use inference_mode

README.md

@@ -148,7 +148,7 @@ eta = 0.0  # <- deterministic sampling
 for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
     # 1. predict noise residual
     orig_t = noise_scheduler.get_orig_t(t, num_inference_steps)
-	with torch.no_grad():
+    with torch.inference_mode():
         residual = unet(image, orig_t)
 
     # 2. predict previous mean of image x_t-1