TIIF-Bench: How Does Your T2I Model Follow Your Instructions?

The rapid advancements of Text-to-Image (T2I) models haveushered in a new phase of AI-generated content, marked by their growingability to interpret and follow user instructions. However, existing T2Imodel evaluation benchmarks fall short in limited prompt diversity andcomplexity, as well as coarse evaluation metrics, making it difficult to eval-uate the fine-grained alignment performance between textual instructionsand generated images. In this paper, we present TIIF-Bench (Text-to-Image Instruction Following Benchmark), aiming to systematicallyassess T2I models’ ability in interpreting and following intricate tex-tual instructions. TIIF-Bench comprises 5,000 prompts organized alongmultiple dimensions and categorized into three levels of difficulty andcomplexity. To rigorously evaluate robustness to prompt length, eachprompt is provided in both short and long versions with identical coresemantics. We further propose a novel Global Normalized Edit Distance(GNED) metric for text rendering and provide aspect-ratio-diverse ref-erence images for each prompt to assess style control. In addition, wecollect 100 high-quality designer-level prompts covering diverse scenariosfor comprehensive evaluation. To enable scalable and fine-grained evalua-tion, we explore the best paradigm for leveraging the world knowledgeencoded in large Vision-Language Models (VLMs) as automated binaryevaluators. Through extensive ablations, we develop a fully reproducibleevaluator that provides interpretable reasoning and reliable verification,enabling our benchmark to discern subtle variations in T2I model out-puts. Through comprehensive benchmarking of mainstream T2I modelson TIIF-Bench, we analyze the strengths and weaknesses of current T2Isystems and reveal the limitations of existing evaluation benchmarks.