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32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import pytest
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import torch
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from refiners.fluxion import manual_seed
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from refiners.fluxion.utils import no_grad
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from refiners.foundationals.latent_diffusion import SD1UNet
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@pytest.fixture(scope="module")
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def refiners_sd15_unet(test_device: torch.device) -> SD1UNet:
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unet = SD1UNet(in_channels=4, device=test_device)
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return unet
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def test_unet_context_flush(refiners_sd15_unet: SD1UNet):
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manual_seed(0)
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text_embedding = torch.randn(1, 77, 768, device=refiners_sd15_unet.device, dtype=refiners_sd15_unet.dtype)
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timestep = torch.randint(0, 999, size=(1, 1), device=refiners_sd15_unet.device)
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x = torch.randn(1, 4, 32, 32, device=refiners_sd15_unet.device, dtype=refiners_sd15_unet.dtype)
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refiners_sd15_unet.set_clip_text_embedding(clip_text_embedding=text_embedding) # not flushed between forward-s
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with no_grad():
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refiners_sd15_unet.set_timestep(timestep=timestep)
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y_1 = refiners_sd15_unet(x.clone())
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with no_grad():
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refiners_sd15_unet.set_timestep(timestep=timestep)
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y_2 = refiners_sd15_unet(x.clone())
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assert torch.equal(y_1, y_2)
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