Papers › Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
Jaemin Cho, Linjie Li, Zhengyuan Yang, Zhe Gan, Lijuan Wang, Mohit Bansal
Spatial control is a core capability in controllable image generation. Advancements in layout-guided image generation have shown promising results on in-distribution (ID) datasets with similar spatial configurations. However, it is unclear how these models perform when facing out-of-distribution (OOD) samples with arbitrary, unseen layouts. In this paper, we propose LayoutBench, a diagnostic benchmark for layout-guided image generation that examines four categories of spatial control skills: number, position, size, and shape. We benchmark two recent representative layout-guided image generation methods and observe that the good ID layout control may not generalize well to arbitrary layouts in the wild (e.g., objects at the boundary). Next, we propose IterInpaint, a new baseline that generates foreground and background regions step-by-step via inpainting, demonstrating stronger generalizability than existing models on OOD layouts in LayoutBench. We perform quantitative and qualitative evaluation and fine-grained analysis on the four LayoutBench skills to pinpoint the weaknesses of existing models. We show comprehensive ablation studies on IterInpaint, including training task ratio, crop&paste vs. repaint, and generation order. Lastly, we evaluate the zero-shot performance of different pretrained layout-guided image generation models on LayoutBench-COCO, our new benchmark for OOD layouts with real objects, where our IterInpaint consistently outperforms SOTA baselines in all four splits. Project website: https://layoutbench.github.io
In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.
Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Layout-to-Image Generation | LayoutBench | IterInpaint | AP | 36.5 | #1 of 3 | Archive leaderboard | report |
| Layout-to-Image Generation | LayoutBench-COCO - Combination | IterInpaint | AP | 44.1 | #1 of 4 | Archive leaderboard | report |
| Layout-to-Image Generation | LayoutBench-COCO - Number | IterInpaint | AP | 31.4 | #1 of 4 | Archive leaderboard | report |
| Layout-to-Image Generation | LayoutBench-COCO - Position | IterInpaint | AP | 39.1 | #1 of 4 | Archive leaderboard | report |
| Layout-to-Image Generation | LayoutBench-COCO - Size | IterInpaint | AP | 33.5 | #1 of 4 | 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.
Methods
Introduced by this paper: IterInpaint
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections