yang-song/score_sde_pytorch
A PyTorch implementation of score-based generative models using stochastic differential equations for high-quality image generation.
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This repository implements the score-based generative modeling framework through stochastic differential equations (SDEs), originally published at ICLR 2021. The method transforms data into noise via a continuous-time stochastic process, then reverses the SDE for sample generation using score matching. It achieves state-of-the-art FID of 2.20 on CIFAR-10 and generates high-fidelity 1024px Celeba-HQ images. The work supports various applications including class-conditional generation, inpainting, and colorization.
Frequently asked
- What is yang-song/score_sde_pytorch?
- A PyTorch implementation of score-based generative models using stochastic differential equations for high-quality image generation.
- Is score_sde_pytorch open source?
- Yes — yang-song/score_sde_pytorch is open source, released under the Apache-2.0 license.
- What language is score_sde_pytorch written in?
- yang-song/score_sde_pytorch is primarily written in Jupyter Notebook.
- How popular is score_sde_pytorch?
- yang-song/score_sde_pytorch has 2.1k stars on GitHub.
- Where can I find score_sde_pytorch?
- yang-song/score_sde_pytorch is on GitHub at https://github.com/yang-song/score_sde_pytorch.