Update README.md

README.md

@@ -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