DiffiT: Diffusion Vision Transformers for Image Generation

Official PyTorch implementation of DiffiT: Diffusion Vision Transformers for Image Generation.

Code and pretrained DiffiT models will be released soon !

DiffiT achieves a new SOTA FID score of 1.73 on ImageNet-256 dataset !

In addition, DiffiT sets a new SOTA FID score of 2.22 on FFHQ-64 dataset !

We introduce a new Time-dependent Multihead Self-Attention (TMSA) mechanism that jointly learns spatial and temporal dependencies and allows for attention conditioning with finegrained control.

💥 News 💥

[04.02.2024] 🔥 Updated manuscript now available on arXiv ! !
[12.04.2023] 🔥 Paper is published on arXiv !

Benchmarks

Latent Space

ImageNet-256

Model	Dataset	Resolution	FID-50K	Inception Score
Latent DiffiT	ImageNet	256x256	1.73	276.49

ImageNet-512

Model	Dataset	Resolution	FID-50K	Inception Score
Latent DiffiT	ImageNet	512x512	2.67	252.12

Image Space

Model	Dataset	Resolution	FID-50K
DiffiT	CIFAR-10	32x32	1.95
DiffiT	FFHQ-64	64x64	2.22

Citation

@article{hatamizadeh2023diffit,
  title={Diffit: Diffusion vision transformers for image generation},
  author={Hatamizadeh, Ali and Song, Jiaming and Liu, Guilin and Kautz, Jan and Vahdat, Arash},
  journal={arXiv preprint arXiv:2312.02139},
  year={2023}
}

creatorcao/DiffiT