Order Is Not Layout: Order-to-Space Bias in Image Generation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917312387350528 |
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| author | Zhang, Yongkang Zhao, Zonglin Zhang, Yuechen Ding, Fei Li, Pei Wang, Wenxuan |
| author_facet | Zhang, Yongkang Zhao, Zonglin Zhang, Yuechen Ding, Fei Li, Pei Wang, Wenxuan |
| contents | We study a systematic bias in modern image generation models: the mention order of entities in text spuriously determines spatial layout and entity--role binding. We term this phenomenon Order-to-Space Bias (OTS) and show that it arises in both text-to-image and image-to-image generation, often overriding grounded cues and causing incorrect layouts or swapped assignments. To quantify OTS, we introduce OTS-Bench, which isolates order effects with paired prompts differing only in entity order and evaluates models along two dimensions: homogenization and correctness. Experiments show that Order-to-Space Bias (OTS) is widespread in modern image generation models, and provide evidence that it is primarily data-driven and manifests during the early stages of layout formation. Motivated by this insight, we show that both targeted fine-tuning and early-stage intervention strategies can substantially reduce OTS, while preserving generation quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03714 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Order Is Not Layout: Order-to-Space Bias in Image Generation Zhang, Yongkang Zhao, Zonglin Zhang, Yuechen Ding, Fei Li, Pei Wang, Wenxuan Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Multimedia We study a systematic bias in modern image generation models: the mention order of entities in text spuriously determines spatial layout and entity--role binding. We term this phenomenon Order-to-Space Bias (OTS) and show that it arises in both text-to-image and image-to-image generation, often overriding grounded cues and causing incorrect layouts or swapped assignments. To quantify OTS, we introduce OTS-Bench, which isolates order effects with paired prompts differing only in entity order and evaluates models along two dimensions: homogenization and correctness. Experiments show that Order-to-Space Bias (OTS) is widespread in modern image generation models, and provide evidence that it is primarily data-driven and manifests during the early stages of layout formation. Motivated by this insight, we show that both targeted fine-tuning and early-stage intervention strategies can substantially reduce OTS, while preserving generation quality. |
| title | Order Is Not Layout: Order-to-Space Bias in Image Generation |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2603.03714 |