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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.
import inspect
import re
from contextlib import nullcontext
from typing import Optional
from huggingface_hub.utils import validate_hf_hub_args
from ..utils import deprecate, is_accelerate_available, logging
from .single_file_utils import (
SingleFileComponentError,
convert_controlnet_checkpoint,
convert_ldm_unet_checkpoint,
convert_ldm_vae_checkpoint,
convert_sd3_transformer_checkpoint_to_diffusers,
convert_stable_cascade_unet_single_file_to_diffusers,
create_controlnet_diffusers_config_from_ldm,
create_unet_diffusers_config_from_ldm,
create_vae_diffusers_config_from_ldm,
fetch_diffusers_config,
fetch_original_config,
load_single_file_checkpoint,
)
logger = logging.get_logger(__name__)
if is_accelerate_available():
from accelerate import init_empty_weights
from ..models.modeling_utils import load_model_dict_into_meta
SINGLE_FILE_LOADABLE_CLASSES = {
"StableCascadeUNet": {
"checkpoint_mapping_fn": convert_stable_cascade_unet_single_file_to_diffusers,
},
"UNet2DConditionModel": {
"checkpoint_mapping_fn": convert_ldm_unet_checkpoint,
"config_mapping_fn": create_unet_diffusers_config_from_ldm,
"default_subfolder": "unet",
"legacy_kwargs": {
"num_in_channels": "in_channels", # Legacy kwargs supported by `from_single_file` mapped to new args
},
},
"AutoencoderKL": {
"checkpoint_mapping_fn": convert_ldm_vae_checkpoint,
"config_mapping_fn": create_vae_diffusers_config_from_ldm,
"default_subfolder": "vae",
},
"ControlNetModel": {
"checkpoint_mapping_fn": convert_controlnet_checkpoint,
"config_mapping_fn": create_controlnet_diffusers_config_from_ldm,
},
"SD3Transformer2DModel": {
"checkpoint_mapping_fn": convert_sd3_transformer_checkpoint_to_diffusers,
"default_subfolder": "transformer",
},
}
def _get_mapping_function_kwargs(mapping_fn, **kwargs):
parameters = inspect.signature(mapping_fn).parameters
mapping_kwargs = {}
for parameter in parameters:
if parameter in kwargs:
mapping_kwargs[parameter] = kwargs[parameter]
return mapping_kwargs
class FromOriginalModelMixin:
"""
Load pretrained weights saved in the `.ckpt` or `.safetensors` format into a model.
"""
@classmethod
@validate_hf_hub_args
def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] = None, **kwargs):
r"""
Instantiate a model from pretrained weights saved in the original `.ckpt` or `.safetensors` format. The model
is set in evaluation mode (`model.eval()`) by default.
Parameters:
pretrained_model_link_or_path_or_dict (`str`, *optional*):
Can be either:
- A link to the `.safetensors` or `.ckpt` file (for example
`"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.safetensors"`) on the Hub.
- A path to a local *file* containing the weights of the component model.
- A state dict containing the component model weights.
config (`str`, *optional*):
- A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline hosted
on the Hub.
- A path to a *directory* (for example `./my_pipeline_directory/`) containing the pipeline component
configs in Diffusers format.
subfolder (`str`, *optional*, defaults to `""`):
The subfolder location of a model file within a larger model repository on the Hub or locally.
original_config (`str`, *optional*):
Dict or path to a yaml file containing the configuration for the model in its original format.
If a dict is provided, it will be used to initialize the model configuration.
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.
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.
