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Unverified Commit 29d2afbf authored by Sayak Paul's avatar Sayak Paul Committed by GitHub
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[LoRA] Propagate `hotswap` better (#11333) 

* propagate hotswap to other load_lora_weights() methods.

* simplify documentations.

* updates

* propagate to load_lora_into_text_encoder.

* empty commit
parent b00a564d
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Showing with 135 additions and 504 deletions
src/diffusers/loaders/lora_pipeline.py
View file @ 29d2afbf
......@@ -127,7 +127,7 @@ class StableDiffusionLoraLoaderMixin(LoraBaseMixin):
def load_lora_weights(
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name=None,
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
......@@ -154,7 +154,7 @@ class StableDiffusionLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
hotswap (`bool`, *optional*):
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
......@@ -368,29 +368,8 @@ class StableDiffusionLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -451,29 +430,8 @@ class StableDiffusionLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
_load_lora_into_text_encoder(
state_dict=state_dict,
......@@ -625,6 +583,7 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
......@@ -651,6 +610,8 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -689,6 +650,7 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
self.load_lora_into_text_encoder(
state_dict,
......@@ -699,6 +661,7 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
self.load_lora_into_text_encoder(
state_dict,
......@@ -709,6 +672,7 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -859,29 +823,8 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -943,29 +886,8 @@ class StableDiffusionXLLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
_load_lora_into_text_encoder(
state_dict=state_dict,
......@@ -1248,29 +1170,8 @@ class SD3LoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -1345,29 +1246,8 @@ class SD3LoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......@@ -1423,29 +1303,8 @@ class SD3LoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
_load_lora_into_text_encoder(
state_dict=state_dict,
......@@ -1701,7 +1560,11 @@ class AuraFlowLoraLoaderMixin(LoraBaseMixin):
# 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
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
......@@ -1719,6 +1582,8 @@ class AuraFlowLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -1748,6 +1613,7 @@ class AuraFlowLoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -1771,29 +1637,8 @@ class AuraFlowLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......@@ -2076,7 +1921,7 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
def load_lora_weights(
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name=None,
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
......@@ -2095,34 +1940,16 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
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*):
`Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter. If the new
adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need to call an
additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -2244,29 +2071,8 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and not is_peft_version(">=", "0.13.1"):
raise ValueError(
......@@ -2376,29 +2182,8 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
_load_lora_into_text_encoder(
state_dict=state_dict,
......@@ -2848,39 +2633,18 @@ class AmusedLoraLoaderMixin(StableDiffusionLoraLoaderMixin):
encoder lora layers.
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).
transformer (`UVit2DModel`):
The Transformer model to load the LoRA layers into.
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*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
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).
transformer (`UVit2DModel`):
The Transformer model to load the LoRA layers into.
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*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and not is_peft_version(">=", "0.13.1"):
raise ValueError(
......@@ -2936,29 +2700,8 @@ class AmusedLoraLoaderMixin(StableDiffusionLoraLoaderMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
_load_lora_into_text_encoder(
state_dict=state_dict,
......@@ -3135,7 +2878,11 @@ class CogVideoXLoraLoaderMixin(LoraBaseMixin):
return state_dict
def load_lora_weights(
self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
......@@ -3153,6 +2900,8 @@ class CogVideoXLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -3182,6 +2931,7 @@ class CogVideoXLoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -3205,29 +2955,8 @@ class CogVideoXLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......@@ -3466,7 +3195,11 @@ class Mochi1LoraLoaderMixin(LoraBaseMixin):
# 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
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
......@@ -3484,6 +3217,8 @@ class Mochi1LoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -3513,6 +3248,7 @@ class Mochi1LoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -3536,29 +3272,8 @@ class Mochi1LoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......@@ -3799,7 +3514,11 @@ class LTXVideoLoraLoaderMixin(LoraBaseMixin):
# 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
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
......@@ -3817,6 +3536,8 @@ class LTXVideoLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -3846,6 +3567,7 @@ class LTXVideoLoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -3869,29 +3591,8 @@ class LTXVideoLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......@@ -4132,7 +3833,11 @@ class SanaLoraLoaderMixin(LoraBaseMixin):
# 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
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
......@@ -4150,6 +3855,8 @@ class SanaLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -4179,6 +3886,7 @@ class SanaLoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -4202,29 +3910,8 @@ class SanaLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......@@ -4468,7 +4155,11 @@ class HunyuanVideoLoraLoaderMixin(LoraBaseMixin):
# 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
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
......@@ -4486,6 +4177,8 @@ class HunyuanVideoLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -4515,6 +4208,7 @@ class HunyuanVideoLoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -4538,29 +4232,8 @@ class HunyuanVideoLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......@@ -4805,7 +4478,11 @@ class Lumina2LoraLoaderMixin(LoraBaseMixin):
# 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
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
......@@ -4823,6 +4500,8 @@ class Lumina2LoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -4852,6 +4531,7 @@ class Lumina2LoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -4875,29 +4555,8 @@ class Lumina2LoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......@@ -5167,7 +4826,11 @@ class WanLoraLoaderMixin(LoraBaseMixin):
return state_dict
def load_lora_weights(
self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], adapter_name=None, **kwargs
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
......@@ -5185,6 +4848,8 @@ class WanLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -5218,6 +4883,7 @@ class WanLoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -5241,29 +4907,8 @@ class WanLoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......@@ -5504,7 +5149,11 @@ class CogView4LoraLoaderMixin(LoraBaseMixin):
# 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
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
adapter_name: Optional[str] = None,
hotswap: bool = False,
**kwargs,
):
"""
Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
......@@ -5522,6 +5171,8 @@ class CogView4LoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
kwargs (`dict`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
"""
......@@ -5551,6 +5202,7 @@ class CogView4LoraLoaderMixin(LoraBaseMixin):
adapter_name=adapter_name,
_pipeline=self,
low_cpu_mem_usage=low_cpu_mem_usage,
hotswap=hotswap,
)
@classmethod
......@@ -5574,29 +5226,8 @@ class CogView4LoraLoaderMixin(LoraBaseMixin):
low_cpu_mem_usage (`bool`, *optional*):
Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
weights.
hotswap : (`bool`, *optional*)
Defaults to `False`. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter
in-place. This means that, instead of loading an additional adapter, this will take the existing
adapter weights and replace them with the weights of the new adapter. This can be faster and more
memory efficient. However, the main advantage of hotswapping is that when the model is compiled with
torch.compile, loading the new adapter does not require recompilation of the model. When using
hotswapping, the passed `adapter_name` should be the name of an already loaded adapter.
If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need
to call an additional method before loading the adapter:
```py
pipeline = ... # load diffusers pipeline
max_rank = ... # the highest rank among all LoRAs that you want to load
# call *before* compiling and loading the LoRA adapter
pipeline.enable_lora_hotswap(target_rank=max_rank)
pipeline.load_lora_weights(file_name)
# optionally compile the model now
```
Note that hotswapping adapters of the text encoder is not yet supported. There are some further
limitations to this technique, which are documented here:
https://huggingface.co/docs/peft/main/en/package_reference/hotswap
hotswap (`bool`, *optional*):
See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
"""
if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
raise ValueError(
......
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