text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to o... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
```py
>>> from diffusers import StableCascadeUNet
>>> ckpt_path = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite.safetensors"
>>> model = StableCascadeUNet.from_single_file(ckpt_path)
```
"""
mapping_class_name = _get_single_file_loadable_mapp... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
pretrained_model_link_or_path = kwargs.get("pretrained_model_link_or_path", None)
if pretrained_model_link_or_path is not None:
deprecation_message = (
"Please use `pretrained_model_link_or_path_or_dict` argument instead for model classes"
)
deprecate("pretrai... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
token = kwargs.pop("token", None)
cache_dir = kwargs.pop("cache_dir", None)
local_files_only = kwargs.pop("local_files_only", None)
subfolder = kwargs.pop("subfolder", None)
revisio... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
if isinstance(pretrained_model_link_or_path_or_dict, dict):
checkpoint = pretrained_model_link_or_path_or_dict
else:
checkpoint = load_single_file_checkpoint(
pretrained_model_link_or_path_or_dict,
force_download=force_download,
proxies=pro... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
checkpoint_mapping_fn = mapping_functions["checkpoint_mapping_fn"]
if original_config is not None:
if "config_mapping_fn" in mapping_functions:
config_mapping_fn = mapping_functions["config_mapping_fn"]
else:
config_mapping_fn = None
if config... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
config_mapping_kwargs = _get_mapping_function_kwargs(config_mapping_fn, **kwargs)
diffusers_model_config = config_mapping_fn(
original_config=original_config, checkpoint=checkpoint, **config_mapping_kwargs
)
else:
if config is not None:
if isin... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
if "default_subfolder" in mapping_functions:
subfolder = mapping_functions["default_subfolder"]
subfolder = subfolder or config.pop(
"subfolder", None
) # some configs contain a subfolder key, e.g. StableCascadeUNet
diffusers_model_c... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
# Map legacy kwargs to new kwargs
if "legacy_kwargs" in mapping_functions:
legacy_kwargs = mapping_functions["legacy_kwargs"]
for legacy_key, new_key in legacy_kwargs.items():
if legacy_key in kwargs:
kwargs[new_key] = kwargs.pop(le... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
model = cls.from_config(diffusers_model_config)
# Check if `_keep_in_fp32_modules` is not None
use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
(torch_dtype == torc... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
if is_accelerate_available():
param_device = torch.device(device) if device else torch.device("cpu")
named_buffers = model.named_buffers()
unexpected_keys = load_model_dict_into_meta(
model,
diffusers_format_checkpoint,
dtype=torch_dtyp... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
if len(unexpected_keys) > 0:
logger.warning(
f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}"
)
if hf_quantizer is not None:
hf_quantizer.postprocess_model(model)
model.hf_qu... | 1,259 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_model.py |
class LoraBaseMixin:
"""Utility class for handling LoRAs."""
_lora_loadable_modules = []
num_fused_loras = 0
def load_lora_weights(self, **kwargs):
raise NotImplementedError("`load_lora_weights()` is not implemented.")
@classmethod
def save_lora_weights(cls, **kwargs):
raise N... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
Returns:
tuple:
A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
"""
return _func_optionally_disable_offloading(_pipeline=_pipeline)
@classmethod
def _fetch_state_dict(cls, *args, **kwargs):
deprecation_message = f"Using th... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
@classmethod
def _best_guess_weight_name(cls, *args, **kwargs):
deprecation_message = f"Using the `_best_guess_weight_name()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _best_guess_weight_name`."
deprecate("_best... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
for component in self._lora_loadable_modules:
model = getattr(self, component, None)
if model is not None:
if issubclass(model.__class__, ModelMixin):
model.unload_lora()
elif issubclass(model.__class__, PreTrainedModel):
_r... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
Args:
components: (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
lora_scale (`float`, defaults to 1.0):
Controls how much to influence the outputs with the LoRA parameters.
safe_fusing (`bool`, defaults to `False`):
Whether t... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.fuse_lora(lora_scale=0.7)
... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
depr_message = "Passing `fuse_transformer` to `fuse_lora()` is deprecated and will be ignored. Please use the `components` argument and provide a list of the components whose LoRAs are to be fused. `fuse_transformer` will be removed in a future version."
deprecate(
"fuse_transformer",
... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
if len(components) == 0:
raise ValueError("`components` cannot be an empty list.")
for fuse_component in components:
if fuse_component not in self._lora_loadable_modules:
raise ValueError(f"{fuse_component} is not found in {self._lora_loadable_modules=}.")
m... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
def unfuse_lora(self, components: List[str] = [], **kwargs):
r"""
Reverses the effect of
[`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora).
