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implement ControlLora and ControlLoraAdapter
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Laurent
Laureηt
parent
a54808e757
commit
41a5ce2052
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from torch import Tensor, device as Device, dtype as DType
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from refiners.fluxion.adapters.adapter import Adapter
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from refiners.fluxion.adapters.lora import Lora, LoraAdapter
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from refiners.fluxion.context import Contexts
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from refiners.fluxion.layers import Chain, Conv2d, Multiply, Passthrough, Residual, SiLU, UseContext
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from refiners.fluxion.layers.module import WeightedModule
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from refiners.foundationals.latent_diffusion.range_adapter import RangeAdapter2d
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualAccumulator
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
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from refiners.foundationals.latent_diffusion.unet import ResidualBlock
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class ConditionEncoder(Chain):
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"""Encode an image into a condition latent tensor.
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Receives:
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(Float[Tensor, "batch in_channels width height"]): The input image.
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Returns:
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(Float[Tensor, "batch out_channels latent_width latent_height"]): The condition latent tensor.
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"""
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def __init__(
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self,
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in_channels: int = 3,
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out_channels: int = 320,
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intermediate_channels: tuple[int, ...] = (16, 32, 96, 256),
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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"""Initialize the ConditionEncoder.
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Args:
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in_channels: The number of channels of the image tensor.
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out_channels: The number of channels of the latent tensor to encode the condition into.
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intermediate_channels: The number of channels of the intermediate layers.
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device: The PyTorch device to use.
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dtype: The PyTorch data type to use.
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"""
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super().__init__(
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Chain(
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Conv2d(
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in_channels=in_channels,
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out_channels=intermediate_channels[0],
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kernel_size=3,
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stride=1,
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padding=1,
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device=device,
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dtype=dtype,
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),
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SiLU(),
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),
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*(
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Chain(
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Conv2d(
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in_channels=intermediate_channels[i],
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out_channels=intermediate_channels[i],
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kernel_size=3,
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padding=1,
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device=device,
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dtype=dtype,
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),
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SiLU(),
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Conv2d(
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in_channels=intermediate_channels[i],
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out_channels=intermediate_channels[i + 1],
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kernel_size=3,
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stride=2,
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padding=1,
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device=device,
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dtype=dtype,
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),
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SiLU(),
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)
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for i in range(len(intermediate_channels) - 1)
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),
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Conv2d(
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in_channels=intermediate_channels[-1],
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out_channels=out_channels,
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kernel_size=3,
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padding=1,
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device=device,
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dtype=dtype,
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),
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)
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class ZeroConvolution(Passthrough):
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"""Transform and store the ControlLora's residuals in the context of the original UNet.
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Receives:
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(Float[Tensor, "batch in_channels width height"]): The input tensor to transform and store.
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Returns: Updates context:
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(Tensor): Add the residual to the nth residual of the target's UNet.
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(context="unet", key="residuals")
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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residual_index: int,
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scale: float = 1.0,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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"""Initialize the ZeroConvolution.
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Args:
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in_channels: The number of channels of the input tensor.
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out_channels: The number of channels of the output tensor/residual.
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residual_index: The index of the residual to store in the target's UNet.
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scale: The scale to multiply the residuals by.
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device: The PyTorch device to use.
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dtype: The PyTorch data type to use.
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"""
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self.scale = scale
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super().__init__(
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Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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device=device,
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dtype=dtype,
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),
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Multiply(scale=scale),
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ResidualAccumulator(n=residual_index),
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)
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class ControlLora(Passthrough):
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"""ControlLora is a Half-UNet clone of the target UNet, patched with LoRAs.
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Like ControlNet, it injects residual tensors into the target UNet.
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See https://github.com/HighCWu/control-lora-v2 for more details.
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Receives: Gets context:
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(Float[Tensor, "batch condition_channels width height"]): The input image.
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Returns: Sets context:
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(list[Tensor]): The residuals to be added to the target UNet's residuals.
