+
+ # Apply RoPE if needed
+ if freqs_cis_img is not None:
+ qq, _ = apply_rotary_emb(q, None, freqs_cis_img)
+ assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}'
+ q = qq
+
+ q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C
+ k = k.transpose(-2, -3).contiguous() # k -> B, L2, H, C - B, H, C, L2
+ v = v.transpose(-2, -3).contiguous()
+
+ context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)
+
+ out = self.out_proj(context) # context.reshape - B, L1, -1
+ out = self.proj_drop(out)
+
+ out_tuple = (out,)
+
+ return out_tuple
+
+
+class Attention(nn.Module):
+ """
+ We rename some layer names to align with flash attention
+ """
+ def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., attn_precision=None, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.attn_precision = attn_precision
+ self.dim = dim
+ self.num_heads = num_heads
+ assert self.dim % num_heads == 0, 'dim should be divisible by num_heads'
+ self.head_dim = self.dim // num_heads
+ # This assertion is aligned with flash attention
+ assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
+ self.scale = self.head_dim ** -0.5
+
+ # qkv --> Wqkv
+ self.Wqkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+ # TODO: eps should be 1 / 65530 if using fp16
+ self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+ self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.out_proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ def forward(self, x, freqs_cis_img=None):
+ B, N, C = x.shape
+ qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d]
+ q, k, v = qkv.unbind(0) # [b, h, s, d]
+ q = self.q_norm(q) # [b, h, s, d]
+ k = self.k_norm(k) # [b, h, s, d]
+
+ # Apply RoPE if needed
+ if freqs_cis_img is not None:
+ qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True)
+ assert qq.shape == q.shape and kk.shape == k.shape, \
+ f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}'
+ q, k = qq, kk
+
+ x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)
+ x = self.out_proj(x)
+ x = self.proj_drop(x)
+
+ out_tuple = (x,)
+
+ return out_tuple
diff --git a/comfy/ldm/hydit/controlnet.py b/comfy/ldm/hydit/controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd71fca31aaa65b7e6b085474852bb2c42bd6579
--- /dev/null
+++ b/comfy/ldm/hydit/controlnet.py
@@ -0,0 +1,321 @@
+from typing import Any, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torch.utils import checkpoint
+
+from comfy.ldm.modules.diffusionmodules.mmdit import (
+ Mlp,
+ TimestepEmbedder,
+ PatchEmbed,
+ RMSNorm,
+)
+from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
+from .poolers import AttentionPool
+
+import comfy.latent_formats
+from .models import HunYuanDiTBlock, calc_rope
+
+from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
+
+
+class HunYuanControlNet(nn.Module):
+ """
+ HunYuanDiT: Diffusion model with a Transformer backbone.
+
+ Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
+
+ Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline.
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ The arguments parsed by argparse.
+ input_size: tuple
+ The size of the input image.
+ patch_size: int
+ The size of the patch.
+ in_channels: int
+ The number of input channels.
+ hidden_size: int
+ The hidden size of the transformer backbone.
+ depth: int
+ The number of transformer blocks.
+ num_heads: int
+ The number of attention heads.
+ mlp_ratio: float
+ The ratio of the hidden size of the MLP in the transformer block.
+ log_fn: callable
+ The logging function.
+ """
+
+ def __init__(
+ self,
+ input_size: tuple = 128,
+ patch_size: int = 2,
+ in_channels: int = 4,
+ hidden_size: int = 1408,
+ depth: int = 40,
+ num_heads: int = 16,
+ mlp_ratio: float = 4.3637,
+ text_states_dim=1024,
+ text_states_dim_t5=2048,
+ text_len=77,
+ text_len_t5=256,
+ qk_norm=True, # See http://arxiv.org/abs/2302.05442 for details.
+ size_cond=False,
+ use_style_cond=False,
+ learn_sigma=True,
+ norm="layer",
+ log_fn: callable = print,
+ attn_precision=None,
+ dtype=None,
+ device=None,
+ operations=None,
+ **kwargs,
+ ):
+ super().__init__()
+ self.log_fn = log_fn
+ self.depth = depth
+ self.learn_sigma = learn_sigma
+ self.in_channels = in_channels
+ self.out_channels = in_channels * 2 if learn_sigma else in_channels
+ self.patch_size = patch_size
+ self.num_heads = num_heads
+ self.hidden_size = hidden_size
+ self.text_states_dim = text_states_dim
+ self.text_states_dim_t5 = text_states_dim_t5
+ self.text_len = text_len
+ self.text_len_t5 = text_len_t5
+ self.size_cond = size_cond
+ self.use_style_cond = use_style_cond
+ self.norm = norm
+ self.dtype = dtype
+ self.latent_format = comfy.latent_formats.SDXL
+
+ self.mlp_t5 = nn.Sequential(
+ nn.Linear(
+ self.text_states_dim_t5,
+ self.text_states_dim_t5 * 4,
+ bias=True,
+ dtype=dtype,
+ device=device,
+ ),
+ nn.SiLU(),
+ nn.Linear(
+ self.text_states_dim_t5 * 4,
+ self.text_states_dim,
+ bias=True,
+ dtype=dtype,
+ device=device,
+ ),
+ )
+ # learnable replace
+ self.text_embedding_padding = nn.Parameter(
+ torch.randn(
+ self.text_len + self.text_len_t5,
+ self.text_states_dim,
+ dtype=dtype,
+ device=device,
+ )
+ )
+
+ # Attention pooling
+ pooler_out_dim = 1024
+ self.pooler = AttentionPool(
+ self.text_len_t5,
+ self.text_states_dim_t5,
+ num_heads=8,
+ output_dim=pooler_out_dim,
+ dtype=dtype,
+ device=device,
+ operations=operations,
+ )
+
+ # Dimension of the extra input vectors
+ self.extra_in_dim = pooler_out_dim
+
+ if self.size_cond:
+ # Image size and crop size conditions
+ self.extra_in_dim += 6 * 256
+
+ if self.use_style_cond:
+ # Here we use a default learned embedder layer for future extension.
+ self.style_embedder = nn.Embedding(
+ 1, hidden_size, dtype=dtype, device=device
+ )
+ self.extra_in_dim += hidden_size
+
+ # Text embedding for `add`
+ self.x_embedder = PatchEmbed(
+ input_size,
+ patch_size,
+ in_channels,
+ hidden_size,
+ dtype=dtype,
+ device=device,
+ operations=operations,
+ )
+ self.t_embedder = TimestepEmbedder(
+ hidden_size, dtype=dtype, device=device, operations=operations
+ )
+ self.extra_embedder = nn.Sequential(
+ operations.Linear(
+ self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device
+ ),
+ nn.SiLU(),
+ operations.Linear(
+ hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device
+ ),
+ )
+
+ # Image embedding
+ num_patches = self.x_embedder.num_patches
+
+ # HUnYuanDiT Blocks
+ self.blocks = nn.ModuleList(
+ [
+ HunYuanDiTBlock(
+ hidden_size=hidden_size,
+ c_emb_size=hidden_size,
+ num_heads=num_heads,
+ mlp_ratio=mlp_ratio,
+ text_states_dim=self.text_states_dim,
+ qk_norm=qk_norm,
+ norm_type=self.norm,
+ skip=False,
+ attn_precision=attn_precision,
+ dtype=dtype,
+ device=device,
+ operations=operations,
+ )
+ for _ in range(19)
+ ]
+ )
+
+ # Input zero linear for the first block
+ self.before_proj = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+
+
+ # Output zero linear for the every block
+ self.after_proj_list = nn.ModuleList(
+ [
+
+ operations.Linear(
+ self.hidden_size, self.hidden_size, dtype=dtype, device=device
+ )
+ for _ in range(len(self.blocks))
+ ]
+ )
+
+ def forward(
+ self,
+ x,
+ hint,
+ timesteps,
+ context,#encoder_hidden_states=None,
+ text_embedding_mask=None,
+ encoder_hidden_states_t5=None,
+ text_embedding_mask_t5=None,
+ image_meta_size=None,
+ style=None,
+ return_dict=False,
+ **kwarg,
+ ):
+ """
+ Forward pass of the encoder.
+
+ Parameters
+ ----------
+ x: torch.Tensor
+ (B, D, H, W)
+ t: torch.Tensor
+ (B)
+ encoder_hidden_states: torch.Tensor
+ CLIP text embedding, (B, L_clip, D)
+ text_embedding_mask: torch.Tensor
+ CLIP text embedding mask, (B, L_clip)
+ encoder_hidden_states_t5: torch.Tensor
+ T5 text embedding, (B, L_t5, D)
+ text_embedding_mask_t5: torch.Tensor
+ T5 text embedding mask, (B, L_t5)
+ image_meta_size: torch.Tensor
+ (B, 6)
+ style: torch.Tensor
+ (B)
+ cos_cis_img: torch.Tensor
+ sin_cis_img: torch.Tensor
+ return_dict: bool
+ Whether to return a dictionary.
+ """
+ condition = hint
+ if condition.shape[0] == 1:
+ condition = torch.repeat_interleave(condition, x.shape[0], dim=0)
+
+ text_states = context # 2,77,1024
+ text_states_t5 = encoder_hidden_states_t5 # 2,256,2048
+ text_states_mask = text_embedding_mask.bool() # 2,77
+ text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256
+ b_t5, l_t5, c_t5 = text_states_t5.shape
+ text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)
+
+ padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states)
+
+ text_states[:, -self.text_len :] = torch.where(
+ text_states_mask[:, -self.text_len :].unsqueeze(2),
+ text_states[:, -self.text_len :],
+ padding[: self.text_len],
+ )
+ text_states_t5[:, -self.text_len_t5 :] = torch.where(
+ text_states_t5_mask[:, -self.text_len_t5 :].unsqueeze(2),
+ text_states_t5[:, -self.text_len_t5 :],
+ padding[self.text_len :],
+ )
+
+ text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205,1024
+
+ # _, _, oh, ow = x.shape
+ # th, tw = oh // self.patch_size, ow // self.patch_size
+
+ # Get image RoPE embedding according to `reso`lution.
+ freqs_cis_img = calc_rope(
+ x, self.patch_size, self.hidden_size // self.num_heads
+ ) # (cos_cis_img, sin_cis_img)
+
+ # ========================= Build time and image embedding =========================
+ t = self.t_embedder(timesteps, dtype=self.dtype)
+ x = self.x_embedder(x)
+
+ # ========================= Concatenate all extra vectors =========================
+ # Build text tokens with pooling
+ extra_vec = self.pooler(encoder_hidden_states_t5)
+
+ # Build image meta size tokens if applicable
+ # if image_meta_size is not None:
+ # image_meta_size = timestep_embedding(image_meta_size.view(-1), 256) # [B * 6, 256]
+ # if image_meta_size.dtype != self.dtype:
+ # image_meta_size = image_meta_size.half()
+ # image_meta_size = image_meta_size.view(-1, 6 * 256)
+ # extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256]
+
+ # Build style tokens
+ if style is not None:
+ style_embedding = self.style_embedder(style)
+ extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
+
+ # Concatenate all extra vectors
+ c = t + self.extra_embedder(extra_vec) # [B, D]
+
+ # ========================= Deal with Condition =========================
+ condition = self.x_embedder(condition)
+
+ # ========================= Forward pass through HunYuanDiT blocks =========================
+ controls = []
+ x = x + self.before_proj(condition) # add condition
+ for layer, block in enumerate(self.blocks):
+ x = block(x, c, text_states, freqs_cis_img)
+ controls.append(self.after_proj_list[layer](x)) # zero linear for output
+
+ return {"output": controls}
diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..88459457d10871141e02145e57e1331c3786ef4e
--- /dev/null
+++ b/comfy/ldm/hydit/models.py
@@ -0,0 +1,422 @@
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm
+from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
+from torch.utils import checkpoint
+
+from .attn_layers import Attention, CrossAttention
+from .poolers import AttentionPool
+from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
+
+def calc_rope(x, patch_size, head_size):
+ th = (x.shape[2] + (patch_size // 2)) // patch_size
+ tw = (x.shape[3] + (patch_size // 2)) // patch_size
+ base_size = 512 // 8 // patch_size
+ start, stop = get_fill_resize_and_crop((th, tw), base_size)
+ sub_args = [start, stop, (th, tw)]
+ # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads']
+ rope = get_2d_rotary_pos_embed(head_size, *sub_args)
+ rope = (rope[0].to(x), rope[1].to(x))
+ return rope
+
+
+def modulate(x, shift, scale):
+ return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+class HunYuanDiTBlock(nn.Module):
+ """
+ A HunYuanDiT block with `add` conditioning.
+ """
+ def __init__(self,
+ hidden_size,
+ c_emb_size,
+ num_heads,
+ mlp_ratio=4.0,
+ text_states_dim=1024,
+ qk_norm=False,
+ norm_type="layer",
+ skip=False,
+ attn_precision=None,
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ use_ele_affine = True
+
+ if norm_type == "layer":
+ norm_layer = operations.LayerNorm
+ elif norm_type == "rms":
+ norm_layer = RMSNorm
+ else:
+ raise ValueError(f"Unknown norm_type: {norm_type}")
+
+ # ========================= Self-Attention =========================
+ self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)
+ self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
+
+ # ========================= FFN =========================
+ self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)
+ mlp_hidden_dim = int(hidden_size * mlp_ratio)
+ approx_gelu = lambda: nn.GELU(approximate="tanh")
+ self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, dtype=dtype, device=device, operations=operations)
+
+ # ========================= Add =========================
+ # Simply use add like SDXL.
+ self.default_modulation = nn.Sequential(
+ nn.SiLU(),
+ operations.Linear(c_emb_size, hidden_size, bias=True, dtype=dtype, device=device)
+ )
+
+ # ========================= Cross-Attention =========================
+ self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True,
+ qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
+ self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
+
+ # ========================= Skip Connection =========================
+ if skip:
+ self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
+ self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, dtype=dtype, device=device)
+ else:
+ self.skip_linear = None
+
+ self.gradient_checkpointing = False
+
+ def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):
+ # Long Skip Connection
+ if self.skip_linear is not None:
+ cat = torch.cat([x, skip], dim=-1)
+ if cat.dtype != x.dtype:
+ cat = cat.to(x.dtype)
+ cat = self.skip_norm(cat)
+ x = self.skip_linear(cat)
+
+ # Self-Attention
+ shift_msa = self.default_modulation(c).unsqueeze(dim=1)
+ attn_inputs = (
+ self.norm1(x) + shift_msa, freq_cis_img,
+ )
+ x = x + self.attn1(*attn_inputs)[0]
+
+ # Cross-Attention
+ cross_inputs = (
+ self.norm3(x), text_states, freq_cis_img
+ )
+ x = x + self.attn2(*cross_inputs)[0]
+
+ # FFN Layer
+ mlp_inputs = self.norm2(x)
+ x = x + self.mlp(mlp_inputs)
+
+ return x
+
+ def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):
+ if self.gradient_checkpointing and self.training:
+ return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip)
+ return self._forward(x, c, text_states, freq_cis_img, skip)
+
+
+class FinalLayer(nn.Module):
+ """
+ The final layer of HunYuanDiT.
+ """
+ def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+ self.adaLN_modulation = nn.Sequential(
+ nn.SiLU(),
+ operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)
+ )
+
+ def forward(self, x, c):
+ shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+ x = modulate(self.norm_final(x), shift, scale)
+ x = self.linear(x)
+ return x
+
+
+class HunYuanDiT(nn.Module):
+ """
+ HunYuanDiT: Diffusion model with a Transformer backbone.
+
+ Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
+
+ Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline.
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ The arguments parsed by argparse.
+ input_size: tuple
+ The size of the input image.
+ patch_size: int
+ The size of the patch.
+ in_channels: int
+ The number of input channels.
+ hidden_size: int
+ The hidden size of the transformer backbone.
+ depth: int
+ The number of transformer blocks.
+ num_heads: int
+ The number of attention heads.
+ mlp_ratio: float
+ The ratio of the hidden size of the MLP in the transformer block.
+ log_fn: callable
+ The logging function.
+ """
+ #@register_to_config
+ def __init__(self,
+ input_size: tuple = 32,
+ patch_size: int = 2,
+ in_channels: int = 4,
+ hidden_size: int = 1152,
+ depth: int = 28,
+ num_heads: int = 16,
+ mlp_ratio: float = 4.0,
+ text_states_dim = 1024,
+ text_states_dim_t5 = 2048,
+ text_len = 77,
+ text_len_t5 = 256,
+ qk_norm = True,# See http://arxiv.org/abs/2302.05442 for details.
+ size_cond = False,
+ use_style_cond = False,
+ learn_sigma = True,
+ norm = "layer",
+ log_fn: callable = print,
+ attn_precision=None,
+ dtype=None,
+ device=None,
+ operations=None,
+ **kwargs,
+ ):
+ super().__init__()
+ self.log_fn = log_fn
+ self.depth = depth
+ self.learn_sigma = learn_sigma
+ self.in_channels = in_channels
+ self.out_channels = in_channels * 2 if learn_sigma else in_channels
+ self.patch_size = patch_size
+ self.num_heads = num_heads
+ self.hidden_size = hidden_size
+ self.text_states_dim = text_states_dim
+ self.text_states_dim_t5 = text_states_dim_t5
+ self.text_len = text_len
+ self.text_len_t5 = text_len_t5
+ self.size_cond = size_cond
+ self.use_style_cond = use_style_cond
+ self.norm = norm
+ self.dtype = dtype
+ #import pdb
+ #pdb.set_trace()
+
+ self.mlp_t5 = nn.Sequential(
+ operations.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device),
+ )
+ # learnable replace
+ self.text_embedding_padding = nn.Parameter(
+ torch.empty(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device))
+
+ # Attention pooling
+ pooler_out_dim = 1024
+ self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations)
+
+ # Dimension of the extra input vectors
+ self.extra_in_dim = pooler_out_dim
+
+ if self.size_cond:
+ # Image size and crop size conditions
+ self.extra_in_dim += 6 * 256
+
+ if self.use_style_cond:
+ # Here we use a default learned embedder layer for future extension.
+ self.style_embedder = operations.Embedding(1, hidden_size, dtype=dtype, device=device)
+ self.extra_in_dim += hidden_size
+
+ # Text embedding for `add`
+ self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations)
+ self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations)
+ self.extra_embedder = nn.Sequential(
+ operations.Linear(self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device),
+ )
+
+ # Image embedding
+ num_patches = self.x_embedder.num_patches
+
+ # HUnYuanDiT Blocks
+ self.blocks = nn.ModuleList([
+ HunYuanDiTBlock(hidden_size=hidden_size,
+ c_emb_size=hidden_size,
+ num_heads=num_heads,
+ mlp_ratio=mlp_ratio,
+ text_states_dim=self.text_states_dim,
+ qk_norm=qk_norm,
+ norm_type=self.norm,
+ skip=layer > depth // 2,
+ attn_precision=attn_precision,
+ dtype=dtype,
+ device=device,
+ operations=operations,
+ )
+ for layer in range(depth)
+ ])
+
+ self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+ self.unpatchify_channels = self.out_channels
+
+
+
+ def forward(self,
+ x,
+ t,
+ context,#encoder_hidden_states=None,
+ text_embedding_mask=None,
+ encoder_hidden_states_t5=None,
+ text_embedding_mask_t5=None,
+ image_meta_size=None,
+ style=None,
+ return_dict=False,
+ control=None,
+ transformer_options={},
+ ):
+ """
+ Forward pass of the encoder.
+
+ Parameters
+ ----------
+ x: torch.Tensor
+ (B, D, H, W)
+ t: torch.Tensor
+ (B)
+ encoder_hidden_states: torch.Tensor
+ CLIP text embedding, (B, L_clip, D)
+ text_embedding_mask: torch.Tensor
+ CLIP text embedding mask, (B, L_clip)
+ encoder_hidden_states_t5: torch.Tensor
+ T5 text embedding, (B, L_t5, D)
+ text_embedding_mask_t5: torch.Tensor
+ T5 text embedding mask, (B, L_t5)
+ image_meta_size: torch.Tensor
+ (B, 6)
+ style: torch.Tensor
+ (B)
+ cos_cis_img: torch.Tensor
+ sin_cis_img: torch.Tensor
+ return_dict: bool
+ Whether to return a dictionary.
+ """
+ patches_replace = transformer_options.get("patches_replace", {})
+ encoder_hidden_states = context
+ text_states = encoder_hidden_states # 2,77,1024
+ text_states_t5 = encoder_hidden_states_t5 # 2,256,2048
+ text_states_mask = text_embedding_mask.bool() # 2,77
+ text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256
+ b_t5, l_t5, c_t5 = text_states_t5.shape
+ text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)
+
+ padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states)
+
+ text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len])
+ text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:])
+
+ text_states = torch.cat([text_states, text_states_t5], dim=1) # 2,205,1024
+ # clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1)
+
+ _, _, oh, ow = x.shape
+ th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size
+
+
+ # Get image RoPE embedding according to `reso`lution.
+ freqs_cis_img = calc_rope(x, self.patch_size, self.hidden_size // self.num_heads) #(cos_cis_img, sin_cis_img)
+
+ # ========================= Build time and image embedding =========================
+ t = self.t_embedder(t, dtype=x.dtype)
+ x = self.x_embedder(x)
+
+ # ========================= Concatenate all extra vectors =========================
+ # Build text tokens with pooling
+ extra_vec = self.pooler(encoder_hidden_states_t5)
+
+ # Build image meta size tokens if applicable
+ if self.size_cond:
+ image_meta_size = timestep_embedding(image_meta_size.view(-1), 256).to(x.dtype) # [B * 6, 256]
+ image_meta_size = image_meta_size.view(-1, 6 * 256)
+ extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256]
+
+ # Build style tokens
+ if self.use_style_cond:
+ if style is None:
+ style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int)
+ style_embedding = self.style_embedder(style, out_dtype=x.dtype)
+ extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
+
+ # Concatenate all extra vectors
+ c = t + self.extra_embedder(extra_vec) # [B, D]
+
+ blocks_replace = patches_replace.get("dit", {})
+
+ controls = None
+ if control:
+ controls = control.get("output", None)
+ # ========================= Forward pass through HunYuanDiT blocks =========================
+ skips = []
+ for layer, block in enumerate(self.blocks):
+ if layer > self.depth // 2:
+ if controls is not None:
+ skip = skips.pop() + controls.pop().to(dtype=x.dtype)
+ else:
+ skip = skips.pop()
+ else:
+ skip = None
+
+ if ("double_block", layer) in blocks_replace:
+ def block_wrap(args):
+ out = {}
+ out["img"] = block(args["img"], args["vec"], args["txt"], args["pe"], args["skip"])
+ return out
+
+ out = blocks_replace[("double_block", layer)]({"img": x, "txt": text_states, "vec": c, "pe": freqs_cis_img, "skip": skip}, {"original_block": block_wrap})
+ x = out["img"]
+ else:
+ x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D)
+
+
+ if layer < (self.depth // 2 - 1):
+ skips.append(x)
+ if controls is not None and len(controls) != 0:
+ raise ValueError("The number of controls is not equal to the number of skip connections.")
+
+ # ========================= Final layer =========================
+ x = self.final_layer(x, c) # (N, L, patch_size ** 2 * out_channels)
+ x = self.unpatchify(x, th, tw) # (N, out_channels, H, W)
+
+ if return_dict:
+ return {'x': x}
+ if self.learn_sigma:
+ return x[:,:self.out_channels // 2,:oh,:ow]
+ return x[:,:,:oh,:ow]
+
+ def unpatchify(self, x, h, w):
+ """
+ x: (N, T, patch_size**2 * C)
+ imgs: (N, H, W, C)
+ """
+ c = self.unpatchify_channels
+ p = self.x_embedder.patch_size[0]
+ # h = w = int(x.shape[1] ** 0.5)
+ assert h * w == x.shape[1]
+
+ x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+ x = torch.einsum('nhwpqc->nchpwq', x)
+ imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+ return imgs
diff --git a/comfy/ldm/hydit/poolers.py b/comfy/ldm/hydit/poolers.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5e5b406fcd4e50a2222c4719e07ea32d4a089f4
--- /dev/null
+++ b/comfy/ldm/hydit/poolers.py
@@ -0,0 +1,37 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from comfy.ldm.modules.attention import optimized_attention
+import comfy.ops
+
+class AttentionPool(nn.Module):
+ def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.positional_embedding = nn.Parameter(torch.empty(spacial_dim + 1, embed_dim, dtype=dtype, device=device))
+ self.k_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)
+ self.q_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)
+ self.v_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)
+ self.c_proj = operations.Linear(embed_dim, output_dim or embed_dim, dtype=dtype, device=device)
+ self.num_heads = num_heads
+ self.embed_dim = embed_dim
+
+ def forward(self, x):
+ x = x[:,:self.positional_embedding.shape[0] - 1]
+ x = x.permute(1, 0, 2) # NLC -> LNC
+ x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC
+ x = x + comfy.ops.cast_to_input(self.positional_embedding[:, None, :], x) # (L+1)NC
+
+ q = self.q_proj(x[:1])
+ k = self.k_proj(x)
+ v = self.v_proj(x)
+
+ batch_size = q.shape[1]
+ head_dim = self.embed_dim // self.num_heads
+ q = q.view(1, batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)
+ k = k.view(k.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)
+ v = v.view(v.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)
+
+ attn_output = optimized_attention(q, k, v, self.num_heads, skip_reshape=True).transpose(0, 1)
+
+ attn_output = self.c_proj(attn_output)
+ return attn_output.squeeze(0)
diff --git a/comfy/ldm/hydit/posemb_layers.py b/comfy/ldm/hydit/posemb_layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcb41a713cd94ea8472ff26e8865066887b1e486
--- /dev/null
+++ b/comfy/ldm/hydit/posemb_layers.py
@@ -0,0 +1,224 @@
+import torch
+import numpy as np
+from typing import Union
+
+
+def _to_tuple(x):
+ if isinstance(x, int):
+ return x, x
+ else:
+ return x
+
+
+def get_fill_resize_and_crop(src, tgt):
+ th, tw = _to_tuple(tgt)
+ h, w = _to_tuple(src)
+
+ tr = th / tw # base resolution
+ r = h / w # target resolution
+
+ # resize
+ if r > tr:
+ resize_height = th
+ resize_width = int(round(th / h * w))
+ else:
+ resize_width = tw
+ resize_height = int(round(tw / w * h)) # resize the target resolution down based on the base resolution
+
+ crop_top = int(round((th - resize_height) / 2.0))
+ crop_left = int(round((tw - resize_width) / 2.0))
+
+ return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+def get_meshgrid(start, *args):
+ if len(args) == 0:
+ # start is grid_size
+ num = _to_tuple(start)
+ start = (0, 0)
+ stop = num
+ elif len(args) == 1:
+ # start is start, args[0] is stop, step is 1
+ start = _to_tuple(start)
+ stop = _to_tuple(args[0])
+ num = (stop[0] - start[0], stop[1] - start[1])
+ elif len(args) == 2:
+ # start is start, args[0] is stop, args[1] is num
+ start = _to_tuple(start)
+ stop = _to_tuple(args[0])
+ num = _to_tuple(args[1])
+ else:
+ raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}")
+
+ grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32)
+ grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32)
+ grid = np.meshgrid(grid_w, grid_h) # here w goes first
+ grid = np.stack(grid, axis=0) # [2, W, H]
+ return grid
+
+#################################################################################
+# Sine/Cosine Positional Embedding Functions #
+#################################################################################
+# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+
+def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0):
+ """
+ grid_size: int of the grid height and width
+ return:
+ pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+ """
+ grid = get_meshgrid(start, *args) # [2, H, w]
+ # grid_h = np.arange(grid_size, dtype=np.float32)
+ # grid_w = np.arange(grid_size, dtype=np.float32)
+ # grid = np.meshgrid(grid_w, grid_h) # here w goes first
+ # grid = np.stack(grid, axis=0) # [2, W, H]
+
+ grid = grid.reshape([2, 1, *grid.shape[1:]])
+ pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+ if cls_token and extra_tokens > 0:
+ pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+ return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+ assert embed_dim % 2 == 0
+
+ # use half of dimensions to encode grid_h
+ emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
+ emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
+
+ emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
+ return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+ """
+ embed_dim: output dimension for each position
+ pos: a list of positions to be encoded: size (W,H)
+ out: (M, D)
+ """
+ assert embed_dim % 2 == 0
+ omega = np.arange(embed_dim // 2, dtype=np.float64)
+ omega /= embed_dim / 2.
+ omega = 1. / 10000**omega # (D/2,)
+
+ pos = pos.reshape(-1) # (M,)
+ out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
+
+ emb_sin = np.sin(out) # (M, D/2)
+ emb_cos = np.cos(out) # (M, D/2)
+
+ emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
+ return emb
+
+
+#################################################################################
+# Rotary Positional Embedding Functions #
+#################################################################################
+# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443
+
+def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True):
+ """
+ This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure.
+
+ Parameters
+ ----------
+ embed_dim: int
+ embedding dimension size
+ start: int or tuple of int
+ If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1;
+ If len(args) == 2, start is start, args[0] is stop, args[1] is num.
+ use_real: bool
+ If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+
+ Returns
+ -------
+ pos_embed: torch.Tensor
+ [HW, D/2]
+ """
+ grid = get_meshgrid(start, *args) # [2, H, w]
+ grid = grid.reshape([2, 1, *grid.shape[1:]]) # Returns a sampling matrix with the same resolution as the target resolution
+ pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
+ return pos_embed
+
+
+def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
+ assert embed_dim % 4 == 0
+
+ # use half of dimensions to encode grid_h
+ emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4)
+ emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4)
+
+ if use_real:
+ cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2)
+ sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2)
+ return cos, sin
+ else:
+ emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2)
+ return emb
+
+
+def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False):
+ """
+ Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+ This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
+ and the end index 'end'. The 'theta' parameter scales the frequencies.
+ The returned tensor contains complex values in complex64 data type.
+
+ Args:
+ dim (int): Dimension of the frequency tensor.
+ pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar
+ theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
+ use_real (bool, optional): If True, return real part and imaginary part separately.
+ Otherwise, return complex numbers.
+
+ Returns:
+ torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2]
+
+ """
+ if isinstance(pos, int):
+ pos = np.arange(pos)
+ freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2]
+ t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S]
+ freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2]
+ if use_real:
+ freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D]
+ freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D]
+ return freqs_cos, freqs_sin
+ else:
+ freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2]
+ return freqs_cis
+
+
+
+def calc_sizes(rope_img, patch_size, th, tw):
+ if rope_img == 'extend':
+ # Expansion mode
+ sub_args = [(th, tw)]
+ elif rope_img.startswith('base'):
+ # Based on the specified dimensions, other dimensions are obtained through interpolation.
+ base_size = int(rope_img[4:]) // 8 // patch_size
+ start, stop = get_fill_resize_and_crop((th, tw), base_size)
+ sub_args = [start, stop, (th, tw)]
+ else:
+ raise ValueError(f"Unknown rope_img: {rope_img}")
+ return sub_args
+
+
+def init_image_posemb(rope_img,
+ resolutions,
+ patch_size,
+ hidden_size,
+ num_heads,
+ log_fn,
+ rope_real=True,
+ ):
+ freqs_cis_img = {}
+ for reso in resolutions:
+ th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size
+ sub_args = calc_sizes(rope_img, patch_size, th, tw)
+ freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real)
+ log_fn(f" Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) "
+ f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}")
+ return freqs_cis_img
diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..f49cef9591701d5e021b03fe663f4bcc733f2f68
--- /dev/null
+++ b/comfy/ldm/lightricks/model.py
@@ -0,0 +1,514 @@
+import torch
+from torch import nn
+import comfy.ldm.modules.attention
+from comfy.ldm.genmo.joint_model.layers import RMSNorm
+import comfy.ldm.common_dit
+from einops import rearrange
+import math
+from typing import Dict, Optional, Tuple
+
+from .symmetric_patchifier import SymmetricPatchifier
+
+
+def get_timestep_embedding(
+ timesteps: torch.Tensor,
+ embedding_dim: int,
+ flip_sin_to_cos: bool = False,
+ downscale_freq_shift: float = 1,
+ scale: float = 1,
+ max_period: int = 10000,
+):
+ """
+ This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+ Args
+ timesteps (torch.Tensor):
+ a 1-D Tensor of N indices, one per batch element. These may be fractional.
+ embedding_dim (int):
+ the dimension of the output.
+ flip_sin_to_cos (bool):
+ Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
+ downscale_freq_shift (float):
+ Controls the delta between frequencies between dimensions
+ scale (float):
+ Scaling factor applied to the embeddings.
+ max_period (int):
+ Controls the maximum frequency of the embeddings
+ Returns
+ torch.Tensor: an [N x dim] Tensor of positional embeddings.
+ """
+ assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+ half_dim = embedding_dim // 2
+ exponent = -math.log(max_period) * torch.arange(
+ start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+ )
+ exponent = exponent / (half_dim - downscale_freq_shift)
+
+ emb = torch.exp(exponent)
+ emb = timesteps[:, None].float() * emb[None, :]
+
+ # scale embeddings
+ emb = scale * emb
+
+ # concat sine and cosine embeddings
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+ # flip sine and cosine embeddings
+ if flip_sin_to_cos:
+ emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+ # zero pad
+ if embedding_dim % 2 == 1:
+ emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+ return emb
+
+
+class TimestepEmbedding(nn.Module):
+ def __init__(
+ self,
+ in_channels: int,
+ time_embed_dim: int,
+ act_fn: str = "silu",
+ out_dim: int = None,
+ post_act_fn: Optional[str] = None,
+ cond_proj_dim=None,
+ sample_proj_bias=True,
+ dtype=None, device=None, operations=None,
+ ):
+ super().__init__()
+
+ self.linear_1 = operations.Linear(in_channels, time_embed_dim, sample_proj_bias, dtype=dtype, device=device)
+
+ if cond_proj_dim is not None:
+ self.cond_proj = operations.Linear(cond_proj_dim, in_channels, bias=False, dtype=dtype, device=device)
+ else:
+ self.cond_proj = None
+
+ self.act = nn.SiLU()
+
+ if out_dim is not None:
+ time_embed_dim_out = out_dim
+ else:
+ time_embed_dim_out = time_embed_dim
+ self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device)
+
+ if post_act_fn is None:
+ self.post_act = None
+ # else:
+ # self.post_act = get_activation(post_act_fn)
+
+ def forward(self, sample, condition=None):
+ if condition is not None:
+ sample = sample + self.cond_proj(condition)
+ sample = self.linear_1(sample)
+
+ if self.act is not None:
+ sample = self.act(sample)
+
+ sample = self.linear_2(sample)
+
+ if self.post_act is not None:
+ sample = self.post_act(sample)
+ return sample
+
+
+class Timesteps(nn.Module):
+ def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
+ super().__init__()
+ self.num_channels = num_channels
+ self.flip_sin_to_cos = flip_sin_to_cos
+ self.downscale_freq_shift = downscale_freq_shift
+ self.scale = scale
+
+ def forward(self, timesteps):
+ t_emb = get_timestep_embedding(
+ timesteps,
+ self.num_channels,
+ flip_sin_to_cos=self.flip_sin_to_cos,
+ downscale_freq_shift=self.downscale_freq_shift,
+ scale=self.scale,
+ )
+ return t_emb
+
+
+class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
+ """
+ For PixArt-Alpha.
+
+ Reference:
+ https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
+ """
+
+ def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+ super().__init__()
+
+ self.outdim = size_emb_dim
+ self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+ self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations)
+
+ def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
+ timesteps_proj = self.time_proj(timestep)
+ timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D)
+ return timesteps_emb
+
+
+class AdaLayerNormSingle(nn.Module):
+ r"""
+ Norm layer adaptive layer norm single (adaLN-single).
+
+ As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
+
+ Parameters:
+ embedding_dim (`int`): The size of each embedding vector.
+ use_additional_conditions (`bool`): To use additional conditions for normalization or not.
+ """
+
+ def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+ super().__init__()
+
+ self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
+ embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions, dtype=dtype, device=device, operations=operations
+ )
+
+ self.silu = nn.SiLU()
+ self.linear = operations.Linear(embedding_dim, 6 * embedding_dim, bias=True, dtype=dtype, device=device)
+
+ def forward(
+ self,
+ timestep: torch.Tensor,
+ added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+ batch_size: Optional[int] = None,
+ hidden_dtype: Optional[torch.dtype] = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ # No modulation happening here.
+ added_cond_kwargs = added_cond_kwargs or {"resolution": None, "aspect_ratio": None}
+ embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)
+ return self.linear(self.silu(embedded_timestep)), embedded_timestep
+
+class PixArtAlphaTextProjection(nn.Module):
+ """
+ Projects caption embeddings. Also handles dropout for classifier-free guidance.
+
+ Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
+ """
+
+ def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None):
+ super().__init__()
+ if out_features is None:
+ out_features = hidden_size
+ self.linear_1 = operations.Linear(in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device)
+ if act_fn == "gelu_tanh":
+ self.act_1 = nn.GELU(approximate="tanh")
+ elif act_fn == "silu":
+ self.act_1 = nn.SiLU()
+ else:
+ raise ValueError(f"Unknown activation function: {act_fn}")
+ self.linear_2 = operations.Linear(in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device)
+
+ def forward(self, caption):
+ hidden_states = self.linear_1(caption)
+ hidden_states = self.act_1(hidden_states)
+ hidden_states = self.linear_2(hidden_states)
+ return hidden_states
+
+
+class GELU_approx(nn.Module):
+ def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.proj = operations.Linear(dim_in, dim_out, dtype=dtype, device=device)
+
+ def forward(self, x):
+ return torch.nn.functional.gelu(self.proj(x), approximate="tanh")
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations)
+
+ self.net = nn.Sequential(
+ project_in,
+ nn.Dropout(dropout),
+ operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+def apply_rotary_emb(input_tensor, freqs_cis): #TODO: remove duplicate funcs and pick the best/fastest one
+ cos_freqs = freqs_cis[0]
+ sin_freqs = freqs_cis[1]
+
+ t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
+ t1, t2 = t_dup.unbind(dim=-1)
+ t_dup = torch.stack((-t2, t1), dim=-1)
+ input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
+
+ out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs
+
+ return out
+
+
+class CrossAttention(nn.Module):
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None):
+ super().__init__()
+ inner_dim = dim_head * heads
+ context_dim = query_dim if context_dim is None else context_dim
+ self.attn_precision = attn_precision
+
+ self.heads = heads
+ self.dim_head = dim_head
+
+ self.q_norm = RMSNorm(inner_dim, dtype=dtype, device=device)
+ self.k_norm = RMSNorm(inner_dim, dtype=dtype, device=device)
+
+ self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device)
+ self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
+ self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
+
+ self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+
+ def forward(self, x, context=None, mask=None, pe=None):
+ q = self.to_q(x)
+ context = x if context is None else context
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ q = self.q_norm(q)
+ k = self.k_norm(k)
+
+ if pe is not None:
+ q = apply_rotary_emb(q, pe)
+ k = apply_rotary_emb(k, pe)
+
+ if mask is None:
+ out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+ else:
+ out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+ return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+ def __init__(self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None):
+ super().__init__()
+
+ self.attn_precision = attn_precision
+ self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+ self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations)
+
+ self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+
+ self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
+
+ def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None):
+ shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
+
+ x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa
+
+ x += self.attn2(x, context=context, mask=attention_mask)
+
+ y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
+ x += self.ff(y) * gate_mlp
+
+ return x
+
+def get_fractional_positions(indices_grid, max_pos):
+ fractional_positions = torch.stack(
+ [
+ indices_grid[:, i] / max_pos[i]
+ for i in range(3)
+ ],
+ dim=-1,
+ )
+ return fractional_positions
+
+
+def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]):
+ dtype = torch.float32 #self.dtype
+
+ fractional_positions = get_fractional_positions(indices_grid, max_pos)
+
+ start = 1
+ end = theta
+ device = fractional_positions.device
+
+ indices = theta ** (
+ torch.linspace(
+ math.log(start, theta),
+ math.log(end, theta),
+ dim // 6,
+ device=device,
+ dtype=dtype,
+ )
+ )
+ indices = indices.to(dtype=dtype)
+
+ indices = indices * math.pi / 2
+
+ freqs = (
+ (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
+ .transpose(-1, -2)
+ .flatten(2)
+ )
+
+ cos_freq = freqs.cos().repeat_interleave(2, dim=-1)
+ sin_freq = freqs.sin().repeat_interleave(2, dim=-1)
+ if dim % 6 != 0:
+ cos_padding = torch.ones_like(cos_freq[:, :, : dim % 6])
+ sin_padding = torch.zeros_like(cos_freq[:, :, : dim % 6])
+ cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)
+ sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)
+ return cos_freq.to(out_dtype), sin_freq.to(out_dtype)
+
+
+class LTXVModel(torch.nn.Module):
+ def __init__(self,
+ in_channels=128,
+ cross_attention_dim=2048,
+ attention_head_dim=64,
+ num_attention_heads=32,
+
+ caption_channels=4096,
+ num_layers=28,
+
+
+ positional_embedding_theta=10000.0,
+ positional_embedding_max_pos=[20, 2048, 2048],
+ dtype=None, device=None, operations=None, **kwargs):
+ super().__init__()
+ self.dtype = dtype
+ self.out_channels = in_channels
+ self.inner_dim = num_attention_heads * attention_head_dim
+
+ self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device)
+
+ self.adaln_single = AdaLayerNormSingle(
+ self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=operations
+ )
+
+ # self.adaln_single.linear = operations.Linear(self.inner_dim, 4 * self.inner_dim, bias=True, dtype=dtype, device=device)
+
+ self.caption_projection = PixArtAlphaTextProjection(
+ in_features=caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations
+ )
+
+ self.transformer_blocks = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ self.inner_dim,
+ num_attention_heads,
+ attention_head_dim,
+ context_dim=cross_attention_dim,
+ # attn_precision=attn_precision,
+ dtype=dtype, device=device, operations=operations
+ )
+ for d in range(num_layers)
+ ]
+ )
+
+ self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device))
+ self.norm_out = operations.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.proj_out = operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)
+
+ self.patchifier = SymmetricPatchifier(1)
+
+ def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, transformer_options={}, **kwargs):
+ patches_replace = transformer_options.get("patches_replace", {})
+
+ indices_grid = self.patchifier.get_grid(
+ orig_num_frames=x.shape[2],
+ orig_height=x.shape[3],
+ orig_width=x.shape[4],
+ batch_size=x.shape[0],
+ scale_grid=((1 / frame_rate) * 8, 32, 32),
+ device=x.device,
+ )
+
+ if guiding_latent is not None:
+ ts = torch.ones([x.shape[0], 1, x.shape[2], x.shape[3], x.shape[4]], device=x.device, dtype=x.dtype)
+ input_ts = timestep.view([timestep.shape[0]] + [1] * (x.ndim - 1))
+ ts *= input_ts
+ ts[:, :, 0] = 0.0
+ timestep = self.patchifier.patchify(ts)
+ input_x = x.clone()
+ x[:, :, 0] = guiding_latent[:, :, 0]
+
+ orig_shape = list(x.shape)
+
+ x = self.patchifier.patchify(x)
+
+ x = self.patchify_proj(x)
+ timestep = timestep * 1000.0
+
+ attention_mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1]))
+ attention_mask = attention_mask.masked_fill(attention_mask.to(torch.bool), float("-inf")) # not sure about this
+ # attention_mask = (context != 0).any(dim=2).to(dtype=x.dtype)
+
+ pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
+
+ batch_size = x.shape[0]
+ timestep, embedded_timestep = self.adaln_single(
+ timestep.flatten(),
+ {"resolution": None, "aspect_ratio": None},
+ batch_size=batch_size,
+ hidden_dtype=x.dtype,
+ )
+ # Second dimension is 1 or number of tokens (if timestep_per_token)
+ timestep = timestep.view(batch_size, -1, timestep.shape[-1])
+ embedded_timestep = embedded_timestep.view(
+ batch_size, -1, embedded_timestep.shape[-1]
+ )
+
+ # 2. Blocks
+ if self.caption_projection is not None:
+ batch_size = x.shape[0]
+ context = self.caption_projection(context)
+ context = context.view(
+ batch_size, -1, x.shape[-1]
+ )
+
+ blocks_replace = patches_replace.get("dit", {})
+ for i, block in enumerate(self.transformer_blocks):
+ if ("double_block", i) in blocks_replace:
+ def block_wrap(args):
+ out = {}
+ out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"])
+ return out
+
+ out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap})
+ x = out["img"]
+ else:
+ x = block(
+ x,
+ context=context,
+ attention_mask=attention_mask,
+ timestep=timestep,
+ pe=pe
+ )
+
+ # 3. Output
+ scale_shift_values = (
+ self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None]
+ )
+ shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
+ x = self.norm_out(x)
+ # Modulation
+ x = x * (1 + scale) + shift
+ x = self.proj_out(x)
+
+ x = self.patchifier.unpatchify(
+ latents=x,
+ output_height=orig_shape[3],
+ output_width=orig_shape[4],
+ output_num_frames=orig_shape[2],
+ out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),
+ )
+
+ if guiding_latent is not None:
+ x[:, :, 0] = (input_x[:, :, 0] - guiding_latent[:, :, 0]) / input_ts[:, :, 0]
+
+ # print("res", x)
+ return x
diff --git a/comfy/ldm/lightricks/symmetric_patchifier.py b/comfy/ldm/lightricks/symmetric_patchifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..51ce505890d792ff662a46b53abb2eafc0d122e1
--- /dev/null
+++ b/comfy/ldm/lightricks/symmetric_patchifier.py
@@ -0,0 +1,105 @@
+from abc import ABC, abstractmethod
+from typing import Tuple
+
+import torch
+from einops import rearrange
+from torch import Tensor
+
+
+def append_dims(x: torch.Tensor, target_dims: int) -> torch.Tensor:
+ """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+ dims_to_append = target_dims - x.ndim
+ if dims_to_append < 0:
+ raise ValueError(
+ f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
+ )
+ elif dims_to_append == 0:
+ return x
+ return x[(...,) + (None,) * dims_to_append]
+
+
+class Patchifier(ABC):
+ def __init__(self, patch_size: int):
+ super().__init__()
+ self._patch_size = (1, patch_size, patch_size)
+
+ @abstractmethod
+ def patchify(
+ self, latents: Tensor, frame_rates: Tensor, scale_grid: bool
+ ) -> Tuple[Tensor, Tensor]:
+ pass
+
+ @abstractmethod
+ def unpatchify(
+ self,
+ latents: Tensor,
+ output_height: int,
+ output_width: int,
+ output_num_frames: int,
+ out_channels: int,
+ ) -> Tuple[Tensor, Tensor]:
+ pass
+
+ @property
+ def patch_size(self):
+ return self._patch_size
+
+ def get_grid(
+ self, orig_num_frames, orig_height, orig_width, batch_size, scale_grid, device
+ ):
+ f = orig_num_frames // self._patch_size[0]
+ h = orig_height // self._patch_size[1]
+ w = orig_width // self._patch_size[2]
+ grid_h = torch.arange(h, dtype=torch.float32, device=device)
+ grid_w = torch.arange(w, dtype=torch.float32, device=device)
+ grid_f = torch.arange(f, dtype=torch.float32, device=device)
+ grid = torch.meshgrid(grid_f, grid_h, grid_w)
+ grid = torch.stack(grid, dim=0)
+ grid = grid.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+
+ if scale_grid is not None:
+ for i in range(3):
+ if isinstance(scale_grid[i], Tensor):
+ scale = append_dims(scale_grid[i], grid.ndim - 1)
+ else:
+ scale = scale_grid[i]
+ grid[:, i, ...] = grid[:, i, ...] * scale * self._patch_size[i]
+
+ grid = rearrange(grid, "b c f h w -> b c (f h w)", b=batch_size)
+ return grid
+
+
+class SymmetricPatchifier(Patchifier):
+ def patchify(
+ self,
+ latents: Tensor,
+ ) -> Tuple[Tensor, Tensor]:
+ latents = rearrange(
+ latents,
+ "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)",
+ p1=self._patch_size[0],
+ p2=self._patch_size[1],
+ p3=self._patch_size[2],
+ )
+ return latents
+
+ def unpatchify(
+ self,
+ latents: Tensor,
+ output_height: int,
+ output_width: int,
+ output_num_frames: int,
+ out_channels: int,
+ ) -> Tuple[Tensor, Tensor]:
+ output_height = output_height // self._patch_size[1]
+ output_width = output_width // self._patch_size[2]
+ latents = rearrange(
+ latents,
+ "b (f h w) (c p q) -> b c f (h p) (w q) ",
+ f=output_num_frames,
+ h=output_height,
+ w=output_width,
+ p=self._patch_size[1],
+ q=self._patch_size[2],
+ )
+ return latents
diff --git a/comfy/ldm/lightricks/vae/causal_conv3d.py b/comfy/ldm/lightricks/vae/causal_conv3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..c572e7e860eff4b2f561a49d0ab427645efa3f5e
--- /dev/null
+++ b/comfy/ldm/lightricks/vae/causal_conv3d.py
@@ -0,0 +1,64 @@
+from typing import Tuple, Union
+
+import torch
+import torch.nn as nn
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+
+class CausalConv3d(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size: int = 3,
+ stride: Union[int, Tuple[int]] = 1,
+ dilation: int = 1,
+ groups: int = 1,
+ **kwargs,
+ ):
+ super().__init__()
+
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ kernel_size = (kernel_size, kernel_size, kernel_size)
+ self.time_kernel_size = kernel_size[0]
+
+ dilation = (dilation, 1, 1)
+
+ height_pad = kernel_size[1] // 2
+ width_pad = kernel_size[2] // 2
+ padding = (0, height_pad, width_pad)
+
+ self.conv = ops.Conv3d(
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=stride,
+ dilation=dilation,
+ padding=padding,
+ padding_mode="zeros",
+ groups=groups,
+ )
+
+ def forward(self, x, causal: bool = True):
+ if causal:
+ first_frame_pad = x[:, :, :1, :, :].repeat(
+ (1, 1, self.time_kernel_size - 1, 1, 1)
+ )
+ x = torch.concatenate((first_frame_pad, x), dim=2)
+ else:
+ first_frame_pad = x[:, :, :1, :, :].repeat(
+ (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
+ )
+ last_frame_pad = x[:, :, -1:, :, :].repeat(
+ (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
+ )
+ x = torch.concatenate((first_frame_pad, x, last_frame_pad), dim=2)
+ x = self.conv(x)
+ return x
+
+ @property
+ def weight(self):
+ return self.conv.weight
diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..33b2c2d4f1866d30b2445cf2db287f54c76e98ba
--- /dev/null
+++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
@@ -0,0 +1,698 @@
+import torch
+from torch import nn
+from functools import partial
+import math
+from einops import rearrange
+from typing import Any, Mapping, Optional, Tuple, Union, List
+from .conv_nd_factory import make_conv_nd, make_linear_nd
+from .pixel_norm import PixelNorm
+
+
+class Encoder(nn.Module):
+ r"""
+ The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.
+
+ Args:
+ dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3):
+ The number of dimensions to use in convolutions.
+ in_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ out_channels (`int`, *optional*, defaults to 3):
+ The number of output channels.
+ blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`):
+ The blocks to use. Each block is a tuple of the block name and the number of layers.
+ base_channels (`int`, *optional*, defaults to 128):
+ The number of output channels for the first convolutional layer.
+ norm_num_groups (`int`, *optional*, defaults to 32):
+ The number of groups for normalization.
+ patch_size (`int`, *optional*, defaults to 1):
+ The patch size to use. Should be a power of 2.
+ norm_layer (`str`, *optional*, defaults to `group_norm`):
+ The normalization layer to use. Can be either `group_norm` or `pixel_norm`.
+ latent_log_var (`str`, *optional*, defaults to `per_channel`):
+ The number of channels for the log variance. Can be either `per_channel`, `uniform`, or `none`.
+ """
+
+ def __init__(
+ self,
+ dims: Union[int, Tuple[int, int]] = 3,
+ in_channels: int = 3,
+ out_channels: int = 3,
+ blocks=[("res_x", 1)],
+ base_channels: int = 128,
+ norm_num_groups: int = 32,
+ patch_size: Union[int, Tuple[int]] = 1,
+ norm_layer: str = "group_norm", # group_norm, pixel_norm
+ latent_log_var: str = "per_channel",
+ ):
+ super().__init__()
+ self.patch_size = patch_size
+ self.norm_layer = norm_layer
+ self.latent_channels = out_channels
+ self.latent_log_var = latent_log_var
+ self.blocks_desc = blocks
+
+ in_channels = in_channels * patch_size**2
+ output_channel = base_channels
+
+ self.conv_in = make_conv_nd(
+ dims=dims,
+ in_channels=in_channels,
+ out_channels=output_channel,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ causal=True,
+ )
+
+ self.down_blocks = nn.ModuleList([])
+
+ for block_name, block_params in blocks:
+ input_channel = output_channel
+ if isinstance(block_params, int):
+ block_params = {"num_layers": block_params}
+
+ if block_name == "res_x":
+ block = UNetMidBlock3D(
+ dims=dims,
+ in_channels=input_channel,
+ num_layers=block_params["num_layers"],
+ resnet_eps=1e-6,
+ resnet_groups=norm_num_groups,
+ norm_layer=norm_layer,
+ )
+ elif block_name == "res_x_y":
+ output_channel = block_params.get("multiplier", 2) * output_channel
+ block = ResnetBlock3D(
+ dims=dims,
+ in_channels=input_channel,
+ out_channels=output_channel,
+ eps=1e-6,
+ groups=norm_num_groups,
+ norm_layer=norm_layer,
+ )
+ elif block_name == "compress_time":
+ block = make_conv_nd(
+ dims=dims,
+ in_channels=input_channel,
+ out_channels=output_channel,
+ kernel_size=3,
+ stride=(2, 1, 1),
+ causal=True,
+ )
+ elif block_name == "compress_space":
+ block = make_conv_nd(
+ dims=dims,
+ in_channels=input_channel,
+ out_channels=output_channel,
+ kernel_size=3,
+ stride=(1, 2, 2),
+ causal=True,
+ )
+ elif block_name == "compress_all":
+ block = make_conv_nd(
+ dims=dims,
+ in_channels=input_channel,
+ out_channels=output_channel,
+ kernel_size=3,
+ stride=(2, 2, 2),
+ causal=True,
+ )
+ elif block_name == "compress_all_x_y":
+ output_channel = block_params.get("multiplier", 2) * output_channel
+ block = make_conv_nd(
+ dims=dims,
+ in_channels=input_channel,
+ out_channels=output_channel,
+ kernel_size=3,
+ stride=(2, 2, 2),
+ causal=True,
+ )
+ else:
+ raise ValueError(f"unknown block: {block_name}")
+
+ self.down_blocks.append(block)
+
+ # out
+ if norm_layer == "group_norm":
+ self.conv_norm_out = nn.GroupNorm(
+ num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6
+ )
+ elif norm_layer == "pixel_norm":
+ self.conv_norm_out = PixelNorm()
+ elif norm_layer == "layer_norm":
+ self.conv_norm_out = LayerNorm(output_channel, eps=1e-6)
+
+ self.conv_act = nn.SiLU()
+
+ conv_out_channels = out_channels
+ if latent_log_var == "per_channel":
+ conv_out_channels *= 2
+ elif latent_log_var == "uniform":
+ conv_out_channels += 1
+ elif latent_log_var != "none":
+ raise ValueError(f"Invalid latent_log_var: {latent_log_var}")
+ self.conv_out = make_conv_nd(
+ dims, output_channel, conv_out_channels, 3, padding=1, causal=True
+ )
+
+ self.gradient_checkpointing = False
+
+ def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+ r"""The forward method of the `Encoder` class."""
+
+ sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+ sample = self.conv_in(sample)
+
+ checkpoint_fn = (
+ partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)
+ if self.gradient_checkpointing and self.training
+ else lambda x: x
+ )
+
+ for down_block in self.down_blocks:
+ sample = checkpoint_fn(down_block)(sample)
+
+ sample = self.conv_norm_out(sample)
+ sample = self.conv_act(sample)
+ sample = self.conv_out(sample)
+
+ if self.latent_log_var == "uniform":
+ last_channel = sample[:, -1:, ...]
+ num_dims = sample.dim()
+
+ if num_dims == 4:
+ # For shape (B, C, H, W)
+ repeated_last_channel = last_channel.repeat(
+ 1, sample.shape[1] - 2, 1, 1
+ )
+ sample = torch.cat([sample, repeated_last_channel], dim=1)
+ elif num_dims == 5:
+ # For shape (B, C, F, H, W)
+ repeated_last_channel = last_channel.repeat(
+ 1, sample.shape[1] - 2, 1, 1, 1
+ )
+ sample = torch.cat([sample, repeated_last_channel], dim=1)
+ else:
+ raise ValueError(f"Invalid input shape: {sample.shape}")
+
+ return sample
+
+
+class Decoder(nn.Module):
+ r"""
+ The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.
+
+ Args:
+ dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3):
+ The number of dimensions to use in convolutions.
+ in_channels (`int`, *optional*, defaults to 3):
+ The number of input channels.
+ out_channels (`int`, *optional*, defaults to 3):
+ The number of output channels.
+ blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`):
+ The blocks to use. Each block is a tuple of the block name and the number of layers.
+ base_channels (`int`, *optional*, defaults to 128):
+ The number of output channels for the first convolutional layer.
+ norm_num_groups (`int`, *optional*, defaults to 32):
+ The number of groups for normalization.
+ patch_size (`int`, *optional*, defaults to 1):
+ The patch size to use. Should be a power of 2.
+ norm_layer (`str`, *optional*, defaults to `group_norm`):
+ The normalization layer to use. Can be either `group_norm` or `pixel_norm`.
