In this work, we show that text-to-image generative models can be ‘inverted’ to assess their own text-image understanding capabilities in a completely automated manner.
Our method, calledSelfEval, uses the generative model to compute the likelihood of real images given text prompts, making the generative model directly applicable to discriminative tasks.
UsingSelfEval, we repurpose standard datasets created for evaluating multimodal text-image discriminative models to evaluate generative models in a fine-grained manner: assessing their performance on attribute binding, color recognition, counting, shape recognition, spatial understanding.
To the best of our knowledgeSelfEvalis the first automated metric to show a high degree of agreement for measuring text-faithfulness with the gold-standard human evaluations across multiple models and benchmarks.
Moreover,SelfEvalenables us to evaluate generative models on challenging tasks such as Winoground image-score where they demonstrate competitive performance to discriminative models.
We also show severe drawbacks of standard automated metrics such as CLIP-score to measure text faithfulness on benchmarks such as DrawBench, and howSelfEvalsidesteps these issues.
We hopeSelfEvalenables easy and reliable automated evaluation for diffusion models.