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for _, module in self.named_modules():
if isinstance(module, BaseTunerLayer):
if hasattr(module, "enable_adapters"):
module.enable_adapters(enabled=False)
else:
# support for older PEFT versions
module.disable_adapters = True
def enable_adapters(self) -> None:
"""
Enable adapters that are attached to the model. The model uses `self.active_adapters()` to retrieve the list of
adapters to enable.
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
[documentation](https://huggingface.co/docs/peft).
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
from peft.tuners.tuners_utils import BaseTunerLayer | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
for _, module in self.named_modules():
if isinstance(module, BaseTunerLayer):
if hasattr(module, "enable_adapters"):
module.enable_adapters(enabled=True)
else:
# support for older PEFT versions
module.disable_adapters = False
def active_adapters(self) -> List[str]:
"""
Gets the current list of active adapters of the model.
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
[documentation](https://huggingface.co/docs/peft).
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not is_peft_available():
raise ImportError("PEFT is not available. Please install PEFT to use this function: `pip install peft`.")
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.") | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
from peft.tuners.tuners_utils import BaseTunerLayer
for _, module in self.named_modules():
if isinstance(module, BaseTunerLayer):
return module.active_adapter
def fuse_lora(self, lora_scale=1.0, safe_fusing=False, adapter_names=None):
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for `fuse_lora()`.")
self.lora_scale = lora_scale
self._safe_fusing = safe_fusing
self.apply(partial(self._fuse_lora_apply, adapter_names=adapter_names))
def _fuse_lora_apply(self, module, adapter_names=None):
from peft.tuners.tuners_utils import BaseTunerLayer
merge_kwargs = {"safe_merge": self._safe_fusing}
if isinstance(module, BaseTunerLayer):
if self.lora_scale != 1.0:
module.scale_layer(self.lora_scale) | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
# For BC with prevous PEFT versions, we need to check the signature
# of the `merge` method to see if it supports the `adapter_names` argument.
supported_merge_kwargs = list(inspect.signature(module.merge).parameters)
if "adapter_names" in supported_merge_kwargs:
merge_kwargs["adapter_names"] = adapter_names
elif "adapter_names" not in supported_merge_kwargs and adapter_names is not None:
raise ValueError(
"The `adapter_names` argument is not supported with your PEFT version. Please upgrade"
" to the latest version of PEFT. `pip install -U peft`"
)
module.merge(**merge_kwargs)
def unfuse_lora(self):
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for `unfuse_lora()`.")
self.apply(self._unfuse_lora_apply) | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
def _unfuse_lora_apply(self, module):
from peft.tuners.tuners_utils import BaseTunerLayer
if isinstance(module, BaseTunerLayer):
module.unmerge()
def unload_lora(self):
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for `unload_lora()`.")
from ..utils import recurse_remove_peft_layers
recurse_remove_peft_layers(self)
if hasattr(self, "peft_config"):
del self.peft_config
def disable_lora(self):
"""
Disables the active LoRA layers of the underlying model.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.disable_lora()
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
set_adapter_layers(self, enabled=False)
def enable_lora(self):
"""
Enables the active LoRA layers of the underlying model.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.enable_lora()
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
set_adapter_layers(self, enabled=True)
def delete_adapters(self, adapter_names: Union[List[str], str]):
"""
Delete an adapter's LoRA layers from the underlying model.
Args:
adapter_names (`Union[List[str], str]`):
The names (single string or list of strings) of the adapter to delete.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
)
pipeline.delete_adapters("cinematic")
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
if isinstance(adapter_names, str):
adapter_names = [adapter_names]
for adapter_name in adapter_names:
delete_adapter_layers(self, adapter_name)
# Pop also the corresponding adapter from the config
if hasattr(self, "peft_config"):
self.peft_config.pop(adapter_name, None) | 1,256 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/peft.py |
class UNet2DConditionLoadersMixin:
"""
Load LoRA layers into a [`UNet2DCondtionModel`].
"""
text_encoder_name = TEXT_ENCODER_NAME
unet_name = UNET_NAME
@validate_hf_hub_args
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
[`attention_processor.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py)
and be a `torch.nn.Module` class. Currently supported: LoRA, Custom Diffusion. For LoRA, one must install
`peft`: `pip install -U peft`.
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
Can be either: | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
- A string, the model id (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A path to a directory (for example `./my_model_directory`) containing the model weights saved
with [`ModelMixin.save_pretrained`].
- 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 where a downloaded pretrained model configuration is cached if the standard cache
is not 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. | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
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.
subfolder (`str`, *optional*, defaults to `""`): | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
The subfolder location of a model file within a larger model repository on the Hub or locally.
network_alphas (`Dict[str, float]`):
The value of the network alpha used for stable learning and preventing underflow. This value has the
same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this
link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning).
adapter_name (`str`, *optional*, defaults to None):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
weight_name (`str`, *optional*, defaults to None):
Name of the serialized state dict file.
