Tencent-Hunyuan / Prism

# Prism
Prism:Dynamic Sparse Attention for Native 2K Joint Video-Audio Generation Model Training
Shuyuan Tu 1 , Qi Tian 2 , Yinming Huang 1 , Yue Wu 2 , Xintong Han 2 , Kaihang Pan 3 , Weijie Kong 2 , Jiangfeng Xiong 2 , Jian-Wei Zhang 2 , Zuxuan Wu 1 , Yu-Gang Jiang 1 
[ 1 Fudan University; 2 Tencent Hunyuan; 3 Zhejiang University]
## Overview
 The overview of the framework of Prism. 
Native training joint video-audio generation models at higher resolutions empowers them to learn richer visual details and sharper motion dynamics.
However, full attention incurs quadratic cost and, as resolution increases, spreads attention over increasingly redundant tokens, diluting learning signals for informative content and disrupting pretrained priors.
Existing sparse attention methods either target training-free acceleration or overlook the unique structure of joint video-audio data, where cross-modal interactions are inherently concentrated around sound-producing regions. 
To address this, we propose Prism, a dynamic sparse attention framework for natively training joint video-audio generation models at 2K. 
In particular, Prism organizes the token sequence into spatiotemporal macro-zones, enabling the attention structure to adapt to local content. 
For each zone, it estimates local information structure via video feature variance along the channel and feature norms from the audio-to-video cross-attention, jointly capturing how visual content varies directionally and how strongly audio influences each visual region. 
Based on these signals, Prism dynamically assigns a tailored block shape to each zone, applying finer partitioning along axes of rapid visual content variation and strong audio-visual coupling.
This encourages tokens within each block to remain semantically coherent, allowing block-level features to capture both visual content and joint video-audio interaction patterns.
Prism further adopts a hybrid block selection strategy to dynamically determine per-query sparsity. 
Experiments show that Prism achieves 2.5$\times$ training speedup compared to full attention, while surpassing it in generation quality.
## News
* `[2026-10-06]`:🔥 The project page, training/inference code, technical report and a preview model checkpoint are released. Stay tuned!
## 🛠️ To-Do List
- [x] Prism-preview-alpha (stable)
- [x] Prism-preview-beta (motion)
- [x] Data Pre-Processing Code (Latent Extraction and Latent Decode)
- [x] Training Code
- [x] Full Finetuning Code
- [x] Inference Code
- [ ] Prism-pro
## 🔑 Quickstart
The Preview Model supports native joint video-audio generation at 720p, 1080p, and 2K resolutions, as well as native joint video-audio training at these resolutions.
### 🧱 Environment Setup
**Prerequisites.** Prism requires Python ≥ 3.10, CUDA ≥ 12.4, and a system-level `ffmpeg` installation.
 [code] 
**(Multi-node only) Quick cluster-wide install via `pdsh`**
If you are running on a multi-node cluster, the following one-liner installs the key
packages