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Created May 13, 2023 18:51
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Posting my loaders.py for a PR I'm testing.
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loaders.py
# 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.
import os
from collections import defaultdict
from pathlib import Path
from typing import Callable, Dict, List, Optional, Union
import torch
from huggingface_hub import hf_hub_download
from .models.attention_processor import (
CustomDiffusionAttnProcessor,
CustomDiffusionXFormersAttnProcessor,
LoRAAttnProcessor,
)
from .utils import (
DIFFUSERS_CACHE,
HF_HUB_OFFLINE,
TEXT_ENCODER_TARGET_MODULES,
_get_model_file,
deprecate,
is_safetensors_available,
is_transformers_available,
logging,
)
if is_safetensors_available():
import safetensors
if is_transformers_available():
from transformers import PreTrainedModel, PreTrainedTokenizer
logger = logging.get_logger(__name__)
TEXT_ENCODER_NAME = "text_encoder"
UNET_NAME = "unet"
LORA_PREFIX_TEXT_ENCODER_NAME = "lora_te"
LORA_PREFIX_UNET_NAME = "lora_unet"
LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"
LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors"
TEXT_INVERSION_NAME = "learned_embeds.bin"
TEXT_INVERSION_NAME_SAFE = "learned_embeds.safetensors"
CUSTOM_DIFFUSION_WEIGHT_NAME = "pytorch_custom_diffusion_weights.bin"
CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE = "pytorch_custom_diffusion_weights.safetensors"
class AttnProcsLayers(torch.nn.Module):
def __init__(self, state_dict: Dict[str, torch.Tensor]):
super().__init__()
self.layers = torch.nn.ModuleList(state_dict.values())
self.mapping = dict(enumerate(state_dict.keys()))
self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}
# we add a hook to state_dict() and load_state_dict() so that the
# naming fits with `unet.attn_processors`
def map_to(module, state_dict, *args, **kwargs):
new_state_dict = {}
for key, value in state_dict.items():
num = int(key.split(".")[1]) # 0 is always "layers"
new_key = key.replace(f"layers.{num}", module.mapping[num])
new_state_dict[new_key] = value
return new_state_dict
def map_from(module, state_dict, *args, **kwargs):
all_keys = list(state_dict.keys())
for key in all_keys:
replace_key = key.split(".processor")[0] + ".processor"
new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}")
state_dict[new_key] = state_dict[key]
del state_dict[key]
self._register_state_dict_hook(map_to)
self._register_load_state_dict_pre_hook(map_from, with_module=True)
class UNet2DConditionLoadersMixin:
def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
r"""
Load pretrained attention processor layers into `UNet2DConditionModel`. Attention processor layers have to be
defined in
[`cross_attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py)
and be a `torch.nn.Module` class.
<Tip warning={true}>
This function is experimental and might change in the future.
</Tip>
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
`./my_model_directory/`.
- A [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
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.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `diffusers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo (either remote in
huggingface.co or downloaded locally), you can specify the folder name here.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models).
