## 2 Related Work
### Evaluation Techniques of Text-to-Image Models  
Text-to-image generative models have made remarkable progress, exemplified by systems like DALL-E, Stable Diffusion, and Imagen, producing high-quality, realistic, and creative images from natural language prompts. However, the evaluation of these models remains an open challenge, as their outputs must be assessed along dimensions such as fidelity, diversity, text-image alignment, compositional reasoning, and ethical considerations. Existing evaluation methods, including human judgment, traditional metrics such as Frechet Inception Distance (FID), and CLIPScore, suffer from limitations such as lack of scalability, difficulty adapting to nuanced prompts, and inadequate assessment of specific challenges like spatial reasoning, numerical reasoning, and biases. This has spurred the development of advanced evaluation frameworks, benchmarks, and metrics, tailored to comprehensively analyze the diverse capabilities and limitations of text-to-image models. To address contextual coherence between text and generated images, methods like Semantic Object Accuracy (SOA) [15] and PaintSkills [16; 17] have introduced diagnostic benchmarks to evaluate fine-grained object alignment and semantic consistency. Frameworks such as TextMatch [18] and likelihood-based evaluation metrics [19] further refine approaches to measure alignment between visual outputs and textual prompts. In the domain of layout fidelity, approaches like Semantic Layout Generation [20], LayoutDiffuse [21], and SmartSpatialEval [22] have introduced mechanisms for bounding box refinement and 3D-aware spatial controls. These advances are supported by spatially enriched datasets like SPRIGHT [23] and SCOP [24], which address historical biases and ambiguities in training data. Several benchmark frameworks have been introduced to address the complexity of text-to-image evaluation. Holistic frameworks such as HEIM [25] and HRS-Bench [19] offer multidimensional evaluations of text-to-image models, covering skills such as text alignment, image quality, compositionality, fairness, robustness, and multilinguality. These frameworks particularly emphasize visual design and aesthetic quality assessment, evaluating aspects such as composition balance, visual hierarchy, and overall appeal through both automated metrics and human evaluation protocols. Compositional benchmarks, including T2I-CompBench [26], GenEval [27], and GenAI-Bench [28], focus on challenges such as object relationships, attribute binding, and spatial reasoning in complex prompts, revealing persistent limitations in handling nuanced compositions. Although manual evaluations remain the gold standard for accuracy and reliability, advances in automated metrics are essential for scalability. VQA-based metrics, such as TIFA [29], I-HallA [30], and VQAScore [31], have shown improved correlation with human judgments by using question-answering systems to evaluate text-image faithfulness and compositional reasoning. Another vital dimension of research is ethical evaluation and bias detection, which aim to identify harmful biases or inequities in the generated outputs. Studies such as PaintSkills [16] and Winoground-T2I [32] have assessed issues such as gender, skin tone bias, and fairness within benchmarks, while interdisciplinary frameworks [33] propose new approaches to examine visual and cultural biases in generative models. However, gaps remain in evaluating specific types of reasoning, such as numerical reasoning for counting and quantifiers [34], and abstract tasks like the generation of embedded text or artistic compositions [3].