| --- |
| license: mit |
| language: |
| - en |
| library_name: diffusers |
| tags: |
| - diffusion |
| - Conditional Diffusion |
| --- |
| ## Diffusion model trained on FER 2013 dataset. |
|
|
|
|
| Here is Custom Pipeline for Class conditioned diffusion model. For training script, pipeline, tutorial nb and sampling please check my Github Repo:- https://github.com/KetanMann/Class_Conditioned_Diffusion_Training_Script |
| Here is Class Conditional Diffusion Pipeline and Sampling. |
|
|
| <div align="center"> |
| <img src="grid_images_fer.gif" alt="Class Conditioned Diffusion GIF"> |
| </div> |
| |
|
|
| ## Firstly install Requirements:- |
|
|
|
|
| ```bash |
| !pip install diffusers |
| ``` |
|
|
|
|
| ## For Sampling run this:- |
|
|
|
|
| ```bash |
| from diffusers import UNet2DModel, DDPMScheduler |
| from diffusers.utils.torch_utils import randn_tensor |
| from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput |
| from huggingface_hub import hf_hub_download |
| import torch |
| import os |
| from PIL import Image |
| import matplotlib.pyplot as plt |
| from typing import List, Optional, Tuple, Union |
| |
| class DDPMPipelinenew(DiffusionPipeline): |
| def __init__(self, unet, scheduler, num_classes: int): |
| super().__init__() |
| self.register_modules(unet=unet, scheduler=scheduler) |
| self.num_classes = num_classes |
| self._device = unet.device # Ensure the pipeline knows the device |
| |
| @torch.no_grad() |
| def __call__( |
| self, |
| batch_size: int = 64, |
| class_labels: Optional[torch.Tensor] = None, |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, |
| num_inference_steps: int = 1000, |
| output_type: Optional[str] = "pil", |
| return_dict: bool = True, |
| ) -> Union[ImagePipelineOutput, Tuple]: |
| |
| # Ensure class_labels is on the same device as the model |
| class_labels = class_labels.to(self._device) |
| if class_labels.ndim == 0: |
| class_labels = class_labels.unsqueeze(0).expand(batch_size) |
| else: |
| class_labels = class_labels.expand(batch_size) |
| |
| # Sample gaussian noise to begin loop |
| if isinstance(self.unet.config.sample_size, int): |
| image_shape = ( |
| batch_size, |
| self.unet.config.in_channels, |
| self.unet.config.sample_size, |
| self.unet.config.sample_size, |
| ) |
| else: |
| image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size) |
| |
| image = randn_tensor(image_shape, generator=generator, device=self._device) |
| |
| # Set step values |
| self.scheduler.set_timesteps(num_inference_steps) |
| |
| for t in self.progress_bar(self.scheduler.timesteps): |
| # Ensure the class labels are correctly broadcast to match the input tensor shape |
| model_output = self.unet(image, t, class_labels).sample |
| |
| image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample |
| |
| image = (image / 2 + 0.5).clamp(0, 1) |
| image = image.cpu().permute(0, 2, 3, 1).numpy() |
| if output_type == "pil": |
| image = self.numpy_to_pil(image) |
| |
| if not return_dict: |
| return (image,) |
| |
| return ImagePipelineOutput(images=image) |
| |
| def to(self, device: torch.device): |
| self._device = device |
| self.unet.to(device) |
| return self |
| |
| def load_pipeline(repo_id, num_classes, device): |
| unet = UNet2DModel.from_pretrained(repo_id, subfolder="unet").to(device) |
| scheduler = DDPMScheduler.from_pretrained(repo_id, subfolder="scheduler") |
| pipeline = DDPMPipelinenew(unet=unet, scheduler=scheduler, num_classes=num_classes) |
| return pipeline.to(device) # Move the entire pipeline to the device |
| |
| def save_images_locally(images, save_dir, epoch, class_label): |
| os.makedirs(save_dir, exist_ok=True) |
| for i, image in enumerate(images): |
| image_path = os.path.join(save_dir, f"image_epoch{epoch}_class{class_label}_idx{i}.png") |
| image.save(image_path) |
| |
| def generate_images(pipeline, class_label, batch_size, num_inference_steps, save_dir, epoch): |
| generator = torch.Generator(device=pipeline._device).manual_seed(0) |
| class_labels = torch.tensor([class_label] * batch_size).to(pipeline._device) |
| images = pipeline( |
| generator=generator, |
| batch_size=batch_size, |
| num_inference_steps=num_inference_steps, |
| class_labels=class_labels, |
| output_type="pil", |
| ).images |
| save_images_locally(images, save_dir, epoch, class_label) |
| return images |
| |
| def create_image_grid(images, grid_size, save_path): |
| total_images = grid_size ** 2 |
| if len(images) < total_images: |
| padding_images = total_images - len(images) |
| images += [Image.new('RGB', images[0].size)] * padding_images # Pad with blank images |
| |
| width, height = images[0].size |
| grid_img = Image.new('RGB', (grid_size * width, grid_size * height)) |
| |
| for i, image in enumerate(images): |
| x = i % grid_size * width |
| y = i // grid_size * height |
| grid_img.paste(image, (x, y)) |
| |
| grid_img.save(save_path) |
| return grid_img |
| |
| if __name__ == "__main__": |
| repo_id = "Ketansomewhere/FER_2013_Conditional_Diffusion" |
| num_classes = 7 # Adjust to your number of classes |
| batch_size = 64 |
| num_inference_steps = 1000 # Can be as low as 50 for faster generation |
| save_dir = "generated_images" |
| epoch = 0 |
| grid_size = 8 # 8x8 grid |
| |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| pipeline = load_pipeline(repo_id, num_classes, device) |
| |
| for class_label in range(num_classes): |
| images = generate_images(pipeline, class_label, batch_size, num_inference_steps, save_dir, epoch) |
| |
| # Create and save the grid image |
| grid_img_path = os.path.join(save_dir, f"grid_image_class{class_label}.png") |
| grid_img = create_image_grid(images, grid_size, grid_img_path) |
| |
| # Plot the grid image |
| plt.figure(figsize=(10, 10)) |
| plt.imshow(grid_img) |
| plt.axis('off') |
| plt.title(f'Class {class_label}') |
| plt.savefig(os.path.join(save_dir, f"grid_image_class{class_label}.png")) |
| plt.show() |
| ``` |
