Papers › InfinityGAN: Towards Infinite-Pixel Image Synthesis

InfinityGAN: Towards Infinite-Pixel Image Synthesis

8 Apr 2021ICLR 2022 4arXiv:2104.03963archive 2025-07-28

Chieh Hubert Lin, Hsin-Ying Lee, Yen-Chi Cheng, Sergey Tulyakov, Ming-Hsuan Yang

We present a novel framework, InfinityGAN, for arbitrary-sized image generation. The task is associated with several key challenges. First, scaling existing models to an arbitrarily large image size is resource-constrained, in terms of both computation and availability of large-field-of-view training data. InfinityGAN trains and infers in a seamless patch-by-patch manner with low computational resources. Second, large images should be locally and globally consistent, avoid repetitive patterns, and look realistic. To address these, InfinityGAN disentangles global appearances, local structures, and textures. With this formulation, we can generate images with spatial size and level of details not attainable before. Experimental evaluation validates that InfinityGAN generates images with superior realism compared to baselines and features parallelizable inference. Finally, we show several applications unlocked by our approach, such as spatial style fusion, multi-modal outpainting, and image inbetweening. All applications can be operated with arbitrary input and output sizes. Please find the full version of the paper at https://openreview.net/forum?id=ufGMqIM0a4b .

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Code

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hubert0527/infinityGAN officialpytorch report

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1ran · honoured contract
3ran · our draft was wrong
1ran · fixture could not drive it
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7unverified

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StructureSynthesizer hubert0527/infinityGAN/models/infinitygan_generator.py official repository unverified licence not identified · pointer only · 2feb7da3dacacaf8 · report
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Tasks

Image GenerationScene Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Scene Generation OSM InfiniteGAN Average FID 183.14 #1 of 2 Archive leaderboard report
Scene Generation OSM InfiniteGAN KID 0.288 #1 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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