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run.py

@@ -19,7 +19,7 @@ unet.to(torch_device)
 vqvae.to(torch_device)
 
 # generate gaussian noise to be decoded
-generator = torch.manual_seed(1)
+generator = torch.manual_seed(0)
 noise = torch.randn(
     (1, unet.in_channels, unet.image_size, unet.image_size),
     generator=generator,
@@ -64,7 +64,7 @@ import tqdm
 pipeline = LatentDiffusionUncondPipeline.from_pretrained("./")
 
 # generatae image by calling the pipeline
-generator = torch.manual_seed(1)
+generator = torch.manual_seed(0)
 image = pipeline(generator=generator, num_inference_steps=200)["sample"]
 
 # process image