</Tip>
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
weight_name = kwargs.pop("weight_name", None)
use_safetensors = kwargs.pop("use_safetensors", None)
if use_safetensors and not is_safetensors_available():
raise ValueError(
"`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"
)
allow_pickle = False
if use_safetensors is None:
use_safetensors = is_safetensors_available()
allow_pickle = True
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
}
model_file = None
if not isinstance(pretrained_model_name_or_path_or_dict, dict):
# Let's first try to load .safetensors weights
if (use_safetensors and weight_name is None) or (
weight_name is not None and weight_name.endswith(".safetensors")
):
try:
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = safetensors.torch.load_file(model_file, device="cpu")
except IOError as e:
if not allow_pickle:
raise e
# try loading non-safetensors weights
pass
if model_file is None:
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = torch.load(model_file, map_location="cpu")
else:
state_dict = pretrained_model_name_or_path_or_dict
# fill attn processors
attn_processors = {}
is_lora = all("lora" in k for k in state_dict.keys())
is_custom_diffusion = any("custom_diffusion" in k for k in state_dict.keys())
if is_lora:
lora_grouped_dict = defaultdict(dict)
for key, value in state_dict.items():
attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])
lora_grouped_dict[attn_processor_key][sub_key] = value
for key, value_dict in lora_grouped_dict.items():
rank = value_dict["to_k_lora.down.weight"].shape[0]
cross_attention_dim = value_dict["to_k_lora.down.weight"].shape[1]
hidden_size = value_dict["to_k_lora.up.weight"].shape[0]
attn_processors[key] = LoRAAttnProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank
)
attn_processors[key].load_state_dict(value_dict)
elif is_custom_diffusion:
custom_diffusion_grouped_dict = defaultdict(dict)
for key, value in state_dict.items():
if len(value) == 0:
custom_diffusion_grouped_dict[key] = {}
else:
if "to_out" in key:
attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])
else:
attn_processor_key, sub_key = ".".join(key.split(".")[:-2]), ".".join(key.split(".")[-2:])
custom_diffusion_grouped_dict[attn_processor_key][sub_key] = value
for key, value_dict in custom_diffusion_grouped_dict.items():
if len(value_dict) == 0:
attn_processors[key] = CustomDiffusionAttnProcessor(
train_kv=False, train_q_out=False, hidden_size=None, cross_attention_dim=None
)
else:
cross_attention_dim = value_dict["to_k_custom_diffusion.weight"].shape[1]
hidden_size = value_dict["to_k_custom_diffusion.weight"].shape[0]
train_q_out = True if "to_q_custom_diffusion.weight" in value_dict else False
attn_processors[key] = CustomDiffusionAttnProcessor(
train_kv=True,
train_q_out=train_q_out,
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
)
attn_processors[key].load_state_dict(value_dict)
else:
raise ValueError(
f"{model_file} does not seem to be in the correct format expected by LoRA or Custom Diffusion training."
)
# set correct dtype & device
attn_processors = {k: v.to(device=self.device, dtype=self.dtype) for k, v in attn_processors.items()}
# set layers
self.set_attn_processor(attn_processors)
def save_attn_procs(
self,
save_directory: Union[str, os.PathLike],
is_main_process: bool = True,
weight_name: str = None,
save_function: Callable = None,
safe_serialization: bool = False,
**kwargs,
):
r"""
Save an attention processor to a directory, so that it can be re-loaded using the
[`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`] method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful when in distributed training like
TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
the main process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful on distributed training like TPUs when one
need to replace `torch.save` by another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
"""
weight_name = weight_name or deprecate(
"weights_name",
"0.18.0",
"`weights_name` is deprecated, please use `weight_name` instead.",
take_from=kwargs,
)
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
if save_function is None:
if safe_serialization:
def save_function(weights, filename):
return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"})
else:
save_function = torch.save
os.makedirs(save_directory, exist_ok=True)
is_custom_diffusion = any(
isinstance(x, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor))
for (_, x) in self.attn_processors.items()
)
if is_custom_diffusion:
model_to_save = AttnProcsLayers(
{
y: x
for (y, x) in self.attn_processors.items()
if isinstance(x, (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor))
}
)
state_dict = model_to_save.state_dict()
for name, attn in self.attn_processors.items():
if len(attn.state_dict()) == 0:
state_dict[name] = {}
else:
model_to_save = AttnProcsLayers(self.attn_processors)
state_dict = model_to_save.state_dict()
if weight_name is None:
if safe_serialization:
weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE if is_custom_diffusion else LORA_WEIGHT_NAME_SAFE
else:
weight_name = CUSTOM_DIFFUSION_WEIGHT_NAME if is_custom_diffusion else LORA_WEIGHT_NAME
# Save the model
save_function(state_dict, os.path.join(save_directory, weight_name))
logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}")
class TextualInversionLoaderMixin:
r"""
Mixin class for loading textual inversion tokens and embeddings to the tokenizer and text encoder.
"""
def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"):
r"""
Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds
to a multi-vector textual inversion embedding, this function will process the prompt so that the special token
is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual
inversion token or a textual inversion token that is a single vector, the input prompt is simply returned.
Parameters:
prompt (`str` or list of `str`):
The prompt or prompts to guide the image generation.
tokenizer (`PreTrainedTokenizer`):
The tokenizer responsible for encoding the prompt into input tokens.
