MaSC is a masked similarity metric that decomposes concept-driven image generation evaluation into subject-specific preservation and background-based prompt following using SigLIP2 embeddings, outperforming global baselines on human correlation and identity benchmarks.
Imagereward: learning and evaluating human preferences for text-to-image generation
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Reward Score Matching unifies reward-based fine-tuning for flow and diffusion models by recasting alignment as score matching to a value-guided target.
Self-Forcing++ scales autoregressive video diffusion to over 4 minutes by using self-generated segments for guidance, reducing error accumulation and outperforming baselines in fidelity and consistency.
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MaSC: A Masked Similarity Metric for Evaluating Concept-Driven Generation
MaSC is a masked similarity metric that decomposes concept-driven image generation evaluation into subject-specific preservation and background-based prompt following using SigLIP2 embeddings, outperforming global baselines on human correlation and identity benchmarks.
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Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
Reward Score Matching unifies reward-based fine-tuning for flow and diffusion models by recasting alignment as score matching to a value-guided target.
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Self-Forcing++: Towards Minute-Scale High-Quality Video Generation
Self-Forcing++ scales autoregressive video diffusion to over 4 minutes by using self-generated segments for guidance, reducing error accumulation and outperforming baselines in fidelity and consistency.
- Flow-OPD: On-Policy Distillation for Flow Matching Models