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Allow users to disable lora patching optimization introduced by #6439 #6628

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Allow users to disable lora patching optimization introduced by #6439 #6628

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4ef3086
Allow users to disable lora patching optimization introduced by #6439
leo9800 Jul 16, 2024
43fb5ca
Merge branch 'main' into configuration-disable-lora-patching-opt
leo9800 Jul 17, 2024
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4 changes: 2 additions & 2 deletions invokeai/app/invocations/compel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
ModelPatcher.apply_lora_text_encoder(
text_encoder,
loras=_lora_loader(),
model_state_dict=model_state_dict,
model_state_dict=None if context.config.get().disable_lora_patching_opt else model_state_dict,
),
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
ModelPatcher.apply_clip_skip(text_encoder, self.clip.skipped_layers),
Expand Down Expand Up @@ -181,7 +181,7 @@ def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
text_encoder,
loras=_lora_loader(),
prefix=lora_prefix,
model_state_dict=state_dict,
model_state_dict=None if context.config.get().disable_lora_patching_opt else state_dict,
),
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
ModelPatcher.apply_clip_skip(text_encoder, clip_field.skipped_layers),
Expand Down
2 changes: 1 addition & 1 deletion invokeai/app/invocations/denoise_latents.py
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Expand Up @@ -762,7 +762,7 @@ def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
ModelPatcher.apply_lora_unet(
unet,
loras=_lora_loader(),
model_state_dict=model_state_dict,
model_state_dict=None if context.config.get().disable_lora_patching_opt else model_state_dict,
),
):
assert isinstance(unet, UNet2DConditionModel)
Expand Down
2 changes: 2 additions & 0 deletions invokeai/app/services/config/config_default.py
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Expand Up @@ -113,6 +113,7 @@ class InvokeAIAppConfig(BaseSettings):
pil_compress_level: The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.
max_queue_size: Maximum number of items in the session queue.
clear_queue_on_startup: Empties session queue on startup.
disable_lora_patching_opt: Disable LoRA patching optimization.
allow_nodes: List of nodes to allow. Omit to allow all.
deny_nodes: List of nodes to deny. Omit to deny none.
node_cache_size: How many cached nodes to keep in memory.
Expand Down Expand Up @@ -186,6 +187,7 @@ class InvokeAIAppConfig(BaseSettings):
pil_compress_level: int = Field(default=1, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.")
max_queue_size: int = Field(default=10000, gt=0, description="Maximum number of items in the session queue.")
clear_queue_on_startup: bool = Field(default=False, description="Empties session queue on startup.")
disable_lora_patching_opt: bool = Field(default=False, description="Disable LoRA patching optimization.")

# NODES
allow_nodes: Optional[list[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.")
Expand Down