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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-to-image |
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tags: |
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- pytorch |
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- diffusers |
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- conditional-image-generation |
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- diffusion-models-class |
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datasets: |
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- dpdl-benchmark/caltech_birds2011 |
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library_name: diffusers |
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--- |
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# class-conditional-diffusion-cub-200 |
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A Diffusion model on Cub 200 dataset for generating bird images. |
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## Usage Predict function to generate images |
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```python |
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def load_model(model_path, device): |
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# Initialize the same model architecture as during training |
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model = ClassConditionedUnet().to(device) |
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# Load the trained weights |
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model.load_state_dict(torch.load(model_path)) |
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# Set model to evaluation mode |
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model.eval() |
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return model |
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def predict(model, class_label, noise_scheduler, num_samples=8, device='cuda'): |
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model.eval() # Ensure the model is in evaluation mode |
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# Prepare a batch of random noise as input |
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shape = (num_samples, 3, 256, 256) # Input shape: (batch_size, channels, height, width) |
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noisy_image = torch.randn(shape).to(device) |
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# Ensure class_label is a tensor and properly repeated for the batch |
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class_labels = torch.tensor([class_label] * num_samples, dtype=torch.long).to(device) |
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# Reverse the diffusion process step by step |
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for t in tqdm(range(49, -1, -1), desc="Reverse Diffusion Steps"): # Iterate backwards through timesteps |
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t_tensor = torch.tensor([t], dtype=torch.long).to(device) # Single time step for the batch |
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# Predict noise with the model and remove it from the image |
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with torch.no_grad(): |
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noise_pred = model(noisy_image, t_tensor.expand(num_samples), class_labels) # Class conditioning here |
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# Step with the scheduler (model_output, timestep, sample) |
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noisy_image = noise_scheduler.step(noise_pred, t, noisy_image).prev_sample |
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# Post-process the output to get image values between [0, 1] |
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generated_images = (noisy_image + 1) / 2 # Rescale from [-1, 1] to [0, 1] |
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return generated_images |
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def display_images(images, num_rows=2): |
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# Create a grid of images |
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grid = torchvision.utils.make_grid(images, nrow=num_rows) |
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np_grid = grid.permute(1, 2, 0).cpu().numpy() # Convert to (H, W, C) format for visualization |
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# Plot the images |
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plt.figure(figsize=(12, 6)) |
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plt.imshow(np.clip(np_grid, 0, 1)) # Clip values to ensure valid range |
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plt.axis('off') |
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plt.show() |
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``` |
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# Example of loading a model and generating predictions |
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```python |
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model_path = "model_epoch_0.pth" # Path to your saved model |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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model = load_model(model_path, device) |
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noise_scheduler = DDPMScheduler(num_train_timesteps=1000, beta_schedule='squaredcos_cap_v2') |
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class_label = 1 # Example class label, change to your desired class |
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generated_images = predict(model, class_label, noise_scheduler, num_samples=2, device=device) |
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display_images(generated_images) |
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``` |
