DDPM CelebA-HQ 256
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Paper | Denoising Diffusion Probabilistic Models |
| Authors | Jonathan Ho, Ajay Jain, Pieter Abbeel |
| Framework | PyTorch |
What is ddpm-celebahq-256?
DDPM CelebA-HQ 256 is a state-of-the-art diffusion model designed for generating high-quality 256x256 face images. Developed by Google, this model implements the Denoising Diffusion Probabilistic Models architecture, which has shown remarkable results in image synthesis tasks. The model has gained significant traction with over 9,800 downloads and 43 likes from the community.
Implementation Details
The model utilizes a diffusion-based approach where noise is gradually removed from random inputs to generate realistic images. It supports multiple scheduler options including DDPM, DDIM, and PNDM, offering flexibility in balancing generation quality and speed.
- DDPM scheduler for highest quality output
- DDIM scheduler for faster inference
- PNDM scheduler as an alternative fast option
- PyTorch-based implementation with Diffusers library support
Core Capabilities
- High-quality 256x256 face image generation
- Unconditional image synthesis
- Progressive lossy decompression
- Flexible inference options through different schedulers
- Easy integration with the Hugging Face ecosystem
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its implementation of the DDPM architecture specifically optimized for face generation, offering state-of-the-art quality while providing multiple scheduling options for different use-case requirements.
Q: What are the recommended use cases?
The model is ideal for generating high-quality face images for various applications including creative content generation, dataset augmentation, and research in facial synthesis. Users can choose between different schedulers based on their specific needs for quality versus speed.
