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Stable diffusion, a generative model used in text-to-image synthesis,frequently encounters resolution-induced composition problems when generatingimages of varying sizes. This issue primarily stems from the model beingtrained on pairs of single-scale images and their corresponding textdescriptions. Moreover, direct training on images of unlimited sizes isunfeasible, as it would require an immense number of text-image pairs andentail substantial computational expenses. To overcome these challenges, wepropose a two-stage pipeline named Any-Size-Diffusion (ASD), designed toefficiently generate well-composed images of any size, while minimizing theneed for high-memory GPU resources. Specifically, the initial stage, dubbed AnyRatio Adaptability Diffusion (ARAD), leverages a selected set of images with arestricted range of ratios to optimize the text-conditional diffusion model,thereby improving its ability to adjust composition to accommodate diverseimage sizes. To support the creation of images at any desired size, we furtherintroduce a technique called Fast Seamless Tiled Diffusion (FSTD) at thesubsequent stage. This method allows for the rapid enlargement of the ASDoutput to any high-resolution size, avoiding seaming artifacts or memoryoverloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarksdemonstrate that ASD can produce well-structured images of arbitrary sizes,cutting down the inference time by 2x compared to the traditional tiledalgorithm.

Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images