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Update README.md
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@@ -137,8 +137,8 @@ unet = UNetModel.from_pretrained("fusing/ddpm-celeba-hq").to(torch_device)
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# 2. Sample gaussian noise
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image = torch.randn(
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(1, unet.in_channels, unet.resolution, unet.resolution),
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generator=generator,
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(1, unet.in_channels, unet.resolution, unet.resolution),
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generator=generator,
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)
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image = image.to(torch_device)
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@@ -147,10 +147,10 @@ num_inference_steps = 50
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eta = 0.0 # <- deterministic sampling
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for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
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# 1. predict noise residual
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# 1. predict noise residual
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orig_t = noise_scheduler.get_orig_t(t, num_inference_steps)
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with torch.no_grad():
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residual = unet(image, orig_t)
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residual = unet(image, orig_t)
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# 2. predict previous mean of image x_t-1
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pred_prev_image = noise_scheduler.step(residual, image, t, num_inference_steps, eta)
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