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32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import torch
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from PIL import Image
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from refiners.fluxion.utils import manual_seed, no_grad
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from refiners.foundationals.latent_diffusion import StableDiffusion_1_Inpainting
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from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
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@no_grad()
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def test_sample_noise():
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manual_seed(2)
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latents_0 = LatentDiffusionModel.sample_noise(size=(1, 4, 64, 64))
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manual_seed(2)
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latents_1 = LatentDiffusionModel.sample_noise(size=(1, 4, 64, 64), offset_noise=0.0)
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assert torch.allclose(latents_0, latents_1, atol=1e-6, rtol=0)
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@no_grad()
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def test_sd1_inpainting(test_device: torch.device) -> None:
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sd = StableDiffusion_1_Inpainting(device=test_device)
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latent_noise = torch.randn(1, 4, 64, 64, device=test_device)
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target_image = Image.new("RGB", (512, 512))
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mask = Image.new("L", (512, 512))
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sd.set_inpainting_conditions(target_image=target_image, mask=mask)
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text_embedding = sd.compute_clip_text_embedding("")
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output = sd(latent_noise, step=0, clip_text_embedding=text_embedding)
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assert output.shape == (1, 4, 64, 64)
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