SourceCAS: A Probability-Based Approach for Universal Condition Alignment Score
The authors measured log pθ(x|c) and log pθ(x) for 100 images generated from the prompt 'woman with green hair, wearing sunglasses and dresses' using Stable Diffusion v1.5. They found that images with higher log pθ(x|c) tend to have markedly lower CLIP scores, and that the ranking tendency of log pθ(x|c) is almost identical to that of log pθ(x). This indicates the conditional probability does not properly reflect alignment with the condition, but is dominated by the unconditional image probability, likely because user-specified conditions are often unseen in the training data.