Latent Diffusion Prior Feature Matching and Zero-Shot Text-to-Image Benchmark Performance

Abstract

Abstract: There has been tremendous progress in large-scale text-to-image synthesis driven by diffusion models enabling versatile downstream applications such as 3D object synthesis from texts, image editing, and customized generation. We present a generic approach using latent diffusion models as powerful image priors for various visual synthesis tasks. Existing methods that utilize such priors fail to use these models' full capabilities. To improve this, our core ideas are 1) a feature matching loss between features from different layers of the decoder to provide detailed guidance and 2) a KL divergen

Research Question

How does feature matching loss in latent diffusion priors impact CLIP score and FID metrics compared to pixel-space reconstruction losses on zero-shot text-to-image benchmarks?

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This record has not completed Gate 2 of the verification pipeline (a type-checked Lean4 proof for mathematical claims, or a sealed-sandbox reproduction for empirical claims). It is a literature synthesis only. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

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Public corrections are additive records. Current status does not claim the synthesis is error-free.

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