  The recent advancements in text-to-image generative models have been
remarkable. Yet, the field suffers from a lack of evaluation metrics that
accurately reflect the performance of these models, particularly lacking
fine-grained metrics that can guide the optimization of the models. In this
paper, we propose EvalAlign, a metric characterized by its accuracy, stability,
and fine granularity. Our approach leverages the capabilities of Multimodal
Large Language Models (MLLMs) pre-trained on extensive datasets. We develop
evaluation protocols that focus on two key dimensions: image faithfulness and
text-image alignment. Each protocol comprises a set of detailed, fine-grained
instructions linked to specific scoring options, enabling precise manual
scoring of the generated images. We Supervised Fine-Tune (SFT) the MLLM to
align closely with human evaluative judgments, resulting in a robust evaluation
model. Our comprehensive tests across 24 text-to-image generation models
demonstrate that EvalAlign not only provides superior metric stability but also
aligns more closely with human preferences than existing metrics, confirming
its effectiveness and utility in model assessment.