low_cpu_mem_usage (`bool`, *optional*): | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights. | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.unet.load_attn_procs(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
```
"""
cache_dir = kwargs.pop("cache_dir", None)
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)
subfolder = kwargs.pop("subfolder", None)
weight_name = kwargs.pop("weight_name", None)
use_safetensors = kwargs.pop("use_safetensors", None)
adapter_name = kwargs.pop("adapter_name", None)
_pipeline = kwargs.pop("_pipeline", None)
network_alphas = kwargs.pop("network_alphas", None) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", False)
allow_pickle = False | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if low_cpu_mem_usage and is_peft_version("<=", "0.13.0"):
raise ValueError(
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
)
if use_safetensors is None:
use_safetensors = True
allow_pickle = True
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
} | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
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,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = safetensors.torch.load_file(model_file, device="cpu") | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
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,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
state_dict = load_state_dict(model_file)
else:
state_dict = pretrained_model_name_or_path_or_dict | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
is_custom_diffusion = any("custom_diffusion" in k for k in state_dict.keys())
is_lora = all(("lora" in k or k.endswith(".alpha")) for k in state_dict.keys())
is_model_cpu_offload = False
is_sequential_cpu_offload = False
if is_lora:
deprecation_message = "Using the `load_attn_procs()` method has been deprecated and will be removed in a future version. Please use `load_lora_adapter()`."
deprecate("load_attn_procs", "0.40.0", deprecation_message) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if is_custom_diffusion:
attn_processors = self._process_custom_diffusion(state_dict=state_dict)
elif is_lora:
is_model_cpu_offload, is_sequential_cpu_offload = self._process_lora(
state_dict=state_dict,
unet_identifier_key=self.unet_name,
network_alphas=network_alphas,
adapter_name=adapter_name,
_pipeline=_pipeline,
low_cpu_mem_usage=low_cpu_mem_usage,
)
else:
raise ValueError(
f"{model_file} does not seem to be in the correct format expected by Custom Diffusion training."
)
# <Unsafe code
# We can be sure that the following works as it just sets attention processors, lora layers and puts all in the same dtype
# Now we remove any existing hooks to `_pipeline`. | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# For LoRA, the UNet is already offloaded at this stage as it is handled inside `_process_lora`.
if is_custom_diffusion and _pipeline is not None:
is_model_cpu_offload, is_sequential_cpu_offload = self._optionally_disable_offloading(_pipeline=_pipeline)
# only custom diffusion needs to set attn processors
self.set_attn_processor(attn_processors)
self.to(dtype=self.dtype, device=self.device)
# Offload back.
if is_model_cpu_offload:
_pipeline.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
_pipeline.enable_sequential_cpu_offload()
# Unsafe code />
def _process_custom_diffusion(self, state_dict):
from ..models.attention_processor import CustomDiffusionAttnProcessor | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
attn_processors = {}
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 | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
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)
return attn_processors | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
def _process_lora(
self, state_dict, unet_identifier_key, network_alphas, adapter_name, _pipeline, low_cpu_mem_usage
):
# This method does the following things:
# 1. Filters the `state_dict` with keys matching `unet_identifier_key` when using the non-legacy
# format. For legacy format no filtering is applied.
# 2. Converts the `state_dict` to the `peft` compatible format.
# 3. Creates a `LoraConfig` and then injects the converted `state_dict` into the UNet per the
# `LoraConfig` specs.
# 4. It also reports if the underlying `_pipeline` has any kind of offloading inside of it.
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
keys = list(state_dict.keys()) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
unet_keys = [k for k in keys if k.startswith(unet_identifier_key)]
unet_state_dict = {
k.replace(f"{unet_identifier_key}.", ""): v for k, v in state_dict.items() if k in unet_keys
}
if network_alphas is not None:
alpha_keys = [k for k in network_alphas.keys() if k.startswith(unet_identifier_key)]
network_alphas = {
k.replace(f"{unet_identifier_key}.", ""): v for k, v in network_alphas.items() if k in alpha_keys
}
is_model_cpu_offload = False
is_sequential_cpu_offload = False
state_dict_to_be_used = unet_state_dict if len(unet_state_dict) > 0 else state_dict
if len(state_dict_to_be_used) > 0:
if adapter_name in getattr(self, "peft_config", {}):
raise ValueError(
f"Adapter name {adapter_name} already in use in the Unet - please select a new adapter name."
) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
state_dict = convert_unet_state_dict_to_peft(state_dict_to_be_used)
if network_alphas is not None:
# The alphas state dict have the same structure as Unet, thus we convert it to peft format using
# `convert_unet_state_dict_to_peft` method.
network_alphas = convert_unet_state_dict_to_peft(network_alphas)
rank = {}
for key, val in state_dict.items():
if "lora_B" in key:
rank[key] = val.shape[1] | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
lora_config_kwargs = get_peft_kwargs(rank, network_alphas, state_dict, is_unet=True)
if "use_dora" in lora_config_kwargs:
if lora_config_kwargs["use_dora"]:
if is_peft_version("<", "0.9.0"):
raise ValueError(
"You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
)
else:
if is_peft_version("<", "0.9.0"):
lora_config_kwargs.pop("use_dora") | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if "lora_bias" in lora_config_kwargs:
if lora_config_kwargs["lora_bias"]:
if is_peft_version("<=", "0.13.2"):
raise ValueError(
"You need `peft` 0.14.0 at least to use `bias` in LoRAs. Please upgrade your installation of `peft`."
