We propose an effective denoising diffusion model for generatinghigh-resolution images (e.g., 1024$\times$512), trained on small-size imagepatches (e.g., 64$\times$64). We name our algorithm Patch-DM, in which a newfeature collage strategy is designed to avoid the boundary artifact whensynthesizing large-size images. Feature collage systematically crops andcombines partial features of the neighboring patches to predict the features ofa shifted image patch, allowing the seamless generation of the entire image dueto the overlap in the patch feature space. Patch-DM produces high-quality imagesynthesis results on our newly collected dataset of nature images(1024$\times$512), as well as on standard benchmarks of smaller sizes(256$\times$256), including LSUN-Bedroom, LSUN-Church, and FFHQ. We compare ourmethod with previous patch-based generation methods and achievestate-of-the-art FID scores on all four datasets. Further, Patch-DM alsoreduces memory complexity compared to the classic diffusion models.