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https://github.com/finegrain-ai/refiners.git
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68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
from typing import Iterator
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import pytest
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import torch
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from refiners.fluxion import manual_seed
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from refiners.foundationals.latent_diffusion import SD1UNet, SDXLUNet
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from refiners.foundationals.latent_diffusion.freeu import FreeUResidualConcatenator, SDFreeUAdapter
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@pytest.fixture(scope="module", params=[True, False])
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def unet(request: pytest.FixtureRequest) -> Iterator[SD1UNet | SDXLUNet]:
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xl: bool = request.param
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unet = SDXLUNet(in_channels=4) if xl else SD1UNet(in_channels=4)
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yield unet
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def test_freeu_adapter(unet: SD1UNet | SDXLUNet) -> None:
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freeu = SDFreeUAdapter(unet, backbone_scales=[1.2, 1.2], skip_scales=[0.9, 0.9])
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assert len(list(unet.walk(FreeUResidualConcatenator))) == 0
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with pytest.raises(AssertionError) as exc:
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freeu.eject()
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assert "could not find" in str(exc.value)
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freeu.inject()
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assert len(list(unet.walk(FreeUResidualConcatenator))) == 2
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freeu.eject()
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assert len(list(unet.walk(FreeUResidualConcatenator))) == 0
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def test_freeu_adapter_too_many_scales(unet: SD1UNet | SDXLUNet) -> None:
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num_blocks = len(unet.UpBlocks)
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with pytest.raises(AssertionError):
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SDFreeUAdapter(unet, backbone_scales=[1.2] * (num_blocks + 1), skip_scales=[0.9] * (num_blocks + 1))
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def test_freeu_adapter_inconsistent_scales(unet: SD1UNet | SDXLUNet) -> None:
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with pytest.raises(AssertionError):
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SDFreeUAdapter(unet, backbone_scales=[1.2, 1.2], skip_scales=[0.9, 0.9, 0.9])
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def test_freeu_identity_scales() -> None:
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manual_seed(0)
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text_embedding = torch.randn(1, 77, 768)
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timestep = torch.randint(0, 999, size=(1, 1))
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x = torch.randn(1, 4, 32, 32)
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unet = SD1UNet(in_channels=4)
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unet.set_clip_text_embedding(clip_text_embedding=text_embedding) # not flushed between forward-s
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with torch.no_grad():
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unet.set_timestep(timestep=timestep)
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y_1 = unet(x.clone())
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freeu = SDFreeUAdapter(unet, backbone_scales=[1.0, 1.0], skip_scales=[1.0, 1.0])
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freeu.inject()
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with torch.no_grad():
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unet.set_timestep(timestep=timestep)
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y_2 = unet(x.clone())
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# The FFT -> inverse FFT sequence (skip features) introduces small numerical differences
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assert torch.allclose(y_1, y_2, atol=1e-5)
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