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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`].

"""

@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