```py
>>> from diffusers import StableCascadeUNet
>>> ckpt_path = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite.safetensors"
>>> model = StableCascadeUNet.from_single_file(ckpt_path)
```
"""
class_name = cls.__name__
if class_name not in SINGLE_FILE_LOADABLE_CLASSES:
raise ValueError(
f"FromOriginalModelMixin is currently only compatible with {', '.join(SINGLE_FILE_LOADABLE_CLASSES.keys())}"
)
pretrained_model_link_or_path = kwargs.get("pretrained_model_link_or_path", None)
if pretrained_model_link_or_path is not None:
deprecation_message = (
"Please use `pretrained_model_link_or_path_or_dict` argument instead for model classes"
)
deprecate("pretrained_model_link_or_path", "1.0.0", deprecation_message)
pretrained_model_link_or_path_or_dict = pretrained_model_link_or_path
config = kwargs.pop("config", None)
original_config = kwargs.pop("original_config", None)
if config is not None and original_config is not None:
raise ValueError(
"`from_single_file` cannot accept both `config` and `original_config` arguments. Please provide only one of these arguments"
)
resume_download = kwargs.pop("resume_download", None)
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)
subfolder = kwargs.pop("subfolder", None)
revision = kwargs.pop("revision", None)
torch_dtype = kwargs.pop("torch_dtype", None)
if isinstance(pretrained_model_link_or_path_or_dict, dict):
checkpoint = pretrained_model_link_or_path_or_dict
else:
checkpoint = load_single_file_checkpoint(
pretrained_model_link_or_path_or_dict,
resume_download=resume_download,
force_download=force_download,
proxies=proxies,
token=token,
cache_dir=cache_dir,
local_files_only=local_files_only,
revision=revision,
)
mapping_functions = SINGLE_FILE_LOADABLE_CLASSES[class_name]
checkpoint_mapping_fn = mapping_functions["checkpoint_mapping_fn"]
if original_config:
if "config_mapping_fn" in mapping_functions:
config_mapping_fn = mapping_functions["config_mapping_fn"]
else:
config_mapping_fn = None
if config_mapping_fn is None:
raise ValueError(
(
f"`original_config` has been provided for {class_name} but no mapping function"
"was found to convert the original config to a Diffusers config in"
"`diffusers.loaders.single_file_utils`"
)
)
if isinstance(original_config, str):
# If original_config is a URL or filepath fetch the original_config dict
original_config = fetch_original_config(original_config, local_files_only=local_files_only)
config_mapping_kwargs = _get_mapping_function_kwargs(config_mapping_fn, **kwargs)
diffusers_model_config = config_mapping_fn(
original_config=original_config, checkpoint=checkpoint, **config_mapping_kwargs
)
else:
if config:
if isinstance(config, str):
default_pretrained_model_config_name = config
else:
raise ValueError(
(
"Invalid `config` argument. Please provide a string representing a repo id"
"or path to a local Diffusers model repo."
)
)
else:
config = fetch_diffusers_config(checkpoint)
default_pretrained_model_config_name = config["pretrained_model_name_or_path"]
if "default_subfolder" in mapping_functions:
subfolder = mapping_functions["default_subfolder"]
subfolder = subfolder or config.pop(
"subfolder", None
) # some configs contain a subfolder key, e.g. StableCascadeUNet
diffusers_model_config = cls.load_config(
pretrained_model_name_or_path=default_pretrained_model_config_name,
subfolder=subfolder,
local_files_only=local_files_only,
)
expected_kwargs, optional_kwargs = cls._get_signature_keys(cls)
# Map legacy kwargs to new kwargs
if "legacy_kwargs" in mapping_functions:
legacy_kwargs = mapping_functions["legacy_kwargs"]
for legacy_key, new_key in legacy_kwargs.items():
if legacy_key in kwargs:
kwargs[new_key] = kwargs.pop(legacy_key)
model_kwargs = {k: kwargs.get(k) for k in kwargs if k in expected_kwargs or k in optional_kwargs}
diffusers_model_config.update(model_kwargs)
checkpoint_mapping_kwargs = _get_mapping_function_kwargs(checkpoint_mapping_fn, **kwargs)
diffusers_format_checkpoint = checkpoint_mapping_fn(
config=diffusers_model_config, checkpoint=checkpoint, **checkpoint_mapping_kwargs
)
if not diffusers_format_checkpoint:
raise SingleFileComponentError(
f"Failed to load {class_name}. Weights for this component appear to be missing in the checkpoint."
)
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
model = cls.from_config(diffusers_model_config)
if is_accelerate_available():
unexpected_keys = load_model_dict_into_meta(model, diffusers_format_checkpoint, dtype=torch_dtype)
else:
_, unexpected_keys = model.load_state_dict(diffusers_format_checkpoint, strict=False)
if model._keys_to_ignore_on_load_unexpected is not None:
for pat in model._keys_to_ignore_on_load_unexpected:
unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
if len(unexpected_keys) > 0:
logger.warning(
f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
)
if torch_dtype is not None:
model.to(torch_dtype)
model.eval()
return model
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