<Tip warning={true}>
This is an experimental API.
</Tip> | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
Args:
components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
unfuse_text_encoder (`bool`, defaults to `True`):
Whether to unfuse the text encoder LoRA para... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
depr_message = "Passing `unfuse_transformer` to `unfuse_lora()` is deprecated and will be ignored. Please use the `components` argument. `unfuse_transformer` will be removed in a future version."
deprecate(
"unfuse_transformer",
"1.0.0",
depr_message,
... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
if len(components) == 0:
raise ValueError("`components` cannot be an empty list.")
for fuse_component in components:
if fuse_component not in self._lora_loadable_modules:
raise ValueError(f"{fuse_component} is not found in {self._lora_loadable_modules=}.")
m... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
# Expand weights into a list, one entry per adapter
if not isinstance(adapter_weights, list):
adapter_weights = [adapter_weights] * len(adapter_names)
if len(adapter_names) != len(adapter_weights):
raise ValueError(
f"Length of adapter names {len(adapter_names)} ... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
# eg {"adapter1": ["unet"], "adapter2": ["unet", "text_encoder"]}
invert_list_adapters = {
adapter: [part for part, adapters in list_adapters.items() if adapter in adapters]
for adapter in all_adapters
}
# Decompose weights into weights for denoiser and text encoders.
... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
if component_adapter_weights is not None and component not in invert_list_adapters[adapter_name]:
logger.warning(
(
f"Lora weight dict for adapter '{adapter_name}' contains {component},"
f"but this will b... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
if issubclass(model.__class__, ModelMixin):
model.set_adapters(adapter_names, _component_adapter_weights[component])
elif issubclass(model.__class__, PreTrainedModel):
set_adapters_for_text_encoder(adapter_names, model, _component_adapter_weights[component])
def disable_... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
for component in self._lora_loadable_modules:
model = getattr(self, component, None)
if model is not None:
if issubclass(model.__class__, ModelMixin):
model.enable_lora()
elif issubclass(model.__class__, PreTrainedModel):
en... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
for component in self._lora_loadable_modules:
model = getattr(self, component, None)
if model is not None:
if issubclass(model.__class__, ModelMixin):
model.delete_adapters(adapter_names)
elif issubclass(model.__class__, PreTrainedModel):
... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
).to("cuda")
pipeline.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy")
pipeline.get_active_adapters()
```
"""
if not U... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
def get_list_adapters(self) -> Dict[str, List[str]]:
"""
Gets the current list of all available adapters in the pipeline.
"""
if not USE_PEFT_BACKEND:
raise ValueError(
"PEFT backend is required for this method. Please install the latest version of PEFT `pip i... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
def set_lora_device(self, adapter_names: List[str], device: Union[torch.device, str, int]) -> None:
"""
Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case
you want to load multiple adapters and free some GPU memory.
Args:
... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
for component in self._lora_loadable_modules:
model = getattr(self, component, None)
if model is not None:
for module in model.modules():
if isinstance(module, BaseTunerLayer):
for adapter_name in adapter_names:
... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
@staticmethod
def pack_weights(layers, prefix):
layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
return layers_state_dict
@staticmethod
def ... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
os.makedirs(save_directory, exist_ok=True)
if weight_name is None:
if safe_serialization:
weight_name = LORA_WEIGHT_NAME_SAFE
else:
weight_name = LORA_WEIGHT_NAME
save_path = Path(save_directory, weight_name).as_posix()
save_function(stat... | 1,260 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_base.py |
class SingleFileComponentError(Exception):
def __init__(self, message=None):
self.message = message
super().__init__(self.message) | 1,261 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/single_file_utils.py |
class StableDiffusionLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into Stable Diffusion [`UNet2DConditionModel`] and
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
"""
_lora_loadable_modules = ["unet", "text_encoder"]
unet_name = ... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state
dict is loaded into `self.text_encoder`. | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specif... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT_LORA)
if low_cpu_mem_usage and not is_peft_version(">=", "0.13.1"):
raise ValueError(
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
self.load_lora_into_unet(
state_dict,
network_alphas=network_alphas,
unet=getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet,
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
)
s... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
<Tip warning={true}>
We support loading A1111 formatted LoRA checkpoints in a limited capacity.
