PRISM shows video diffusion models inherently encode preference information in noisy latents, achieving SOTA accuracy and enabling noise-robust early-stage sampling with a correlation to generative performance.
Dpm-solver-v3: Improved diffusion ode solver with empirical model statistics
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PNAPO augments preference data with prior noise pairs and uses straight-line interpolation to create a tighter surrogate objective for offline alignment of rectified flow models.
APEX derives self-adversarial gradients from condition-shifted velocity fields in flow models to achieve high-fidelity one-step generation, outperforming much larger models and multi-step teachers.
The work introduces rCM, a score-regularized continuous-time consistency model that matches DMD2 quality on large models up to 14B parameters while improving diversity and enabling 1-4 step sampling.
citing papers explorer
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Through the PRISM: Preference Representation in Intermediate States of Video Diffusion Models
PRISM shows video diffusion models inherently encode preference information in noisy latents, achieving SOTA accuracy and enabling noise-robust early-stage sampling with a correlation to generative performance.
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Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs
PNAPO augments preference data with prior noise pairs and uses straight-line interpolation to create a tighter surrogate objective for offline alignment of rectified flow models.
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Self-Adversarial One Step Generation via Condition Shifting
APEX derives self-adversarial gradients from condition-shifted velocity fields in flow models to achieve high-fidelity one-step generation, outperforming much larger models and multi-step teachers.
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Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency
The work introduces rCM, a score-regularized continuous-time consistency model that matches DMD2 quality on large models up to 14B parameters while improving diversity and enabling 1-4 step sampling.