ddpm cifar10 32

Introduction

Denoising Diffusion Probabilistic Models (DDPM) are a class of models used for high-quality image synthesis, inspired by nonequilibrium thermodynamics. These models achieve impressive results on datasets like CIFAR10 and LSUN by utilizing a novel connection between diffusion probabilistic models and denoising score matching.

Architecture

DDPM models are latent variable models that incorporate a progressive lossy decompression scheme, which generalizes autoregressive decoding. They leverage discrete noise schedulers for inference, with options including scheduling_ddpm, scheduling_ddim, and scheduling_pndm.

Training

Training your own DDPM model can be done by following the official training example available on Hugging Face's platform. These models are trained using a weighted variational bound.

Guide: Running Locally

1. Install the necessary library:

   pip install diffusers
   

2. Load the model and scheduler:

   from diffusers import DDPMPipeline
   
   model_id = "google/ddpm-cifar10-32"
   ddpm = DDPMPipeline.from_pretrained(model_id)
   

3. Run the pipeline to generate an image:

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

4. Suggestion: Use cloud services like Google Colab with GPU support to accelerate model inference and training processes.

License

This model is licensed under the Apache 2.0 License.