---
title: "并行解码蒸馏：实现快速图像与视频生成"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.26004v1"
permalink: "https://feed.astralor.com/items/itm_1ae47a9cc16625ee55333ee5"
published: "2026-07-28T17:20:00.000Z"
score: 78
channel: "ai"
tags: ["LTX-2.3","Qwen-Image","Wan 14B","并行解码蒸馏","扩散模型","视频生成"]
---

# 并行解码蒸馏：实现快速图像与视频生成

## AI 点评

> 这篇论文提出的PDD方法很巧妙，直接通过多步预测跳过复杂蒸馏损失，在保持质量的同时大幅提升效率和多样性。对从事视频生成模型加速的研究者和工程师来说，这是最值得关注的进展之一：它可能替代现有VSD范式，让少步生成真正落地。

## 速览

论文提出并行解码蒸馏（PDD），一种基于轨迹的蒸馏方法，用于加速扩散模型和流匹配模型的推理。PDD通过每次网络评估预测多个去噪步骤，显著减少采样步数（4-8 NFE即达SOTA），同时避免传统VSD和对抗损失的模式崩溃问题，提升生成视频的多样性。在LTX-2.3、Wan 14B、Qwen-Image等模型上验证了有效性。

## 关键点

- PDD通过一次网络前向预测多个去噪步，将推理步数降至4-8步。
- 无需VSD或对抗损失，训练更简单稳定，避免模式崩溃。
- 在LTX-2.3文生视频/音频、Wan 14B文生视频、Qwen-Image文生图上达到SOTA。
- 生成视频的多样性和运动质量显著提升。

## 全文

Generation in video diffusion or flow models is computationally expensive due to the slow and iterative sampling process. Current state-of-the-art (SOTA) acceleration methods heavily rely on variational score distillation (VSD) and adversarial losses to distill diffusion models into few-step generators. Albeit achieving high-quality video generation, these training losses are notoriously hard to optimize and suffer from mode collapse, leading to loss of video diversity and lack of motion. In this paper, we introduce Parallel Decoding Distillation (PDD), a simplified and scalable trajectory-based distillation method for fast inference of diffusion and flow matching models. Our architecture and training procedure are compatible with any pre-trained model and support sampling with a varying number of function evaluations (NFE). PDD accelerates generation by predicting multiple denoising steps per network evaluation. Conceptually, it learns a representation of the mean velocity without regressing its derivative using JVPs or finite-difference approximations. Our method achieves SOTA performance with 4-8 NFE on LTX-2.3 Text-to-Video/Audio, Wan 14B Text-to-Video, and Qwen-Image Text-to-Image. Moreover, PDD presents a significant improvement in generated video diversity.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_1ae47a9cc16625ee55333ee5