+ causal (`bool`, *optional*, defaults to `True`):
+ Whether to use causal convolutions or not.
+ """
+
+ def __init__(
+ self,
+ dims,
+ in_channels: int = 3,
+ out_channels: int = 3,
+ blocks=[("res_x", 1)],
+ base_channels: int = 128,
+ layers_per_block: int = 2,
+ norm_num_groups: int = 32,
+ patch_size: int = 1,
+ norm_layer: str = "group_norm",
+ causal: bool = True,
+ ):
+ super().__init__()
+ self.patch_size = patch_size
+ self.layers_per_block = layers_per_block
+ out_channels = out_channels * patch_size**2
+ self.causal = causal
+ self.blocks_desc = blocks
+
+ # Compute output channel to be product of all channel-multiplier blocks
+ output_channel = base_channels
+ for block_name, block_params in list(reversed(blocks)):
+ block_params = block_params if isinstance(block_params, dict) else {}
+ if block_name == "res_x_y":
+ output_channel = output_channel * block_params.get("multiplier", 2)
+
+ self.conv_in = make_conv_nd(
+ dims,
+ in_channels,
+ output_channel,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ causal=True,
+ )
+
+ self.up_blocks = nn.ModuleList([])
+
+ for block_name, block_params in list(reversed(blocks)):
+ input_channel = output_channel
+ if isinstance(block_params, int):
+ block_params = {"num_layers": block_params}
+
+ if block_name == "res_x":
+ block = UNetMidBlock3D(
+ dims=dims,
+ in_channels=input_channel,
+ num_layers=block_params["num_layers"],
+ resnet_eps=1e-6,
+ resnet_groups=norm_num_groups,
+ norm_layer=norm_layer,
+ )
+ elif block_name == "res_x_y":
+ output_channel = output_channel // block_params.get("multiplier", 2)
+ block = ResnetBlock3D(
+ dims=dims,
+ in_channels=input_channel,
+ out_channels=output_channel,
+ eps=1e-6,
+ groups=norm_num_groups,
+ norm_layer=norm_layer,
+ )
+ elif block_name == "compress_time":
+ block = DepthToSpaceUpsample(
+ dims=dims, in_channels=input_channel, stride=(2, 1, 1)
+ )
+ elif block_name == "compress_space":
+ block = DepthToSpaceUpsample(
+ dims=dims, in_channels=input_channel, stride=(1, 2, 2)
+ )
+ elif block_name == "compress_all":
+ block = DepthToSpaceUpsample(
+ dims=dims,
+ in_channels=input_channel,
+ stride=(2, 2, 2),
+ residual=block_params.get("residual", False),
+ )
+ else:
+ raise ValueError(f"unknown layer: {block_name}")
+
+ self.up_blocks.append(block)
+
+ if norm_layer == "group_norm":
+ self.conv_norm_out = nn.GroupNorm(
+ num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6
+ )
+ elif norm_layer == "pixel_norm":
+ self.conv_norm_out = PixelNorm()
+ elif norm_layer == "layer_norm":
+ self.conv_norm_out = LayerNorm(output_channel, eps=1e-6)
+
+ self.conv_act = nn.SiLU()
+ self.conv_out = make_conv_nd(
+ dims, output_channel, out_channels, 3, padding=1, causal=True
+ )
+
+ self.gradient_checkpointing = False
+
+ # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
+ def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+ r"""The forward method of the `Decoder` class."""
+ # assert target_shape is not None, "target_shape must be provided"
+
+ sample = self.conv_in(sample, causal=self.causal)
+
+ upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
+
+ checkpoint_fn = (
+ partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)
+ if self.gradient_checkpointing and self.training
+ else lambda x: x
+ )
+
+ sample = sample.to(upscale_dtype)
+
+ for up_block in self.up_blocks:
+ sample = checkpoint_fn(up_block)(sample, causal=self.causal)
+
+ sample = self.conv_norm_out(sample)
+ sample = self.conv_act(sample)
+ sample = self.conv_out(sample, causal=self.causal)
+
+ sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+
+ return sample
+
+
+class UNetMidBlock3D(nn.Module):
+ """
+ A 3D UNet mid-block [`UNetMidBlock3D`] with multiple residual blocks.
+
+ Args:
+ in_channels (`int`): The number of input channels.
+ dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
+ num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
+ resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
+ resnet_groups (`int`, *optional*, defaults to 32):
+ The number of groups to use in the group normalization layers of the resnet blocks.
+
+ Returns:
+ `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
+ in_channels, height, width)`.
+
+ """
+
+ def __init__(
+ self,
+ dims: Union[int, Tuple[int, int]],
+ in_channels: int,
+ dropout: float = 0.0,
+ num_layers: int = 1,
+ resnet_eps: float = 1e-6,
+ resnet_groups: int = 32,
+ norm_layer: str = "group_norm",
+ ):
+ super().__init__()
+ resnet_groups = (
+ resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+ )
+
+ self.res_blocks = nn.ModuleList(
+ [
+ ResnetBlock3D(
+ dims=dims,
+ in_channels=in_channels,
+ out_channels=in_channels,
+ eps=resnet_eps,
+ groups=resnet_groups,
+ dropout=dropout,
+ norm_layer=norm_layer,
+ )
+ for _ in range(num_layers)
+ ]
+ )
+
+ def forward(
+ self, hidden_states: torch.FloatTensor, causal: bool = True
+ ) -> torch.FloatTensor:
+ for resnet in self.res_blocks:
+ hidden_states = resnet(hidden_states, causal=causal)
+
+ return hidden_states
+
+
+class DepthToSpaceUpsample(nn.Module):
+ def __init__(self, dims, in_channels, stride, residual=False):
+ super().__init__()
+ self.stride = stride
+ self.out_channels = math.prod(stride) * in_channels
+ self.conv = make_conv_nd(
+ dims=dims,
+ in_channels=in_channels,
+ out_channels=self.out_channels,
+ kernel_size=3,
+ stride=1,
+ causal=True,
+ )
+ self.residual = residual
+
+ def forward(self, x, causal: bool = True):
+ if self.residual:
+ # Reshape and duplicate the input to match the output shape
+ x_in = rearrange(
+ x,
+ "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+ p1=self.stride[0],
+ p2=self.stride[1],
+ p3=self.stride[2],
+ )
+ x_in = x_in.repeat(1, math.prod(self.stride), 1, 1, 1)
+ if self.stride[0] == 2:
+ x_in = x_in[:, :, 1:, :, :]
+ x = self.conv(x, causal=causal)
+ x = rearrange(
+ x,
+ "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+ p1=self.stride[0],
+ p2=self.stride[1],
+ p3=self.stride[2],
+ )
+ if self.stride[0] == 2:
+ x = x[:, :, 1:, :, :]
+ if self.residual:
+ x = x + x_in
+ return x
+
+
+class LayerNorm(nn.Module):
+ def __init__(self, dim, eps, elementwise_affine=True) -> None:
+ super().__init__()
+ self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine)
+
+ def forward(self, x):
+ x = rearrange(x, "b c d h w -> b d h w c")
+ x = self.norm(x)
+ x = rearrange(x, "b d h w c -> b c d h w")
+ return x
+
+
+class ResnetBlock3D(nn.Module):
+ r"""
+ A Resnet block.
+
+ Parameters:
+ in_channels (`int`): The number of channels in the input.
+ out_channels (`int`, *optional*, default to be `None`):
+ The number of output channels for the first conv layer. If None, same as `in_channels`.
+ dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
+ groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
+ eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
+ """
+
+ def __init__(
+ self,
+ dims: Union[int, Tuple[int, int]],
+ in_channels: int,
+ out_channels: Optional[int] = None,
+ dropout: float = 0.0,
+ groups: int = 32,
+ eps: float = 1e-6,
+ norm_layer: str = "group_norm",
+ ):
+ super().__init__()
+ self.in_channels = in_channels
+ out_channels = in_channels if out_channels is None else out_channels
+ self.out_channels = out_channels
+
+ if norm_layer == "group_norm":
+ self.norm1 = nn.GroupNorm(
+ num_groups=groups, num_channels=in_channels, eps=eps, affine=True
+ )
+ elif norm_layer == "pixel_norm":
+ self.norm1 = PixelNorm()
+ elif norm_layer == "layer_norm":
+ self.norm1 = LayerNorm(in_channels, eps=eps, elementwise_affine=True)
+
+ self.non_linearity = nn.SiLU()
+
+ self.conv1 = make_conv_nd(
+ dims,
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ causal=True,
+ )
+
+ if norm_layer == "group_norm":
+ self.norm2 = nn.GroupNorm(
+ num_groups=groups, num_channels=out_channels, eps=eps, affine=True
+ )
+ elif norm_layer == "pixel_norm":
+ self.norm2 = PixelNorm()
+ elif norm_layer == "layer_norm":
+ self.norm2 = LayerNorm(out_channels, eps=eps, elementwise_affine=True)
+
+ self.dropout = torch.nn.Dropout(dropout)
+
+ self.conv2 = make_conv_nd(
+ dims,
+ out_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ causal=True,
+ )
+
+ self.conv_shortcut = (
+ make_linear_nd(
+ dims=dims, in_channels=in_channels, out_channels=out_channels
+ )
+ if in_channels != out_channels
+ else nn.Identity()
+ )
+
+ self.norm3 = (
+ LayerNorm(in_channels, eps=eps, elementwise_affine=True)
+ if in_channels != out_channels
+ else nn.Identity()
+ )
+
+ def forward(
+ self,
+ input_tensor: torch.FloatTensor,
+ causal: bool = True,
+ ) -> torch.FloatTensor:
+ hidden_states = input_tensor
+
+ hidden_states = self.norm1(hidden_states)
+
+ hidden_states = self.non_linearity(hidden_states)
+
+ hidden_states = self.conv1(hidden_states, causal=causal)
+
+ hidden_states = self.norm2(hidden_states)
+
+ hidden_states = self.non_linearity(hidden_states)
+
+ hidden_states = self.dropout(hidden_states)
+
+ hidden_states = self.conv2(hidden_states, causal=causal)
+
+ input_tensor = self.norm3(input_tensor)
+
+ input_tensor = self.conv_shortcut(input_tensor)
+
+ output_tensor = input_tensor + hidden_states
+
+ return output_tensor
+
+
+def patchify(x, patch_size_hw, patch_size_t=1):
+ if patch_size_hw == 1 and patch_size_t == 1:
+ return x
+ if x.dim() == 4:
+ x = rearrange(
+ x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size_hw, r=patch_size_hw
+ )
+ elif x.dim() == 5:
+ x = rearrange(
+ x,
+ "b c (f p) (h q) (w r) -> b (c p r q) f h w",
+ p=patch_size_t,
+ q=patch_size_hw,
+ r=patch_size_hw,
+ )
+ else:
+ raise ValueError(f"Invalid input shape: {x.shape}")
+
+ return x
+
+
+def unpatchify(x, patch_size_hw, patch_size_t=1):
+ if patch_size_hw == 1 and patch_size_t == 1:
+ return x
+
+ if x.dim() == 4:
+ x = rearrange(
+ x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size_hw, r=patch_size_hw
+ )
+ elif x.dim() == 5:
+ x = rearrange(
+ x,
+ "b (c p r q) f h w -> b c (f p) (h q) (w r)",
+ p=patch_size_t,
+ q=patch_size_hw,
+ r=patch_size_hw,
+ )
+
+ return x
+
+class processor(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.register_buffer("std-of-means", torch.empty(128))
+ self.register_buffer("mean-of-means", torch.empty(128))
+ self.register_buffer("mean-of-stds", torch.empty(128))
+ self.register_buffer("mean-of-stds_over_std-of-means", torch.empty(128))
+ self.register_buffer("channel", torch.empty(128))
+
+ def un_normalize(self, x):
+ return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)
+
+ def normalize(self, x):
+ return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)
+
+class VideoVAE(nn.Module):
+ def __init__(self):
+ super().__init__()
+ config = {
+ "_class_name": "CausalVideoAutoencoder",
+ "dims": 3,
+ "in_channels": 3,
+ "out_channels": 3,
+ "latent_channels": 128,
+ "blocks": [
+ ["res_x", 4],
+ ["compress_all", 1],
+ ["res_x_y", 1],
+ ["res_x", 3],
+ ["compress_all", 1],
+ ["res_x_y", 1],
+ ["res_x", 3],
+ ["compress_all", 1],
+ ["res_x", 3],
+ ["res_x", 4],
+ ],
+ "scaling_factor": 1.0,
+ "norm_layer": "pixel_norm",
+ "patch_size": 4,
+ "latent_log_var": "uniform",
+ "use_quant_conv": False,
+ "causal_decoder": False,
+ }
+
+ double_z = config.get("double_z", True)
+ latent_log_var = config.get(
+ "latent_log_var", "per_channel" if double_z else "none"
+ )
+
+ self.encoder = Encoder(
+ dims=config["dims"],
+ in_channels=config.get("in_channels", 3),
+ out_channels=config["latent_channels"],
+ blocks=config.get("encoder_blocks", config.get("blocks")),
+ patch_size=config.get("patch_size", 1),
+ latent_log_var=latent_log_var,
+ norm_layer=config.get("norm_layer", "group_norm"),
+ )
+
+ self.decoder = Decoder(
+ dims=config["dims"],
+ in_channels=config["latent_channels"],
+ out_channels=config.get("out_channels", 3),
+ blocks=config.get("decoder_blocks", config.get("blocks")),
+ patch_size=config.get("patch_size", 1),
+ norm_layer=config.get("norm_layer", "group_norm"),
+ causal=config.get("causal_decoder", False),
+ )
+
+ self.per_channel_statistics = processor()
+
+ def encode(self, x):
+ means, logvar = torch.chunk(self.encoder(x), 2, dim=1)
+ return self.per_channel_statistics.normalize(means)
+
+ def decode(self, x):
+ return self.decoder(self.per_channel_statistics.un_normalize(x))
+
diff --git a/comfy/ldm/lightricks/vae/conv_nd_factory.py b/comfy/ldm/lightricks/vae/conv_nd_factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5f067bf09ebb56351850abec5fca0b74f4741a8
--- /dev/null
+++ b/comfy/ldm/lightricks/vae/conv_nd_factory.py
@@ -0,0 +1,83 @@
+from typing import Tuple, Union
+
+import torch
+
+from .dual_conv3d import DualConv3d
+from .causal_conv3d import CausalConv3d
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def make_conv_nd(
+ dims: Union[int, Tuple[int, int]],
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ stride=1,
+ padding=0,
+ dilation=1,
+ groups=1,
+ bias=True,
+ causal=False,
+):
+ if dims == 2:
+ return ops.Conv2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ bias=bias,
+ )
+ elif dims == 3:
+ if causal:
+ return CausalConv3d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ bias=bias,
+ )
+ return ops.Conv3d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ bias=bias,
+ )
+ elif dims == (2, 1):
+ return DualConv3d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ bias=bias,
+ )
+ else:
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def make_linear_nd(
+ dims: int,
+ in_channels: int,
+ out_channels: int,
+ bias=True,
+):
+ if dims == 2:
+ return ops.Conv2d(
+ in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
+ )
+ elif dims == 3 or dims == (2, 1):
+ return ops.Conv3d(
+ in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
+ )
+ else:
+ raise ValueError(f"unsupported dimensions: {dims}")
diff --git a/comfy/ldm/lightricks/vae/dual_conv3d.py b/comfy/ldm/lightricks/vae/dual_conv3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bd54c0a6712857e5f9e62d26144d3a450b58571
--- /dev/null
+++ b/comfy/ldm/lightricks/vae/dual_conv3d.py
@@ -0,0 +1,195 @@
+import math
+from typing import Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+
+class DualConv3d(nn.Module):
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride: Union[int, Tuple[int, int, int]] = 1,
+ padding: Union[int, Tuple[int, int, int]] = 0,
+ dilation: Union[int, Tuple[int, int, int]] = 1,
+ groups=1,
+ bias=True,
+ ):
+ super(DualConv3d, self).__init__()
+
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ # Ensure kernel_size, stride, padding, and dilation are tuples of length 3
+ if isinstance(kernel_size, int):
+ kernel_size = (kernel_size, kernel_size, kernel_size)
+ if kernel_size == (1, 1, 1):
+ raise ValueError(
+ "kernel_size must be greater than 1. Use make_linear_nd instead."
+ )
+ if isinstance(stride, int):
+ stride = (stride, stride, stride)
+ if isinstance(padding, int):
+ padding = (padding, padding, padding)
+ if isinstance(dilation, int):
+ dilation = (dilation, dilation, dilation)
+
+ # Set parameters for convolutions
+ self.groups = groups
+ self.bias = bias
+
+ # Define the size of the channels after the first convolution
+ intermediate_channels = (
+ out_channels if in_channels < out_channels else in_channels
+ )
+
+ # Define parameters for the first convolution
+ self.weight1 = nn.Parameter(
+ torch.Tensor(
+ intermediate_channels,
+ in_channels // groups,
+ 1,
+ kernel_size[1],
+ kernel_size[2],
+ )
+ )
+ self.stride1 = (1, stride[1], stride[2])
+ self.padding1 = (0, padding[1], padding[2])
+ self.dilation1 = (1, dilation[1], dilation[2])
+ if bias:
+ self.bias1 = nn.Parameter(torch.Tensor(intermediate_channels))
+ else:
+ self.register_parameter("bias1", None)
+
+ # Define parameters for the second convolution
+ self.weight2 = nn.Parameter(
+ torch.Tensor(
+ out_channels, intermediate_channels // groups, kernel_size[0], 1, 1
+ )
+ )
+ self.stride2 = (stride[0], 1, 1)
+ self.padding2 = (padding[0], 0, 0)
+ self.dilation2 = (dilation[0], 1, 1)
+ if bias:
+ self.bias2 = nn.Parameter(torch.Tensor(out_channels))
+ else:
+ self.register_parameter("bias2", None)
+
+ # Initialize weights and biases
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ nn.init.kaiming_uniform_(self.weight1, a=math.sqrt(5))
+ nn.init.kaiming_uniform_(self.weight2, a=math.sqrt(5))
+ if self.bias:
+ fan_in1, _ = nn.init._calculate_fan_in_and_fan_out(self.weight1)
+ bound1 = 1 / math.sqrt(fan_in1)
+ nn.init.uniform_(self.bias1, -bound1, bound1)
+ fan_in2, _ = nn.init._calculate_fan_in_and_fan_out(self.weight2)
+ bound2 = 1 / math.sqrt(fan_in2)
+ nn.init.uniform_(self.bias2, -bound2, bound2)
+
+ def forward(self, x, use_conv3d=False, skip_time_conv=False):
+ if use_conv3d:
+ return self.forward_with_3d(x=x, skip_time_conv=skip_time_conv)
+ else:
+ return self.forward_with_2d(x=x, skip_time_conv=skip_time_conv)
+
+ def forward_with_3d(self, x, skip_time_conv):
+ # First convolution
+ x = F.conv3d(
+ x,
+ self.weight1,
+ self.bias1,
+ self.stride1,
+ self.padding1,
+ self.dilation1,
+ self.groups,
+ )
+
+ if skip_time_conv:
+ return x
+
+ # Second convolution
+ x = F.conv3d(
+ x,
+ self.weight2,
+ self.bias2,
+ self.stride2,
+ self.padding2,
+ self.dilation2,
+ self.groups,
+ )
+
+ return x
+
+ def forward_with_2d(self, x, skip_time_conv):
+ b, c, d, h, w = x.shape
+
+ # First 2D convolution
+ x = rearrange(x, "b c d h w -> (b d) c h w")
+ # Squeeze the depth dimension out of weight1 since it's 1
+ weight1 = self.weight1.squeeze(2)
+ # Select stride, padding, and dilation for the 2D convolution
+ stride1 = (self.stride1[1], self.stride1[2])
+ padding1 = (self.padding1[1], self.padding1[2])
+ dilation1 = (self.dilation1[1], self.dilation1[2])
+ x = F.conv2d(x, weight1, self.bias1, stride1, padding1, dilation1, self.groups)
+
+ _, _, h, w = x.shape
+
+ if skip_time_conv:
+ x = rearrange(x, "(b d) c h w -> b c d h w", b=b)
+ return x
+
+ # Second convolution which is essentially treated as a 1D convolution across the 'd' dimension
+ x = rearrange(x, "(b d) c h w -> (b h w) c d", b=b)
+
+ # Reshape weight2 to match the expected dimensions for conv1d
+ weight2 = self.weight2.squeeze(-1).squeeze(-1)
+ # Use only the relevant dimension for stride, padding, and dilation for the 1D convolution
+ stride2 = self.stride2[0]
+ padding2 = self.padding2[0]
+ dilation2 = self.dilation2[0]
+ x = F.conv1d(x, weight2, self.bias2, stride2, padding2, dilation2, self.groups)
+ x = rearrange(x, "(b h w) c d -> b c d h w", b=b, h=h, w=w)
+
+ return x
+
+ @property
+ def weight(self):
+ return self.weight2
+
+
+def test_dual_conv3d_consistency():
+ # Initialize parameters
+ in_channels = 3
+ out_channels = 5
+ kernel_size = (3, 3, 3)
+ stride = (2, 2, 2)
+ padding = (1, 1, 1)
+
+ # Create an instance of the DualConv3d class
+ dual_conv3d = DualConv3d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ padding=padding,
+ bias=True,
+ )
+
+ # Example input tensor
+ test_input = torch.randn(1, 3, 10, 10, 10)
+
+ # Perform forward passes with both 3D and 2D settings
+ output_conv3d = dual_conv3d(test_input, use_conv3d=True)
+ output_2d = dual_conv3d(test_input, use_conv3d=False)
+
+ # Assert that the outputs from both methods are sufficiently close
+ assert torch.allclose(
+ output_conv3d, output_2d, atol=1e-6
+ ), "Outputs are not consistent between 3D and 2D convolutions."
diff --git a/comfy/ldm/lightricks/vae/pixel_norm.py b/comfy/ldm/lightricks/vae/pixel_norm.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bc3ea60e8a6453e7e12a7fb5aca4de3958a2567
--- /dev/null
+++ b/comfy/ldm/lightricks/vae/pixel_norm.py
@@ -0,0 +1,12 @@
+import torch
+from torch import nn
+
+
+class PixelNorm(nn.Module):
+ def __init__(self, dim=1, eps=1e-8):
+ super(PixelNorm, self).__init__()
+ self.dim = dim
+ self.eps = eps
+
+ def forward(self, x):
+ return x / torch.sqrt(torch.mean(x**2, dim=self.dim, keepdim=True) + self.eps)
diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5f4de2883078dadee058aef437901069588321b
--- /dev/null
+++ b/comfy/ldm/models/autoencoder.py
@@ -0,0 +1,226 @@
+import torch
+from contextlib import contextmanager
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
+from comfy.ldm.util import instantiate_from_config
+from comfy.ldm.modules.ema import LitEma
+import comfy.ops
+
+class DiagonalGaussianRegularizer(torch.nn.Module):
+ def __init__(self, sample: bool = True):
+ super().__init__()
+ self.sample = sample
+
+ def get_trainable_parameters(self) -> Any:
+ yield from ()
+
+ def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+ log = dict()
+ posterior = DiagonalGaussianDistribution(z)
+ if self.sample:
+ z = posterior.sample()
+ else:
+ z = posterior.mode()
+ kl_loss = posterior.kl()
+ kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+ log["kl_loss"] = kl_loss
+ return z, log
+
+
+class AbstractAutoencoder(torch.nn.Module):
+ """
+ This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,
+ unCLIP models, etc. Hence, it is fairly general, and specific features
+ (e.g. discriminator training, encoding, decoding) must be implemented in subclasses.
+ """
+
+ def __init__(
+ self,
+ ema_decay: Union[None, float] = None,
+ monitor: Union[None, str] = None,
+ input_key: str = "jpg",
+ **kwargs,
+ ):
+ super().__init__()
+
+ self.input_key = input_key
+ self.use_ema = ema_decay is not None
+ if monitor is not None:
+ self.monitor = monitor
+
+ if self.use_ema:
+ self.model_ema = LitEma(self, decay=ema_decay)
+ logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ def get_input(self, batch) -> Any:
+ raise NotImplementedError()
+
+ def on_train_batch_end(self, *args, **kwargs):
+ # for EMA computation
+ if self.use_ema:
+ self.model_ema(self)
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.parameters())
+ self.model_ema.copy_to(self)
+ if context is not None:
+ logpy.info(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.parameters())
+ if context is not None:
+ logpy.info(f"{context}: Restored training weights")
+
+ def encode(self, *args, **kwargs) -> torch.Tensor:
+ raise NotImplementedError("encode()-method of abstract base class called")
+
+ def decode(self, *args, **kwargs) -> torch.Tensor:
+ raise NotImplementedError("decode()-method of abstract base class called")
+
+ def instantiate_optimizer_from_config(self, params, lr, cfg):
+ logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config")
+ return get_obj_from_str(cfg["target"])(
+ params, lr=lr, **cfg.get("params", dict())
+ )
+
+ def configure_optimizers(self) -> Any:
+ raise NotImplementedError()
+
+
+class AutoencodingEngine(AbstractAutoencoder):
+ """
+ Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL
+ (we also restore them explicitly as special cases for legacy reasons).
+ Regularizations such as KL or VQ are moved to the regularizer class.
+ """
+
+ def __init__(
+ self,
+ *args,
+ encoder_config: Dict,
+ decoder_config: Dict,
+ regularizer_config: Dict,
+ **kwargs,
+ ):
+ super().__init__(*args, **kwargs)
+
+ self.encoder: torch.nn.Module = instantiate_from_config(encoder_config)
+ self.decoder: torch.nn.Module = instantiate_from_config(decoder_config)
+ self.regularization: AbstractRegularizer = instantiate_from_config(
+ regularizer_config
+ )
+
+ def get_last_layer(self):
+ return self.decoder.get_last_layer()
+
+ def encode(
+ self,
+ x: torch.Tensor,
+ return_reg_log: bool = False,
+ unregularized: bool = False,
+ ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
+ z = self.encoder(x)
+ if unregularized:
+ return z, dict()
+ z, reg_log = self.regularization(z)
+ if return_reg_log:
+ return z, reg_log
+ return z
+
+ def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
+ x = self.decoder(z, **kwargs)
+ return x
+
+ def forward(
+ self, x: torch.Tensor, **additional_decode_kwargs
+ ) -> Tuple[torch.Tensor, torch.Tensor, dict]:
+ z, reg_log = self.encode(x, return_reg_log=True)
+ dec = self.decode(z, **additional_decode_kwargs)
+ return z, dec, reg_log
+
+
+class AutoencodingEngineLegacy(AutoencodingEngine):
+ def __init__(self, embed_dim: int, **kwargs):
+ self.max_batch_size = kwargs.pop("max_batch_size", None)
+ ddconfig = kwargs.pop("ddconfig")
+ super().__init__(
+ encoder_config={
+ "target": "comfy.ldm.modules.diffusionmodules.model.Encoder",
+ "params": ddconfig,
+ },
+ decoder_config={
+ "target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
+ "params": ddconfig,
+ },
+ **kwargs,
+ )
+ self.quant_conv = comfy.ops.disable_weight_init.Conv2d(
+ (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
+ (1 + ddconfig["double_z"]) * embed_dim,
+ 1,
+ )
+ self.post_quant_conv = comfy.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ self.embed_dim = embed_dim
+
+ def get_autoencoder_params(self) -> list:
+ params = super().get_autoencoder_params()
+ return params
+
+ def encode(
+ self, x: torch.Tensor, return_reg_log: bool = False
+ ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
+ if self.max_batch_size is None:
+ z = self.encoder(x)
+ z = self.quant_conv(z)
+ else:
+ N = x.shape[0]
+ bs = self.max_batch_size
+ n_batches = int(math.ceil(N / bs))
+ z = list()
+ for i_batch in range(n_batches):
+ z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])
+ z_batch = self.quant_conv(z_batch)
+ z.append(z_batch)
+ z = torch.cat(z, 0)
+
+ z, reg_log = self.regularization(z)
+ if return_reg_log:
+ return z, reg_log
+ return z
+
+ def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
+ if self.max_batch_size is None:
+ dec = self.post_quant_conv(z)
+ dec = self.decoder(dec, **decoder_kwargs)
+ else:
+ N = z.shape[0]
+ bs = self.max_batch_size
+ n_batches = int(math.ceil(N / bs))
+ dec = list()
+ for i_batch in range(n_batches):
+ dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])
+ dec_batch = self.decoder(dec_batch, **decoder_kwargs)
+ dec.append(dec_batch)
+ dec = torch.cat(dec, 0)
+
+ return dec
+
+
+class AutoencoderKL(AutoencodingEngineLegacy):
+ def __init__(self, **kwargs):
+ if "lossconfig" in kwargs:
+ kwargs["loss_config"] = kwargs.pop("lossconfig")
+ super().__init__(
+ regularizer_config={
+ "target": (
+ "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"
+ )
+ },
+ **kwargs,
+ )
diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..885b24019109ca6b0c0e7b93612350b0f3b20514
--- /dev/null
+++ b/comfy/ldm/modules/attention.py
@@ -0,0 +1,880 @@
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from typing import Optional
+import logging
+
+from .diffusionmodules.util import AlphaBlender, timestep_embedding
+from .sub_quadratic_attention import efficient_dot_product_attention
+
+from comfy import model_management
+
+if model_management.xformers_enabled():
+ import xformers
+ import xformers.ops
+
+from comfy.cli_args import args
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype()
+
+def get_attn_precision(attn_precision):
+ if args.dont_upcast_attention:
+ return None
+ if FORCE_UPCAST_ATTENTION_DTYPE is not None:
+ return FORCE_UPCAST_ATTENTION_DTYPE
+ return attn_precision
+
+def exists(val):
+ return val is not None
+
+
+def uniq(arr):
+ return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d
+
+
+def max_neg_value(t):
+ return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+ dim = tensor.shape[-1]
+ std = 1 / math.sqrt(dim)
+ tensor.uniform_(-std, std)
+ return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+ def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops):
+ super().__init__()
+ self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
+
+ def forward(self, x):
+ x, gate = self.proj(x).chunk(2, dim=-1)
+ return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = default(dim_out, dim)
+ project_in = nn.Sequential(
+ operations.Linear(dim, inner_dim, dtype=dtype, device=device),
+ nn.GELU()
+ ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)
+
+ self.net = nn.Sequential(
+ project_in,
+ nn.Dropout(dropout),
+ operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+def Normalize(in_channels, dtype=None, device=None):
+ return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
+
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+ attn_precision = get_attn_precision(attn_precision)
+
+ if skip_reshape:
+ b, _, _, dim_head = q.shape
+ else:
+ b, _, dim_head = q.shape
+ dim_head //= heads
+
+ scale = dim_head ** -0.5
+
+ h = heads
+ if skip_reshape:
+ q, k, v = map(
+ lambda t: t.reshape(b * heads, -1, dim_head),
+ (q, k, v),
+ )
+ else:
+ q, k, v = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(b, -1, heads, dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b * heads, -1, dim_head)
+ .contiguous(),
+ (q, k, v),
+ )
+
+ # force cast to fp32 to avoid overflowing
+ if attn_precision == torch.float32:
+ sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
+ else:
+ sim = einsum('b i d, b j d -> b i j', q, k) * scale
+
+ del q, k
+
+ if exists(mask):
+ if mask.dtype == torch.bool:
+ mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention
+ max_neg_value = -torch.finfo(sim.dtype).max
+ mask = repeat(mask, 'b j -> (b h) () j', h=h)
+ sim.masked_fill_(~mask, max_neg_value)
+ else:
+ if len(mask.shape) == 2:
+ bs = 1
+ else:
+ bs = mask.shape[0]
+ mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+ sim.add_(mask)
+
+ # attention, what we cannot get enough of
+ sim = sim.softmax(dim=-1)
+
+ out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
+ out = (
+ out.unsqueeze(0)
+ .reshape(b, heads, -1, dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b, -1, heads * dim_head)
+ )
+ return out
+
+
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False):
+ attn_precision = get_attn_precision(attn_precision)
+
+ if skip_reshape:
+ b, _, _, dim_head = query.shape
+ else:
+ b, _, dim_head = query.shape
+ dim_head //= heads
+
+ scale = dim_head ** -0.5
+
+ if skip_reshape:
+ query = query.reshape(b * heads, -1, dim_head)
+ value = value.reshape(b * heads, -1, dim_head)
+ key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)
+ else:
+ query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+ value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+ key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+
+
+ dtype = query.dtype
+ upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32
+ if upcast_attention:
+ bytes_per_token = torch.finfo(torch.float32).bits//8
+ else:
+ bytes_per_token = torch.finfo(query.dtype).bits//8
+ batch_x_heads, q_tokens, _ = query.shape
+ _, _, k_tokens = key.shape
+ qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
+
+ mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
+
+ kv_chunk_size_min = None
+ kv_chunk_size = None
+ query_chunk_size = None
+
+ for x in [4096, 2048, 1024, 512, 256]:
+ count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0)
+ if count >= k_tokens:
+ kv_chunk_size = k_tokens
+ query_chunk_size = x
+ break
+
+ if query_chunk_size is None:
+ query_chunk_size = 512
+
+ if mask is not None:
+ if len(mask.shape) == 2:
+ bs = 1
+ else:
+ bs = mask.shape[0]
+ mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+
+ hidden_states = efficient_dot_product_attention(
+ query,
+ key,
+ value,
+ query_chunk_size=query_chunk_size,
+ kv_chunk_size=kv_chunk_size,
+ kv_chunk_size_min=kv_chunk_size_min,
+ use_checkpoint=False,
+ upcast_attention=upcast_attention,
+ mask=mask,
+ )
+
+ hidden_states = hidden_states.to(dtype)
+
+ hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
+ return hidden_states
+
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+ attn_precision = get_attn_precision(attn_precision)
+
+ if skip_reshape:
+ b, _, _, dim_head = q.shape
+ else:
+ b, _, dim_head = q.shape
+ dim_head //= heads
+
+ scale = dim_head ** -0.5
+
+ h = heads
+ if skip_reshape:
+ q, k, v = map(
+ lambda t: t.reshape(b * heads, -1, dim_head),
+ (q, k, v),
+ )
+ else:
+ q, k, v = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(b, -1, heads, dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b * heads, -1, dim_head)
+ .contiguous(),
+ (q, k, v),
+ )
+
+ r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+
+ mem_free_total = model_management.get_free_memory(q.device)
+
+ if attn_precision == torch.float32:
+ element_size = 4
+ upcast = True
+ else:
+ element_size = q.element_size()
+ upcast = False
+
+ gb = 1024 ** 3
+ tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
+ modifier = 3
+ mem_required = tensor_size * modifier
+ steps = 1
+
+
+ if mem_required > mem_free_total:
+ steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+ # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+ # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+
+ if steps > 64:
+ max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+ raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+ f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
+
+ if mask is not None:
+ if len(mask.shape) == 2:
+ bs = 1
+ else:
+ bs = mask.shape[0]
+ mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+
+ # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
+ first_op_done = False
+ cleared_cache = False
+ while True:
+ try:
+ slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+ for i in range(0, q.shape[1], slice_size):
+ end = i + slice_size
+ if upcast:
+ with torch.autocast(enabled=False, device_type = 'cuda'):
+ s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
+ else:
+ s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale
+
+ if mask is not None:
+ if len(mask.shape) == 2:
+ s1 += mask[i:end]
+ else:
+ if mask.shape[1] == 1:
+ s1 += mask
+ else:
+ s1 += mask[:, i:end]
+
+ s2 = s1.softmax(dim=-1).to(v.dtype)
+ del s1
+ first_op_done = True
+
+ r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+ del s2
+ break
+ except model_management.OOM_EXCEPTION as e:
+ if first_op_done == False:
+ model_management.soft_empty_cache(True)
+ if cleared_cache == False:
+ cleared_cache = True
+ logging.warning("out of memory error, emptying cache and trying again")
+ continue
+ steps *= 2
+ if steps > 64:
+ raise e
+ logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
+ else:
+ raise e
+
+ del q, k, v
+
+ r1 = (
+ r1.unsqueeze(0)
+ .reshape(b, heads, -1, dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b, -1, heads * dim_head)
+ )
+ return r1
+
+BROKEN_XFORMERS = False
+try:
+ x_vers = xformers.__version__
+ # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error)
+ BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20")
+except:
+ pass
+
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+ if skip_reshape:
+ b, _, _, dim_head = q.shape
+ else:
+ b, _, dim_head = q.shape
+ dim_head //= heads
+
+ disabled_xformers = False
+
+ if BROKEN_XFORMERS:
+ if b * heads > 65535:
+ disabled_xformers = True
+
+ if not disabled_xformers:
+ if torch.jit.is_tracing() or torch.jit.is_scripting():
+ disabled_xformers = True
+
+ if disabled_xformers:
+ return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape)
+
+ if skip_reshape:
+ q, k, v = map(
+ lambda t: t.reshape(b * heads, -1, dim_head),
+ (q, k, v),
+ )
+ else:
+ q, k, v = map(
+ lambda t: t.reshape(b, -1, heads, dim_head),
+ (q, k, v),
+ )
+
+ if mask is not None:
+ pad = 8 - mask.shape[-1] % 8
+ mask_out = torch.empty([q.shape[0], q.shape[2], q.shape[1], mask.shape[-1] + pad], dtype=q.dtype, device=q.device)
+ mask_out[..., :mask.shape[-1]] = mask
+ mask = mask_out[..., :mask.shape[-1]]
+
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
+
+ if skip_reshape:
+ out = (
+ out.unsqueeze(0)
+ .reshape(b, heads, -1, dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b, -1, heads * dim_head)
+ )
+ else:
+ out = (
+ out.reshape(b, -1, heads * dim_head)
+ )
+
+ return out
+
+if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue.
+ SDP_BATCH_LIMIT = 2**15
+else:
+ #TODO: other GPUs ?
+ SDP_BATCH_LIMIT = 2**31
+
+
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+ if skip_reshape:
+ b, _, _, dim_head = q.shape
+ else:
+ b, _, dim_head = q.shape
+ dim_head //= heads
+ q, k, v = map(
+ lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+ (q, k, v),
+ )
+
+ if SDP_BATCH_LIMIT >= q.shape[0]:
+ out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+ out = (
+ out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+ )
+ else:
+ out = torch.empty((q.shape[0], q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device)
+ for i in range(0, q.shape[0], SDP_BATCH_LIMIT):
+ out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention(q[i : i + SDP_BATCH_LIMIT], k[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT], attn_mask=mask, dropout_p=0.0, is_causal=False).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
+ return out
+
+
+optimized_attention = attention_basic
+
+if model_management.xformers_enabled():
+ logging.info("Using xformers cross attention")
+ optimized_attention = attention_xformers
+elif model_management.pytorch_attention_enabled():
+ logging.info("Using pytorch cross attention")
+ optimized_attention = attention_pytorch
+else:
+ if args.use_split_cross_attention:
+ logging.info("Using split optimization for cross attention")
+ optimized_attention = attention_split
+ else:
+ logging.info("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
+ optimized_attention = attention_sub_quad
+
+optimized_attention_masked = optimized_attention
+
+def optimized_attention_for_device(device, mask=False, small_input=False):
+ if small_input:
+ if model_management.pytorch_attention_enabled():
+ return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases
+ else:
+ return attention_basic
+
+ if device == torch.device("cpu"):
+ return attention_sub_quad
+
+ if mask:
+ return optimized_attention_masked
+
+ return optimized_attention
+
+
+class CrossAttention(nn.Module):
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops):
+ super().__init__()
+ inner_dim = dim_head * heads
+ context_dim = default(context_dim, query_dim)
+ self.attn_precision = attn_precision
+
+ self.heads = heads
+ self.dim_head = dim_head
+
+ self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+ self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+ self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+
+ self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+
+ def forward(self, x, context=None, value=None, mask=None):
+ q = self.to_q(x)
+ context = default(context, x)
+ k = self.to_k(context)
+ if value is not None:
+ v = self.to_v(value)
+ del value
+ else:
+ v = self.to_v(context)
+
+ if mask is None:
+ out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+ else:
+ out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+ return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+ def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None,
+ disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops):
+ super().__init__()
+
+ self.ff_in = ff_in or inner_dim is not None
+ if inner_dim is None:
+ inner_dim = dim
+
+ self.is_res = inner_dim == dim
+ self.attn_precision = attn_precision
+
+ if self.ff_in:
+ self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device)
+ self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
+
+ self.disable_self_attn = disable_self_attn
+ self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout,
+ context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn
+ self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
+
+ if disable_temporal_crossattention:
+ if switch_temporal_ca_to_sa:
+ raise ValueError
+ else:
+ self.attn2 = None
+ else:
+ context_dim_attn2 = None
+ if not switch_temporal_ca_to_sa:
+ context_dim_attn2 = context_dim
+
+ self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,
+ heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations) # is self-attn if context is none
+ self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
+
+ self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
+ self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
+ self.n_heads = n_heads
+ self.d_head = d_head
+ self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
+
+ def forward(self, x, context=None, transformer_options={}):
+ extra_options = {}
+ block = transformer_options.get("block", None)
+ block_index = transformer_options.get("block_index", 0)
+ transformer_patches = {}
+ transformer_patches_replace = {}
+
+ for k in transformer_options:
+ if k == "patches":
+ transformer_patches = transformer_options[k]
+ elif k == "patches_replace":
+ transformer_patches_replace = transformer_options[k]
+ else:
+ extra_options[k] = transformer_options[k]
+
+ extra_options["n_heads"] = self.n_heads
+ extra_options["dim_head"] = self.d_head
+ extra_options["attn_precision"] = self.attn_precision
+
+ if self.ff_in:
+ x_skip = x
+ x = self.ff_in(self.norm_in(x))
+ if self.is_res:
+ x += x_skip
+
+ n = self.norm1(x)
+ if self.disable_self_attn:
+ context_attn1 = context
+ else:
+ context_attn1 = None
+ value_attn1 = None
+
+ if "attn1_patch" in transformer_patches:
+ patch = transformer_patches["attn1_patch"]
+ if context_attn1 is None:
+ context_attn1 = n
+ value_attn1 = context_attn1
+ for p in patch:
+ n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options)
+
+ if block is not None:
+ transformer_block = (block[0], block[1], block_index)
+ else:
+ transformer_block = None
+ attn1_replace_patch = transformer_patches_replace.get("attn1", {})
+ block_attn1 = transformer_block
+ if block_attn1 not in attn1_replace_patch:
+ block_attn1 = block
+
+ if block_attn1 in attn1_replace_patch:
+ if context_attn1 is None:
+ context_attn1 = n
+ value_attn1 = n
+ n = self.attn1.to_q(n)
+ context_attn1 = self.attn1.to_k(context_attn1)
+ value_attn1 = self.attn1.to_v(value_attn1)
+ n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
+ n = self.attn1.to_out(n)
+ else:
+ n = self.attn1(n, context=context_attn1, value=value_attn1)
+
+ if "attn1_output_patch" in transformer_patches:
+ patch = transformer_patches["attn1_output_patch"]
+ for p in patch:
+ n = p(n, extra_options)
+
+ x += n
+ if "middle_patch" in transformer_patches:
+ patch = transformer_patches["middle_patch"]
+ for p in patch:
+ x = p(x, extra_options)
+
+ if self.attn2 is not None:
+ n = self.norm2(x)
+ if self.switch_temporal_ca_to_sa:
+ context_attn2 = n
+ else:
+ context_attn2 = context
+ value_attn2 = None
+ if "attn2_patch" in transformer_patches:
+ patch = transformer_patches["attn2_patch"]
+ value_attn2 = context_attn2
+ for p in patch:
+ n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options)
+
+ attn2_replace_patch = transformer_patches_replace.get("attn2", {})
+ block_attn2 = transformer_block
+ if block_attn2 not in attn2_replace_patch:
+ block_attn2 = block
+
+ if block_attn2 in attn2_replace_patch:
+ if value_attn2 is None:
+ value_attn2 = context_attn2
+ n = self.attn2.to_q(n)
+ context_attn2 = self.attn2.to_k(context_attn2)
+ value_attn2 = self.attn2.to_v(value_attn2)
+ n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
+ n = self.attn2.to_out(n)
+ else:
+ n = self.attn2(n, context=context_attn2, value=value_attn2)
+
+ if "attn2_output_patch" in transformer_patches:
+ patch = transformer_patches["attn2_output_patch"]
+ for p in patch:
+ n = p(n, extra_options)
+
+ x += n
+ if self.is_res:
+ x_skip = x
+ x = self.ff(self.norm3(x))
+ if self.is_res:
+ x += x_skip
+
+ return x
+
+
+class SpatialTransformer(nn.Module):
+ """
+ Transformer block for image-like data.
+ First, project the input (aka embedding)
+ and reshape to b, t, d.
+ Then apply standard transformer action.
+ Finally, reshape to image
+ NEW: use_linear for more efficiency instead of the 1x1 convs
+ """
+ def __init__(self, in_channels, n_heads, d_head,
+ depth=1, dropout=0., context_dim=None,
+ disable_self_attn=False, use_linear=False,
+ use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops):
+ super().__init__()
+ if exists(context_dim) and not isinstance(context_dim, list):
+ context_dim = [context_dim] * depth
+ self.in_channels = in_channels
+ inner_dim = n_heads * d_head
+ self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
+ if not use_linear:
+ self.proj_in = operations.Conv2d(in_channels,
+ inner_dim,
+ kernel_size=1,
+ stride=1,
+ padding=0, dtype=dtype, device=device)
+ else:
+ self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
+
+ self.transformer_blocks = nn.ModuleList(
+ [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
+ disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
+ for d in range(depth)]
+ )
+ if not use_linear:
+ self.proj_out = operations.Conv2d(inner_dim,in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0, dtype=dtype, device=device)
+ else:
+ self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
+ self.use_linear = use_linear
+
+ def forward(self, x, context=None, transformer_options={}):
+ # note: if no context is given, cross-attention defaults to self-attention
+ if not isinstance(context, list):
+ context = [context] * len(self.transformer_blocks)
+ b, c, h, w = x.shape
+ x_in = x
+ x = self.norm(x)
+ if not self.use_linear:
+ x = self.proj_in(x)
+ x = x.movedim(1, 3).flatten(1, 2).contiguous()
+ if self.use_linear:
+ x = self.proj_in(x)
+ for i, block in enumerate(self.transformer_blocks):
+ transformer_options["block_index"] = i
+ x = block(x, context=context[i], transformer_options=transformer_options)
+ if self.use_linear:
+ x = self.proj_out(x)
+ x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous()
+ if not self.use_linear:
+ x = self.proj_out(x)
+ return x + x_in
+
+
+class SpatialVideoTransformer(SpatialTransformer):
+ def __init__(
+ self,
+ in_channels,
+ n_heads,
+ d_head,
+ depth=1,
+ dropout=0.0,
+ use_linear=False,
+ context_dim=None,
+ use_spatial_context=False,
+ timesteps=None,
+ merge_strategy: str = "fixed",
+ merge_factor: float = 0.5,
+ time_context_dim=None,
+ ff_in=False,
+ checkpoint=False,
+ time_depth=1,
+ disable_self_attn=False,
+ disable_temporal_crossattention=False,
+ max_time_embed_period: int = 10000,
+ attn_precision=None,
+ dtype=None, device=None, operations=ops
+ ):
+ super().__init__(
+ in_channels,
+ n_heads,
+ d_head,
+ depth=depth,
+ dropout=dropout,
+ use_checkpoint=checkpoint,
+ context_dim=context_dim,
+ use_linear=use_linear,
+ disable_self_attn=disable_self_attn,
+ attn_precision=attn_precision,
+ dtype=dtype, device=device, operations=operations
+ )
+ self.time_depth = time_depth
+ self.depth = depth
+ self.max_time_embed_period = max_time_embed_period
+
+ time_mix_d_head = d_head
+ n_time_mix_heads = n_heads
+
+ time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads)
+
+ inner_dim = n_heads * d_head
+ if use_spatial_context:
+ time_context_dim = context_dim
+
+ self.time_stack = nn.ModuleList(
+ [
+ BasicTransformerBlock(
+ inner_dim,
+ n_time_mix_heads,
+ time_mix_d_head,
+ dropout=dropout,
+ context_dim=time_context_dim,
+ # timesteps=timesteps,
+ checkpoint=checkpoint,
+ ff_in=ff_in,
+ inner_dim=time_mix_inner_dim,
+ disable_self_attn=disable_self_attn,
+ disable_temporal_crossattention=disable_temporal_crossattention,
+ attn_precision=attn_precision,
+ dtype=dtype, device=device, operations=operations
+ )
+ for _ in range(self.depth)
+ ]
+ )
+
+ assert len(self.time_stack) == len(self.transformer_blocks)
+
+ self.use_spatial_context = use_spatial_context
+ self.in_channels = in_channels
+
+ time_embed_dim = self.in_channels * 4
+ self.time_pos_embed = nn.Sequential(
+ operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device),
+ )
+
+ self.time_mixer = AlphaBlender(
+ alpha=merge_factor, merge_strategy=merge_strategy
+ )
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ context: Optional[torch.Tensor] = None,
+ time_context: Optional[torch.Tensor] = None,
+ timesteps: Optional[int] = None,
+ image_only_indicator: Optional[torch.Tensor] = None,
+ transformer_options={}
+ ) -> torch.Tensor:
+ _, _, h, w = x.shape
+ x_in = x
+ spatial_context = None
+ if exists(context):
+ spatial_context = context
+
+ if self.use_spatial_context:
+ assert (
+ context.ndim == 3
+ ), f"n dims of spatial context should be 3 but are {context.ndim}"
+
+ if time_context is None:
+ time_context = context
+ time_context_first_timestep = time_context[::timesteps]
+ time_context = repeat(
+ time_context_first_timestep, "b ... -> (b n) ...", n=h * w
+ )
+ elif time_context is not None and not self.use_spatial_context:
+ time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w)
+ if time_context.ndim == 2:
+ time_context = rearrange(time_context, "b c -> b 1 c")
+
+ x = self.norm(x)
+ if not self.use_linear:
+ x = self.proj_in(x)
+ x = rearrange(x, "b c h w -> b (h w) c")
+ if self.use_linear:
+ x = self.proj_in(x)
+
+ num_frames = torch.arange(timesteps, device=x.device)
+ num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
+ num_frames = rearrange(num_frames, "b t -> (b t)")
+ t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype)
+ emb = self.time_pos_embed(t_emb)
+ emb = emb[:, None, :]
+
+ for it_, (block, mix_block) in enumerate(
+ zip(self.transformer_blocks, self.time_stack)
+ ):
+ transformer_options["block_index"] = it_
+ x = block(
+ x,
+ context=spatial_context,
+ transformer_options=transformer_options,
+ )
+
+ x_mix = x
+ x_mix = x_mix + emb
+
+ B, S, C = x_mix.shape
+ x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps)
+ x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options
+ x_mix = rearrange(
+ x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
+ )
+
+ x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator)
+
+ if self.use_linear:
+ x = self.proj_out(x)
+ x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
+ if not self.use_linear:
+ x = self.proj_out(x)
+ out = x + x_in
+ return out
+
+
diff --git a/comfy/ldm/modules/diffusionmodules/__init__.py b/comfy/ldm/modules/diffusionmodules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f8f506ce02d40cb3372eca166148cc9e9746294
--- /dev/null
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -0,0 +1,1042 @@
+import logging
+import math
+from typing import Dict, Optional, List
+
+import numpy as np
+import torch
+import torch.nn as nn
+from ..attention import optimized_attention
+from einops import rearrange, repeat
+from .util import timestep_embedding
+import comfy.ops
+import comfy.ldm.common_dit
+
+def default(x, y):
+ if x is not None:
+ return x
+ return y
+
+class Mlp(nn.Module):
+ """ MLP as used in Vision Transformer, MLP-Mixer and related networks
+ """
+ def __init__(
+ self,
+ in_features,
+ hidden_features=None,
+ out_features=None,
+ act_layer=nn.GELU,
+ norm_layer=None,
+ bias=True,
+ drop=0.,
+ use_conv=False,
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ drop_probs = drop
+ linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear
+
+ self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
+ self.act = act_layer()
+ self.drop1 = nn.Dropout(drop_probs)
+ self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
+ self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
+ self.drop2 = nn.Dropout(drop_probs)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop1(x)
+ x = self.norm(x)
+ x = self.fc2(x)
+ x = self.drop2(x)
+ return x
+
+class PatchEmbed(nn.Module):
+ """ 2D Image to Patch Embedding
+ """
+ dynamic_img_pad: torch.jit.Final[bool]
+
+ def __init__(
+ self,
+ img_size: Optional[int] = 224,
+ patch_size: int = 16,
+ in_chans: int = 3,
+ embed_dim: int = 768,
+ norm_layer = None,
+ flatten: bool = True,
+ bias: bool = True,
+ strict_img_size: bool = True,
+ dynamic_img_pad: bool = True,
+ padding_mode='circular',
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.patch_size = (patch_size, patch_size)
+ self.padding_mode = padding_mode
+ if img_size is not None:
+ self.img_size = (img_size, img_size)
+ self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
+ self.num_patches = self.grid_size[0] * self.grid_size[1]
+ else:
+ self.img_size = None
+ self.grid_size = None
+ self.num_patches = None
+
+ # flatten spatial dim and transpose to channels last, kept for bwd compat
+ self.flatten = flatten
+ self.strict_img_size = strict_img_size
+ self.dynamic_img_pad = dynamic_img_pad
+
+ self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
+ self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+ def forward(self, x):
+ # B, C, H, W = x.shape
+ # if self.img_size is not None:
+ # if self.strict_img_size:
+ # _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
+ # _assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
+ # elif not self.dynamic_img_pad:
+ # _assert(
+ # H % self.patch_size[0] == 0,
+ # f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
+ # )
+ # _assert(
+ # W % self.patch_size[1] == 0,
+ # f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
+ # )
+ if self.dynamic_img_pad:
+ x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode)
+ x = self.proj(x)
+ if self.flatten:
+ x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
+ x = self.norm(x)
+ return x
+
+def modulate(x, shift, scale):
+ if shift is None:
+ shift = torch.zeros_like(scale)
+ return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+#################################################################################
+# Sine/Cosine Positional Embedding Functions #
+#################################################################################
+
+
+def get_2d_sincos_pos_embed(
+ embed_dim,
+ grid_size,
+ cls_token=False,
+ extra_tokens=0,
+ scaling_factor=None,
+ offset=None,
+):
+ """
+ grid_size: int of the grid height and width
+ return:
+ pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+ """
+ grid_h = np.arange(grid_size, dtype=np.float32)
+ grid_w = np.arange(grid_size, dtype=np.float32)
+ grid = np.meshgrid(grid_w, grid_h) # here w goes first
+ grid = np.stack(grid, axis=0)
+ if scaling_factor is not None:
+ grid = grid / scaling_factor
+ if offset is not None:
+ grid = grid - offset
+
+ grid = grid.reshape([2, 1, grid_size, grid_size])
+ pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+ if cls_token and extra_tokens > 0:
+ pos_embed = np.concatenate(
+ [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
+ )
+ return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+ assert embed_dim % 2 == 0
+
+ # use half of dimensions to encode grid_h
+ emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
+ emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
+
+ emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
+ return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+ """
+ embed_dim: output dimension for each position
+ pos: a list of positions to be encoded: size (M,)
+ out: (M, D)
+ """
+ assert embed_dim % 2 == 0
+ omega = np.arange(embed_dim // 2, dtype=np.float64)
+ omega /= embed_dim / 2.0
+ omega = 1.0 / 10000**omega # (D/2,)
+
+ pos = pos.reshape(-1) # (M,)
+ out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
+
+ emb_sin = np.sin(out) # (M, D/2)
+ emb_cos = np.cos(out) # (M, D/2)
+
+ emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
+ return emb
+
+def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32):
+ omega = torch.arange(embed_dim // 2, device=device, dtype=dtype)
+ omega /= embed_dim / 2.0
+ omega = 1.0 / 10000**omega # (D/2,)
+ pos = pos.reshape(-1) # (M,)
+ out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product
+ emb_sin = torch.sin(out) # (M, D/2)
+ emb_cos = torch.cos(out) # (M, D/2)
+ emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D)
+ return emb
+
+def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32):
+ small = min(h, w)
+ val_h = (h / small) * val_magnitude
+ val_w = (w / small) * val_magnitude
+ grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij')
+ emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
+ emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
+ emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D)
+ return emb
+
+
+#################################################################################
+# Embedding Layers for Timesteps and Class Labels #
+#################################################################################
+
+
+class TimestepEmbedder(nn.Module):
+ """
+ Embeds scalar timesteps into vector representations.
