text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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if not transformer_lora_layers:
raise ValueError("You must pass `transformer_lora_layers`.")
if transformer_lora_layers:
state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
# Save the model
cls.write_lora_layers(
state_dict=sta... | 1,267 | /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... | 1,267 | /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)
... | 1,267 | /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_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
"""
super().unfuse_lora(components=components) | 1,267 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
class Mochi1LoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`MochiTransformer3DModel`]. Specific to [`MochiPipeline`].
"""
_lora_loadable_modules = ["transformer"]
transformer_name = TRANSFORMER_NAME
@classmethod
@validate_hf_hub_args
# Copied from diffusers.loaders.lora_pi... | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.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`].
... | 1,268 | /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`):
... | 1,268 | /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. | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
"""
# Load the main state dict first which has the LoRA layers for either of
# transformer and text encoder or both.
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwa... | 1,268 | /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... | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
is_dora_scale_present = any("dora_scale" in k for k in state_dict)
if is_dora_scale_present:
warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you thi... | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.load_lora_weights
def load_lora_weights(
self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
):
"""
Load LoRA weights specified in `pretrained_model_name_or_pa... | 1,268 | /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... | 1,268 | /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 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`."
... | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
self.load_lora_into_transformer(
state_dict,
transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
)
@classmethod... | 1,268 | /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.
... | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
) | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# Load the layers corresponding to transformer.
logger.info(f"Loading {cls.transformer_name}.")
transformer.load_lora_adapter(
state_dict,
network_alphas=None,
adapter_name=adapter_name,
_pipeline=_pipeline,
low_cpu_mem_usage=low_cpu_mem_usage,... | 1,268 | /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.
transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `... | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
"""
state_dict = {} | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
if not transformer_lora_layers:
raise ValueError("You must pass `transformer_lora_layers`.")
if transformer_lora_layers:
state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
# Save the model
cls.write_lora_layers(
state_dict=sta... | 1,268 | /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... | 1,268 | /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)
... | 1,268 | /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_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
"""
super().unfuse_lora(components=components) | 1,268 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
class LTXVideoLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`LTXVideoTransformer3DModel`]. Specific to [`LTXPipeline`].
"""
_lora_loadable_modules = ["transformer"]
transformer_name = TRANSFORMER_NAME
@classmethod
@validate_hf_hub_args
# Copied from diffusers.loaders.lora... | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.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`].
... | 1,269 | /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`):
... | 1,269 | /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. | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
"""
# Load the main state dict first which has the LoRA layers for either of
# transformer and text encoder or both.
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwa... | 1,269 | /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... | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
is_dora_scale_present = any("dora_scale" in k for k in state_dict)
if is_dora_scale_present:
warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you thi... | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.load_lora_weights
def load_lora_weights(
self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
):
"""
Load LoRA weights specified in `pretrained_model_name_or_pa... | 1,269 | /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... | 1,269 | /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 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`."
... | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
self.load_lora_into_transformer(
state_dict,
transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
)
@classmethod... | 1,269 | /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.
... | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
) | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# Load the layers corresponding to transformer.
logger.info(f"Loading {cls.transformer_name}.")
transformer.load_lora_adapter(
state_dict,
network_alphas=None,
adapter_name=adapter_name,
_pipeline=_pipeline,
low_cpu_mem_usage=low_cpu_mem_usage,... | 1,269 | /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.
transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `... | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
"""
state_dict = {} | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
if not transformer_lora_layers:
raise ValueError("You must pass `transformer_lora_layers`.")
if transformer_lora_layers:
state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
# Save the model
cls.write_lora_layers(
state_dict=sta... | 1,269 | /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... | 1,269 | /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)
... | 1,269 | /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_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
"""
super().unfuse_lora(components=components) | 1,269 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
class SanaLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`SanaTransformer2DModel`]. Specific to [`SanaPipeline`].
"""
_lora_loadable_modules = ["transformer"]
transformer_name = TRANSFORMER_NAME
@classmethod
@validate_hf_hub_args
# Copied from diffusers.loaders.lora_pipeli... | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.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`].
... | 1,270 | /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`):
... | 1,270 | /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. | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
"""
# Load the main state dict first which has the LoRA layers for either of
# transformer and text encoder or both.
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwa... | 1,270 | /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... | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
is_dora_scale_present = any("dora_scale" in k for k in state_dict)
if is_dora_scale_present:
warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you thi... | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.load_lora_weights
def load_lora_weights(
self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
):
"""
Load LoRA weights specified in `pretrained_model_name_or_pa... | 1,270 | /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... | 1,270 | /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 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`."
... | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
self.load_lora_into_transformer(
state_dict,
transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
)
@classmethod... | 1,270 | /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.
... | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
) | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# Load the layers corresponding to transformer.
logger.info(f"Loading {cls.transformer_name}.")
transformer.load_lora_adapter(
state_dict,
network_alphas=None,
adapter_name=adapter_name,
_pipeline=_pipeline,
low_cpu_mem_usage=low_cpu_mem_usage,... | 1,270 | /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.
transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `... | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
"""
state_dict = {} | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
if not transformer_lora_layers:
raise ValueError("You must pass `transformer_lora_layers`.")
if transformer_lora_layers:
state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
# Save the model
cls.write_lora_layers(
state_dict=sta... | 1,270 | /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... | 1,270 | /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)
... | 1,270 | /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_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
"""
super().unfuse_lora(components=components) | 1,270 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
class HunyuanVideoLoraLoaderMixin(LoraBaseMixin):
r"""
Load LoRA layers into [`HunyuanVideoTransformer3DModel`]. Specific to [`HunyuanVideoPipeline`].
"""
_lora_loadable_modules = ["transformer"]
transformer_name = TRANSFORMER_NAME
@classmethod
@validate_hf_hub_args
def lora_state_dict... | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.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`].
... | 1,271 | /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`):
... | 1,271 | /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. | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
"""
# Load the main state dict first which has the LoRA layers for either of
# transformer and text encoder or both.
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwa... | 1,271 | /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... | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
is_dora_scale_present = any("dora_scale" in k for k in state_dict)
if is_dora_scale_present:
warn_msg = "It seems like you are using a DoRA checkpoint that is not compatible in Diffusers at the moment. So, we are going to filter out the keys associated to 'dora_scale` from the state dict. If you thi... | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.load_lora_weights
def load_lora_weights(
self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
):
"""
Load LoRA weights specified in `pretrained_model_name_or_pa... | 1,271 | /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... | 1,271 | /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 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`."
... | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
self.load_lora_into_transformer(
state_dict,
transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
)
@classmethod... | 1,271 | /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.
... | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
"`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
) | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
# Load the layers corresponding to transformer.
logger.info(f"Loading {cls.transformer_name}.")
transformer.load_lora_adapter(
state_dict,
network_alphas=None,
adapter_name=adapter_name,
_pipeline=_pipeline,
low_cpu_mem_usage=low_cpu_mem_usage,... | 1,271 | /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.
transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `... | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
"""
state_dict = {} | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
if not transformer_lora_layers:
raise ValueError("You must pass `transformer_lora_layers`.")
if transformer_lora_layers:
state_dict.update(cls.pack_weights(transformer_lora_layers, cls.transformer_name))
# Save the model
cls.write_lora_layers(
state_dict=sta... | 1,271 | /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... | 1,271 | /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)
... | 1,271 | /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_transformer (`bool`, defaults to `True`): Whether to unfuse the UNet LoRA parameters.
"""
super().unfuse_lora(components=components) | 1,271 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
class LoraLoaderMixin(StableDiffusionLoraLoaderMixin):
def __init__(self, *args, **kwargs):
deprecation_message = "LoraLoaderMixin is deprecated and this will be removed in a future version. Please use `StableDiffusionLoraLoaderMixin`, instead."
deprecate("LoraLoaderMixin", "1.0.0", deprecation_mess... | 1,272 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/lora_pipeline.py |
class IPAdapterMixin:
"""Mixin for handling IP Adapters."""
@validate_hf_hub_args
def load_ip_adapter(
self,
pretrained_model_name_or_path_or_dict: Union[str, List[str], Dict[str, torch.Tensor]],
subfolder: Union[str, List[str]],
weight_name: Union[str, List[str]],
i... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.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`].
... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
The subfolder location of the image encoder within a larger model repository on the Hub or locally.
Pass `None` to not load the image encoder. If the image encoder is located in a folder inside
`subfolder`, you only need to pass the name of the folder that contains image encoder weights,... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.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`):
... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while l... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# handle the list inputs for multiple IP Adapters
if not isinstance(weight_name, list):
weight_name = [weight_name]
if not isinstance(pretrained_model_name_or_path_or_dict, list):
pretrained_model_name_or_path_or_dict = [pretrained_model_name_or_path_or_dict]
if len(pret... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# Load the main state dict first.
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 = kwar... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
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`."
) | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
}
state_dicts = []
for pretrained_model_name_or_path_or_dict, weight_name, subfolder in zip(
pretrained_model_name_or_path_or_dict, weight_name, subfolder
):
if not i... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
with safe_open(model_file, framework="pt", device="cpu") as f:
for key in f.keys():
if key.startswith("image_proj."):
state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
elif key.star... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
keys = list(state_dict.keys())
if "image_proj" not in keys and "ip_adapter" not in keys:
raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.")
state_dicts.append(state_dict)
# load CLIP image encoder here if it has not be... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
pretrained_model_name_or_path_or_dict,
subfolder=image_encoder_subfolder,
low_cpu_mem_usage=low_cpu_mem_usage,
cache_dir=cache_dir,
... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
"Use `ip_adapter_image_embeds` to pass pre-generated image embedding instead."
) | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
# create feature extractor if it has not been registered to the pipeline yet
if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None:
# FaceID IP adapters don't need the image encoder so it's not present, in this case we default to 224
defau... | 1,273 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/loaders/ip_adapter.py |
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