# Copyright 2023 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 contextlib import nullcontext
from io import BytesIO
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import validate_hf_hub_args
from ..utils import deprecate, is_accelerate_available, is_transformers_available, logging
if is_transformers_available():
pass
if is_accelerate_available():
from accelerate import init_empty_weights
logger = logging.get_logger(__name__)
class FromSingleFileMixin:
"""
Load model weights saved in the `.ckpt` format into a [`DiffusionPipeline`].
"""
@classmethod
def from_ckpt(cls, *args, **kwargs):
deprecation_message = "The function `from_ckpt` is deprecated in favor of `from_single_file` and will be removed in diffusers v.0.21. Please make sure to use `StableDiffusionPipeline.from_single_file(...)` instead."
deprecate("from_ckpt", "0.21.0", deprecation_message, standard_warn=False)
return cls.from_single_file(*args, **kwargs)
@classmethod
@validate_hf_hub_args
def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
r"""
Instantiate a [`DiffusionPipeline`] from pretrained pipeline weights saved in the `.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.
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.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the safetensors weights are downloaded if they're available **and** if the
safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
weights. If set to `False`, safetensors weights are not loaded.
extract_ema (`bool`, *optional*, defaults to `False`):
Whether to extract the EMA weights or not. Pass `True` to extract the EMA weights which usually yield
higher quality images for inference. Non-EMA weights are usually better for continuing finetuning.
upcast_attention (`bool`, *optional*, defaults to `None`):
Whether the attention computation should always be upcasted.
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.
prediction_type (`str`, *optional*):
The prediction type the model was trained on. Use `'epsilon'` for all Stable Diffusion v1 models and
the Stable Diffusion v2 base model. Use `'v_prediction'` for Stable Diffusion v2.
num_in_channels (`int`, *optional*, defaults to `None`):
The number of input channels. If `None`, it is automatically inferred.
scheduler_type (`str`, *optional*, defaults to `"pndm"`):
Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm",
"ddim"]`.
load_safety_checker (`bool`, *optional*, defaults to `True`):
Whether to load the safety checker or not.
text_encoder ([`~transformers.CLIPTextModel`], *optional*, defaults to `None`):
An instance of `CLIPTextModel` to use, specifically the
[clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. If this
parameter is `None`, the function loads a new instance of `CLIPTextModel` by itself if needed.
vae (`AutoencoderKL`, *optional*, defaults to `None`):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. If
this parameter is `None`, the function will load a new instance of [CLIP] by itself, if needed.
tokenizer ([`~transformers.CLIPTokenizer`], *optional*, defaults to `None`):
An instance of `CLIPTokenizer` to use. If this parameter is `None`, the function loads a new instance
of `CLIPTokenizer` by itself if needed.
original_config_file (`str`):
Path to `.yaml` config file corresponding to the original architecture. If `None`, will be
automatically inferred by looking for a key that only exists in SD2.0 models.
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 StableDiffusionPipeline
>>> # Download pipeline from huggingface.co and cache.
>>> pipeline = StableDiffusionPipeline.from_single_file(
... "https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix.safetensors"
... )
>>> # Download pipeline from local file
>>> # file is downloaded under ./v1-5-pruned-emaonly.ckpt
>>> pipeline = StableDiffusionPipeline.from_single_file("./v1-5-pruned-emaonly")
>>> # Enable float16 and move to GPU
>>> pipeline = StableDiffusionPipeline.from_single_file(
... "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt",
... torch_dtype=torch.float16,
... )
>>> pipeline.to("cuda")
```
"""
# import here to avoid circular dependency
from ..pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
original_config_file = kwargs.pop("original_config_file", None)
config_files = kwargs.pop("config_files", None)
cache_dir = kwargs.pop("cache_dir", None)
resume_download = kwargs.pop("resume_download", False)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", None)
token = kwargs.pop("token", None)
revision = kwargs.pop("revision", None)
extract_ema = kwargs.pop("extract_ema", False)
image_size = kwargs.pop("image_size", None)
scheduler_type = kwargs.pop("scheduler_type", "pndm")
num_in_channels = kwargs.pop("num_in_channels", None)
upcast_attention = kwargs.pop("upcast_attention", None)
load_safety_checker = kwargs.pop("load_safety_checker", True)
prediction_type = kwargs.pop("prediction_type", None)
text_encoder = kwargs.pop("text_encoder", None)
text_encoder_2 = kwargs.pop("text_encoder_2", None)
vae = kwargs.pop("vae", None)
controlnet = kwargs.pop("controlnet", None)
adapter = kwargs.pop("adapter", None)
tokenizer = kwargs.pop("tokenizer", None)
tokenizer_2 = kwargs.pop("tokenizer_2", None)
torch_dtype = kwargs.pop("torch_dtype", None)
use_safetensors = kwargs.pop("use_safetensors", None)
pipeline_name = cls.__name__
file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1]
from_safetensors = file_extension == "safetensors"
if from_safetensors and use_safetensors is False:
raise ValueError("Make sure to install `safetensors` with `pip install safetensors`.")
