Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion Transformer

Zhuoyi Yang, Heyang Jiang, Wenyi Hong, Jiayan Teng, Wendi Zheng, Yuxiao Dong, Ming Ding, Jie Tang

Abstract: Diffusion models have shown remarkable performance in image generation in recent years. However, due to a quadratic increase in memory during generating ultra-high-resolution images (e.g. \(4096\times 4096\)), the resolution of generated images is often limited to \(1024\times 1024\). In this work. We propose a unidirectional block attention mechanism that can adaptively adjust the memory overhead during the inference process and handle global dependencies. Building on this module, we adopt the DiT structure for upsampling and develop an infinite super-resolution model capable of upsampling images of various shapes and resolutions. Comprehensive experiments show that our model achieves SOTA performance in generating ultra-high-resolution images in both machine and human evaluation. Compared to commonly used UNet structures, our model can save more than \(5{\times }\) memory when generating \(4096\times 4096\) images. The project URL is https://github.com/THUDM/Inf-DiT.

External IDs:dblp:conf/eccv/YangJHTZDDT24