Returns:
`str` or list of `str`: The converted prompt
"""
if not isinstance(prompt, List):
prompts = [prompt]
else:
prompts = prompt
prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts]
if not isinstance(prompt, List):
return prompts[0]
return prompts
def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"):
r"""
Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds
to a multi-vector textual inversion embedding, this function will process the prompt so that the special token
is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual
inversion token or a textual inversion token that is a single vector, the input prompt is simply returned.
Parameters:
prompt (`str`):
The prompt to guide the image generation.
tokenizer (`PreTrainedTokenizer`):
The tokenizer responsible for encoding the prompt into input tokens.
Returns:
`str`: The converted prompt
"""
tokens = tokenizer.tokenize(prompt)
for token in tokens:
if token in tokenizer.added_tokens_encoder:
replacement = token
i = 1
while f"{token}_{i}" in tokenizer.added_tokens_encoder:
replacement += f" {token}_{i}"
i += 1
prompt = prompt.replace(token, replacement)
return prompt
def load_textual_inversion(
self, pretrained_model_name_or_path: Union[str, Dict[str, torch.Tensor]], token: Optional[str] = None, **kwargs
):
r"""
Load textual inversion embeddings into the text encoder of stable diffusion pipelines. Both `diffusers` and
`Automatic1111` formats are supported (see example below).
<Tip warning={true}>
This function is experimental and might change in the future.
</Tip>
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids should have an organization name, like
`"sd-concepts-library/low-poly-hd-logos-icons"`.
- A path to a *directory* containing textual inversion weights, e.g.
`./my_text_inversion_directory/`.
weight_name (`str`, *optional*):
Name of a custom weight file. This should be used in two cases:
- The saved textual inversion file is in `diffusers` format, but was saved under a specific weight
name, such as `text_inv.bin`.
- The saved textual inversion file is in the "Automatic1111" form.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
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.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `diffusers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo (either remote in
huggingface.co or downloaded locally), you can specify the folder name here.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models).
</Tip>
Example:
To load a textual inversion embedding vector in `diffusers` format:
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
pipe.load_textual_inversion("sd-concepts-library/cat-toy")
prompt = "A <cat-toy> backpack"
image = pipe(prompt, num_inference_steps=50).images[0]
image.save("cat-backpack.png")
```
To load a textual inversion embedding vector in Automatic1111 format, make sure to first download the vector,
e.g. from [civitAI](https://civitai.com/models/3036?modelVersionId=9857) and then load the vector locally:
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")
prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details."
image = pipe(prompt, num_inference_steps=50).images[0]
image.save("character.png")
```
"""
if not hasattr(self, "tokenizer") or not isinstance(self.tokenizer, PreTrainedTokenizer):
raise ValueError(
f"{self.__class__.__name__} requires `self.tokenizer` of type `PreTrainedTokenizer` for calling"
f" `{self.load_textual_inversion.__name__}`"
)
if not hasattr(self, "text_encoder") or not isinstance(self.text_encoder, PreTrainedModel):
raise ValueError(
f"{self.__class__.__name__} requires `self.text_encoder` of type `PreTrainedModel` for calling"
f" `{self.load_textual_inversion.__name__}`"
)
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
weight_name = kwargs.pop("weight_name", None)
use_safetensors = kwargs.pop("use_safetensors", None)
if use_safetensors and not is_safetensors_available():
raise ValueError(
"`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"
)
allow_pickle = False
if use_safetensors is None:
use_safetensors = is_safetensors_available()
allow_pickle = True
user_agent = {
"file_type": "text_inversion",
"framework": "pytorch",
}
# 1. Load textual inversion file
model_file = None
# Let's first try to load .safetensors weights
if (use_safetensors and weight_name is None) or (
weight_name is not None and weight_name.endswith(".safetensors")
):
try:
model_file = _get_model_file(
pretrained_model_name_or_path,
weights_name=weight_name or TEXT_INVERSION_NAME_SAFE,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = safetensors.torch.load_file(model_file, device="cpu")
except Exception as e:
if not allow_pickle:
raise e
model_file = None
if model_file is None:
model_file = _get_model_file(
pretrained_model_name_or_path,
weights_name=weight_name or TEXT_INVERSION_NAME,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = torch.load(model_file, map_location="cpu")
# 2. Load token and embedding correcly from file
if isinstance(state_dict, torch.Tensor):
if token is None:
raise ValueError(
"You are trying to load a textual inversion embedding that has been saved as a PyTorch tensor. Make sure to pass the name of the corresponding token in this case: `token=...`."