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 b... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
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-)dow... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.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`):
... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
The subfolder location of a model file within a larger model repository on the Hub or locally.
weight_name (`str`, *optional*, defaults to None):
Name of the serialized state dict file.
"""
# Load the main state dict first which has the LoRA layers for either of
# UNe... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
allow_pickle = False
if use_safetensors is None:
use_safetensors = True
allow_pickle = True
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
} | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
state_dict = _fetch_state_dict(
pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
weight_name=weight_name,
use_safetensors=use_safetensors,
local_files_only=local_files_only,
cache_dir=cache_dir,
force_download=force_down... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k} | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
network_alphas = None
# TODO: replace it with a method from `state_dict_utils`
if all(
(
k.startswith("lora_te_")
or k.startswith("lora_unet_")
or k.startswith("lora_te1_")
or k.startswith("lora_te2_")
)
... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional `unet` which can be used to distinguish between text
encoder lora layers.
... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
`default_{i}` where i is the total number of adapters being loaded.
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading only loading the pretrained LoRA weights and not initializing the random
weights.
"""
if not USE_PEFT_BACKEND:
raise ... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
if low_cpu_mem_usage and not is_peft_version(">=", "0.13.1"):
raise ValueError(
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
) | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
# then the `state_dict` keys should have `cls.unet_name` and/or `cls.text_encoder_name` as
# their prefixes.
keys = list(state_dict.keys())
only_text_encoder = all(key.startswith(cls.... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
@classmethod
def load_lora_into_text_encoder(
cls,
state_dict,
network_alphas,
text_encoder,
prefix=None,
lora_scale=1.0,
adapter_name=None,
_pipeline=None,
low_cpu_mem_usage=False,
):
"""
This will load the LoRA layers spec... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional `text_encoder` to distinguish between unet lora layers.
network_alphas (`Dict[str, float]`):
The value of the... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
lora layer.
adapter_name (`str`, *optional*):
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.
low_cpu_mem_usage (`bool`, *optional*):
... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
@classmethod
def save_lora_weights(
cls,
save_directory: Union[str, os.PathLike],
unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_lora_layers: Dict[str, torch.nn.Module] = None,
is_main_process: bool = True,
weight_name: str = No... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save LoRA parameters to. Will be created if it doesn't exist.
unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `unet`.
... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
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`):
... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
if not (unet_lora_layers or text_encoder_lora_layers):
raise ValueError("You must pass at least one of `unet_lora_layers` and `text_encoder_lora_layers`.")
if unet_lora_layers:
state_dict.update(cls.pack_weights(unet_lora_layers, cls.unet_name))
if text_encoder_lora_layers:
... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
def fuse_lora(
self,
components: List[str] = ["unet", "text_encoder"],
lora_scale: float = 1.0,
safe_fusing: bool = False,
adapter_names: Optional[List[str]] = None,
**kwargs,
):
r"""
Fuses the LoRA parameters into the original parameters of the corres... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Args:
components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
unfuse_text_encoder (`bool`, defaults to `True`):
Whether to unfuse the text encoder LoRA para... | 1,262 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into Stable Diffusion XL [`UNet2DConditionModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), and
[`CLIPTextModelWithProjection`](https://huggingface.co/docs/transform... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`] for more details on how the state dict is
loaded.
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is
loaded into `self.unet`.
See [`~loaders.StableDiffusionLoraLoa... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specif... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT_LORA)
if low_cpu_mem_usage and not is_peft_version(">=", "0.13.1"):
raise ValueError(
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# First, ensure that the checkpoint is a compatible one and can be successfully loaded.
state_dict, network_alphas = self.lora_state_dict(
pretrained_model_name_or_path_or_dict,
unet_config=self.unet.config,
**kwargs,
)
is_correct_format = all("lora" in key f... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
self.load_lora_into_unet(
state_dict,
network_alphas=network_alphas,
unet=self.unet,
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
)
text_encoder_state_dict = {k: v for k, v in state_dict.items() if... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
if len(text_encoder_2_state_dict) > 0:
self.load_lora_into_text_encoder(
text_encoder_2_state_dict,
network_alphas=network_alphas,
text_encoder=self.text_encode... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
We support loading A1111 formatted LoRA checkpoints in a limited capacity.