+ """
+
+ def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.mlp = nn.Sequential(
+ operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+ )
+ self.frequency_embedding_size = frequency_embedding_size
+
+ def forward(self, t, dtype, **kwargs):
+ t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+ t_emb = self.mlp(t_freq)
+ return t_emb
+
+
+class VectorEmbedder(nn.Module):
+ """
+ Embeds a flat vector of dimension input_dim
+ """
+
+ def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.mlp = nn.Sequential(
+ operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+ )
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ emb = self.mlp(x)
+ return emb
+
+
+#################################################################################
+# Core DiT Model #
+#################################################################################
+
+
+def split_qkv(qkv, head_dim):
+ qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
+ return qkv[0], qkv[1], qkv[2]
+
+
+class SelfAttention(nn.Module):
+ ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
+
+ def __init__(
+ self,
+ dim: int,
+ num_heads: int = 8,
+ qkv_bias: bool = False,
+ qk_scale: Optional[float] = None,
+ proj_drop: float = 0.0,
+ attn_mode: str = "xformers",
+ pre_only: bool = False,
+ qk_norm: Optional[str] = None,
+ rmsnorm: bool = False,
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.num_heads = num_heads
+ self.head_dim = dim // num_heads
+
+ self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+ if not pre_only:
+ self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+ self.proj_drop = nn.Dropout(proj_drop)
+ assert attn_mode in self.ATTENTION_MODES
+ self.attn_mode = attn_mode
+ self.pre_only = pre_only
+
+ if qk_norm == "rms":
+ self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+ self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+ elif qk_norm == "ln":
+ self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+ self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+ elif qk_norm is None:
+ self.ln_q = nn.Identity()
+ self.ln_k = nn.Identity()
+ else:
+ raise ValueError(qk_norm)
+
+ def pre_attention(self, x: torch.Tensor) -> torch.Tensor:
+ B, L, C = x.shape
+ qkv = self.qkv(x)
+ q, k, v = split_qkv(qkv, self.head_dim)
+ q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
+ k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
+ return (q, k, v)
+
+ def post_attention(self, x: torch.Tensor) -> torch.Tensor:
+ assert not self.pre_only
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ q, k, v = self.pre_attention(x)
+ x = optimized_attention(
+ q, k, v, heads=self.num_heads
+ )
+ x = self.post_attention(x)
+ return x
+
+
+class RMSNorm(torch.nn.Module):
+ def __init__(
+ self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
+ ):
+ """
+ Initialize the RMSNorm normalization layer.
+ Args:
+ dim (int): The dimension of the input tensor.
+ eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+ Attributes:
+ eps (float): A small value added to the denominator for numerical stability.
+ weight (nn.Parameter): Learnable scaling parameter.
+ """
+ super().__init__()
+ self.eps = eps
+ self.learnable_scale = elementwise_affine
+ if self.learnable_scale:
+ self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+ else:
+ self.register_parameter("weight", None)
+
+ def forward(self, x):
+ return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)
+
+
+
+class SwiGLUFeedForward(nn.Module):
+ def __init__(
+ self,
+ dim: int,
+ hidden_dim: int,
+ multiple_of: int,
+ ffn_dim_multiplier: Optional[float] = None,
+ ):
+ """
+ Initialize the FeedForward module.
+
+ Args:
+ dim (int): Input dimension.
+ hidden_dim (int): Hidden dimension of the feedforward layer.
+ multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+ ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
+
+ Attributes:
+ w1 (ColumnParallelLinear): Linear transformation for the first layer.
+ w2 (RowParallelLinear): Linear transformation for the second layer.
+ w3 (ColumnParallelLinear): Linear transformation for the third layer.
+
+ """
+ super().__init__()
+ hidden_dim = int(2 * hidden_dim / 3)
+ # custom dim factor multiplier
+ if ffn_dim_multiplier is not None:
+ hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+ hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+ self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+ self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+ self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+ def forward(self, x):
+ return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))
+
+
+class DismantledBlock(nn.Module):
+ """
+ A DiT block with gated adaptive layer norm (adaLN) conditioning.
+ """
+
+ ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
+
+ def __init__(
+ self,
+ hidden_size: int,
+ num_heads: int,
+ mlp_ratio: float = 4.0,
+ attn_mode: str = "xformers",
+ qkv_bias: bool = False,
+ pre_only: bool = False,
+ rmsnorm: bool = False,
+ scale_mod_only: bool = False,
+ swiglu: bool = False,
+ qk_norm: Optional[str] = None,
+ x_block_self_attn: bool = False,
+ dtype=None,
+ device=None,
+ operations=None,
+ **block_kwargs,
+ ):
+ super().__init__()
+ assert attn_mode in self.ATTENTION_MODES
+ if not rmsnorm:
+ self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ else:
+ self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+ self.attn = SelfAttention(
+ dim=hidden_size,
+ num_heads=num_heads,
+ qkv_bias=qkv_bias,
+ attn_mode=attn_mode,
+ pre_only=pre_only,
+ qk_norm=qk_norm,
+ rmsnorm=rmsnorm,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+ if x_block_self_attn:
+ assert not pre_only
+ assert not scale_mod_only
+ self.x_block_self_attn = True
+ self.attn2 = SelfAttention(
+ dim=hidden_size,
+ num_heads=num_heads,
+ qkv_bias=qkv_bias,
+ attn_mode=attn_mode,
+ pre_only=False,
+ qk_norm=qk_norm,
+ rmsnorm=rmsnorm,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+ else:
+ self.x_block_self_attn = False
+ if not pre_only:
+ if not rmsnorm:
+ self.norm2 = operations.LayerNorm(
+ hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
+ )
+ else:
+ self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+ mlp_hidden_dim = int(hidden_size * mlp_ratio)
+ if not pre_only:
+ if not swiglu:
+ self.mlp = Mlp(
+ in_features=hidden_size,
+ hidden_features=mlp_hidden_dim,
+ act_layer=lambda: nn.GELU(approximate="tanh"),
+ drop=0,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+ else:
+ self.mlp = SwiGLUFeedForward(
+ dim=hidden_size,
+ hidden_dim=mlp_hidden_dim,
+ multiple_of=256,
+ )
+ self.scale_mod_only = scale_mod_only
+ if x_block_self_attn:
+ assert not pre_only
+ assert not scale_mod_only
+ n_mods = 9
+ elif not scale_mod_only:
+ n_mods = 6 if not pre_only else 2
+ else:
+ n_mods = 4 if not pre_only else 1
+ self.adaLN_modulation = nn.Sequential(
+ nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device)
+ )
+ self.pre_only = pre_only
+
+ def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+ if not self.pre_only:
+ if not self.scale_mod_only:
+ (
+ shift_msa,
+ scale_msa,
+ gate_msa,
+ shift_mlp,
+ scale_mlp,
+ gate_mlp,
+ ) = self.adaLN_modulation(c).chunk(6, dim=1)
+ else:
+ shift_msa = None
+ shift_mlp = None
+ (
+ scale_msa,
+ gate_msa,
+ scale_mlp,
+ gate_mlp,
+ ) = self.adaLN_modulation(
+ c
+ ).chunk(4, dim=1)
+ qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
+ return qkv, (
+ x,
+ gate_msa,
+ shift_mlp,
+ scale_mlp,
+ gate_mlp,
+ )
+ else:
+ if not self.scale_mod_only:
+ (
+ shift_msa,
+ scale_msa,
+ ) = self.adaLN_modulation(
+ c
+ ).chunk(2, dim=1)
+ else:
+ shift_msa = None
+ scale_msa = self.adaLN_modulation(c)
+ qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
+ return qkv, None
+
+ def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):
+ assert not self.pre_only
+ x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
+ x = x + gate_mlp.unsqueeze(1) * self.mlp(
+ modulate(self.norm2(x), shift_mlp, scale_mlp)
+ )
+ return x
+
+ def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+ assert self.x_block_self_attn
+ (
+ shift_msa,
+ scale_msa,
+ gate_msa,
+ shift_mlp,
+ scale_mlp,
+ gate_mlp,
+ shift_msa2,
+ scale_msa2,
+ gate_msa2,
+ ) = self.adaLN_modulation(c).chunk(9, dim=1)
+ x_norm = self.norm1(x)
+ qkv = self.attn.pre_attention(modulate(x_norm, shift_msa, scale_msa))
+ qkv2 = self.attn2.pre_attention(modulate(x_norm, shift_msa2, scale_msa2))
+ return qkv, qkv2, (
+ x,
+ gate_msa,
+ shift_mlp,
+ scale_mlp,
+ gate_mlp,
+ gate_msa2,
+ )
+
+ def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2):
+ assert not self.pre_only
+ attn1 = self.attn.post_attention(attn)
+ attn2 = self.attn2.post_attention(attn2)
+ out1 = gate_msa.unsqueeze(1) * attn1
+ out2 = gate_msa2.unsqueeze(1) * attn2
+ x = x + out1
+ x = x + out2
+ x = x + gate_mlp.unsqueeze(1) * self.mlp(
+ modulate(self.norm2(x), shift_mlp, scale_mlp)
+ )
+ return x
+
+ def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+ assert not self.pre_only
+ if self.x_block_self_attn:
+ qkv, qkv2, intermediates = self.pre_attention_x(x, c)
+ attn, _ = optimized_attention(
+ qkv[0], qkv[1], qkv[2],
+ num_heads=self.attn.num_heads,
+ )
+ attn2, _ = optimized_attention(
+ qkv2[0], qkv2[1], qkv2[2],
+ num_heads=self.attn2.num_heads,
+ )
+ return self.post_attention_x(attn, attn2, *intermediates)
+ else:
+ qkv, intermediates = self.pre_attention(x, c)
+ attn = optimized_attention(
+ qkv[0], qkv[1], qkv[2],
+ heads=self.attn.num_heads,
+ )
+ return self.post_attention(attn, *intermediates)
+
+
+def block_mixing(*args, use_checkpoint=True, **kwargs):
+ if use_checkpoint:
+ return torch.utils.checkpoint.checkpoint(
+ _block_mixing, *args, use_reentrant=False, **kwargs
+ )
+ else:
+ return _block_mixing(*args, **kwargs)
+
+
+def _block_mixing(context, x, context_block, x_block, c):
+ context_qkv, context_intermediates = context_block.pre_attention(context, c)
+
+ if x_block.x_block_self_attn:
+ x_qkv, x_qkv2, x_intermediates = x_block.pre_attention_x(x, c)
+ else:
+ x_qkv, x_intermediates = x_block.pre_attention(x, c)
+
+ o = []
+ for t in range(3):
+ o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
+ qkv = tuple(o)
+
+ attn = optimized_attention(
+ qkv[0], qkv[1], qkv[2],
+ heads=x_block.attn.num_heads,
+ )
+ context_attn, x_attn = (
+ attn[:, : context_qkv[0].shape[1]],
+ attn[:, context_qkv[0].shape[1] :],
+ )
+
+ if not context_block.pre_only:
+ context = context_block.post_attention(context_attn, *context_intermediates)
+
+ else:
+ context = None
+ if x_block.x_block_self_attn:
+ attn2 = optimized_attention(
+ x_qkv2[0], x_qkv2[1], x_qkv2[2],
+ heads=x_block.attn2.num_heads,
+ )
+ x = x_block.post_attention_x(x_attn, attn2, *x_intermediates)
+ else:
+ x = x_block.post_attention(x_attn, *x_intermediates)
+ return context, x
+
+
+class JointBlock(nn.Module):
+ """just a small wrapper to serve as a fsdp unit"""
+
+ def __init__(
+ self,
+ *args,
+ **kwargs,
+ ):
+ super().__init__()
+ pre_only = kwargs.pop("pre_only")
+ qk_norm = kwargs.pop("qk_norm", None)
+ x_block_self_attn = kwargs.pop("x_block_self_attn", False)
+ self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
+ self.x_block = DismantledBlock(*args,
+ pre_only=False,
+ qk_norm=qk_norm,
+ x_block_self_attn=x_block_self_attn,
+ **kwargs)
+
+ def forward(self, *args, **kwargs):
+ return block_mixing(
+ *args, context_block=self.context_block, x_block=self.x_block, **kwargs
+ )
+
+
+class FinalLayer(nn.Module):
+ """
+ The final layer of DiT.
+ """
+
+ def __init__(
+ self,
+ hidden_size: int,
+ patch_size: int,
+ out_channels: int,
+ total_out_channels: Optional[int] = None,
+ dtype=None,
+ device=None,
+ operations=None,
+ ):
+ super().__init__()
+ self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.linear = (
+ operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+ if (total_out_channels is None)
+ else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
+ )
+ self.adaLN_modulation = nn.Sequential(
+ nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
+ )
+
+ def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+ shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+ x = modulate(self.norm_final(x), shift, scale)
+ x = self.linear(x)
+ return x
+
+class SelfAttentionContext(nn.Module):
+ def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None):
+ super().__init__()
+ dim_head = dim // heads
+ inner_dim = dim
+
+ self.heads = heads
+ self.dim_head = dim_head
+
+ self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device)
+
+ self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device)
+
+ def forward(self, x):
+ qkv = self.qkv(x)
+ q, k, v = split_qkv(qkv, self.dim_head)
+ x = optimized_attention(q.reshape(q.shape[0], q.shape[1], -1), k, v, heads=self.heads)
+ return self.proj(x)
+
+class ContextProcessorBlock(nn.Module):
+ def __init__(self, context_size, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations)
+ self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+ self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations)
+
+ def forward(self, x):
+ x += self.attn(self.norm1(x))
+ x += self.mlp(self.norm2(x))
+ return x
+
+class ContextProcessor(nn.Module):
+ def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)])
+ self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+ def forward(self, x):
+ for i, l in enumerate(self.layers):
+ x = l(x)
+ return self.norm(x)
+
+class MMDiT(nn.Module):
+ """
+ Diffusion model with a Transformer backbone.
+ """
+
+ def __init__(
+ self,
+ input_size: int = 32,
+ patch_size: int = 2,
+ in_channels: int = 4,
+ depth: int = 28,
+ # hidden_size: Optional[int] = None,
+ # num_heads: Optional[int] = None,
+ mlp_ratio: float = 4.0,
+ learn_sigma: bool = False,
+ adm_in_channels: Optional[int] = None,
+ context_embedder_config: Optional[Dict] = None,
+ compile_core: bool = False,
+ use_checkpoint: bool = False,
+ register_length: int = 0,
+ attn_mode: str = "torch",
+ rmsnorm: bool = False,
+ scale_mod_only: bool = False,
+ swiglu: bool = False,
+ out_channels: Optional[int] = None,
+ pos_embed_scaling_factor: Optional[float] = None,
+ pos_embed_offset: Optional[float] = None,
+ pos_embed_max_size: Optional[int] = None,
+ num_patches = None,
+ qk_norm: Optional[str] = None,
+ qkv_bias: bool = True,
+ context_processor_layers = None,
+ x_block_self_attn: bool = False,
+ x_block_self_attn_layers: Optional[List[int]] = [],
+ context_size = 4096,
+ num_blocks = None,
+ final_layer = True,
+ skip_blocks = False,
+ dtype = None, #TODO
+ device = None,
+ operations = None,
+ ):
+ super().__init__()
+ self.dtype = dtype
+ self.learn_sigma = learn_sigma
+ self.in_channels = in_channels
+ default_out_channels = in_channels * 2 if learn_sigma else in_channels
+ self.out_channels = default(out_channels, default_out_channels)
+ self.patch_size = patch_size
+ self.pos_embed_scaling_factor = pos_embed_scaling_factor
+ self.pos_embed_offset = pos_embed_offset
+ self.pos_embed_max_size = pos_embed_max_size
+ self.x_block_self_attn_layers = x_block_self_attn_layers
+
+ # hidden_size = default(hidden_size, 64 * depth)
+ # num_heads = default(num_heads, hidden_size // 64)
+
+ # apply magic --> this defines a head_size of 64
+ self.hidden_size = 64 * depth
+ num_heads = depth
+ if num_blocks is None:
+ num_blocks = depth
+
+ self.depth = depth
+ self.num_heads = num_heads
+
+ self.x_embedder = PatchEmbed(
+ input_size,
+ patch_size,
+ in_channels,
+ self.hidden_size,
+ bias=True,
+ strict_img_size=self.pos_embed_max_size is None,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+ self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+ self.y_embedder = None
+ if adm_in_channels is not None:
+ assert isinstance(adm_in_channels, int)
+ self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+ if context_processor_layers is not None:
+ self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations)
+ else:
+ self.context_processor = None
+
+ self.context_embedder = nn.Identity()
+ if context_embedder_config is not None:
+ if context_embedder_config["target"] == "torch.nn.Linear":
+ self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device)
+
+ self.register_length = register_length
+ if self.register_length > 0:
+ self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device))
+
+ # num_patches = self.x_embedder.num_patches
+ # Will use fixed sin-cos embedding:
+ # just use a buffer already
+ if num_patches is not None:
+ self.register_buffer(
+ "pos_embed",
+ torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device),
+ )
+ else:
+ self.pos_embed = None
+
+ self.use_checkpoint = use_checkpoint
+ if not skip_blocks:
+ self.joint_blocks = nn.ModuleList(
+ [
+ JointBlock(
+ self.hidden_size,
+ num_heads,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ attn_mode=attn_mode,
+ pre_only=(i == num_blocks - 1) and final_layer,
+ rmsnorm=rmsnorm,
+ scale_mod_only=scale_mod_only,
+ swiglu=swiglu,
+ qk_norm=qk_norm,
+ x_block_self_attn=(i in self.x_block_self_attn_layers) or x_block_self_attn,
+ dtype=dtype,
+ device=device,
+ operations=operations,
+ )
+ for i in range(num_blocks)
+ ]
+ )
+
+ if final_layer:
+ self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+
+ if compile_core:
+ assert False
+ self.forward_core_with_concat = torch.compile(self.forward_core_with_concat)
+
+ def cropped_pos_embed(self, hw, device=None):
+ p = self.x_embedder.patch_size[0]
+ h, w = hw
+ # patched size
+ h = (h + 1) // p
+ w = (w + 1) // p
+ if self.pos_embed is None:
+ return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device)
+ assert self.pos_embed_max_size is not None
+ assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
+ assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
+ top = (self.pos_embed_max_size - h) // 2
+ left = (self.pos_embed_max_size - w) // 2
+ spatial_pos_embed = rearrange(
+ self.pos_embed,
+ "1 (h w) c -> 1 h w c",
+ h=self.pos_embed_max_size,
+ w=self.pos_embed_max_size,
+ )
+ spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
+ spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
+ # print(spatial_pos_embed, top, left, h, w)
+ # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res
+ # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better
+ # # print(t)
+ # return t
+ return spatial_pos_embed
+
+ def unpatchify(self, x, hw=None):
+ """
+ x: (N, T, patch_size**2 * C)
+ imgs: (N, H, W, C)
+ """
+ c = self.out_channels
+ p = self.x_embedder.patch_size[0]
+ if hw is None:
+ h = w = int(x.shape[1] ** 0.5)
+ else:
+ h, w = hw
+ h = (h + 1) // p
+ w = (w + 1) // p
+ assert h * w == x.shape[1]
+
+ x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+ x = torch.einsum("nhwpqc->nchpwq", x)
+ imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+ return imgs
+
+ def forward_core_with_concat(
+ self,
+ x: torch.Tensor,
+ c_mod: torch.Tensor,
+ context: Optional[torch.Tensor] = None,
+ control = None,
+ transformer_options = {},
+ ) -> torch.Tensor:
+ patches_replace = transformer_options.get("patches_replace", {})
+ if self.register_length > 0:
+ context = torch.cat(
+ (
+ repeat(self.register, "1 ... -> b ...", b=x.shape[0]),
+ default(context, torch.Tensor([]).type_as(x)),
+ ),
+ 1,
+ )
+
+ # context is B, L', D
+ # x is B, L, D
+ blocks_replace = patches_replace.get("dit", {})
+ blocks = len(self.joint_blocks)
+ for i in range(blocks):
+ if ("double_block", i) in blocks_replace:
+ def block_wrap(args):
+ out = {}
+ out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"])
+ return out
+
+ out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap})
+ context = out["txt"]
+ x = out["img"]
+ else:
+ context, x = self.joint_blocks[i](
+ context,
+ x,
+ c=c_mod,
+ use_checkpoint=self.use_checkpoint,
+ )
+ if control is not None:
+ control_o = control.get("output")
+ if i < len(control_o):
+ add = control_o[i]
+ if add is not None:
+ x += add
+
+ x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels)
+ return x
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ t: torch.Tensor,
+ y: Optional[torch.Tensor] = None,
+ context: Optional[torch.Tensor] = None,
+ control = None,
+ transformer_options = {},
+ ) -> torch.Tensor:
+ """
+ Forward pass of DiT.
+ x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+ t: (N,) tensor of diffusion timesteps
+ y: (N,) tensor of class labels
+ """
+
+ if self.context_processor is not None:
+ context = self.context_processor(context)
+
+ hw = x.shape[-2:]
+ x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x)
+ c = self.t_embedder(t, dtype=x.dtype) # (N, D)
+ if y is not None and self.y_embedder is not None:
+ y = self.y_embedder(y) # (N, D)
+ c = c + y # (N, D)
+
+ if context is not None:
+ context = self.context_embedder(context)
+
+ x = self.forward_core_with_concat(x, c, context, control, transformer_options)
+
+ x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W)
+ return x[:,:,:hw[-2],:hw[-1]]
+
+
+class OpenAISignatureMMDITWrapper(MMDiT):
+ def forward(
+ self,
+ x: torch.Tensor,
+ timesteps: torch.Tensor,
+ context: Optional[torch.Tensor] = None,
+ y: Optional[torch.Tensor] = None,
+ control = None,
+ transformer_options = {},
+ **kwargs,
+ ) -> torch.Tensor:
+ return super().forward(x, timesteps, context=context, y=y, control=control, transformer_options=transformer_options)
+
diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..04eb83b2181253e3a88f7945f75e017060e02ebf
--- /dev/null
+++ b/comfy/ldm/modules/diffusionmodules/model.py
@@ -0,0 +1,650 @@
+# pytorch_diffusion + derived encoder decoder
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from typing import Optional, Any
+import logging
+
+from comfy import model_management
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+if model_management.xformers_enabled_vae():
+ import xformers
+ import xformers.ops
+
+def get_timestep_embedding(timesteps, embedding_dim):
+ """
+ This matches the implementation in Denoising Diffusion Probabilistic Models:
+ From Fairseq.
+ Build sinusoidal embeddings.
+ This matches the implementation in tensor2tensor, but differs slightly
+ from the description in Section 3.5 of "Attention Is All You Need".
+ """
+ assert len(timesteps.shape) == 1
+
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+ emb = emb.to(device=timesteps.device)
+ emb = timesteps.float()[:, None] * emb[None, :]
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+ if embedding_dim % 2 == 1: # zero pad
+ emb = torch.nn.functional.pad(emb, (0,1,0,0))
+ return emb
+
+
+def nonlinearity(x):
+ # swish
+ return x*torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+ return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class Upsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv:
+ self.conv = ops.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ try:
+ x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+ except: #operation not implemented for bf16
+ b, c, h, w = x.shape
+ out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device)
+ split = 8
+ l = out.shape[1] // split
+ for i in range(0, out.shape[1], l):
+ out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype)
+ del x
+ x = out
+
+ if self.with_conv:
+ x = self.conv(x)
+ return x
+
+
+class Downsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv:
+ # no asymmetric padding in torch conv, must do it ourselves
+ self.conv = ops.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=2,
+ padding=0)
+
+ def forward(self, x):
+ if self.with_conv:
+ pad = (0,1,0,1)
+ x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+ x = self.conv(x)
+ else:
+ x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+ return x
+
+
+class ResnetBlock(nn.Module):
+ def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+ dropout, temb_channels=512):
+ super().__init__()
+ self.in_channels = in_channels
+ out_channels = in_channels if out_channels is None else out_channels
+ self.out_channels = out_channels
+ self.use_conv_shortcut = conv_shortcut
+
+ self.swish = torch.nn.SiLU(inplace=True)
+ self.norm1 = Normalize(in_channels)
+ self.conv1 = ops.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if temb_channels > 0:
+ self.temb_proj = ops.Linear(temb_channels,
+ out_channels)
+ self.norm2 = Normalize(out_channels)
+ self.dropout = torch.nn.Dropout(dropout, inplace=True)
+ self.conv2 = ops.Conv2d(out_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ self.conv_shortcut = ops.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ else:
+ self.nin_shortcut = ops.Conv2d(in_channels,
+ out_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x, temb):
+ h = x
+ h = self.norm1(h)
+ h = self.swish(h)
+ h = self.conv1(h)
+
+ if temb is not None:
+ h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
+
+ h = self.norm2(h)
+ h = self.swish(h)
+ h = self.dropout(h)
+ h = self.conv2(h)
+
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ x = self.conv_shortcut(x)
+ else:
+ x = self.nin_shortcut(x)
+
+ return x+h
+
+def slice_attention(q, k, v):
+ r1 = torch.zeros_like(k, device=q.device)
+ scale = (int(q.shape[-1])**(-0.5))
+
+ mem_free_total = model_management.get_free_memory(q.device)
+
+ gb = 1024 ** 3
+ tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+ modifier = 3 if q.element_size() == 2 else 2.5
+ mem_required = tensor_size * modifier
+ steps = 1
+
+ if mem_required > mem_free_total:
+ steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+ while True:
+ try:
+ slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+ for i in range(0, q.shape[1], slice_size):
+ end = i + slice_size
+ s1 = torch.bmm(q[:, i:end], k) * scale
+
+ s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1)
+ del s1
+
+ r1[:, :, i:end] = torch.bmm(v, s2)
+ del s2
+ break
+ except model_management.OOM_EXCEPTION as e:
+ model_management.soft_empty_cache(True)
+ steps *= 2
+ if steps > 128:
+ raise e
+ logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
+
+ return r1
+
+def normal_attention(q, k, v):
+ # compute attention
+ b,c,h,w = q.shape
+
+ q = q.reshape(b,c,h*w)
+ q = q.permute(0,2,1) # b,hw,c
+ k = k.reshape(b,c,h*w) # b,c,hw
+ v = v.reshape(b,c,h*w)
+
+ r1 = slice_attention(q, k, v)
+ h_ = r1.reshape(b,c,h,w)
+ del r1
+ return h_
+
+def xformers_attention(q, k, v):
+ # compute attention
+ B, C, H, W = q.shape
+ q, k, v = map(
+ lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
+ (q, k, v),
+ )
+
+ try:
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
+ out = out.transpose(1, 2).reshape(B, C, H, W)
+ except NotImplementedError as e:
+ out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
+ return out
+
+def pytorch_attention(q, k, v):
+ # compute attention
+ B, C, H, W = q.shape
+ q, k, v = map(
+ lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
+ (q, k, v),
+ )
+
+ try:
+ out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
+ out = out.transpose(2, 3).reshape(B, C, H, W)
+ except model_management.OOM_EXCEPTION as e:
+ logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
+ out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
+ return out
+
+
+class AttnBlock(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = ops.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = ops.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = ops.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = ops.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ if model_management.xformers_enabled_vae():
+ logging.info("Using xformers attention in VAE")
+ self.optimized_attention = xformers_attention
+ elif model_management.pytorch_attention_enabled():
+ logging.info("Using pytorch attention in VAE")
+ self.optimized_attention = pytorch_attention
+ else:
+ logging.info("Using split attention in VAE")
+ self.optimized_attention = normal_attention
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ h_ = self.optimized_attention(q, k, v)
+
+ h_ = self.proj_out(h_)
+
+ return x+h_
+
+
+def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
+ return AttnBlock(in_channels)
+
+
+class Model(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = self.ch*4
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ self.use_timestep = use_timestep
+ if self.use_timestep:
+ # timestep embedding
+ self.temb = nn.Module()
+ self.temb.dense = nn.ModuleList([
+ ops.Linear(self.ch,
+ self.temb_ch),
+ ops.Linear(self.temb_ch,
+ self.temb_ch),
+ ])
+
+ # downsampling
+ self.conv_in = ops.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ skip_in = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ if i_block == self.num_res_blocks:
+ skip_in = ch*in_ch_mult[i_level]
+ block.append(ResnetBlock(in_channels=block_in+skip_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = ops.Conv2d(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x, t=None, context=None):
+ #assert x.shape[2] == x.shape[3] == self.resolution
+ if context is not None:
+ # assume aligned context, cat along channel axis
+ x = torch.cat((x, context), dim=1)
+ if self.use_timestep:
+ # timestep embedding
+ assert t is not None
+ temb = get_timestep_embedding(t, self.ch)
+ temb = self.temb.dense[0](temb)
+ temb = nonlinearity(temb)
+ temb = self.temb.dense[1](temb)
+ else:
+ temb = None
+
+ # downsampling
+ hs = [self.conv_in(x)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != self.num_resolutions-1:
+ hs.append(self.down[i_level].downsample(hs[-1]))
+
+ # middle
+ h = hs[-1]
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](
+ torch.cat([h, hs.pop()], dim=1), temb)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h)
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+ def get_last_layer(self):
+ return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
+ **ignore_kwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ # downsampling
+ self.conv_in = ops.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.in_ch_mult = in_ch_mult
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = ops.Conv2d(block_in,
+ 2*z_channels if double_z else z_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ # timestep embedding
+ temb = None
+ # downsampling
+ h = self.conv_in(x)
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](h, temb)
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ if i_level != self.num_resolutions-1:
+ h = self.down[i_level].downsample(h)
+
+ # middle
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+
+class Decoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+ conv_out_op=ops.Conv2d,
+ resnet_op=ResnetBlock,
+ attn_op=AttnBlock,
+ **ignorekwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+ self.give_pre_end = give_pre_end
+ self.tanh_out = tanh_out
+
+ # compute in_ch_mult, block_in and curr_res at lowest res
+ in_ch_mult = (1,)+tuple(ch_mult)
+ block_in = ch*ch_mult[self.num_resolutions-1]
+ curr_res = resolution // 2**(self.num_resolutions-1)
+ self.z_shape = (1,z_channels,curr_res,curr_res)
+ logging.debug("Working with z of shape {} = {} dimensions.".format(
+ self.z_shape, np.prod(self.z_shape)))
+
+ # z to block_in
+ self.conv_in = ops.Conv2d(z_channels,
+ block_in,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = resnet_op(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = attn_op(block_in)
+ self.mid.block_2 = resnet_op(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ block.append(resnet_op(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(attn_op(block_in))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = conv_out_op(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, z, **kwargs):
+ #assert z.shape[1:] == self.z_shape[1:]
+ self.last_z_shape = z.shape
+
+ # timestep embedding
+ temb = None
+
+ # z to block_in
+ h = self.conv_in(z)
+
+ # middle
+ h = self.mid.block_1(h, temb, **kwargs)
+ h = self.mid.attn_1(h, **kwargs)
+ h = self.mid.block_2(h, temb, **kwargs)
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](h, temb, **kwargs)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h, **kwargs)
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+
+ # end
+ if self.give_pre_end:
+ return h
+
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h, **kwargs)
+ if self.tanh_out:
+ h = torch.tanh(h)
+ return h
diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py
new file mode 100644
index 0000000000000000000000000000000000000000..2902073d5ea777aabdd93dc95dc823949ca2f08f
--- /dev/null
+++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py
@@ -0,0 +1,897 @@
+from abc import abstractmethod
+
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+import logging
+
+from .util import (
+ checkpoint,
+ avg_pool_nd,
+ zero_module,
+ timestep_embedding,
+ AlphaBlender,
+)
+from ..attention import SpatialTransformer, SpatialVideoTransformer, default
+from comfy.ldm.util import exists
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+class TimestepBlock(nn.Module):
+ """
+ Any module where forward() takes timestep embeddings as a second argument.
+ """
+
+ @abstractmethod
+ def forward(self, x, emb):
+ """
+ Apply the module to `x` given `emb` timestep embeddings.
+ """
+
+#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index"
+def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None):
+ for layer in ts:
+ if isinstance(layer, VideoResBlock):
+ x = layer(x, emb, num_video_frames, image_only_indicator)
+ elif isinstance(layer, TimestepBlock):
+ x = layer(x, emb)
+ elif isinstance(layer, SpatialVideoTransformer):
+ x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options)
+ if "transformer_index" in transformer_options:
+ transformer_options["transformer_index"] += 1
+ elif isinstance(layer, SpatialTransformer):
+ x = layer(x, context, transformer_options)
+ if "transformer_index" in transformer_options:
+ transformer_options["transformer_index"] += 1
+ elif isinstance(layer, Upsample):
+ x = layer(x, output_shape=output_shape)
+ else:
+ x = layer(x)
+ return x
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+ """
+ A sequential module that passes timestep embeddings to the children that
+ support it as an extra input.
+ """
+
+ def forward(self, *args, **kwargs):
+ return forward_timestep_embed(self, *args, **kwargs)
+
+class Upsample(nn.Module):
+ """
+ An upsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ upsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ if use_conv:
+ self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
+
+ def forward(self, x, output_shape=None):
+ assert x.shape[1] == self.channels
+ if self.dims == 3:
+ shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2]
+ if output_shape is not None:
+ shape[1] = output_shape[3]
+ shape[2] = output_shape[4]
+ else:
+ shape = [x.shape[2] * 2, x.shape[3] * 2]
+ if output_shape is not None:
+ shape[0] = output_shape[2]
+ shape[1] = output_shape[3]
+
+ x = F.interpolate(x, size=shape, mode="nearest")
+ if self.use_conv:
+ x = self.conv(x)
+ return x
+
+class Downsample(nn.Module):
+ """
+ A downsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ downsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ stride = 2 if dims != 3 else (1, 2, 2)
+ if use_conv:
+ self.op = operations.conv_nd(
+ dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
+ )
+ else:
+ assert self.channels == self.out_channels
+ self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+ """
+ A residual block that can optionally change the number of channels.
+ :param channels: the number of input channels.
+ :param emb_channels: the number of timestep embedding channels.
+ :param dropout: the rate of dropout.
+ :param out_channels: if specified, the number of out channels.
+ :param use_conv: if True and out_channels is specified, use a spatial
+ convolution instead of a smaller 1x1 convolution to change the
+ channels in the skip connection.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param use_checkpoint: if True, use gradient checkpointing on this module.
+ :param up: if True, use this block for upsampling.
+ :param down: if True, use this block for downsampling.
+ """
+
+ def __init__(
+ self,
+ channels,
+ emb_channels,
+ dropout,
+ out_channels=None,
+ use_conv=False,
+ use_scale_shift_norm=False,
+ dims=2,
+ use_checkpoint=False,
+ up=False,
+ down=False,
+ kernel_size=3,
+ exchange_temb_dims=False,
+ skip_t_emb=False,
+ dtype=None,
+ device=None,
+ operations=ops
+ ):
+ super().__init__()
+ self.channels = channels
+ self.emb_channels = emb_channels
+ self.dropout = dropout
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.use_checkpoint = use_checkpoint
+ self.use_scale_shift_norm = use_scale_shift_norm
+ self.exchange_temb_dims = exchange_temb_dims
+
+ if isinstance(kernel_size, list):
+ padding = [k // 2 for k in kernel_size]
+ else:
+ padding = kernel_size // 2
+
+ self.in_layers = nn.Sequential(
+ operations.GroupNorm(32, channels, dtype=dtype, device=device),
+ nn.SiLU(),
+ operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device),
+ )
+
+ self.updown = up or down
+
+ if up:
+ self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
+ self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
+ elif down:
+ self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
+ self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
+ else:
+ self.h_upd = self.x_upd = nn.Identity()
+
+ self.skip_t_emb = skip_t_emb
+ if self.skip_t_emb:
+ self.emb_layers = None
+ self.exchange_temb_dims = False
+ else:
+ self.emb_layers = nn.Sequential(
+ nn.SiLU(),
+ operations.Linear(
+ emb_channels,
+ 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
+ ),
+ )
+ self.out_layers = nn.Sequential(
+ operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
+ nn.SiLU(),
+ nn.Dropout(p=dropout),
+ operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device)
+ ,
+ )
+
+ if self.out_channels == channels:
+ self.skip_connection = nn.Identity()
+ elif use_conv:
+ self.skip_connection = operations.conv_nd(
+ dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device
+ )
+ else:
+ self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
+
+ def forward(self, x, emb):
+ """
+ Apply the block to a Tensor, conditioned on a timestep embedding.
+ :param x: an [N x C x ...] Tensor of features.
+ :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ return checkpoint(
+ self._forward, (x, emb), self.parameters(), self.use_checkpoint
+ )
+
+
+ def _forward(self, x, emb):
+ if self.updown:
+ in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+ h = in_rest(x)
+ h = self.h_upd(h)
+ x = self.x_upd(x)
+ h = in_conv(h)
+ else:
+ h = self.in_layers(x)
+
+ emb_out = None
+ if not self.skip_t_emb:
+ emb_out = self.emb_layers(emb).type(h.dtype)
+ while len(emb_out.shape) < len(h.shape):
+ emb_out = emb_out[..., None]
+ if self.use_scale_shift_norm:
+ out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+ h = out_norm(h)
+ if emb_out is not None:
+ scale, shift = th.chunk(emb_out, 2, dim=1)
+ h *= (1 + scale)
+ h += shift
+ h = out_rest(h)
+ else:
+ if emb_out is not None:
+ if self.exchange_temb_dims:
+ emb_out = emb_out.movedim(1, 2)
+ h = h + emb_out
+ h = self.out_layers(h)
+ return self.skip_connection(x) + h
+
+
+class VideoResBlock(ResBlock):
+ def __init__(
+ self,
+ channels: int,
+ emb_channels: int,
+ dropout: float,
+ video_kernel_size=3,
+ merge_strategy: str = "fixed",
+ merge_factor: float = 0.5,
+ out_channels=None,
+ use_conv: bool = False,
+ use_scale_shift_norm: bool = False,
+ dims: int = 2,
+ use_checkpoint: bool = False,
+ up: bool = False,
+ down: bool = False,
+ dtype=None,
+ device=None,
+ operations=ops
+ ):
+ super().__init__(
+ channels,
+ emb_channels,
+ dropout,
+ out_channels=out_channels,
+ use_conv=use_conv,
+ use_scale_shift_norm=use_scale_shift_norm,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ up=up,
+ down=down,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+
+ self.time_stack = ResBlock(
+ default(out_channels, channels),
+ emb_channels,
+ dropout=dropout,
+ dims=3,
+ out_channels=default(out_channels, channels),
+ use_scale_shift_norm=False,
+ use_conv=False,
+ up=False,
+ down=False,
+ kernel_size=video_kernel_size,
+ use_checkpoint=use_checkpoint,
+ exchange_temb_dims=True,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+ self.time_mixer = AlphaBlender(
+ alpha=merge_factor,
+ merge_strategy=merge_strategy,
+ rearrange_pattern="b t -> b 1 t 1 1",
+ )
+
+ def forward(
+ self,
+ x: th.Tensor,
+ emb: th.Tensor,
+ num_video_frames: int,
+ image_only_indicator = None,
+ ) -> th.Tensor:
+ x = super().forward(x, emb)
+
+ x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
+ x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
+
+ x = self.time_stack(
+ x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames)
+ )
+ x = self.time_mixer(
+ x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator
+ )
+ x = rearrange(x, "b c t h w -> (b t) c h w")
+ return x
+
+
+class Timestep(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.dim = dim
+
+ def forward(self, t):
+ return timestep_embedding(t, self.dim)
+
+def apply_control(h, control, name):
+ if control is not None and name in control and len(control[name]) > 0:
+ ctrl = control[name].pop()
+ if ctrl is not None:
+ try:
+ h += ctrl
+ except:
+ logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape))
+ return h
+
+class UNetModel(nn.Module):
+ """
+ The full UNet model with attention and timestep embedding.
+ :param in_channels: channels in the input Tensor.
+ :param model_channels: base channel count for the model.
+ :param out_channels: channels in the output Tensor.
+ :param num_res_blocks: number of residual blocks per downsample.
+ :param dropout: the dropout probability.
+ :param channel_mult: channel multiplier for each level of the UNet.
+ :param conv_resample: if True, use learned convolutions for upsampling and
+ downsampling.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param num_classes: if specified (as an int), then this model will be
+ class-conditional with `num_classes` classes.
+ :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+ :param num_heads: the number of attention heads in each attention layer.
+ :param num_heads_channels: if specified, ignore num_heads and instead use
+ a fixed channel width per attention head.
+ :param num_heads_upsample: works with num_heads to set a different number
+ of heads for upsampling. Deprecated.
+ :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+ :param resblock_updown: use residual blocks for up/downsampling.
+ :param use_new_attention_order: use a different attention pattern for potentially
+ increased efficiency.
+ """
+
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ out_channels,
+ num_res_blocks,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ num_classes=None,
+ use_checkpoint=False,
+ dtype=th.float32,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ disable_self_attentions=None,
+ num_attention_blocks=None,
+ disable_middle_self_attn=False,
+ use_linear_in_transformer=False,
+ adm_in_channels=None,
+ transformer_depth_middle=None,
+ transformer_depth_output=None,
+ use_temporal_resblock=False,
+ use_temporal_attention=False,
+ time_context_dim=None,
+ extra_ff_mix_layer=False,
+ use_spatial_context=False,
+ merge_strategy=None,
+ merge_factor=0.0,
+ video_kernel_size=None,
+ disable_temporal_crossattention=False,
+ max_ddpm_temb_period=10000,
+ attn_precision=None,
+ device=None,
+ operations=ops,
+ ):
+ super().__init__()
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ # from omegaconf.listconfig import ListConfig
+ # if type(context_dim) == ListConfig:
+ # context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ self.out_channels = out_channels
+
+ if isinstance(num_res_blocks, int):
+ self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+ else:
+ if len(num_res_blocks) != len(channel_mult):
+ raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+ "as a list/tuple (per-level) with the same length as channel_mult")
+ self.num_res_blocks = num_res_blocks
+
+ if disable_self_attentions is not None:
+ # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+ assert len(disable_self_attentions) == len(channel_mult)
+ if num_attention_blocks is not None:
+ assert len(num_attention_blocks) == len(self.num_res_blocks)
+
+ transformer_depth = transformer_depth[:]
+ transformer_depth_output = transformer_depth_output[:]
+
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.num_classes = num_classes
+ self.use_checkpoint = use_checkpoint
+ self.dtype = dtype
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.use_temporal_resblocks = use_temporal_resblock
+ self.predict_codebook_ids = n_embed is not None
+
+ self.default_num_video_frames = None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
+ )
+
+ if self.num_classes is not None:
+ if isinstance(self.num_classes, int):
+ self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device)
+ elif self.num_classes == "continuous":
+ logging.debug("setting up linear c_adm embedding layer")
+ self.label_emb = nn.Linear(1, time_embed_dim)
+ elif self.num_classes == "sequential":
+ assert adm_in_channels is not None
+ self.label_emb = nn.Sequential(
+ nn.Sequential(
+ operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
+ )
+ )
+ else:
+ raise ValueError()
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
+ )
+ ]
+ )
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+
+ def get_attention_layer(
+ ch,
+ num_heads,
+ dim_head,
+ depth=1,
+ context_dim=None,
+ use_checkpoint=False,
+ disable_self_attn=False,
+ ):
+ if use_temporal_attention:
+ return SpatialVideoTransformer(
+ ch,
+ num_heads,
+ dim_head,
+ depth=depth,
+ context_dim=context_dim,
+ time_context_dim=time_context_dim,
+ dropout=dropout,
+ ff_in=extra_ff_mix_layer,
+ use_spatial_context=use_spatial_context,
+ merge_strategy=merge_strategy,
+ merge_factor=merge_factor,
+ checkpoint=use_checkpoint,
+ use_linear=use_linear_in_transformer,
+ disable_self_attn=disable_self_attn,
+ disable_temporal_crossattention=disable_temporal_crossattention,
+ max_time_embed_period=max_ddpm_temb_period,
+ attn_precision=attn_precision,
+ dtype=self.dtype, device=device, operations=operations
+ )
+ else:
+ return SpatialTransformer(
+ ch, num_heads, dim_head, depth=depth, context_dim=context_dim,
+ disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
+ )
+
+ def get_resblock(
+ merge_factor,
+ merge_strategy,
+ video_kernel_size,
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels,
+ dims,
+ use_checkpoint,
+ use_scale_shift_norm,
+ down=False,
+ up=False,
+ dtype=None,
+ device=None,
+ operations=ops
+ ):
+ if self.use_temporal_resblocks:
+ return VideoResBlock(
+ merge_factor=merge_factor,
+ merge_strategy=merge_strategy,
+ video_kernel_size=video_kernel_size,
+ channels=ch,
+ emb_channels=time_embed_dim,
+ dropout=dropout,
+ out_channels=out_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=down,
+ up=up,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+ else:
+ return ResBlock(
+ channels=ch,
+ emb_channels=time_embed_dim,
+ dropout=dropout,
+ out_channels=out_channels,
+ use_checkpoint=use_checkpoint,
+ dims=dims,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=down,
+ up=up,
+ dtype=dtype,
+ device=device,
+ operations=operations
+ )
+
+ for level, mult in enumerate(channel_mult):
+ for nr in range(self.num_res_blocks[level]):
+ layers = [
+ get_resblock(
+ merge_factor=merge_factor,
+ merge_strategy=merge_strategy,
+ video_kernel_size=video_kernel_size,
+ ch=ch,
+ time_embed_dim=time_embed_dim,
+ dropout=dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ dtype=self.dtype,
+ device=device,
+ operations=operations,
+ )
+ ]
+ ch = mult * model_channels
+ num_transformers = transformer_depth.pop(0)
+ if num_transformers > 0:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+ layers.append(get_attention_layer(
+ ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint)
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ get_resblock(
+ merge_factor=merge_factor,
+ merge_strategy=merge_strategy,
+ video_kernel_size=video_kernel_size,
+ ch=ch,
+ time_embed_dim=time_embed_dim,
+ dropout=dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ dtype=self.dtype,
+ device=device,
+ operations=operations
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ mid_block = [
+ get_resblock(
+ merge_factor=merge_factor,
+ merge_strategy=merge_strategy,
+ video_kernel_size=video_kernel_size,
+ ch=ch,
+ time_embed_dim=time_embed_dim,
+ dropout=dropout,
+ out_channels=None,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ dtype=self.dtype,
+ device=device,
+ operations=operations
+ )]
+
+ self.middle_block = None
+ if transformer_depth_middle >= -1:
+ if transformer_depth_middle >= 0:
+ mid_block += [get_attention_layer( # always uses a self-attn
+ ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
+ disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint
+ ),
+ get_resblock(
+ merge_factor=merge_factor,
+ merge_strategy=merge_strategy,
+ video_kernel_size=video_kernel_size,
+ ch=ch,
+ time_embed_dim=time_embed_dim,
+ dropout=dropout,
+ out_channels=None,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ dtype=self.dtype,
+ device=device,
+ operations=operations
+ )]
+ self.middle_block = TimestepEmbedSequential(*mid_block)
+ self._feature_size += ch
+
+ self.output_blocks = nn.ModuleList([])
+ for level, mult in list(enumerate(channel_mult))[::-1]:
+ for i in range(self.num_res_blocks[level] + 1):
+ ich = input_block_chans.pop()
+ layers = [
+ get_resblock(
+ merge_factor=merge_factor,
+ merge_strategy=merge_strategy,
+ video_kernel_size=video_kernel_size,
+ ch=ch + ich,
+ time_embed_dim=time_embed_dim,
+ dropout=dropout,
+ out_channels=model_channels * mult,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ dtype=self.dtype,
+ device=device,
+ operations=operations
+ )
+ ]
+ ch = model_channels * mult
+ num_transformers = transformer_depth_output.pop()
+ if num_transformers > 0:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+ layers.append(
+ get_attention_layer(
+ ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint
+ )
+ )
+ if level and i == self.num_res_blocks[level]:
+ out_ch = ch
+ layers.append(
+ get_resblock(
+ merge_factor=merge_factor,
+ merge_strategy=merge_strategy,
+ video_kernel_size=video_kernel_size,
+ ch=ch,
+ time_embed_dim=time_embed_dim,
+ dropout=dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ up=True,
+ dtype=self.dtype,
+ device=device,
+ operations=operations
+ )
+ if resblock_updown
+ else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
+ )
+ ds //= 2
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+
+ self.out = nn.Sequential(
+ operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
+ nn.SiLU(),
+ operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device),
+ )
+ if self.predict_codebook_ids:
+ self.id_predictor = nn.Sequential(
+ operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
+ operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
+ #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
+ )
+
+ def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
+ """
+ Apply the model to an input batch.
+ :param x: an [N x C x ...] Tensor of inputs.
+ :param timesteps: a 1-D batch of timesteps.
+ :param context: conditioning plugged in via crossattn
+ :param y: an [N] Tensor of labels, if class-conditional.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ transformer_options["original_shape"] = list(x.shape)
+ transformer_options["transformer_index"] = 0
+ transformer_patches = transformer_options.get("patches", {})
+
+ num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames)
+ image_only_indicator = kwargs.get("image_only_indicator", None)
+ time_context = kwargs.get("time_context", None)
+
+ assert (y is not None) == (
+ self.num_classes is not None
+ ), "must specify y if and only if the model is class-conditional"
+ hs = []
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
+ emb = self.time_embed(t_emb)
+
+ if "emb_patch" in transformer_patches:
+ patch = transformer_patches["emb_patch"]
+ for p in patch:
+ emb = p(emb, self.model_channels, transformer_options)
+
+ if self.num_classes is not None:
+ assert y.shape[0] == x.shape[0]
+ emb = emb + self.label_emb(y)
+
+ h = x
+ for id, module in enumerate(self.input_blocks):
+ transformer_options["block"] = ("input", id)
+ h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
+ h = apply_control(h, control, 'input')
+ if "input_block_patch" in transformer_patches:
+ patch = transformer_patches["input_block_patch"]
+ for p in patch:
+ h = p(h, transformer_options)
+
+ hs.append(h)
+ if "input_block_patch_after_skip" in transformer_patches:
+ patch = transformer_patches["input_block_patch_after_skip"]
+ for p in patch:
+ h = p(h, transformer_options)
+
+ transformer_options["block"] = ("middle", 0)
+ if self.middle_block is not None:
+ h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
+ h = apply_control(h, control, 'middle')
+
+
+ for id, module in enumerate(self.output_blocks):
+ transformer_options["block"] = ("output", id)
+ hsp = hs.pop()
+ hsp = apply_control(hsp, control, 'output')
+
+ if "output_block_patch" in transformer_patches:
+ patch = transformer_patches["output_block_patch"]
+ for p in patch:
+ h, hsp = p(h, hsp, transformer_options)
+
+ h = th.cat([h, hsp], dim=1)
+ del hsp
+ if len(hs) > 0:
+ output_shape = hs[-1].shape
+ else:
+ output_shape = None
+ h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
+ h = h.type(x.dtype)
+ if self.predict_codebook_ids:
+ return self.id_predictor(h)
+ else:
+ return self.out(h)
diff --git a/comfy/ldm/modules/diffusionmodules/upscaling.py b/comfy/ldm/modules/diffusionmodules/upscaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5ac7c2f9138d6d34cda735d2201225d46831154
--- /dev/null
+++ b/comfy/ldm/modules/diffusionmodules/upscaling.py
@@ -0,0 +1,85 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from functools import partial
+
+from .util import extract_into_tensor, make_beta_schedule
+from comfy.ldm.util import default
+
+
+class AbstractLowScaleModel(nn.Module):
+ # for concatenating a downsampled image to the latent representation
+ def __init__(self, noise_schedule_config=None):
+ super(AbstractLowScaleModel, self).__init__()
+ if noise_schedule_config is not None:
+ self.register_schedule(**noise_schedule_config)
+
+ def register_schedule(self, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ def q_sample(self, x_start, t, noise=None, seed=None):
+ if noise is None:
+ if seed is None:
+ noise = torch.randn_like(x_start)
+ else:
+ noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device)
+ return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise)
+
+ def forward(self, x):
+ return x, None
+
+ def decode(self, x):
+ return x
+
+
+class SimpleImageConcat(AbstractLowScaleModel):
+ # no noise level conditioning
+ def __init__(self):
+ super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
+ self.max_noise_level = 0
+
+ def forward(self, x):
+ # fix to constant noise level
+ return x, torch.zeros(x.shape[0], device=x.device).long()
+
+
+class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
+ def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
+ super().__init__(noise_schedule_config=noise_schedule_config)
+ self.max_noise_level = max_noise_level
+
+ def forward(self, x, noise_level=None, seed=None):
+ if noise_level is None:
+ noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
+ else:
+ assert isinstance(noise_level, torch.Tensor)
+ z = self.q_sample(x, noise_level, seed=seed)
+ return z, noise_level
+
+
+
diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce14ad5e18cf1c8f821878f395cc1bab50fad476
--- /dev/null
+++ b/comfy/ldm/modules/diffusionmodules/util.py
@@ -0,0 +1,306 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+
+import os
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import repeat, rearrange
+
+from comfy.ldm.util import instantiate_from_config
+
+class AlphaBlender(nn.Module):
+ strategies = ["learned", "fixed", "learned_with_images"]
+
+ def __init__(
+ self,
+ alpha: float,
+ merge_strategy: str = "learned_with_images",
+ rearrange_pattern: str = "b t -> (b t) 1 1",
+ ):
+ super().__init__()
+ self.merge_strategy = merge_strategy
+ self.rearrange_pattern = rearrange_pattern
+
+ assert (
+ merge_strategy in self.strategies
+ ), f"merge_strategy needs to be in {self.strategies}"
+
+ if self.merge_strategy == "fixed":
+ self.register_buffer("mix_factor", torch.Tensor([alpha]))
+ elif (
+ self.merge_strategy == "learned"
+ or self.merge_strategy == "learned_with_images"
+ ):
+ self.register_parameter(
+ "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+ )
+ else:
+ raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+ def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor:
+ # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t)
+ if self.merge_strategy == "fixed":
+ # make shape compatible
+ # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs)
+ alpha = self.mix_factor.to(device)
+ elif self.merge_strategy == "learned":
+ alpha = torch.sigmoid(self.mix_factor.to(device))
+ # make shape compatible
+ # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
+ elif self.merge_strategy == "learned_with_images":
+ if image_only_indicator is None:
+ alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1")
+ else:
+ alpha = torch.where(
+ image_only_indicator.bool(),
+ torch.ones(1, 1, device=image_only_indicator.device),
+ rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
+ )
+ alpha = rearrange(alpha, self.rearrange_pattern)
+ # make shape compatible
+ # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
+ else:
+ raise NotImplementedError()
+ return alpha
+
+ def forward(
+ self,
+ x_spatial,
+ x_temporal,
+ image_only_indicator=None,
+ ) -> torch.Tensor:
+ alpha = self.get_alpha(image_only_indicator, x_spatial.device)
+ x = (
+ alpha.to(x_spatial.dtype) * x_spatial
+ + (1.0 - alpha).to(x_spatial.dtype) * x_temporal
+ )
+ return x
+
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if schedule == "linear":
+ betas = (
+ torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+ )
+
+ elif schedule == "cosine":
+ timesteps = (
+ torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+ )
+ alphas = timesteps / (1 + cosine_s) * np.pi / 2
+ alphas = torch.cos(alphas).pow(2)
+ alphas = alphas / alphas[0]
+ betas = 1 - alphas[1:] / alphas[:-1]
+ betas = torch.clamp(betas, min=0, max=0.999)
+
+ elif schedule == "squaredcos_cap_v2": # used for karlo prior
+ # return early
+ return betas_for_alpha_bar(
+ n_timestep,
+ lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+ )
+
+ elif schedule == "sqrt_linear":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+ elif schedule == "sqrt":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+ else:
+ raise ValueError(f"schedule '{schedule}' unknown.")
+ return betas
+
+
+def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+ if ddim_discr_method == 'uniform':
+ c = num_ddpm_timesteps // num_ddim_timesteps
+ ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+ elif ddim_discr_method == 'quad':
+ ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+ else:
+ raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+
+ # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+ # add one to get the final alpha values right (the ones from first scale to data during sampling)
+ steps_out = ddim_timesteps + 1
+ if verbose:
+ print(f'Selected timesteps for ddim sampler: {steps_out}')
+ return steps_out
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+ # select alphas for computing the variance schedule
+ alphas = alphacums[ddim_timesteps]
+ alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+ # according the the formula provided in https://arxiv.org/abs/2010.02502
+ sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+ if verbose:
+ print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+ print(f'For the chosen value of eta, which is {eta}, '
+ f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+ return sigmas, alphas, alphas_prev
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+ """
+ Create a beta schedule that discretizes the given alpha_t_bar function,
+ which defines the cumulative product of (1-beta) over time from t = [0,1].
+ :param num_diffusion_timesteps: the number of betas to produce.
+ :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+ produces the cumulative product of (1-beta) up to that
+ part of the diffusion process.
+ :param max_beta: the maximum beta to use; use values lower than 1 to
+ prevent singularities.
+ """
+ betas = []
+ for i in range(num_diffusion_timesteps):
+ t1 = i / num_diffusion_timesteps
+ t2 = (i + 1) / num_diffusion_timesteps
+ betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+ return np.array(betas)
+
+
+def extract_into_tensor(a, t, x_shape):
+ b, *_ = t.shape
+ out = a.gather(-1, t)
+ return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def checkpoint(func, inputs, params, flag):
+ """
+ Evaluate a function without caching intermediate activations, allowing for
+ reduced memory at the expense of extra compute in the backward pass.
+ :param func: the function to evaluate.
+ :param inputs: the argument sequence to pass to `func`.
+ :param params: a sequence of parameters `func` depends on but does not
+ explicitly take as arguments.
+ :param flag: if False, disable gradient checkpointing.
+ """
+ if flag:
+ args = tuple(inputs) + tuple(params)
+ return CheckpointFunction.apply(func, len(inputs), *args)
+ else:
+ return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+ @staticmethod
+ def forward(ctx, run_function, length, *args):
+ ctx.run_function = run_function
+ ctx.input_tensors = list(args[:length])
+ ctx.input_params = list(args[length:])
+ ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
+ "dtype": torch.get_autocast_gpu_dtype(),
+ "cache_enabled": torch.is_autocast_cache_enabled()}
+ with torch.no_grad():
+ output_tensors = ctx.run_function(*ctx.input_tensors)
+ return output_tensors
+
+ @staticmethod
+ def backward(ctx, *output_grads):
+ ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+ with torch.enable_grad(), \
+ torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+ # Fixes a bug where the first op in run_function modifies the
+ # Tensor storage in place, which is not allowed for detach()'d
+ # Tensors.
+ shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+ output_tensors = ctx.run_function(*shallow_copies)
+ input_grads = torch.autograd.grad(
+ output_tensors,
+ ctx.input_tensors + ctx.input_params,
+ output_grads,
+ allow_unused=True,
+ )
+ del ctx.input_tensors
+ del ctx.input_params
+ del output_tensors
+ return (None, None) + input_grads
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+ """
+ Create sinusoidal timestep embeddings.
+ :param timesteps: a 1-D Tensor of N indices, one per batch element.
+ These may be fractional.
+ :param dim: the dimension of the output.
+ :param max_period: controls the minimum frequency of the embeddings.
+ :return: an [N x dim] Tensor of positional embeddings.
+ """
+ if not repeat_only:
+ half = dim // 2
+ freqs = torch.exp(
+ -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half
+ )
+ args = timesteps[:, None].float() * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ else:
+ embedding = repeat(timesteps, 'b -> b d', d=dim)
+ return embedding
+
+
+def zero_module(module):
+ """
+ Zero out the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+
+def scale_module(module, scale):
+ """
+ Scale the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().mul_(scale)
+ return module
+
+
+def mean_flat(tensor):
+ """
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D average pooling module.
+ """
+ if dims == 1:
+ return nn.AvgPool1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.AvgPool2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.AvgPool3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class HybridConditioner(nn.Module):
+
+ def __init__(self, c_concat_config, c_crossattn_config):
+ super().__init__()
+ self.concat_conditioner = instantiate_from_config(c_concat_config)
+ self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+ def forward(self, c_concat, c_crossattn):
+ c_concat = self.concat_conditioner(c_concat)
+ c_crossattn = self.crossattn_conditioner(c_crossattn)
+ return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
+
+
+def noise_like(shape, device, repeat=False):
+ repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+ noise = lambda: torch.randn(shape, device=device)
+ return repeat_noise() if repeat else noise()
diff --git a/comfy/ldm/modules/distributions/__init__.py b/comfy/ldm/modules/distributions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/comfy/ldm/modules/distributions/distributions.py b/comfy/ldm/modules/distributions/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2b8ef901130efc171aa69742ca0244d94d3f2e9
--- /dev/null
+++ b/comfy/ldm/modules/distributions/distributions.py
@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+ def sample(self):
+ raise NotImplementedError()
+
+ def mode(self):
+ raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+ def __init__(self, value):
+ self.value = value
+
+ def sample(self):
+ return self.value
+
+ def mode(self):
+ return self.value
+
+
+class DiagonalGaussianDistribution(object):
+ def __init__(self, parameters, deterministic=False):
+ self.parameters = parameters
+ self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+ self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+ self.deterministic = deterministic
+ self.std = torch.exp(0.5 * self.logvar)
+ self.var = torch.exp(self.logvar)
+ if self.deterministic:
+ self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+ def sample(self):
+ x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+ return x
+
+ def kl(self, other=None):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ else:
+ if other is None:
+ return 0.5 * torch.sum(torch.pow(self.mean, 2)
+ + self.var - 1.0 - self.logvar,
+ dim=[1, 2, 3])
+ else:
+ return 0.5 * torch.sum(
+ torch.pow(self.mean - other.mean, 2) / other.var
+ + self.var / other.var - 1.0 - self.logvar + other.logvar,
+ dim=[1, 2, 3])
+
+ def nll(self, sample, dims=[1,2,3]):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ logtwopi = np.log(2.0 * np.pi)
+ return 0.5 * torch.sum(
+ logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+ dim=dims)
+
+ def mode(self):
+ return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+ """
+ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+ Compute the KL divergence between two gaussians.
+ Shapes are automatically broadcasted, so batches can be compared to
+ scalars, among other use cases.
+ """
+ tensor = None
+ for obj in (mean1, logvar1, mean2, logvar2):
+ if isinstance(obj, torch.Tensor):
+ tensor = obj
+ break
+ assert tensor is not None, "at least one argument must be a Tensor"
+
+ # Force variances to be Tensors. Broadcasting helps convert scalars to
+ # Tensors, but it does not work for torch.exp().
+ logvar1, logvar2 = [
+ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+ for x in (logvar1, logvar2)
+ ]
+
+ return 0.5 * (
+ -1.0
+ + logvar2
+ - logvar1
+ + torch.exp(logvar1 - logvar2)
+ + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+ )
diff --git a/comfy/ldm/modules/ema.py b/comfy/ldm/modules/ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..bded25019b9bcbcd0260f0b8185f8c7859ca58c4
--- /dev/null
+++ b/comfy/ldm/modules/ema.py
@@ -0,0 +1,80 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+ def __init__(self, model, decay=0.9999, use_num_upates=True):
+ super().__init__()
+ if decay < 0.0 or decay > 1.0:
+ raise ValueError('Decay must be between 0 and 1')
+
+ self.m_name2s_name = {}
+ self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+ self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
+ else torch.tensor(-1, dtype=torch.int))
+
+ for name, p in model.named_parameters():
+ if p.requires_grad:
+ # remove as '.'-character is not allowed in buffers
+ s_name = name.replace('.', '')
+ self.m_name2s_name.update({name: s_name})
+ self.register_buffer(s_name, p.clone().detach().data)
+
+ self.collected_params = []
+
+ def reset_num_updates(self):
+ del self.num_updates
+ self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
+
+ def forward(self, model):
+ decay = self.decay
+
+ if self.num_updates >= 0:
+ self.num_updates += 1
+ decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
+
+ one_minus_decay = 1.0 - decay
+
+ with torch.no_grad():
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+
+ for key in m_param:
+ if m_param[key].requires_grad:
+ sname = self.m_name2s_name[key]
+ shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+ shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+ else:
+ assert not key in self.m_name2s_name
+
+ def copy_to(self, model):
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+ for key in m_param:
+ if m_param[key].requires_grad:
+ m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+ else:
+ assert not key in self.m_name2s_name
+
+ def store(self, parameters):
+ """
+ Save the current parameters for restoring later.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ temporarily stored.
+ """
+ self.collected_params = [param.clone() for param in parameters]
+
+ def restore(self, parameters):
+ """
+ Restore the parameters stored with the `store` method.
+ Useful to validate the model with EMA parameters without affecting the
+ original optimization process. Store the parameters before the
+ `copy_to` method. After validation (or model saving), use this to
+ restore the former parameters.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ updated with the stored parameters.
+ """
+ for c_param, param in zip(self.collected_params, parameters):
+ param.data.copy_(c_param.data)
diff --git a/comfy/ldm/modules/encoders/__init__.py b/comfy/ldm/modules/encoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/comfy/ldm/modules/encoders/noise_aug_modules.py b/comfy/ldm/modules/encoders/noise_aug_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5d8660301636fde75808cba50afa539cf1162e0
--- /dev/null
+++ b/comfy/ldm/modules/encoders/noise_aug_modules.py
@@ -0,0 +1,35 @@
+from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
+from ..diffusionmodules.openaimodel import Timestep
+import torch
+
+class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
+ def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
+ super().__init__(*args, **kwargs)
+ if clip_stats_path is None:
+ clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
+ else:
+ clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
+ self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
+ self.register_buffer("data_std", clip_std[None, :], persistent=False)
+ self.time_embed = Timestep(timestep_dim)
+
+ def scale(self, x):
+ # re-normalize to centered mean and unit variance
+ x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device)
+ return x
+
+ def unscale(self, x):
+ # back to original data stats
+ x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device)
+ return x
+
+ def forward(self, x, noise_level=None, seed=None):
+ if noise_level is None:
+ noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
+ else:
+ assert isinstance(noise_level, torch.Tensor)
+ x = self.scale(x)
+ z = self.q_sample(x, noise_level, seed=seed)
+ z = self.unscale(z)
+ noise_level = self.time_embed(noise_level)
+ return z, noise_level
diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..47b8b1510ea4e136271370ff36889822f5edd429
--- /dev/null
+++ b/comfy/ldm/modules/sub_quadratic_attention.py
@@ -0,0 +1,276 @@
+# original source:
+# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py
+# license:
+# MIT
+# credit:
+# Amin Rezaei (original author)
+# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
+# implementation of:
+# Self-attention Does Not Need O(n2) Memory":
+# https://arxiv.org/abs/2112.05682v2
+
+from functools import partial
+import torch
+from torch import Tensor
+from torch.utils.checkpoint import checkpoint
+import math
+import logging
+
+try:
+ from typing import Optional, NamedTuple, List, Protocol
+except ImportError:
+ from typing import Optional, NamedTuple, List
+ from typing_extensions import Protocol
+
+from torch import Tensor
+from typing import List
+
+from comfy import model_management
+
+def dynamic_slice(
+ x: Tensor,
+ starts: List[int],
+ sizes: List[int],
+) -> Tensor:
+ slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
+ return x[slicing]
+
+class AttnChunk(NamedTuple):
+ exp_values: Tensor
+ exp_weights_sum: Tensor
+ max_score: Tensor
+
+class SummarizeChunk(Protocol):
+ @staticmethod
+ def __call__(
+ query: Tensor,
+ key_t: Tensor,
+ value: Tensor,
+ ) -> AttnChunk: ...
+
+class ComputeQueryChunkAttn(Protocol):
+ @staticmethod
+ def __call__(
+ query: Tensor,
+ key_t: Tensor,
+ value: Tensor,
+ ) -> Tensor: ...
+
+def _summarize_chunk(
+ query: Tensor,
+ key_t: Tensor,
+ value: Tensor,
+ scale: float,
+ upcast_attention: bool,
+ mask,
+) -> AttnChunk:
+ if upcast_attention:
+ with torch.autocast(enabled=False, device_type = 'cuda'):
+ query = query.float()
+ key_t = key_t.float()
+ attn_weights = torch.baddbmm(
+ torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+ query,
+ key_t,
+ alpha=scale,
+ beta=0,
+ )
+ else:
+ attn_weights = torch.baddbmm(
+ torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+ query,
+ key_t,
+ alpha=scale,
+ beta=0,
+ )
+ max_score, _ = torch.max(attn_weights, -1, keepdim=True)
+ max_score = max_score.detach()
+ attn_weights -= max_score
+ if mask is not None:
+ attn_weights += mask
+ torch.exp(attn_weights, out=attn_weights)
+ exp_weights = attn_weights.to(value.dtype)
+ exp_values = torch.bmm(exp_weights, value)
+ max_score = max_score.squeeze(-1)
+ return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
+
+def _query_chunk_attention(
+ query: Tensor,
+ key_t: Tensor,
+ value: Tensor,
+ summarize_chunk: SummarizeChunk,
+ kv_chunk_size: int,
+ mask,
+) -> Tensor:
+ batch_x_heads, k_channels_per_head, k_tokens = key_t.shape
+ _, _, v_channels_per_head = value.shape
+
+ def chunk_scanner(chunk_idx: int, mask) -> AttnChunk:
+ key_chunk = dynamic_slice(
+ key_t,
+ (0, 0, chunk_idx),
+ (batch_x_heads, k_channels_per_head, kv_chunk_size)
+ )
+ value_chunk = dynamic_slice(
+ value,
+ (0, chunk_idx, 0),
+ (batch_x_heads, kv_chunk_size, v_channels_per_head)
+ )
+ if mask is not None:
+ mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size]
+
+ return summarize_chunk(query, key_chunk, value_chunk, mask=mask)
+
+ chunks: List[AttnChunk] = [
+ chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
+ ]
+ acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
+ chunk_values, chunk_weights, chunk_max = acc_chunk
+
+ global_max, _ = torch.max(chunk_max, 0, keepdim=True)
+ max_diffs = torch.exp(chunk_max - global_max)
+ chunk_values *= torch.unsqueeze(max_diffs, -1)
+ chunk_weights *= max_diffs
+
+ all_values = chunk_values.sum(dim=0)
+ all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
+ return all_values / all_weights
+
+# TODO: refactor CrossAttention#get_attention_scores to share code with this
+def _get_attention_scores_no_kv_chunking(
+ query: Tensor,
+ key_t: Tensor,
+ value: Tensor,
+ scale: float,
+ upcast_attention: bool,
+ mask,
+) -> Tensor:
+ if upcast_attention:
+ with torch.autocast(enabled=False, device_type = 'cuda'):
+ query = query.float()
+ key_t = key_t.float()
+ attn_scores = torch.baddbmm(
+ torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+ query,
+ key_t,
+ alpha=scale,
+ beta=0,
+ )
+ else:
+ attn_scores = torch.baddbmm(
+ torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+ query,
+ key_t,
+ alpha=scale,
+ beta=0,
+ )
+
+ if mask is not None:
+ attn_scores += mask
+ try:
+ attn_probs = attn_scores.softmax(dim=-1)
+ del attn_scores
+ except model_management.OOM_EXCEPTION:
+ logging.warning("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
+ attn_scores -= attn_scores.max(dim=-1, keepdim=True).values
+ torch.exp(attn_scores, out=attn_scores)
+ summed = torch.sum(attn_scores, dim=-1, keepdim=True)
+ attn_scores /= summed
+ attn_probs = attn_scores
+
+ hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value)
+ return hidden_states_slice
+
+class ScannedChunk(NamedTuple):
+ chunk_idx: int
+ attn_chunk: AttnChunk
+
+def efficient_dot_product_attention(
+ query: Tensor,
+ key_t: Tensor,
+ value: Tensor,
+ query_chunk_size=1024,
+ kv_chunk_size: Optional[int] = None,
+ kv_chunk_size_min: Optional[int] = None,
+ use_checkpoint=True,
+ upcast_attention=False,
+ mask = None,
+):
+ """Computes efficient dot-product attention given query, transposed key, and value.
+ This is efficient version of attention presented in
+ https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.
+ Args:
+ query: queries for calculating attention with shape of
+ `[batch * num_heads, tokens, channels_per_head]`.
+ key_t: keys for calculating attention with shape of
+ `[batch * num_heads, channels_per_head, tokens]`.
+ value: values to be used in attention with shape of
+ `[batch * num_heads, tokens, channels_per_head]`.
+ query_chunk_size: int: query chunks size
+ kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)
+ kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).
+ use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)
+ Returns:
+ Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.
+ """
+ batch_x_heads, q_tokens, q_channels_per_head = query.shape
+ _, _, k_tokens = key_t.shape
+ scale = q_channels_per_head ** -0.5
+
+ kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)
+ if kv_chunk_size_min is not None:
+ kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
+
+ if mask is not None and len(mask.shape) == 2:
+ mask = mask.unsqueeze(0)
+
+ def get_query_chunk(chunk_idx: int) -> Tensor:
+ return dynamic_slice(
+ query,
+ (0, chunk_idx, 0),
+ (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)
+ )
+
+ def get_mask_chunk(chunk_idx: int) -> Tensor:
+ if mask is None:
+ return None
+ if mask.shape[1] == 1:
+ return mask
+ chunk = min(query_chunk_size, q_tokens)
+ return mask[:,chunk_idx:chunk_idx + chunk]
+
+ summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention)
+ summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
+ compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
+ _get_attention_scores_no_kv_chunking,
+ scale=scale,
+ upcast_attention=upcast_attention
+ ) if k_tokens <= kv_chunk_size else (
+ # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
+ partial(
+ _query_chunk_attention,
+ kv_chunk_size=kv_chunk_size,
+ summarize_chunk=summarize_chunk,
+ )
+ )
+
+ if q_tokens <= query_chunk_size:
+ # fast-path for when there's just 1 query chunk
+ return compute_query_chunk_attn(
+ query=query,
+ key_t=key_t,
+ value=value,
+ mask=mask,
+ )
+
+ # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
+ # and pass slices to be mutated, instead of torch.cat()ing the returned slices
+ res = torch.cat([
+ compute_query_chunk_attn(
+ query=get_query_chunk(i * query_chunk_size),
+ key_t=key_t,
+ value=value,
+ mask=get_mask_chunk(i * query_chunk_size)
+ ) for i in range(math.ceil(q_tokens / query_chunk_size))
+ ], dim=1)
+ return res
diff --git a/comfy/ldm/modules/temporal_ae.py b/comfy/ldm/modules/temporal_ae.py
new file mode 100644
index 0000000000000000000000000000000000000000..2992aeafc35ae8ca9e4ecac236810fa5a1fb84ad
--- /dev/null
+++ b/comfy/ldm/modules/temporal_ae.py
@@ -0,0 +1,245 @@
+import functools
+from typing import Callable, Iterable, Union
+
+import torch
+from einops import rearrange, repeat
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+from .diffusionmodules.model import (
+ AttnBlock,
+ Decoder,
+ ResnetBlock,
+)
+from .diffusionmodules.openaimodel import ResBlock, timestep_embedding
+from .attention import BasicTransformerBlock
+
+def partialclass(cls, *args, **kwargs):
+ class NewCls(cls):
+ __init__ = functools.partialmethod(cls.__init__, *args, **kwargs)
+
+ return NewCls
+
+
+class VideoResBlock(ResnetBlock):
+ def __init__(
+ self,
+ out_channels,
+ *args,
+ dropout=0.0,
+ video_kernel_size=3,
+ alpha=0.0,
+ merge_strategy="learned",
+ **kwargs,
+ ):
+ super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs)
+ if video_kernel_size is None:
+ video_kernel_size = [3, 1, 1]
+ self.time_stack = ResBlock(
+ channels=out_channels,
+ emb_channels=0,
+ dropout=dropout,
+ dims=3,
+ use_scale_shift_norm=False,
+ use_conv=False,
+ up=False,
+ down=False,
+ kernel_size=video_kernel_size,
+ use_checkpoint=False,
+ skip_t_emb=True,
+ )
+
+ self.merge_strategy = merge_strategy
+ if self.merge_strategy == "fixed":
+ self.register_buffer("mix_factor", torch.Tensor([alpha]))
+ elif self.merge_strategy == "learned":
+ self.register_parameter(
+ "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+ )
+ else:
+ raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+ def get_alpha(self, bs):
+ if self.merge_strategy == "fixed":
+ return self.mix_factor
+ elif self.merge_strategy == "learned":
+ return torch.sigmoid(self.mix_factor)
+ else:
+ raise NotImplementedError()
+
+ def forward(self, x, temb, skip_video=False, timesteps=None):
+ b, c, h, w = x.shape
+ if timesteps is None:
+ timesteps = b
+
+ x = super().forward(x, temb)
+
+ if not skip_video:
+ x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+
+ x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+
+ x = self.time_stack(x, temb)
+
+ alpha = self.get_alpha(bs=b // timesteps).to(x.device)
+ x = alpha * x + (1.0 - alpha) * x_mix
+
+ x = rearrange(x, "b c t h w -> (b t) c h w")
+ return x
+
+
+class AE3DConv(ops.Conv2d):
+ def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs):
+ super().__init__(in_channels, out_channels, *args, **kwargs)
+ if isinstance(video_kernel_size, Iterable):
+ padding = [int(k // 2) for k in video_kernel_size]
+ else:
+ padding = int(video_kernel_size // 2)
+
+ self.time_mix_conv = ops.Conv3d(
+ in_channels=out_channels,
+ out_channels=out_channels,
+ kernel_size=video_kernel_size,
+ padding=padding,
+ )
+
+ def forward(self, input, timesteps=None, skip_video=False):
+ if timesteps is None:
+ timesteps = input.shape[0]
+ x = super().forward(input)
+ if skip_video:
+ return x
+ x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+ x = self.time_mix_conv(x)
+ return rearrange(x, "b c t h w -> (b t) c h w")
+
+
+class AttnVideoBlock(AttnBlock):
+ def __init__(
+ self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned"
+ ):
+ super().__init__(in_channels)
+ # no context, single headed, as in base class
+ self.time_mix_block = BasicTransformerBlock(
+ dim=in_channels,
+ n_heads=1,
+ d_head=in_channels,
+ checkpoint=False,
+ ff_in=True,
+ )
+
+ time_embed_dim = self.in_channels * 4
+ self.video_time_embed = torch.nn.Sequential(
+ ops.Linear(self.in_channels, time_embed_dim),
+ torch.nn.SiLU(),
+ ops.Linear(time_embed_dim, self.in_channels),
+ )
+
+ self.merge_strategy = merge_strategy
+ if self.merge_strategy == "fixed":
+ self.register_buffer("mix_factor", torch.Tensor([alpha]))
+ elif self.merge_strategy == "learned":
+ self.register_parameter(
+ "mix_factor", torch.nn.Parameter(torch.Tensor([alpha]))
+ )
+ else:
+ raise ValueError(f"unknown merge strategy {self.merge_strategy}")
+
+ def forward(self, x, timesteps=None, skip_time_block=False):
+ if skip_time_block:
+ return super().forward(x)
+
+ if timesteps is None:
+ timesteps = x.shape[0]
+
+ x_in = x
+ x = self.attention(x)
+ h, w = x.shape[2:]
+ x = rearrange(x, "b c h w -> b (h w) c")
+
+ x_mix = x
+ num_frames = torch.arange(timesteps, device=x.device)
+ num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
+ num_frames = rearrange(num_frames, "b t -> (b t)")
+ t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False)
+ emb = self.video_time_embed(t_emb) # b, n_channels
+ emb = emb[:, None, :]
+ x_mix = x_mix + emb
+
+ alpha = self.get_alpha().to(x.device)
+ x_mix = self.time_mix_block(x_mix, timesteps=timesteps)
+ x = alpha * x + (1.0 - alpha) * x_mix # alpha merge
+
+ x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
+ x = self.proj_out(x)
+
+ return x_in + x
+
+ def get_alpha(
+ self,
+ ):
+ if self.merge_strategy == "fixed":
+ return self.mix_factor
+ elif self.merge_strategy == "learned":
+ return torch.sigmoid(self.mix_factor)
+ else:
+ raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}")
+
+
+
+def make_time_attn(
+ in_channels,
+ attn_type="vanilla",
+ attn_kwargs=None,
+ alpha: float = 0,
+ merge_strategy: str = "learned",
+):
+ return partialclass(
+ AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy
+ )
+
+
+class Conv2DWrapper(torch.nn.Conv2d):
+ def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor:
+ return super().forward(input)
+
+
+class VideoDecoder(Decoder):
+ available_time_modes = ["all", "conv-only", "attn-only"]
+
+ def __init__(
+ self,
+ *args,
+ video_kernel_size: Union[int, list] = 3,
+ alpha: float = 0.0,
+ merge_strategy: str = "learned",
+ time_mode: str = "conv-only",
+ **kwargs,
+ ):
+ self.video_kernel_size = video_kernel_size
+ self.alpha = alpha
+ self.merge_strategy = merge_strategy
+ self.time_mode = time_mode
+ assert (
+ self.time_mode in self.available_time_modes
+ ), f"time_mode parameter has to be in {self.available_time_modes}"
+
+ if self.time_mode != "attn-only":
+ kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size)
+ if self.time_mode not in ["conv-only", "only-last-conv"]:
+ kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy)
+ if self.time_mode not in ["attn-only", "only-last-conv"]:
+ kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy)
+
+ super().__init__(*args, **kwargs)
+
+ def get_last_layer(self, skip_time_mix=False, **kwargs):
+ if self.time_mode == "attn-only":
+ raise NotImplementedError("TODO")
+ else:
+ return (
+ self.conv_out.time_mix_conv.weight
+ if not skip_time_mix
+ else self.conv_out.weight
+ )
diff --git a/comfy/ldm/util.py b/comfy/ldm/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c09ca1c72f7ceb3f9d7f9546aae5561baf62b13
--- /dev/null
+++ b/comfy/ldm/util.py
@@ -0,0 +1,197 @@
+import importlib
+
+import torch
+from torch import optim
+import numpy as np
+
+from inspect import isfunction
+from PIL import Image, ImageDraw, ImageFont
+
+
+def log_txt_as_img(wh, xc, size=10):
+ # wh a tuple of (width, height)
+ # xc a list of captions to plot
+ b = len(xc)
+ txts = list()
+ for bi in range(b):
+ txt = Image.new("RGB", wh, color="white")
+ draw = ImageDraw.Draw(txt)
+ font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
+ nc = int(40 * (wh[0] / 256))
+ lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
+
+ try:
+ draw.text((0, 0), lines, fill="black", font=font)
+ except UnicodeEncodeError:
+ print("Cant encode string for logging. Skipping.")
+
+ txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
+ txts.append(txt)
+ txts = np.stack(txts)
+ txts = torch.tensor(txts)
+ return txts
+
+
+def ismap(x):
+ if not isinstance(x, torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+
+def isimage(x):
+ if not isinstance(x,torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+
+def exists(x):
+ return x is not None
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+def mean_flat(tensor):
+ """
+ https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def count_params(model, verbose=False):
+ total_params = sum(p.numel() for p in model.parameters())
+ if verbose:
+ print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+ return total_params
+
+
+def instantiate_from_config(config):
+ if not "target" in config:
+ if config == '__is_first_stage__':
+ return None
+ elif config == "__is_unconditional__":
+ return None
+ raise KeyError("Expected key `target` to instantiate.")
+ return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False):
+ module, cls = string.rsplit(".", 1)
+ if reload:
+ module_imp = importlib.import_module(module)
+ importlib.reload(module_imp)
+ return getattr(importlib.import_module(module, package=None), cls)
+
+
+class AdamWwithEMAandWings(optim.Optimizer):
+ # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
+ def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using
+ weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code
+ ema_power=1., param_names=()):
+ """AdamW that saves EMA versions of the parameters."""
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= eps:
+ raise ValueError("Invalid epsilon value: {}".format(eps))
+ if not 0.0 <= betas[0] < 1.0:
+ raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
+ if not 0.0 <= betas[1] < 1.0:
+ raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
+ if not 0.0 <= weight_decay:
+ raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+ if not 0.0 <= ema_decay <= 1.0:
+ raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
+ defaults = dict(lr=lr, betas=betas, eps=eps,
+ weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
+ ema_power=ema_power, param_names=param_names)
+ super().__init__(params, defaults)
+
+ def __setstate__(self, state):
+ super().__setstate__(state)
+ for group in self.param_groups:
+ group.setdefault('amsgrad', False)
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ """Performs a single optimization step.
+ Args:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+
+ for group in self.param_groups:
+ params_with_grad = []
+ grads = []
+ exp_avgs = []
+ exp_avg_sqs = []
+ ema_params_with_grad = []
+ state_sums = []
+ max_exp_avg_sqs = []
+ state_steps = []
+ amsgrad = group['amsgrad']
+ beta1, beta2 = group['betas']
+ ema_decay = group['ema_decay']
+ ema_power = group['ema_power']
+
+ for p in group['params']:
+ if p.grad is None:
+ continue
+ params_with_grad.append(p)
+ if p.grad.is_sparse:
+ raise RuntimeError('AdamW does not support sparse gradients')
+ grads.append(p.grad)
+
+ state = self.state[p]
+
+ # State initialization
+ if len(state) == 0:
+ state['step'] = 0
+ # Exponential moving average of gradient values
+ state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ # Exponential moving average of squared gradient values
+ state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ if amsgrad:
+ # Maintains max of all exp. moving avg. of sq. grad. values
+ state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ # Exponential moving average of parameter values
+ state['param_exp_avg'] = p.detach().float().clone()
+
+ exp_avgs.append(state['exp_avg'])
+ exp_avg_sqs.append(state['exp_avg_sq'])
+ ema_params_with_grad.append(state['param_exp_avg'])
+
+ if amsgrad:
+ max_exp_avg_sqs.append(state['max_exp_avg_sq'])
+
+ # update the steps for each param group update
+ state['step'] += 1
+ # record the step after step update
+ state_steps.append(state['step'])
+
+ optim._functional.adamw(params_with_grad,
+ grads,
+ exp_avgs,
+ exp_avg_sqs,
+ max_exp_avg_sqs,
+ state_steps,
+ amsgrad=amsgrad,
+ beta1=beta1,
+ beta2=beta2,
+ lr=group['lr'],
+ weight_decay=group['weight_decay'],
+ eps=group['eps'],
+ maximize=False)
+
+ cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
+ for param, ema_param in zip(params_with_grad, ema_params_with_grad):
+ ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
+
+ return loss
\ No newline at end of file
diff --git a/comfy/lora.py b/comfy/lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..1080169b1913a202906aed06b5cb67d6a5c3c98f
--- /dev/null
+++ b/comfy/lora.py
@@ -0,0 +1,631 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Comfy
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+from __future__ import annotations
+import comfy.utils
+import comfy.model_management
+import comfy.model_base
+import logging
+import torch
+
+LORA_CLIP_MAP = {
+ "mlp.fc1": "mlp_fc1",
+ "mlp.fc2": "mlp_fc2",
+ "self_attn.k_proj": "self_attn_k_proj",
+ "self_attn.q_proj": "self_attn_q_proj",
+ "self_attn.v_proj": "self_attn_v_proj",
+ "self_attn.out_proj": "self_attn_out_proj",
+}
+
+
+def load_lora(lora, to_load):
+ patch_dict = {}
+ loaded_keys = set()
+ for x in to_load:
+ alpha_name = "{}.alpha".format(x)
+ alpha = None
+ if alpha_name in lora.keys():
+ alpha = lora[alpha_name].item()
+ loaded_keys.add(alpha_name)
+
+ dora_scale_name = "{}.dora_scale".format(x)
+ dora_scale = None
+ if dora_scale_name in lora.keys():
+ dora_scale = lora[dora_scale_name]
+ loaded_keys.add(dora_scale_name)
+
+ reshape_name = "{}.reshape_weight".format(x)
+ reshape = None
+ if reshape_name in lora.keys():
+ try:
+ reshape = lora[reshape_name].tolist()
+ loaded_keys.add(reshape_name)
+ except:
+ pass
+
+ regular_lora = "{}.lora_up.weight".format(x)
+ diffusers_lora = "{}_lora.up.weight".format(x)
+ diffusers2_lora = "{}.lora_B.weight".format(x)
+ diffusers3_lora = "{}.lora.up.weight".format(x)
+ mochi_lora = "{}.lora_B".format(x)
+ transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
+ A_name = None
+
+ if regular_lora in lora.keys():
+ A_name = regular_lora
+ B_name = "{}.lora_down.weight".format(x)
+ mid_name = "{}.lora_mid.weight".format(x)
+ elif diffusers_lora in lora.keys():
+ A_name = diffusers_lora
+ B_name = "{}_lora.down.weight".format(x)
+ mid_name = None
+ elif diffusers2_lora in lora.keys():
+ A_name = diffusers2_lora
+ B_name = "{}.lora_A.weight".format(x)
+ mid_name = None
+ elif diffusers3_lora in lora.keys():
+ A_name = diffusers3_lora
+ B_name = "{}.lora.down.weight".format(x)
+ mid_name = None
+ elif mochi_lora in lora.keys():
+ A_name = mochi_lora
+ B_name = "{}.lora_A".format(x)
+ mid_name = None
+ elif transformers_lora in lora.keys():
+ A_name = transformers_lora
+ B_name ="{}.lora_linear_layer.down.weight".format(x)
+ mid_name = None
+
+ if A_name is not None:
+ mid = None
+ if mid_name is not None and mid_name in lora.keys():
+ mid = lora[mid_name]
+ loaded_keys.add(mid_name)
+ patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale, reshape))
+ loaded_keys.add(A_name)
+ loaded_keys.add(B_name)
+
+
+ ######## loha
+ hada_w1_a_name = "{}.hada_w1_a".format(x)
+ hada_w1_b_name = "{}.hada_w1_b".format(x)
+ hada_w2_a_name = "{}.hada_w2_a".format(x)
+ hada_w2_b_name = "{}.hada_w2_b".format(x)
+ hada_t1_name = "{}.hada_t1".format(x)
+ hada_t2_name = "{}.hada_t2".format(x)
+ if hada_w1_a_name in lora.keys():
+ hada_t1 = None
+ hada_t2 = None
+ if hada_t1_name in lora.keys():
+ hada_t1 = lora[hada_t1_name]
+ hada_t2 = lora[hada_t2_name]
+ loaded_keys.add(hada_t1_name)
+ loaded_keys.add(hada_t2_name)
+
+ patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale))
+ loaded_keys.add(hada_w1_a_name)
+ loaded_keys.add(hada_w1_b_name)
+ loaded_keys.add(hada_w2_a_name)
+ loaded_keys.add(hada_w2_b_name)
+
+
+ ######## lokr
+ lokr_w1_name = "{}.lokr_w1".format(x)
+ lokr_w2_name = "{}.lokr_w2".format(x)
+ lokr_w1_a_name = "{}.lokr_w1_a".format(x)
+ lokr_w1_b_name = "{}.lokr_w1_b".format(x)
+ lokr_t2_name = "{}.lokr_t2".format(x)
+ lokr_w2_a_name = "{}.lokr_w2_a".format(x)
+ lokr_w2_b_name = "{}.lokr_w2_b".format(x)
+
+ lokr_w1 = None
+ if lokr_w1_name in lora.keys():
+ lokr_w1 = lora[lokr_w1_name]
+ loaded_keys.add(lokr_w1_name)
+
+ lokr_w2 = None
+ if lokr_w2_name in lora.keys():
+ lokr_w2 = lora[lokr_w2_name]
+ loaded_keys.add(lokr_w2_name)
+
+ lokr_w1_a = None
+ if lokr_w1_a_name in lora.keys():
+ lokr_w1_a = lora[lokr_w1_a_name]
+ loaded_keys.add(lokr_w1_a_name)
+
+ lokr_w1_b = None
+ if lokr_w1_b_name in lora.keys():
+ lokr_w1_b = lora[lokr_w1_b_name]
+ loaded_keys.add(lokr_w1_b_name)
+
+ lokr_w2_a = None
+ if lokr_w2_a_name in lora.keys():
+ lokr_w2_a = lora[lokr_w2_a_name]
+ loaded_keys.add(lokr_w2_a_name)
+
+ lokr_w2_b = None
+ if lokr_w2_b_name in lora.keys():
+ lokr_w2_b = lora[lokr_w2_b_name]
+ loaded_keys.add(lokr_w2_b_name)
+
+ lokr_t2 = None
+ if lokr_t2_name in lora.keys():
+ lokr_t2 = lora[lokr_t2_name]
+ loaded_keys.add(lokr_t2_name)
+
+ if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
+ patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale))
+
+ #glora
+ a1_name = "{}.a1.weight".format(x)
+ a2_name = "{}.a2.weight".format(x)
+ b1_name = "{}.b1.weight".format(x)
+ b2_name = "{}.b2.weight".format(x)
+ if a1_name in lora:
+ patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale))
+ loaded_keys.add(a1_name)
+ loaded_keys.add(a2_name)
+ loaded_keys.add(b1_name)
+ loaded_keys.add(b2_name)
+
+ w_norm_name = "{}.w_norm".format(x)
+ b_norm_name = "{}.b_norm".format(x)
+ w_norm = lora.get(w_norm_name, None)
+ b_norm = lora.get(b_norm_name, None)
+
+ if w_norm is not None:
+ loaded_keys.add(w_norm_name)
+ patch_dict[to_load[x]] = ("diff", (w_norm,))
+ if b_norm is not None:
+ loaded_keys.add(b_norm_name)
+ patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,))
+
+ diff_name = "{}.diff".format(x)
+ diff_weight = lora.get(diff_name, None)
+ if diff_weight is not None:
+ patch_dict[to_load[x]] = ("diff", (diff_weight,))
+ loaded_keys.add(diff_name)
+
+ diff_bias_name = "{}.diff_b".format(x)
+ diff_bias = lora.get(diff_bias_name, None)
+ if diff_bias is not None:
+ patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,))
+ loaded_keys.add(diff_bias_name)
+
+ set_weight_name = "{}.set_weight".format(x)
+ set_weight = lora.get(set_weight_name, None)
+ if set_weight is not None:
+ patch_dict[to_load[x]] = ("set", (set_weight,))
+ loaded_keys.add(set_weight_name)
+
+ for x in lora.keys():
+ if x not in loaded_keys:
+ logging.warning("lora key not loaded: {}".format(x))
+
+ return patch_dict
+
+def model_lora_keys_clip(model, key_map={}):
+ sdk = model.state_dict().keys()
+ for k in sdk:
+ if k.endswith(".weight"):
+ key_map["text_encoders.{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
+
+ text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
+ clip_l_present = False
+ clip_g_present = False
+ for b in range(32): #TODO: clean up
+ for c in LORA_CLIP_MAP:
+ k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
+ if k in sdk:
+ lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
+ key_map[lora_key] = k
+ lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c])
+ key_map[lora_key] = k
+ lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
+ key_map[lora_key] = k
+
+ k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
+ if k in sdk:
+ lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
+ key_map[lora_key] = k
+ lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
+ key_map[lora_key] = k
+ clip_l_present = True
+ lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
+ key_map[lora_key] = k
+
+ k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
+ if k in sdk:
+ clip_g_present = True
+ if clip_l_present:
+ lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
+ key_map[lora_key] = k
+ lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
+ key_map[lora_key] = k
+ else:
+ lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner
+ key_map[lora_key] = k
+ lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
+ key_map[lora_key] = k
+ lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config
+ key_map[lora_key] = k
+
+ for k in sdk:
+ if k.endswith(".weight"):
+ if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora
+ l_key = k[len("t5xxl.transformer."):-len(".weight")]
+ t5_index = 1
+ if clip_g_present:
+ t5_index += 1
+ if clip_l_present:
+ t5_index += 1
+ if t5_index == 2:
+ key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux
+ t5_index += 1
+
+ key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k
+ elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora
+ l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")]
+ lora_key = "lora_te1_{}".format(l_key.replace(".", "_"))
+ key_map[lora_key] = k
+
+
+ k = "clip_g.transformer.text_projection.weight"
+ if k in sdk:
+ key_map["lora_prior_te_text_projection"] = k #cascade lora?
+ # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too
+ key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora
+
+ k = "clip_l.transformer.text_projection.weight"
+ if k in sdk:
+ key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning
+
+ return key_map
+
+def model_lora_keys_unet(model, key_map={}):
+ sd = model.state_dict()
+ sdk = sd.keys()
+
+ for k in sdk:
+ if k.startswith("diffusion_model."):
+ if k.endswith(".weight"):
+ key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
+ key_map["lora_unet_{}".format(key_lora)] = k
+ key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config
+ key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
+ else:
+ key_map["{}".format(k)] = k #generic lora format for not .weight without any weird key names
+
+ diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config)
+ for k in diffusers_keys:
+ if k.endswith(".weight"):
+ unet_key = "diffusion_model.{}".format(diffusers_keys[k])
+ key_lora = k[:-len(".weight")].replace(".", "_")
+ key_map["lora_unet_{}".format(key_lora)] = unet_key
+ key_map["lycoris_{}".format(key_lora)] = unet_key #simpletuner lycoris format
+
+ diffusers_lora_prefix = ["", "unet."]
+ for p in diffusers_lora_prefix:
+ diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_"))
+ if diffusers_lora_key.endswith(".to_out.0"):
+ diffusers_lora_key = diffusers_lora_key[:-2]
+ key_map[diffusers_lora_key] = unet_key
+
+ if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
+ diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+ for k in diffusers_keys:
+ if k.endswith(".weight"):
+ to = diffusers_keys[k]
+ key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format
+ key_map[key_lora] = to
+
+ key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others?
+ key_map[key_lora] = to
+
+ key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora
+ key_map[key_lora] = to
+
+ key_lora = "lycoris_{}".format(k[:-len(".weight")].replace(".", "_")) #simpletuner lycoris format
+ key_map[key_lora] = to
+
+
+ if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow
+ diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+ for k in diffusers_keys:
+ if k.endswith(".weight"):
+ to = diffusers_keys[k]
+ key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format
+ key_map[key_lora] = to
+
+ if isinstance(model, comfy.model_base.HunyuanDiT):
+ for k in sdk:
+ if k.startswith("diffusion_model.") and k.endswith(".weight"):
+ key_lora = k[len("diffusion_model."):-len(".weight")]
+ key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format
+
+ if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux
+ diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+ for k in diffusers_keys:
+ if k.endswith(".weight"):
+ to = diffusers_keys[k]
+ key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format
+ key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
+ key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer
+
+ if isinstance(model, comfy.model_base.GenmoMochi):
+ for k in sdk:
+ if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official Mochi lora format
+ key_lora = k[len("diffusion_model."):-len(".weight")]
+ key_map["{}".format(key_lora)] = k
+
+ return key_map
+
+
+def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function):
+ dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype)
+ lora_diff *= alpha
+ weight_calc = weight + function(lora_diff).type(weight.dtype)
+ weight_norm = (
+ weight_calc.transpose(0, 1)
+ .reshape(weight_calc.shape[1], -1)
+ .norm(dim=1, keepdim=True)
+ .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
+ .transpose(0, 1)
+ )
+
+ weight_calc *= (dora_scale / weight_norm).type(weight.dtype)
+ if strength != 1.0:
+ weight_calc -= weight
+ weight += strength * (weight_calc)
+ else:
+ weight[:] = weight_calc
+ return weight
+
+def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor:
+ """
+ Pad a tensor to a new shape with zeros.
+
+ Args:
+ tensor (torch.Tensor): The original tensor to be padded.
+ new_shape (List[int]): The desired shape of the padded tensor.
+
+ Returns:
+ torch.Tensor: A new tensor padded with zeros to the specified shape.
+
+ Note:
+ If the new shape is smaller than the original tensor in any dimension,
+ the original tensor will be truncated in that dimension.
+ """
+ if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]):
+ raise ValueError("The new shape must be larger than the original tensor in all dimensions")
+
+ if len(new_shape) != len(tensor.shape):
+ raise ValueError("The new shape must have the same number of dimensions as the original tensor")
+
+ # Create a new tensor filled with zeros
+ padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device)
+
+ # Create slicing tuples for both tensors
+ orig_slices = tuple(slice(0, dim) for dim in tensor.shape)
+ new_slices = tuple(slice(0, dim) for dim in tensor.shape)
+
+ # Copy the original tensor into the new tensor
+ padded_tensor[new_slices] = tensor[orig_slices]
+
+ return padded_tensor
+
+def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
+ for p in patches:
+ strength = p[0]
+ v = p[1]
+ strength_model = p[2]
+ offset = p[3]
+ function = p[4]
+ if function is None:
+ function = lambda a: a
+
+ old_weight = None
+ if offset is not None:
+ old_weight = weight
+ weight = weight.narrow(offset[0], offset[1], offset[2])
+
+ if strength_model != 1.0:
+ weight *= strength_model
+
+ if isinstance(v, list):
+ v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), )
+
+ if len(v) == 1:
+ patch_type = "diff"
+ elif len(v) == 2:
+ patch_type = v[0]
+ v = v[1]
+
+ if patch_type == "diff":
+ diff: torch.Tensor = v[0]
+ # An extra flag to pad the weight if the diff's shape is larger than the weight
+ do_pad_weight = len(v) > 1 and v[1]['pad_weight']
+ if do_pad_weight and diff.shape != weight.shape:
+ logging.info("Pad weight {} from {} to shape: {}".format(key, weight.shape, diff.shape))
+ weight = pad_tensor_to_shape(weight, diff.shape)
+
+ if strength != 0.0:
+ if diff.shape != weight.shape:
+ logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, diff.shape, weight.shape))
+ else:
+ weight += function(strength * comfy.model_management.cast_to_device(diff, weight.device, weight.dtype))
+ elif patch_type == "set":
+ weight.copy_(v[0])
+ elif patch_type == "lora": #lora/locon
+ mat1 = comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype)
+ mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype)
+ dora_scale = v[4]
+ reshape = v[5]
+
+ if reshape is not None:
+ weight = pad_tensor_to_shape(weight, reshape)
+
+ if v[2] is not None:
+ alpha = v[2] / mat2.shape[0]
+ else:
+ alpha = 1.0
+
+ if v[3] is not None:
+ #locon mid weights, hopefully the math is fine because I didn't properly test it
+ mat3 = comfy.model_management.cast_to_device(v[3], weight.device, intermediate_dtype)
+ final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
+ mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
+ try:
+ lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
+ if dora_scale is not None:
+ weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
+ else:
+ weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+ except Exception as e:
+ logging.error("ERROR {} {} {}".format(patch_type, key, e))
+ elif patch_type == "lokr":
+ w1 = v[0]
+ w2 = v[1]
+ w1_a = v[3]
+ w1_b = v[4]
+ w2_a = v[5]
+ w2_b = v[6]
+ t2 = v[7]
+ dora_scale = v[8]
+ dim = None
+
+ if w1 is None:
+ dim = w1_b.shape[0]
+ w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype))
+ else:
+ w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype)
+
+ if w2 is None:
+ dim = w2_b.shape[0]
+ if t2 is None:
+ w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype))
+ else:
+ w2 = torch.einsum('i j k l, j r, i p -> p r k l',
+ comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype))
+ else:
+ w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype)
+
+ if len(w2.shape) == 4:
+ w1 = w1.unsqueeze(2).unsqueeze(2)
+ if v[2] is not None and dim is not None:
+ alpha = v[2] / dim
+ else:
+ alpha = 1.0
+
+ try:
+ lora_diff = torch.kron(w1, w2).reshape(weight.shape)
+ if dora_scale is not None:
+ weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
+ else:
+ weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+ except Exception as e:
+ logging.error("ERROR {} {} {}".format(patch_type, key, e))
+ elif patch_type == "loha":
+ w1a = v[0]
+ w1b = v[1]
+ if v[2] is not None:
+ alpha = v[2] / w1b.shape[0]
+ else:
+ alpha = 1.0
+
+ w2a = v[3]
+ w2b = v[4]
+ dora_scale = v[7]
+ if v[5] is not None: #cp decomposition
+ t1 = v[5]
+ t2 = v[6]
+ m1 = torch.einsum('i j k l, j r, i p -> p r k l',
+ comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype))
+
+ m2 = torch.einsum('i j k l, j r, i p -> p r k l',
+ comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype))
+ else:
+ m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype))
+ m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype),
+ comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype))
+
+ try:
+ lora_diff = (m1 * m2).reshape(weight.shape)
+ if dora_scale is not None:
+ weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
+ else:
+ weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+ except Exception as e:
+ logging.error("ERROR {} {} {}".format(patch_type, key, e))
+ elif patch_type == "glora":
+ dora_scale = v[5]
+
+ old_glora = False
+ if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]:
+ rank = v[0].shape[0]
+ old_glora = True
+
+ if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]:
+ if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]:
+ pass
+ else:
+ old_glora = False
+ rank = v[1].shape[0]
+
+ a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
+ a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
+ b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
+ b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)
+
+ if v[4] is not None:
+ alpha = v[4] / rank
+ else:
+ alpha = 1.0
+
+ try:
+ if old_glora:
+ lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora
+ else:
+ if weight.dim() > 2:
+ lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
+ else:
+ lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
+ lora_diff += torch.mm(b1, b2).reshape(weight.shape)
+
+ if dora_scale is not None:
+ weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
+ else:
+ weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+ except Exception as e:
+ logging.error("ERROR {} {} {}".format(patch_type, key, e))
+ else:
+ logging.warning("patch type not recognized {} {}".format(patch_type, key))
+
+ if old_weight is not None:
+ weight = old_weight
+
+ return weight
diff --git a/comfy/lora_convert.py b/comfy/lora_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..05032c6900dd3e2f873ab17008735d434664058c
--- /dev/null
+++ b/comfy/lora_convert.py
@@ -0,0 +1,17 @@
+import torch
+
+
+def convert_lora_bfl_control(sd): #BFL loras for Flux
+ sd_out = {}
+ for k in sd:
+ k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.scale.set_weight"))
+ sd_out[k_to] = sd[k]
+
+ sd_out["diffusion_model.img_in.reshape_weight"] = torch.tensor([sd["img_in.lora_B.weight"].shape[0], sd["img_in.lora_A.weight"].shape[1]])
+ return sd_out
+
+
+def convert_lora(sd):
+ if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd:
+ return convert_lora_bfl_control(sd)
+ return sd
diff --git a/comfy/model_base.py b/comfy/model_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..c305014a4a374205ee1b3a93dad1e5f488ae582f
--- /dev/null
+++ b/comfy/model_base.py
@@ -0,0 +1,796 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Comfy
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+import logging
+from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
+from comfy.ldm.cascade.stage_c import StageC
+from comfy.ldm.cascade.stage_b import StageB
+from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
+from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
+from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
+import comfy.ldm.genmo.joint_model.asymm_models_joint
+import comfy.ldm.aura.mmdit
+import comfy.ldm.hydit.models
+import comfy.ldm.audio.dit
+import comfy.ldm.audio.embedders
+import comfy.ldm.flux.model
+import comfy.ldm.lightricks.model
+
+import comfy.model_management
+import comfy.conds
+import comfy.ops
+from enum import Enum
+from . import utils
+import comfy.latent_formats
+import math
+
+class ModelType(Enum):
+ EPS = 1
+ V_PREDICTION = 2
+ V_PREDICTION_EDM = 3
+ STABLE_CASCADE = 4
+ EDM = 5
+ FLOW = 6
+ V_PREDICTION_CONTINUOUS = 7
+ FLUX = 8
+
+
+from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
+
+
+def model_sampling(model_config, model_type):
+ s = ModelSamplingDiscrete
+
+ if model_type == ModelType.EPS:
+ c = EPS
+ elif model_type == ModelType.V_PREDICTION:
+ c = V_PREDICTION
+ elif model_type == ModelType.V_PREDICTION_EDM:
+ c = V_PREDICTION
+ s = ModelSamplingContinuousEDM
+ elif model_type == ModelType.FLOW:
+ c = comfy.model_sampling.CONST
+ s = comfy.model_sampling.ModelSamplingDiscreteFlow
+ elif model_type == ModelType.STABLE_CASCADE:
+ c = EPS
+ s = StableCascadeSampling
+ elif model_type == ModelType.EDM:
+ c = EDM
+ s = ModelSamplingContinuousEDM
+ elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
+ c = V_PREDICTION
+ s = ModelSamplingContinuousV
+ elif model_type == ModelType.FLUX:
+ c = comfy.model_sampling.CONST
+ s = comfy.model_sampling.ModelSamplingFlux
+
+ class ModelSampling(s, c):
+ pass
+
+ return ModelSampling(model_config)
+
+
+class BaseModel(torch.nn.Module):
+ def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):
+ super().__init__()
+
+ unet_config = model_config.unet_config
+ self.latent_format = model_config.latent_format
+ self.model_config = model_config
+ self.manual_cast_dtype = model_config.manual_cast_dtype
+ self.device = device
+
+ if not unet_config.get("disable_unet_model_creation", False):
+ if model_config.custom_operations is None:
+ fp8 = model_config.optimizations.get("fp8", model_config.scaled_fp8 is not None)
+ operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8)
+ else:
+ operations = model_config.custom_operations
+ self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
+ if comfy.model_management.force_channels_last():
+ self.diffusion_model.to(memory_format=torch.channels_last)
+ logging.debug("using channels last mode for diffusion model")
+ logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
+ self.model_type = model_type
+ self.model_sampling = model_sampling(model_config, model_type)
+
+ self.adm_channels = unet_config.get("adm_in_channels", None)
+ if self.adm_channels is None:
+ self.adm_channels = 0
+
+ self.concat_keys = ()
+ logging.info("model_type {}".format(model_type.name))
+ logging.debug("adm {}".format(self.adm_channels))
+ self.memory_usage_factor = model_config.memory_usage_factor
+
+ def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
+ sigma = t
+ xc = self.model_sampling.calculate_input(sigma, x)
+ if c_concat is not None:
+ xc = torch.cat([xc] + [c_concat], dim=1)
+
+ context = c_crossattn
+ dtype = self.get_dtype()
+
+ if self.manual_cast_dtype is not None:
+ dtype = self.manual_cast_dtype
+
+ xc = xc.to(dtype)
+ t = self.model_sampling.timestep(t).float()
+ context = context.to(dtype)
+ extra_conds = {}
+ for o in kwargs:
+ extra = kwargs[o]
+ if hasattr(extra, "dtype"):
+ if extra.dtype != torch.int and extra.dtype != torch.long:
+ extra = extra.to(dtype)
+ extra_conds[o] = extra
+
+ model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
+ return self.model_sampling.calculate_denoised(sigma, model_output, x)
+
+ def get_dtype(self):
+ return self.diffusion_model.dtype
+
+ def is_adm(self):
+ return self.adm_channels > 0
+
+ def encode_adm(self, **kwargs):
+ return None
+
+ def concat_cond(self, **kwargs):
+ if len(self.concat_keys) > 0:
+ cond_concat = []
+ denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+ concat_latent_image = kwargs.get("concat_latent_image", None)
+ if concat_latent_image is None:
+ concat_latent_image = kwargs.get("latent_image", None)
+ else:
+ concat_latent_image = self.process_latent_in(concat_latent_image)
+
+ noise = kwargs.get("noise", None)
+ device = kwargs["device"]
+
+ if concat_latent_image.shape[1:] != noise.shape[1:]:
+ concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
+
+ concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0])
+
+ if denoise_mask is not None:
+ if len(denoise_mask.shape) == len(noise.shape):
+ denoise_mask = denoise_mask[:,:1]
+
+ denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
+ if denoise_mask.shape[-2:] != noise.shape[-2:]:
+ denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
+ denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
+
+ for ck in self.concat_keys:
+ if denoise_mask is not None:
+ if ck == "mask":
+ cond_concat.append(denoise_mask.to(device))
+ elif ck == "masked_image":
+ cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space
+ else:
+ if ck == "mask":
+ cond_concat.append(torch.ones_like(noise)[:,:1])
+ elif ck == "masked_image":
+ cond_concat.append(self.blank_inpaint_image_like(noise))
+ data = torch.cat(cond_concat, dim=1)
+ return data
+ return None
+
+ def extra_conds(self, **kwargs):
+ out = {}
+ concat_cond = self.concat_cond(**kwargs)
+ if concat_cond is not None:
+ out['c_concat'] = comfy.conds.CONDNoiseShape(concat_cond)
+
+ adm = self.encode_adm(**kwargs)
+ if adm is not None:
+ out['y'] = comfy.conds.CONDRegular(adm)
+
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
+
+ cross_attn_cnet = kwargs.get("cross_attn_controlnet", None)
+ if cross_attn_cnet is not None:
+ out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet)
+
+ c_concat = kwargs.get("noise_concat", None)
+ if c_concat is not None:
+ out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat)
+
+ return out
+
+ def load_model_weights(self, sd, unet_prefix=""):
+ to_load = {}
+ keys = list(sd.keys())
+ for k in keys:
+ if k.startswith(unet_prefix):
+ to_load[k[len(unet_prefix):]] = sd.pop(k)
+
+ to_load = self.model_config.process_unet_state_dict(to_load)
+ m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
+ if len(m) > 0:
+ logging.warning("unet missing: {}".format(m))
+
+ if len(u) > 0:
+ logging.warning("unet unexpected: {}".format(u))
+ del to_load
+ return self
+
+ def process_latent_in(self, latent):
+ return self.latent_format.process_in(latent)
+
+ def process_latent_out(self, latent):
+ return self.latent_format.process_out(latent)
+
+ def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):
+ extra_sds = []
+ if clip_state_dict is not None:
+ extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict))
+ if vae_state_dict is not None:
+ extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict))
+ if clip_vision_state_dict is not None:
+ extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict))
+
+ unet_state_dict = self.diffusion_model.state_dict()
+
+ if self.model_config.scaled_fp8 is not None:
+ unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8)
+
+ unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
+
+ if self.model_type == ModelType.V_PREDICTION:
+ unet_state_dict["v_pred"] = torch.tensor([])
+
+ for sd in extra_sds:
+ unet_state_dict.update(sd)
+
+ return unet_state_dict
+
+ def set_inpaint(self):
+ self.concat_keys = ("mask", "masked_image")
+ def blank_inpaint_image_like(latent_image):
+ blank_image = torch.ones_like(latent_image)
+ # these are the values for "zero" in pixel space translated to latent space
+ blank_image[:,0] *= 0.8223
+ blank_image[:,1] *= -0.6876
+ blank_image[:,2] *= 0.6364
+ blank_image[:,3] *= 0.1380
+ return blank_image
+ self.blank_inpaint_image_like = blank_inpaint_image_like
+
+ def memory_required(self, input_shape):
+ if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
+ dtype = self.get_dtype()
+ if self.manual_cast_dtype is not None:
+ dtype = self.manual_cast_dtype
+ #TODO: this needs to be tweaked
+ area = input_shape[0] * math.prod(input_shape[2:])
+ return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024)
+ else:
+ #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
+ area = input_shape[0] * math.prod(input_shape[2:])
+ return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024)
+
+
+def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
+ adm_inputs = []
+ weights = []
+ noise_aug = []
+ for unclip_cond in unclip_conditioning:
+ for adm_cond in unclip_cond["clip_vision_output"].image_embeds:
+ weight = unclip_cond["strength"]
+ noise_augment = unclip_cond["noise_augmentation"]
+ noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
+ c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed)
+ adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
+ weights.append(weight)
+ noise_aug.append(noise_augment)
+ adm_inputs.append(adm_out)
+
+ if len(noise_aug) > 1:
+ adm_out = torch.stack(adm_inputs).sum(0)
+ noise_augment = noise_augment_merge
+ noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
+ c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device))
+ adm_out = torch.cat((c_adm, noise_level_emb), 1)
+
+ return adm_out
+
+class SD21UNCLIP(BaseModel):
+ def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None):
+ super().__init__(model_config, model_type, device=device)
+ self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config)
+
+ def encode_adm(self, **kwargs):
+ unclip_conditioning = kwargs.get("unclip_conditioning", None)
+ device = kwargs["device"]
+ if unclip_conditioning is None:
+ return torch.zeros((1, self.adm_channels))
+ else:
+ return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10)
+
+def sdxl_pooled(args, noise_augmentor):
+ if "unclip_conditioning" in args:
+ return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280]
+ else:
+ return args["pooled_output"]
+
+class SDXLRefiner(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+ super().__init__(model_config, model_type, device=device)
+ self.embedder = Timestep(256)
+ self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
+
+ def encode_adm(self, **kwargs):
+ clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
+ width = kwargs.get("width", 768)
+ height = kwargs.get("height", 768)
+ crop_w = kwargs.get("crop_w", 0)
+ crop_h = kwargs.get("crop_h", 0)
+
+ if kwargs.get("prompt_type", "") == "negative":
+ aesthetic_score = kwargs.get("aesthetic_score", 2.5)
+ else:
+ aesthetic_score = kwargs.get("aesthetic_score", 6)
+
+ out = []
+ out.append(self.embedder(torch.Tensor([height])))
+ out.append(self.embedder(torch.Tensor([width])))
+ out.append(self.embedder(torch.Tensor([crop_h])))
+ out.append(self.embedder(torch.Tensor([crop_w])))
+ out.append(self.embedder(torch.Tensor([aesthetic_score])))
+ flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
+ return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
+
+class SDXL(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+ super().__init__(model_config, model_type, device=device)
+ self.embedder = Timestep(256)
+ self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
+
+ def encode_adm(self, **kwargs):
+ clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
+ width = kwargs.get("width", 768)
+ height = kwargs.get("height", 768)
+ crop_w = kwargs.get("crop_w", 0)
+ crop_h = kwargs.get("crop_h", 0)
+ target_width = kwargs.get("target_width", width)
+ target_height = kwargs.get("target_height", height)
+
+ out = []
+ out.append(self.embedder(torch.Tensor([height])))
+ out.append(self.embedder(torch.Tensor([width])))
+ out.append(self.embedder(torch.Tensor([crop_h])))
+ out.append(self.embedder(torch.Tensor([crop_w])))
+ out.append(self.embedder(torch.Tensor([target_height])))
+ out.append(self.embedder(torch.Tensor([target_width])))
+ flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
+ return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
+
+
+class SVD_img2vid(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None):
+ super().__init__(model_config, model_type, device=device)
+ self.embedder = Timestep(256)
+
+ def encode_adm(self, **kwargs):
+ fps_id = kwargs.get("fps", 6) - 1
+ motion_bucket_id = kwargs.get("motion_bucket_id", 127)
+ augmentation = kwargs.get("augmentation_level", 0)
+
+ out = []
+ out.append(self.embedder(torch.Tensor([fps_id])))
+ out.append(self.embedder(torch.Tensor([motion_bucket_id])))
+ out.append(self.embedder(torch.Tensor([augmentation])))
+
+ flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0)
+ return flat
+
+ def extra_conds(self, **kwargs):
+ out = {}
+ adm = self.encode_adm(**kwargs)
+ if adm is not None:
+ out['y'] = comfy.conds.CONDRegular(adm)
+
+ latent_image = kwargs.get("concat_latent_image", None)
+ noise = kwargs.get("noise", None)
+ device = kwargs["device"]
+
+ if latent_image is None:
+ latent_image = torch.zeros_like(noise)
+
+ if latent_image.shape[1:] != noise.shape[1:]:
+ latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
+
+ latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])
+
+ out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image)
+
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
+
+ if "time_conditioning" in kwargs:
+ out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"])
+
+ out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0])
+ return out
+
+class SV3D_u(SVD_img2vid):
+ def encode_adm(self, **kwargs):
+ augmentation = kwargs.get("augmentation_level", 0)
+
+ out = []
+ out.append(self.embedder(torch.flatten(torch.Tensor([augmentation]))))
+
+ flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0)
+ return flat
+
+class SV3D_p(SVD_img2vid):
+ def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None):
+ super().__init__(model_config, model_type, device=device)
+ self.embedder_512 = Timestep(512)
+
+ def encode_adm(self, **kwargs):
+ augmentation = kwargs.get("augmentation_level", 0)
+ elevation = kwargs.get("elevation", 0) #elevation and azimuth are in degrees here
+ azimuth = kwargs.get("azimuth", 0)
+ noise = kwargs.get("noise", None)
+
+ out = []
+ out.append(self.embedder(torch.flatten(torch.Tensor([augmentation]))))
+ out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0))))
+ out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0))))
+
+ out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out))
+ return torch.cat(out, dim=1)
+
+
+class Stable_Zero123(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
+ super().__init__(model_config, model_type, device=device)
+ self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)
+ self.cc_projection.weight.copy_(cc_projection_weight)
+ self.cc_projection.bias.copy_(cc_projection_bias)
+
+ def extra_conds(self, **kwargs):
+ out = {}
+
+ latent_image = kwargs.get("concat_latent_image", None)
+ noise = kwargs.get("noise", None)
+
+ if latent_image is None:
+ latent_image = torch.zeros_like(noise)
+
+ if latent_image.shape[1:] != noise.shape[1:]:
+ latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
+
+ latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])
+
+ out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image)
+
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ if cross_attn.shape[-1] != 768:
+ cross_attn = self.cc_projection(cross_attn)
+ out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
+ return out
+
+class SD_X4Upscaler(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
+ super().__init__(model_config, model_type, device=device)
+ self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350)
+
+ def extra_conds(self, **kwargs):
+ out = {}
+
+ image = kwargs.get("concat_image", None)
+ noise = kwargs.get("noise", None)
+ noise_augment = kwargs.get("noise_augmentation", 0.0)
+ device = kwargs["device"]
+ seed = kwargs["seed"] - 10
+
+ noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment)
+
+ if image is None:
+ image = torch.zeros_like(noise)[:,:3]
+
+ if image.shape[1:] != noise.shape[1:]:
+ image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+
+ noise_level = torch.tensor([noise_level], device=device)
+ if noise_augment > 0:
+ image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed)
+
+ image = utils.resize_to_batch_size(image, noise.shape[0])
+
+ out['c_concat'] = comfy.conds.CONDNoiseShape(image)
+ out['y'] = comfy.conds.CONDRegular(noise_level)
+ return out
+
+class IP2P:
+ def concat_cond(self, **kwargs):
+ image = kwargs.get("concat_latent_image", None)
+ noise = kwargs.get("noise", None)
+ device = kwargs["device"]
+
+ if image is None:
+ image = torch.zeros_like(noise)
+
+ if image.shape[1:] != noise.shape[1:]:
+ image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+
+ image = utils.resize_to_batch_size(image, noise.shape[0])
+ return self.process_ip2p_image_in(image)
+
+
+class SD15_instructpix2pix(IP2P, BaseModel):
+ def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+ super().__init__(model_config, model_type, device=device)
+ self.process_ip2p_image_in = lambda image: image
+
+
+class SDXL_instructpix2pix(IP2P, SDXL):
+ def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+ super().__init__(model_config, model_type, device=device)
+ if model_type == ModelType.V_PREDICTION_EDM:
+ self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p
+ else:
+ self.process_ip2p_image_in = lambda image: image #diffusers ip2p
+
+
+class StableCascade_C(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=StageC)
+ self.diffusion_model.eval().requires_grad_(False)
+
+ def extra_conds(self, **kwargs):
+ out = {}
+ clip_text_pooled = kwargs["pooled_output"]
+ if clip_text_pooled is not None:
+ out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)
+
+ if "unclip_conditioning" in kwargs:
+ embeds = []
+ for unclip_cond in kwargs["unclip_conditioning"]:
+ weight = unclip_cond["strength"]
+ embeds.append(unclip_cond["clip_vision_output"].image_embeds.unsqueeze(0) * weight)
+ clip_img = torch.cat(embeds, dim=1)
+ else:
+ clip_img = torch.zeros((1, 1, 768))
+ out["clip_img"] = comfy.conds.CONDRegular(clip_img)
+ out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
+ out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,)))
+
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn)
+ return out
+
+
+class StableCascade_B(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=StageB)
+ self.diffusion_model.eval().requires_grad_(False)
+
+ def extra_conds(self, **kwargs):
+ out = {}
+ noise = kwargs.get("noise", None)
+
+ clip_text_pooled = kwargs["pooled_output"]
+ if clip_text_pooled is not None:
+ out['clip'] = comfy.conds.CONDRegular(clip_text_pooled)
+
+ #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched
+ prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device))
+
+ out["effnet"] = comfy.conds.CONDRegular(prior)
+ out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
+ return out
+
+
+class SD3(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper)
+
+ def encode_adm(self, **kwargs):
+ return kwargs["pooled_output"]
+
+ def extra_conds(self, **kwargs):
+ out = super().extra_conds(**kwargs)
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+ return out
+
+
+class AuraFlow(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT)
+
+ def extra_conds(self, **kwargs):
+ out = super().extra_conds(**kwargs)
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+ return out
+
+
+class StableAudio1(BaseModel):
+ def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer)
+ self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+ self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+ self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights)
+ self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights)
+
+ def extra_conds(self, **kwargs):
+ out = {}
+
+ noise = kwargs.get("noise", None)
+ device = kwargs["device"]
+
+ seconds_start = kwargs.get("seconds_start", 0)
+ seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53))
+
+ seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device)
+ seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device)
+
+ global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1))
+ out['global_embed'] = comfy.conds.CONDRegular(global_embed)
+
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1)
+ out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+ return out
+
+ def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):
+ sd = super().state_dict_for_saving(clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict)
+ d = {"conditioner.conditioners.seconds_start.": self.seconds_start_embedder.state_dict(), "conditioner.conditioners.seconds_total.": self.seconds_total_embedder.state_dict()}
+ for k in d:
+ s = d[k]
+ for l in s:
+ sd["{}{}".format(k, l)] = s[l]
+ return sd
+
+class HunyuanDiT(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hydit.models.HunYuanDiT)
+
+ def extra_conds(self, **kwargs):
+ out = super().extra_conds(**kwargs)
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+ attention_mask = kwargs.get("attention_mask", None)
+ if attention_mask is not None:
+ out['text_embedding_mask'] = comfy.conds.CONDRegular(attention_mask)
+
+ conditioning_mt5xl = kwargs.get("conditioning_mt5xl", None)
+ if conditioning_mt5xl is not None:
+ out['encoder_hidden_states_t5'] = comfy.conds.CONDRegular(conditioning_mt5xl)
+
+ attention_mask_mt5xl = kwargs.get("attention_mask_mt5xl", None)
+ if attention_mask_mt5xl is not None:
+ out['text_embedding_mask_t5'] = comfy.conds.CONDRegular(attention_mask_mt5xl)
+
+ width = kwargs.get("width", 768)
+ height = kwargs.get("height", 768)
+ crop_w = kwargs.get("crop_w", 0)
+ crop_h = kwargs.get("crop_h", 0)
+ target_width = kwargs.get("target_width", width)
+ target_height = kwargs.get("target_height", height)
+
+ out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
+ return out
+
+class Flux(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux)
+
+ def concat_cond(self, **kwargs):
+ try:
+ #Handle Flux control loras dynamically changing the img_in weight.
