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arXiv · 2508.16644

CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance

Anindya Mondal·Ayan Banerjee·Sauradip Nag·Josep Llados·Xiatian Zhu·Anjan Dutta
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Abstract

Diffusion models excel at photorealistic synthesis but struggle with precise object counts, especially in high-density settings. We introduce COUNTLOOP, a training-free framework that achieves precise instance control through iterative, structured feedback. Our method alternates between synthesis and evaluation: a VLM-based planner generates structured scene layouts, while a VLM-based critic provides explicit feedback on object counts, spatial arrangements, and visual quality to refine the layout iteratively. Instance-driven attention masking and cumulative attention composition further prevent semantic leakage, ensuring clear object separation even in densely occluded scenes. Evaluations on COCO-Count, T2I-CompBench, and two newly introduced high instance benchmarks show that COUNTLOOP reduces counting error by up to 57% and achieves the highest or comparable spatial quality scores across all benchmarks, while maintaining photorealism.

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Anindya Mondal, Ayan Banerjee, Sauradip Nag, Josep Llados, Xiatian Zhu, Anjan Dutta. 2025-08-18. CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance. https://arxiv.org/abs/2508.16644

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