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(context="unet", key="residuals")
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"""
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def __init__(
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self,
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name: str,
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unet: SDXLUNet,
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scale: float = 1.0,
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condition_channels: int = 3,
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) -> None:
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"""Initialize the ControlLora.
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Args:
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name: The name of the ControlLora.
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unet: The target UNet.
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scale: The scale to multiply the residuals by.
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condition_channels: The number of channels of the input condition tensor.
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"""
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self.name = name
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super().__init__(
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timestep_encoder := unet.layer("TimestepEncoder", Chain).structural_copy(),
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downblocks := unet.layer("DownBlocks", Chain).structural_copy(),
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middle_block := unet.layer("MiddleBlock", Chain).structural_copy(),
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)
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# modify the context_key of the copied TimestepEncoder to avoid conflicts
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timestep_encoder.context_key = f"timestep_embedding_control_lora_{name}"
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# modify the context_key of each RangeAdapter2d to avoid conflicts
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for range_adapter in self.layers(RangeAdapter2d):
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range_adapter.context_key = f"timestep_embedding_control_lora_{name}"
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# insert the ConditionEncoder in the first DownBlock
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first_downblock = downblocks.layer(0, Chain)
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out_channels = first_downblock.layer(0, Conv2d).out_channels
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first_downblock.append(
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Residual(
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UseContext(f"control_lora_{name}", f"condition"),
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ConditionEncoder(
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in_channels=condition_channels,
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out_channels=out_channels,
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device=unet.device,
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dtype=unet.dtype,
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),
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)
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)
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# replace each ResidualAccumulator by a ZeroConvolution
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for residual_accumulator in self.layers(ResidualAccumulator):
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downblock = self.ensure_find_parent(residual_accumulator)
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first_layer = downblock[0]
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assert hasattr(first_layer, "out_channels"), f"{first_layer} has no out_channels attribute"
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block_channels = first_layer.out_channels
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assert isinstance(block_channels, int)
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downblock.replace(
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residual_accumulator,
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ZeroConvolution(
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scale=scale,
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residual_index=residual_accumulator.n,
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in_channels=block_channels,
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out_channels=block_channels,
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device=unet.device,
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dtype=unet.dtype,
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),
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)
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# append a ZeroConvolution to middle_block
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middle_block_channels = middle_block.layer(0, ResidualBlock).out_channels
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middle_block.append(
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ZeroConvolution(
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scale=scale,
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residual_index=len(downblocks),
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in_channels=middle_block_channels,
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out_channels=middle_block_channels,
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device=unet.device,
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dtype=unet.dtype,
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)
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)
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@property
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def scale(self) -> float:
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"""The scale of the injected residuals."""
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zero_convolution_module = self.ensure_find(ZeroConvolution)
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return zero_convolution_module.scale
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@scale.setter
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def scale(self, value: float) -> None:
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for zero_convolution_module in self.layers(ZeroConvolution):
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zero_convolution_module.scale = value
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class ControlLoraAdapter(Chain, Adapter[SDXLUNet]):
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"""Adapter for ControlLora.
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This adapter simply prepends a ControlLora model inside the target's UNet.
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"""
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def __init__(
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self,
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name: str,
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target: SDXLUNet,
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scale: float = 1.0,
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condition_channels: int = 3,
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weights: dict[str, Tensor] | None = None,
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) -> None:
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with self.setup_adapter(target):
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self.name = name
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self._control_lora = [
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ControlLora(
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name=name,
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unet=target,
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scale=scale,
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condition_channels=condition_channels,
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),
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]
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super().__init__(target)
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if weights:
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self.load_weights(weights)
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@property
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def control_lora(self) -> ControlLora:
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"""The ControlLora model."""
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return self._control_lora[0]
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def init_context(self) -> Contexts:
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return {
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f"control_lora_{self.name}": {
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"condition": None,
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}
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}
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def inject(self, parent: Chain | None = None) -> "ControlLoraAdapter":
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self.target.insert(index=0, module=self.control_lora)
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return super().inject(parent)
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def eject(self) -> None:
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self.target.remove(self.control_lora)
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return super().eject()
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def structural_copy(self) -> "ControlLoraAdapter":
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raise RuntimeError("ControlLoraAdapter cannot be copied, eject it first.")