)
else:
if is_peft_version("<=", "0.13.2"):
lora_config_kwargs.pop("lora_bias")
lora_config = LoraConfig(**lora_config_kwargs)
# adapter_name
if adapter_name is None:
adapter_name = get_adapter_name(self) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
# otherwise loading LoRA weights will lead to an error
is_model_cpu_offload, is_sequential_cpu_offload = self._optionally_disable_offloading(_pipeline)
peft_kwargs = {}
if is_peft_version(">=", "0.13.1"):
peft_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
inject_adapter_in_model(lora_config, self, adapter_name=adapter_name, **peft_kwargs)
incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name, **peft_kwargs) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
warn_msg = ""
if incompatible_keys is not None:
# Check only for unexpected keys.
unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
if unexpected_keys:
lora_unexpected_keys = [k for k in unexpected_keys if "lora_" in k and adapter_name in k]
if lora_unexpected_keys:
warn_msg = (
f"Loading adapter weights from state_dict led to unexpected keys found in the model:"
f" {', '.join(lora_unexpected_keys)}. "
) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# Filter missing keys specific to the current adapter.
missing_keys = getattr(incompatible_keys, "missing_keys", None)
if missing_keys:
lora_missing_keys = [k for k in missing_keys if "lora_" in k and adapter_name in k]
if lora_missing_keys:
warn_msg += (
f"Loading adapter weights from state_dict led to missing keys in the model:"
f" {', '.join(lora_missing_keys)}."
)
if warn_msg:
logger.warning(warn_msg)
return is_model_cpu_offload, is_sequential_cpu_offload
@classmethod
# Copied from diffusers.loaders.lora_base.LoraBaseMixin._optionally_disable_offloading
def _optionally_disable_offloading(cls, _pipeline):
"""
Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU. | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
Args:
_pipeline (`DiffusionPipeline`):
The pipeline to disable offloading for.
Returns:
tuple:
A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
"""
return _func_optionally_disable_offloading(_pipeline=_pipeline)
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 = True,
**kwargs,
):
r"""
Save attention processor layers to a directory so that it can be reloaded with the
[`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`] method. | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save an attention processor to (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 during distributed training and you
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 during distributed training when you need to
replace `torch.save` with another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or with `pickle`.
Example: | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
).to("cuda")
pipeline.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
pipeline.unet.save_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
```
"""
from ..models.attention_processor import (
CustomDiffusionAttnProcessor,
CustomDiffusionAttnProcessor2_0,
CustomDiffusionXFormersAttnProcessor,
)
if os.path.isfile(save_directory):
logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
return | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
is_custom_diffusion = any(
isinstance(
x,
(CustomDiffusionAttnProcessor, CustomDiffusionAttnProcessor2_0, CustomDiffusionXFormersAttnProcessor),
)
for (_, x) in self.attn_processors.items()
)
if is_custom_diffusion:
state_dict = self._get_custom_diffusion_state_dict()
if save_function is None and safe_serialization:
# safetensors does not support saving dicts with non-tensor values
empty_state_dict = {k: v for k, v in state_dict.items() if not isinstance(v, torch.Tensor)}
if len(empty_state_dict) > 0:
logger.warning(
f"Safetensors does not support saving dicts with non-tensor values. "
f"The following keys will be ignored: {empty_state_dict.keys()}"
)
state_dict = {k: v for k, v in state_dict.items() if isinstance(v, torch.Tensor)} | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
else:
deprecation_message = "Using the `save_attn_procs()` method has been deprecated and will be removed in a future version. Please use `save_lora_adapter()`."
deprecate("save_attn_procs", "0.40.0", deprecation_message) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for saving LoRAs using the `save_attn_procs()` method.")
from peft.utils import get_peft_model_state_dict
state_dict = get_peft_model_state_dict(self)
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)
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 | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# Save the model
save_path = Path(save_directory, weight_name).as_posix()
save_function(state_dict, save_path)
logger.info(f"Model weights saved in {save_path}")
def _get_custom_diffusion_state_dict(self):
from ..models.attention_processor import (
CustomDiffusionAttnProcessor,
CustomDiffusionAttnProcessor2_0,
CustomDiffusionXFormersAttnProcessor,
) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
model_to_save = AttnProcsLayers(
{
y: x
for (y, x) in self.attn_processors.items()
if isinstance(
x,
(
CustomDiffusionAttnProcessor,
CustomDiffusionAttnProcessor2_0,
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] = {}
return state_dict
def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_usage=False):
if low_cpu_mem_usage:
if is_accelerate_available():
from accelerate import init_empty_weights | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
else:
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
" install accelerate\n```\n."
)
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
)
updated_state_dict = {}
image_projection = None
init_context = init_empty_weights if low_cpu_mem_usage else nullcontext | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if "proj.weight" in state_dict:
# IP-Adapter
num_image_text_embeds = 4
clip_embeddings_dim = state_dict["proj.weight"].shape[-1]
cross_attention_dim = state_dict["proj.weight"].shape[0] // 4
with init_context():
image_projection = ImageProjection(
cross_attention_dim=cross_attention_dim,
image_embed_dim=clip_embeddings_dim,
num_image_text_embeds=num_image_text_embeds,
)
for key, value in state_dict.items():
diffusers_name = key.replace("proj", "image_embeds")
updated_state_dict[diffusers_name] = value
elif "proj.3.weight" in state_dict:
# IP-Adapter Full
clip_embeddings_dim = state_dict["proj.0.weight"].shape[0]
cross_attention_dim = state_dict["proj.3.weight"].shape[0] | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
with init_context():
image_projection = IPAdapterFullImageProjection(
cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim
)
for key, value in state_dict.items():
diffusers_name = key.replace("proj.0", "ff.net.0.proj")
diffusers_name = diffusers_name.replace("proj.2", "ff.net.2")
diffusers_name = diffusers_name.replace("proj.3", "norm")
updated_state_dict[diffusers_name] = value | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
elif "perceiver_resampler.proj_in.weight" in state_dict:
# IP-Adapter Face ID Plus
id_embeddings_dim = state_dict["proj.0.weight"].shape[1]
embed_dims = state_dict["perceiver_resampler.proj_in.weight"].shape[0]
hidden_dims = state_dict["perceiver_resampler.proj_in.weight"].shape[1]
output_dims = state_dict["perceiver_resampler.proj_out.weight"].shape[0]
heads = state_dict["perceiver_resampler.layers.0.0.to_q.weight"].shape[0] // 64
with init_context():
image_projection = IPAdapterFaceIDPlusImageProjection(
embed_dims=embed_dims,
output_dims=output_dims,
hidden_dims=hidden_dims,
heads=heads,
id_embeddings_dim=id_embeddings_dim,
) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
for key, value in state_dict.items():
diffusers_name = key.replace("perceiver_resampler.", "")
diffusers_name = diffusers_name.replace("0.to", "attn.to")
diffusers_name = diffusers_name.replace("0.1.0.", "0.ff.0.")
