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b933fabf31
It is implicitly defined by the underlying cross-attention layer. This also makes it consistent with SDXL.
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
import torch
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import pytest
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from refiners.foundationals.latent_diffusion import SD1UNet
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from refiners.foundationals.latent_diffusion.reference_only_control import (
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ReferenceOnlyControlAdapter,
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SaveLayerNormAdapter,
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SelfAttentionInjectionAdapter,
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SelfAttentionInjectionPassthrough,
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)
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from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock
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@torch.no_grad()
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def test_sai_inject_eject() -> None:
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unet = SD1UNet(in_channels=9)
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sai = ReferenceOnlyControlAdapter(unet)
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nb_cross_attention_blocks = len(list(unet.walk(CrossAttentionBlock)))
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assert nb_cross_attention_blocks > 0
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assert unet.parent is None
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assert len(list(unet.walk(SelfAttentionInjectionPassthrough))) == 0
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assert len(list(unet.walk(SaveLayerNormAdapter))) == 0
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assert len(list(unet.walk(SelfAttentionInjectionAdapter))) == 0
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with pytest.raises(AssertionError) as exc:
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sai.eject()
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assert "not the first element" in str(exc.value)
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sai.inject()
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assert unet.parent == sai
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assert len(list(unet.walk(SelfAttentionInjectionPassthrough))) == 1
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assert len(list(unet.walk(SaveLayerNormAdapter))) == nb_cross_attention_blocks
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assert len(list(unet.walk(SelfAttentionInjectionAdapter))) == nb_cross_attention_blocks
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with pytest.raises(AssertionError) as exc:
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sai.inject()
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assert "already injected" in str(exc.value)
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sai.eject()
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assert unet.parent is None
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assert len(list(unet.walk(SelfAttentionInjectionPassthrough))) == 0
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assert len(list(unet.walk(SaveLayerNormAdapter))) == 0
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assert len(list(unet.walk(SelfAttentionInjectionAdapter))) == 0
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