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24 lines
693 B
Python
24 lines
693 B
Python
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from refiners.foundationals.latent_diffusion.unet import UNet
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from refiners.fluxion import manual_seed
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import torch
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def test_unet_context_flush():
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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 = UNet(in_channels=4, clip_embedding_dim=768)
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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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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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assert torch.equal(y_1, y_2)
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