)
embedding = state_dict
elif len(state_dict) == 1:
# diffusers
loaded_token, embedding = next(iter(state_dict.items()))
elif "string_to_param" in state_dict:
# A1111
loaded_token = state_dict["name"]
embedding = state_dict["string_to_param"]["*"]
if token is not None and loaded_token != token:
logger.warn(f"The loaded token: {loaded_token} is overwritten by the passed token {token}.")
else:
token = loaded_token
embedding = embedding.to(dtype=self.text_encoder.dtype, device=self.text_encoder.device)
# 3. Make sure we don't mess up the tokenizer or text encoder
vocab = self.tokenizer.get_vocab()
if token in vocab:
raise ValueError(
f"Token {token} already in tokenizer vocabulary. Please choose a different token name or remove {token} and embedding from the tokenizer and text encoder."
)
elif f"{token}_1" in vocab:
multi_vector_tokens = [token]
i = 1
while f"{token}_{i}" in self.tokenizer.added_tokens_encoder:
multi_vector_tokens.append(f"{token}_{i}")
i += 1
raise ValueError(
f"Multi-vector Token {multi_vector_tokens} already in tokenizer vocabulary. Please choose a different token name or remove the {multi_vector_tokens} and embedding from the tokenizer and text encoder."
)
is_multi_vector = len(embedding.shape) > 1 and embedding.shape[0] > 1
if is_multi_vector:
tokens = [token] + [f"{token}_{i}" for i in range(1, embedding.shape[0])]
embeddings = [e for e in embedding] # noqa: C416
else:
tokens = [token]
embeddings = [embedding[0]] if len(embedding.shape) > 1 else [embedding]
# add tokens and get ids
self.tokenizer.add_tokens(tokens)
token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
# resize token embeddings and set new embeddings
self.text_encoder.resize_token_embeddings(len(self.tokenizer))
for token_id, embedding in zip(token_ids, embeddings):
self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding
logger.info(f"Loaded textual inversion embedding for {token}.")
class LoraLoaderMixin:
r"""
Utility class for handling the loading LoRA layers into UNet (of class [`UNet2DConditionModel`]) and Text Encoder
(of class [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)).
<Tip warning={true}>
This function is experimental and might change in the future.
</Tip>
"""
text_encoder_name = "text_encoder"
unet_name = "unet"
lora_prefix_text_encoder_name = LORA_PREFIX_TEXT_ENCODER_NAME
lora_prefix_unet_name = LORA_PREFIX_UNET_NAME
def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
r"""
Load pretrained attention processor layers (such as LoRA) into [`UNet2DConditionModel`] and
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel)).
<Tip warning={true}>
This function is experimental and might change in the future.
</Tip>
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
`./my_model_directory/`.
- A [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
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.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `diffusers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo (either remote in
huggingface.co or downloaded locally), you can specify the folder name here.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
lora_weight (`float`, *optional*, defaults to `1.0`):
The specific weight to apply to whole lora model, between 0 and 1.
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models).
</Tip>
"""
# Load the main state dict first which has the LoRA layers for either of
# UNet and text encoder or both.
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
weight_name = kwargs.pop("weight_name", None)
use_safetensors = kwargs.pop("use_safetensors", None)
lora_weight = kwargs.pop("lora_weight", 1.0)
if use_safetensors and not is_safetensors_available():
raise ValueError(
"`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"
)
allow_pickle = False
if use_safetensors is None:
use_safetensors = is_safetensors_available()
allow_pickle = True
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
}
model_file = None
if not isinstance(pretrained_model_name_or_path_or_dict, dict):
# Let's first try to load .safetensors weights
if (use_safetensors and weight_name is None) or (
weight_name is not None and weight_name.endswith(".safetensors")
):
try:
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = safetensors.torch.load_file(model_file, device="cpu")
except IOError as e:
if not allow_pickle:
raise e
# try loading non-safetensors weights
pass
if model_file is None:
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = torch.load(model_file, map_location="cpu")
else:
state_dict = pretrained_model_name_or_path_or_dict
# If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
# then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
# their prefixes.
keys = list(state_dict.keys())
if all(
key.startswith(self.lora_prefix_unet_name) or key.startswith(self.lora_prefix_text_encoder_name)
for key in keys
):
self._load_lora_weights(state_dict, weight=lora_weight)
elif all(key.startswith(self.unet_name) or key.startswith(self.text_encoder_name) for key in keys):
# Load the layers corresponding to UNet.
logger.info(f"Loading {self.unet_name}.")
unet_lora_state_dict = {k: v for k, v in state_dict.items() if k.startswith(self.unet_name)}
self.unet.load_attn_procs(unet_lora_state_dict)
# Load the layers corresponding to text encoder and make necessary adjustments.
elif all(key.startswith(self.text_encoder_name) for key in keys):
logger.info(f"Loading {self.text_encoder_name}.")
text_encoder_lora_state_dict = {
k: v for k, v in state_dict.items() if k.startswith(self.text_encoder_name)
}
attn_procs_text_encoder = self.load_attn_procs(text_encoder_lora_state_dict)
self._modify_text_encoder(attn_procs_text_encoder)
# Otherwise, we're dealing with the old format. This means the `state_dict` should only
# contain the module names of the `unet` as its keys WITHOUT any prefix.
elif not all(
key.startswith(self.unet_name) or key.startswith(self.text_encoder_name) for key in state_dict.keys()
):
self.unet.load_attn_procs(state_dict)
deprecation_message = "You have saved the LoRA weights using the old format. This will be"
" deprecated soon. To convert the old LoRA weights to the new format, you can first load them"
" in a dictionary and then create a new dictionary like the following:"
" `new_state_dict = {f'unet'.{module_name}: params for module_name, params in old_state_dict.items()}`."
deprecate("legacy LoRA weights", "1.0.0", deprecation_message, standard_warn=False)
def _load_lora_weights(self, pretrained_model_dict: Dict[str, torch.Tensor], weight: float):
state_dict = pretrained_model_dict
visited = []
# directly update weight in diffusers model
for key in state_dict:
# key fromat
# 'lora_unet_down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0.alpha'
# 'lora_unet_down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0.lora_down.weight'
# 'lora_unet_down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0.lora_up.weight'
# alpha will handled, continue for skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
layer_infos = key.split(".")[0].split(self.lora_prefix_text_encoder_name + "_")[-1].split("_")
curr_layer = self.text_encoder
dtype = self.text_encoder.dtype
else:
layer_infos = key.split(".")[0].split(self.lora_prefix_unet_name + "_")[-1].split("_")
curr_layer = self.unet
dtype = self.unet.dtype
# traverse layer_infos to find the key-specific layer
temp_name = layer_infos.pop(0)
while len(layer_infos) > -1:
try:
curr_layer = curr_layer.__getattr__(temp_name)
if len(layer_infos) > 0:
temp_name = layer_infos.pop(0)
elif len(layer_infos) == 0:
break
except Exception:
if len(temp_name) > 0:
temp_name += "_" + layer_infos.pop(0)
else:
temp_name = layer_infos.pop(0)
layer_keys = [key.split(".", 1)[0] + v for v in [".lora_up.weight", ".lora_down.weight", ".alpha"]]
weight_up = state_dict[layer_keys[0]].to(dtype)
weight_down = state_dict[layer_keys[1]].to(dtype)
alpha = state_dict[layer_keys[2]]
alpha = alpha.item() / weight_up.shape[1] if alpha else 1.0
# update weights
if len(state_dict[layer_keys[0]].shape) == 4:
weight_up = weight_up.squeeze(3).squeeze(2)
weight_down = weight_down.squeeze(3).squeeze(2)
curr_layer.weight.data += weight * alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
else:
curr_layer.weight.data += weight * alpha * torch.mm(weight_up, weight_down)
# update visited list
for item in layer_keys:
visited.append(item)
def _modify_text_encoder(self, attn_processors: Dict[str, LoRAAttnProcessor]):
r"""
Monkey-patches the forward passes of attention modules of the text encoder.
