  Diffusion models have revolutionized image generation in recent years, yet
they are still limited to a few sizes and aspect ratios. We propose
ElasticDiffusion, a novel training-free decoding method that enables pretrained
text-to-image diffusion models to generate images with various sizes.
ElasticDiffusion attempts to decouple the generation trajectory of a pretrained
model into local and global signals. The local signal controls low-level pixel
information and can be estimated on local patches, while the global signal is
used to maintain overall structural consistency and is estimated with a
reference image. We test our method on CelebA-HQ (faces) and LAION-COCO
(objects/indoor/outdoor scenes). Our experiments and qualitative results show
superior image coherence quality across aspect ratios compared to
MultiDiffusion and the standard decoding strategy of Stable Diffusion. Project
page: https://elasticdiffusion.github.io/
