Commit a14d774b authored by Patrick von Platen's avatar Patrick von Platen
Browse files

fix readme again

parent d90a7367
...@@ -23,7 +23,7 @@ ...@@ -23,7 +23,7 @@
``` ```
git clone https://github.com/huggingface/diffusers.git git clone https://github.com/huggingface/diffusers.git
cd diffusers && pip install -e . cd diffusers && pip install -e .
`` ```
### 1. `diffusers` as a central modular diffusion and sampler library ### 1. `diffusers` as a central modular diffusion and sampler library
...@@ -55,7 +55,7 @@ num_prediction_steps = len(noise_scheduler) ...@@ -55,7 +55,7 @@ num_prediction_steps = len(noise_scheduler)
for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps): for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps):
# predict noise residual # predict noise residual
with torch.no_grad(): with torch.no_grad():
residual = self.unet(image, t) residual = unet(image, t)
# predict previous mean of image x_t-1 # predict previous mean of image x_t-1
pred_prev_image = noise_scheduler.compute_prev_image_step(residual, image, t) pred_prev_image = noise_scheduler.compute_prev_image_step(residual, image, t)
...@@ -105,7 +105,7 @@ eta = 0.0 # <- deterministic sampling ...@@ -105,7 +105,7 @@ eta = 0.0 # <- deterministic sampling
for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps): for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
# 1. predict noise residual # 1. predict noise residual
with torch.no_grad(): with torch.no_grad():
residual = self.unet(image, inference_step_times[t]) residual = unet(image, inference_step_times[t])
# 2. predict previous mean of image x_t-1 # 2. predict previous mean of image x_t-1
pred_prev_image = noise_scheduler.compute_prev_image_step(residual, image, t, num_inference_steps, eta) pred_prev_image = noise_scheduler.compute_prev_image_step(residual, image, t, num_inference_steps, eta)
......
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