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:
... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
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-)dow... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.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`):
... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
The subfolder location of a model file within a larger model repository on the Hub or locally.
weight_name (`str`, *optional*, defaults to None):
Name of the serialized state dict file.
"""
# Load the main state dict first which has the LoRA layers for either of
# UNe... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
allow_pickle = False
if use_safetensors is None:
use_safetensors = True
allow_pickle = True
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
} | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
state_dict = _fetch_state_dict(
pretrained_model_name_or_path_or_dict=pretrained_model_name_or_path_or_dict,
weight_name=weight_name,
use_safetensors=use_safetensors,
local_files_only=local_files_only,
cache_dir=cache_dir,
force_download=force_down... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
state_dict = {k: v for k, v in state_dict.items() if "dora_scale" not in k} | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
network_alphas = None
# TODO: replace it with a method from `state_dict_utils`
if all(
(
k.startswith("lora_te_")
or k.startswith("lora_unet_")
or k.startswith("lora_te1_")
or k.startswith("lora_te2_")
)
... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
@classmethod
# Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.load_lora_into_unet
def load_lora_into_unet(
cls, state_dict, network_alphas, unet, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False
):
"""
This will load the LoRA layers specified in ... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional `unet` which can be used to distinguish between text
encoder lora layers.
... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
`default_{i}` where i is the total number of adapters being loaded.
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading only loading the pretrained LoRA weights and not initializing the random
weights.
"""
if not USE_PEFT_BACKEND:
raise ... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
if low_cpu_mem_usage and not is_peft_version(">=", "0.13.1"):
raise ValueError(
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
) | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
# then the `state_dict` keys should have `cls.unet_name` and/or `cls.text_encoder_name` as
# their prefixes.
keys = list(state_dict.keys())
only_text_encoder = all(key.startswith(cls.... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
@classmethod
# Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.load_lora_into_text_encoder
def load_lora_into_text_encoder(
cls,
state_dict,
network_alphas,
text_encoder,
prefix=None,
lora_scale=1.0,
adapter_name=None,
_p... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional `text_encoder` to distinguish between unet lora layers.
network_alphas (`Dict[str, float]`):
The value of the... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
lora layer.
adapter_name (`str`, *optional*):
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.
low_cpu_mem_usage (`bool`, *optional*):
... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
@classmethod
def save_lora_weights(
cls,
save_directory: Union[str, os.PathLike],
unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_2_lora_layers: Dict[str, ... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save LoRA parameters to. Will be created if it doesn't exist.
unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `unet`.
... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
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`):
... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
if not (unet_lora_layers or text_encoder_lora_layers or text_encoder_2_lora_layers):
raise ValueError(
"You must pass at least one of `unet_lora_layers`, `text_encoder_lora_layers` or `text_encoder_2_lora_layers`."
)
if unet_lora_layers:
state_dict.update(cls... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
def fuse_lora(
self,
components: List[str] = ["unet", "text_encoder", "text_encoder_2"],
lora_scale: float = 1.0,
safe_fusing: bool = False,
adapter_names: Optional[List[str]] = None,
**kwargs,
):
r"""
Fuses the LoRA parameters into the original parame... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Args:
components: (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
lora_scale (`float`, defaults to 1.0):
Controls how much to influence the outputs with the LoRA parameters.
safe_fusing (`bool`, defaults to `False`):
Whether t... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.fuse_lora(lora_scale=0.7)
... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
Args:
components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
unfuse_unet (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
unfuse_text_encoder (`bool`, defaults to `True`):
Whether to unfuse the text encoder LoRA para... | 1,263 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
class SD3LoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`SD3Transformer2DModel`],
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), and
[`CLIPTextModelWithProjection`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.C... | 1,264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
</Tip>
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
Can be either:
- A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A... | 1,264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
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-)dow... | 1,264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.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`):
... | 1,264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
The subfolder location of a model file within a larger model repository on the Hub or locally. | 1,264 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
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