CutDiffusion: A Simple, Fast, Cheap, and Strong Diffusion Extrapolation Method
<|reference_start|>CutDiffusion: A Simple, Fast, Cheap, and Strong Diffusion Extrapolation Method: Transforming large pre-trained low-resolution diffusion models to cater to higher-resolution demands, i.e., diffusion extrapolation, significantly improves diffusion adaptability. We propose tuning-free CutDiffusion, aimed at simplifying and accelerating the diffusion extrapolation process, making it more affordable and improving performance. CutDiffusion abides by the existing patch-wise extrapolation but cuts a standard patch diffusion process into an initial phase focused on comprehensive structure denoising and a subsequent phase dedicated to specific detail refinement. Comprehensive experiments highlight the numerous almighty advantages of CutDiffusion: (1) simple method construction that enables a concise higher-resolution diffusion process without third-party engagement; (2) fast inference speed achieved through a single-step higher-resolution diffusion process, and fewer inference patches required; (3) cheap GPU cost resulting from patch-wise inference and fewer patches during the comprehensive structure denoising; (4) strong generation performance, stemming from the emphasis on specific detail refinement.<|reference_end|>
@article{lin2024cutdiffusion:,
    title={CutDiffusion: A Simple, Fast, Cheap, and Strong Diffusion Extrapolation
  Method},
    author={Mingbao Lin, Zhihang Lin, Wengyi Zhan, Liujuan Cao, Rongrong Ji},
    journal={arXiv preprint arXiv:2404.15141},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15141},
    primaryClass={cs.CV cs.AI}
}