+ num_channels = self.diffusion_model.img_in.weight.shape[1] // (self.diffusion_model.patch_size * self.diffusion_model.patch_size)
+ except:
+ #Some cases like tensorrt might not have the weights accessible
+ num_channels = self.model_config.unet_config["in_channels"]
+
+ out_channels = self.model_config.unet_config["out_channels"]
+
+ if num_channels <= out_channels:
+ return None
+
+ image = kwargs.get("concat_latent_image", None)
+ noise = kwargs.get("noise", None)
+ device = kwargs["device"]
+
+ if image is None:
+ image = torch.zeros_like(noise)
+
+ image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+ image = utils.resize_to_batch_size(image, noise.shape[0])
+ image = self.process_latent_in(image)
+ if num_channels <= out_channels * 2:
+ return image
+
+ #inpaint model
+ mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+ if mask is None:
+ mask = torch.ones_like(noise)[:, :1]
+
+ mask = torch.mean(mask, dim=1, keepdim=True)
+ print(mask.shape)
+ mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center")
+ mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8)
+ mask = utils.resize_to_batch_size(mask, noise.shape[0])
+ return torch.cat((image, mask), dim=1)
+
+ def encode_adm(self, **kwargs):
+ return kwargs["pooled_output"]
+
+ def extra_conds(self, **kwargs):
+ out = super().extra_conds(**kwargs)
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+ out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
+ return out
+
+class GenmoMochi(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint)
+
+ def extra_conds(self, **kwargs):
+ out = super().extra_conds(**kwargs)
+ attention_mask = kwargs.get("attention_mask", None)
+ if attention_mask is not None:
+ out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+ out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item()))
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+ return out
+
+class LTXV(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel) #TODO
+
+ def extra_conds(self, **kwargs):
+ out = super().extra_conds(**kwargs)
+ attention_mask = kwargs.get("attention_mask", None)
+ if attention_mask is not None:
+ out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+ cross_attn = kwargs.get("cross_attn", None)
+ if cross_attn is not None:
+ out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+ guiding_latent = kwargs.get("guiding_latent", None)
+ if guiding_latent is not None:
+ out['guiding_latent'] = comfy.conds.CONDRegular(guiding_latent)
+
+ out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))
+ return out
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5c5dbb2af437604d76aaef33df89c01c9dd589b
--- /dev/null
+++ b/comfy/model_detection.py
@@ -0,0 +1,603 @@
+import comfy.supported_models
+import comfy.supported_models_base
+import comfy.utils
+import math
+import logging
+import torch
+
+def count_blocks(state_dict_keys, prefix_string):
+ count = 0
+ while True:
+ c = False
+ for k in state_dict_keys:
+ if k.startswith(prefix_string.format(count)):
+ c = True
+ break
+ if c == False:
+ break
+ count += 1
+ return count
+
+def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
+ context_dim = None
+ use_linear_in_transformer = False
+
+ transformer_prefix = prefix + "1.transformer_blocks."
+ transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)))
+ if len(transformer_keys) > 0:
+ last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}')
+ context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]
+ use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2
+ time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict
+ time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict
+ return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross
+ return None
+
+def detect_unet_config(state_dict, key_prefix):
+ state_dict_keys = list(state_dict.keys())
+
+ if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model
+ unet_config = {}
+ unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
+ patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
+ unet_config["patch_size"] = patch_size
+ final_layer = '{}final_layer.linear.weight'.format(key_prefix)
+ if final_layer in state_dict:
+ unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size)
+
+ unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
+ unet_config["input_size"] = None
+ y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix)
+ if y_key in state_dict_keys:
+ unet_config["adm_in_channels"] = state_dict[y_key].shape[1]
+
+ context_key = '{}context_embedder.weight'.format(key_prefix)
+ if context_key in state_dict_keys:
+ in_features = state_dict[context_key].shape[1]
+ out_features = state_dict[context_key].shape[0]
+ unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}}
+ num_patches_key = '{}pos_embed'.format(key_prefix)
+ if num_patches_key in state_dict_keys:
+ num_patches = state_dict[num_patches_key].shape[1]
+ unet_config["num_patches"] = num_patches
+ unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches))
+
+ rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix)
+ if rms_qk in state_dict_keys:
+ unet_config["qk_norm"] = "rms"
+
+ unet_config["pos_embed_scaling_factor"] = None #unused for inference
+ context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)
+ if context_processor in state_dict_keys:
+ unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')
+ unet_config["x_block_self_attn_layers"] = []
+ for key in state_dict_keys:
+ if key.startswith('{}joint_blocks.'.format(key_prefix)) and key.endswith('.x_block.attn2.qkv.weight'):
+ layer = key[len('{}joint_blocks.'.format(key_prefix)):-len('.x_block.attn2.qkv.weight')]
+ unet_config["x_block_self_attn_layers"].append(int(layer))
+ return unet_config
+
+ if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade
+ unet_config = {}
+ text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix)
+ if text_mapper_name in state_dict_keys:
+ unet_config['stable_cascade_stage'] = 'c'
+ w = state_dict[text_mapper_name]
+ if w.shape[0] == 1536: #stage c lite
+ unet_config['c_cond'] = 1536
+ unet_config['c_hidden'] = [1536, 1536]
+ unet_config['nhead'] = [24, 24]
+ unet_config['blocks'] = [[4, 12], [12, 4]]
+ elif w.shape[0] == 2048: #stage c full
+ unet_config['c_cond'] = 2048
+ elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys:
+ unet_config['stable_cascade_stage'] = 'b'
+ w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)]
+ if w.shape[-1] == 640:
+ unet_config['c_hidden'] = [320, 640, 1280, 1280]
+ unet_config['nhead'] = [-1, -1, 20, 20]
+ unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]]
+ unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]]
+ elif w.shape[-1] == 576: #stage b lite
+ unet_config['c_hidden'] = [320, 576, 1152, 1152]
+ unet_config['nhead'] = [-1, 9, 18, 18]
+ unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]]
+ unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]
+ return unet_config
+
+ if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit
+ unet_config = {}
+ unet_config["audio_model"] = "dit1.0"
+ return unet_config
+
+ if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit
+ unet_config = {}
+ unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
+ unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
+ double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.')
+ single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.')
+ unet_config["n_double_layers"] = double_layers
+ unet_config["n_layers"] = double_layers + single_layers
+ return unet_config
+
+ if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT
+ unet_config = {}
+ unet_config["image_model"] = "hydit"
+ unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
+ unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0]
+ if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2
+ unet_config["mlp_ratio"] = 4.3637
+ if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968:
+ unet_config["size_cond"] = True
+ unet_config["use_style_cond"] = True
+ unet_config["image_model"] = "hydit1"
+ return unet_config
+
+ if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: #Flux
+ dit_config = {}
+ dit_config["image_model"] = "flux"
+ dit_config["in_channels"] = 16
+ patch_size = 2
+ dit_config["patch_size"] = patch_size
+ in_key = "{}img_in.weight".format(key_prefix)
+ if in_key in state_dict_keys:
+ dit_config["in_channels"] = state_dict[in_key].shape[1] // (patch_size * patch_size)
+ dit_config["out_channels"] = 16
+ dit_config["vec_in_dim"] = 768
+ dit_config["context_in_dim"] = 4096
+ dit_config["hidden_size"] = 3072
+ dit_config["mlp_ratio"] = 4.0
+ dit_config["num_heads"] = 24
+ dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
+ dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
+ dit_config["axes_dim"] = [16, 56, 56]
+ dit_config["theta"] = 10000
+ dit_config["qkv_bias"] = True
+ dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
+ return dit_config
+
+ if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview
+ dit_config = {}
+ dit_config["image_model"] = "mochi_preview"
+ dit_config["depth"] = 48
+ dit_config["patch_size"] = 2
+ dit_config["num_heads"] = 24
+ dit_config["hidden_size_x"] = 3072
+ dit_config["hidden_size_y"] = 1536
+ dit_config["mlp_ratio_x"] = 4.0
+ dit_config["mlp_ratio_y"] = 4.0
+ dit_config["learn_sigma"] = False
+ dit_config["in_channels"] = 12
+ dit_config["qk_norm"] = True
+ dit_config["qkv_bias"] = False
+ dit_config["out_bias"] = True
+ dit_config["attn_drop"] = 0.0
+ dit_config["patch_embed_bias"] = True
+ dit_config["posenc_preserve_area"] = True
+ dit_config["timestep_mlp_bias"] = True
+ dit_config["attend_to_padding"] = False
+ dit_config["timestep_scale"] = 1000.0
+ dit_config["use_t5"] = True
+ dit_config["t5_feat_dim"] = 4096
+ dit_config["t5_token_length"] = 256
+ dit_config["rope_theta"] = 10000.0
+ return dit_config
+
+ if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv
+ dit_config = {}
+ dit_config["image_model"] = "ltxv"
+ return dit_config
+
+ if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
+ return None
+
+ unet_config = {
+ "use_checkpoint": False,
+ "image_size": 32,
+ "use_spatial_transformer": True,
+ "legacy": False
+ }
+
+ y_input = '{}label_emb.0.0.weight'.format(key_prefix)
+ if y_input in state_dict_keys:
+ unet_config["num_classes"] = "sequential"
+ unet_config["adm_in_channels"] = state_dict[y_input].shape[1]
+ else:
+ unet_config["adm_in_channels"] = None
+
+ model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
+ in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
+
+ out_key = '{}out.2.weight'.format(key_prefix)
+ if out_key in state_dict:
+ out_channels = state_dict[out_key].shape[0]
+ else:
+ out_channels = 4
+
+ num_res_blocks = []
+ channel_mult = []
+ attention_resolutions = []
+ transformer_depth = []
+ transformer_depth_output = []
+ context_dim = None
+ use_linear_in_transformer = False
+
+ video_model = False
+ video_model_cross = False
+
+ current_res = 1
+ count = 0
+
+ last_res_blocks = 0
+ last_channel_mult = 0
+
+ input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.')
+ for count in range(input_block_count):
+ prefix = '{}input_blocks.{}.'.format(key_prefix, count)
+ prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1)
+
+ block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys)))
+ if len(block_keys) == 0:
+ break
+
+ block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)))
+
+ if "{}0.op.weight".format(prefix) in block_keys: #new layer
+ num_res_blocks.append(last_res_blocks)
+ channel_mult.append(last_channel_mult)
+
+ current_res *= 2
+ last_res_blocks = 0
+ last_channel_mult = 0
+ out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
+ if out is not None:
+ transformer_depth_output.append(out[0])
+ else:
+ transformer_depth_output.append(0)
+ else:
+ res_block_prefix = "{}0.in_layers.0.weight".format(prefix)
+ if res_block_prefix in block_keys:
+ last_res_blocks += 1
+ last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels
+
+ out = calculate_transformer_depth(prefix, state_dict_keys, state_dict)
+ if out is not None:
+ transformer_depth.append(out[0])
+ if context_dim is None:
+ context_dim = out[1]
+ use_linear_in_transformer = out[2]
+ video_model = out[3]
+ video_model_cross = out[4]
+ else:
+ transformer_depth.append(0)
+
+ res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output)
+ if res_block_prefix in block_keys_output:
+ out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
+ if out is not None:
+ transformer_depth_output.append(out[0])
+ else:
+ transformer_depth_output.append(0)
+
+
+ num_res_blocks.append(last_res_blocks)
+ channel_mult.append(last_channel_mult)
+ if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys:
+ transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
+ elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys:
+ transformer_depth_middle = -1
+ else:
+ transformer_depth_middle = -2
+
+ unet_config["in_channels"] = in_channels
+ unet_config["out_channels"] = out_channels
+ unet_config["model_channels"] = model_channels
+ unet_config["num_res_blocks"] = num_res_blocks
+ unet_config["transformer_depth"] = transformer_depth
+ unet_config["transformer_depth_output"] = transformer_depth_output
+ unet_config["channel_mult"] = channel_mult
+ unet_config["transformer_depth_middle"] = transformer_depth_middle
+ unet_config['use_linear_in_transformer'] = use_linear_in_transformer
+ unet_config["context_dim"] = context_dim
+
+ if video_model:
+ unet_config["extra_ff_mix_layer"] = True
+ unet_config["use_spatial_context"] = True
+ unet_config["merge_strategy"] = "learned_with_images"
+ unet_config["merge_factor"] = 0.0
+ unet_config["video_kernel_size"] = [3, 1, 1]
+ unet_config["use_temporal_resblock"] = True
+ unet_config["use_temporal_attention"] = True
+ unet_config["disable_temporal_crossattention"] = not video_model_cross
+ else:
+ unet_config["use_temporal_resblock"] = False
+ unet_config["use_temporal_attention"] = False
+
+ return unet_config
+
+def model_config_from_unet_config(unet_config, state_dict=None):
+ for model_config in comfy.supported_models.models:
+ if model_config.matches(unet_config, state_dict):
+ return model_config(unet_config)
+
+ logging.error("no match {}".format(unet_config))
+ return None
+
+def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False):
+ unet_config = detect_unet_config(state_dict, unet_key_prefix)
+ if unet_config is None:
+ return None
+ model_config = model_config_from_unet_config(unet_config, state_dict)
+ if model_config is None and use_base_if_no_match:
+ model_config = comfy.supported_models_base.BASE(unet_config)
+
+ scaled_fp8_key = "{}scaled_fp8".format(unet_key_prefix)
+ if scaled_fp8_key in state_dict:
+ scaled_fp8_weight = state_dict.pop(scaled_fp8_key)
+ model_config.scaled_fp8 = scaled_fp8_weight.dtype
+ if model_config.scaled_fp8 == torch.float32:
+ model_config.scaled_fp8 = torch.float8_e4m3fn
+
+ return model_config
+
+def unet_prefix_from_state_dict(state_dict):
+ candidates = ["model.diffusion_model.", #ldm/sgm models
+ "model.model.", #audio models
+ ]
+ counts = {k: 0 for k in candidates}
+ for k in state_dict:
+ for c in candidates:
+ if k.startswith(c):
+ counts[c] += 1
+ break
+
+ top = max(counts, key=counts.get)
+ if counts[top] > 5:
+ return top
+ else:
+ return "model." #aura flow and others
+
+
+def convert_config(unet_config):
+ new_config = unet_config.copy()
+ num_res_blocks = new_config.get("num_res_blocks", None)
+ channel_mult = new_config.get("channel_mult", None)
+
+ if isinstance(num_res_blocks, int):
+ num_res_blocks = len(channel_mult) * [num_res_blocks]
+
+ if "attention_resolutions" in new_config:
+ attention_resolutions = new_config.pop("attention_resolutions")
+ transformer_depth = new_config.get("transformer_depth", None)
+ transformer_depth_middle = new_config.get("transformer_depth_middle", None)
+
+ if isinstance(transformer_depth, int):
+ transformer_depth = len(channel_mult) * [transformer_depth]
+ if transformer_depth_middle is None:
+ transformer_depth_middle = transformer_depth[-1]
+ t_in = []
+ t_out = []
+ s = 1
+ for i in range(len(num_res_blocks)):
+ res = num_res_blocks[i]
+ d = 0
+ if s in attention_resolutions:
+ d = transformer_depth[i]
+
+ t_in += [d] * res
+ t_out += [d] * (res + 1)
+ s *= 2
+ transformer_depth = t_in
+ transformer_depth_output = t_out
+ new_config["transformer_depth"] = t_in
+ new_config["transformer_depth_output"] = t_out
+ new_config["transformer_depth_middle"] = transformer_depth_middle
+
+ new_config["num_res_blocks"] = num_res_blocks
+ return new_config
+
+
+def unet_config_from_diffusers_unet(state_dict, dtype=None):
+ match = {}
+ transformer_depth = []
+
+ attn_res = 1
+ down_blocks = count_blocks(state_dict, "down_blocks.{}")
+ for i in range(down_blocks):
+ attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
+ res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}')
+ for ab in range(attn_blocks):
+ transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
+ transformer_depth.append(transformer_count)
+ if transformer_count > 0:
+ match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1]
+
+ attn_res *= 2
+ if attn_blocks == 0:
+ for i in range(res_blocks):
+ transformer_depth.append(0)
+
+ match["transformer_depth"] = transformer_depth
+
+ match["model_channels"] = state_dict["conv_in.weight"].shape[0]
+ match["in_channels"] = state_dict["conv_in.weight"].shape[1]
+ match["adm_in_channels"] = None
+ if "class_embedding.linear_1.weight" in state_dict:
+ match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
+ elif "add_embedding.linear_1.weight" in state_dict:
+ match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
+
+ SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
+ 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
+ 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4,
+ 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2],
+ 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True,
+ 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
+ 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
+ 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
+ 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
+ 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
+ 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,
+ 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,
+ 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
+ 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
+ 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],
+ 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2],
+ 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5],
+ 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6],
+ 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
+ 'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True,
+ 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]}
+
+ SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
+ 'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,
+ 'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
+ 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
+ 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
+ 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+
+ supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint]
+
+ for unet_config in supported_models:
+ matches = True
+ for k in match:
+ if match[k] != unet_config[k]:
+ matches = False
+ break
+ if matches:
+ return convert_config(unet_config)
+ return None
+
+def model_config_from_diffusers_unet(state_dict):
+ unet_config = unet_config_from_diffusers_unet(state_dict)
+ if unet_config is not None:
+ return model_config_from_unet_config(unet_config)
+ return None
+
+def convert_diffusers_mmdit(state_dict, output_prefix=""):
+ out_sd = {}
+
+ if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
+ num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
+ num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+ sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
+ elif 'x_embedder.weight' in state_dict: #Flux
+ depth = count_blocks(state_dict, 'transformer_blocks.{}.')
+ depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+ hidden_size = state_dict["x_embedder.bias"].shape[0]
+ sd_map = comfy.utils.flux_to_diffusers({"depth": depth, "depth_single_blocks": depth_single_blocks, "hidden_size": hidden_size}, output_prefix=output_prefix)
+ elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: #SD3
+ num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
+ depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
+ sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
+ else:
+ return None
+
+ for k in sd_map:
+ weight = state_dict.get(k, None)
+ if weight is not None:
+ t = sd_map[k]
+
+ if not isinstance(t, str):
+ if len(t) > 2:
+ fun = t[2]
+ else:
+ fun = lambda a: a
+ offset = t[1]
+ if offset is not None:
+ old_weight = out_sd.get(t[0], None)
+ if old_weight is None:
+ old_weight = torch.empty_like(weight)
+ if old_weight.shape[offset[0]] < offset[1] + offset[2]:
+ exp = list(weight.shape)
+ exp[offset[0]] = offset[1] + offset[2]
+ new = torch.empty(exp, device=weight.device, dtype=weight.dtype)
+ new[:old_weight.shape[0]] = old_weight
+ old_weight = new
+
+ w = old_weight.narrow(offset[0], offset[1], offset[2])
+ else:
+ old_weight = weight
+ w = weight
+ w[:] = fun(weight)
+ t = t[0]
+ out_sd[t] = old_weight
+ else:
+ out_sd[t] = weight
+ state_dict.pop(k)
+
+ return out_sd
diff --git a/comfy/model_management.py b/comfy/model_management.py
new file mode 100644
index 0000000000000000000000000000000000000000..a793cab3eea86dd42d930319f9f56c9f421f3eff
--- /dev/null
+++ b/comfy/model_management.py
@@ -0,0 +1,1135 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Comfy
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import psutil
+import logging
+from enum import Enum
+from comfy.cli_args import args
+import torch
+import sys
+import platform
+
+class VRAMState(Enum):
+ DISABLED = 0 #No vram present: no need to move models to vram
+ NO_VRAM = 1 #Very low vram: enable all the options to save vram
+ LOW_VRAM = 2
+ NORMAL_VRAM = 3
+ HIGH_VRAM = 4
+ SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both.
+
+class CPUState(Enum):
+ GPU = 0
+ CPU = 1
+ MPS = 2
+
+# Determine VRAM State
+vram_state = VRAMState.NORMAL_VRAM
+set_vram_to = VRAMState.NORMAL_VRAM
+cpu_state = CPUState.GPU
+
+total_vram = 0
+
+xpu_available = False
+torch_version = ""
+try:
+ torch_version = torch.version.__version__
+ xpu_available = (int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)) and torch.xpu.is_available()
+except:
+ pass
+
+lowvram_available = True
+if args.deterministic:
+ logging.info("Using deterministic algorithms for pytorch")
+ torch.use_deterministic_algorithms(True, warn_only=True)
+
+directml_enabled = False
+if args.directml is not None:
+ import torch_directml
+ directml_enabled = True
+ device_index = args.directml
+ if device_index < 0:
+ directml_device = torch_directml.device()
+ else:
+ directml_device = torch_directml.device(device_index)
+ logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index)))
+ # torch_directml.disable_tiled_resources(True)
+ lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
+
+try:
+ import intel_extension_for_pytorch as ipex
+ _ = torch.xpu.device_count()
+ xpu_available = torch.xpu.is_available()
+except:
+ xpu_available = xpu_available or (hasattr(torch, "xpu") and torch.xpu.is_available())
+
+try:
+ if torch.backends.mps.is_available():
+ cpu_state = CPUState.MPS
+ import torch.mps
+except:
+ pass
+
+if args.cpu:
+ cpu_state = CPUState.CPU
+
+def is_intel_xpu():
+ global cpu_state
+ global xpu_available
+ if cpu_state == CPUState.GPU:
+ if xpu_available:
+ return True
+ return False
+
+def get_torch_device():
+ global directml_enabled
+ global cpu_state
+ if directml_enabled:
+ global directml_device
+ return directml_device
+ if cpu_state == CPUState.MPS:
+ return torch.device("mps")
+ if cpu_state == CPUState.CPU:
+ return torch.device("cpu")
+ else:
+ if is_intel_xpu():
+ return torch.device("xpu", torch.xpu.current_device())
+ else:
+ return torch.device(torch.cuda.current_device())
+
+def get_total_memory(dev=None, torch_total_too=False):
+ global directml_enabled
+ if dev is None:
+ dev = get_torch_device()
+
+ if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
+ mem_total = psutil.virtual_memory().total
+ mem_total_torch = mem_total
+ else:
+ if directml_enabled:
+ mem_total = 1024 * 1024 * 1024 #TODO
+ mem_total_torch = mem_total
+ elif is_intel_xpu():
+ stats = torch.xpu.memory_stats(dev)
+ mem_reserved = stats['reserved_bytes.all.current']
+ mem_total_torch = mem_reserved
+ mem_total = torch.xpu.get_device_properties(dev).total_memory
+ else:
+ stats = torch.cuda.memory_stats(dev)
+ mem_reserved = stats['reserved_bytes.all.current']
+ _, mem_total_cuda = torch.cuda.mem_get_info(dev)
+ mem_total_torch = mem_reserved
+ mem_total = mem_total_cuda
+
+ if torch_total_too:
+ return (mem_total, mem_total_torch)
+ else:
+ return mem_total
+
+total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
+total_ram = psutil.virtual_memory().total / (1024 * 1024)
+logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
+
+try:
+ logging.info("pytorch version: {}".format(torch_version))
+except:
+ pass
+
+try:
+ OOM_EXCEPTION = torch.cuda.OutOfMemoryError
+except:
+ OOM_EXCEPTION = Exception
+
+XFORMERS_VERSION = ""
+XFORMERS_ENABLED_VAE = True
+if args.disable_xformers:
+ XFORMERS_IS_AVAILABLE = False
+else:
+ try:
+ import xformers
+ import xformers.ops
+ XFORMERS_IS_AVAILABLE = True
+ try:
+ XFORMERS_IS_AVAILABLE = xformers._has_cpp_library
+ except:
+ pass
+ try:
+ XFORMERS_VERSION = xformers.version.__version__
+ logging.info("xformers version: {}".format(XFORMERS_VERSION))
+ if XFORMERS_VERSION.startswith("0.0.18"):
+ logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
+ logging.warning("Please downgrade or upgrade xformers to a different version.\n")
+ XFORMERS_ENABLED_VAE = False
+ except:
+ pass
+ except:
+ XFORMERS_IS_AVAILABLE = False
+
+def is_nvidia():
+ global cpu_state
+ if cpu_state == CPUState.GPU:
+ if torch.version.cuda:
+ return True
+ return False
+
+ENABLE_PYTORCH_ATTENTION = False
+if args.use_pytorch_cross_attention:
+ ENABLE_PYTORCH_ATTENTION = True
+ XFORMERS_IS_AVAILABLE = False
+
+VAE_DTYPES = [torch.float32]
+
+try:
+ if is_nvidia():
+ if int(torch_version[0]) >= 2:
+ if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
+ ENABLE_PYTORCH_ATTENTION = True
+ if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
+ VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
+ if is_intel_xpu():
+ if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
+ ENABLE_PYTORCH_ATTENTION = True
+except:
+ pass
+
+if is_intel_xpu():
+ VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
+
+if args.cpu_vae:
+ VAE_DTYPES = [torch.float32]
+
+
+if ENABLE_PYTORCH_ATTENTION:
+ torch.backends.cuda.enable_math_sdp(True)
+ torch.backends.cuda.enable_flash_sdp(True)
+ torch.backends.cuda.enable_mem_efficient_sdp(True)
+
+if args.lowvram:
+ set_vram_to = VRAMState.LOW_VRAM
+ lowvram_available = True
+elif args.novram:
+ set_vram_to = VRAMState.NO_VRAM
+elif args.highvram or args.gpu_only:
+ vram_state = VRAMState.HIGH_VRAM
+
+FORCE_FP32 = False
+FORCE_FP16 = False
+if args.force_fp32:
+ logging.info("Forcing FP32, if this improves things please report it.")
+ FORCE_FP32 = True
+
+if args.force_fp16:
+ logging.info("Forcing FP16.")
+ FORCE_FP16 = True
+
+if lowvram_available:
+ if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
+ vram_state = set_vram_to
+
+
+if cpu_state != CPUState.GPU:
+ vram_state = VRAMState.DISABLED
+
+if cpu_state == CPUState.MPS:
+ vram_state = VRAMState.SHARED
+
+logging.info(f"Set vram state to: {vram_state.name}")
+
+DISABLE_SMART_MEMORY = args.disable_smart_memory
+
+if DISABLE_SMART_MEMORY:
+ logging.info("Disabling smart memory management")
+
+def get_torch_device_name(device):
+ if hasattr(device, 'type'):
+ if device.type == "cuda":
+ try:
+ allocator_backend = torch.cuda.get_allocator_backend()
+ except:
+ allocator_backend = ""
+ return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend)
+ else:
+ return "{}".format(device.type)
+ elif is_intel_xpu():
+ return "{} {}".format(device, torch.xpu.get_device_name(device))
+ else:
+ return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
+
+try:
+ logging.info("Device: {}".format(get_torch_device_name(get_torch_device())))
+except:
+ logging.warning("Could not pick default device.")
+
+
+current_loaded_models = []
+
+def module_size(module):
+ module_mem = 0
+ sd = module.state_dict()
+ for k in sd:
+ t = sd[k]
+ module_mem += t.nelement() * t.element_size()
+ return module_mem
+
+class LoadedModel:
+ def __init__(self, model):
+ self.model = model
+ self.device = model.load_device
+ self.weights_loaded = False
+ self.real_model = None
+ self.currently_used = True
+
+ def model_memory(self):
+ return self.model.model_size()
+
+ def model_offloaded_memory(self):
+ return self.model.model_size() - self.model.loaded_size()
+
+ def model_memory_required(self, device):
+ if device == self.model.current_loaded_device():
+ return self.model_offloaded_memory()
+ else:
+ return self.model_memory()
+
+ def model_load(self, lowvram_model_memory=0, force_patch_weights=False):
+ patch_model_to = self.device
+
+ self.model.model_patches_to(self.device)
+ self.model.model_patches_to(self.model.model_dtype())
+
+ load_weights = not self.weights_loaded
+
+ if self.model.loaded_size() > 0:
+ use_more_vram = lowvram_model_memory
+ if use_more_vram == 0:
+ use_more_vram = 1e32
+ self.model_use_more_vram(use_more_vram)
+ else:
+ try:
+ self.real_model = self.model.patch_model(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, load_weights=load_weights, force_patch_weights=force_patch_weights)
+ except Exception as e:
+ self.model.unpatch_model(self.model.offload_device)
+ self.model_unload()
+ raise e
+
+ if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and self.real_model is not None:
+ with torch.no_grad():
+ self.real_model = ipex.optimize(self.real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)
+
+ self.weights_loaded = True
+ return self.real_model
+
+ def should_reload_model(self, force_patch_weights=False):
+ if force_patch_weights and self.model.lowvram_patch_counter() > 0:
+ return True
+ return False
+
+ def model_unload(self, memory_to_free=None, unpatch_weights=True):
+ if memory_to_free is not None:
+ if memory_to_free < self.model.loaded_size():
+ freed = self.model.partially_unload(self.model.offload_device, memory_to_free)
+ if freed >= memory_to_free:
+ return False
+ self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights)
+ self.model.model_patches_to(self.model.offload_device)
+ self.weights_loaded = self.weights_loaded and not unpatch_weights
+ self.real_model = None
+ return True
+
+ def model_use_more_vram(self, extra_memory):
+ return self.model.partially_load(self.device, extra_memory)
+
+ def __eq__(self, other):
+ return self.model is other.model
+
+def use_more_memory(extra_memory, loaded_models, device):
+ for m in loaded_models:
+ if m.device == device:
+ extra_memory -= m.model_use_more_vram(extra_memory)
+ if extra_memory <= 0:
+ break
+
+def offloaded_memory(loaded_models, device):
+ offloaded_mem = 0
+ for m in loaded_models:
+ if m.device == device:
+ offloaded_mem += m.model_offloaded_memory()
+ return offloaded_mem
+
+WINDOWS = any(platform.win32_ver())
+
+EXTRA_RESERVED_VRAM = 400 * 1024 * 1024
+if WINDOWS:
+ EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue
+
+if args.reserve_vram is not None:
+ EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024
+ logging.debug("Reserving {}MB vram for other applications.".format(EXTRA_RESERVED_VRAM / (1024 * 1024)))
+
+def extra_reserved_memory():
+ return EXTRA_RESERVED_VRAM
+
+def minimum_inference_memory():
+ return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory()
+
+def unload_model_clones(model, unload_weights_only=True, force_unload=True):
+ to_unload = []
+ for i in range(len(current_loaded_models)):
+ if model.is_clone(current_loaded_models[i].model):
+ to_unload = [i] + to_unload
+
+ if len(to_unload) == 0:
+ return True
+
+ same_weights = 0
+ for i in to_unload:
+ if model.clone_has_same_weights(current_loaded_models[i].model):
+ same_weights += 1
+
+ if same_weights == len(to_unload):
+ unload_weight = False
+ else:
+ unload_weight = True
+
+ if not force_unload:
+ if unload_weights_only and unload_weight == False:
+ return None
+ else:
+ unload_weight = True
+
+ for i in to_unload:
+ logging.debug("unload clone {} {}".format(i, unload_weight))
+ current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight)
+
+ return unload_weight
+
+def free_memory(memory_required, device, keep_loaded=[]):
+ unloaded_model = []
+ can_unload = []
+ unloaded_models = []
+
+ for i in range(len(current_loaded_models) -1, -1, -1):
+ shift_model = current_loaded_models[i]
+ if shift_model.device == device:
+ if shift_model not in keep_loaded:
+ can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
+ shift_model.currently_used = False
+
+ for x in sorted(can_unload):
+ i = x[-1]
+ memory_to_free = None
+ if not DISABLE_SMART_MEMORY:
+ free_mem = get_free_memory(device)
+ if free_mem > memory_required:
+ break
+ memory_to_free = memory_required - free_mem
+ logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}")
+ if current_loaded_models[i].model_unload(memory_to_free):
+ unloaded_model.append(i)
+
+ for i in sorted(unloaded_model, reverse=True):
+ unloaded_models.append(current_loaded_models.pop(i))
+
+ if len(unloaded_model) > 0:
+ soft_empty_cache()
+ else:
+ if vram_state != VRAMState.HIGH_VRAM:
+ mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
+ if mem_free_torch > mem_free_total * 0.25:
+ soft_empty_cache()
+ return unloaded_models
+
+def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
+ global vram_state
+
+ inference_memory = minimum_inference_memory()
+ extra_mem = max(inference_memory, memory_required + extra_reserved_memory())
+ if minimum_memory_required is None:
+ minimum_memory_required = extra_mem
+ else:
+ minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory())
+
+ models = set(models)
+
+ models_to_load = []
+ models_already_loaded = []
+ for x in models:
+ loaded_model = LoadedModel(x)
+ loaded = None
+
+ try:
+ loaded_model_index = current_loaded_models.index(loaded_model)
+ except:
+ loaded_model_index = None
+
+ if loaded_model_index is not None:
+ loaded = current_loaded_models[loaded_model_index]
+ if loaded.should_reload_model(force_patch_weights=force_patch_weights): #TODO: cleanup this model reload logic
+ current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True)
+ loaded = None
+ else:
+ loaded.currently_used = True
+ models_already_loaded.append(loaded)
+
+ if loaded is None:
+ if hasattr(x, "model"):
+ logging.info(f"Requested to load {x.model.__class__.__name__}")
+ models_to_load.append(loaded_model)
+
+ if len(models_to_load) == 0:
+ devs = set(map(lambda a: a.device, models_already_loaded))
+ for d in devs:
+ if d != torch.device("cpu"):
+ free_memory(extra_mem + offloaded_memory(models_already_loaded, d), d, models_already_loaded)
+ free_mem = get_free_memory(d)
+ if free_mem < minimum_memory_required:
+ logging.info("Unloading models for lowram load.") #TODO: partial model unloading when this case happens, also handle the opposite case where models can be unlowvramed.
+ models_to_load = free_memory(minimum_memory_required, d)
+ logging.info("{} models unloaded.".format(len(models_to_load)))
+ else:
+ use_more_memory(free_mem - minimum_memory_required, models_already_loaded, d)
+ if len(models_to_load) == 0:
+ return
+
+ logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
+
+ total_memory_required = {}
+ for loaded_model in models_to_load:
+ unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) #unload clones where the weights are different
+ total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
+
+ for loaded_model in models_already_loaded:
+ total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
+
+ for loaded_model in models_to_load:
+ weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) #unload the rest of the clones where the weights can stay loaded
+ if weights_unloaded is not None:
+ loaded_model.weights_loaded = not weights_unloaded
+
+ for device in total_memory_required:
+ if device != torch.device("cpu"):
+ free_memory(total_memory_required[device] * 1.1 + extra_mem, device, models_already_loaded)
+
+ for loaded_model in models_to_load:
+ model = loaded_model.model
+ torch_dev = model.load_device
+ if is_device_cpu(torch_dev):
+ vram_set_state = VRAMState.DISABLED
+ else:
+ vram_set_state = vram_state
+ lowvram_model_memory = 0
+ if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load:
+ model_size = loaded_model.model_memory_required(torch_dev)
+ current_free_mem = get_free_memory(torch_dev)
+ lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), min(current_free_mem * 0.4, current_free_mem - minimum_inference_memory()))
+ if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
+ lowvram_model_memory = 0
+
+ if vram_set_state == VRAMState.NO_VRAM:
+ lowvram_model_memory = 64 * 1024 * 1024
+
+ cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
+ current_loaded_models.insert(0, loaded_model)
+
+
+ devs = set(map(lambda a: a.device, models_already_loaded))
+ for d in devs:
+ if d != torch.device("cpu"):
+ free_mem = get_free_memory(d)
+ if free_mem > minimum_memory_required:
+ use_more_memory(free_mem - minimum_memory_required, models_already_loaded, d)
+ return
+
+
+def load_model_gpu(model):
+ return load_models_gpu([model])
+
+def loaded_models(only_currently_used=False):
+ output = []
+ for m in current_loaded_models:
+ if only_currently_used:
+ if not m.currently_used:
+ continue
+
+ output.append(m.model)
+ return output
+
+def cleanup_models(keep_clone_weights_loaded=False):
+ to_delete = []
+ for i in range(len(current_loaded_models)):
+ #TODO: very fragile function needs improvement
+ num_refs = sys.getrefcount(current_loaded_models[i].model)
+ if num_refs <= 2:
+ if not keep_clone_weights_loaded:
+ to_delete = [i] + to_delete
+ #TODO: find a less fragile way to do this.
+ elif sys.getrefcount(current_loaded_models[i].real_model) <= 3: #references from .real_model + the .model
+ to_delete = [i] + to_delete
+
+ for i in to_delete:
+ x = current_loaded_models.pop(i)
+ x.model_unload()
+ del x
+
+def dtype_size(dtype):
+ dtype_size = 4
+ if dtype == torch.float16 or dtype == torch.bfloat16:
+ dtype_size = 2
+ elif dtype == torch.float32:
+ dtype_size = 4
+ else:
+ try:
+ dtype_size = dtype.itemsize
+ except: #Old pytorch doesn't have .itemsize
+ pass
+ return dtype_size
+
+def unet_offload_device():
+ if vram_state == VRAMState.HIGH_VRAM:
+ return get_torch_device()
+ else:
+ return torch.device("cpu")
+
+def unet_inital_load_device(parameters, dtype):
+ torch_dev = get_torch_device()
+ if vram_state == VRAMState.HIGH_VRAM:
+ return torch_dev
+
+ cpu_dev = torch.device("cpu")
+ if DISABLE_SMART_MEMORY:
+ return cpu_dev
+
+ model_size = dtype_size(dtype) * parameters
+
+ mem_dev = get_free_memory(torch_dev)
+ mem_cpu = get_free_memory(cpu_dev)
+ if mem_dev > mem_cpu and model_size < mem_dev:
+ return torch_dev
+ else:
+ return cpu_dev
+
+def maximum_vram_for_weights(device=None):
+ return (get_total_memory(device) * 0.88 - minimum_inference_memory())
+
+def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
+ if model_params < 0:
+ model_params = 1000000000000000000000
+ if args.fp32_unet:
+ return torch.float32
+ if args.fp64_unet:
+ return torch.float64
+ if args.bf16_unet:
+ return torch.bfloat16
+ if args.fp16_unet:
+ return torch.float16
+ if args.fp8_e4m3fn_unet:
+ return torch.float8_e4m3fn
+ if args.fp8_e5m2_unet:
+ return torch.float8_e5m2
+
+ fp8_dtype = None
+ try:
+ for dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
+ if dtype in supported_dtypes:
+ fp8_dtype = dtype
+ break
+ except:
+ pass
+
+ if fp8_dtype is not None:
+ if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive
+ return fp8_dtype
+
+ free_model_memory = maximum_vram_for_weights(device)
+ if model_params * 2 > free_model_memory:
+ return fp8_dtype
+
+ for dt in supported_dtypes:
+ if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params):
+ if torch.float16 in supported_dtypes:
+ return torch.float16
+ if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params):
+ if torch.bfloat16 in supported_dtypes:
+ return torch.bfloat16
+
+ for dt in supported_dtypes:
+ if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True):
+ if torch.float16 in supported_dtypes:
+ return torch.float16
+ if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True):
+ if torch.bfloat16 in supported_dtypes:
+ return torch.bfloat16
+
+ return torch.float32
+
+# None means no manual cast
+def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
+ if weight_dtype == torch.float32 or weight_dtype == torch.float64:
+ return None
+
+ fp16_supported = should_use_fp16(inference_device, prioritize_performance=False)
+ if fp16_supported and weight_dtype == torch.float16:
+ return None
+
+ bf16_supported = should_use_bf16(inference_device)
+ if bf16_supported and weight_dtype == torch.bfloat16:
+ return None
+
+ fp16_supported = should_use_fp16(inference_device, prioritize_performance=True)
+ for dt in supported_dtypes:
+ if dt == torch.float16 and fp16_supported:
+ return torch.float16
+ if dt == torch.bfloat16 and bf16_supported:
+ return torch.bfloat16
+
+ return torch.float32
+
+def text_encoder_offload_device():
+ if args.gpu_only:
+ return get_torch_device()
+ else:
+ return torch.device("cpu")
+
+def text_encoder_device():
+ if args.gpu_only:
+ return get_torch_device()
+ elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM:
+ if should_use_fp16(prioritize_performance=False):
+ return get_torch_device()
+ else:
+ return torch.device("cpu")
+ else:
+ return torch.device("cpu")
+
+def text_encoder_initial_device(load_device, offload_device, model_size=0):
+ if load_device == offload_device or model_size <= 1024 * 1024 * 1024:
+ return offload_device
+
+ if is_device_mps(load_device):
+ return offload_device
+
+ mem_l = get_free_memory(load_device)
+ mem_o = get_free_memory(offload_device)
+ if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l:
+ return load_device
+ else:
+ return offload_device
+
+def text_encoder_dtype(device=None):
+ if args.fp8_e4m3fn_text_enc:
+ return torch.float8_e4m3fn
+ elif args.fp8_e5m2_text_enc:
+ return torch.float8_e5m2
+ elif args.fp16_text_enc:
+ return torch.float16
+ elif args.fp32_text_enc:
+ return torch.float32
+
+ if is_device_cpu(device):
+ return torch.float16
+
+ return torch.float16
+
+
+def intermediate_device():
+ if args.gpu_only:
+ return get_torch_device()
+ else:
+ return torch.device("cpu")
+
+def vae_device():
+ if args.cpu_vae:
+ return torch.device("cpu")
+ return get_torch_device()
+
+def vae_offload_device():
+ if args.gpu_only:
+ return get_torch_device()
+ else:
+ return torch.device("cpu")
+
+def vae_dtype(device=None, allowed_dtypes=[]):
+ global VAE_DTYPES
+ if args.fp16_vae:
+ return torch.float16
+ elif args.bf16_vae:
+ return torch.bfloat16
+ elif args.fp32_vae:
+ return torch.float32
+
+ for d in allowed_dtypes:
+ if d == torch.float16 and should_use_fp16(device, prioritize_performance=False):
+ return d
+ if d in VAE_DTYPES:
+ return d
+
+ return VAE_DTYPES[0]
+
+def get_autocast_device(dev):
+ if hasattr(dev, 'type'):
+ return dev.type
+ return "cuda"
+
+def supports_dtype(device, dtype): #TODO
+ if dtype == torch.float32:
+ return True
+ if is_device_cpu(device):
+ return False
+ if dtype == torch.float16:
+ return True
+ if dtype == torch.bfloat16:
+ return True
+ return False
+
+def supports_cast(device, dtype): #TODO
+ if dtype == torch.float32:
+ return True
+ if dtype == torch.float16:
+ return True
+ if directml_enabled: #TODO: test this
+ return False
+ if dtype == torch.bfloat16:
+ return True
+ if is_device_mps(device):
+ return False
+ if dtype == torch.float8_e4m3fn:
+ return True
+ if dtype == torch.float8_e5m2:
+ return True
+ return False
+
+def pick_weight_dtype(dtype, fallback_dtype, device=None):
+ if dtype is None:
+ dtype = fallback_dtype
+ elif dtype_size(dtype) > dtype_size(fallback_dtype):
+ dtype = fallback_dtype
+
+ if not supports_cast(device, dtype):
+ dtype = fallback_dtype
+
+ return dtype
+
+def device_supports_non_blocking(device):
+ if is_device_mps(device):
+ return False #pytorch bug? mps doesn't support non blocking
+ if is_intel_xpu():
+ return False
+ if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)
+ return False
+ if directml_enabled:
+ return False
+ return True
+
+def device_should_use_non_blocking(device):
+ if not device_supports_non_blocking(device):
+ return False
+ return False
+ # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others
+
+def force_channels_last():
+ if args.force_channels_last:
+ return True
+
+ #TODO
+ return False
+
+def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False):
+ if device is None or weight.device == device:
+ if not copy:
+ if dtype is None or weight.dtype == dtype:
+ return weight
+ return weight.to(dtype=dtype, copy=copy)
+
+ r = torch.empty_like(weight, dtype=dtype, device=device)
+ r.copy_(weight, non_blocking=non_blocking)
+ return r
+
+def cast_to_device(tensor, device, dtype, copy=False):
+ non_blocking = device_supports_non_blocking(device)
+ return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy)
+
+
+def xformers_enabled():
+ global directml_enabled
+ global cpu_state
+ if cpu_state != CPUState.GPU:
+ return False
+ if is_intel_xpu():
+ return False
+ if directml_enabled:
+ return False
+ return XFORMERS_IS_AVAILABLE
+
+
+def xformers_enabled_vae():
+ enabled = xformers_enabled()
+ if not enabled:
+ return False
+
+ return XFORMERS_ENABLED_VAE
+
+def pytorch_attention_enabled():
+ global ENABLE_PYTORCH_ATTENTION
+ return ENABLE_PYTORCH_ATTENTION
+
+def pytorch_attention_flash_attention():
+ global ENABLE_PYTORCH_ATTENTION
+ if ENABLE_PYTORCH_ATTENTION:
+ #TODO: more reliable way of checking for flash attention?
+ if is_nvidia(): #pytorch flash attention only works on Nvidia
+ return True
+ if is_intel_xpu():
+ return True
+ return False
+
+def force_upcast_attention_dtype():
+ upcast = args.force_upcast_attention
+ try:
+ macos_version = tuple(int(n) for n in platform.mac_ver()[0].split("."))
