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| # Copyright 2024 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from huggingface_hub.utils import validate_hf_hub_args | |
| from .single_file_utils import ( | |
| create_diffusers_controlnet_model_from_ldm, | |
| fetch_ldm_config_and_checkpoint, | |
| ) | |
| class FromOriginalControlNetMixin: | |
| """ | |
| Load pretrained ControlNet weights saved in the `.ckpt` or `.safetensors` format into a [`ControlNetModel`]. | |
| """ | |
| def from_single_file(cls, pretrained_model_link_or_path, **kwargs): | |
| r""" | |
| Instantiate a [`ControlNetModel`] from pretrained ControlNet weights saved in the original `.ckpt` or | |
| `.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default. | |
| Parameters: | |
| pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*): | |
| Can be either: | |
| - A link to the `.ckpt` file (for example | |
| `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub. | |
| - A path to a *file* containing all pipeline weights. | |
| config_file (`str`, *optional*): | |
| Filepath to the configuration YAML file associated with the model. If not provided it will default to: | |
| https://raw.githubusercontent.com/lllyasviel/ControlNet/main/models/cldm_v15.yaml | |
| torch_dtype (`str` or `torch.dtype`, *optional*): | |
| Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the | |
| dtype is automatically derived from the model's weights. | |
| force_download (`bool`, *optional*, defaults to `False`): | |
| Whether or not to force the (re-)download of the model weights and configuration files, overriding the | |
| cached versions if they exist. | |
| cache_dir (`Union[str, os.PathLike]`, *optional*): | |
| Path to a directory where a downloaded pretrained model configuration is cached if the standard cache | |
| is not used. | |
| resume_download (`bool`, *optional*, defaults to `False`): | |
| Whether or not to resume downloading the model weights and configuration files. If set to `False`, any | |
| incompletely downloaded files are deleted. | |
| proxies (`Dict[str, str]`, *optional*): | |
| A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', | |
| 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. | |
| local_files_only (`bool`, *optional*, defaults to `False`): | |
| Whether to only load local model weights and configuration files or not. If set to True, the model | |
| won't be downloaded from the Hub. | |
| token (`str` or *bool*, *optional*): | |
| The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from | |
| `diffusers-cli login` (stored in `~/.huggingface`) is used. | |
| revision (`str`, *optional*, defaults to `"main"`): | |
| The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier | |
| allowed by Git. | |
| image_size (`int`, *optional*, defaults to 512): | |
| The image size the model was trained on. Use 512 for all Stable Diffusion v1 models and the Stable | |
| Diffusion v2 base model. Use 768 for Stable Diffusion v2. | |
| upcast_attention (`bool`, *optional*, defaults to `None`): | |
| Whether the attention computation should always be upcasted. | |
| kwargs (remaining dictionary of keyword arguments, *optional*): | |
| Can be used to overwrite load and saveable variables (for example the pipeline components of the | |
| specific pipeline class). The overwritten components are directly passed to the pipelines `__init__` | |
| method. See example below for more information. | |
| Examples: | |
| ```py | |
| from diffusers import StableDiffusionControlNetPipeline, ControlNetModel | |
| url = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth" # can also be a local path | |
| model = ControlNetModel.from_single_file(url) | |
| url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors" # can also be a local path | |
| pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet) | |
| ``` | |
| """ | |
| original_config_file = kwargs.pop("original_config_file", None) | |
| config_file = kwargs.pop("config_file", None) | |
| resume_download = kwargs.pop("resume_download", False) | |
| force_download = kwargs.pop("force_download", False) | |
| proxies = kwargs.pop("proxies", None) | |
| token = kwargs.pop("token", None) | |
| cache_dir = kwargs.pop("cache_dir", None) | |
| local_files_only = kwargs.pop("local_files_only", None) | |
| revision = kwargs.pop("revision", None) | |
| torch_dtype = kwargs.pop("torch_dtype", None) | |
| class_name = cls.__name__ | |
| if (config_file is not None) and (original_config_file is not None): | |
| raise ValueError( | |
| "You cannot pass both `config_file` and `original_config_file` to `from_single_file`. Please use only one of these arguments." | |
| ) | |
| original_config_file = config_file or original_config_file | |
| original_config, checkpoint = fetch_ldm_config_and_checkpoint( | |
| pretrained_model_link_or_path=pretrained_model_link_or_path, | |
| class_name=class_name, | |
| original_config_file=original_config_file, | |
| resume_download=resume_download, | |
| force_download=force_download, | |
| proxies=proxies, | |
| token=token, | |
| revision=revision, | |
| local_files_only=local_files_only, | |
| cache_dir=cache_dir, | |
| ) | |
| upcast_attention = kwargs.pop("upcast_attention", False) | |
| image_size = kwargs.pop("image_size", None) | |
| component = create_diffusers_controlnet_model_from_ldm( | |
| class_name, | |
| original_config, | |
| checkpoint, | |
| upcast_attention=upcast_attention, | |
| image_size=image_size, | |
| torch_dtype=torch_dtype, | |
| ) | |
| controlnet = component["controlnet"] | |
| if torch_dtype is not None: | |
| controlnet = controlnet.to(torch_dtype) | |
| return controlnet | |