diffusers_name = diffusers_name.replace("0.1.1.weight", "0.ff.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("0.1.3.weight", "0.ff.1.net.2.weight")
diffusers_name = diffusers_name.replace("1.1.0.", "1.ff.0.")
diffusers_name = diffusers_name.replace("1.1.1.weight", "1.ff.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("1.1.3.weight", "1.ff.1.net.2.weight")
diffusers_name = diffusers_name.replace("2.1.0.", "2.ff.0.")
diffusers_name = diffusers_name.replace("2.1.1.weight", "2.ff.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("2.1.3.weight", "2.ff.1.net.2.weight") | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
diffusers_name = diffusers_name.replace("3.1.0.", "3.ff.0.")
diffusers_name = diffusers_name.replace("3.1.1.weight", "3.ff.1.net.0.proj.weight")
diffusers_name = diffusers_name.replace("3.1.3.weight", "3.ff.1.net.2.weight")
diffusers_name = diffusers_name.replace("layers.0.0", "layers.0.ln0")
diffusers_name = diffusers_name.replace("layers.0.1", "layers.0.ln1")
diffusers_name = diffusers_name.replace("layers.1.0", "layers.1.ln0")
diffusers_name = diffusers_name.replace("layers.1.1", "layers.1.ln1")
diffusers_name = diffusers_name.replace("layers.2.0", "layers.2.ln0")
diffusers_name = diffusers_name.replace("layers.2.1", "layers.2.ln1")
diffusers_name = diffusers_name.replace("layers.3.0", "layers.3.ln0")
diffusers_name = diffusers_name.replace("layers.3.1", "layers.3.ln1") | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if "norm1" in diffusers_name:
updated_state_dict[diffusers_name.replace("0.norm1", "0")] = value
elif "norm2" in diffusers_name:
updated_state_dict[diffusers_name.replace("0.norm2", "1")] = value
elif "to_kv" in diffusers_name:
v_chunk = value.chunk(2, dim=0)
updated_state_dict[diffusers_name.replace("to_kv", "to_k")] = v_chunk[0]
updated_state_dict[diffusers_name.replace("to_kv", "to_v")] = v_chunk[1]
elif "to_out" in diffusers_name:
updated_state_dict[diffusers_name.replace("to_out", "to_out.0")] = value
elif "proj.0.weight" == diffusers_name:
updated_state_dict["proj.net.0.proj.weight"] = value
elif "proj.0.bias" == diffusers_name:
updated_state_dict["proj.net.0.proj.bias"] = value
elif "proj.2.weight" == diffusers_name: | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
updated_state_dict["proj.net.2.weight"] = value
elif "proj.2.bias" == diffusers_name:
updated_state_dict["proj.net.2.bias"] = value
else:
updated_state_dict[diffusers_name] = value | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
elif "norm.weight" in state_dict:
# IP-Adapter Face ID
id_embeddings_dim_in = state_dict["proj.0.weight"].shape[1]
id_embeddings_dim_out = state_dict["proj.0.weight"].shape[0]
multiplier = id_embeddings_dim_out // id_embeddings_dim_in
norm_layer = "norm.weight"
cross_attention_dim = state_dict[norm_layer].shape[0]
num_tokens = state_dict["proj.2.weight"].shape[0] // cross_attention_dim
with init_context():
image_projection = IPAdapterFaceIDImageProjection(
cross_attention_dim=cross_attention_dim,
image_embed_dim=id_embeddings_dim_in,
mult=multiplier,
num_tokens=num_tokens,
) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
for key, value in state_dict.items():
diffusers_name = key.replace("proj.0", "ff.net.0.proj")
diffusers_name = diffusers_name.replace("proj.2", "ff.net.2")
updated_state_dict[diffusers_name] = value
else:
# IP-Adapter Plus
num_image_text_embeds = state_dict["latents"].shape[1]
embed_dims = state_dict["proj_in.weight"].shape[1]
output_dims = state_dict["proj_out.weight"].shape[0]
hidden_dims = state_dict["latents"].shape[2]
attn_key_present = any("attn" in k for k in state_dict)
heads = (
state_dict["layers.0.attn.to_q.weight"].shape[0] // 64
if attn_key_present
else state_dict["layers.0.0.to_q.weight"].shape[0] // 64
) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
with init_context():
image_projection = IPAdapterPlusImageProjection(
embed_dims=embed_dims,
output_dims=output_dims,
hidden_dims=hidden_dims,
heads=heads,
num_queries=num_image_text_embeds,
)
for key, value in state_dict.items():
diffusers_name = key.replace("0.to", "2.to") | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
diffusers_name = diffusers_name.replace("0.0.norm1", "0.ln0")
diffusers_name = diffusers_name.replace("0.0.norm2", "0.ln1")
diffusers_name = diffusers_name.replace("1.0.norm1", "1.ln0")
diffusers_name = diffusers_name.replace("1.0.norm2", "1.ln1")
diffusers_name = diffusers_name.replace("2.0.norm1", "2.ln0")
diffusers_name = diffusers_name.replace("2.0.norm2", "2.ln1")
diffusers_name = diffusers_name.replace("3.0.norm1", "3.ln0")
diffusers_name = diffusers_name.replace("3.0.norm2", "3.ln1") | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if "to_kv" in diffusers_name:
parts = diffusers_name.split(".")