Parameters:
attn_processors: Dict[str, `LoRAAttnProcessor`]:
A dictionary mapping the module names and their corresponding [`~LoRAAttnProcessor`].
"""
# Loop over the original attention modules.
for name, _ in self.text_encoder.named_modules():
if any(x in name for x in TEXT_ENCODER_TARGET_MODULES):
# Retrieve the module and its corresponding LoRA processor.
module = self.text_encoder.get_submodule(name)
# Construct a new function that performs the LoRA merging. We will monkey patch
# this forward pass.
lora_layer = getattr(attn_processors[name], self._get_lora_layer_attribute(name))
old_forward = module.forward
def new_forward(x):
return old_forward(x) + lora_layer(x)
# Monkey-patch.
module.forward = new_forward
def _get_lora_layer_attribute(self, name: str) -> str:
if "q_proj" in name:
return "to_q_lora"
elif "v_proj" in name:
return "to_v_lora"
elif "k_proj" in name:
return "to_k_lora"
else:
return "to_out_lora"
def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
r"""
Load pretrained attention processor layers for
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
<Tip warning={true}>
This function is experimental and might change in the future.
</Tip>
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
- A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
`./my_model_directory/`.
- A [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
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.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `diffusers-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo (either remote in
huggingface.co or downloaded locally), you can specify the folder name here.
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more information.
Returns:
`Dict[name, LoRAAttnProcessor]`: Mapping between the module names and their corresponding
[`LoRAAttnProcessor`].
<Tip>
It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
models](https://huggingface.co/docs/hub/models-gated#gated-models).
</Tip>
"""
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
subfolder = kwargs.pop("subfolder", None)
weight_name = kwargs.pop("weight_name", None)
use_safetensors = kwargs.pop("use_safetensors", None)
if use_safetensors and not is_safetensors_available():
raise ValueError(
"`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"
)
allow_pickle = False
if use_safetensors is None:
use_safetensors = is_safetensors_available()
allow_pickle = True
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
}
model_file = None
if not isinstance(pretrained_model_name_or_path_or_dict, dict):
# Let's first try to load .safetensors weights
if (use_safetensors and weight_name is None) or (
weight_name is not None and weight_name.endswith(".safetensors")
):
try:
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = safetensors.torch.load_file(model_file, device="cpu")
except IOError as e:
if not allow_pickle:
raise e
# try loading non-safetensors weights
pass
if model_file is None:
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name or LORA_WEIGHT_NAME,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = torch.load(model_file, map_location="cpu")
else:
state_dict = pretrained_model_name_or_path_or_dict
# fill attn processors
attn_processors = {}
is_lora = all("lora" in k for k in state_dict.keys())
if is_lora:
lora_grouped_dict = defaultdict(dict)
for key, value in state_dict.items():
attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])
lora_grouped_dict[attn_processor_key][sub_key] = value
for key, value_dict in lora_grouped_dict.items():
rank = value_dict["to_k_lora.down.weight"].shape[0]
cross_attention_dim = value_dict["to_k_lora.down.weight"].shape[1]
hidden_size = value_dict["to_k_lora.up.weight"].shape[0]
attn_processors[key] = LoRAAttnProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank
)
attn_processors[key].load_state_dict(value_dict)
else:
raise ValueError(f"{model_file} does not seem to be in the correct format expected by LoRA training.")
# set correct dtype & device
attn_processors = {
k: v.to(device=self.device, dtype=self.text_encoder.dtype) for k, v in attn_processors.items()
}
return attn_processors
@classmethod
def save_lora_weights(
self,
save_directory: Union[str, os.PathLike],
unet_lora_layers: Dict[str, torch.nn.Module] = None,
text_encoder_lora_layers: Dict[str, torch.nn.Module] = None,
is_main_process: bool = True,
weight_name: str = None,
save_function: Callable = None,
safe_serialization: bool = False,
):
r"""
Save the LoRA parameters corresponding to the UNet and the text encoder.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to which to save. Will be created if it doesn't exist.
unet_lora_layers (`Dict[str, torch.nn.Module`]):
State dict of the LoRA layers corresponding to the UNet. Specifying this helps to make the
serialization process easier and cleaner.
text_encoder_lora_layers (`Dict[str, torch.nn.Module`]):
State dict of the LoRA layers corresponding to the `text_encoder`. Since the `text_encoder` comes from
`transformers`, we cannot rejig it. That is why we have to explicitly pass the text encoder LoRA state
dict.
