(doc/foundationals) add SDLoraManager, related docstrings

This commit is contained in:
Laurent 2024-02-02 15:50:20 +00:00 committed by Laureηt
parent 7406d8e01f
commit 6b35f1cc84
2 changed files with 97 additions and 3 deletions

View file

@ -5,3 +5,5 @@
::: refiners.foundationals.latent_diffusion.stable_diffusion_1
::: refiners.foundationals.latent_diffusion.solvers
::: refiners.foundationals.latent_diffusion.lora

View file

@ -8,20 +8,34 @@ from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
class SDLoraManager:
"""Manage LoRAs for a Stable Diffusion model.
Note:
In the context of SDLoraManager, a "LoRA" is a set of ["LoRA layers"][refiners.fluxion.adapters.lora.Lora]
that can be attached to a target model.
"""
def __init__(
self,
target: LatentDiffusionModel,
) -> None:
"""Initialize the LoRA manager.
Args:
target: The target model to manage the LoRAs for.
"""
self.target = target
@property
def unet(self) -> fl.Chain:
"""The Stable Diffusion's U-Net model."""
unet = self.target.unet
assert isinstance(unet, fl.Chain)
return unet
@property
def clip_text_encoder(self) -> fl.Chain:
"""The Stable Diffusion's text encoder."""
clip_text_encoder = self.target.clip_text_encoder
assert isinstance(clip_text_encoder, fl.Chain)
return clip_text_encoder
@ -33,23 +47,44 @@ class SDLoraManager:
tensors: dict[str, Tensor],
scale: float = 1.0,
) -> None:
"""Load the LoRA weights from a dictionary of tensors.
"""Load a single LoRA from a `state_dict`.
Expects the keys to be in the commonly found formats on CivitAI's hub.
Warning:
This method expects the keys of the `state_dict` to be in the commonly found formats on CivitAI's hub.
Args:
name: The name of the LoRA.
tensors: The `state_dict` of the LoRA to load.
scale: The scale to use for the LoRA.
Raises:
AssertionError: If the Manager already has a LoRA with the same name.
"""
assert name not in self.names, f"LoRA {name} already exists"
# load LoRA the state_dict
loras = Lora.from_dict(
name, {key: value.to(device=self.target.device, dtype=self.target.dtype) for key, value in tensors.items()}
name,
state_dict={
key: value.to(
device=self.target.device,
dtype=self.target.dtype,
)
for key, value in tensors.items()
},
)
# sort all the LoRA's keys using the `sort_keys` method
loras = {key: loras[key] for key in sorted(loras.keys(), key=SDLoraManager.sort_keys)}
# if no key contains "unet" or "text", assume all keys are for the unet
if all("unet" not in key and "text" not in key for key in loras.keys()):
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)
# set the scale of the LoRA
self.set_scale(name, scale)
def add_multiple_loras(
@ -58,14 +93,36 @@ class SDLoraManager:
tensors: dict[str, dict[str, Tensor]],
scale: dict[str, float] | None = None,
) -> None:
"""Load multiple LoRAs from a dictionary of `state_dict`.
Args:
tensors: The dictionary of `state_dict` of the LoRAs to load
(keys are the names of the LoRAs, values are the `state_dict` of the LoRAs).
scale: The scales to use for the LoRAs.
Raises:
AssertionError: If the manager already has a LoRA with the same name.
"""
for name, lora_tensors in tensors.items():
self.add_loras(name, tensors=lora_tensors, scale=scale[name] if scale else 1.0)
def add_loras_to_text_encoder(self, loras: dict[str, Lora], /) -> None:
"""Add multiple LoRAs to the text encoder.
Args:
loras: The dictionary of LoRAs to add to the text encoder.
(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}
SDLoraManager.auto_attach(text_encoder_loras, self.clip_text_encoder)
def add_loras_to_unet(self, loras: dict[str, Lora], /) -> None:
"""Add multiple LoRAs to the U-Net.
Args:
loras: The dictionary of LoRAs to add to the U-Net.
(keys are the names of the LoRAs, values are the LoRAs to add to the U-Net)
"""
unet_loras = {key: loras[key] for key in loras.keys() if "unet" in key}
exclude = [
block for s, block in self.unet_exclusions.items() if all([s not in key for key in unet_loras.keys()])
@ -73,6 +130,11 @@ class SDLoraManager:
SDLoraManager.auto_attach(unet_loras, self.unet, exclude=exclude)
def remove_loras(self, *names: str) -> None:
"""Remove mulitple LoRAs from the target.
Args:
names: The names of the LoRAs to remove.
"""
for lora_adapter in self.lora_adapters:
for name in names:
lora_adapter.remove_lora(name)
@ -81,21 +143,47 @@ class SDLoraManager:
lora_adapter.eject()
def remove_all(self) -> None:
"""Remove all the LoRAs from the target."""
for lora_adapter in self.lora_adapters:
lora_adapter.eject()
def get_loras_by_name(self, name: str, /) -> list[Lora]:
"""Get the LoRA layers with the given name.
Args:
name: The name of the LoRA.
"""
return [lora for lora in self.loras if lora.name == name]
def get_scale(self, name: str, /) -> float:
"""Get the scale of the LoRA with the given name.
Args:
name: The name of the LoRA.
Returns:
The scale of the LoRA layers with the given name.
"""
loras = self.get_loras_by_name(name)
assert all([lora.scale == loras[0].scale for lora in loras]), "lora scales are not all the same"
return loras[0].scale
def set_scale(self, name: str, scale: float, /) -> None:
"""Set the scale of the LoRA with the given name.
Args:
name: The name of the LoRA.
scale: The new scale to set.
"""
self.update_scales({name: scale})
def update_scales(self, scales: dict[str, float], /) -> None:
"""Update the scales of mulitple LoRAs.
Args:
scales: The scales to update.
(keys are the names of the LoRAs, values are the new scales to set)
"""
assert all([name in self.names for name in scales]), f"Scales keys must be a subset of {self.names}"
for name, scale in scales.items():
for lora in self.get_loras_by_name(name):
@ -103,14 +191,17 @@ class SDLoraManager:
@property
def loras(self) -> list[Lora]:
"""List of all the LoRA layers managed by the SDLoraManager."""
return list(self.unet.layers(Lora)) + list(self.clip_text_encoder.layers(Lora))
@property
def names(self) -> list[str]:
"""List of all the LoRA names managed the SDLoraManager"""
return list(set(lora.name for lora in self.loras))
@property
def lora_adapters(self) -> list[LoraAdapter]:
"""List of all the LoraAdapters managed by the SDLoraManager."""
return list(self.unet.layers(LoraAdapter)) + list(self.clip_text_encoder.layers(LoraAdapter))
@property
@ -124,6 +215,7 @@ class SDLoraManager:
@property
def scales(self) -> dict[str, float]:
"""The scales of all the LoRAs managed by the SDLoraManager."""
return {name: self.get_scale(name) for name in self.names}
@staticmethod