Title: Scalable Evaluation of Closed-Set and Open-Set Semantic and Spatial Alignment in Layout-Guided Diffusion Models
Primary area: datasets and benchmarks
Keywords: layout-guided diffusion models, benchmark, closed-set, open-set

Abstract: Evaluating layout-guided text-to-image generative models requires measuring both semantic alignment with textual prompts and spatial fidelity to prescribed layouts. Existing benchmarks are limited in scale and coverage, hindering systematic comparison and reducing interpretability of model capabilities. In this paper, we introduce a scalable closed-set benchmark (C-Bench), automatically
built through a pipeline combining template- and LLM-based prompt generation with constraint-driven layout synthesis. C-Bench spans seven scenarios designed to isolate key generative capabilities and provides varying levels of complexity in both prompt structure and layout. To complement this controlled setting, we propose an open-set benchmark (O-Bench) derived from Flickr30k Entities, enabling evaluation on natural prompts and layouts. We further develop a unified evaluation protocol that combines semantic and spatial accuracy into a single score, enabling consistent model ranking. Using our benchmarks, we conduct a large-scale evaluation of six state-of-the-art layout-guided diffusion models, totaling 319,086 generated and evaluated images. Results show that MIGC achieves the highest overall performance (0.7082 on C-Bench and 0.7548 on O-Bench), establishing it as the most reliable model, particularly in layout alignment. Models trained explicitly with layout information consistently outperform Stable Diffusion–based approaches, which lag significantly behind. Overall, our benchmarks and evaluation protocol provide a scalable and interpretable framework for assessing progress in controllable image generation. Code and benchmarks will be released upon acceptance.

TL;DR: We introduce scalable closed-set benchmark, an open-set benchmark and a unified evaluation protocol to assess layout-guided text-to-image models, revealing performance differences across six state-of-the-art models.