# TODO: For now we only support stable diffusion
stable_unclip = None
model_type = None
if pipeline_name in [
"StableDiffusionControlNetPipeline",
"StableDiffusionControlNetImg2ImgPipeline",
"StableDiffusionControlNetInpaintPipeline",
]:
from ..models.controlnet import ControlNetModel
from ..pipelines.controlnet.multicontrolnet import MultiControlNetModel
# list/tuple or a single instance of ControlNetModel or MultiControlNetModel
if not (
isinstance(controlnet, (ControlNetModel, MultiControlNetModel))
or isinstance(controlnet, (list, tuple))
and isinstance(controlnet[0], ControlNetModel)
):
raise ValueError("ControlNet needs to be passed if loading from ControlNet pipeline.")
elif "StableDiffusion" in pipeline_name:
# Model type will be inferred from the checkpoint.
pass
elif pipeline_name == "StableUnCLIPPipeline":
model_type = "FrozenOpenCLIPEmbedder"
stable_unclip = "txt2img"
elif pipeline_name == "StableUnCLIPImg2ImgPipeline":
model_type = "FrozenOpenCLIPEmbedder"
stable_unclip = "img2img"
elif pipeline_name == "PaintByExamplePipeline":
model_type = "PaintByExample"
elif pipeline_name == "LDMTextToImagePipeline":
model_type = "LDMTextToImage"
else:
raise ValueError(f"Unhandled pipeline class: {pipeline_name}")
# remove huggingface url
has_valid_url_prefix = False
valid_url_prefixes = ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]
for prefix in valid_url_prefixes:
if pretrained_model_link_or_path.startswith(prefix):
pretrained_model_link_or_path = pretrained_model_link_or_path[len(prefix) :]
has_valid_url_prefix = True
# Code based on diffusers.pipelines.pipeline_utils.DiffusionPipeline.from_pretrained
ckpt_path = Path(pretrained_model_link_or_path)
if not ckpt_path.is_file():
if not has_valid_url_prefix:
raise ValueError(
f"The provided path is either not a file or a valid huggingface URL was not provided. Valid URLs begin with {', '.join(valid_url_prefixes)}"
)
# get repo_id and (potentially nested) file path of ckpt in repo
repo_id = "/".join(ckpt_path.parts[:2])
file_path = "/".join(ckpt_path.parts[2:])
if file_path.startswith("blob/"):
file_path = file_path[len("blob/") :]
if file_path.startswith("main/"):
file_path = file_path[len("main/") :]
pretrained_model_link_or_path = hf_hub_download(
repo_id,
filename=file_path,
cache_dir=cache_dir,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
force_download=force_download,
)
pipe = download_from_original_stable_diffusion_ckpt(
pretrained_model_link_or_path,
pipeline_class=cls,
model_type=model_type,
stable_unclip=stable_unclip,
controlnet=controlnet,
adapter=adapter,
from_safetensors=from_safetensors,
extract_ema=extract_ema,
image_size=image_size,
scheduler_type=scheduler_type,
num_in_channels=num_in_channels,
upcast_attention=upcast_attention,
load_safety_checker=load_safety_checker,
prediction_type=prediction_type,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
vae=vae,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
original_config_file=original_config_file,
config_files=config_files,
local_files_only=local_files_only,
)
if torch_dtype is not None:
pipe.to(dtype=torch_dtype)
return pipe
class FromOriginalVAEMixin:
"""
Load pretrained ControlNet weights saved in the `.ckpt` or `.safetensors` format into an [`AutoencoderKL`].
"""
@classmethod
@validate_hf_hub_args
def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
r"""
Instantiate a [`AutoencoderKL`] 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.
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.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the safetensors weights are downloaded if they're available **and** if the
safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
weights. If set to `False`, safetensors weights are not loaded.
upcast_attention (`bool`, *optional*, defaults to `None`):
Whether the attention computation should always be upcasted.
scaling_factor (`float`, *optional*, defaults to 0.18215):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z
= 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution
Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
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.
<Tip warning={true}>
Make sure to pass both `image_size` and `scaling_factor` to `from_single_file()` if you're loading
a VAE from SDXL or a Stable Diffusion v2 model or higher.
</Tip>
Examples:
```py
from diffusers import AutoencoderKL
url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors" # can also be local file
model = AutoencoderKL.from_single_file(url)
```
"""
from ..models import AutoencoderKL
# import here to avoid circular dependency
from ..pipelines.stable_diffusion.convert_from_ckpt import (
convert_ldm_vae_checkpoint,
create_vae_diffusers_config,
)
config_file = kwargs.pop("config_file", None)
cache_dir = kwargs.pop("cache_dir", None)
resume_download = kwargs.pop("resume_download", False)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", None)
token = kwargs.pop("token", None)
revision = kwargs.pop("revision", None)
image_size = kwargs.pop("image_size", None)
scaling_factor = kwargs.pop("scaling_factor", None)
kwargs.pop("upcast_attention", None)
torch_dtype = kwargs.pop("torch_dtype", None)
use_safetensors = kwargs.pop("use_safetensors", None)
file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1]
from_safetensors = file_extension == "safetensors"
if from_safetensors and use_safetensors is False:
raise ValueError("Make sure to install `safetensors` with `pip install safetensors`.")
