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(doc/foundationals) add SDLoraManager
, related docstrings
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@ -5,3 +5,5 @@
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::: refiners.foundationals.latent_diffusion.stable_diffusion_1
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::: refiners.foundationals.latent_diffusion.solvers
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::: refiners.foundationals.latent_diffusion.lora
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@ -8,20 +8,34 @@ from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
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class SDLoraManager:
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"""Manage LoRAs for a Stable Diffusion model.
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Note:
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In the context of SDLoraManager, a "LoRA" is a set of ["LoRA layers"][refiners.fluxion.adapters.lora.Lora]
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that can be attached to a target model.
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"""
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def __init__(
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self,
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target: LatentDiffusionModel,
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) -> None:
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"""Initialize the LoRA manager.
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Args:
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target: The target model to manage the LoRAs for.
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"""
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self.target = target
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@property
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def unet(self) -> fl.Chain:
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"""The Stable Diffusion's U-Net model."""
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unet = self.target.unet
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assert isinstance(unet, fl.Chain)
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return unet
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@property
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def clip_text_encoder(self) -> fl.Chain:
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"""The Stable Diffusion's text encoder."""
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clip_text_encoder = self.target.clip_text_encoder
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assert isinstance(clip_text_encoder, fl.Chain)
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return clip_text_encoder
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@ -33,23 +47,44 @@ class SDLoraManager:
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tensors: dict[str, Tensor],
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scale: float = 1.0,
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) -> None:
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"""Load the LoRA weights from a dictionary of tensors.
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"""Load a single LoRA from a `state_dict`.
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Expects the keys to be in the commonly found formats on CivitAI's hub.
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Warning:
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This method expects the keys of the `state_dict` to be in the commonly found formats on CivitAI's hub.
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Args:
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name: The name of the LoRA.
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tensors: The `state_dict` of the LoRA to load.
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scale: The scale to use for the LoRA.
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Raises:
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AssertionError: If the Manager already has a LoRA with the same name.
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"""
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assert name not in self.names, f"LoRA {name} already exists"
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# load LoRA the state_dict
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loras = Lora.from_dict(
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name, {key: value.to(device=self.target.device, dtype=self.target.dtype) for key, value in tensors.items()}
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name,
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state_dict={
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key: value.to(
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device=self.target.device,
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dtype=self.target.dtype,
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)
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for key, value in tensors.items()
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},
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)
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# sort all the LoRA's keys using the `sort_keys` method
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loras = {key: loras[key] for key in sorted(loras.keys(), key=SDLoraManager.sort_keys)}
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# if no key contains "unet" or "text", assume all keys are for the unet
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if all("unet" not in key and "text" not in key for key in loras.keys()):
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loras = {f"unet_{key}": value for key, value in loras.items()}
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# attach the LoRA to the target
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self.add_loras_to_unet(loras)
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self.add_loras_to_text_encoder(loras)
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# set the scale of the LoRA
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self.set_scale(name, scale)
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def add_multiple_loras(
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@ -58,14 +93,36 @@ class SDLoraManager:
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tensors: dict[str, dict[str, Tensor]],
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scale: dict[str, float] | None = None,
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) -> None:
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"""Load multiple LoRAs from a dictionary of `state_dict`.
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Args:
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tensors: The dictionary of `state_dict` of the LoRAs to load
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(keys are the names of the LoRAs, values are the `state_dict` of the LoRAs).
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scale: The scales to use for the LoRAs.
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Raises:
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AssertionError: If the manager already has a LoRA with the same name.
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"""
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for name, lora_tensors in tensors.items():
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self.add_loras(name, tensors=lora_tensors, scale=scale[name] if scale else 1.0)
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def add_loras_to_text_encoder(self, loras: dict[str, Lora], /) -> None:
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"""Add multiple LoRAs to the text encoder.
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Args:
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loras: The dictionary of LoRAs to add to the text encoder.
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(keys are the names of the LoRAs, values are the LoRAs to add to the text encoder)
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"""
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text_encoder_loras = {key: loras[key] for key in loras.keys() if "text" in key}
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SDLoraManager.auto_attach(text_encoder_loras, self.clip_text_encoder)
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def add_loras_to_unet(self, loras: dict[str, Lora], /) -> None:
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"""Add multiple LoRAs to the U-Net.
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Args:
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loras: The dictionary of LoRAs to add to the U-Net.
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(keys are the names of the LoRAs, values are the LoRAs to add to the U-Net)
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"""
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unet_loras = {key: loras[key] for key in loras.keys() if "unet" in key}
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exclude = [
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block for s, block in self.unet_exclusions.items() if all([s not in key for key in unet_loras.keys()])
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@ -73,6 +130,11 @@ class SDLoraManager:
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SDLoraManager.auto_attach(unet_loras, self.unet, exclude=exclude)
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def remove_loras(self, *names: str) -> None:
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"""Remove mulitple LoRAs from the target.
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Args:
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names: The names of the LoRAs to remove.
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"""
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for lora_adapter in self.lora_adapters:
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for name in names:
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lora_adapter.remove_lora(name)
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@ -81,21 +143,47 @@ class SDLoraManager:
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lora_adapter.eject()
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def remove_all(self) -> None:
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"""Remove all the LoRAs from the target."""
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for lora_adapter in self.lora_adapters:
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lora_adapter.eject()
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def get_loras_by_name(self, name: str, /) -> list[Lora]:
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"""Get the LoRA layers with the given name.
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Args:
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name: The name of the LoRA.
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"""
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return [lora for lora in self.loras if lora.name == name]
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def get_scale(self, name: str, /) -> float:
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"""Get the scale of the LoRA with the given name.
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Args:
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name: The name of the LoRA.
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Returns:
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The scale of the LoRA layers with the given name.
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"""
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loras = self.get_loras_by_name(name)
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assert all([lora.scale == loras[0].scale for lora in loras]), "lora scales are not all the same"
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return loras[0].scale
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def set_scale(self, name: str, scale: float, /) -> None:
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"""Set the scale of the LoRA with the given name.
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Args:
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name: The name of the LoRA.
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scale: The new scale to set.
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"""
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self.update_scales({name: scale})
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def update_scales(self, scales: dict[str, float], /) -> None:
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"""Update the scales of mulitple LoRAs.
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Args:
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scales: The scales to update.
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(keys are the names of the LoRAs, values are the new scales to set)
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"""
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assert all([name in self.names for name in scales]), f"Scales keys must be a subset of {self.names}"
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for name, scale in scales.items():
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for lora in self.get_loras_by_name(name):
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@ -103,14 +191,17 @@ class SDLoraManager:
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@property
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def loras(self) -> list[Lora]:
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"""List of all the LoRA layers managed by the SDLoraManager."""
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return list(self.unet.layers(Lora)) + list(self.clip_text_encoder.layers(Lora))
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@property
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def names(self) -> list[str]:
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"""List of all the LoRA names managed the SDLoraManager"""
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return list(set(lora.name for lora in self.loras))
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@property
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def lora_adapters(self) -> list[LoraAdapter]:
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"""List of all the LoraAdapters managed by the SDLoraManager."""
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return list(self.unet.layers(LoraAdapter)) + list(self.clip_text_encoder.layers(LoraAdapter))
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@property
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@ -124,6 +215,7 @@ class SDLoraManager:
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@property
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def scales(self) -> dict[str, float]:
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"""The scales of all the LoRAs managed by the SDLoraManager."""
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return {name: self.get_scale(name) for name in self.names}
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@staticmethod
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