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

TL;DR: CountLoop is a training-free framework that achieves precise instance control using iterative, structured feedback. Our method alternates between synthesis and evaluation, using a VLM-guided agent as both a layout planner and a critic.

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..

Pipeline Overview

CountLoop operates in three stages: (1) A Design VLM interprets the prompt to produce realistic layouts. (2) These layouts guide style-consistent image generation via a cumulative attention mechanism. (3) A Critic VLM assesses the output for counting accuracy and aesthetic quality, providing structured feedback to refine both the layout and prompt. This iterative loop runs until a target quality score is reached, enabling complex, high-instance images without retraining the diffusion model.

Visual Results

CountLoop consistently avoids semantic drift, grid artifacts, and count inaccuracies that outperform competitors for high-instance image generation. It scales reliably to 100+ instances per image.

Comparison with state-of-the-art methods. CountLoop accurately renders high counts (e.g., "17 vases", "104 hot air balloons") with natural arrangements, whereas competitors often under-generate or produce artificial clusters.

Benchmarks & Evaluation

We evaluate on four sets spanning instance count and compositional difficulty: COCO-Count, T2I-CompBenchCount, and newly proposed CountLoop-S and CountLoop-M.