parts[2] = "attn"
diffusers_name = ".".join(parts)
v_chunk = value.chunk(2, dim=0)
updated_state_dict[diffusers_name.replace("to_kv", "to_k")] = v_chunk[0]
updated_state_dict[diffusers_name.replace("to_kv", "to_v")] = v_chunk[1]
elif "to_q" in diffusers_name:
parts = diffusers_name.split(".")
parts[2] = "attn"
diffusers_name = ".".join(parts)
updated_state_dict[diffusers_name] = value
elif "to_out" in diffusers_name:
parts = diffusers_name.split(".")
parts[2] = "attn"
diffusers_name = ".".join(parts)
updated_state_dict[diffusers_name.replace("to_out", "to_out.0")] = value
else: | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
diffusers_name = diffusers_name.replace("0.1.0", "0.ff.0")
diffusers_name = diffusers_name.replace("0.1.1", "0.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("0.1.3", "0.ff.1.net.2") | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
diffusers_name = diffusers_name.replace("1.1.0", "1.ff.0")
diffusers_name = diffusers_name.replace("1.1.1", "1.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("1.1.3", "1.ff.1.net.2")
diffusers_name = diffusers_name.replace("2.1.0", "2.ff.0")
diffusers_name = diffusers_name.replace("2.1.1", "2.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("2.1.3", "2.ff.1.net.2")
diffusers_name = diffusers_name.replace("3.1.0", "3.ff.0")
diffusers_name = diffusers_name.replace("3.1.1", "3.ff.1.net.0.proj")
diffusers_name = diffusers_name.replace("3.1.3", "3.ff.1.net.2")
updated_state_dict[diffusers_name] = value | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if not low_cpu_mem_usage:
image_projection.load_state_dict(updated_state_dict, strict=True)
else:
load_model_dict_into_meta(image_projection, updated_state_dict, device=self.device, dtype=self.dtype)
return image_projection
def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=False):
from ..models.attention_processor import (
IPAdapterAttnProcessor,
IPAdapterAttnProcessor2_0,
IPAdapterXFormersAttnProcessor,
)
if low_cpu_mem_usage:
if is_accelerate_available():
from accelerate import init_empty_weights | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
else:
low_cpu_mem_usage = False
logger.warning(
"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
" install accelerate\n```\n."
)
if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
raise NotImplementedError(
"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
" `low_cpu_mem_usage=False`."
) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# set ip-adapter cross-attention processors & load state_dict
attn_procs = {}
key_id = 1
init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
for name in self.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else self.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = self.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(self.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = self.config.block_out_channels[block_id] | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
if cross_attention_dim is None or "motion_modules" in name:
attn_processor_class = self.attn_processors[name].__class__
attn_procs[name] = attn_processor_class()
else:
if "XFormers" in str(self.attn_processors[name].__class__):
attn_processor_class = IPAdapterXFormersAttnProcessor
else:
attn_processor_class = (
IPAdapterAttnProcessor2_0
if hasattr(F, "scaled_dot_product_attention")
else IPAdapterAttnProcessor
)
num_image_text_embeds = []
for state_dict in state_dicts:
if "proj.weight" in state_dict["image_proj"]:
# IP-Adapter
num_image_text_embeds += [4]
elif "proj.3.weight" in state_dict["image_proj"]:
# IP-Adapter Full Face | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
num_image_text_embeds += [257] # 256 CLIP tokens + 1 CLS token
elif "perceiver_resampler.proj_in.weight" in state_dict["image_proj"]:
# IP-Adapter Face ID Plus
num_image_text_embeds += [4]
elif "norm.weight" in state_dict["image_proj"]:
# IP-Adapter Face ID
num_image_text_embeds += [4]
else:
# IP-Adapter Plus
num_image_text_embeds += [state_dict["image_proj"]["latents"].shape[1]] | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
with init_context():
attn_procs[name] = attn_processor_class(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
scale=1.0,
num_tokens=num_image_text_embeds,
)
value_dict = {}
for i, state_dict in enumerate(state_dicts):
value_dict.update({f"to_k_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_k_ip.weight"]})
value_dict.update({f"to_v_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_v_ip.weight"]})
if not low_cpu_mem_usage:
attn_procs[name].load_state_dict(value_dict)
else:
device = next(iter(value_dict.values())).device
dtype = next(iter(value_dict.values())).dtype
load_model_dict_into_meta(attn_procs[name], value_dict, device=device, dtype=dtype) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
key_id += 2
return attn_procs
def _load_ip_adapter_weights(self, state_dicts, low_cpu_mem_usage=False):
if not isinstance(state_dicts, list):
state_dicts = [state_dicts]
# Kolors Unet already has a `encoder_hid_proj`
if (
self.encoder_hid_proj is not None
and self.config.encoder_hid_dim_type == "text_proj"
and not hasattr(self, "text_encoder_hid_proj")
):
