LoRA manager: move exclude / include to add_loras call

Always exclude the TimestepEncoder by default.
This is because some keys include both e.g. `resnet` and `time_emb_proj`.

Preprocess blocks that tend to mix up with others in a separate
auto_attach call.
This commit is contained in:
Pierre Chapuis 2024-03-05 16:51:02 +01:00
parent 052a20b897
commit 8c7fcbc00f
3 changed files with 42 additions and 30 deletions

View file

@ -18,10 +18,6 @@ class SDLoraManager:
def __init__(
self,
target: LatentDiffusionModel,
unet_inclusions: list[str] | None = None,
unet_exclusions: list[str] | None = None,
text_encoder_inclusions: list[str] | None = None,
text_encoder_exclusions: list[str] | None = None,
) -> None:
"""Initialize the LoRA manager.
@ -29,10 +25,6 @@ class SDLoraManager:
target: The target model to manage the LoRAs for.
"""
self.target = target
self.unet_inclusions = unet_inclusions
self.unet_exclusions = unet_exclusions
self.text_encoder_inclusions = text_encoder_inclusions
self.text_encoder_exclusions = text_encoder_exclusions
@property
def unet(self) -> fl.Chain:
@ -54,6 +46,11 @@ class SDLoraManager:
/,
tensors: dict[str, Tensor],
scale: float = 1.0,
unet_inclusions: list[str] | None = None,
unet_exclusions: list[str] | None = None,
unet_preprocess: dict[str, str] | None = None,
text_encoder_inclusions: list[str] | None = None,
text_encoder_exclusions: list[str] | None = None,
) -> None:
"""Load a single LoRA from a `state_dict`.
@ -89,13 +86,19 @@ class SDLoraManager:
loras = {f"unet_{key}": value for key, value in loras.items()}
# attach the LoRA to the target
self.add_loras_to_unet(loras)
self.add_loras_to_text_encoder(loras)
self.add_loras_to_unet(loras, include=unet_inclusions, exclude=unet_exclusions, preprocess=unet_preprocess)
self.add_loras_to_text_encoder(loras, include=text_encoder_inclusions, exclude=text_encoder_exclusions)
# set the scale of the LoRA
self.set_scale(name, scale)
def add_loras_to_text_encoder(self, loras: dict[str, Lora[Any]], /) -> None:
def add_loras_to_text_encoder(
self,
loras: dict[str, Lora[Any]],
/,
include: list[str] | None = None,
exclude: list[str] | None = None,
) -> None:
"""Add multiple LoRAs to the text encoder.
Args:
@ -103,14 +106,16 @@ class SDLoraManager:
(keys are the names of the LoRAs, values are the LoRAs to add to the text encoder)
"""
text_encoder_loras = {key: loras[key] for key in loras.keys() if "text" in key}
auto_attach_loras(
text_encoder_loras,
self.clip_text_encoder,
exclude=self.text_encoder_exclusions,
include=self.text_encoder_inclusions,
)
auto_attach_loras(text_encoder_loras, self.clip_text_encoder, exclude=exclude, include=include)
def add_loras_to_unet(self, loras: dict[str, Lora[Any]], /) -> None:
def add_loras_to_unet(
self,
loras: dict[str, Lora[Any]],
/,
include: list[str] | None = None,
exclude: list[str] | None = None,
preprocess: dict[str, str] | None = None,
) -> None:
"""Add multiple LoRAs to the U-Net.
Args:
@ -119,20 +124,29 @@ class SDLoraManager:
"""
unet_loras = {key: loras[key] for key in loras.keys() if "unet" in key}
if self.unet_exclusions is None:
auto_exclusions = {
"time": "TimestepEncoder",
if exclude is None:
exclude = ["TimestepEncoder"]
if preprocess is None:
preprocess = {
"res": "ResidualBlock",
"downsample": "Downsample",
"upsample": "Upsample",
}
exclusions = [
block for s, block in auto_exclusions.items() if all([s not in key for key in unet_loras.keys()])
]
else:
exclusions = self.unet_exclusions
auto_attach_loras(unet_loras, self.unet, exclude=exclusions, include=self.unet_inclusions)
if include is not None:
preprocess = {k: v for k, v in preprocess.items() if v in include}
preprocess = {k: v for k, v in preprocess.items() if v not in exclude}
loras_excluded = {k: v for k, v in unet_loras.items() if any(x in k for x in preprocess.keys())}
loras_remaining = {k: v for k, v in unet_loras.items() if k not in loras_excluded}
for exc, v in preprocess.items():
ls = {k: v for k, v in loras_excluded.items() if exc in k}
auto_attach_loras(ls, self.unet, include=[v])
auto_attach_loras(loras_remaining, self.unet, exclude=exclude, include=include)
def remove_loras(self, *names: str) -> None:
"""Remove multiple LoRAs from the target.

View file

@ -66,7 +66,6 @@ def add_lcm_lora(
debug_map=debug_map,
)
# Do *not* check for time because some keys include both `resnets` and `time_emb_proj`.
exclusions = {
"res": "ResidualBlock",
"downsample": "Downsample",

View file

@ -1394,8 +1394,7 @@ def test_diffusion_sdxl_lora(
prompt = "professional portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography"
negative_prompt = "3d render, cartoon, drawing, art, low light, blur, pixelated, low resolution, black and white"
manager = SDLoraManager(sdxl, unet_inclusions=["CrossAttentionBlock"])
manager.add_loras("dpo", lora_weights, scale=lora_scale)
SDLoraManager(sdxl).add_loras("dpo", lora_weights, scale=lora_scale, unet_inclusions=["CrossAttentionBlock"])
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
text=prompt, negative_text=negative_prompt