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https://github.com/finegrain-ai/refiners.git
synced 2024-11-21 13:48:46 +00:00
add T2I-Adapter to foundationals/latent_diffusion
This commit is contained in:
parent
d72e1d3478
commit
14864857b1
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@ -11,11 +11,13 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1 import (
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SD1UNet,
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SD1ControlnetAdapter,
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SD1IPAdapter,
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SD1T2IAdapter,
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)
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl import (
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SDXLUNet,
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DoubleTextEncoder,
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SDXLIPAdapter,
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SDXLT2IAdapter,
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)
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@ -25,9 +27,11 @@ __all__ = [
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"SD1UNet",
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"SD1ControlnetAdapter",
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"SD1IPAdapter",
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"SD1T2IAdapter",
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"SDXLUNet",
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"DoubleTextEncoder",
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"SDXLIPAdapter",
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"SDXLT2IAdapter",
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"DPMSolver",
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"Scheduler",
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"CLIPTextEncoderL",
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@ -5,6 +5,7 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (
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)
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1ControlnetAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.image_prompt import SD1IPAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.t2i_adapter import SD1T2IAdapter
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__all__ = [
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"StableDiffusion_1",
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@ -12,4 +13,5 @@ __all__ = [
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"SD1UNet",
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"SD1ControlnetAdapter",
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"SD1IPAdapter",
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"SD1T2IAdapter",
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]
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@ -0,0 +1,46 @@
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from typing import cast, Iterable
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from torch import Tensor
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from refiners.foundationals.latent_diffusion.t2i_adapter import T2IAdapter, T2IFeatures, ConditionEncoder
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet, ResidualAccumulator
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import refiners.fluxion.layers as fl
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class SD1T2IAdapter(T2IAdapter[SD1UNet]):
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def __init__(
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self,
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target: SD1UNet,
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name: str,
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condition_encoder: ConditionEncoder | None = None,
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weights: dict[str, Tensor] | None = None,
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) -> None:
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self.residual_indices = (2, 5, 8, 11)
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super().__init__(
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target=target,
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name=name,
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condition_encoder=condition_encoder or ConditionEncoder(device=target.device, dtype=target.dtype),
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weights=weights,
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)
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def inject(self: "SD1T2IAdapter", parent: fl.Chain | None = None) -> "SD1T2IAdapter":
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for n, block in enumerate(cast(Iterable[fl.Chain], self.target.DownBlocks)):
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if n not in self.residual_indices:
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continue
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for t2i_layer in block.layers(layer_type=T2IFeatures):
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assert t2i_layer.name != self.name, f"T2I-Adapter named {self.name} is already injected"
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block.insert_before_type(
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ResidualAccumulator, T2IFeatures(name=self.name, index=self.residual_indices.index(n))
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)
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return super().inject(parent)
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def eject(self: "SD1T2IAdapter") -> None:
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for n, block in enumerate(cast(Iterable[fl.Chain], self.target.DownBlocks)):
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if n not in self.residual_indices:
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continue
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t2i_layers = [
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t2i_layer for t2i_layer in block.layers(layer_type=T2IFeatures) if t2i_layer.name == self.name
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]
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assert len(t2i_layers) == 1
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block.remove(t2i_layers.pop())
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super().eject()
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@ -2,6 +2,7 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDX
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt import SDXLIPAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.t2i_adapter import SDXLT2IAdapter
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__all__ = [
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@ -9,4 +10,5 @@ __all__ = [
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"DoubleTextEncoder",
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"StableDiffusion_XL",
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"SDXLIPAdapter",
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"SDXLT2IAdapter",
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]
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@ -0,0 +1,57 @@
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from typing import cast, Iterable
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from torch import Tensor
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from refiners.foundationals.latent_diffusion.t2i_adapter import T2IAdapter, T2IFeatures, ConditionEncoderXL
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl import SDXLUNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualAccumulator
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import refiners.fluxion.layers as fl
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class SDXLT2IAdapter(T2IAdapter[SDXLUNet]):
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def __init__(
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self,
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target: SDXLUNet,
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name: str,
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condition_encoder: ConditionEncoderXL | None = None,
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weights: dict[str, Tensor] | None = None,
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) -> None:
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self.residual_indices = (3, 5, 8) # the UNet's middle block is handled separately (see `inject` and `eject`)