# remove huggingface url
for prefix in ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]:
if pretrained_model_link_or_path.startswith(prefix):
pretrained_model_link_or_path = pretrained_model_link_or_path[len(prefix) :]
# Code based on diffusers.pipelines.pipeline_utils.DiffusionPipeline.from_pretrained
ckpt_path = Path(pretrained_model_link_or_path)
if not ckpt_path.is_file():
# get repo_id and (potentially nested) file path of ckpt in repo
repo_id = "/".join(ckpt_path.parts[:2])
file_path = "/".join(ckpt_path.parts[2:])
if file_path.startswith("blob/"):
file_path = file_path[len("blob/") :]
if file_path.startswith("main/"):
file_path = file_path[len("main/") :]
pretrained_model_link_or_path = hf_hub_download(
repo_id,
filename=file_path,
cache_dir=cache_dir,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
force_download=force_download,
)
if from_safetensors:
from safetensors import safe_open
checkpoint = {}
with safe_open(pretrained_model_link_or_path, framework="pt", device="cpu") as f:
for key in f.keys():
checkpoint[key] = f.get_tensor(key)
else:
checkpoint = torch.load(pretrained_model_link_or_path, map_location="cpu")
if "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
if config_file is None:
config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
config_file = BytesIO(requests.get(config_url).content)
original_config = yaml.safe_load(config_file)
# default to sd-v1-5
image_size = image_size or 512
vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
if scaling_factor is None:
if (
"model" in original_config
and "params" in original_config["model"]
and "scale_factor" in original_config["model"]["params"]
):
vae_scaling_factor = original_config["model"]["params"]["scale_factor"]
else:
vae_scaling_factor = 0.18215 # default SD scaling factor
vae_config["scaling_factor"] = vae_scaling_factor
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
vae = AutoencoderKL(**vae_config)
if is_accelerate_available():
from ..models.modeling_utils import load_model_dict_into_meta
load_model_dict_into_meta(vae, converted_vae_checkpoint, device="cpu")
else:
vae.load_state_dict(converted_vae_checkpoint)
if torch_dtype is not None:
vae.to(dtype=torch_dtype)
return vae
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.
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.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the safetensors weights are downloaded if they're available **and** if the
safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
weights. If set to `False`, safetensors weights are not loaded.
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)
```
"""
# import here to avoid circular dependency
from ..pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
config_file = kwargs.pop("config_file", None)
cache_dir = kwargs.pop("cache_dir", None)
resume_download = kwargs.pop("resume_download", False)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", None)
token = kwargs.pop("token", None)
num_in_channels = kwargs.pop("num_in_channels", None)
use_linear_projection = kwargs.pop("use_linear_projection", None)
revision = kwargs.pop("revision", None)
extract_ema = kwargs.pop("extract_ema", False)
image_size = kwargs.pop("image_size", None)
upcast_attention = kwargs.pop("upcast_attention", None)
torch_dtype = kwargs.pop("torch_dtype", None)
use_safetensors = kwargs.pop("use_safetensors", None)
file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1]
from_safetensors = file_extension == "safetensors"
if from_safetensors and use_safetensors is False:
raise ValueError("Make sure to install `safetensors` with `pip install safetensors`.")
# remove huggingface url
for prefix in ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]:
if pretrained_model_link_or_path.startswith(prefix):
pretrained_model_link_or_path = pretrained_model_link_or_path[len(prefix) :]
# Code based on diffusers.pipelines.pipeline_utils.DiffusionPipeline.from_pretrained
ckpt_path = Path(pretrained_model_link_or_path)
if not ckpt_path.is_file():
# get repo_id and (potentially nested) file path of ckpt in repo
repo_id = "/".join(ckpt_path.parts[:2])
file_path = "/".join(ckpt_path.parts[2:])
if file_path.startswith("blob/"):
file_path = file_path[len("blob/") :]
if file_path.startswith("main/"):
file_path = file_path[len("main/") :]
pretrained_model_link_or_path = hf_hub_download(
repo_id,
filename=file_path,
cache_dir=cache_dir,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
force_download=force_download,
)
if config_file is None:
config_url = "https://raw.githubusercontent.com/lllyasviel/ControlNet/main/models/cldm_v15.yaml"
config_file = BytesIO(requests.get(config_url).content)
image_size = image_size or 512
controlnet = download_controlnet_from_original_ckpt(
pretrained_model_link_or_path,
original_config_file=config_file,
image_size=image_size,
extract_ema=extract_ema,
num_in_channels=num_in_channels,
upcast_attention=upcast_attention,
from_safetensors=from_safetensors,
use_linear_projection=use_linear_projection,
)
if torch_dtype is not None:
controlnet.to(dtype=torch_dtype)
return controlnet