facebookresearch/DiT
A PyTorch implementation of diffusion transformers for high-quality class-conditional image generation.
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This repository provides the official implementation of Scalable Diffusion Models with Transformers (DiT). It replaces the traditional U-Net backbone in diffusion models with a transformer operating on latent patches. The work demonstrates that DiTs with higher computational complexity (measured in Gflops) consistently achieve better FID scores. Pre-trained DiT-XL/2 models are provided that achieve state-of-the-art results on class-conditional ImageNet generation at both 256×256 and 512×512 resolutions.
Frequently asked
- What is facebookresearch/DiT?
- A PyTorch implementation of diffusion transformers for high-quality class-conditional image generation.
- Is DiT open source?
- Yes — facebookresearch/DiT is an open-source project tracked on heatdrop.
- What language is DiT written in?
- facebookresearch/DiT is primarily written in Python.
- How popular is DiT?
- facebookresearch/DiT has 8.6k stars on GitHub.
- Where can I find DiT?
- facebookresearch/DiT is on GitHub at https://github.com/facebookresearch/DiT.