license: mit
pipeline_tag: unconditional-image-generation
HART: Efficient Visual Generation with Hybrid Autoregressive Transformer
Abstract
We introduce Hybrid Autoregressive Transformer (HART), an autoregressive (AR) visual generation model capable of directly generating 1024x1024 images, rivaling diffusion models in image generation quality. Existing AR models face limitations due to the poor image reconstruction quality of their discrete tokenizers and the prohibitive training costs associated with generating 1024px images. To address these challenges, we present the hybrid tokenizer, which decomposes the continuous latents from the autoencoder into two components: discrete tokens representing the big picture and continuous tokens representing the residual components that cannot be represented by the discrete tokens. The discrete component is modeled by a scalable-resolution discrete AR model, while the continuous component is learned with a lightweight residual diffusion module with only 37M parameters. Compared with the discrete-only VAR tokenizer, our hybrid approach improves reconstruction FID from 2.11 to 0.30 on MJHQ-30K, leading to a 31% generation FID improvement from 7.85 to 5.38. HART also outperforms state-of-the-art diffusion models in both FID and CLIP score, with 4.5-7.7x higher throughput and 6.9-13.4x lower MACs.
Useful links
- Open source: https://github.com/mit-han-lab/hart
- Project: https://hanlab.mit.edu/projects/hart
- Demo: https://hart.mit.edu
- Paper page: https://huggingface.co/papers/2410.10812
Citation
@article{tang2024hart,
title={HART: Efficient Visual Generation with Hybrid Autoregressive Transformer},
author={Tang, Haotian and Wu, Yecheng and Yang, Shang and Xie, Enze and Chen, Junsong and Chen, Junyu and Zhang, Zhuoyang and Cai, Han and Lu, Yao and Han, Song},
journal={arXiv preprint},
year={2024}
}