ddpm cat 256

Introduction

The DDPM-CAT-256 model provides high-quality image synthesis using Denoising Diffusion Probabilistic Models (DDPM). This approach is rooted in nonequilibrium thermodynamics and offers a novel training methodology that connects diffusion probabilistic models with denoising score matching through Langevin dynamics. The model achieves state-of-the-art results on datasets like CIFAR10 and LSUN.

Architecture

DDPMs operate by progressively denoising a sample of random noise to generate an image. They use a latent variable model framework, which allows for a progressive lossy decompression scheme. The model can be used with different schedulers like DDPM, DDIM, and PNDM to balance quality and inference speed.

Training

To train a DDPM model, you can refer to the official training example available on Google Colab. This guide provides step-by-step instructions to set up your environment and execute the training process.

Guide: Running Locally

1. Install the Required Package:

   pip install diffusers
   

2. Load the Model and Scheduler:

   from diffusers import DDPMPipeline
   
   model_id = "google/ddpm-cat-256"
   ddpm = DDPMPipeline.from_pretrained(model_id)
   

3. Run Inference:

   image = ddpm().images[0]
   image.save("ddpm_generated_image.png")
   

4. Use Cloud GPUs: For faster processing, consider using cloud platforms like AWS, Google Cloud, or Azure that offer GPU instances.

License

The DDPM-CAT-256 model is released under the Apache-2.0 license, allowing for open-source usage and modification.