self.text_encoder_hid_proj = self.encoder_hid_proj
# Set encoder_hid_proj after loading ip_adapter weights,
# because `IPAdapterPlusImageProjection` also has `attn_processors`.
self.encoder_hid_proj = None
attn_procs = self._convert_ip_adapter_attn_to_diffusers(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage)
self.set_attn_processor(attn_procs) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
# convert IP-Adapter Image Projection layers to diffusers
image_projection_layers = []
for state_dict in state_dicts:
image_projection_layer = self._convert_ip_adapter_image_proj_to_diffusers(
state_dict["image_proj"], low_cpu_mem_usage=low_cpu_mem_usage
)
image_projection_layers.append(image_projection_layer)
self.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
self.config.encoder_hid_dim_type = "ip_image_proj"
self.to(dtype=self.dtype, device=self.device) | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
def _load_ip_adapter_loras(self, state_dicts):
lora_dicts = {}
for key_id, name in enumerate(self.attn_processors.keys()):
for i, state_dict in enumerate(state_dicts):
if f"{key_id}.to_k_lora.down.weight" in state_dict["ip_adapter"]:
if i not in lora_dicts:
lora_dicts[i] = {}
lora_dicts[i].update(
{
f"unet.{name}.to_k_lora.down.weight": state_dict["ip_adapter"][
f"{key_id}.to_k_lora.down.weight"
]
}
)
lora_dicts[i].update(
{
f"unet.{name}.to_q_lora.down.weight": state_dict["ip_adapter"][
f"{key_id}.to_q_lora.down.weight"
]
}
)
lora_dicts[i].update( | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
{
f"unet.{name}.to_v_lora.down.weight": state_dict["ip_adapter"][
f"{key_id}.to_v_lora.down.weight"
]
}
)
lora_dicts[i].update(
{
f"unet.{name}.to_out_lora.down.weight": state_dict["ip_adapter"][
f"{key_id}.to_out_lora.down.weight"
]
}
)
lora_dicts[i].update(
{f"unet.{name}.to_k_lora.up.weight": state_dict["ip_adapter"][f"{key_id}.to_k_lora.up.weight"]}
)
lora_dicts[i].update(
{f"unet.{name}.to_q_lora.up.weight": state_dict["ip_adapter"][f"{key_id}.to_q_lora.up.weight"]}
)
lora_dicts[i].update( | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
{f"unet.{name}.to_v_lora.up.weight": state_dict["ip_adapter"][f"{key_id}.to_v_lora.up.weight"]}
)
lora_dicts[i].update(
{
f"unet.{name}.to_out_lora.up.weight": state_dict["ip_adapter"][
f"{key_id}.to_out_lora.up.weight"
]
}
)
return lora_dicts | 1,257 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/unet.py |
class TextualInversionLoaderMixin:
r"""
Load Textual Inversion tokens and embeddings to the tokenizer and text encoder.
"""
def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"): # noqa: F821
r"""
Processes prompts that include a special token corresponding to a multi-vector textual inversion embedding to
be replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual
inversion token or if the textual inversion token is a single vector, the input prompt is 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. | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
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"): # noqa: F821
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. | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
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)
unique_tokens = set(tokens)
for token in unique_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 | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
def _check_text_inv_inputs(self, tokenizer, text_encoder, pretrained_model_name_or_paths, tokens):
if tokenizer is None:
raise ValueError(
f"{self.__class__.__name__} requires `self.tokenizer` or passing a `tokenizer` of type `PreTrainedTokenizer` for calling"
f" `{self.load_textual_inversion.__name__}`"
)
if text_encoder is None:
raise ValueError(
f"{self.__class__.__name__} requires `self.text_encoder` or passing a `text_encoder` of type `PreTrainedModel` for calling"
f" `{self.load_textual_inversion.__name__}`"
)
if len(pretrained_model_name_or_paths) > 1 and len(pretrained_model_name_or_paths) != len(tokens):
raise ValueError(
f"You have passed a list of models of length {len(pretrained_model_name_or_paths)}, and list of tokens of length {len(tokens)} "
f"Make sure both lists have the same length."
) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
valid_tokens = [t for t in tokens if t is not None]
if len(set(valid_tokens)) < len(valid_tokens):
raise ValueError(f"You have passed a list of tokens that contains duplicates: {tokens}") | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
@staticmethod
def _retrieve_tokens_and_embeddings(tokens, state_dicts, tokenizer):
all_tokens = []
all_embeddings = []
for state_dict, token in zip(state_dicts, tokens):
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=...`."
)
loaded_token = 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"]["*"]
else:
raise ValueError( | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
f"Loaded state dictionary is incorrect: {state_dict}. \n\n"
"Please verify that the loaded state dictionary of the textual embedding either only has a single key or includes the `string_to_param`"
" input key."
) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
if token is not None and loaded_token != token:
logger.info(f"The loaded token: {loaded_token} is overwritten by the passed token {token}.")
else:
token = loaded_token
if token in tokenizer.get_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."
)
all_tokens.append(token)
all_embeddings.append(embedding)
return all_tokens, all_embeddings
@staticmethod
def _extend_tokens_and_embeddings(tokens, embeddings, tokenizer):
all_tokens = []
all_embeddings = [] | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
for embedding, token in zip(embeddings, tokens):
if f"{token}_1" in tokenizer.get_vocab():
multi_vector_tokens = [token]
i = 1
while f"{token}_{i}" in 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."
) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
is_multi_vector = len(embedding.shape) > 1 and embedding.shape[0] > 1
if is_multi_vector:
all_tokens += [token] + [f"{token}_{i}" for i in range(1, embedding.shape[0])]
all_embeddings += [e for e in embedding] # noqa: C416
else:
all_tokens += [token]
all_embeddings += [embedding[0]] if len(embedding.shape) > 1 else [embedding]
return all_tokens, all_embeddings | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
@validate_hf_hub_args
def load_textual_inversion(
self,
pretrained_model_name_or_path: Union[str, List[str], Dict[str, torch.Tensor], List[Dict[str, torch.Tensor]]],
token: Optional[Union[str, List[str]]] = None,
tokenizer: Optional["PreTrainedTokenizer"] = None, # noqa: F821
text_encoder: Optional["PreTrainedModel"] = None, # noqa: F821
**kwargs,
):
r"""
Load Textual Inversion embeddings into the text encoder of [`StableDiffusionPipeline`] (both 🤗 Diffusers and
Automatic1111 formats are supported).
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike` or `List[str or os.PathLike]` or `Dict` or `List[Dict]`):
Can be either one of the following or a list of them: | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
- A string, the *model id* (for example `sd-concepts-library/low-poly-hd-logos-icons`) of a
pretrained model hosted on the Hub.
- A path to a *directory* (for example `./my_text_inversion_directory/`) containing the textual
inversion weights.
- A path to a *file* (for example `./my_text_inversions.pt`) containing textual inversion weights.
- A [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict). | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
token (`str` or `List[str]`, *optional*):
Override the token to use for the textual inversion weights. If `pretrained_model_name_or_path` is a
list, then `token` must also be a list of equal length.
text_encoder ([`~transformers.CLIPTextModel`], *optional*):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
If not specified, function will take self.tokenizer.
tokenizer ([`~transformers.CLIPTokenizer`], *optional*):
A `CLIPTokenizer` to tokenize text. If not specified, function will take self.tokenizer.
weight_name (`str`, *optional*):
Name of a custom weight file. This should be used when: | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
- 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 format.
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.
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. | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
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.
hf_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.
subfolder (`str`, *optional*, defaults to `""`): | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
The subfolder location of a model file within a larger model repository on the Hub or locally.
mirror (`str`, *optional*):
Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information. | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
Example:
To load a Textual Inversion embedding vector in 🤗 Diffusers format:
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "stable-diffusion-v1-5/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 download the vector first
(for example from [civitAI](https://civitai.com/models/3036?modelVersionId=9857)) and then load the vector
locally:
```py
from diffusers import StableDiffusionPipeline
import torch | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
model_id = "stable-diffusion-v1-5/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")
```
"""
# 1. Set correct tokenizer and text encoder
tokenizer = tokenizer or getattr(self, "tokenizer", None)
text_encoder = text_encoder or getattr(self, "text_encoder", None) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 2. Normalize inputs
pretrained_model_name_or_paths = (
[pretrained_model_name_or_path]
if not isinstance(pretrained_model_name_or_path, list)
else pretrained_model_name_or_path
)
tokens = [token] if not isinstance(token, list) else token
if tokens[0] is None:
tokens = tokens * len(pretrained_model_name_or_paths)
# 3. Check inputs
self._check_text_inv_inputs(tokenizer, text_encoder, pretrained_model_name_or_paths, tokens)
# 4. Load state dicts of textual embeddings
state_dicts = load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 4.1 Handle the special case when state_dict is a tensor that contains n embeddings for n tokens
if len(tokens) > 1 and len(state_dicts) == 1:
if isinstance(state_dicts[0], torch.Tensor):
state_dicts = list(state_dicts[0])
if len(tokens) != len(state_dicts):
raise ValueError(
f"You have passed a state_dict contains {len(state_dicts)} embeddings, and list of tokens of length {len(tokens)} "
f"Make sure both have the same length."
)
# 4. Retrieve tokens and embeddings
tokens, embeddings = self._retrieve_tokens_and_embeddings(tokens, state_dicts, tokenizer)
# 5. Extend tokens and embeddings for multi vector
tokens, embeddings = self._extend_tokens_and_embeddings(tokens, embeddings, tokenizer) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 6. Make sure all embeddings have the correct size
expected_emb_dim = text_encoder.get_input_embeddings().weight.shape[-1]
if any(expected_emb_dim != emb.shape[-1] for emb in embeddings):
raise ValueError(
"Loaded embeddings are of incorrect shape. Expected each textual inversion embedding "
"to be of shape {input_embeddings.shape[-1]}, but are {embeddings.shape[-1]} "
)
# 7. Now we can be sure that loading the embedding matrix works
# < Unsafe code: | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 7.1 Offload all hooks in case the pipeline was cpu offloaded before make sure, we offload and onload again
is_model_cpu_offload = False
is_sequential_cpu_offload = False
if self.hf_device_map is None:
for _, component in self.components.items():
if isinstance(component, nn.Module):
if hasattr(component, "_hf_hook"):
is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
is_sequential_cpu_offload = (
isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
or hasattr(component._hf_hook, "hooks")
and isinstance(component._hf_hook.hooks[0], AlignDevicesHook)
)
logger.info( | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
"Accelerate hooks detected. Since you have called `load_textual_inversion()`, the previous hooks will be first removed. Then the textual inversion parameters will be loaded and the hooks will be applied again."
