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README.md

@@ -65,18 +65,21 @@ from diffusers import UNet2DModel, DDPMScheduler
 import tqdm
 
 # 1. Initialize the model
+# Choose a model ID, use google's with use_safetensors=False, use Mou11209203's with use_safetensors=True
 repo_id = "google/ddpm-celebahq-256"
-model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True)
+repo_id1 = "Mou11209203/ddpm-celebahq-256"
+model = UNet2DModel.from_pretrained(repo_id1, use_safetensors=True)
 model.to("cuda")  # Move the model to GPU
-display(model.config)
+print("model.config: ", model.config)
 
 # 2. Initialize the scheduler
-scheduler = DDPMScheduler.from_pretrained(repo_id)
+scheduler = DDPMScheduler.from_pretrained(repo_id1)
+print("scheduler.config: ", scheduler.config)
 
 # 3. Create an image with Gaussian noise
-torch.manual_seed(0)  # Set random seed for reproducibility
+torch.manual_seed(0)  # Fix the random seed for reproducibility
 noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size).to("cuda")
-display(f"Noisy sample shape: {noisy_sample.shape}")
+print(f"Noisy sample shape: {noisy_sample.shape}")
 
 # 4. Define a function to display the image
 def display_sample(sample, i):
@@ -85,13 +88,13 @@ def display_sample(sample, i):
     image_processed = image_processed.numpy().astype(np.uint8)
 
     image_pil = PIL.Image.fromarray(image_processed[0])
-    display(f"Image at step {i}")
-    display(image_pil)
+    print(f"Image at step {i}")
+    image_pil.show()
 
 # 5. Reverse diffusion process
 sample = noisy_sample
 for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
-    # 1. Predict noise residual
+    # 1. Predict the noise residual
     with torch.no_grad():
         residual = model(sample, t).sample
 
@@ -102,7 +105,7 @@ for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
     if (i + 1) % 50 == 0:
         display_sample(sample, i + 1)
 
-display("Denoising complete.")
+print("Denoising complete.")
 ```
 
 ## Training