  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, called SelfEval, uses the generative model to compute the
likelihood of real images given text prompts, making the generative model
directly applicable to discriminative tasks.
  Using SelfEval, 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 knowledge SelfEval is 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, SelfEval enables 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
how SelfEval sidesteps these issues.
  We hope SelfEval enables easy and reliable automated evaluation for diffusion
models.
