Update README.md

README.md

@@ -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)