Update README.md

README.md

@@ -19,8 +19,73 @@ library_name: diffusers
     
     ## Usage
     ```python
-    from diffusers import DDPMPipeline
-    pipeline = DDPMPipeline.from_pretrained('muneebable/class-conditional-diffusion-cub-200')
-    image = pipeline().images[0]
-    image
+
+# Load the model
+def load_model(model_path, device):
+    # Initialize the same model architecture as during training
+    model = ClassConditionedUnet().to(device)
+    
+    # Load the trained weights
+    model.load_state_dict(torch.load(model_path))
+    
+    # Set model to evaluation mode
+    model.eval()
+    
+    return model
+
+# Predict function to generate images
+def predict(model, class_label, noise_scheduler, num_samples=8, device='cuda'):
+    model.eval()  # Ensure the model is in evaluation mode
+    
+    # Prepare a batch of random noise as input
+    shape = (num_samples, 3, 256, 256)  # Input shape: (batch_size, channels, height, width)
+    noisy_image = torch.randn(shape).to(device)
+    
+    # Ensure class_label is a tensor and properly repeated for the batch
+    class_labels = torch.tensor([class_label] * num_samples, dtype=torch.long).to(device)
+
+    # Reverse the diffusion process step by step
+    for t in tqdm(range(49, -1, -1), desc="Reverse Diffusion Steps"):  # Iterate backwards through timesteps
+        t_tensor = torch.tensor([t], dtype=torch.long).to(device)  # Single time step for the batch
+        
+        # Predict noise with the model and remove it from the image
+        with torch.no_grad():
+            noise_pred = model(noisy_image, t_tensor.expand(num_samples), class_labels)  # Class conditioning here
+        
+        # Step with the scheduler (model_output, timestep, sample)
+        noisy_image = noise_scheduler.step(noise_pred, t, noisy_image).prev_sample
+    
+    # Post-process the output to get image values between [0, 1]
+    generated_images = (noisy_image + 1) / 2  # Rescale from [-1, 1] to [0, 1]
+    
+    return generated_images
+
+# Display predicted images
+def display_images(images, num_rows=2):
+    # Create a grid of images
+    grid = torchvision.utils.make_grid(images, nrow=num_rows)
+    np_grid = grid.permute(1, 2, 0).cpu().numpy()  # Convert to (H, W, C) format for visualization
+    
+    # Plot the images
+    plt.figure(figsize=(12, 6))
+    plt.imshow(np.clip(np_grid, 0, 1))  # Clip values to ensure valid range
+    plt.axis('off')
+    plt.show()
+
+# Example of loading a model and generating predictions
+model_path = "model_epoch_0.pth"  # Path to your saved model
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Load the model
+model = load_model(model_path, device)
+
+# Create a noise scheduler
+noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule='squaredcos_cap_v2')
+
+# Predict and generate samples for a specific class label
+class_label = 1  # Example class label, change to your desired class
+generated_images = predict(model, class_label, noise_scheduler, num_samples=2, device=device)
+
+# Display the generated images
+display_images(generated_images)
     ```