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generation/maisi/README.md

@@ -50,7 +50,7 @@ We retrained several state-of-the-art diffusion model-based methods using our da
 | 512x512x768   | [80,80,112], 8 patches                 | 4                           | 55G         | 904s    | 48s      |
 
 
-The experiment was tested on A100 80G GPU. 
+The experiment was tested on A100 80G GPU.
 
 During inference, the peak GPU memory usage happens during the autoencoder decoding latent features.
 To reduce GPU memory usage, we can either increasing `autoencoder_tp_num_splits` or reduce `autoencoder_sliding_window_infer_size`.

generation/maisi/scripts/inference.py

@@ -231,5 +231,5 @@ def main():
     )
     torch.cuda.reset_peak_memory_stats()
     main()
-    peak_memory_gb = torch.cuda.max_memory_allocated() / (1024 ** 3)  # Convert to GB
+    peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)  # Convert to GB
     print(f"Peak GPU memory usage: {peak_memory_gb:.2f} GB")