)
remove_hook_from_module(component, recurse=is_sequential_cpu_offload) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# 7.2 save expected device and dtype
device = text_encoder.device
dtype = text_encoder.dtype
# 7.3 Increase token embedding matrix
text_encoder.resize_token_embeddings(len(tokenizer) + len(tokens))
input_embeddings = text_encoder.get_input_embeddings().weight
# 7.4 Load token and embedding
for token, embedding in zip(tokens, embeddings):
# add tokens and get ids
tokenizer.add_tokens(token)
token_id = tokenizer.convert_tokens_to_ids(token)
input_embeddings.data[token_id] = embedding
logger.info(f"Loaded textual inversion embedding for {token}.")
input_embeddings.to(dtype=dtype, device=device)
# 7.5 Offload the model again
if is_model_cpu_offload:
self.enable_model_cpu_offload()
elif is_sequential_cpu_offload:
self.enable_sequential_cpu_offload()
# / Unsafe Code > | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
def unload_textual_inversion(
self,
tokens: Optional[Union[str, List[str]]] = None,
tokenizer: Optional["PreTrainedTokenizer"] = None,
text_encoder: Optional["PreTrainedModel"] = None,
):
r"""
Unload Textual Inversion embeddings from the text encoder of [`StableDiffusionPipeline`]
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
# Example 1
pipeline.load_textual_inversion("sd-concepts-library/gta5-artwork")
pipeline.load_textual_inversion("sd-concepts-library/moeb-style")
# Remove all token embeddings
pipeline.unload_textual_inversion()
# Example 2
pipeline.load_textual_inversion("sd-concepts-library/moeb-style")
pipeline.load_textual_inversion("sd-concepts-library/gta5-artwork") | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# Remove just one token
pipeline.unload_textual_inversion("<moe-bius>")
# Example 3: unload from SDXL
pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
embedding_path = hf_hub_download(
repo_id="linoyts/web_y2k", filename="web_y2k_emb.safetensors", repo_type="model"
)
# load embeddings to the text encoders
state_dict = load_file(embedding_path)
# load embeddings of text_encoder 1 (CLIP ViT-L/14)
pipeline.load_textual_inversion(
state_dict["clip_l"],
tokens=["<s0>", "<s1>"],
text_encoder=pipeline.text_encoder,
tokenizer=pipeline.tokenizer,
)
# load embeddings of text_encoder 2 (CLIP ViT-G/14)
pipeline.load_textual_inversion(
state_dict["clip_g"],
tokens=["<s0>", "<s1>"],
text_encoder=pipeline.text_encoder_2,
tokenizer=pipeline.tokenizer_2,
) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# Unload explicitly from both text encoders and tokenizers
pipeline.unload_textual_inversion(
tokens=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer
)
pipeline.unload_textual_inversion(
tokens=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2
)
```
"""
tokenizer = tokenizer or getattr(self, "tokenizer", None)
text_encoder = text_encoder or getattr(self, "text_encoder", None)
# Get textual inversion tokens and ids
token_ids = []
last_special_token_id = None | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
if tokens:
if isinstance(tokens, str):
tokens = [tokens]
for added_token_id, added_token in tokenizer.added_tokens_decoder.items():
if not added_token.special:
if added_token.content in tokens:
token_ids.append(added_token_id)
else:
last_special_token_id = added_token_id
if len(token_ids) == 0:
raise ValueError("No tokens to remove found")
else:
tokens = []
for added_token_id, added_token in tokenizer.added_tokens_decoder.items():
if not added_token.special:
token_ids.append(added_token_id)
tokens.append(added_token.content)
else:
last_special_token_id = added_token_id | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# Delete from tokenizer
for token_id, token_to_remove in zip(token_ids, tokens):
del tokenizer._added_tokens_decoder[token_id]
del tokenizer._added_tokens_encoder[token_to_remove]
# Make all token ids sequential in tokenizer
key_id = 1
for token_id in tokenizer.added_tokens_decoder:
if token_id > last_special_token_id and token_id > last_special_token_id + key_id:
token = tokenizer._added_tokens_decoder[token_id]
tokenizer._added_tokens_decoder[last_special_token_id + key_id] = token
del tokenizer._added_tokens_decoder[token_id]
tokenizer._added_tokens_encoder[token.content] = last_special_token_id + key_id
key_id += 1
tokenizer._update_trie()
# set correct total vocab size after removing tokens
tokenizer._update_total_vocab_size() | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
# Delete from text encoder
text_embedding_dim = text_encoder.get_input_embeddings().embedding_dim
temp_text_embedding_weights = text_encoder.get_input_embeddings().weight
text_embedding_weights = temp_text_embedding_weights[: last_special_token_id + 1]
to_append = []
for i in range(last_special_token_id + 1, temp_text_embedding_weights.shape[0]):
if i not in token_ids:
to_append.append(temp_text_embedding_weights[i].unsqueeze(0))
if len(to_append) > 0:
to_append = torch.cat(to_append, dim=0)
text_embedding_weights = torch.cat([text_embedding_weights, to_append], dim=0)
text_embeddings_filtered = nn.Embedding(text_embedding_weights.shape[0], text_embedding_dim)
text_embeddings_filtered.weight.data = text_embedding_weights
text_encoder.set_input_embeddings(text_embeddings_filtered) | 1,258 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/textual_inversion.py |
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. | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
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. | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
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. | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
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.
disable_mmap ('bool', *optional*, defaults to 'False'): | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
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