DiffusionAi/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI.git synced 2025-01-07 03:17:05 +08:00
Code Issues Packages Projects Releases Wiki Activity

Enable LoRAPatcher.apply_smart_lora_patches(...) throughout the stack.

Browse Source
This commit is contained in:
Ryan Dick 2024-12-10 17:27:33 +00:00
parent 52c9646f84
commit 3ed6e65a6e
6 changed files with 17 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
invokeai/app/invocations/compel.py
View File

@ -82,10 +82,11 @@ class CompelInvocation(BaseInvocation):
# apply all patches while the model is on the target device
text_encoder_info.model_on_device() as (cached_weights, text_encoder),
tokenizer_info as tokenizer,
LoRAPatcher.apply_lora_patches(
LoRAPatcher.apply_smart_lora_patches(
model=text_encoder,
patches=_lora_loader(),
prefix="lora_te_",
dtype=TorchDevice.choose_torch_dtype(),
cached_weights=cached_weights,
),
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
@ -179,10 +180,11 @@ class SDXLPromptInvocationBase:
# apply all patches while the model is on the target device
text_encoder_info.model_on_device() as (cached_weights, text_encoder),
tokenizer_info as tokenizer,
LoRAPatcher.apply_lora_patches(
LoRAPatcher.apply_smart_lora_patches(
text_encoder,
patches=_lora_loader(),
prefix=lora_prefix,
dtype=TorchDevice.choose_torch_dtype(),
cached_weights=cached_weights,
),
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.

3
invokeai/app/invocations/denoise_latents.py
View File

@ -1003,10 +1003,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
SeamlessExt.static_patch_model(unet, self.unet.seamless_axes), # FIXME
# Apply the LoRA after unet has been moved to its target device for faster patching.
LoRAPatcher.apply_lora_patches(
LoRAPatcher.apply_smart_lora_patches(
model=unet,
patches=_lora_loader(),
prefix="lora_unet_",
dtype=unet.dtype,
cached_weights=cached_weights,
),
):

3
invokeai/app/invocations/flux_denoise.py
View File

@ -310,10 +310,11 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
if config.format in [ModelFormat.Checkpoint]:
# The model is non-quantized, so we can apply the LoRA weights directly into the model.
exit_stack.enter_context(
LoRAPatcher.apply_lora_patches(
LoRAPatcher.apply_smart_lora_patches(
model=transformer,
patches=self._lora_iterator(context),
prefix=FLUX_LORA_TRANSFORMER_PREFIX,
dtype=inference_dtype,
cached_weights=cached_weights,
)
)

4
invokeai/app/invocations/flux_text_encoder.py
View File

@ -22,6 +22,7 @@ from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
from invokeai.backend.lora.lora_patcher import LoRAPatcher
from invokeai.backend.model_manager.config import ModelFormat
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
from invokeai.backend.util.devices import TorchDevice
@invocation(
@ -111,10 +112,11 @@ class FluxTextEncoderInvocation(BaseInvocation):
if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
# The model is non-quantized, so we can apply the LoRA weights directly into the model.
exit_stack.enter_context(
LoRAPatcher.apply_lora_patches(
LoRAPatcher.apply_smart_lora_patches(
model=clip_text_encoder,
patches=self._clip_lora_iterator(context),
prefix=FLUX_LORA_CLIP_PREFIX,
dtype=TorchDevice.choose_torch_dtype(),
cached_weights=cached_weights,
)
)

4
invokeai/app/invocations/sd3_text_encoder.py
View File

@ -21,6 +21,7 @@ from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
from invokeai.backend.lora.lora_patcher import LoRAPatcher
from invokeai.backend.model_manager.config import ModelFormat
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, SD3ConditioningInfo
from invokeai.backend.util.devices import TorchDevice
# The SD3 T5 Max Sequence Length set based on the default in diffusers.
SD3_T5_MAX_SEQ_LEN = 256
@ -150,10 +151,11 @@ class Sd3TextEncoderInvocation(BaseInvocation):
if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
# The model is non-quantized, so we can apply the LoRA weights directly into the model.
exit_stack.enter_context(
LoRAPatcher.apply_lora_patches(
LoRAPatcher.apply_smart_lora_patches(
model=clip_text_encoder,
patches=self._clip_lora_iterator(context, clip_model),
prefix=FLUX_LORA_CLIP_PREFIX,
dtype=TorchDevice.choose_torch_dtype(),
cached_weights=cached_weights,
)
)

4
invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
View File

@ -207,7 +207,9 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
with (
ExitStack() as exit_stack,
unet_info as unet,
LoRAPatcher.apply_lora_patches(model=unet, patches=_lora_loader(), prefix="lora_unet_"),
LoRAPatcher.apply_smart_lora_patches(
model=unet, patches=_lora_loader(), prefix="lora_unet_", dtype=unet.dtype
),
):
assert isinstance(unet, UNet2DConditionModel)
latents = latents.to(device=unet.device, dtype=unet.dtype)