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful when in distributed training like
TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
the main process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful on distributed training like TPUs when one
need to replace `torch.save` by another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
"""
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return
if save_function is None:
if safe_serialization:
def save_function(weights, filename):
return safetensors.torch.save_file(weights, filename, metadata={"format": "pt"})
else:
save_function = torch.save
os.makedirs(save_directory, exist_ok=True)
# Create a flat dictionary.
state_dict = {}
if unet_lora_layers is not None:
unet_lora_state_dict = {
f"{self.unet_name}.{module_name}": param
for module_name, param in unet_lora_layers.state_dict().items()
}
state_dict.update(unet_lora_state_dict)
if text_encoder_lora_layers is not None:
text_encoder_lora_state_dict = {
f"{self.text_encoder_name}.{module_name}": param
for module_name, param in text_encoder_lora_layers.state_dict().items()
}
state_dict.update(text_encoder_lora_state_dict)
# Save the model
if weight_name is None:
if safe_serialization:
weight_name = LORA_WEIGHT_NAME_SAFE
else:
weight_name = LORA_WEIGHT_NAME
save_function(state_dict, os.path.join(save_directory, weight_name))
logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}")
class FromCkptMixin:
"""This helper class allows to directly load .ckpt stable diffusion file_extension
into the respective classes."""
@classmethod
def from_ckpt(cls, pretrained_model_link_or_path, **kwargs):
r"""
Instantiate a PyTorch diffusion pipeline from pre-trained pipeline weights saved in the original .ckpt format.
The pipeline is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated).
Parameters:
pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A link to the .ckpt file on the Hub. Should be in the format
`"https://huggingface.co/<repo_id>/blob/main/<path_to_file>"`
- 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 under this dtype. If `"auto"` is passed the dtype
will be 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 in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to delete incompletely received files. Will attempt to resume the download if such a
file exists.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model).
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
use_safetensors (`bool`, *optional* ):
If set to `True`, the pipeline will be loaded from `safetensors` weights. If set to `None` (the
default). The pipeline will load using `safetensors` if the safetensors weights are available *and* if
`safetensors` is installed. If the to `False` the pipeline will *not* use `safetensors`.
extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for
checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults
to `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for
inference. Non-EMA weights are usually better to continue fine-tuning.
upcast_attention (`bool`, *optional*, defaults to `None`):
Whether the attention computation should always be upcasted. This is necessary when running stable
image_size (`int`, *optional*, defaults to 512):
The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2
Base. Use 768 for Stable Diffusion v2.
prediction_type (`str`, *optional*):
The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable
Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.
num_in_channels (`int`, *optional*, defaults to None):
The number of input channels. If `None`, it will be 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. Defaults to `True`.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
specific pipeline class. The overwritten components are then 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_ckpt(
... "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_ckpt("./v1-5-pruned-emaonly")
>>> # Enable float16 and move to GPU
>>> pipeline = StableDiffusionPipeline.from_ckpt(
... "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
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
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", HF_HUB_OFFLINE)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
extract_ema = kwargs.pop("extract_ema", False)
image_size = kwargs.pop("image_size", 512)
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)
torch_dtype = kwargs.pop("torch_dtype", None)
use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)
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 True:
raise ValueError("Make sure to install `safetensors` with `pip install safetensors`.")
# TODO: For now we only support stable diffusion
stable_unclip = None
controlnet = False
if pipeline_name == "StableDiffusionControlNetPipeline":
model_type = "FrozenCLIPEmbedder"
controlnet = True
elif "StableDiffusion" in pipeline_name:
model_type = "FrozenCLIPEmbedder"
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
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 = str(Path().joinpath(*ckpt_path.parts[:2]))
file_path = str(Path().joinpath(*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,
use_auth_token=use_auth_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,
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,
)
if torch_dtype is not None:
pipe.to(torch_dtype=torch_dtype)
return pipe
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