Patched Denoising Diffusion Models For High-Resolution Image Synthesis
Zheng Ding Mengqi Zhang Jiajun Wu Zhuowen Tu
We propose an effective denoising diffusion model for generating
high-resolution images (e.g., 1024$\times$512), trained on small-size image
patches (e.g., 64$\times$64). We name our algorithm Patch-DM, in which a new
feature collage strategy is designed to avoid the boundary artifact when
synthesizing large-size images. Feature collage systematically crops and
combines partial features of the neighboring patches to predict the features of
a shifted image patch, allowing the seamless generation of the entire image due
to the overlap in the patch feature space. Patch-DM produces high-quality image
synthesis results on our newly collected dataset of nature images
(1024$\times$512), as well as on standard benchmarks of smaller sizes
(256$\times$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.