Papers › Patched Denoising Diffusion Models For High-Resolution Image Synthesis

Patched Denoising Diffusion Models For High-Resolution Image Synthesis

2 Aug 2023arXiv:2308.01316archive 2025-07-28

Zheng Ding, Mengqi Zhang, Jiajun Wu, Zhuowen Tu

We propose an effective denoising diffusion model for generating high-resolution images (e.g., 1024×512), trained on small-size image patches (e.g., 64×64). We name our algorithm Patch-DM, in which a new feature collage strategy is designed to avoid the boundary artifact when synthesizing large-size images. Feature collage systematically crops and combines partial features of the neighboring patches to predict the features of a shifted image patch, allowing the seamless generation of the entire image due to the overlap in the patch feature space. Patch-DM produces high-quality image synthesis results on our newly collected dataset of nature images (1024×512), as well as on standard benchmarks of smaller sizes (256×256), including LSUN-Bedroom, LSUN-Church, and FFHQ. We compare our method with previous patch-based generation methods and achieve state-of-the-art FID scores on all four datasets. Further, Patch-DM also reduces memory complexity compared to the classic diffusion models.

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