muneebable/class-conditional-diffusion-cub-200

# class-conditional-diffusion-cub-200

A Diffusion model on Cub 200 dataset for generating bird images.

## Usage Predict function to generate images
```python

  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
  
  
  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
  
  
  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

```python
model_path = "model_epoch_0.pth"  # Path to your saved model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = load_model(model_path, device)
noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule='squaredcos_cap_v2')
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_images(generated_images)
```