| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| 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/<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 |
|
|
