Salt🧂: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation

Add some Salt 🧂 to your video generation distillation

Xingtong Ge^1,2, Yi Zhang², Yushi Huang¹, Dailan He², Xiahong Wang², Bingqi Ma², Guanglu Song²,
Yu Liu², Jun Zhang¹

¹ Hong Kong University of Science and Technology ² Vivix Group Limited

Abstract

Distilling video generation models to extremely low inference budgets (e.g., 2-4 NFEs) is crucial for real-time deployment, yet remains challenging. Trajectory-style consistency distillation often becomes conservative under complex video dynamics, yielding over-smoothed appearance and weak motion. Distribution matching distillation (DMD) can recover sharp, mode-seeking samples, but its local training signals do not explicitly regularize how denoising updates compose across timesteps, making composed rollouts prone to drift. To overcome this challenge, we propose Self-Consistent Distribution Matching Distillation (SC-DMD), which explicitly regularizes the endpoint-consistent composition of consecutive denoising updates. For real-time autoregressive video generation, we further treat the KV cache as a quality-parameterized condition and propose cache-distribution-aware training. This training scheme applies SC-DMD over multi-step rollouts and introduces a cache-conditioned feature alignment objective that steers low-quality outputs toward high-quality references. Across extensive experiments on both non-autoregressive backbones (e.g., Wan 2.1) and autoregressive real-time paradigms (e.g., Self Forcing, Causal Forcing, and LongLive), Salt consistently improves low-NFE video generation quality while remaining compatible with diverse KV-cache memory mechanisms.

Selected Results

Text-to-video generation on VBench

Diffusion models

Model	NFE	Total	Quality	Semantic
rCM	4	82.73	83.65	79.04
DMD	4	82.78	84.39	76.36
Salt (SC-DMD)	4	83.19	84.42	78.30

Autoregressive models

Model	NFE	Total	Quality	Semantic
Self Forcing	4	84.20	84.74	82.05
Salt + Self Forcing	4	84.47	85.27	81.28
LongLive	4	84.40	85.12	81.53
Salt + LongLive	4	84.93	85.41	83.00
Causal Forcing	4	84.62	85.41	81.47
Salt + Causal Forcing	4	85.08	85.96	81.59
Salt + Causal Forcing	2	84.80	85.63	81.49

Image-to-video generation on VBench-I2V

Method	NFE	I2V Score	Quality	Background Consistency	Motion Smoothness	Dynamic Degree	Imaging Quality	Temporal Flicker
PCM	8	93.63	78.52	97.34	98.24	30.98	70.42	97.67
DMD	4	93.09	78.89	92.79	97.99	58.46	70.35	95.21
LightX2V	4	93.50	80.92	95.87	97.89	60.33	71.67	96.30
Salt (SC-DMD)	4	93.90	80.86	95.97	98.37	52.85	72.16	97.41
Salt-alpha	4	93.88	81.71	95.46	98.30	68.13	72.08	96.48

Long-horizon autoregressive generation on VBench-Long

Backbone	Total	Quality	Semantic
Causal Forcing	78.11	82.57	60.25
Salt + Causal Forcing	78.28	82.15	62.77
LongLive	79.03	82.82	63.88
Salt + LongLive	79.27	82.90	64.74

Qualitative Results

The figure below shows additional qualitative comparisons with the Causal Forcing baseline. Salt better preserves subject identity, object geometry, scene composition, and motion smoothness across challenging examples.

Figure: Qualitative comparisons with the Causal Forcing baseline. Salt better preserves subject identity, object geometry, scene composition, and temporal coherence across challenging examples, including the umbrella, trombone, reading-girl, and grape cases.