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Update README.md

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Patrick von Platen 2022-06-15 13:27:05 +02:00 committed by GitHub
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@ -98,7 +98,7 @@ num_prediction_steps = len(noise_scheduler)
for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps):
# predict noise residual
with torch.no_grad():
residual = unet(image, t)
residual = unet(image, t)
# predict previous mean of image x_t-1
pred_prev_image = noise_scheduler.step(residual, image, t)
@ -107,7 +107,7 @@ for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_s
variance = 0
if t > 0:
noise = torch.randn(image.shape, generator=generator).to(image.device)
variance = noise_scheduler.get_variance(t).sqrt() * noise
variance = noise_scheduler.get_variance(t).sqrt() * noise
# set current image to prev_image: x_t -> x_t-1
image = pred_prev_image + variance