CAS: A Probability-Based Approach for Universal Condition Alignment Score

Published: 16 Jan 2024, Last Modified: 11 Feb 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
Primary Area: generative models
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Keywords: Generative model, diffusion model, score-based prior, conditional diffusion model, text-to-image alignment score, inversion process, image quality assessment, T2I alignment score
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Abstract: Recent conditional diffusion models have shown remarkable advancements and have been widely applied in fascinating real-world applications. However, samples generated by these models often do not strictly comply with user-provided conditions. Due to this, there have been few attempts to evaluate this alignment via pre-trained scoring models to select well-generated samples. Nonetheless, current studies are confined to the text-to-image domain and require large training datasets. This suggests that crafting alignment scores for various conditions will demand considerable resources in the future. In this context, we introduce a universal condition alignment score that leverages the conditional probability measurable through the diffusion process. Our technique operates across all conditions and requires no additional models beyond the diffusion model used for generation, effectively enabling self-rejection. Our experiments validate that our met- ric effectively applies in diverse conditional generations, such as text-to-image, {instruction, image}-to-image, edge-/scribble-to-image, and text-to-audio.
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Submission Number: 7028