title: Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size
    HD Images
summary: Stable diffusion, a generative model used in text-to-image synthesis,
  frequently encounters resolution-induced composition problems when generating
  images of varying sizes. This issue primarily stems from the model being
  trained on pairs of single-scale images and their corresponding text
  descriptions. Moreover, direct training on images of unlimited sizes is
  unfeasible, as it would require an immense number of text-image pairs and
  entail substantial computational expenses. To overcome these challenges, we
  propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to
  efficiently generate well-composed images of any size, while minimizing the
  need for high-memory GPU resources. Specifically, the initial stage, dubbed Any
  Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a
  restricted range of ratios to optimize the text-conditional diffusion model,
  thereby improving its ability to adjust composition to accommodate diverse
  image sizes. To support the creation of images at any desired size, we further
  introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the
  subsequent stage. This method allows for the rapid enlargement of the ASD
  output to any high-resolution size, avoiding seaming artifacts or memory
  overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks
  demonstrate that ASD can produce well-structured images of arbitrary sizes,
  cutting down the inference time by 2x compared to the traditional tiled
  algorithm.
paper_authors._id: 64f19cd017c909b4f5e6ce60
paper_authors.name: Qingping Zheng
paper_authors._id: 64f19cd017c909b4f5e6ce61
paper_authors.name: Yuanfan Guo
paper_authors._id: 64f19cd017c909b4f5e6ce62
paper_authors.name: Jiankang Deng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-01T12:05:26.222Z
paper_authors._id: 62cc7a38376917c0223dd24b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1657566065867-noauth.png
paper_authors.fullname: JiankangDeng
paper_authors.type: user
paper_authors.user: JiankangDeng
paper_authors._id: 64f19cd017c909b4f5e6ce63
paper_authors.name: Jianhua Han
paper_authors._id: 64f19cd017c909b4f5e6ce64
paper_authors.name: Ying Li
paper_authors._id: 64f19cd017c909b4f5e6ce65
paper_authors.name: Songcen Xu
paper_authors._id: 64f19cd017c909b4f5e6ce66
paper_authors.name: Hang Xu
paper_title: Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size
    HD Images
paper_summary: Stable diffusion, a generative model used in text-to-image synthesis,
  frequently encounters resolution-induced composition problems when generating
  images of varying sizes. This issue primarily stems from the model being
  trained on pairs of single-scale images and their corresponding text
  descriptions. Moreover, direct training on images of unlimited sizes is
  unfeasible, as it would require an immense number of text-image pairs and
  entail substantial computational expenses. To overcome these challenges, we
  propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to
  efficiently generate well-composed images of any size, while minimizing the
  need for high-memory GPU resources. Specifically, the initial stage, dubbed Any
  Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a
  restricted range of ratios to optimize the text-conditional diffusion model,
  thereby improving its ability to adjust composition to accommodate diverse
  image sizes. To support the creation of images at any desired size, we further
  introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the
  subsequent stage. This method allows for the rapid enlargement of the ASD
  output to any high-resolution size, avoiding seaming artifacts or memory
  overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks
  demonstrate that ASD can produce well-structured images of arbitrary sizes,
  cutting down the inference time by 2x compared to the traditional tiled
  algorithm.
paper_ai_summary: The Any-Size-Diffusion model addresses resolution issues in text-to-image synthesis by using a two-stage pipeline that optimizes text-conditional diffusion and allows for rapid enlargement of images to any size without artifacts.
paper_ai_keywords: stable diffusion generative model text-to-image synthesis resolution-induced composition single-scale images text-image pairs Any-Size-Diffusion ARAD Fast Seamless Tiled Diffusion text-conditional diffusion model image sizes high-resolution inference time tiled algorithm