FineGRAIN: Evaluating Failure Modes of Text-to-Image Models with Vision Language Model Judges

Kevin David Hayes, Micah Goldblum, Vikash Sehwag, Gowthami Somepalli, Ashwinee Panda, Tom Goldstein

Keywords: Diffusion, Benchmark, Evaluation, VLM

TL;DR: We jointly evaluate VLMs and diffusion models by seeing whether VLMs can evaluate diffusion model failure modes.

Abstract: Text-to-image (T2I) models are capable of generating visually impressive images, yet they often fail to accurately capture specific attributes in user prompts, such as the correct number of objects with the specified colors. The diversity of such errors underscores the need for a hierarchical evaluation framework that can compare prompt adherence abilities of different image generation models. Simultaneously, benchmarks of vision language models (VLMs) have not kept pace with the complexity of scenes that VLMs are used to annotate. In this work, we propose a structured methodology for jointly evaluating T2I models and VLMs by testing whether VLMs can identify 27 specific failure modes in the images generated by T2I models conditioned on challenging prompts. Our second contribution is a dataset of prompts and images generated by 5 T2I models (Flux, SD3-Medium, SD3-Large, SD3.5-Medium, SD3.5-Large) and the corresponding annotations from VLMs (Molmo, InternVL3, Pixtral) annotated by an LLM (Llama3) to test whether VLMs correctly identify the failure mode in a generated image. By analyzing failure modes on a curated set of prompts, we reveal systematic errors in attribute fidelity and object representation. Our findings suggest that current metrics are insufficient to capture these nuanced errors, highlighting the importance of targeted benchmarks for advancing generative model reliability and interpretability.

Dataset URL: https://huggingface.co/datasets/KevinDavidHayes/t2i-finegrain

Code URL: https://github.com/khayes95/FineGRAIN_Eval

Primary Area: Datasets & Benchmarks for applications in language modeling and vision language modeling

Submission Number: 2317