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Update README.md

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Suraj Patil 2022-06-16 10:22:55 +02:00 committed by GitHub
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@ -137,8 +137,8 @@ unet = UNetModel.from_pretrained("fusing/ddpm-celeba-hq").to(torch_device)
# 2. Sample gaussian noise
image = torch.randn(
(1, unet.in_channels, unet.resolution, unet.resolution),
generator=generator,
(1, unet.in_channels, unet.resolution, unet.resolution),
generator=generator,
)
image = image.to(torch_device)
@ -147,10 +147,10 @@ num_inference_steps = 50
eta = 0.0 # <- deterministic sampling
for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
# 1. predict noise residual
# 1. predict noise residual
orig_t = noise_scheduler.get_orig_t(t, num_inference_steps)
with torch.no_grad():
residual = unet(image, orig_t)
residual = unet(image, orig_t)
# 2. predict previous mean of image x_t-1
pred_prev_image = noise_scheduler.step(residual, image, t, num_inference_steps, eta)