ContentV: Efficient Training of Video Generation Models with Limited Compute

This project presents ContentV, an efficient framework for accelerating the training of DiT-based video generation models through three key innovations:

A minimalist architecture that maximizes reuse of pre-trained image generation models for video synthesis
A systematic multi-stage training strategy leveraging flow matching for enhanced efficiency
A cost-effective reinforcement learning with human feedback framework that improves generation quality without requiring additional human annotations

Our open-source 8B model (based on Stable Diffusion 3.5 Large and Wan-VAE) achieves state-of-the-art result (85.14 on VBench) in only 4 weeks of training with 256×64GB NPUs.

⚡ Quickstart

Recommended PyTorch Version

GPU: torch >= 2.3.1 (CUDA >= 12.2)
NPU: torch and torch-npu >= 2.1.0 (CANN >= 8.0.RC2). Please refer to Ascend Extension for PyTorch for the installation of torch-npu.

Installation

git clone https://github.com/bytedance/ContentV.git
cd ContentV
pip3 install -r requirements.txt

T2V Generation

## For GPU
python3 demo.py
## For NPU
USE_ASCEND_NPU=1 python3 demo.py

📊 VBench

Model	Total Score	Quality Score	Semantic Score	Human Action	Scene	Dynamic Degree	Multiple Objects	Appear. Style
Wan2.1-14B	86.22	86.67	84.44	99.20	61.24	94.26	86.59	21.59
ContentV (Long)	85.14	86.64	79.12	96.80	57.38	83.05	71.41	23.02
Goku†	84.85	85.60	81.87	97.60	57.08	76.11	79.48	23.08
Open-Sora 2.0	84.34	85.40	80.12	95.40	52.71	71.39	77.72	22.98
Sora†	84.28	85.51	79.35	98.20	56.95	79.91	70.85	24.76
ContentV (Short)	84.11	86.23	75.61	89.60	44.02	79.26	74.58	21.21
EasyAnimate 5.1	83.42	85.03	77.01	95.60	54.31	57.15	66.85	23.06
Kling 1.6†	83.40	85.00	76.99	96.20	55.57	62.22	63.99	20.75
HunyuanVideo	83.24	85.09	75.82	94.40	53.88	70.83	68.55	19.80
CogVideoX-5B	81.61	82.75	77.04	99.40	53.20	70.97	62.11	24.91
Pika-1.0†	80.69	82.92	71.77	86.20	49.83	47.50	43.08	22.26
VideoCrafter-2.0	80.44	82.20	73.42	95.00	55.29	42.50	40.66	25.13
AnimateDiff-V2	80.27	82.90	69.75	92.60	50.19	40.83	36.88	22.42
OpenSora 1.2	79.23	80.71	73.30	85.80	42.47	47.22	58.41	23.89

✅ Todo List

Inference code and checkpoints
Training code of RLHF

🧾 License

This code repository and part of the model weights are licensed under the Apache 2.0 License. Please note that:

MMDiT are derived from Stable Diffusion 3.5 Large and trained with video samples. This Stability AI Model is licensed under the Stability AI Community License, Copyright © Stability AI Ltd. All Rights Reserved
Video VAE from Wan2.1 is licensed under Apache 2.0 License

❤️ Acknowledgement

🔗 Citation

@article{contentv2025,
  title     = {ContentV: Efficient Training of Video Generation Models with Limited Compute},
  author    = {Bytedance Douyin Content Team},
  year      = {2025},
  }