Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size
  HD Images
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.
64f19cd017c909b4f5e6ce60
Qingping Zheng
64f19cd017c909b4f5e6ce61
Yuanfan Guo
64f19cd017c909b4f5e6ce62
Jiankang Deng
admin_assigned
2023-09-01T12:05:26.222Z
62cc7a38376917c0223dd24b
https://cdn-avatars.huggingface.co/v1/production/uploads/1657566065867-noauth.png
JiankangDeng
user
JiankangDeng
64f19cd017c909b4f5e6ce63
Jianhua Han
64f19cd017c909b4f5e6ce64
Ying Li
64f19cd017c909b4f5e6ce65
Songcen Xu
64f19cd017c909b4f5e6ce66
Hang Xu
Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size
  HD Images
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.
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.
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