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Merge branch 'main' of https://github.com/huggingface/diffusers

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Patrick von Platen
2022-06-15 12:43:26 +02:00
parent 30c76cb437 1ab81f3b5b
commit 7971a05a2a
1 changed files with 18 additions and 14 deletions
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README.md
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@@ -53,6 +53,10 @@ The class provides functionality to compute previous image according to alpha, b
## Quickstart
### Installation
**Note**: If you want to run PyTorch on GPU on a CUDA-compatible machine, please make sure to install the corresponding `torch` version from the
[official website](
```
git clone https://github.com/huggingface/diffusers.git
cd diffusers && pip install -e .
@@ -84,29 +88,29 @@ unet = UNetModel.from_pretrained("fusing/ddpm-lsun-church").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)
# 3. Denoise
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)
# predict noise residual
with torch.no_grad():
residual = unet(image, t)
# predict previous mean of image x_t-1
pred_prev_image = noise_scheduler.step(residual, image, t)
# predict previous mean of image x_t-1
pred_prev_image = noise_scheduler.step(residual, image, t)
# optionally sample variance
variance = 0
if t > 0:
noise = torch.randn(image.shape, generator=generator).to(image.device)
variance = noise_scheduler.get_variance(t).sqrt() * noise
# optionally sample variance
variance = 0
if t > 0:
noise = torch.randn(image.shape, generator=generator).to(image.device)
variance = noise_scheduler.get_variance(t).sqrt() * noise
# set current image to prev_image: x_t -> x_t-1
image = pred_prev_image + variance
# set current image to prev_image: x_t -> x_t-1
image = pred_prev_image + variance
# 5. process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)