+ if (14, 5) <= macos_version <= (15, 2): # black image bug on recent versions of macOS
+ upcast = True
+ except:
+ pass
+ if upcast:
+ return torch.float32
+ else:
+ return None
+
+def get_free_memory(dev=None, torch_free_too=False):
+ global directml_enabled
+ if dev is None:
+ dev = get_torch_device()
+
+ if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
+ mem_free_total = psutil.virtual_memory().available
+ mem_free_torch = mem_free_total
+ else:
+ if directml_enabled:
+ mem_free_total = 1024 * 1024 * 1024 #TODO
+ mem_free_torch = mem_free_total
+ elif is_intel_xpu():
+ stats = torch.xpu.memory_stats(dev)
+ mem_active = stats['active_bytes.all.current']
+ mem_reserved = stats['reserved_bytes.all.current']
+ mem_free_torch = mem_reserved - mem_active
+ mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved
+ mem_free_total = mem_free_xpu + mem_free_torch
+ else:
+ stats = torch.cuda.memory_stats(dev)
+ mem_active = stats['active_bytes.all.current']
+ mem_reserved = stats['reserved_bytes.all.current']
+ mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
+ mem_free_torch = mem_reserved - mem_active
+ mem_free_total = mem_free_cuda + mem_free_torch
+
+ if torch_free_too:
+ return (mem_free_total, mem_free_torch)
+ else:
+ return mem_free_total
+
+def cpu_mode():
+ global cpu_state
+ return cpu_state == CPUState.CPU
+
+def mps_mode():
+ global cpu_state
+ return cpu_state == CPUState.MPS
+
+def is_device_type(device, type):
+ if hasattr(device, 'type'):
+ if (device.type == type):
+ return True
+ return False
+
+def is_device_cpu(device):
+ return is_device_type(device, 'cpu')
+
+def is_device_mps(device):
+ return is_device_type(device, 'mps')
+
+def is_device_cuda(device):
+ return is_device_type(device, 'cuda')
+
+def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
+ global directml_enabled
+
+ if device is not None:
+ if is_device_cpu(device):
+ return False
+
+ if FORCE_FP16:
+ return True
+
+ if device is not None:
+ if is_device_mps(device):
+ return True
+
+ if FORCE_FP32:
+ return False
+
+ if directml_enabled:
+ return False
+
+ if mps_mode():
+ return True
+
+ if cpu_mode():
+ return False
+
+ if is_intel_xpu():
+ return True
+
+ if torch.version.hip:
+ return True
+
+ props = torch.cuda.get_device_properties(device)
+ if props.major >= 8:
+ return True
+
+ if props.major < 6:
+ return False
+
+ #FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32
+ nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"]
+ for x in nvidia_10_series:
+ if x in props.name.lower():
+ if WINDOWS or manual_cast:
+ return True
+ else:
+ return False #weird linux behavior where fp32 is faster
+
+ if manual_cast:
+ free_model_memory = maximum_vram_for_weights(device)
+ if (not prioritize_performance) or model_params * 4 > free_model_memory:
+ return True
+
+ if props.major < 7:
+ return False
+
+ #FP16 is just broken on these cards
+ nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"]
+ for x in nvidia_16_series:
+ if x in props.name:
+ return False
+
+ return True
+
+def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
+ if device is not None:
+ if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow
+ return False
+
+ if device is not None:
+ if is_device_mps(device):
+ return True
+
+ if FORCE_FP32:
+ return False
+
+ if directml_enabled:
+ return False
+
+ if mps_mode():
+ return True
+
+ if cpu_mode():
+ return False
+
+ if is_intel_xpu():
+ return True
+
+ props = torch.cuda.get_device_properties(device)
+ if props.major >= 8:
+ return True
+
+ bf16_works = torch.cuda.is_bf16_supported()
+
+ if bf16_works or manual_cast:
+ free_model_memory = maximum_vram_for_weights(device)
+ if (not prioritize_performance) or model_params * 4 > free_model_memory:
+ return True
+
+ return False
+
+def supports_fp8_compute(device=None):
+ if not is_nvidia():
+ return False
+
+ props = torch.cuda.get_device_properties(device)
+ if props.major >= 9:
+ return True
+ if props.major < 8:
+ return False
+ if props.minor < 9:
+ return False
+
+ if int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) < 3):
+ return False
+
+ if WINDOWS:
+ if (int(torch_version[0]) == 2 and int(torch_version[2]) < 4):
+ return False
+
+ return True
+
+def soft_empty_cache(force=False):
+ global cpu_state
+ if cpu_state == CPUState.MPS:
+ torch.mps.empty_cache()
+ elif is_intel_xpu():
+ torch.xpu.empty_cache()
+ elif torch.cuda.is_available():
+ if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda
+ torch.cuda.empty_cache()
+ torch.cuda.ipc_collect()
+
+def unload_all_models():
+ free_memory(1e30, get_torch_device())
+
+
+def resolve_lowvram_weight(weight, model, key): #TODO: remove
+ print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
+ return weight
+
+#TODO: might be cleaner to put this somewhere else
+import threading
+
+class InterruptProcessingException(Exception):
+ pass
+
+interrupt_processing_mutex = threading.RLock()
+
+interrupt_processing = False
+def interrupt_current_processing(value=True):
+ global interrupt_processing
+ global interrupt_processing_mutex
+ with interrupt_processing_mutex:
+ interrupt_processing = value
+
+def processing_interrupted():
+ global interrupt_processing
+ global interrupt_processing_mutex
+ with interrupt_processing_mutex:
+ return interrupt_processing
+
+def throw_exception_if_processing_interrupted():
+ global interrupt_processing
+ global interrupt_processing_mutex
+ with interrupt_processing_mutex:
+ if interrupt_processing:
+ interrupt_processing = False
+ raise InterruptProcessingException()
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..f53f10749b0130de6e7fb9c7130d25cc2f22de26
--- /dev/null
+++ b/comfy/model_patcher.py
@@ -0,0 +1,588 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Comfy
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+import copy
+import inspect
+import logging
+import uuid
+import collections
+import math
+
+import comfy.utils
+import comfy.float
+import comfy.model_management
+import comfy.lora
+from comfy.comfy_types import UnetWrapperFunction
+
+def string_to_seed(data):
+ crc = 0xFFFFFFFF
+ for byte in data:
+ if isinstance(byte, str):
+ byte = ord(byte)
+ crc ^= byte
+ for _ in range(8):
+ if crc & 1:
+ crc = (crc >> 1) ^ 0xEDB88320
+ else:
+ crc >>= 1
+ return crc ^ 0xFFFFFFFF
+
+def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
+ to = model_options["transformer_options"].copy()
+
+ if "patches_replace" not in to:
+ to["patches_replace"] = {}
+ else:
+ to["patches_replace"] = to["patches_replace"].copy()
+
+ if name not in to["patches_replace"]:
+ to["patches_replace"][name] = {}
+ else:
+ to["patches_replace"][name] = to["patches_replace"][name].copy()
+
+ if transformer_index is not None:
+ block = (block_name, number, transformer_index)
+ else:
+ block = (block_name, number)
+ to["patches_replace"][name][block] = patch
+ model_options["transformer_options"] = to
+ return model_options
+
+def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False):
+ model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
+ if disable_cfg1_optimization:
+ model_options["disable_cfg1_optimization"] = True
+ return model_options
+
+def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False):
+ model_options["sampler_pre_cfg_function"] = model_options.get("sampler_pre_cfg_function", []) + [pre_cfg_function]
+ if disable_cfg1_optimization:
+ model_options["disable_cfg1_optimization"] = True
+ return model_options
+
+def wipe_lowvram_weight(m):
+ if hasattr(m, "prev_comfy_cast_weights"):
+ m.comfy_cast_weights = m.prev_comfy_cast_weights
+ del m.prev_comfy_cast_weights
+ m.weight_function = None
+ m.bias_function = None
+
+class LowVramPatch:
+ def __init__(self, key, patches):
+ self.key = key
+ self.patches = patches
+ def __call__(self, weight):
+ intermediate_dtype = weight.dtype
+ if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
+ intermediate_dtype = torch.float32
+ return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key))
+
+ return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
+
+def get_key_weight(model, key):
+ set_func = None
+ convert_func = None
+ op_keys = key.rsplit('.', 1)
+ if len(op_keys) < 2:
+ weight = comfy.utils.get_attr(model, key)
+ else:
+ op = comfy.utils.get_attr(model, op_keys[0])
+ try:
+ set_func = getattr(op, "set_{}".format(op_keys[1]))
+ except AttributeError:
+ pass
+
+ try:
+ convert_func = getattr(op, "convert_{}".format(op_keys[1]))
+ except AttributeError:
+ pass
+
+ weight = getattr(op, op_keys[1])
+ if convert_func is not None:
+ weight = comfy.utils.get_attr(model, key)
+
+ return weight, set_func, convert_func
+
+class ModelPatcher:
+ def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
+ self.size = size
+ self.model = model
+ if not hasattr(self.model, 'device'):
+ logging.debug("Model doesn't have a device attribute.")
+ self.model.device = offload_device
+ elif self.model.device is None:
+ self.model.device = offload_device
+
+ self.patches = {}
+ self.backup = {}
+ self.object_patches = {}
+ self.object_patches_backup = {}
+ self.model_options = {"transformer_options":{}}
+ self.model_size()
+ self.load_device = load_device
+ self.offload_device = offload_device
+ self.weight_inplace_update = weight_inplace_update
+ self.patches_uuid = uuid.uuid4()
+
+ if not hasattr(self.model, 'model_loaded_weight_memory'):
+ self.model.model_loaded_weight_memory = 0
+
+ if not hasattr(self.model, 'lowvram_patch_counter'):
+ self.model.lowvram_patch_counter = 0
+
+ if not hasattr(self.model, 'model_lowvram'):
+ self.model.model_lowvram = False
+
+ def model_size(self):
+ if self.size > 0:
+ return self.size
+ self.size = comfy.model_management.module_size(self.model)
+ return self.size
+
+ def loaded_size(self):
+ return self.model.model_loaded_weight_memory
+
+ def lowvram_patch_counter(self):
+ return self.model.lowvram_patch_counter
+
+ def clone(self):
+ n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update)
+ n.patches = {}
+ for k in self.patches:
+ n.patches[k] = self.patches[k][:]
+ n.patches_uuid = self.patches_uuid
+
+ n.object_patches = self.object_patches.copy()
+ n.model_options = copy.deepcopy(self.model_options)
+ n.backup = self.backup
+ n.object_patches_backup = self.object_patches_backup
+ return n
+
+ def is_clone(self, other):
+ if hasattr(other, 'model') and self.model is other.model:
+ return True
+ return False
+
+ def clone_has_same_weights(self, clone):
+ if not self.is_clone(clone):
+ return False
+
+ if len(self.patches) == 0 and len(clone.patches) == 0:
+ return True
+
+ if self.patches_uuid == clone.patches_uuid:
+ if len(self.patches) != len(clone.patches):
+ logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.")
+ else:
+ return True
+
+ def memory_required(self, input_shape):
+ return self.model.memory_required(input_shape=input_shape)
+
+ def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False):
+ if len(inspect.signature(sampler_cfg_function).parameters) == 3:
+ self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
+ else:
+ self.model_options["sampler_cfg_function"] = sampler_cfg_function
+ if disable_cfg1_optimization:
+ self.model_options["disable_cfg1_optimization"] = True
+
+ def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
+ self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization)
+
+ def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False):
+ self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization)
+
+ def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
+ self.model_options["model_function_wrapper"] = unet_wrapper_function
+
+ def set_model_denoise_mask_function(self, denoise_mask_function):
+ self.model_options["denoise_mask_function"] = denoise_mask_function
+
+ def set_model_patch(self, patch, name):
+ to = self.model_options["transformer_options"]
+ if "patches" not in to:
+ to["patches"] = {}
+ to["patches"][name] = to["patches"].get(name, []) + [patch]
+
+ def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None):
+ self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index)
+
+ def set_model_attn1_patch(self, patch):
+ self.set_model_patch(patch, "attn1_patch")
+
+ def set_model_attn2_patch(self, patch):
+ self.set_model_patch(patch, "attn2_patch")
+
+ def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None):
+ self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index)
+
+ def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None):
+ self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index)
+
+ def set_model_attn1_output_patch(self, patch):
+ self.set_model_patch(patch, "attn1_output_patch")
+
+ def set_model_attn2_output_patch(self, patch):
+ self.set_model_patch(patch, "attn2_output_patch")
+
+ def set_model_input_block_patch(self, patch):
+ self.set_model_patch(patch, "input_block_patch")
+
+ def set_model_input_block_patch_after_skip(self, patch):
+ self.set_model_patch(patch, "input_block_patch_after_skip")
+
+ def set_model_output_block_patch(self, patch):
+ self.set_model_patch(patch, "output_block_patch")
+
+ def add_object_patch(self, name, obj):
+ self.object_patches[name] = obj
+
+ def get_model_object(self, name):
+ if name in self.object_patches:
+ return self.object_patches[name]
+ else:
+ if name in self.object_patches_backup:
+ return self.object_patches_backup[name]
+ else:
+ return comfy.utils.get_attr(self.model, name)
+
+ def model_patches_to(self, device):
+ to = self.model_options["transformer_options"]
+ if "patches" in to:
+ patches = to["patches"]
+ for name in patches:
+ patch_list = patches[name]
+ for i in range(len(patch_list)):
+ if hasattr(patch_list[i], "to"):
+ patch_list[i] = patch_list[i].to(device)
+ if "patches_replace" in to:
+ patches = to["patches_replace"]
+ for name in patches:
+ patch_list = patches[name]
+ for k in patch_list:
+ if hasattr(patch_list[k], "to"):
+ patch_list[k] = patch_list[k].to(device)
+ if "model_function_wrapper" in self.model_options:
+ wrap_func = self.model_options["model_function_wrapper"]
+ if hasattr(wrap_func, "to"):
+ self.model_options["model_function_wrapper"] = wrap_func.to(device)
+
+ def model_dtype(self):
+ if hasattr(self.model, "get_dtype"):
+ return self.model.get_dtype()
+
+ def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
+ p = set()
+ model_sd = self.model.state_dict()
+ for k in patches:
+ offset = None
+ function = None
+ if isinstance(k, str):
+ key = k
+ else:
+ offset = k[1]
+ key = k[0]
+ if len(k) > 2:
+ function = k[2]
+
+ if key in model_sd:
+ p.add(k)
+ current_patches = self.patches.get(key, [])
+ current_patches.append((strength_patch, patches[k], strength_model, offset, function))
+ self.patches[key] = current_patches
+
+ self.patches_uuid = uuid.uuid4()
+ return list(p)
+
+ def get_key_patches(self, filter_prefix=None):
+ model_sd = self.model_state_dict()
+ p = {}
+ for k in model_sd:
+ if filter_prefix is not None:
+ if not k.startswith(filter_prefix):
+ continue
+ bk = self.backup.get(k, None)
+ weight, set_func, convert_func = get_key_weight(self.model, k)
+ if bk is not None:
+ weight = bk.weight
+ if convert_func is None:
+ convert_func = lambda a, **kwargs: a
+
+ if k in self.patches:
+ p[k] = [(weight, convert_func)] + self.patches[k]
+ else:
+ p[k] = [(weight, convert_func)]
+ return p
+
+ def model_state_dict(self, filter_prefix=None):
+ sd = self.model.state_dict()
+ keys = list(sd.keys())
+ if filter_prefix is not None:
+ for k in keys:
+ if not k.startswith(filter_prefix):
+ sd.pop(k)
+ return sd
+
+ def patch_weight_to_device(self, key, device_to=None, inplace_update=False):
+ if key not in self.patches:
+ return
+
+ weight, set_func, convert_func = get_key_weight(self.model, key)
+ inplace_update = self.weight_inplace_update or inplace_update
+
+ if key not in self.backup:
+ self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
+
+ if device_to is not None:
+ temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+ else:
+ temp_weight = weight.to(torch.float32, copy=True)
+ if convert_func is not None:
+ temp_weight = convert_func(temp_weight, inplace=True)
+
+ out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key)
+ if set_func is None:
+ out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key))
+ if inplace_update:
+ comfy.utils.copy_to_param(self.model, key, out_weight)
+ else:
+ comfy.utils.set_attr_param(self.model, key, out_weight)
+ else:
+ set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
+
+ def _load_list(self):
+ loading = []
+ for n, m in self.model.named_modules():
+ params = []
+ skip = False
+ for name, param in m.named_parameters(recurse=False):
+ params.append(name)
+ for name, param in m.named_parameters(recurse=True):
+ if name not in params:
+ skip = True # skip random weights in non leaf modules
+ break
+ if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
+ loading.append((comfy.model_management.module_size(m), n, m, params))
+ return loading
+
+ def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
+ mem_counter = 0
+ patch_counter = 0
+ lowvram_counter = 0
+ loading = self._load_list()
+
+ load_completely = []
+ loading.sort(reverse=True)
+ for x in loading:
+ n = x[1]
+ m = x[2]
+ params = x[3]
+ module_mem = x[0]
+
+ lowvram_weight = False
+
+ if not full_load and hasattr(m, "comfy_cast_weights"):
+ if mem_counter + module_mem >= lowvram_model_memory:
+ lowvram_weight = True
+ lowvram_counter += 1
+ if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed
+ continue
+
+ weight_key = "{}.weight".format(n)
+ bias_key = "{}.bias".format(n)
+
+ if lowvram_weight:
+ if weight_key in self.patches:
+ if force_patch_weights:
+ self.patch_weight_to_device(weight_key)
+ else:
+ m.weight_function = LowVramPatch(weight_key, self.patches)
+ patch_counter += 1
+ if bias_key in self.patches:
+ if force_patch_weights:
+ self.patch_weight_to_device(bias_key)
+ else:
+ m.bias_function = LowVramPatch(bias_key, self.patches)
+ patch_counter += 1
+
+ m.prev_comfy_cast_weights = m.comfy_cast_weights
+ m.comfy_cast_weights = True
+ else:
+ if hasattr(m, "comfy_cast_weights"):
+ if m.comfy_cast_weights:
+ wipe_lowvram_weight(m)
+
+ if full_load or mem_counter + module_mem < lowvram_model_memory:
+ mem_counter += module_mem
+ load_completely.append((module_mem, n, m, params))
+
+ load_completely.sort(reverse=True)
+ for x in load_completely:
+ n = x[1]
+ m = x[2]
+ params = x[3]
+ if hasattr(m, "comfy_patched_weights"):
+ if m.comfy_patched_weights == True:
+ continue
+
+ for param in params:
+ self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to)
+
+ logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
+ m.comfy_patched_weights = True
+
+ for x in load_completely:
+ x[2].to(device_to)
+
+ if lowvram_counter > 0:
+ logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
+ self.model.model_lowvram = True
+ else:
+ logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
+ self.model.model_lowvram = False
+ if full_load:
+ self.model.to(device_to)
+ mem_counter = self.model_size()
+
+ self.model.lowvram_patch_counter += patch_counter
+ self.model.device = device_to
+ self.model.model_loaded_weight_memory = mem_counter
+
+ def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False):
+ for k in self.object_patches:
+ old = comfy.utils.set_attr(self.model, k, self.object_patches[k])
+ if k not in self.object_patches_backup:
+ self.object_patches_backup[k] = old
+
+ if lowvram_model_memory == 0:
+ full_load = True
+ else:
+ full_load = False
+
+ if load_weights:
+ self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load)
+ return self.model
+
+ def unpatch_model(self, device_to=None, unpatch_weights=True):
+ if unpatch_weights:
+ if self.model.model_lowvram:
+ for m in self.model.modules():
+ wipe_lowvram_weight(m)
+
+ self.model.model_lowvram = False
+ self.model.lowvram_patch_counter = 0
+
+ keys = list(self.backup.keys())
+
+ for k in keys:
+ bk = self.backup[k]
+ if bk.inplace_update:
+ comfy.utils.copy_to_param(self.model, k, bk.weight)
+ else:
+ comfy.utils.set_attr_param(self.model, k, bk.weight)
+
+ self.backup.clear()
+
+ if device_to is not None:
+ self.model.to(device_to)
+ self.model.device = device_to
+ self.model.model_loaded_weight_memory = 0
+
+ for m in self.model.modules():
+ if hasattr(m, "comfy_patched_weights"):
+ del m.comfy_patched_weights
+
+ keys = list(self.object_patches_backup.keys())
+ for k in keys:
+ comfy.utils.set_attr(self.model, k, self.object_patches_backup[k])
+
+ self.object_patches_backup.clear()
+
+ def partially_unload(self, device_to, memory_to_free=0):
+ memory_freed = 0
+ patch_counter = 0
+ unload_list = self._load_list()
+ unload_list.sort()
+ for unload in unload_list:
+ if memory_to_free < memory_freed:
+ break
+ module_mem = unload[0]
+ n = unload[1]
+ m = unload[2]
+ params = unload[3]
+
+ lowvram_possible = hasattr(m, "comfy_cast_weights")
+ if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True:
+ move_weight = True
+ for param in params:
+ key = "{}.{}".format(n, param)
+ bk = self.backup.get(key, None)
+ if bk is not None:
+ if not lowvram_possible:
+ move_weight = False
+ break
+
+ if bk.inplace_update:
+ comfy.utils.copy_to_param(self.model, key, bk.weight)
+ else:
+ comfy.utils.set_attr_param(self.model, key, bk.weight)
+ self.backup.pop(key)
+
+ weight_key = "{}.weight".format(n)
+ bias_key = "{}.bias".format(n)
+ if move_weight:
+ m.to(device_to)
+ if lowvram_possible:
+ if weight_key in self.patches:
+ m.weight_function = LowVramPatch(weight_key, self.patches)
+ patch_counter += 1
+ if bias_key in self.patches:
+ m.bias_function = LowVramPatch(bias_key, self.patches)
+ patch_counter += 1
+
+ m.prev_comfy_cast_weights = m.comfy_cast_weights
+ m.comfy_cast_weights = True
+ m.comfy_patched_weights = False
+ memory_freed += module_mem
+ logging.debug("freed {}".format(n))
+
+ self.model.model_lowvram = True
+ self.model.lowvram_patch_counter += patch_counter
+ self.model.model_loaded_weight_memory -= memory_freed
+ return memory_freed
+
+ def partially_load(self, device_to, extra_memory=0):
+ self.unpatch_model(unpatch_weights=False)
+ self.patch_model(load_weights=False)
+ full_load = False
+ if self.model.model_lowvram == False:
+ return 0
+ if self.model.model_loaded_weight_memory + extra_memory > self.model_size():
+ full_load = True
+ current_used = self.model.model_loaded_weight_memory
+ self.load(device_to, lowvram_model_memory=current_used + extra_memory, full_load=full_load)
+ return self.model.model_loaded_weight_memory - current_used
+
+ def current_loaded_device(self):
+ return self.model.device
+
+ def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):
+ print("WARNING the ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead")
+ return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)
diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b4e095d9201a89cca632caa182a8d2e02df77f0
--- /dev/null
+++ b/comfy/model_sampling.py
@@ -0,0 +1,339 @@
+import torch
+from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
+import math
+
+def rescale_zero_terminal_snr_sigmas(sigmas):
+ alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
+ alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+ # Store old values.
+ alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+ alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+ # Shift so the last timestep is zero.
+ alphas_bar_sqrt -= (alphas_bar_sqrt_T)
+
+ # Scale so the first timestep is back to the old value.
+ alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+ # Convert alphas_bar_sqrt to betas
+ alphas_bar = alphas_bar_sqrt**2 # Revert sqrt
+ alphas_bar[-1] = 4.8973451890853435e-08
+ return ((1 - alphas_bar) / alphas_bar) ** 0.5
+
+class EPS:
+ def calculate_input(self, sigma, noise):
+ sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+ return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
+
+ def calculate_denoised(self, sigma, model_output, model_input):
+ sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+ return model_input - model_output * sigma
+
+ def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+ if max_denoise:
+ noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
+ else:
+ noise = noise * sigma
+
+ noise += latent_image
+ return noise
+
+ def inverse_noise_scaling(self, sigma, latent):
+ return latent
+
+class V_PREDICTION(EPS):
+ def calculate_denoised(self, sigma, model_output, model_input):
+ sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+ return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
+
+class EDM(V_PREDICTION):
+ def calculate_denoised(self, sigma, model_output, model_input):
+ sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+ return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
+
+class CONST:
+ def calculate_input(self, sigma, noise):
+ return noise
+
+ def calculate_denoised(self, sigma, model_output, model_input):
+ sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+ return model_input - model_output * sigma
+
+ def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+ return sigma * noise + (1.0 - sigma) * latent_image
+
+ def inverse_noise_scaling(self, sigma, latent):
+ return latent / (1.0 - sigma)
+
+class ModelSamplingDiscrete(torch.nn.Module):
+ def __init__(self, model_config=None, zsnr=None):
+ super().__init__()
+
+ if model_config is not None:
+ sampling_settings = model_config.sampling_settings
+ else:
+ sampling_settings = {}
+
+ beta_schedule = sampling_settings.get("beta_schedule", "linear")
+ linear_start = sampling_settings.get("linear_start", 0.00085)
+ linear_end = sampling_settings.get("linear_end", 0.012)
+ timesteps = sampling_settings.get("timesteps", 1000)
+
+ if zsnr is None:
+ zsnr = sampling_settings.get("zsnr", False)
+
+ self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3, zsnr=zsnr)
+ self.sigma_data = 1.0
+
+ def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, zsnr=False):
+ if given_betas is not None:
+ betas = given_betas
+ else:
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = torch.cumprod(alphas, dim=0)
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+
+ # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32))
+ # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32))
+ # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
+
+ sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+ if zsnr:
+ sigmas = rescale_zero_terminal_snr_sigmas(sigmas)
+
+ self.set_sigmas(sigmas)
+
+ def set_sigmas(self, sigmas):
+ self.register_buffer('sigmas', sigmas.float())
+ self.register_buffer('log_sigmas', sigmas.log().float())
+
+ @property
+ def sigma_min(self):
+ return self.sigmas[0]
+
+ @property
+ def sigma_max(self):
+ return self.sigmas[-1]
+
+ def timestep(self, sigma):
+ log_sigma = sigma.log()
+ dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None]
+ return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device)
+
+ def sigma(self, timestep):
+ t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1))
+ low_idx = t.floor().long()
+ high_idx = t.ceil().long()
+ w = t.frac()
+ log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx]
+ return log_sigma.exp().to(timestep.device)
+
+ def percent_to_sigma(self, percent):
+ if percent <= 0.0:
+ return 999999999.9
+ if percent >= 1.0:
+ return 0.0
+ percent = 1.0 - percent
+ return self.sigma(torch.tensor(percent * 999.0)).item()
+
+class ModelSamplingDiscreteEDM(ModelSamplingDiscrete):
+ def timestep(self, sigma):
+ return 0.25 * sigma.log()
+
+ def sigma(self, timestep):
+ return (timestep / 0.25).exp()
+
+class ModelSamplingContinuousEDM(torch.nn.Module):
+ def __init__(self, model_config=None):
+ super().__init__()
+ if model_config is not None:
+ sampling_settings = model_config.sampling_settings
+ else:
+ sampling_settings = {}
+
+ sigma_min = sampling_settings.get("sigma_min", 0.002)
+ sigma_max = sampling_settings.get("sigma_max", 120.0)
+ sigma_data = sampling_settings.get("sigma_data", 1.0)
+ self.set_parameters(sigma_min, sigma_max, sigma_data)
+
+ def set_parameters(self, sigma_min, sigma_max, sigma_data):
+ self.sigma_data = sigma_data
+ sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
+
+ self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
+ self.register_buffer('log_sigmas', sigmas.log())
+
+ @property
+ def sigma_min(self):
+ return self.sigmas[0]
+
+ @property
+ def sigma_max(self):
+ return self.sigmas[-1]
+
+ def timestep(self, sigma):
+ return 0.25 * sigma.log()
+
+ def sigma(self, timestep):
+ return (timestep / 0.25).exp()
+
+ def percent_to_sigma(self, percent):
+ if percent <= 0.0:
+ return 999999999.9
+ if percent >= 1.0:
+ return 0.0
+ percent = 1.0 - percent
+
+ log_sigma_min = math.log(self.sigma_min)
+ return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
+
+
+class ModelSamplingContinuousV(ModelSamplingContinuousEDM):
+ def timestep(self, sigma):
+ return sigma.atan() / math.pi * 2
+
+ def sigma(self, timestep):
+ return (timestep * math.pi / 2).tan()
+
+
+def time_snr_shift(alpha, t):
+ if alpha == 1.0:
+ return t
+ return alpha * t / (1 + (alpha - 1) * t)
+
+class ModelSamplingDiscreteFlow(torch.nn.Module):
+ def __init__(self, model_config=None):
+ super().__init__()
+ if model_config is not None:
+ sampling_settings = model_config.sampling_settings
+ else:
+ sampling_settings = {}
+
+ self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000))
+
+ def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000):
+ self.shift = shift
+ self.multiplier = multiplier
+ ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier)
+ self.register_buffer('sigmas', ts)
+
+ @property
+ def sigma_min(self):
+ return self.sigmas[0]
+
+ @property
+ def sigma_max(self):
+ return self.sigmas[-1]
+
+ def timestep(self, sigma):
+ return sigma * self.multiplier
+
+ def sigma(self, timestep):
+ return time_snr_shift(self.shift, timestep / self.multiplier)
+
+ def percent_to_sigma(self, percent):
+ if percent <= 0.0:
+ return 1.0
+ if percent >= 1.0:
+ return 0.0
+ return 1.0 - percent
+
+class StableCascadeSampling(ModelSamplingDiscrete):
+ def __init__(self, model_config=None):
+ super().__init__()
+
+ if model_config is not None:
+ sampling_settings = model_config.sampling_settings
+ else:
+ sampling_settings = {}
+
+ self.set_parameters(sampling_settings.get("shift", 1.0))
+
+ def set_parameters(self, shift=1.0, cosine_s=8e-3):
+ self.shift = shift
+ self.cosine_s = torch.tensor(cosine_s)
+ self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
+
+ #This part is just for compatibility with some schedulers in the codebase
+ self.num_timesteps = 10000
+ sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
+ for x in range(self.num_timesteps):
+ t = (x + 1) / self.num_timesteps
+ sigmas[x] = self.sigma(t)
+
+ self.set_sigmas(sigmas)
+
+ def sigma(self, timestep):
+ alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)
+
+ if self.shift != 1.0:
+ var = alpha_cumprod
+ logSNR = (var/(1-var)).log()
+ logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
+ alpha_cumprod = logSNR.sigmoid()
+
+ alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)
+ return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5
+
+ def timestep(self, sigma):
+ var = 1 / ((sigma * sigma) + 1)
+ var = var.clamp(0, 1.0)
+ s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)
+ t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
+ return t
+
+ def percent_to_sigma(self, percent):
+ if percent <= 0.0:
+ return 999999999.9
+ if percent >= 1.0:
+ return 0.0
+
+ percent = 1.0 - percent
+ return self.sigma(torch.tensor(percent))
+
+
+def flux_time_shift(mu: float, sigma: float, t):
+ return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+class ModelSamplingFlux(torch.nn.Module):
+ def __init__(self, model_config=None):
+ super().__init__()
+ if model_config is not None:
+ sampling_settings = model_config.sampling_settings
+ else:
+ sampling_settings = {}
+
+ self.set_parameters(shift=sampling_settings.get("shift", 1.15))
+
+ def set_parameters(self, shift=1.15, timesteps=10000):
+ self.shift = shift
+ ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps))
+ self.register_buffer('sigmas', ts)
+
+ @property
+ def sigma_min(self):
+ return self.sigmas[0]
+
+ @property
+ def sigma_max(self):
+ return self.sigmas[-1]
+
+ def timestep(self, sigma):
+ return sigma
+
+ def sigma(self, timestep):
+ return flux_time_shift(self.shift, 1.0, timestep)
+
+ def percent_to_sigma(self, percent):
+ if percent <= 0.0:
+ return 1.0
+ if percent >= 1.0:
+ return 0.0
+ return 1.0 - percent
diff --git a/comfy/ops.py b/comfy/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c5ba0124b5ad59fc72f31e557c558f8eaf1c23e
--- /dev/null
+++ b/comfy/ops.py
@@ -0,0 +1,366 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Stability AI
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+import comfy.model_management
+from comfy.cli_args import args
+import comfy.float
+
+cast_to = comfy.model_management.cast_to #TODO: remove once no more references
+
+def cast_to_input(weight, input, non_blocking=False, copy=True):
+ return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
+
+def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+ if input is not None:
+ if dtype is None:
+ dtype = input.dtype
+ if bias_dtype is None:
+ bias_dtype = dtype
+ if device is None:
+ device = input.device
+
+ bias = None
+ non_blocking = comfy.model_management.device_supports_non_blocking(device)
+ if s.bias is not None:
+ has_function = s.bias_function is not None
+ bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function)
+ if has_function:
+ bias = s.bias_function(bias)
+
+ has_function = s.weight_function is not None
+ weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function)
+ if has_function:
+ weight = s.weight_function(weight)
+ return weight, bias
+
+class CastWeightBiasOp:
+ comfy_cast_weights = False
+ weight_function = None
+ bias_function = None
+
+class disable_weight_init:
+ class Linear(torch.nn.Linear, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.linear(input, weight, bias)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return self._conv_forward(input, weight, bias)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return self._conv_forward(input, weight, bias)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return self._conv_forward(input, weight, bias)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+
+ class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ if self.weight is not None:
+ weight, bias = cast_bias_weight(self, input)
+ else:
+ weight = None
+ bias = None
+ return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input, output_size=None):
+ num_spatial_dims = 2
+ output_padding = self._output_padding(
+ input, output_size, self.stride, self.padding, self.kernel_size,
+ num_spatial_dims, self.dilation)
+
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.conv_transpose2d(
+ input, weight, bias, self.stride, self.padding,
+ output_padding, self.groups, self.dilation)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input, output_size=None):
+ num_spatial_dims = 1
+ output_padding = self._output_padding(
+ input, output_size, self.stride, self.padding, self.kernel_size,
+ num_spatial_dims, self.dilation)
+
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.conv_transpose1d(
+ input, weight, bias, self.stride, self.padding,
+ output_padding, self.groups, self.dilation)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class Embedding(torch.nn.Embedding, CastWeightBiasOp):
+ def reset_parameters(self):
+ self.bias = None
+ return None
+
+ def forward_comfy_cast_weights(self, input, out_dtype=None):
+ output_dtype = out_dtype
+ if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
+ out_dtype = None
+ weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
+ return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ if "out_dtype" in kwargs:
+ kwargs.pop("out_dtype")
+ return super().forward(*args, **kwargs)
+
+ @classmethod
+ def conv_nd(s, dims, *args, **kwargs):
+ if dims == 2:
+ return s.Conv2d(*args, **kwargs)
+ elif dims == 3:
+ return s.Conv3d(*args, **kwargs)
+ else:
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class manual_cast(disable_weight_init):
+ class Linear(disable_weight_init.Linear):
+ comfy_cast_weights = True
+
+ class Conv1d(disable_weight_init.Conv1d):
+ comfy_cast_weights = True
+
+ class Conv2d(disable_weight_init.Conv2d):
+ comfy_cast_weights = True
+
+ class Conv3d(disable_weight_init.Conv3d):
+ comfy_cast_weights = True
+
+ class GroupNorm(disable_weight_init.GroupNorm):
+ comfy_cast_weights = True
+
+ class LayerNorm(disable_weight_init.LayerNorm):
+ comfy_cast_weights = True
+
+ class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
+ comfy_cast_weights = True
+
+ class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
+ comfy_cast_weights = True
+
+ class Embedding(disable_weight_init.Embedding):
+ comfy_cast_weights = True
+
+
+def fp8_linear(self, input):
+ dtype = self.weight.dtype
+ if dtype not in [torch.float8_e4m3fn]:
+ return None
+
+ tensor_2d = False
+ if len(input.shape) == 2:
+ tensor_2d = True
+ input = input.unsqueeze(1)
+
+
+ if len(input.shape) == 3:
+ w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input.dtype)
+ w = w.t()
+
+ scale_weight = self.scale_weight
+ scale_input = self.scale_input
+ if scale_weight is None:
+ scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
+ else:
+ scale_weight = scale_weight.to(input.device)
+
+ if scale_input is None:
+ scale_input = torch.ones((), device=input.device, dtype=torch.float32)
+ inn = input.reshape(-1, input.shape[2]).to(dtype)
+ else:
+ scale_input = scale_input.to(input.device)
+ inn = (input * (1.0 / scale_input).to(input.dtype)).reshape(-1, input.shape[2]).to(dtype)
+
+ if bias is not None:
+ o = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
+ else:
+ o = torch._scaled_mm(inn, w, out_dtype=input.dtype, scale_a=scale_input, scale_b=scale_weight)
+
+ if isinstance(o, tuple):
+ o = o[0]
+
+ if tensor_2d:
+ return o.reshape(input.shape[0], -1)
+
+ return o.reshape((-1, input.shape[1], self.weight.shape[0]))
+
+ return None
+
+class fp8_ops(manual_cast):
+ class Linear(manual_cast.Linear):
+ def reset_parameters(self):
+ self.scale_weight = None
+ self.scale_input = None
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ out = fp8_linear(self, input)
+ if out is not None:
+ return out
+
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.linear(input, weight, bias)
+
+def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
+ class scaled_fp8_op(manual_cast):
+ class Linear(manual_cast.Linear):
+ def __init__(self, *args, **kwargs):
+ if override_dtype is not None:
+ kwargs['dtype'] = override_dtype
+ super().__init__(*args, **kwargs)
+
+ def reset_parameters(self):
+ if not hasattr(self, 'scale_weight'):
+ self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
+
+ if not scale_input:
+ self.scale_input = None
+
+ if not hasattr(self, 'scale_input'):
+ self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ if fp8_matrix_mult:
+ out = fp8_linear(self, input)
+ if out is not None:
+ return out
+
+ weight, bias = cast_bias_weight(self, input)
+
+ if weight.numel() < input.numel(): #TODO: optimize
+ return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
+ else:
+ return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
+
+ def convert_weight(self, weight, inplace=False, **kwargs):
+ if inplace:
+ weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype)
+ return weight
+ else:
+ return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype)
+
+ def set_weight(self, weight, inplace_update=False, seed=None, **kwargs):
+ weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
+ if inplace_update:
+ self.weight.data.copy_(weight)
+ else:
+ self.weight = torch.nn.Parameter(weight, requires_grad=False)
+
+ return scaled_fp8_op
+
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None):
+ fp8_compute = comfy.model_management.supports_fp8_compute(load_device)
+ if scaled_fp8 is not None:
+ return scaled_fp8_ops(fp8_matrix_mult=fp8_compute, scale_input=True, override_dtype=scaled_fp8)
+
+ if fp8_compute and (fp8_optimizations or args.fast) and not disable_fast_fp8:
+ return fp8_ops
+
+ if compute_dtype is None or weight_dtype == compute_dtype:
+ return disable_weight_init
+
+ return manual_cast
diff --git a/comfy/options.py b/comfy/options.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7f8af41ebd8b9669ef0ef21827ea6195bcb4752
--- /dev/null
+++ b/comfy/options.py
@@ -0,0 +1,6 @@
+
+args_parsing = False
+
+def enable_args_parsing(enable=True):
+ global args_parsing
+ args_parsing = enable
diff --git a/comfy/sample.py b/comfy/sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..98dcaca7f38e76754bdce7fffaccf620fd0ba497
--- /dev/null
+++ b/comfy/sample.py
@@ -0,0 +1,50 @@
+import torch
+import comfy.model_management
+import comfy.samplers
+import comfy.utils
+import numpy as np
+import logging
+
+def prepare_noise(latent_image, seed, noise_inds=None):
+ """
+ creates random noise given a latent image and a seed.
+ optional arg skip can be used to skip and discard x number of noise generations for a given seed
+ """
+ generator = torch.manual_seed(seed)
+ if noise_inds is None:
+ return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
+
+ unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
+ noises = []
+ for i in range(unique_inds[-1]+1):
+ noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
+ if i in unique_inds:
+ noises.append(noise)
+ noises = [noises[i] for i in inverse]
+ noises = torch.cat(noises, axis=0)
+ return noises
+
+def fix_empty_latent_channels(model, latent_image):
+ latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels
+ if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
+ latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
+ return latent_image
+
+def prepare_sampling(model, noise_shape, positive, negative, noise_mask):
+ logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed")
+ return model, positive, negative, noise_mask, []
+
+def cleanup_additional_models(models):
+ logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed")
+
+def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
+ sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
+
+ samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
+ samples = samples.to(comfy.model_management.intermediate_device())
+ return samples
+
+def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
+ samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
+ samples = samples.to(comfy.model_management.intermediate_device())
+ return samples
diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..1879e670a533a007f6f41f5ed30144c3fdd2e30c
--- /dev/null
+++ b/comfy/sampler_helpers.py
@@ -0,0 +1,74 @@
+import torch
+import comfy.model_management
+import comfy.conds
+import comfy.utils
+
+def prepare_mask(noise_mask, shape, device):
+ return comfy.utils.reshape_mask(noise_mask, shape).to(device)
+
+def get_models_from_cond(cond, model_type):
+ models = []
+ for c in cond:
+ if model_type in c:
+ models += [c[model_type]]
+ return models
+
+def convert_cond(cond):
+ out = []
+ for c in cond:
+ temp = c[1].copy()
+ model_conds = temp.get("model_conds", {})
+ if c[0] is not None:
+ model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
+ temp["cross_attn"] = c[0]
+ temp["model_conds"] = model_conds
+ out.append(temp)
+ return out
+
+def get_additional_models(conds, dtype):
+ """loads additional models in conditioning"""
+ cnets = []
+ gligen = []
+
+ for k in conds:
+ cnets += get_models_from_cond(conds[k], "control")
+ gligen += get_models_from_cond(conds[k], "gligen")
+
+ control_nets = set(cnets)
+
+ inference_memory = 0
+ control_models = []
+ for m in control_nets:
+ control_models += m.get_models()
+ inference_memory += m.inference_memory_requirements(dtype)
+
+ gligen = [x[1] for x in gligen]
+ models = control_models + gligen
+ return models, inference_memory
+
+def cleanup_additional_models(models):
+ """cleanup additional models that were loaded"""
+ for m in models:
+ if hasattr(m, 'cleanup'):
+ m.cleanup()
+
+
+def prepare_sampling(model, noise_shape, conds):
+ device = model.load_device
+ real_model = None
+ models, inference_memory = get_additional_models(conds, model.model_dtype())
+ memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory
+ minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory
+ comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required)
+ real_model = model.model
+
+ return real_model, conds, models
+
+def cleanup_models(conds, models):
+ cleanup_additional_models(models)
+
+ control_cleanup = []
+ for k in conds:
+ control_cleanup += get_models_from_cond(conds[k], "control")
+
+ cleanup_additional_models(set(control_cleanup))
diff --git a/comfy/samplers.py b/comfy/samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..94cba03b88fd1b40ff60b99e04998868077948b1
--- /dev/null
+++ b/comfy/samplers.py
@@ -0,0 +1,855 @@
+from .k_diffusion import sampling as k_diffusion_sampling
+from .extra_samplers import uni_pc
+import torch
+import collections
+from comfy import model_management
+import math
+import logging
+import comfy.sampler_helpers
+import scipy.stats
+import numpy
+
+def get_area_and_mult(conds, x_in, timestep_in):
+ dims = tuple(x_in.shape[2:])
+ area = None
+ strength = 1.0
+
+ if 'timestep_start' in conds:
+ timestep_start = conds['timestep_start']
+ if timestep_in[0] > timestep_start:
+ return None
+ if 'timestep_end' in conds:
+ timestep_end = conds['timestep_end']
+ if timestep_in[0] < timestep_end:
+ return None
+ if 'area' in conds:
+ area = list(conds['area'])
+ if 'strength' in conds:
+ strength = conds['strength']
+
+ input_x = x_in
+ if area is not None:
+ for i in range(len(dims)):
+ area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i])
+ input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i])
+
+ if 'mask' in conds:
+ # Scale the mask to the size of the input
+ # The mask should have been resized as we began the sampling process
+ mask_strength = 1.0
+ if "mask_strength" in conds:
+ mask_strength = conds["mask_strength"]
+ mask = conds['mask']
+ assert(mask.shape[1:] == x_in.shape[2:])
+
+ mask = mask[:input_x.shape[0]]
+ if area is not None:
+ for i in range(len(dims)):
+ mask = mask.narrow(i + 1, area[len(dims) + i], area[i])
+
+ mask = mask * mask_strength
+ mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
+ else:
+ mask = torch.ones_like(input_x)
+ mult = mask * strength
+
+ if 'mask' not in conds and area is not None:
+ rr = 8
+ for i in range(len(dims)):
+ if area[len(dims) + i] != 0:
+ for t in range(rr):
+ m = mult.narrow(i + 2, t, 1)
+ m *= ((1.0/rr) * (t + 1))
+ if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]:
+ for t in range(rr):
+ m = mult.narrow(i + 2, area[i] - 1 - t, 1)
+ m *= ((1.0/rr) * (t + 1))
+
+ conditioning = {}
+ model_conds = conds["model_conds"]
+ for c in model_conds:
+ conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
+
+ control = conds.get('control', None)
+
+ patches = None
+ if 'gligen' in conds:
+ gligen = conds['gligen']
+ patches = {}
+ gligen_type = gligen[0]
+ gligen_model = gligen[1]
+ if gligen_type == "position":
+ gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
+ else:
+ gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
+
+ patches['middle_patch'] = [gligen_patch]
+
+ cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches'])
+ return cond_obj(input_x, mult, conditioning, area, control, patches)
+
+def cond_equal_size(c1, c2):
+ if c1 is c2:
+ return True
+ if c1.keys() != c2.keys():
+ return False
+ for k in c1:
+ if not c1[k].can_concat(c2[k]):
+ return False
+ return True
+
+def can_concat_cond(c1, c2):
+ if c1.input_x.shape != c2.input_x.shape:
+ return False
+
+ def objects_concatable(obj1, obj2):
+ if (obj1 is None) != (obj2 is None):
+ return False
+ if obj1 is not None:
+ if obj1 is not obj2:
+ return False
+ return True
+
+ if not objects_concatable(c1.control, c2.control):
+ return False
+
+ if not objects_concatable(c1.patches, c2.patches):
+ return False
+
+ return cond_equal_size(c1.conditioning, c2.conditioning)
+
+def cond_cat(c_list):
+ c_crossattn = []
+ c_concat = []
+ c_adm = []
+ crossattn_max_len = 0
+
+ temp = {}
+ for x in c_list:
+ for k in x:
+ cur = temp.get(k, [])
+ cur.append(x[k])
+ temp[k] = cur
+
+ out = {}
+ for k in temp:
+ conds = temp[k]
+ out[k] = conds[0].concat(conds[1:])
+
+ return out
+
+def calc_cond_batch(model, conds, x_in, timestep, model_options):
+ out_conds = []
+ out_counts = []
+ to_run = []
+
+ for i in range(len(conds)):
+ out_conds.append(torch.zeros_like(x_in))
+ out_counts.append(torch.ones_like(x_in) * 1e-37)
+
+ cond = conds[i]
+ if cond is not None:
+ for x in cond:
+ p = get_area_and_mult(x, x_in, timestep)
+ if p is None:
+ continue
+
+ to_run += [(p, i)]
+
+ while len(to_run) > 0:
+ first = to_run[0]
+ first_shape = first[0][0].shape
+ to_batch_temp = []
+ for x in range(len(to_run)):
+ if can_concat_cond(to_run[x][0], first[0]):
+ to_batch_temp += [x]
+
+ to_batch_temp.reverse()
+ to_batch = to_batch_temp[:1]
+
+ free_memory = model_management.get_free_memory(x_in.device)
+ for i in range(1, len(to_batch_temp) + 1):
+ batch_amount = to_batch_temp[:len(to_batch_temp)//i]
+ input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
+ if model.memory_required(input_shape) * 1.5 < free_memory:
+ to_batch = batch_amount
+ break
+
+ input_x = []
+ mult = []
+ c = []
+ cond_or_uncond = []
+ area = []
+ control = None
+ patches = None
+ for x in to_batch:
+ o = to_run.pop(x)
+ p = o[0]
+ input_x.append(p.input_x)
+ mult.append(p.mult)
+ c.append(p.conditioning)
+ area.append(p.area)
+ cond_or_uncond.append(o[1])
+ control = p.control
+ patches = p.patches
+
+ batch_chunks = len(cond_or_uncond)
+ input_x = torch.cat(input_x)
+ c = cond_cat(c)
+ timestep_ = torch.cat([timestep] * batch_chunks)
+
+ if control is not None:
+ c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
+
+ transformer_options = {}
+ if 'transformer_options' in model_options:
+ transformer_options = model_options['transformer_options'].copy()
+
+ if patches is not None:
+ if "patches" in transformer_options:
+ cur_patches = transformer_options["patches"].copy()
+ for p in patches:
+ if p in cur_patches:
+ cur_patches[p] = cur_patches[p] + patches[p]
+ else:
+ cur_patches[p] = patches[p]
+ transformer_options["patches"] = cur_patches
+ else:
+ transformer_options["patches"] = patches
+
+ transformer_options["cond_or_uncond"] = cond_or_uncond[:]
+ transformer_options["sigmas"] = timestep
+
+ c['transformer_options'] = transformer_options
+
+ if 'model_function_wrapper' in model_options:
+ output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
+ else:
+ output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
+
+ for o in range(batch_chunks):
+ cond_index = cond_or_uncond[o]
+ a = area[o]
+ if a is None:
+ out_conds[cond_index] += output[o] * mult[o]
+ out_counts[cond_index] += mult[o]
+ else:
+ out_c = out_conds[cond_index]
+ out_cts = out_counts[cond_index]
+ dims = len(a) // 2
+ for i in range(dims):
+ out_c = out_c.narrow(i + 2, a[i + dims], a[i])
+ out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
+ out_c += output[o] * mult[o]
+ out_cts += mult[o]
+
+ for i in range(len(out_conds)):
+ out_conds[i] /= out_counts[i]
+
+ return out_conds
+
+def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
+ logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
+ return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
+
+def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
+ if "sampler_cfg_function" in model_options:
+ args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
+ "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
+ cfg_result = x - model_options["sampler_cfg_function"](args)
+ else:
+ cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
+
+ for fn in model_options.get("sampler_post_cfg_function", []):
+ args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
+ "sigma": timestep, "model_options": model_options, "input": x}
+ cfg_result = fn(args)
+
+ return cfg_result
+
+#The main sampling function shared by all the samplers
+#Returns denoised
+def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
+ if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
+ uncond_ = None
+ else:
+ uncond_ = uncond
+
+ conds = [cond, uncond_]
+ out = calc_cond_batch(model, conds, x, timestep, model_options)
+
+ for fn in model_options.get("sampler_pre_cfg_function", []):
+ args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
+ "input": x, "sigma": timestep, "model": model, "model_options": model_options}
+ out = fn(args)
+
+ return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
+
+
+class KSamplerX0Inpaint:
+ def __init__(self, model, sigmas):
+ self.inner_model = model
+ self.sigmas = sigmas
+ def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None):
+ if denoise_mask is not None:
+ if "denoise_mask_function" in model_options:
+ denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
+ latent_mask = 1. - denoise_mask
+ x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask
+ out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
+ if denoise_mask is not None:
+ out = out * denoise_mask + self.latent_image * latent_mask
+ return out
+
+def simple_scheduler(model_sampling, steps):
+ s = model_sampling
+ sigs = []
+ ss = len(s.sigmas) / steps
+ for x in range(steps):
+ sigs += [float(s.sigmas[-(1 + int(x * ss))])]
+ sigs += [0.0]
+ return torch.FloatTensor(sigs)
+
+def ddim_scheduler(model_sampling, steps):
+ s = model_sampling
+ sigs = []
+ x = 1
+ if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001):
+ steps += 1
+ sigs = []
+ else:
+ sigs = [0.0]
+
+ ss = max(len(s.sigmas) // steps, 1)
+ while x < len(s.sigmas):
+ sigs += [float(s.sigmas[x])]
+ x += ss
+ sigs = sigs[::-1]
+ return torch.FloatTensor(sigs)
+
+def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
+ s = model_sampling
+ start = s.timestep(s.sigma_max)
+ end = s.timestep(s.sigma_min)
+
+ append_zero = True
+ if sgm:
+ timesteps = torch.linspace(start, end, steps + 1)[:-1]
+ else:
+ if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001):
+ steps += 1
+ append_zero = False
+ timesteps = torch.linspace(start, end, steps)
+
+ sigs = []
+ for x in range(len(timesteps)):
+ ts = timesteps[x]
+ sigs.append(float(s.sigma(ts)))
+
+ if append_zero:
+ sigs += [0.0]
+
+ return torch.FloatTensor(sigs)
+
+# Implemented based on: https://arxiv.org/abs/2407.12173
+def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):
+ total_timesteps = (len(model_sampling.sigmas) - 1)
+ ts = 1 - numpy.linspace(0, 1, steps, endpoint=False)
+ ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps)
+
+ sigs = []
+ last_t = -1
+ for t in ts:
+ if t != last_t:
+ sigs += [float(model_sampling.sigmas[int(t)])]
+ last_t = t
+ sigs += [0.0]
+ return torch.FloatTensor(sigs)
+
+# from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41
+def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None):
+ if steps == 1:
+ sigma_schedule = [1.0, 0.0]
+ else:
+ if linear_steps is None:
+ linear_steps = steps // 2
+ linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)]
+ threshold_noise_step_diff = linear_steps - threshold_noise * steps
+ quadratic_steps = steps - linear_steps
+ quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps ** 2)
+ linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps ** 2)
+ const = quadratic_coef * (linear_steps ** 2)
+ quadratic_sigma_schedule = [
+ quadratic_coef * (i ** 2) + linear_coef * i + const
+ for i in range(linear_steps, steps)
+ ]
+ sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0]
+ sigma_schedule = [1.0 - x for x in sigma_schedule]
+ return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu()
+
+def get_mask_aabb(masks):
+ if masks.numel() == 0:
+ return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
+
+ b = masks.shape[0]
+
+ bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int)
+ is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool)
+ for i in range(b):
+ mask = masks[i]
+ if mask.numel() == 0:
+ continue
+ if torch.max(mask != 0) == False:
+ is_empty[i] = True
+ continue
+ y, x = torch.where(mask)
+ bounding_boxes[i, 0] = torch.min(x)
+ bounding_boxes[i, 1] = torch.min(y)
+ bounding_boxes[i, 2] = torch.max(x)
+ bounding_boxes[i, 3] = torch.max(y)
+
+ return bounding_boxes, is_empty
+
+def resolve_areas_and_cond_masks_multidim(conditions, dims, device):
+ # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
+ # While we're doing this, we can also resolve the mask device and scaling for performance reasons
+ for i in range(len(conditions)):
+ c = conditions[i]
+ if 'area' in c:
+ area = c['area']
+ if area[0] == "percentage":
+ modified = c.copy()
+ a = area[1:]
+ a_len = len(a) // 2
+ area = ()
+ for d in range(len(dims)):
+ area += (max(1, round(a[d] * dims[d])),)
+ for d in range(len(dims)):
+ area += (round(a[d + a_len] * dims[d]),)
+
+ modified['area'] = area
+ c = modified
+ conditions[i] = c
+
+ if 'mask' in c:
+ mask = c['mask']
+ mask = mask.to(device=device)
+ modified = c.copy()
+ if len(mask.shape) == len(dims):
+ mask = mask.unsqueeze(0)
+ if mask.shape[1:] != dims:
+ mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1)
+
+ if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2
+ bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
+ boxes, is_empty = get_mask_aabb(bounds)
+ if is_empty[0]:
+ # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway)
+ modified['area'] = (8, 8, 0, 0)
+ else:
+ box = boxes[0]
+ H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0])
+ H = max(8, H)
+ W = max(8, W)
+ area = (int(H), int(W), int(Y), int(X))
+ modified['area'] = area
+
+ modified['mask'] = mask
+ conditions[i] = modified
+
+def resolve_areas_and_cond_masks(conditions, h, w, device):
+ logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.")
+ return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device)
+
+def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2
+ if 'area' not in c:
+ return
+
+ c_area = c['area']
+ smallest = None
+ for x in conds:
+ if 'area' in x:
+ a = x['area']
+ if c_area[2] >= a[2] and c_area[3] >= a[3]:
+ if a[0] + a[2] >= c_area[0] + c_area[2]:
+ if a[1] + a[3] >= c_area[1] + c_area[3]:
+ if smallest is None:
+ smallest = x
+ elif 'area' not in smallest:
+ smallest = x
+ else:
+ if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]:
+ smallest = x
+ else:
+ if smallest is None:
+ smallest = x
+ if smallest is None:
+ return
+ if 'area' in smallest:
+ if smallest['area'] == c_area:
+ return
+
+ out = c.copy()
+ out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied?
+ conds += [out]
+
+def calculate_start_end_timesteps(model, conds):
+ s = model.model_sampling
+ for t in range(len(conds)):
+ x = conds[t]
+
+ timestep_start = None
+ timestep_end = None
+ if 'start_percent' in x:
+ timestep_start = s.percent_to_sigma(x['start_percent'])
+ if 'end_percent' in x:
+ timestep_end = s.percent_to_sigma(x['end_percent'])
+
+ if (timestep_start is not None) or (timestep_end is not None):
+ n = x.copy()
+ if (timestep_start is not None):
+ n['timestep_start'] = timestep_start
+ if (timestep_end is not None):
+ n['timestep_end'] = timestep_end
+ conds[t] = n
+
+def pre_run_control(model, conds):
+ s = model.model_sampling
+ for t in range(len(conds)):
+ x = conds[t]
+
+ timestep_start = None
+ timestep_end = None
+ percent_to_timestep_function = lambda a: s.percent_to_sigma(a)
+ if 'control' in x:
+ x['control'].pre_run(model, percent_to_timestep_function)
+
+def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
+ cond_cnets = []
+ cond_other = []
+ uncond_cnets = []
+ uncond_other = []
+ for t in range(len(conds)):
+ x = conds[t]
+ if 'area' not in x:
+ if name in x and x[name] is not None:
+ cond_cnets.append(x[name])
+ else:
+ cond_other.append((x, t))
+ for t in range(len(uncond)):
+ x = uncond[t]
+ if 'area' not in x:
+ if name in x and x[name] is not None:
+ uncond_cnets.append(x[name])
+ else:
+ uncond_other.append((x, t))
+
+ if len(uncond_cnets) > 0:
+ return
+
+ for x in range(len(cond_cnets)):
+ temp = uncond_other[x % len(uncond_other)]
+ o = temp[0]
+ if name in o and o[name] is not None:
+ n = o.copy()
+ n[name] = uncond_fill_func(cond_cnets, x)
+ uncond += [n]
+ else:
+ n = o.copy()
+ n[name] = uncond_fill_func(cond_cnets, x)
+ uncond[temp[1]] = n
+
+def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs):
+ for t in range(len(conds)):
+ x = conds[t]
+ params = x.copy()
+ params["device"] = device
+ params["noise"] = noise
+ default_width = None
+ if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model
+ default_width = noise.shape[3] * 8
+ params["width"] = params.get("width", default_width)
+ params["height"] = params.get("height", noise.shape[2] * 8)
+ params["prompt_type"] = params.get("prompt_type", prompt_type)
+ for k in kwargs:
+ if k not in params:
+ params[k] = kwargs[k]
+
+ out = model_function(**params)
+ x = x.copy()
+ model_conds = x['model_conds'].copy()
+ for k in out:
+ model_conds[k] = out[k]
+ x['model_conds'] = model_conds
+ conds[t] = x
+ return conds
+
+class Sampler:
+ def sample(self):
+ pass
+
+ def max_denoise(self, model_wrap, sigmas):
+ max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max)
+ sigma = float(sigmas[0])
+ return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
+
+KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+ "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
+ "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
+ "ipndm", "ipndm_v", "deis"]
+
+class KSAMPLER(Sampler):
+ def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
+ self.sampler_function = sampler_function
+ self.extra_options = extra_options
+ self.inpaint_options = inpaint_options
+
+ def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
+ extra_args["denoise_mask"] = denoise_mask
+ model_k = KSamplerX0Inpaint(model_wrap, sigmas)
+ model_k.latent_image = latent_image
+ if self.inpaint_options.get("random", False): #TODO: Should this be the default?
+ generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
+ model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
+ else:
+ model_k.noise = noise
+
+ noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))
+
+ k_callback = None
+ total_steps = len(sigmas) - 1
+ if callback is not None:
+ k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
+
+ samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
+ samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)
+ return samples
+
+
+def ksampler(sampler_name, extra_options={}, inpaint_options={}):
+ if sampler_name == "dpm_fast":
+ def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable):
+ if len(sigmas) <= 1:
+ return noise
+
+ sigma_min = sigmas[-1]
+ if sigma_min == 0:
+ sigma_min = sigmas[-2]
+ total_steps = len(sigmas) - 1
+ return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable)
+ sampler_function = dpm_fast_function
+ elif sampler_name == "dpm_adaptive":
+ def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options):
+ if len(sigmas) <= 1:
+ return noise
+
+ sigma_min = sigmas[-1]
+ if sigma_min == 0:
+ sigma_min = sigmas[-2]
+ return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options)
+ sampler_function = dpm_adaptive_function
+ else:
+ sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
+
+ return KSAMPLER(sampler_function, extra_options, inpaint_options)
+
+
+def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None):
+ for k in conds:
+ conds[k] = conds[k][:]
+ resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device)
+
+ for k in conds:
+ calculate_start_end_timesteps(model, conds[k])
+
+ if hasattr(model, 'extra_conds'):
+ for k in conds:
+ conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
+
+ #make sure each cond area has an opposite one with the same area
+ for k in conds:
+ for c in conds[k]:
+ for kk in conds:
+ if k != kk:
+ create_cond_with_same_area_if_none(conds[kk], c)
+
+ for k in conds:
+ pre_run_control(model, conds[k])
+
+ if "positive" in conds:
+ positive = conds["positive"]
+ for k in conds:
+ if k != "positive":
+ apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x])
+ apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x])
+
+ return conds
+
+class CFGGuider:
+ def __init__(self, model_patcher):
+ self.model_patcher = model_patcher
+ self.model_options = model_patcher.model_options
+ self.original_conds = {}
+ self.cfg = 1.0
+
+ def set_conds(self, positive, negative):
+ self.inner_set_conds({"positive": positive, "negative": negative})
+
+ def set_cfg(self, cfg):
+ self.cfg = cfg
+
+ def inner_set_conds(self, conds):
+ for k in conds:
+ self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
+
+ def __call__(self, *args, **kwargs):
+ return self.predict_noise(*args, **kwargs)
+
+ def predict_noise(self, x, timestep, model_options={}, seed=None):
+ return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
+
+ def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed):
+ if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
+ latent_image = self.inner_model.process_latent_in(latent_image)
+
+ self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
+
+ extra_args = {"model_options": self.model_options, "seed":seed}
+
+ samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
+ return self.inner_model.process_latent_out(samples.to(torch.float32))
+
+ def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+ if sigmas.shape[-1] == 0:
+ return latent_image
+
+ self.conds = {}
+ for k in self.original_conds:
+ self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
+
+ self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds)
+ device = self.model_patcher.load_device
+
+ if denoise_mask is not None:
+ denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
+
+ noise = noise.to(device)
+ latent_image = latent_image.to(device)
+ sigmas = sigmas.to(device)
+
+ output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+
+ comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
+ del self.inner_model
+ del self.conds
+ del self.loaded_models
+ return output
+
+
+def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+ cfg_guider = CFGGuider(model)
+ cfg_guider.set_conds(positive, negative)
+ cfg_guider.set_cfg(cfg)
+ return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+
+
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta", "linear_quadratic"]
+SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
+
+def calculate_sigmas(model_sampling, scheduler_name, steps):
+ if scheduler_name == "karras":
+ sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
+ elif scheduler_name == "exponential":
+ sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
+ elif scheduler_name == "normal":
+ sigmas = normal_scheduler(model_sampling, steps)
+ elif scheduler_name == "simple":
+ sigmas = simple_scheduler(model_sampling, steps)
+ elif scheduler_name == "ddim_uniform":
+ sigmas = ddim_scheduler(model_sampling, steps)
+ elif scheduler_name == "sgm_uniform":
+ sigmas = normal_scheduler(model_sampling, steps, sgm=True)
+ elif scheduler_name == "beta":
+ sigmas = beta_scheduler(model_sampling, steps)
+ elif scheduler_name == "linear_quadratic":
+ sigmas = linear_quadratic_schedule(model_sampling, steps)
+ else:
+ logging.error("error invalid scheduler {}".format(scheduler_name))
+ return sigmas
+
+def sampler_object(name):
+ if name == "uni_pc":
+ sampler = KSAMPLER(uni_pc.sample_unipc)
+ elif name == "uni_pc_bh2":
+ sampler = KSAMPLER(uni_pc.sample_unipc_bh2)
+ elif name == "ddim":
+ sampler = ksampler("euler", inpaint_options={"random": True})
+ else:
+ sampler = ksampler(name)
+ return sampler
+
+class KSampler:
+ SCHEDULERS = SCHEDULER_NAMES
+ SAMPLERS = SAMPLER_NAMES
+ DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2'))
+
+ def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
+ self.model = model
+ self.device = device
+ if scheduler not in self.SCHEDULERS:
+ scheduler = self.SCHEDULERS[0]
+ if sampler not in self.SAMPLERS:
+ sampler = self.SAMPLERS[0]
+ self.scheduler = scheduler
+ self.sampler = sampler
+ self.set_steps(steps, denoise)
+ self.denoise = denoise
+ self.model_options = model_options
+
+ def calculate_sigmas(self, steps):
+ sigmas = None
+
+ discard_penultimate_sigma = False
+ if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS:
+ steps += 1
+ discard_penultimate_sigma = True
+
+ sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps)
+
+ if discard_penultimate_sigma:
+ sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+ return sigmas
+
+ def set_steps(self, steps, denoise=None):
+ self.steps = steps
+ if denoise is None or denoise > 0.9999:
+ self.sigmas = self.calculate_sigmas(steps).to(self.device)
+ else:
+ if denoise <= 0.0:
+ self.sigmas = torch.FloatTensor([])
+ else:
+ new_steps = int(steps/denoise)
+ sigmas = self.calculate_sigmas(new_steps).to(self.device)
+ self.sigmas = sigmas[-(steps + 1):]
+
+ def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
+ if sigmas is None:
+ sigmas = self.sigmas
+
+ if last_step is not None and last_step < (len(sigmas) - 1):
+ sigmas = sigmas[:last_step + 1]
+ if force_full_denoise:
+ sigmas[-1] = 0
+
+ if start_step is not None:
+ if start_step < (len(sigmas) - 1):
+ sigmas = sigmas[start_step:]
+ else:
+ if latent_image is not None:
+ return latent_image
+ else:
+ return torch.zeros_like(noise)
+
+ sampler = sampler_object(self.sampler)
+
+ return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
diff --git a/comfy/sd.py b/comfy/sd.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2af707812159ae7e740044e4cea1fc5b471564f
--- /dev/null
+++ b/comfy/sd.py
@@ -0,0 +1,819 @@
+import torch
+from enum import Enum
+import logging
+
+from comfy import model_management
+from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
+from .ldm.cascade.stage_a import StageA
+from .ldm.cascade.stage_c_coder import StageC_coder
+from .ldm.audio.autoencoder import AudioOobleckVAE
+import comfy.ldm.genmo.vae.model
+import comfy.ldm.lightricks.vae.causal_video_autoencoder
+import yaml
+
+import comfy.utils
+
+from . import clip_vision
+from . import gligen
+from . import diffusers_convert
+from . import model_detection
+
+from . import sd1_clip
+from . import sdxl_clip
+import comfy.text_encoders.sd2_clip
+import comfy.text_encoders.sd3_clip
+import comfy.text_encoders.sa_t5
+import comfy.text_encoders.aura_t5
+import comfy.text_encoders.hydit
+import comfy.text_encoders.flux
+import comfy.text_encoders.long_clipl
+import comfy.text_encoders.genmo
+import comfy.text_encoders.lt
+
+import comfy.model_patcher
+import comfy.lora
+import comfy.lora_convert
+import comfy.t2i_adapter.adapter
+import comfy.taesd.taesd
+
+import comfy.ldm.flux.redux
+
+def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
+ key_map = {}
+ if model is not None:
+ key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
+ if clip is not None:
+ key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
+
+ lora = comfy.lora_convert.convert_lora(lora)
+ loaded = comfy.lora.load_lora(lora, key_map)
+ if model is not None:
+ new_modelpatcher = model.clone()
+ k = new_modelpatcher.add_patches(loaded, strength_model)
+ else:
+ k = ()
+ new_modelpatcher = None
+
+ if clip is not None:
+ new_clip = clip.clone()
+ k1 = new_clip.add_patches(loaded, strength_clip)
+ else:
+ k1 = ()
+ new_clip = None
+ k = set(k)
+ k1 = set(k1)
+ for x in loaded:
+ if (x not in k) and (x not in k1):
+ logging.warning("NOT LOADED {}".format(x))
+
+ return (new_modelpatcher, new_clip)
+
+
+class CLIP:
+ def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, model_options={}):
+ if no_init:
+ return
+ params = target.params.copy()
+ clip = target.clip
+ tokenizer = target.tokenizer
+
+ load_device = model_options.get("load_device", model_management.text_encoder_device())
+ offload_device = model_options.get("offload_device", model_management.text_encoder_offload_device())
+ dtype = model_options.get("dtype", None)
+ if dtype is None:
+ dtype = model_management.text_encoder_dtype(load_device)
+
+ params['dtype'] = dtype
+ params['device'] = model_options.get("initial_device", model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype)))
+ params['model_options'] = model_options
+
+ self.cond_stage_model = clip(**(params))
+
+ for dt in self.cond_stage_model.dtypes:
+ if not model_management.supports_cast(load_device, dt):
+ load_device = offload_device
+ if params['device'] != offload_device:
+ self.cond_stage_model.to(offload_device)
+ logging.warning("Had to shift TE back.")
+
+ self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
+ if params['device'] == load_device:
+ model_management.load_models_gpu([self.patcher], force_full_load=True)
+ self.layer_idx = None
+ logging.debug("CLIP model load device: {}, offload device: {}, current: {}".format(load_device, offload_device, params['device']))
+
+ def clone(self):
+ n = CLIP(no_init=True)
+ n.patcher = self.patcher.clone()
+ n.cond_stage_model = self.cond_stage_model
+ n.tokenizer = self.tokenizer
+ n.layer_idx = self.layer_idx
+ return n
+
+ def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
+ return self.patcher.add_patches(patches, strength_patch, strength_model)
+
+ def clip_layer(self, layer_idx):
+ self.layer_idx = layer_idx
+
+ def tokenize(self, text, return_word_ids=False):
+ return self.tokenizer.tokenize_with_weights(text, return_word_ids)
+
+ def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False):
+ self.cond_stage_model.reset_clip_options()
+
+ if self.layer_idx is not None:
+ self.cond_stage_model.set_clip_options({"layer": self.layer_idx})
+
+ if return_pooled == "unprojected":
+ self.cond_stage_model.set_clip_options({"projected_pooled": False})
+
+ self.load_model()
+ o = self.cond_stage_model.encode_token_weights(tokens)
+ cond, pooled = o[:2]
+ if return_dict:
+ out = {"cond": cond, "pooled_output": pooled}
+ if len(o) > 2:
+ for k in o[2]:
+ out[k] = o[2][k]
+ return out
+
+ if return_pooled:
+ return cond, pooled
+ return cond
+
+ def encode(self, text):
+ tokens = self.tokenize(text)
+ return self.encode_from_tokens(tokens)
+
+ def load_sd(self, sd, full_model=False):
+ if full_model:
+ return self.cond_stage_model.load_state_dict(sd, strict=False)
+ else:
+ return self.cond_stage_model.load_sd(sd)
+
+ def get_sd(self):
+ sd_clip = self.cond_stage_model.state_dict()
+ sd_tokenizer = self.tokenizer.state_dict()
+ for k in sd_tokenizer:
+ sd_clip[k] = sd_tokenizer[k]
+ return sd_clip
+
+ def load_model(self):
+ model_management.load_model_gpu(self.patcher)
+ return self.patcher
+
+ def get_key_patches(self):
+ return self.patcher.get_key_patches()
+
+class VAE:
+ def __init__(self, sd=None, device=None, config=None, dtype=None):
+ if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
+ sd = diffusers_convert.convert_vae_state_dict(sd)
+
+ self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)
+ self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
+ self.downscale_ratio = 8
+ self.upscale_ratio = 8
+ self.latent_channels = 4
+ self.latent_dim = 2
+ self.output_channels = 3
+ self.process_input = lambda image: image * 2.0 - 1.0
+ self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
+ self.working_dtypes = [torch.bfloat16, torch.float32]
+
+ if config is None:
+ if "decoder.mid.block_1.mix_factor" in sd:
+ encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
+ decoder_config = encoder_config.copy()
+ decoder_config["video_kernel_size"] = [3, 1, 1]
+ decoder_config["alpha"] = 0.0
+ self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
+ encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
+ decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
+ elif "taesd_decoder.1.weight" in sd:
+ self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
+ self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
+ elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
+ self.first_stage_model = StageA()
+ self.downscale_ratio = 4
+ self.upscale_ratio = 4
+ #TODO
+ #self.memory_used_encode
+ #self.memory_used_decode
+ self.process_input = lambda image: image
+ self.process_output = lambda image: image
+ elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade
+ self.first_stage_model = StageC_coder()
+ self.downscale_ratio = 32
+ self.latent_channels = 16
+ new_sd = {}
+ for k in sd:
+ new_sd["encoder.{}".format(k)] = sd[k]
+ sd = new_sd
+ elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade
+ self.first_stage_model = StageC_coder()
+ self.latent_channels = 16
+ new_sd = {}
+ for k in sd:
+ new_sd["previewer.{}".format(k)] = sd[k]
+ sd = new_sd
+ elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade
+ self.first_stage_model = StageC_coder()
+ self.downscale_ratio = 32
+ self.latent_channels = 16
+ elif "decoder.conv_in.weight" in sd:
+ #default SD1.x/SD2.x VAE parameters
+ ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
+
+ if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
+ ddconfig['ch_mult'] = [1, 2, 4]
+ self.downscale_ratio = 4
+ self.upscale_ratio = 4
+
+ self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
+ if 'quant_conv.weight' in sd:
+ self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)
+ else:
+ self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
+ encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
+ decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
+ elif "decoder.layers.1.layers.0.beta" in sd:
+ self.first_stage_model = AudioOobleckVAE()
+ self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
+ self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype)
+ self.latent_channels = 64
+ self.output_channels = 2
+ self.upscale_ratio = 2048
+ self.downscale_ratio = 2048
+ self.latent_dim = 1
+ self.process_output = lambda audio: audio
+ self.process_input = lambda audio: audio
+ self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+ elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae
+ if "blocks.2.blocks.3.stack.5.weight" in sd:
+ sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."})
+ if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd:
+ sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."})
+ self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE()
+ self.latent_channels = 12
+ self.latent_dim = 3
+ self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
+ self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
+ self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
+ self.working_dtypes = [torch.float16, torch.float32]
+ elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv
+ self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE()
+ self.latent_channels = 128
+ self.latent_dim = 3
+ self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
+ self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+ self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32)
+ self.working_dtypes = [torch.bfloat16, torch.float32]
+ else:
+ logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
+ self.first_stage_model = None
+ return
+ else:
+ self.first_stage_model = AutoencoderKL(**(config['params']))
+ self.first_stage_model = self.first_stage_model.eval()
+
+ m, u = self.first_stage_model.load_state_dict(sd, strict=False)
+ if len(m) > 0:
+ logging.warning("Missing VAE keys {}".format(m))
+
+ if len(u) > 0:
+ logging.debug("Leftover VAE keys {}".format(u))
+
+ if device is None:
+ device = model_management.vae_device()
+ self.device = device
+ offload_device = model_management.vae_offload_device()
+ if dtype is None:
+ dtype = model_management.vae_dtype(self.device, self.working_dtypes)
+ self.vae_dtype = dtype
+ self.first_stage_model.to(self.vae_dtype)
+ self.output_device = model_management.intermediate_device()
+
+ self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
+ logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
+
+ def vae_encode_crop_pixels(self, pixels):
+ dims = pixels.shape[1:-1]
+ for d in range(len(dims)):
+ x = (dims[d] // self.downscale_ratio) * self.downscale_ratio
+ x_offset = (dims[d] % self.downscale_ratio) // 2
+ if x != dims[d]:
+ pixels = pixels.narrow(d + 1, x_offset, x)
+ return pixels
+
+ def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
+ steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
+ steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+ steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+ pbar = comfy.utils.ProgressBar(steps)
+
+ decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+ output = self.process_output(
+ (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
+ comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
+ comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar))
+ / 3.0)
+ return output
+
+ def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
+ decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+ return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)
+
+ def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)):
+ decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+ return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device))
+
+ def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
+ steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
+ steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+ steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+ pbar = comfy.utils.ProgressBar(steps)
+
+ encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+ samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
+ samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
+ samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
+ samples /= 3.0
+ return samples
+
+ def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
+ encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+ return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
+
+ def decode(self, samples_in):
+ pixel_samples = None
+ try:
+ memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
+ model_management.load_models_gpu([self.patcher], memory_required=memory_used)
+ free_memory = model_management.get_free_memory(self.device)
+ batch_number = int(free_memory / memory_used)
+ batch_number = max(1, batch_number)
+
+ for x in range(0, samples_in.shape[0], batch_number):
+ samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
+ out = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
+ if pixel_samples is None:
+ pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+ pixel_samples[x:x+batch_number] = out
+ except model_management.OOM_EXCEPTION as e:
+ logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
+ dims = samples_in.ndim - 2
+ if dims == 1:
+ pixel_samples = self.decode_tiled_1d(samples_in)
+ elif dims == 2:
+ pixel_samples = self.decode_tiled_(samples_in)
+ elif dims == 3:
+ tile = 256 // self.spacial_compression_decode()
+ overlap = tile // 4
+ pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+
+ pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
+ return pixel_samples
+
+ def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None):
+ memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile
+ model_management.load_models_gpu([self.patcher], memory_required=memory_used)
+ dims = samples.ndim - 2
+ args = {}
+ if tile_x is not None:
+ args["tile_x"] = tile_x
+ if tile_y is not None:
+ args["tile_y"] = tile_y
+ if overlap is not None:
+ args["overlap"] = overlap
+
+ if dims == 1:
+ args.pop("tile_y")
+ output = self.decode_tiled_1d(samples, **args)
+ elif dims == 2:
+ output = self.decode_tiled_(samples, **args)
+ elif dims == 3:
+ output = self.decode_tiled_3d(samples, **args)
+ return output.movedim(1, -1)
+
+ def encode(self, pixel_samples):
+ pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
+ pixel_samples = pixel_samples.movedim(-1, 1)
+ if self.latent_dim == 3:
+ pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+ try:
+ memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
+ model_management.load_models_gpu([self.patcher], memory_required=memory_used)
+ free_memory = model_management.get_free_memory(self.device)
+ batch_number = int(free_memory / max(1, memory_used))
+ batch_number = max(1, batch_number)
+ samples = None
+ for x in range(0, pixel_samples.shape[0], batch_number):
+ pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
+ out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+ if samples is None:
+ samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+ samples[x:x + batch_number] = out
+
+ except model_management.OOM_EXCEPTION as e:
+ logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
+ if len(pixel_samples.shape) == 3:
+ samples = self.encode_tiled_1d(pixel_samples)
+ else:
+ samples = self.encode_tiled_(pixel_samples)
+
+ return samples
+
+ def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
+ pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
+ model_management.load_model_gpu(self.patcher)
+ pixel_samples = pixel_samples.movedim(-1,1)
+ samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
+ return samples
+
+ def get_sd(self):
+ return self.first_stage_model.state_dict()
+
+ def spacial_compression_decode(self):
+ try:
+ return self.upscale_ratio[-1]
+ except:
+ return self.upscale_ratio
+
+class StyleModel:
+ def __init__(self, model, device="cpu"):
+ self.model = model
+
+ def get_cond(self, input):
+ return self.model(input.last_hidden_state)
+
+
+def load_style_model(ckpt_path):
+ model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+ keys = model_data.keys()
+ if "style_embedding" in keys:
+ model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
+ elif "redux_down.weight" in keys:
+ model = comfy.ldm.flux.redux.ReduxImageEncoder()
+ else:
+ raise Exception("invalid style model {}".format(ckpt_path))
+ model.load_state_dict(model_data)
+ return StyleModel(model)
+
+class CLIPType(Enum):
+ STABLE_DIFFUSION = 1
+ STABLE_CASCADE = 2
+ SD3 = 3
+ STABLE_AUDIO = 4
+ HUNYUAN_DIT = 5
+ FLUX = 6
+ MOCHI = 7
+ LTXV = 8
+
+def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
+ clip_data = []
+ for p in ckpt_paths:
+ clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
+ return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
+
+
+class TEModel(Enum):
+ CLIP_L = 1
+ CLIP_H = 2
+ CLIP_G = 3
+ T5_XXL = 4
+ T5_XL = 5
+ T5_BASE = 6
+
+def detect_te_model(sd):
+ if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+ return TEModel.CLIP_G
+ if "text_model.encoder.layers.22.mlp.fc1.weight" in sd:
+ return TEModel.CLIP_H
+ if "text_model.encoder.layers.0.mlp.fc1.weight" in sd:
+ return TEModel.CLIP_L
+ if "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd:
+ weight = sd["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
+ if weight.shape[-1] == 4096:
+ return TEModel.T5_XXL
+ elif weight.shape[-1] == 2048:
+ return TEModel.T5_XL
+ if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd:
+ return TEModel.T5_BASE
+ return None
+
+
+def t5xxl_detect(clip_data):
+ weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+
+ for sd in clip_data:
+ if weight_name in sd:
+ return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd)
+
+ return {}
+
+
+def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
+ clip_data = state_dicts
+
+ class EmptyClass:
+ pass
+
+ for i in range(len(clip_data)):
+ if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
+ clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
+ else:
+ if "text_projection" in clip_data[i]:
+ clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
+
+ clip_target = EmptyClass()
+ clip_target.params = {}
+ if len(clip_data) == 1:
+ te_model = detect_te_model(clip_data[0])
+ if te_model == TEModel.CLIP_G:
+ if clip_type == CLIPType.STABLE_CASCADE:
+ clip_target.clip = sdxl_clip.StableCascadeClipModel
+ clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer
+ elif clip_type == CLIPType.SD3:
+ clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False)
+ clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+ else:
+ clip_target.clip = sdxl_clip.SDXLRefinerClipModel
+ clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+ elif te_model == TEModel.CLIP_H:
+ clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel
+ clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer
+ elif te_model == TEModel.T5_XXL:
+ if clip_type == CLIPType.SD3:
+ clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data))
+ clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+ elif clip_type == CLIPType.LTXV:
+ clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data))
+ clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer
+ else: #CLIPType.MOCHI
+ clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
+ clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer
+ elif te_model == TEModel.T5_XL:
+ clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
+ clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
+ elif te_model == TEModel.T5_BASE:
+ clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
+ clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
+ else:
+ if clip_type == CLIPType.SD3:
+ clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
+ clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+ else:
+ clip_target.clip = sd1_clip.SD1ClipModel
+ clip_target.tokenizer = sd1_clip.SD1Tokenizer
+ elif len(clip_data) == 2:
+ if clip_type == CLIPType.SD3:
+ te_models = [detect_te_model(clip_data[0]), detect_te_model(clip_data[1])]
+ clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=TEModel.CLIP_L in te_models, clip_g=TEModel.CLIP_G in te_models, t5=TEModel.T5_XXL in te_models, **t5xxl_detect(clip_data))
+ clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+ elif clip_type == CLIPType.HUNYUAN_DIT:
+ clip_target.clip = comfy.text_encoders.hydit.HyditModel
+ clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
+ elif clip_type == CLIPType.FLUX:
+ clip_target.clip = comfy.text_encoders.flux.flux_clip(**t5xxl_detect(clip_data))
+ clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
+ else:
+ clip_target.clip = sdxl_clip.SDXLClipModel
+ clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+ elif len(clip_data) == 3:
+ clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data))
+ clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+
+ parameters = 0
+ tokenizer_data = {}
+ for c in clip_data:
+ parameters += comfy.utils.calculate_parameters(c)
+ tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
+
+ clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options)
+ for c in clip_data:
+ m, u = clip.load_sd(c)
+ if len(m) > 0:
+ logging.warning("clip missing: {}".format(m))
+
+ if len(u) > 0:
+ logging.debug("clip unexpected: {}".format(u))
+ return clip
+
+def load_gligen(ckpt_path):
+ data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+ model = gligen.load_gligen(data)
+ if model_management.should_use_fp16():
+ model = model.half()
+ return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
+
+def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
+ logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.")
+ model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True)
+ #TODO: this function is a mess and should be removed eventually
+ if config is None:
+ with open(config_path, 'r') as stream:
+ config = yaml.safe_load(stream)
+ model_config_params = config['model']['params']
+ clip_config = model_config_params['cond_stage_config']
+ scale_factor = model_config_params['scale_factor']
+
+ if "parameterization" in model_config_params:
+ if model_config_params["parameterization"] == "v":
+ m = model.clone()
+ class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION):
+ pass
+ m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config))
+ model = m
+
+ layer_idx = clip_config.get("params", {}).get("layer_idx", None)
+ if layer_idx is not None:
+ clip.clip_layer(layer_idx)
+
+ return (model, clip, vae)
+
+def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}):
+ sd = comfy.utils.load_torch_file(ckpt_path)
+ out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options)
+ if out is None:
+ raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
+ return out
+
+def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}):
+ clip = None
+ clipvision = None
+ vae = None
+ model = None
+ model_patcher = None
+
+ diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
+ parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
+ weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)
+ load_device = model_management.get_torch_device()
+
+ model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix)
+ if model_config is None:
+ return None
+
+ unet_weight_dtype = list(model_config.supported_inference_dtypes)
+ if weight_dtype is not None and model_config.scaled_fp8 is None:
+ unet_weight_dtype.append(weight_dtype)
+
+ model_config.custom_operations = model_options.get("custom_operations", None)
+ unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
+
+ if unet_dtype is None:
+ unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
+
+ manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+ model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+
+ if model_config.clip_vision_prefix is not None:
+ if output_clipvision:
+ clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
+
+ if output_model:
+ inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
+ model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
+ model.load_model_weights(sd, diffusion_model_prefix)
+
+ if output_vae:
+ vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
+ vae_sd = model_config.process_vae_state_dict(vae_sd)
+ vae = VAE(sd=vae_sd)
+
+ if output_clip:
+ clip_target = model_config.clip_target(state_dict=sd)
+ if clip_target is not None:
+ clip_sd = model_config.process_clip_state_dict(sd)
+ if len(clip_sd) > 0:
+ parameters = comfy.utils.calculate_parameters(clip_sd)
+ clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options)
+ m, u = clip.load_sd(clip_sd, full_model=True)
+ if len(m) > 0:
+ m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
+ if len(m_filter) > 0:
+ logging.warning("clip missing: {}".format(m))
+ else:
+ logging.debug("clip missing: {}".format(m))
+
+ if len(u) > 0:
+ logging.debug("clip unexpected {}:".format(u))
+ else:
+ logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
+
+ left_over = sd.keys()
+ if len(left_over) > 0:
+ logging.debug("left over keys: {}".format(left_over))
+
+ if output_model:
+ model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device())
+ if inital_load_device != torch.device("cpu"):
+ logging.info("loaded straight to GPU")
+ model_management.load_models_gpu([model_patcher], force_full_load=True)
+
+ return (model_patcher, clip, vae, clipvision)
+
+
+def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffusers or regular format
+ dtype = model_options.get("dtype", None)
+
+ #Allow loading unets from checkpoint files
+ diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
+ temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
+ if len(temp_sd) > 0:
+ sd = temp_sd
+
+ parameters = comfy.utils.calculate_parameters(sd)
+ weight_dtype = comfy.utils.weight_dtype(sd)
+
+ load_device = model_management.get_torch_device()
+ model_config = model_detection.model_config_from_unet(sd, "")
+
+ if model_config is not None:
+ new_sd = sd
+ else:
+ new_sd = model_detection.convert_diffusers_mmdit(sd, "")
+ if new_sd is not None: #diffusers mmdit
+ model_config = model_detection.model_config_from_unet(new_sd, "")
+ if model_config is None:
+ return None
+ else: #diffusers unet
+ model_config = model_detection.model_config_from_diffusers_unet(sd)
+ if model_config is None:
+ return None
+
+ diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
+
+ new_sd = {}
+ for k in diffusers_keys:
+ if k in sd:
+ new_sd[diffusers_keys[k]] = sd.pop(k)
+ else:
+ logging.warning("{} {}".format(diffusers_keys[k], k))
+
+ offload_device = model_management.unet_offload_device()
+ unet_weight_dtype = list(model_config.supported_inference_dtypes)
+ if weight_dtype is not None and model_config.scaled_fp8 is None:
+ unet_weight_dtype.append(weight_dtype)
+
+ if dtype is None:
+ unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
+ else:
+ unet_dtype = dtype
+
+ manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+ model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+ model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
+ if model_options.get("fp8_optimizations", False):
+ model_config.optimizations["fp8"] = True
+
+ model = model_config.get_model(new_sd, "")
+ model = model.to(offload_device)
+ model.load_model_weights(new_sd, "")
+ left_over = sd.keys()
+ if len(left_over) > 0:
+ logging.info("left over keys in unet: {}".format(left_over))
+ return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
+
+
+def load_diffusion_model(unet_path, model_options={}):
+ sd = comfy.utils.load_torch_file(unet_path)
+ model = load_diffusion_model_state_dict(sd, model_options=model_options)
+ if model is None:
+ logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
+ raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
+ return model
+
+def load_unet(unet_path, dtype=None):
+ print("WARNING: the load_unet function has been deprecated and will be removed please switch to: load_diffusion_model")
+ return load_diffusion_model(unet_path, model_options={"dtype": dtype})
+
+def load_unet_state_dict(sd, dtype=None):
+ print("WARNING: the load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict")
+ return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype})
+
+def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}):
+ clip_sd = None
+ load_models = [model]
+ if clip is not None:
+ load_models.append(clip.load_model())
+ clip_sd = clip.get_sd()
+ vae_sd = None
+ if vae is not None:
+ vae_sd = vae.get_sd()
+
+ model_management.load_models_gpu(load_models, force_patch_weights=True)
+ clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
+ sd = model.model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd)
+ for k in extra_keys:
+ sd[k] = extra_keys[k]
+
+ for k in sd:
+ t = sd[k]
+ if not t.is_contiguous():
+ sd[k] = t.contiguous()
+
+ comfy.utils.save_torch_file(sd, output_path, metadata=metadata)
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..a454f3bb3f1a6f9f5895bef039524e3867666361
--- /dev/null
+++ b/comfy/sd1_clip.py
@@ -0,0 +1,602 @@
+import os
+
+from transformers import CLIPTokenizer
+import comfy.ops
+import torch
+import traceback
+import zipfile
+from . import model_management
+import comfy.clip_model
+import json
+import logging
+import numbers
+
+def gen_empty_tokens(special_tokens, length):
+ start_token = special_tokens.get("start", None)
+ end_token = special_tokens.get("end", None)
+ pad_token = special_tokens.get("pad")
+ output = []
+ if start_token is not None:
+ output.append(start_token)
+ if end_token is not None:
+ output.append(end_token)
+ output += [pad_token] * (length - len(output))
+ return output
+
+class ClipTokenWeightEncoder:
+ def encode_token_weights(self, token_weight_pairs):
+ to_encode = list()
+ max_token_len = 0
+ has_weights = False
+ for x in token_weight_pairs:
+ tokens = list(map(lambda a: a[0], x))
+ max_token_len = max(len(tokens), max_token_len)
+ has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
+ to_encode.append(tokens)
+
+ sections = len(to_encode)
+ if has_weights or sections == 0:
+ to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
+
+ o = self.encode(to_encode)
+ out, pooled = o[:2]
+
+ if pooled is not None:
+ first_pooled = pooled[0:1].to(model_management.intermediate_device())
+ else:
+ first_pooled = pooled
+
+ output = []
+ for k in range(0, sections):
+ z = out[k:k+1]
+ if has_weights:
+ z_empty = out[-1]
+ for i in range(len(z)):
+ for j in range(len(z[i])):
+ weight = token_weight_pairs[k][j][1]
+ if weight != 1.0:
+ z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
+ output.append(z)
+
+ if (len(output) == 0):
+ r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
+ else:
+ r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
+
+ if len(o) > 2:
+ extra = {}
+ for k in o[2]:
+ v = o[2][k]
+ if k == "attention_mask":
+ v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device())
+ extra[k] = v
+
+ r = r + (extra,)
+ return r
+
+class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
+ LAYERS = [
+ "last",
+ "pooled",
+ "hidden"
+ ]
+ def __init__(self, device="cpu", max_length=77,
+ freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
+ special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
+ return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32
+ super().__init__()
+ assert layer in self.LAYERS
+
+ if textmodel_json_config is None:
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
+
+ with open(textmodel_json_config) as f:
+ config = json.load(f)
+
+ operations = model_options.get("custom_operations", None)
+ scaled_fp8 = None
+
+ if operations is None:
+ scaled_fp8 = model_options.get("scaled_fp8", None)
+ if scaled_fp8 is not None:
+ operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8)
+ else:
+ operations = comfy.ops.manual_cast
+
+ self.operations = operations
+ self.transformer = model_class(config, dtype, device, self.operations)
+ if scaled_fp8 is not None:
+ self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8))
+
+ self.num_layers = self.transformer.num_layers
+
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ self.layer_idx = None
+ self.special_tokens = special_tokens
+
+ self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
+ self.enable_attention_masks = enable_attention_masks
+ self.zero_out_masked = zero_out_masked
+
+ self.layer_norm_hidden_state = layer_norm_hidden_state
+ self.return_projected_pooled = return_projected_pooled
+ self.return_attention_masks = return_attention_masks
+
+ if layer == "hidden":
+ assert layer_idx is not None
+ assert abs(layer_idx) < self.num_layers
+ self.set_clip_options({"layer": layer_idx})
+ self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ #self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def set_clip_options(self, options):
+ layer_idx = options.get("layer", self.layer_idx)
+ self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
+ if layer_idx is None or abs(layer_idx) > self.num_layers:
+ self.layer = "last"
+ else:
+ self.layer = "hidden"
+ self.layer_idx = layer_idx
+
+ def reset_clip_options(self):
+ self.layer = self.options_default[0]
+ self.layer_idx = self.options_default[1]
+ self.return_projected_pooled = self.options_default[2]
+
+ def set_up_textual_embeddings(self, tokens, current_embeds):
+ out_tokens = []
+ next_new_token = token_dict_size = current_embeds.weight.shape[0]
+ embedding_weights = []
+
+ for x in tokens:
+ tokens_temp = []
+ for y in x:
+ if isinstance(y, numbers.Integral):
+ tokens_temp += [int(y)]
+ else:
+ if y.shape[0] == current_embeds.weight.shape[1]:
+ embedding_weights += [y]
+ tokens_temp += [next_new_token]
+ next_new_token += 1
+ else:
+ logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1]))
+ while len(tokens_temp) < len(x):
+ tokens_temp += [self.special_tokens["pad"]]
+ out_tokens += [tokens_temp]
+
+ n = token_dict_size
+ if len(embedding_weights) > 0:
+ new_embedding = self.operations.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
+ new_embedding.weight[:token_dict_size] = current_embeds.weight
+ for x in embedding_weights:
+ new_embedding.weight[n] = x
+ n += 1
+ self.transformer.set_input_embeddings(new_embedding)
+
+ processed_tokens = []
+ for x in out_tokens:
+ processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one
+
+ return processed_tokens
+
+ def forward(self, tokens):
+ backup_embeds = self.transformer.get_input_embeddings()
+ device = backup_embeds.weight.device
+ tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
+ tokens = torch.LongTensor(tokens).to(device)
+
+ attention_mask = None
+ if self.enable_attention_masks or self.zero_out_masked or self.return_attention_masks:
+ attention_mask = torch.zeros_like(tokens)
+ end_token = self.special_tokens.get("end", -1)
+ for x in range(attention_mask.shape[0]):
+ for y in range(attention_mask.shape[1]):
+ attention_mask[x, y] = 1
+ if tokens[x, y] == end_token:
+ break
+
+ attention_mask_model = None
+ if self.enable_attention_masks:
+ attention_mask_model = attention_mask
+
+ outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32)
+ self.transformer.set_input_embeddings(backup_embeds)
+
+ if self.layer == "last":
+ z = outputs[0].float()
+ else:
+ z = outputs[1].float()
+
+ if self.zero_out_masked:
+ z *= attention_mask.unsqueeze(-1).float()
+
+ pooled_output = None
+ if len(outputs) >= 3:
+ if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
+ pooled_output = outputs[3].float()
+ elif outputs[2] is not None:
+ pooled_output = outputs[2].float()
+
+ extra = {}
+ if self.return_attention_masks:
+ extra["attention_mask"] = attention_mask
+
+ if len(extra) > 0:
+ return z, pooled_output, extra
+
+ return z, pooled_output
+
+ def encode(self, tokens):
+ return self(tokens)
+
+ def load_sd(self, sd):
+ return self.transformer.load_state_dict(sd, strict=False)
+
+def parse_parentheses(string):
+ result = []
+ current_item = ""
+ nesting_level = 0
+ for char in string:
+ if char == "(":
+ if nesting_level == 0:
+ if current_item:
+ result.append(current_item)
+ current_item = "("
+ else:
+ current_item = "("
+ else:
+ current_item += char
+ nesting_level += 1
+ elif char == ")":
+ nesting_level -= 1
+ if nesting_level == 0:
+ result.append(current_item + ")")
+ current_item = ""
+ else:
+ current_item += char
+ else:
+ current_item += char
+ if current_item:
+ result.append(current_item)
+ return result
+
+def token_weights(string, current_weight):
+ a = parse_parentheses(string)
+ out = []
+ for x in a:
+ weight = current_weight
+ if len(x) >= 2 and x[-1] == ')' and x[0] == '(':
+ x = x[1:-1]
+ xx = x.rfind(":")
+ weight *= 1.1
+ if xx > 0:
+ try:
+ weight = float(x[xx+1:])
+ x = x[:xx]
+ except:
+ pass
+ out += token_weights(x, weight)
+ else:
+ out += [(x, current_weight)]
+ return out
+
+def escape_important(text):
+ text = text.replace("\\)", "\0\1")
+ text = text.replace("\\(", "\0\2")
+ return text
+
+def unescape_important(text):
+ text = text.replace("\0\1", ")")
+ text = text.replace("\0\2", "(")
+ return text
+
+def safe_load_embed_zip(embed_path):
+ with zipfile.ZipFile(embed_path) as myzip:
+ names = list(filter(lambda a: "data/" in a, myzip.namelist()))
+ names.reverse()
+ for n in names:
+ with myzip.open(n) as myfile:
+ data = myfile.read()
+ number = len(data) // 4
+ length_embed = 1024 #sd2.x
+ if number < 768:
+ continue
+ if number % 768 == 0:
+ length_embed = 768 #sd1.x
+ num_embeds = number // length_embed
+ embed = torch.frombuffer(data, dtype=torch.float)
+ out = embed.reshape((num_embeds, length_embed)).clone()
+ del embed
+ return out
+
+def expand_directory_list(directories):
+ dirs = set()
+ for x in directories:
+ dirs.add(x)
+ for root, subdir, file in os.walk(x, followlinks=True):
+ dirs.add(root)
+ return list(dirs)
+
+def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format
+ i = 0
+ out_list = []
+ for k in embed:
+ if k.startswith(prefix) and k.endswith(suffix):
+ out_list.append(embed[k])
+ if len(out_list) == 0:
+ return None
+
+ return torch.cat(out_list, dim=0)
+
+def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
+ if isinstance(embedding_directory, str):
+ embedding_directory = [embedding_directory]
+
+ embedding_directory = expand_directory_list(embedding_directory)
+
+ valid_file = None
+ for embed_dir in embedding_directory:
+ embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name))
+ embed_dir = os.path.abspath(embed_dir)
+ try:
+ if os.path.commonpath((embed_dir, embed_path)) != embed_dir:
+ continue
+ except:
+ continue
+ if not os.path.isfile(embed_path):
+ extensions = ['.safetensors', '.pt', '.bin']
+ for x in extensions:
+ t = embed_path + x
+ if os.path.isfile(t):
+ valid_file = t
+ break
+ else:
+ valid_file = embed_path
+ if valid_file is not None:
+ break
+
+ if valid_file is None:
+ return None
+
+ embed_path = valid_file
+
+ embed_out = None
+
+ try:
+ if embed_path.lower().endswith(".safetensors"):
+ import safetensors.torch
+ embed = safetensors.torch.load_file(embed_path, device="cpu")
+ else:
+ if 'weights_only' in torch.load.__code__.co_varnames:
+ try:
+ embed = torch.load(embed_path, weights_only=True, map_location="cpu")
+ except:
+ embed_out = safe_load_embed_zip(embed_path)
+ else:
+ embed = torch.load(embed_path, map_location="cpu")
+ except Exception as e:
+ logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
+ return None
+
+ if embed_out is None:
+ if 'string_to_param' in embed:
+ values = embed['string_to_param'].values()
+ embed_out = next(iter(values))
+ elif isinstance(embed, list):
+ out_list = []
+ for x in range(len(embed)):
+ for k in embed[x]:
+ t = embed[x][k]
+ if t.shape[-1] != embedding_size:
+ continue
+ out_list.append(t.reshape(-1, t.shape[-1]))
+ embed_out = torch.cat(out_list, dim=0)
+ elif embed_key is not None and embed_key in embed:
+ embed_out = embed[embed_key]
+ else:
+ embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*')
+ if embed_out is None:
+ embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key))
+ if embed_out is None:
+ values = embed.values()
+ embed_out = next(iter(values))
+ return embed_out
+
+class SDTokenizer:
+ def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None, tokenizer_data={}):
+ if tokenizer_path is None:
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
+ self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
+ self.max_length = max_length
+ self.min_length = min_length
+
+ empty = self.tokenizer('')["input_ids"]
+ if has_start_token:
+ self.tokens_start = 1
+ self.start_token = empty[0]
+ self.end_token = empty[1]
+ else:
+ self.tokens_start = 0
+ self.start_token = None
+ self.end_token = empty[0]
+
+ if pad_token is not None:
+ self.pad_token = pad_token
+ elif pad_with_end:
+ self.pad_token = self.end_token
+ else:
+ self.pad_token = 0
+
+ self.pad_with_end = pad_with_end
+ self.pad_to_max_length = pad_to_max_length
+
+ vocab = self.tokenizer.get_vocab()
+ self.inv_vocab = {v: k for k, v in vocab.items()}
+ self.embedding_directory = embedding_directory
+ self.max_word_length = 8
+ self.embedding_identifier = "embedding:"
+ self.embedding_size = embedding_size
+ self.embedding_key = embedding_key
+
+ def _try_get_embedding(self, embedding_name:str):
+ '''
+ Takes a potential embedding name and tries to retrieve it.
+ Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
+ '''
+ embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
+ if embed is None:
+ stripped = embedding_name.strip(',')
+ if len(stripped) < len(embedding_name):
+ embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
+ return (embed, embedding_name[len(stripped):])
+ return (embed, "")
+
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False):
+ '''
+ Takes a prompt and converts it to a list of (token, weight, word id) elements.
+ Tokens can both be integer tokens and pre computed CLIP tensors.
+ Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
+ Returned list has the dimensions NxM where M is the input size of CLIP
+ '''
+
+ text = escape_important(text)
+ parsed_weights = token_weights(text, 1.0)
+
+ #tokenize words
+ tokens = []
+ for weighted_segment, weight in parsed_weights:
+ to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ')
+ to_tokenize = [x for x in to_tokenize if x != ""]
+ for word in to_tokenize:
+ #if we find an embedding, deal with the embedding
+ if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
+ embedding_name = word[len(self.embedding_identifier):].strip('\n')
+ embed, leftover = self._try_get_embedding(embedding_name)
+ if embed is None:
+ logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
+ else:
+ if len(embed.shape) == 1:
+ tokens.append([(embed, weight)])
+ else:
+ tokens.append([(embed[x], weight) for x in range(embed.shape[0])])
+ #if we accidentally have leftover text, continue parsing using leftover, else move on to next word
+ if leftover != "":
+ word = leftover
+ else:
+ continue
+ #parse word
+ tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]])
+
+ #reshape token array to CLIP input size
+ batched_tokens = []
+ batch = []
+ if self.start_token is not None:
+ batch.append((self.start_token, 1.0, 0))
+ batched_tokens.append(batch)
+ for i, t_group in enumerate(tokens):
+ #determine if we're going to try and keep the tokens in a single batch
+ is_large = len(t_group) >= self.max_word_length
+
+ while len(t_group) > 0:
+ if len(t_group) + len(batch) > self.max_length - 1:
+ remaining_length = self.max_length - len(batch) - 1
+ #break word in two and add end token
+ if is_large:
+ batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
+ batch.append((self.end_token, 1.0, 0))
+ t_group = t_group[remaining_length:]
+ #add end token and pad
+ else:
+ batch.append((self.end_token, 1.0, 0))
+ if self.pad_to_max_length:
+ batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
+ #start new batch
+ batch = []
+ if self.start_token is not None:
+ batch.append((self.start_token, 1.0, 0))
+ batched_tokens.append(batch)
+ else:
+ batch.extend([(t,w,i+1) for t,w in t_group])
+ t_group = []
+
+ #fill last batch
+ batch.append((self.end_token, 1.0, 0))
+ if self.pad_to_max_length:
+ batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch)))
+ if self.min_length is not None and len(batch) < self.min_length:
+ batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch)))
+
+ if not return_word_ids:
+ batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
+
+ return batched_tokens
+
+
+ def untokenize(self, token_weight_pair):
+ return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
+
+ def state_dict(self):
+ return {}
+
+class SD1Tokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer):
+ self.clip_name = clip_name
+ self.clip = "clip_{}".format(self.clip_name)
+ tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
+ setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False):
+ out = {}
+ out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return getattr(self, self.clip).untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {}
+
+class SD1CheckpointClipModel(SDClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options)
+
+class SD1ClipModel(torch.nn.Module):
+ def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs):
+ super().__init__()
+
+ if name is not None:
+ self.clip_name = name
+ self.clip = "{}".format(self.clip_name)
+ else:
+ self.clip_name = clip_name
+ self.clip = "clip_{}".format(self.clip_name)
+
+ clip_model = model_options.get("{}_class".format(self.clip), clip_model)
+ setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs))
+
+ self.dtypes = set()
+ if dtype is not None:
+ self.dtypes.add(dtype)
+
+ def set_clip_options(self, options):
+ getattr(self, self.clip).set_clip_options(options)
+
+ def reset_clip_options(self):
+ getattr(self, self.clip).reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs = token_weight_pairs[self.clip_name]
+ out = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
+ return out
+
+ def load_sd(self, sd):
+ return getattr(self, self.clip).load_sd(sd)
diff --git a/comfy/sd1_clip_config.json b/comfy/sd1_clip_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..3ba8c6b5bc3d6389fb6c9e2c8231729ad9d663a4
--- /dev/null
+++ b/comfy/sd1_clip_config.json
@@ -0,0 +1,25 @@
+{
+ "_name_or_path": "openai/clip-vit-large-patch14",
+ "architectures": [
+ "CLIPTextModel"
+ ],
+ "attention_dropout": 0.0,
+ "bos_token_id": 0,
+ "dropout": 0.0,
+ "eos_token_id": 49407,
+ "hidden_act": "quick_gelu",
+ "hidden_size": 768,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "layer_norm_eps": 1e-05,
+ "max_position_embeddings": 77,
+ "model_type": "clip_text_model",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "pad_token_id": 1,
+ "projection_dim": 768,
+ "torch_dtype": "float32",
+ "transformers_version": "4.24.0",
+ "vocab_size": 49408
+}
diff --git a/comfy/sd1_tokenizer/merges.txt b/comfy/sd1_tokenizer/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..76e821f1b6f0a9709293c3b6b51ed90980b3166b
--- /dev/null
+++ b/comfy/sd1_tokenizer/merges.txt
@@ -0,0 +1,48895 @@
+#version: 0.2
+i n
+t h
+a n
+r e
+a r
+e r
+th e
+in g
+o u
+o n
+s t
+o r
+e n
+o n
+a l
+a t
+e r
+i t
+i n
+t o
+r o
+i s
+l e
+i c
+a t
+an d
+e d
+o f
+c h
+o r
+e s
+i l
+e l
+s t
+a c
+o m
+a m
+l o
+a n
+a y
+s h
+r i
+l i
+t i
+f or
+n e
+ð Ł
+r a
+h a
+d e
+o l
+v e
+s i
+u r
+a l
+s e
+' s
+u n
+d i
+b e
+l a
+w h
+o o
+d ay
+e n
+m a
+n o
+l e
+t o
+ou r
+i r
+g h
+w it
+i t
+y o
+a s
+s p
+th is
+t s
+at i
+yo u
+wit h
+a d
+i s
+a b
+l y
+w e
+th e
+t e
+a s
+a g
+v i
+p p
+s u
+h o
+m y
+. .
+b u
+c om
+s e
+er s
+m e
+m e
+al l
+c on
+m o
+k e
+g e
+ou t
+en t
+c o
+f e
+v er
+a r
+f ro
+a u
+p o
+c e
+gh t
+ar e
+s s
+fro m
+c h
+t r
+ou n
+on e
+b y
+d o
+t h
+w or
+er e
+k e
+p ro
+f or
+d s
+b o
+t a
+w e
+g o
+h e
+t er
+in g
+d e
+b e
+ati on
+m or
+a y
+e x
+il l
+p e
+k s
+s c
+l u
+f u
+q u
+v er
+ðŁ ĺ
+j u
+m u
+at e
+an d
+v e
+k ing
+m ar
+o p
+h i
+.. .
+p re
+a d
+r u
+th at
+j o
+o f
+c e
+ne w
+a m
+a p
+g re
+s s
+d u
+no w
+y e
+t ing
+y our
+it y
+n i
+c i
+p ar
+g u
+f i
+a f
+p er
+t er
+u p
+s o
+g i
+on s
+g r
+g e
+b r
+p l
+' t
+m i
+in e
+we e
+b i
+u s
+sh o
+ha ve
+to day
+a v
+m an
+en t
+ac k
+ur e
+ou r
+â Ģ
+c u
+l d
+lo o
+i m
+ic e
+s om
+f in
+re d
+re n
+oo d
+w as
+ti on
+p i
+i r
+th er
+t y
+p h
+ar d
+e c
+! !
+m on
+mor e
+w ill
+t ra
+c an
+c ol
+p u
+t e
+w n
+m b
+s o
+it i
+ju st
+n ing
+h ere
+t u
+p a
+p r
+bu t
+wh at
+al ly
+f ir
+m in
+c a
+an t
+s a
+t ed
+e v
+m ent
+f a
+ge t
+am e
+ab out
+g ra
+no t
+ha pp
+ay s
+m an
+h is
+ti me
+li ke
+g h
+ha s
+th an
+lo ve
+ar t
+st e
+d ing
+h e
+c re
+w s
+w at
+d er
+it e
+s er
+ac e
+ag e
+en d
+st r
+a w
+st or
+r e
+c ar
+el l
+al l
+p s
+f ri
+p ho
+p or
+d o
+a k
+w i
+f re
+wh o
+sh i
+b oo
+s on
+el l
+wh en
+il l
+ho w
+gre at
+w in
+e l
+b l
+s si
+al i
+som e
+ðŁ Ĵ
+t on
+d er
+le s
+p la
+ï ¸
+e d
+s ch
+h u
+on g
+d on
+k i
+s h
+an n
+c or
+. .
+oun d
+a z
+in e
+ar y
+fu l
+st u
+ou ld
+st i
+g o
+se e
+ab le
+ar s
+l l
+m is
+b er
+c k
+w a
+en ts
+n o
+si g
+f e
+fir st
+e t
+sp e
+ac k
+i f
+ou s
+' m
+st er
+a pp
+an g
+an ce
+an s
+g ood
+b re
+e ver
+the y
+t ic
+com e
+of f
+b ack
+as e
+ing s
+ol d
+i ght
+f o
+h er
+happ y
+p ic
+it s
+v ing
+u s
+m at
+h om
+d y
+e m
+s k
+y ing
+the ir
+le d
+r y
+u l
+h ar
+c k
+t on
+on al
+h el
+r ic
+b ir
+vi e
+w ay
+t ri
+d a
+p le
+b ro
+st o
+oo l
+ni ght
+tr u
+b a
+re ad
+re s
+ye ar
+f r
+t or
+al s
+c oun
+c la
+t ure
+v el
+at ed
+le c
+en d
+th ing
+v o
+ic i
+be st
+c an
+wor k
+la st
+af ter
+en ce
+p ri
+p e
+e s
+i l
+âĢ ¦
+d re
+y s
+o ver
+i es
+ðŁ ij
+com m
+t w
+in k
+s un
+c l
+li fe
+t t
+a ch
+l and
+s y
+t re
+t al
+p ol
+s m
+du c
+s al
+f t
+' re
+ch e
+w ar
+t ur
+ati ons
+ac h
+m s
+il e
+p m
+ou gh
+at e
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diff --git a/comfy/sd1_tokenizer/special_tokens_map.json b/comfy/sd1_tokenizer/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..2c2130b544c0c5a72d5d00da071ba130a9800fb2
--- /dev/null
+++ b/comfy/sd1_tokenizer/special_tokens_map.json
@@ -0,0 +1,24 @@
+{
+ "bos_token": {
+ "content": "<|startoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "pad_token": "<|endoftext|>",
+ "unk_token": {
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/comfy/sd1_tokenizer/tokenizer_config.json b/comfy/sd1_tokenizer/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..5ba7bf706515bc60487ad0e1816b4929b82542d6
--- /dev/null
+++ b/comfy/sd1_tokenizer/tokenizer_config.json
@@ -0,0 +1,34 @@
+{
+ "add_prefix_space": false,
+ "bos_token": {
+ "__type": "AddedToken",
+ "content": "<|startoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "do_lower_case": true,
+ "eos_token": {
+ "__type": "AddedToken",
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "errors": "replace",
+ "model_max_length": 77,
+ "name_or_path": "openai/clip-vit-large-patch14",
+ "pad_token": "<|endoftext|>",
+ "special_tokens_map_file": "./special_tokens_map.json",
+ "tokenizer_class": "CLIPTokenizer",
+ "unk_token": {
+ "__type": "AddedToken",
+ "content": "<|endoftext|>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ }
+}
diff --git a/comfy/sd1_tokenizer/vocab.json b/comfy/sd1_tokenizer/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..469be27c5c010538f845f518c4f5e8574c78f7c8
--- /dev/null
+++ b/comfy/sd1_tokenizer/vocab.json
@@ -0,0 +1,49410 @@
+{
+ "!": 0,
+ "!!": 1443,
+ "!!!": 11194,
+ "!!!!": 4003,
+ "!!!!!!!!": 11281,
+ "!!!!!!!!!!!!!!!!": 30146,
+ "!!!!!!!!!!!": 49339,
+ "!!!!!!!!!!": 35579,
+ "!!!!!!!!!": 28560,
+ "!!!!!!!!": 21622,
+ "!!!!!!!": 15203,
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+}
diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d0a4e8e75a0a4e069a671fce113e1798c29751a
--- /dev/null
+++ b/comfy/sdxl_clip.py
@@ -0,0 +1,95 @@
+from comfy import sd1_clip
+import torch
+import os
+
+class SDXLClipG(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}):
+ if layer == "penultimate":
+ layer="hidden"
+ layer_idx=-2
+
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
+ super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype,
+ special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False, return_projected_pooled=True, model_options=model_options)
+
+ def load_sd(self, sd):
+ return super().load_sd(sd)
+
+class SDXLClipGTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
+
+
+class SDXLTokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
+ self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
+ self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False):
+ out = {}
+ out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
+ out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.clip_g.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {}
+
+class SDXLClipModel(torch.nn.Module):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__()
+ clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
+ self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options)
+ self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options)
+ self.dtypes = set([dtype])
+
+ def set_clip_options(self, options):
+ self.clip_l.set_clip_options(options)
+ self.clip_g.set_clip_options(options)
+
+ def reset_clip_options(self):
+ self.clip_g.reset_clip_options()
+ self.clip_l.reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs_g = token_weight_pairs["g"]
+ token_weight_pairs_l = token_weight_pairs["l"]
+ g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
+ l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+ cut_to = min(l_out.shape[1], g_out.shape[1])
+ return torch.cat([l_out[:,:cut_to], g_out[:,:cut_to]], dim=-1), g_pooled
+
+ def load_sd(self, sd):
+ if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+ return self.clip_g.load_sd(sd)
+ else:
+ return self.clip_l.load_sd(sd)
+
+class SDXLRefinerClipModel(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG, model_options=model_options)
+
+
+class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):
+ super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
+
+class StableCascadeTokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="g", tokenizer=StableCascadeClipGTokenizer)
+
+class StableCascadeClipG(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
+ super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype,
+ special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, return_projected_pooled=True, model_options=model_options)
+
+ def load_sd(self, sd):
+ return super().load_sd(sd)
+
+class StableCascadeClipModel(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG, model_options=model_options)
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..b426d30850b8e7f3fda7b2382a052641db572253
--- /dev/null
+++ b/comfy/supported_models.py
@@ -0,0 +1,745 @@
+import torch
+from . import model_base
+from . import utils
+
+from . import sd1_clip
+from . import sdxl_clip
+import comfy.text_encoders.sd2_clip
+import comfy.text_encoders.sd3_clip
+import comfy.text_encoders.sa_t5
+import comfy.text_encoders.aura_t5
+import comfy.text_encoders.hydit
+import comfy.text_encoders.flux
+import comfy.text_encoders.genmo
+import comfy.text_encoders.lt
+
+from . import supported_models_base
+from . import latent_formats
+
+from . import diffusers_convert
+
+class SD15(supported_models_base.BASE):
+ unet_config = {
+ "context_dim": 768,
+ "model_channels": 320,
+ "use_linear_in_transformer": False,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ }
+
+ unet_extra_config = {
+ "num_heads": 8,
+ "num_head_channels": -1,
+ }
+
+ latent_format = latent_formats.SD15
+ memory_usage_factor = 1.0
+
+ def process_clip_state_dict(self, state_dict):
+ k = list(state_dict.keys())
+ for x in k:
+ if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
+ y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
+ state_dict[y] = state_dict.pop(x)
+
+ if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict:
+ ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids']
+ if ids.dtype == torch.float32:
+ state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
+
+ replace_prefix = {}
+ replace_prefix["cond_stage_model."] = "clip_l."
