# 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`]. """ @classmethod @validate_hf_hub_args 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//blob/main/.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