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38 lines
1.6 KiB
Python
38 lines
1.6 KiB
Python
from torch import Tensor
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import imaginairy.vendored.refiners.fluxion.layers as fl
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from imaginairy.vendored.refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualAccumulator, SD1UNet
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from imaginairy.vendored.refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoder, T2IAdapter, T2IFeatures
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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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scale: float = 1.0,
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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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self._features = [T2IFeatures(name=name, index=i, scale=scale) for i in range(4)]
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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, feat in zip(self.residual_indices, self._features, strict=True):
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block = self.target.DownBlocks[n]
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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(ResidualAccumulator, feat)
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return super().inject(parent)
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def eject(self: "SD1T2IAdapter") -> None:
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for n, feat in zip(self.residual_indices, self._features, strict=True):
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self.target.DownBlocks[n].remove(feat)
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super().eject()
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