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
+ for p in pop_keys:
+ if p in state_dict:
+ state_dict.pop(p)
+
+ replace_prefix = {"clip_l.": "cond_stage_model."}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)
+
+class SD20(supported_models_base.BASE):
+ unet_config = {
+ "context_dim": 1024,
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ }
+
+ unet_extra_config = {
+ "num_heads": -1,
+ "num_head_channels": 64,
+ "attn_precision": torch.float32,
+ }
+
+ latent_format = latent_formats.SD15
+ memory_usage_factor = 1.0
+
+ def model_type(self, state_dict, prefix=""):
+ if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
+ k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
+ out = state_dict.get(k, None)
+ if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
+ return model_base.ModelType.V_PREDICTION
+ return model_base.ModelType.EPS
+
+ def process_clip_state_dict(self, state_dict):
+ replace_prefix = {}
+ replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format
+ replace_prefix["cond_stage_model.model."] = "clip_h."
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+ state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.")
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ replace_prefix = {}
+ replace_prefix["clip_h"] = "cond_stage_model.model"
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
+ state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
+ return state_dict
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.sd2_clip.SD2ClipModel)
+
+class SD21UnclipL(SD20):
+ unet_config = {
+ "context_dim": 1024,
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "adm_in_channels": 1536,
+ "use_temporal_attention": False,
+ }
+
+ clip_vision_prefix = "embedder.model.visual."
+ noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768}
+
+
+class SD21UnclipH(SD20):
+ unet_config = {
+ "context_dim": 1024,
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "adm_in_channels": 2048,
+ "use_temporal_attention": False,
+ }
+
+ clip_vision_prefix = "embedder.model.visual."
+ noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024}
+
+class SDXLRefiner(supported_models_base.BASE):
+ unet_config = {
+ "model_channels": 384,
+ "use_linear_in_transformer": True,
+ "context_dim": 1280,
+ "adm_in_channels": 2560,
+ "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0],
+ "use_temporal_attention": False,
+ }
+
+ latent_format = latent_formats.SDXL
+ memory_usage_factor = 1.0
+
+ def get_model(self, state_dict, prefix="", device=None):
+ return model_base.SDXLRefiner(self, device=device)
+
+ def process_clip_state_dict(self, state_dict):
+ keys_to_replace = {}
+ replace_prefix = {}
+ replace_prefix["conditioner.embedders.0.model."] = "clip_g."
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+
+ state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
+ state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ replace_prefix = {}
+ state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
+ if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g:
+ state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
+ replace_prefix["clip_g"] = "conditioner.embedders.0.model"
+ state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
+ return state_dict_g
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)
+
+class SDXL(supported_models_base.BASE):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 0, 2, 2, 10, 10],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+ latent_format = latent_formats.SDXL
+
+ memory_usage_factor = 0.8
+
+ def model_type(self, state_dict, prefix=""):
+ if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5
+ self.latent_format = latent_formats.SDXL_Playground_2_5()
+ self.sampling_settings["sigma_data"] = 0.5
+ self.sampling_settings["sigma_max"] = 80.0
+ self.sampling_settings["sigma_min"] = 0.002
+ return model_base.ModelType.EDM
+ elif "edm_vpred.sigma_max" in state_dict:
+ self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item())
+ if "edm_vpred.sigma_min" in state_dict:
+ self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
+ return model_base.ModelType.V_PREDICTION_EDM
+ elif "v_pred" in state_dict:
+ if "ztsnr" in state_dict: #Some zsnr anime checkpoints
+ self.sampling_settings["zsnr"] = True
+ return model_base.ModelType.V_PREDICTION
+ else:
+ return model_base.ModelType.EPS
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device)
+ if self.inpaint_model():
+ out.set_inpaint()
+ return out
+
+ def process_clip_state_dict(self, state_dict):
+ keys_to_replace = {}
+ replace_prefix = {}
+
+ replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model"
+ replace_prefix["conditioner.embedders.1.model."] = "clip_g."
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+
+ state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
+ state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ replace_prefix = {}
+ keys_to_replace = {}
+ state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
+ for k in state_dict:
+ if k.startswith("clip_l"):
+ state_dict_g[k] = state_dict[k]
+
+ state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1))
+ pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
+ for p in pop_keys:
+ if p in state_dict_g:
+ state_dict_g.pop(p)
+
+ replace_prefix["clip_g"] = "conditioner.embedders.1.model"
+ replace_prefix["clip_l"] = "conditioner.embedders.0"
+ state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
+ return state_dict_g
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)
+
+class SSD1B(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 0, 2, 2, 4, 4],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+class Segmind_Vega(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 0, 1, 1, 2, 2],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+class KOALA_700M(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 2, 5],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+class KOALA_1B(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 2, 6],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+class SVD_img2vid(supported_models_base.BASE):
+ unet_config = {
+ "model_channels": 320,
+ "in_channels": 8,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
+ "context_dim": 1024,
+ "adm_in_channels": 768,
+ "use_temporal_attention": True,
+ "use_temporal_resblock": True
+ }
+
+ unet_extra_config = {
+ "num_heads": -1,
+ "num_head_channels": 64,
+ "attn_precision": torch.float32,
+ }
+
+ clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual."
+
+ latent_format = latent_formats.SD15
+
+ sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002}
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SVD_img2vid(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return None
+
+class SV3D_u(SVD_img2vid):
+ unet_config = {
+ "model_channels": 320,
+ "in_channels": 8,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
+ "context_dim": 1024,
+ "adm_in_channels": 256,
+ "use_temporal_attention": True,
+ "use_temporal_resblock": True
+ }
+
+ vae_key_prefix = ["conditioner.embedders.1.encoder."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SV3D_u(self, device=device)
+ return out
+
+class SV3D_p(SV3D_u):
+ unet_config = {
+ "model_channels": 320,
+ "in_channels": 8,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
+ "context_dim": 1024,
+ "adm_in_channels": 1280,
+ "use_temporal_attention": True,
+ "use_temporal_resblock": True
+ }
+
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SV3D_p(self, device=device)
+ return out
+
+class Stable_Zero123(supported_models_base.BASE):
+ unet_config = {
+ "context_dim": 768,
+ "model_channels": 320,
+ "use_linear_in_transformer": False,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ "in_channels": 8,
+ }
+
+ unet_extra_config = {
+ "num_heads": 8,
+ "num_head_channels": -1,
+ }
+
+ required_keys = {
+ "cc_projection.weight": None,
+ "cc_projection.bias": None,
+ }
+
+ clip_vision_prefix = "cond_stage_model.model.visual."
+
+ latent_format = latent_formats.SD15
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"])
+ return out
+
+ def clip_target(self, state_dict={}):
+ return None
+
+class SD_X4Upscaler(SD20):
+ unet_config = {
+ "context_dim": 1024,
+ "model_channels": 256,
+ 'in_channels': 7,
+ "use_linear_in_transformer": True,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ }
+
+ unet_extra_config = {
+ "disable_self_attentions": [True, True, True, False],
+ "num_classes": 1000,
+ "num_heads": 8,
+ "num_head_channels": -1,
+ }
+
+ latent_format = latent_formats.SD_X4
+
+ sampling_settings = {
+ "linear_start": 0.0001,
+ "linear_end": 0.02,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SD_X4Upscaler(self, device=device)
+ return out
+
+class Stable_Cascade_C(supported_models_base.BASE):
+ unet_config = {
+ "stable_cascade_stage": 'c',
+ }
+
+ unet_extra_config = {}
+
+ latent_format = latent_formats.SC_Prior
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ sampling_settings = {
+ "shift": 2.0,
+ }
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoder."]
+ clip_vision_prefix = "clip_l_vision."
+
+ def process_unet_state_dict(self, state_dict):
+ key_list = list(state_dict.keys())
+ for y in ["weight", "bias"]:
+ suffix = "in_proj_{}".format(y)
+ keys = filter(lambda a: a.endswith(suffix), key_list)
+ for k_from in keys:
+ weights = state_dict.pop(k_from)
+ prefix = k_from[:-(len(suffix) + 1)]
+ shape_from = weights.shape[0] // 3
+ for x in range(3):
+ p = ["to_q", "to_k", "to_v"]
+ k_to = "{}.{}.{}".format(prefix, p[x], y)
+ state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)]
+ return state_dict
+
+ def process_clip_state_dict(self, state_dict):
+ state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
+ if "clip_g.text_projection" in state_dict:
+ state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1)
+ return state_dict
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.StableCascade_C(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel)
+
+class Stable_Cascade_B(Stable_Cascade_C):
+ unet_config = {
+ "stable_cascade_stage": 'b',
+ }
+
+ unet_extra_config = {}
+
+ latent_format = latent_formats.SC_B
+ supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+ sampling_settings = {
+ "shift": 1.0,
+ }
+
+ clip_vision_prefix = None
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.StableCascade_B(self, device=device)
+ return out
+
+class SD15_instructpix2pix(SD15):
+ unet_config = {
+ "context_dim": 768,
+ "model_channels": 320,
+ "use_linear_in_transformer": False,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ "in_channels": 8,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ return model_base.SD15_instructpix2pix(self, device=device)
+
+class SDXL_instructpix2pix(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 0, 2, 2, 10, 10],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ "in_channels": 8,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device)
+
+class SD3(supported_models_base.BASE):
+ unet_config = {
+ "in_channels": 16,
+ "pos_embed_scaling_factor": None,
+ }
+
+ sampling_settings = {
+ "shift": 3.0,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.SD3
+
+ memory_usage_factor = 1.2
+
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SD3(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ clip_l = False
+ clip_g = False
+ t5 = False
+ dtype_t5 = None
+ pref = self.text_encoder_key_prefix[0]
+ if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
+ clip_l = True
+ if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
+ clip_g = True
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ if "dtype_t5" in t5_detect:
+ t5 = True
+
+ return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, **t5_detect))
+
+class StableAudio(supported_models_base.BASE):
+ unet_config = {
+ "audio_model": "dit1.0",
+ }
+
+ sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03}
+
+ unet_extra_config = {}
+ latent_format = latent_formats.StableAudio1
+
+ text_encoder_key_prefix = ["text_encoders."]
+ vae_key_prefix = ["pretransform.model."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True)
+ seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True)
+ return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device)
+
+ def process_unet_state_dict(self, state_dict):
+ for k in list(state_dict.keys()):
+ if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero
+ state_dict.pop(k)
+ return state_dict
+
+ def process_unet_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": "model.model."}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model)
+
+class AuraFlow(supported_models_base.BASE):
+ unet_config = {
+ "cond_seq_dim": 2048,
+ }
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ "shift": 1.73,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.SDXL
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.AuraFlow(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)
+
+class HunyuanDiT(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "hydit",
+ }
+
+ unet_extra_config = {
+ "attn_precision": torch.float32,
+ }
+
+ sampling_settings = {
+ "linear_start": 0.00085,
+ "linear_end": 0.018,
+ }
+
+ latent_format = latent_formats.SDXL
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.HunyuanDiT(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel)
+
+class HunyuanDiT1(HunyuanDiT):
+ unet_config = {
+ "image_model": "hydit1",
+ }
+
+ unet_extra_config = {}
+
+ sampling_settings = {
+ "linear_start" : 0.00085,
+ "linear_end" : 0.03,
+ }
+
+class Flux(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "flux",
+ "guidance_embed": True,
+ }
+
+ sampling_settings = {
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Flux
+
+ memory_usage_factor = 2.8
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Flux(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect))
+
+class FluxInpaint(Flux):
+ unet_config = {
+ "image_model": "flux",
+ "guidance_embed": True,
+ "in_channels": 96,
+ }
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+class FluxSchnell(Flux):
+ unet_config = {
+ "image_model": "flux",
+ "guidance_embed": False,
+ }
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ "shift": 1.0,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)
+ return out
+
+class GenmoMochi(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "mochi_preview",
+ }
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ "shift": 6.0,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Mochi
+
+ memory_usage_factor = 2.0 #TODO
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.GenmoMochi(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect))
+
+class LTXV(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "ltxv",
+ }
+
+ sampling_settings = {
+ "shift": 2.37,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.LTXV
+
+ memory_usage_factor = 2.7
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.LTXV(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect))
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV]
+
+models += [SVD_img2vid]
diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..54573abb110d8cc5e190ecefa0f9aecf95da0b99
--- /dev/null
+++ b/comfy/supported_models_base.py
@@ -0,0 +1,119 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Comfy
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+from . import model_base
+from . import utils
+from . import latent_formats
+
+class ClipTarget:
+ def __init__(self, tokenizer, clip):
+ self.clip = clip
+ self.tokenizer = tokenizer
+ self.params = {}
+
+class BASE:
+ unet_config = {}
+ unet_extra_config = {
+ "num_heads": -1,
+ "num_head_channels": 64,
+ }
+
+ required_keys = {}
+
+ clip_prefix = []
+ clip_vision_prefix = None
+ noise_aug_config = None
+ sampling_settings = {}
+ latent_format = latent_formats.LatentFormat
+ vae_key_prefix = ["first_stage_model."]
+ text_encoder_key_prefix = ["cond_stage_model."]
+ supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+ memory_usage_factor = 2.0
+
+ manual_cast_dtype = None
+ custom_operations = None
+ scaled_fp8 = None
+ optimizations = {"fp8": False}
+
+ @classmethod
+ def matches(s, unet_config, state_dict=None):
+ for k in s.unet_config:
+ if k not in unet_config or s.unet_config[k] != unet_config[k]:
+ return False
+ if state_dict is not None:
+ for k in s.required_keys:
+ if k not in state_dict:
+ return False
+ return True
+
+ def model_type(self, state_dict, prefix=""):
+ return model_base.ModelType.EPS
+
+ def inpaint_model(self):
+ return self.unet_config["in_channels"] > 4
+
+ def __init__(self, unet_config):
+ self.unet_config = unet_config.copy()
+ self.sampling_settings = self.sampling_settings.copy()
+ self.latent_format = self.latent_format()
+ self.optimizations = self.optimizations.copy()
+ for x in self.unet_extra_config:
+ self.unet_config[x] = self.unet_extra_config[x]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ if self.noise_aug_config is not None:
+ out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device)
+ else:
+ out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device)
+ if self.inpaint_model():
+ out.set_inpaint()
+ return out
+
+ def process_clip_state_dict(self, state_dict):
+ state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
+ return state_dict
+
+ def process_unet_state_dict(self, state_dict):
+ return state_dict
+
+ def process_vae_state_dict(self, state_dict):
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": self.text_encoder_key_prefix[0]}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def process_clip_vision_state_dict_for_saving(self, state_dict):
+ replace_prefix = {}
+ if self.clip_vision_prefix is not None:
+ replace_prefix[""] = self.clip_vision_prefix
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def process_unet_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": "model.diffusion_model."}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def process_vae_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": self.vae_key_prefix[0]}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def set_inference_dtype(self, dtype, manual_cast_dtype):
+ self.unet_config['dtype'] = dtype
+ self.manual_cast_dtype = manual_cast_dtype
diff --git a/comfy/t2i_adapter/adapter.py b/comfy/t2i_adapter/adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..10ea18e326693f237b3b219970c86e3808f6d334
--- /dev/null
+++ b/comfy/t2i_adapter/adapter.py
@@ -0,0 +1,299 @@
+#taken from https://github.com/TencentARC/T2I-Adapter
+import torch
+import torch.nn as nn
+from collections import OrderedDict
+
+
+def conv_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D convolution module.
+ """
+ if dims == 1:
+ return nn.Conv1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.Conv2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.Conv3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D average pooling module.
+ """
+ if dims == 1:
+ return nn.AvgPool1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.AvgPool2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.AvgPool3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class Downsample(nn.Module):
+ """
+ A downsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ downsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ stride = 2 if dims != 3 else (1, 2, 2)
+ if use_conv:
+ self.op = conv_nd(
+ dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+ )
+ else:
+ assert self.channels == self.out_channels
+ self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ if not self.use_conv:
+ padding = [x.shape[2] % 2, x.shape[3] % 2]
+ self.op.padding = padding
+
+ x = self.op(x)
+ return x
+
+
+class ResnetBlock(nn.Module):
+ def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
+ super().__init__()
+ ps = ksize // 2
+ if in_c != out_c or sk == False:
+ self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
+ else:
+ # print('n_in')
+ self.in_conv = None
+ self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
+ self.act = nn.ReLU()
+ self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
+ if sk == False:
+ self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
+ else:
+ self.skep = None
+
+ self.down = down
+ if self.down == True:
+ self.down_opt = Downsample(in_c, use_conv=use_conv)
+
+ def forward(self, x):
+ if self.down == True:
+ x = self.down_opt(x)
+ if self.in_conv is not None: # edit
+ x = self.in_conv(x)
+
+ h = self.block1(x)
+ h = self.act(h)
+ h = self.block2(h)
+ if self.skep is not None:
+ return h + self.skep(x)
+ else:
+ return h + x
+
+
+class Adapter(nn.Module):
+ def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True):
+ super(Adapter, self).__init__()
+ self.unshuffle_amount = 8
+ resblock_no_downsample = []
+ resblock_downsample = [3, 2, 1]
+ self.xl = xl
+ if self.xl:
+ self.unshuffle_amount = 16
+ resblock_no_downsample = [1]
+ resblock_downsample = [2]
+
+ self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)
+ self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)
+ self.channels = channels
+ self.nums_rb = nums_rb
+ self.body = []
+ for i in range(len(channels)):
+ for j in range(nums_rb):
+ if (i in resblock_downsample) and (j == 0):
+ self.body.append(
+ ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
+ elif (i in resblock_no_downsample) and (j == 0):
+ self.body.append(
+ ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
+ else:
+ self.body.append(
+ ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
+ self.body = nn.ModuleList(self.body)
+ self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
+
+ def forward(self, x):
+ # unshuffle
+ x = self.unshuffle(x)
+ # extract features
+ features = []
+ x = self.conv_in(x)
+ for i in range(len(self.channels)):
+ for j in range(self.nums_rb):
+ idx = i * self.nums_rb + j
+ x = self.body[idx](x)
+ if self.xl:
+ features.append(None)
+ if i == 0:
+ features.append(None)
+ features.append(None)
+ if i == 2:
+ features.append(None)
+ else:
+ features.append(None)
+ features.append(None)
+ features.append(x)
+
+ features = features[::-1]
+
+ if self.xl:
+ return {"input": features[1:], "middle": features[:1]}
+ else:
+ return {"input": features}
+
+
+
+class LayerNorm(nn.LayerNorm):
+ """Subclass torch's LayerNorm to handle fp16."""
+
+ def forward(self, x: torch.Tensor):
+ orig_type = x.dtype
+ ret = super().forward(x.type(torch.float32))
+ return ret.type(orig_type)
+
+
+class QuickGELU(nn.Module):
+
+ def forward(self, x: torch.Tensor):
+ return x * torch.sigmoid(1.702 * x)
+
+
+class ResidualAttentionBlock(nn.Module):
+
+ def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+ super().__init__()
+
+ self.attn = nn.MultiheadAttention(d_model, n_head)
+ self.ln_1 = LayerNorm(d_model)
+ self.mlp = nn.Sequential(
+ OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()),
+ ("c_proj", nn.Linear(d_model * 4, d_model))]))
+ self.ln_2 = LayerNorm(d_model)
+ self.attn_mask = attn_mask
+
+ def attention(self, x: torch.Tensor):
+ self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+ return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+ def forward(self, x: torch.Tensor):
+ x = x + self.attention(self.ln_1(x))
+ x = x + self.mlp(self.ln_2(x))
+ return x
+
+
+class StyleAdapter(nn.Module):
+
+ def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4):
+ super().__init__()
+
+ scale = width ** -0.5
+ self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)])
+ self.num_token = num_token
+ self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale)
+ self.ln_post = LayerNorm(width)
+ self.ln_pre = LayerNorm(width)
+ self.proj = nn.Parameter(scale * torch.randn(width, context_dim))
+
+ def forward(self, x):
+ # x shape [N, HW+1, C]
+ style_embedding = self.style_embedding + torch.zeros(
+ (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device)
+ x = torch.cat([x, style_embedding], dim=1)
+ x = self.ln_pre(x)
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.transformer_layes(x)
+ x = x.permute(1, 0, 2) # LND -> NLD
+
+ x = self.ln_post(x[:, -self.num_token:, :])
+ x = x @ self.proj
+
+ return x
+
+
+class ResnetBlock_light(nn.Module):
+ def __init__(self, in_c):
+ super().__init__()
+ self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1)
+ self.act = nn.ReLU()
+ self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1)
+
+ def forward(self, x):
+ h = self.block1(x)
+ h = self.act(h)
+ h = self.block2(h)
+
+ return h + x
+
+
+class extractor(nn.Module):
+ def __init__(self, in_c, inter_c, out_c, nums_rb, down=False):
+ super().__init__()
+ self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0)
+ self.body = []
+ for _ in range(nums_rb):
+ self.body.append(ResnetBlock_light(inter_c))
+ self.body = nn.Sequential(*self.body)
+ self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0)
+ self.down = down
+ if self.down == True:
+ self.down_opt = Downsample(in_c, use_conv=False)
+
+ def forward(self, x):
+ if self.down == True:
+ x = self.down_opt(x)
+ x = self.in_conv(x)
+ x = self.body(x)
+ x = self.out_conv(x)
+
+ return x
+
+
+class Adapter_light(nn.Module):
+ def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):
+ super(Adapter_light, self).__init__()
+ self.unshuffle_amount = 8
+ self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)
+ self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)
+ self.channels = channels
+ self.nums_rb = nums_rb
+ self.body = []
+ self.xl = False
+
+ for i in range(len(channels)):
+ if i == 0:
+ self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False))
+ else:
+ self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True))
+ self.body = nn.ModuleList(self.body)
+
+ def forward(self, x):
+ # unshuffle
+ x = self.unshuffle(x)
+ # extract features
+ features = []
+ for i in range(len(self.channels)):
+ x = self.body[i](x)
+ features.append(None)
+ features.append(None)
+ features.append(x)
+
+ return {"input": features[::-1]}
diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce36f1a84dae599a35e84a8da3462408c0f0ccc6
--- /dev/null
+++ b/comfy/taesd/taesd.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""
+Tiny AutoEncoder for Stable Diffusion
+(DNN for encoding / decoding SD's latent space)
+"""
+import torch
+import torch.nn as nn
+
+import comfy.utils
+import comfy.ops
+
+def conv(n_in, n_out, **kwargs):
+ return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+
+class Clamp(nn.Module):
+ def forward(self, x):
+ return torch.tanh(x / 3) * 3
+
+class Block(nn.Module):
+ def __init__(self, n_in, n_out):
+ super().__init__()
+ self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
+ self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
+ self.fuse = nn.ReLU()
+ def forward(self, x):
+ return self.fuse(self.conv(x) + self.skip(x))
+
+def Encoder(latent_channels=4):
+ return nn.Sequential(
+ conv(3, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, latent_channels),
+ )
+
+
+def Decoder(latent_channels=4):
+ return nn.Sequential(
+ Clamp(), conv(latent_channels, 64), nn.ReLU(),
+ Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+ Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+ Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+ Block(64, 64), conv(64, 3),
+ )
+
+class TAESD(nn.Module):
+ latent_magnitude = 3
+ latent_shift = 0.5
+
+ def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4):
+ """Initialize pretrained TAESD on the given device from the given checkpoints."""
+ super().__init__()
+ self.taesd_encoder = Encoder(latent_channels=latent_channels)
+ self.taesd_decoder = Decoder(latent_channels=latent_channels)
+ self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
+ self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))
+ if encoder_path is not None:
+ self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
+ if decoder_path is not None:
+ self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True))
+
+ @staticmethod
+ def scale_latents(x):
+ """raw latents -> [0, 1]"""
+ return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1)
+
+ @staticmethod
+ def unscale_latents(x):
+ """[0, 1] -> raw latents"""
+ return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
+
+ def decode(self, x):
+ x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale)
+ x_sample = x_sample.sub(0.5).mul(2)
+ return x_sample
+
+ def encode(self, x):
+ return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9ad45a7fcb3e31b9e08b36fb58b2f86c2a2084a
--- /dev/null
+++ b/comfy/text_encoders/aura_t5.py
@@ -0,0 +1,22 @@
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.t5
+import os
+
+class PT5XlModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json")
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options)
+
+class PT5XlTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model")
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
+
+class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer)
+
+class AuraT5Model(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+ super().__init__(device=device, dtype=dtype, model_options=model_options, name="pile_t5xl", clip_model=PT5XlModel, **kwargs)
diff --git a/comfy/text_encoders/bert.py b/comfy/text_encoders/bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc9bac1d24c088cf17cae4f3af86b8646960f808
--- /dev/null
+++ b/comfy/text_encoders/bert.py
@@ -0,0 +1,140 @@
+import torch
+from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
+
+class BertAttention(torch.nn.Module):
+ def __init__(self, embed_dim, heads, dtype, device, operations):
+ super().__init__()
+
+ self.heads = heads
+ self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+ self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+ self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+
+
+ def forward(self, x, mask=None, optimized_attention=None):
+ q = self.query(x)
+ k = self.key(x)
+ v = self.value(x)
+
+ out = optimized_attention(q, k, v, self.heads, mask)
+ return out
+
+class BertOutput(torch.nn.Module):
+ def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device)
+ self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device)
+ # self.dropout = nn.Dropout(0.0)
+
+ def forward(self, x, y):
+ x = self.dense(x)
+ # hidden_states = self.dropout(hidden_states)
+ x = self.LayerNorm(x + y)
+ return x
+
+class BertAttentionBlock(torch.nn.Module):
+ def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.self = BertAttention(embed_dim, heads, dtype, device, operations)
+ self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+ def forward(self, x, mask, optimized_attention):
+ y = self.self(x, mask, optimized_attention)
+ return self.output(y, x)
+
+class BertIntermediate(torch.nn.Module):
+ def __init__(self, embed_dim, intermediate_dim, dtype, device, operations):
+ super().__init__()
+ self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device)
+
+ def forward(self, x):
+ x = self.dense(x)
+ return torch.nn.functional.gelu(x)
+
+
+class BertBlock(torch.nn.Module):
+ def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations)
+ self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations)
+ self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+ def forward(self, x, mask, optimized_attention):
+ x = self.attention(x, mask, optimized_attention)
+ y = self.intermediate(x)
+ return self.output(y, x)
+
+class BertEncoder(torch.nn.Module):
+ def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations) for i in range(num_layers)])
+
+ def forward(self, x, mask=None, intermediate_output=None):
+ optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
+
+ if intermediate_output is not None:
+ if intermediate_output < 0:
+ intermediate_output = len(self.layer) + intermediate_output
+
+ intermediate = None
+ for i, l in enumerate(self.layer):
+ x = l(x, mask, optimized_attention)
+ if i == intermediate_output:
+ intermediate = x.clone()
+ return x, intermediate
+
+class BertEmbeddings(torch.nn.Module):
+ def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)
+ self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)
+ self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)
+
+ self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device)
+
+ def forward(self, input_tokens, token_type_ids=None, dtype=None):
+ x = self.word_embeddings(input_tokens, out_dtype=dtype)
+ x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x)
+ if token_type_ids is not None:
+ x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype)
+ else:
+ x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x)
+ x = self.LayerNorm(x)
+ return x
+
+
+class BertModel_(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ embed_dim = config_dict["hidden_size"]
+ layer_norm_eps = config_dict["layer_norm_eps"]
+
+ self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations)
+ self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations)
+
+ def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
+ x = self.embeddings(input_tokens, dtype=dtype)
+ mask = None
+ if attention_mask is not None:
+ mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+ mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+
+ x, i = self.encoder(x, mask, intermediate_output)
+ return x, i
+
+
+class BertModel(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ self.bert = BertModel_(config_dict, dtype, device, operations)
+ self.num_layers = config_dict["num_hidden_layers"]
+
+ def get_input_embeddings(self):
+ return self.bert.embeddings.word_embeddings
+
+ def set_input_embeddings(self, embeddings):
+ self.bert.embeddings.word_embeddings = embeddings
+
+ def forward(self, *args, **kwargs):
+ return self.bert(*args, **kwargs)
diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py
new file mode 100644
index 0000000000000000000000000000000000000000..b945b1aaace8c1776caf2f6387caecdabe561e8d
--- /dev/null
+++ b/comfy/text_encoders/flux.py
@@ -0,0 +1,72 @@
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+import comfy.text_encoders.sd3_clip
+import comfy.model_management
+from transformers import T5TokenizerFast
+import torch
+import os
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+
+
+class FluxTokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
+ self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
+ self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False):
+ out = {}
+ out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+ out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.clip_l.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {}
+
+
+class FluxClipModel(torch.nn.Module):
+ def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}):
+ super().__init__()
+ dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
+ clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
+ self.clip_l = clip_l_class(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
+ self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
+ self.dtypes = set([dtype, dtype_t5])
+
+ def set_clip_options(self, options):
+ self.clip_l.set_clip_options(options)
+ self.t5xxl.set_clip_options(options)
+
+ def reset_clip_options(self):
+ self.clip_l.reset_clip_options()
+ self.t5xxl.reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs_l = token_weight_pairs["l"]
+ token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
+
+ t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+ l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+ return t5_out, l_pooled
+
+ def load_sd(self, sd):
+ if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+ return self.clip_l.load_sd(sd)
+ else:
+ return self.t5xxl.load_sd(sd)
+
+def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None):
+ class FluxClipModel_(FluxClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
+ return FluxClipModel_
diff --git a/comfy/text_encoders/genmo.py b/comfy/text_encoders/genmo.py
new file mode 100644
index 0000000000000000000000000000000000000000..45987a480e4210d6ee0e4852811cb9eb6b5f6247
--- /dev/null
+++ b/comfy/text_encoders/genmo.py
@@ -0,0 +1,38 @@
+from comfy import sd1_clip
+import comfy.text_encoders.sd3_clip
+import os
+from transformers import T5TokenizerFast
+
+
+class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
+ def __init__(self, **kwargs):
+ kwargs["attention_mask"] = True
+ super().__init__(**kwargs)
+
+
+class MochiT5XXL(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+
+
+class MochiT5Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+
+def mochi_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+ class MochiTEModel_(MochiT5XXL):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ if dtype is None:
+ dtype = dtype_t5
+ super().__init__(device=device, dtype=dtype, model_options=model_options)
+ return MochiTEModel_
diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cb790f45e2d87dba0ba8e4d74aa0537bcd1068e
--- /dev/null
+++ b/comfy/text_encoders/hydit.py
@@ -0,0 +1,79 @@
+from comfy import sd1_clip
+from transformers import BertTokenizer
+from .spiece_tokenizer import SPieceTokenizer
+from .bert import BertModel
+import comfy.text_encoders.t5
+import os
+import torch
+
+class HyditBertModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json")
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True, model_options=model_options)
+
+class HyditBertTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip_tokenizer")
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77)
+
+
+class MT5XLModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.json")
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True, model_options=model_options)
+
+class MT5XLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ #tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_tokenizer"), "spiece.model")
+ tokenizer = tokenizer_data.get("spiece_model", None)
+ super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+
+ def state_dict(self):
+ return {"spiece_model": self.tokenizer.serialize_model()}
+
+class HyditTokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ mt5_tokenizer_data = tokenizer_data.get("mt5xl.spiece_model", None)
+ self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory)
+ self.mt5xl = MT5XLTokenizer(tokenizer_data={"spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False):
+ out = {}
+ out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids)
+ out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.hydit_clip.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]}
+
+class HyditModel(torch.nn.Module):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__()
+ self.hydit_clip = HyditBertModel(dtype=dtype, model_options=model_options)
+ self.mt5xl = MT5XLModel(dtype=dtype, model_options=model_options)
+
+ self.dtypes = set()
+ if dtype is not None:
+ self.dtypes.add(dtype)
+
+ def encode_token_weights(self, token_weight_pairs):
+ hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs["hydit_clip"])
+ mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs["mt5xl"])
+ return hydit_out[0], hydit_out[1], {"attention_mask": hydit_out[2]["attention_mask"], "conditioning_mt5xl": mt5_out[0], "attention_mask_mt5xl": mt5_out[2]["attention_mask"]}
+
+ def load_sd(self, sd):
+ if "bert.encoder.layer.0.attention.self.query.weight" in sd:
+ return self.hydit_clip.load_sd(sd)
+ else:
+ return self.mt5xl.load_sd(sd)
+
+ def set_clip_options(self, options):
+ self.hydit_clip.set_clip_options(options)
+ self.mt5xl.set_clip_options(options)
+
+ def reset_clip_options(self):
+ self.hydit_clip.reset_clip_options()
+ self.mt5xl.reset_clip_options()
diff --git a/comfy/text_encoders/hydit_clip.json b/comfy/text_encoders/hydit_clip.json
new file mode 100644
index 0000000000000000000000000000000000000000..c41c7c1ff376407f42e3ff20ab26faaa98bb5e65
--- /dev/null
+++ b/comfy/text_encoders/hydit_clip.json
@@ -0,0 +1,35 @@
+{
+ "_name_or_path": "hfl/chinese-roberta-wwm-ext-large",
+ "architectures": [
+ "BertModel"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "bos_token_id": 0,
+ "classifier_dropout": null,
+ "directionality": "bidi",
+ "eos_token_id": 2,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 1024,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-12,
+ "max_position_embeddings": 512,
+ "model_type": "bert",
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "output_past": true,
+ "pad_token_id": 0,
+ "pooler_fc_size": 768,
+ "pooler_num_attention_heads": 12,
+ "pooler_num_fc_layers": 3,
+ "pooler_size_per_head": 128,
+ "pooler_type": "first_token_transform",
+ "position_embedding_type": "absolute",
+ "torch_dtype": "float32",
+ "transformers_version": "4.22.1",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 47020
+}
+
diff --git a/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json b/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..a8b3208c2884c4efb86e49300fdd3dc877220cdf
--- /dev/null
+++ b/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json
@@ -0,0 +1,7 @@
+{
+ "cls_token": "[CLS]",
+ "mask_token": "[MASK]",
+ "pad_token": "[PAD]",
+ "sep_token": "[SEP]",
+ "unk_token": "[UNK]"
+}
diff --git a/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json b/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a14356073e11a885074a7cdbddc749463cefd911
--- /dev/null
+++ b/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json
@@ -0,0 +1,16 @@
+{
+ "cls_token": "[CLS]",
+ "do_basic_tokenize": true,
+ "do_lower_case": true,
+ "mask_token": "[MASK]",
+ "name_or_path": "hfl/chinese-roberta-wwm-ext",
+ "never_split": null,
+ "pad_token": "[PAD]",
+ "sep_token": "[SEP]",
+ "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json",
+ "strip_accents": null,
+ "tokenize_chinese_chars": true,
+ "tokenizer_class": "BertTokenizer",
+ "unk_token": "[UNK]",
+ "model_max_length": 77
+}
diff --git a/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt b/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6246906805d02aca01714c71e4c8d77b69a7a131
--- /dev/null
+++ b/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt
@@ -0,0 +1,47020 @@
+[PAD]
+[unused1]
+[unused2]
+[unused3]
+[unused4]
+[unused5]
+[unused6]
+[unused7]
+[unused8]
+[unused9]
+[unused10]
+[unused11]
+[unused12]
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+[unused15]
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+[unused70]
+[unused71]
+[unused72]
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+[unused77]
+[unused78]
+[unused79]
+[unused80]
+[unused81]
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+[unused83]
+[unused84]
+[unused85]
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+[unused88]
+[unused89]
+[unused90]
+[unused91]
+[unused92]
+[unused93]
+[unused94]
+[unused95]
+[unused96]
+[unused97]
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+[unused99]
+[UNK]
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+##ম
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+##ு
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+##ை
+##ನ
+##ರ
+##ಾ
+##ක
+##ය
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+##ල
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+##ා
+##ต
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+##พ
+##ล
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+##疒
+##糹
+##訁
+##辶
+##阝
+##龸
+##fi
+##fl
diff --git a/comfy/text_encoders/long_clipl.json b/comfy/text_encoders/long_clipl.json
new file mode 100644
index 0000000000000000000000000000000000000000..5e2056ff37ec907462bac7a557e12bb728a15990
--- /dev/null
+++ b/comfy/text_encoders/long_clipl.json
@@ -0,0 +1,25 @@
+{
+ "_name_or_path": "openai/clip-vit-large-patch14",
+ "architectures": [
+ "CLIPTextModel"
+ ],
+ "attention_dropout": 0.0,
+ "bos_token_id": 0,
+ "dropout": 0.0,
+ "eos_token_id": 49407,
+ "hidden_act": "quick_gelu",
+ "hidden_size": 768,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "layer_norm_eps": 1e-05,
+ "max_position_embeddings": 248,
+ "model_type": "clip_text_model",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "pad_token_id": 1,
+ "projection_dim": 768,
+ "torch_dtype": "float32",
+ "transformers_version": "4.24.0",
+ "vocab_size": 49408
+}
diff --git a/comfy/text_encoders/long_clipl.py b/comfy/text_encoders/long_clipl.py
new file mode 100644
index 0000000000000000000000000000000000000000..b81912cb3d38ca9dafccd11a74ef96b97dfa5839
--- /dev/null
+++ b/comfy/text_encoders/long_clipl.py
@@ -0,0 +1,30 @@
+from comfy import sd1_clip
+import os
+
+class LongClipTokenizer_(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(max_length=248, embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+
+class LongClipModel_(sd1_clip.SDClipModel):
+ def __init__(self, *args, **kwargs):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "long_clipl.json")
+ super().__init__(*args, textmodel_json_config=textmodel_json_config, **kwargs)
+
+class LongClipTokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, tokenizer=LongClipTokenizer_)
+
+class LongClipModel(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+ super().__init__(device=device, dtype=dtype, model_options=model_options, clip_model=LongClipModel_, **kwargs)
+
+def model_options_long_clip(sd, tokenizer_data, model_options):
+ w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None)
+ if w is None:
+ w = sd.get("text_model.embeddings.position_embedding.weight", None)
+ if w is not None and w.shape[0] == 248:
+ tokenizer_data = tokenizer_data.copy()
+ model_options = model_options.copy()
+ tokenizer_data["clip_l_tokenizer_class"] = LongClipTokenizer_
+ model_options["clip_l_class"] = LongClipModel_
+ return tokenizer_data, model_options
diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c2ce583ff15ce44788ef152a75b88a8ac800fee
--- /dev/null
+++ b/comfy/text_encoders/lt.py
@@ -0,0 +1,18 @@
+from comfy import sd1_clip
+import os
+from transformers import T5TokenizerFast
+import comfy.text_encoders.genmo
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128) #pad to 128?
+
+
+class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+
+def ltxv_te(*args, **kwargs):
+ return comfy.text_encoders.genmo.mochi_te(*args, **kwargs)
diff --git a/comfy/text_encoders/mt5_config_xl.json b/comfy/text_encoders/mt5_config_xl.json
new file mode 100644
index 0000000000000000000000000000000000000000..092fefd6e32dac566e443fc03eae53f6a8b57400
--- /dev/null
+++ b/comfy/text_encoders/mt5_config_xl.json
@@ -0,0 +1,22 @@
+{
+ "d_ff": 5120,
+ "d_kv": 64,
+ "d_model": 2048,
+ "decoder_start_token_id": 0,
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "dense_act_fn": "gelu_pytorch_tanh",
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": true,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "mt5",
+ "num_decoder_layers": 24,
+ "num_heads": 32,
+ "num_layers": 24,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_num_buckets": 32,
+ "tie_word_embeddings": false,
+ "vocab_size": 250112
+}
diff --git a/comfy/text_encoders/sa_t5.py b/comfy/text_encoders/sa_t5.py
new file mode 100644
index 0000000000000000000000000000000000000000..7778ce47ad98ed7baabda3ce29dca76e25c6880f
--- /dev/null
+++ b/comfy/text_encoders/sa_t5.py
@@ -0,0 +1,22 @@
+from comfy import sd1_clip
+from transformers import T5TokenizerFast
+import comfy.text_encoders.t5
+import os
+
+class T5BaseModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json")
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+
+class T5BaseTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128)
+
+class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5base", tokenizer=T5BaseTokenizer)
+
+class SAT5Model(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+ super().__init__(device=device, dtype=dtype, model_options=model_options, name="t5base", clip_model=T5BaseModel, **kwargs)
diff --git a/comfy/text_encoders/sd2_clip.py b/comfy/text_encoders/sd2_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..31fc89869e6e134b6de260d30443654a6cc4ac83
--- /dev/null
+++ b/comfy/text_encoders/sd2_clip.py
@@ -0,0 +1,23 @@
+from comfy import sd1_clip
+import os
+
+class SD2ClipHModel(sd1_clip.SDClipModel):
+ def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}):
+ if layer == "penultimate":
+ layer="hidden"
+ layer_idx=-2
+
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json")
+ super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=True, model_options=model_options)
+
+class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024)
+
+class SD2Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="h", tokenizer=SD2ClipHTokenizer)
+
+class SD2ClipModel(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+ super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="h", clip_model=SD2ClipHModel, **kwargs)
diff --git a/comfy/text_encoders/sd2_clip_config.json b/comfy/text_encoders/sd2_clip_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..00893cfdc9b00f8eb7cf5aaa9c343e7fcd298d82
--- /dev/null
+++ b/comfy/text_encoders/sd2_clip_config.json
@@ -0,0 +1,23 @@
+{
+ "architectures": [
+ "CLIPTextModel"
+ ],
+ "attention_dropout": 0.0,
+ "bos_token_id": 0,
+ "dropout": 0.0,
+ "eos_token_id": 49407,
+ "hidden_act": "gelu",
+ "hidden_size": 1024,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-05,
+ "max_position_embeddings": 77,
+ "model_type": "clip_text_model",
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "pad_token_id": 1,
+ "projection_dim": 1024,
+ "torch_dtype": "float32",
+ "vocab_size": 49408
+}
diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..00d7e31ad32d495169e6bf3a7d511714326ca04a
--- /dev/null
+++ b/comfy/text_encoders/sd3_clip.py
@@ -0,0 +1,167 @@
+from comfy import sd1_clip
+from comfy import sdxl_clip
+from transformers import T5TokenizerFast
+import comfy.text_encoders.t5
+import torch
+import os
+import comfy.model_management
+import logging
+
+class T5XXLModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
+ t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
+ if t5xxl_scaled_fp8 is not None:
+ model_options = model_options.copy()
+ model_options["scaled_fp8"] = t5xxl_scaled_fp8
+
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+def t5_xxl_detect(state_dict, prefix=""):
+ out = {}
+ t5_key = "{}encoder.final_layer_norm.weight".format(prefix)
+ if t5_key in state_dict:
+ out["dtype_t5"] = state_dict[t5_key].dtype
+
+ scaled_fp8_key = "{}scaled_fp8".format(prefix)
+ if scaled_fp8_key in state_dict:
+ out["t5xxl_scaled_fp8"] = state_dict[scaled_fp8_key].dtype
+
+ return out
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77)
+
+
+class SD3Tokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer)
+ self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory)
+ self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
+ self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False):
+ out = {}
+ out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
+ out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+ out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.clip_g.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {}
+
+class SD3ClipModel(torch.nn.Module):
+ def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}):
+ super().__init__()
+ self.dtypes = set()
+ if clip_l:
+ clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel)
+ self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options)
+ self.dtypes.add(dtype)
+ else:
+ self.clip_l = None
+
+ if clip_g:
+ self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options)
+ self.dtypes.add(dtype)
+ else:
+ self.clip_g = None
+
+ if t5:
+ dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
+ self.t5_attention_mask = t5_attention_mask
+ self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask)
+ self.dtypes.add(dtype_t5)
+ else:
+ self.t5xxl = None
+
+ logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5))
+
+ def set_clip_options(self, options):
+ if self.clip_l is not None:
+ self.clip_l.set_clip_options(options)
+ if self.clip_g is not None:
+ self.clip_g.set_clip_options(options)
+ if self.t5xxl is not None:
+ self.t5xxl.set_clip_options(options)
+
+ def reset_clip_options(self):
+ if self.clip_l is not None:
+ self.clip_l.reset_clip_options()
+ if self.clip_g is not None:
+ self.clip_g.reset_clip_options()
+ if self.t5xxl is not None:
+ self.t5xxl.reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs_l = token_weight_pairs["l"]
+ token_weight_pairs_g = token_weight_pairs["g"]
+ token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
+ lg_out = None
+ pooled = None
+ out = None
+ extra = {}
+
+ if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
+ if self.clip_l is not None:
+ lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+ else:
+ l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
+
+ if self.clip_g is not None:
+ g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
+ if lg_out is not None:
+ cut_to = min(lg_out.shape[1], g_out.shape[1])
+ lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1)
+ else:
+ lg_out = torch.nn.functional.pad(g_out, (768, 0))
+ else:
+ g_out = None
+ g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
+
+ if lg_out is not None:
+ lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
+ out = lg_out
+ pooled = torch.cat((l_pooled, g_pooled), dim=-1)
+
+ if self.t5xxl is not None:
+ t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+ t5_out, t5_pooled = t5_output[:2]
+ if self.t5_attention_mask:
+ extra["attention_mask"] = t5_output[2]["attention_mask"]
+
+ if lg_out is not None:
+ out = torch.cat([lg_out, t5_out], dim=-2)
+ else:
+ out = t5_out
+
+ if out is None:
+ out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())
+
+ if pooled is None:
+ pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
+
+ return out, pooled, extra
+
+ def load_sd(self, sd):
+ if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+ return self.clip_g.load_sd(sd)
+ elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+ return self.clip_l.load_sd(sd)
+ else:
+ return self.t5xxl.load_sd(sd)
+
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5xxl_scaled_fp8=None, t5_attention_mask=False):
+ class SD3ClipModel_(SD3ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options)
+ return SD3ClipModel_
diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..73739553d47c140ae7b985ed76004a72e6ddbf2b
--- /dev/null
+++ b/comfy/text_encoders/spiece_tokenizer.py
@@ -0,0 +1,32 @@
+import os
+import torch
+
+class SPieceTokenizer:
+ add_eos = True
+
+ @staticmethod
+ def from_pretrained(path):
+ return SPieceTokenizer(path)
+
+ def __init__(self, tokenizer_path):
+ import sentencepiece
+ if torch.is_tensor(tokenizer_path):
+ tokenizer_path = tokenizer_path.numpy().tobytes()
+
+ if isinstance(tokenizer_path, bytes):
+ self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_eos=self.add_eos)
+ else:
+ self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_eos=self.add_eos)
+
+ def get_vocab(self):
+ out = {}
+ for i in range(self.tokenizer.get_piece_size()):
+ out[self.tokenizer.id_to_piece(i)] = i
+ return out
+
+ def __call__(self, string):
+ out = self.tokenizer.encode(string)
+ return {"input_ids": out}
+
+ def serialize_model(self):
+ return torch.ByteTensor(list(self.tokenizer.serialized_model_proto()))
diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1420c6cd2f208c9731d42b09fe077980a952aad
--- /dev/null
+++ b/comfy/text_encoders/t5.py
@@ -0,0 +1,241 @@
+import torch
+import math
+from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
+
+class T5LayerNorm(torch.nn.Module):
+ def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+ self.variance_epsilon = eps
+
+ def forward(self, x):
+ variance = x.pow(2).mean(-1, keepdim=True)
+ x = x * torch.rsqrt(variance + self.variance_epsilon)
+ return comfy.ops.cast_to_input(self.weight, x) * x
+
+activations = {
+ "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
+ "relu": torch.nn.functional.relu,
+}
+
+class T5DenseActDense(torch.nn.Module):
+ def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+ super().__init__()
+ self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+ self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+ self.act = activations[ff_activation]
+
+ def forward(self, x):
+ x = self.act(self.wi(x))
+ # x = self.dropout(x)
+ x = self.wo(x)
+ return x
+
+class T5DenseGatedActDense(torch.nn.Module):
+ def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+ super().__init__()
+ self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+ self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+ self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+ self.act = activations[ff_activation]
+
+ def forward(self, x):
+ hidden_gelu = self.act(self.wi_0(x))
+ hidden_linear = self.wi_1(x)
+ x = hidden_gelu * hidden_linear
+ # x = self.dropout(x)
+ x = self.wo(x)
+ return x
+
+class T5LayerFF(torch.nn.Module):
+ def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations):
+ super().__init__()
+ if gated_act:
+ self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+ else:
+ self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+
+ self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(self, x):
+ forwarded_states = self.layer_norm(x)
+ forwarded_states = self.DenseReluDense(forwarded_states)
+ # x = x + self.dropout(forwarded_states)
+ x += forwarded_states
+ return x
+
+class T5Attention(torch.nn.Module):
+ def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations):
+ super().__init__()
+
+ # Mesh TensorFlow initialization to avoid scaling before softmax
+ self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+ self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+ self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+ self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
+ self.num_heads = num_heads
+
+ self.relative_attention_bias = None
+ if relative_attention_bias:
+ self.relative_attention_num_buckets = 32
+ self.relative_attention_max_distance = 128
+ self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype)
+
+ @staticmethod
+ def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+ small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+ positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+ This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+ Args:
+ relative_position: an int32 Tensor
+ bidirectional: a boolean - whether the attention is bidirectional
+ num_buckets: an integer
+ max_distance: an integer
+
+ Returns:
+ a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+ """
+ relative_buckets = 0
+ if bidirectional:
+ num_buckets //= 2
+ relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+ relative_position = torch.abs(relative_position)
+ else:
+ relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+ # now relative_position is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = relative_position < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ relative_position_if_large = max_exact + (
+ torch.log(relative_position.float() / max_exact)
+ / math.log(max_distance / max_exact)
+ * (num_buckets - max_exact)
+ ).to(torch.long)
+ relative_position_if_large = torch.min(
+ relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+ )
+
+ relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+ return relative_buckets
+
+ def compute_bias(self, query_length, key_length, device, dtype):
+ """Compute binned relative position bias"""
+ context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
+ memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
+ relative_position = memory_position - context_position # shape (query_length, key_length)
+ relative_position_bucket = self._relative_position_bucket(
+ relative_position, # shape (query_length, key_length)
+ bidirectional=True,
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+ values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype) # shape (query_length, key_length, num_heads)
+ values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
+ return values
+
+ def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+ q = self.q(x)
+ k = self.k(x)
+ v = self.v(x)
+ if self.relative_attention_bias is not None:
+ past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype)
+
+ if past_bias is not None:
+ if mask is not None:
+ mask = mask + past_bias
+ else:
+ mask = past_bias
+
+ out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
+ return self.o(out), past_bias
+
+class T5LayerSelfAttention(torch.nn.Module):
+ def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations):
+ super().__init__()
+ self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations)
+ self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+ normed_hidden_states = self.layer_norm(x)
+ output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention)
+ # x = x + self.dropout(attention_output)
+ x += output
+ return x, past_bias
+
+class T5Block(torch.nn.Module):
+ def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations):
+ super().__init__()
+ self.layer = torch.nn.ModuleList()
+ self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
+ self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations))
+
+ def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+ x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
+ x = self.layer[-1](x)
+ return x, past_bias
+
+class T5Stack(torch.nn.Module):
+ def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations):
+ super().__init__()
+
+ self.block = torch.nn.ModuleList(
+ [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
+ )
+ self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
+ mask = None
+ if attention_mask is not None:
+ mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+ mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+
+ intermediate = None
+ optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
+ past_bias = None
+ for i, l in enumerate(self.block):
+ x, past_bias = l(x, mask, past_bias, optimized_attention)
+ if i == intermediate_output:
+ intermediate = x.clone()
+ x = self.final_layer_norm(x)
+ if intermediate is not None and final_layer_norm_intermediate:
+ intermediate = self.final_layer_norm(intermediate)
+ return x, intermediate
+
+class T5(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ self.num_layers = config_dict["num_layers"]
+ model_dim = config_dict["d_model"]
+
+ self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations)
+ self.dtype = dtype
+ self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype)
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, embeddings):
+ self.shared = embeddings
+
+ def forward(self, input_ids, *args, **kwargs):
+ x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32))
+ if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]:
+ x = torch.nan_to_num(x) #Fix for fp8 T5 base
+ return self.encoder(x, *args, **kwargs)
diff --git a/comfy/text_encoders/t5_config_base.json b/comfy/text_encoders/t5_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..71f68327c27280ce150d0c8e92fd61eca0b52a63
--- /dev/null
+++ b/comfy/text_encoders/t5_config_base.json
@@ -0,0 +1,22 @@
+{
+ "d_ff": 3072,
+ "d_kv": 64,
+ "d_model": 768,
+ "decoder_start_token_id": 0,
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "dense_act_fn": "relu",
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": false,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "t5",
+ "num_decoder_layers": 12,
+ "num_heads": 12,
+ "num_layers": 12,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_num_buckets": 32,
+ "tie_word_embeddings": false,
+ "vocab_size": 32128
+}
diff --git a/comfy/text_encoders/t5_config_xxl.json b/comfy/text_encoders/t5_config_xxl.json
new file mode 100644
index 0000000000000000000000000000000000000000..28283b51a11bed6a874499f82d411c16cc646eb1
--- /dev/null
+++ b/comfy/text_encoders/t5_config_xxl.json
@@ -0,0 +1,22 @@
+{
+ "d_ff": 10240,
+ "d_kv": 64,
+ "d_model": 4096,
+ "decoder_start_token_id": 0,
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "dense_act_fn": "gelu_pytorch_tanh",
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": true,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "t5",
+ "num_decoder_layers": 24,
+ "num_heads": 64,
+ "num_layers": 24,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_num_buckets": 32,
+ "tie_word_embeddings": false,
+ "vocab_size": 32128
+}
diff --git a/comfy/text_encoders/t5_pile_config_xl.json b/comfy/text_encoders/t5_pile_config_xl.json
new file mode 100644
index 0000000000000000000000000000000000000000..ee4e03f97a5b3a9927fc676816f210a364ee234b
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_config_xl.json
@@ -0,0 +1,22 @@
+{
+ "d_ff": 5120,
+ "d_kv": 64,
+ "d_model": 2048,
+ "decoder_start_token_id": 0,
+ "dropout_rate": 0.1,
+ "eos_token_id": 2,
+ "dense_act_fn": "gelu_pytorch_tanh",
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": true,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "umt5",
+ "num_decoder_layers": 24,
+ "num_heads": 32,
+ "num_layers": 24,
+ "output_past": true,
+ "pad_token_id": 1,
+ "relative_attention_num_buckets": 32,
+ "tie_word_embeddings": false,
+ "vocab_size": 32128
+}
diff --git a/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model b/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model
new file mode 100644
index 0000000000000000000000000000000000000000..6c00c742ce03c627d6cd5b795984876fa49fa899
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723
diff --git a/comfy/text_encoders/t5_tokenizer/special_tokens_map.json b/comfy/text_encoders/t5_tokenizer/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..17ade346a1042cbe0c1436f5bedcbd85c099d582
--- /dev/null
+++ b/comfy/text_encoders/t5_tokenizer/special_tokens_map.json
@@ -0,0 +1,125 @@
+{
+ "additional_special_tokens": [
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+ "",
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+ }
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diff --git a/comfy/text_encoders/t5_tokenizer/tokenizer.json b/comfy/text_encoders/t5_tokenizer/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..b11c92d7184d265f0dc857ec5d676aa81aa16262
--- /dev/null
+++ b/comfy/text_encoders/t5_tokenizer/tokenizer.json
@@ -0,0 +1,129428 @@
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diff --git a/comfy/text_encoders/t5_tokenizer/tokenizer_config.json b/comfy/text_encoders/t5_tokenizer/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..02020eb6d20746871e1ea93f14c4475cf9368f98
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