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@property
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def scale(self) -> float:
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"""The scale of the injected residuals."""
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return self.control_lora.scale
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@scale.setter
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def scale(self, value: float) -> None:
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self.control_lora.scale = value
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def set_condition(self, condition: Tensor) -> None:
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self.set_context(
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context=f"control_lora_{self.name}",
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value={"condition": condition},
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)
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def load_weights(
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self,
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state_dict: dict[str, Tensor],
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) -> None:
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"""Load the weights from the state_dict into the ControlLora.
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Args:
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state_dict: The state_dict containing the weights to load.
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"""
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ControlLoraAdapter.load_lora_layers(self.name, state_dict, self.control_lora)
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ControlLoraAdapter.load_zero_convolution_layers(state_dict, self.control_lora)
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ControlLoraAdapter.load_condition_encoder(state_dict, self.control_lora)
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@staticmethod
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def load_lora_layers(
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name: str,
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state_dict: dict[str, Tensor],
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control_lora: ControlLora,
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) -> None:
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"""Load the LoRA layers from the state_dict into the ControlLora.
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Args:
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name: The name of the ControlLora.
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state_dict: The state_dict containing the LoRA layers to load.
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control_lora: The ControlLora to load the LoRA layers into.
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"""
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# filter the LoraAdapters from the state_dict
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lora_weights = {
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key.removeprefix("ControlLora."): value for key, value in state_dict.items() if "ControlLora" in key
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}
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lora_weights = {f"{key}.weight": value for key, value in lora_weights.items()}
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# move the tensors to the device and dtype of the ControlLora
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lora_weights = {
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key: value.to(
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dtype=control_lora.dtype,
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device=control_lora.device,
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)
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for key, value in lora_weights.items()
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}
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# load every LoRA layers from the filtered state_dict
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loras = Lora.from_dict(name, state_dict=lora_weights)
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# attach the LoRA layers to the ControlLora
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adapters: list[LoraAdapter] = []
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for key, lora in loras.items():
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target = control_lora.layer(key.split("."), WeightedModule)
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assert lora.is_compatible(target)
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adapter = LoraAdapter(target, lora)
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adapters.append(adapter)
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for adapter in adapters:
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adapter.inject(control_lora)
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@staticmethod
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def load_zero_convolution_layers(
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state_dict: dict[str, Tensor],
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control_lora: ControlLora,
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):
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"""Load the ZeroConvolution layers from the state_dict into the ControlLora.
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Args:
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state_dict: The state_dict containing the ZeroConvolution layers to load.
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control_lora: The ControlLora to load the ZeroConvolution layers into.
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"""
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zero_convolution_layers = list(control_lora.layers(ZeroConvolution))
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for i, zero_convolution_layer in enumerate(zero_convolution_layers):
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zero_convolution_state_dict = {
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key.removeprefix(f"ZeroConvolution_{i+1:02d}."): value
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for key, value in state_dict.items()
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if f"ZeroConvolution_{i+1:02d}" in key
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}
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zero_convolution_layer.load_state_dict(zero_convolution_state_dict)
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@staticmethod
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def load_condition_encoder(
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state_dict: dict[str, Tensor],
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control_lora: ControlLora,
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):
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"""Load the ConditionEncoder layers from the state_dict into the ControlLora.
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Args:
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state_dict: The state_dict containing the ConditionEncoder layers to load.
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control_lora: The ControlLora to load the ConditionEncoder layers into.
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"""
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condition_encoder_layer = control_lora.ensure_find(ConditionEncoder)
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condition_encoder_state_dict = {
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key.removeprefix("ConditionEncoder."): value
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for key, value in state_dict.items()
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if "ConditionEncoder" in key
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}
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condition_encoder_layer.load_state_dict(condition_encoder_state_dict)
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