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super().__init__(
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target=target,
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name=name,
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condition_encoder=condition_encoder or ConditionEncoderXL(device=target.device, dtype=target.dtype),
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weights=weights,
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)
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def inject(self: "SDXLT2IAdapter", parent: fl.Chain | None = None) -> "SDXLT2IAdapter":
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def sanity_check_t2i(block: fl.Module) -> None:
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for t2i_layer in block.layers(layer_type=T2IFeatures):
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assert t2i_layer.name != self.name, f"T2I-Adapter named {self.name} is already injected"
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for n, block in enumerate(cast(Iterable[fl.Chain], self.target.DownBlocks)):
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if n not in self.residual_indices:
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continue
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sanity_check_t2i(block)
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block.insert_before_type(
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ResidualAccumulator, T2IFeatures(name=self.name, index=self.residual_indices.index(n))
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)
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sanity_check_t2i(self.target.MiddleBlock)
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# Special case: the MiddleBlock has no ResidualAccumulator (this is done via a subsequent layer) so just append
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self.target.MiddleBlock.append(T2IFeatures(name=self.name, index=-1))
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return super().inject(parent)
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def eject(self: "SDXLT2IAdapter") -> None:
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def eject_t2i(block: fl.Module) -> None:
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t2i_layers = [
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t2i_layer for t2i_layer in block.layers(layer_type=T2IFeatures) if t2i_layer.name == self.name
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]
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assert len(t2i_layers) == 1
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block.remove(t2i_layers.pop())
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for n, block in enumerate(cast(Iterable[fl.Chain], self.target.DownBlocks)):
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if n not in self.residual_indices:
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continue
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eject_t2i(block)
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eject_t2i(self.target.MiddleBlock)
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super().eject()
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216
src/refiners/foundationals/latent_diffusion/t2i_adapter.py
Normal file
216
src/refiners/foundationals/latent_diffusion/t2i_adapter.py
Normal file
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@ -0,0 +1,216 @@
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from typing import Generic, TypeVar, Any, TYPE_CHECKING
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from torch import Tensor, device as Device, dtype as DType
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from torch.nn import AvgPool2d as _AvgPool2d
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from refiners.fluxion.adapters.adapter import Adapter
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from refiners.fluxion.context import Contexts
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from refiners.fluxion.layers.module import Module
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import refiners.fluxion.layers as fl
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if TYPE_CHECKING:
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
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T = TypeVar("T", bound="SD1UNet | SDXLUNet")
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TT2IAdapter = TypeVar("TT2IAdapter", bound="T2IAdapter[Any]") # Self (see PEP 673)
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class Downsample2d(_AvgPool2d, Module):
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def __init__(self, scale_factor: int) -> None:
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_AvgPool2d.__init__(self, kernel_size=scale_factor, stride=scale_factor)
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class ResidualBlock(fl.Residual):
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def __init__(
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self,
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channels: int,
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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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super().__init__(
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fl.Conv2d(
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in_channels=channels, out_channels=channels, kernel_size=3, padding=1, device=device, dtype=dtype
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),
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fl.ReLU(),
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fl.Conv2d(in_channels=channels, out_channels=channels, kernel_size=1, device=device, dtype=dtype),
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)
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class ResidualBlocks(fl.Chain):
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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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num_residual_blocks: int = 2,
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downsample: bool = False,
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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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preproc = Downsample2d(scale_factor=2) if downsample else fl.Identity()
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shortcut = (
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fl.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, device=device, dtype=dtype)
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if in_channels != out_channels
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else fl.Identity()
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)
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super().__init__(
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preproc,
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shortcut,
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fl.Chain(
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ResidualBlock(channels=out_channels, device=device, dtype=dtype) for _ in range(num_residual_blocks)
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),
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)
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class StatefulResidualBlocks(fl.Chain):
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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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num_residual_blocks: int = 2,
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downsample: bool = False,
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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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super().__init__(
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ResidualBlocks(
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in_channels=in_channels,
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out_channels=out_channels,
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num_residual_blocks=num_residual_blocks,
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downsample=downsample,
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device=device,
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dtype=dtype,
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),
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fl.SetContext(context="t2iadapter", key="features", callback=self.push),
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)
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def push(self, features: list[Tensor], x: Tensor) -> None:
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features.append(x)
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class ConditionEncoder(fl.Chain):
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def __init__(
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self,
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in_channels: int = 3,
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channels: tuple[int, int, int, int] = (320, 640, 1280, 1280),
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num_residual_blocks: int = 2,
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downscale_factor: int = 8,
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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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self.scale = scale
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super().__init__(
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fl.PixelUnshuffle(downscale_factor=downscale_factor),
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fl.Conv2d(
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in_channels=in_channels * downscale_factor**2,
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out_channels=channels[0],
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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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StatefulResidualBlocks(channels[0], channels[0], num_residual_blocks, device=device, dtype=dtype),
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*(
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StatefulResidualBlocks(
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channels[i - 1], channels[i], num_residual_blocks, downsample=True, device=device, dtype=dtype
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)
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for i in range(1, len(channels))
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),
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fl.UseContext(context="t2iadapter", key="features").compose(func=self.scale_outputs),
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)
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def scale_outputs(self, features: list[Tensor]) -> tuple[Tensor, ...]:
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assert len(features) == 4
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return tuple([x * self.scale for x in features])
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def init_context(self) -> Contexts:
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return {"t2iadapter": {"features": []}}
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class ConditionEncoderXL(ConditionEncoder, fl.Chain):
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def __init__(
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self,
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in_channels: int = 3,
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channels: tuple[int, int, int, int] = (320, 640, 1280, 1280),
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num_residual_blocks: int = 2,
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downscale_factor: int = 16,
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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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self.scale = scale
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fl.Chain.__init__(
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self,
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fl.PixelUnshuffle(downscale_factor=downscale_factor),
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fl.Conv2d(
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in_channels=in_channels * downscale_factor**2,
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out_channels=channels[0],
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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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StatefulResidualBlocks(channels[0], channels[0], num_residual_blocks, device=device, dtype=dtype),
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StatefulResidualBlocks(channels[0], channels[1], num_residual_blocks, device=device, dtype=dtype),
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StatefulResidualBlocks(
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channels[1], channels[2], num_residual_blocks, downsample=True, device=device, dtype=dtype
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),
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StatefulResidualBlocks(channels[2], channels[3], num_residual_blocks, device=device, dtype=dtype),
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fl.UseContext(context="t2iadapter", key="features").compose(func=self.scale_outputs),
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)
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class T2IFeatures(fl.Residual):
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def __init__(self, name: str, index: int) -> None:
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self.name = name
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self.index = index
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super().__init__(
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fl.UseContext(context="t2iadapter", key=f"condition_features_{self.name}").compose(
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func=lambda features: features[self.index]
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)
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)
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class T2IAdapter(Generic[T], fl.Chain, Adapter[T]):
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_condition_encoder: list[ConditionEncoder] # prevent PyTorch module registration
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def __init__(
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self,
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target: T,
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name: str,
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condition_encoder: ConditionEncoder,
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weights: dict[str, Tensor] | None = None,
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) -> None:
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self.name = name
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if weights is not None:
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condition_encoder.load_state_dict(weights)
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self._condition_encoder = [condition_encoder]
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with self.setup_adapter(target):
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super().__init__(target)
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def inject(self: TT2IAdapter, parent: fl.Chain | None = None) -> TT2IAdapter:
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return super().inject(parent)
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def eject(self) -> None:
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super().eject()
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@property
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def condition_encoder(self) -> ConditionEncoder:
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return self._condition_encoder[0]
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def compute_condition_features(self, condition: Tensor) -> tuple[Tensor, ...]:
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return self.condition_encoder(condition)
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def set_condition_features(self, features: tuple[Tensor, ...]) -> None:
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self.set_context("t2iadapter", {f"condition_features_{self.name}": features})
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def set_scale(self, scale: float) -> None:
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self.condition_encoder.scale = scale
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def init_context(self) -> Contexts:
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return {"t2iadapter": {f"condition_features_{self.name}": None}}
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def structural_copy(self: "TT2IAdapter") -> "TT2IAdapter":
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raise RuntimeError("T2I-Adapter cannot be copied, eject it first.")
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