ContentV: Efficient Training of Video Generation Models with Limited Compute
This work addresses the challenge of efficient training for video generation models, which is crucial for researchers and practitioners with limited compute resources, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of high computational costs in video generation by introducing ContentV, an 8B-parameter text-to-video model that achieves state-of-the-art performance with a score of 85.14 on VBench after training on 256 x 64GB NPUs for only four weeks.
Recent advances in video generation demand increasingly efficient training recipes to mitigate escalating computational costs. In this report, we present ContentV, an 8B-parameter text-to-video model that achieves state-of-the-art performance (85.14 on VBench) after training on 256 x 64GB Neural Processing Units (NPUs) for merely four weeks. ContentV generates diverse, high-quality videos across multiple resolutions and durations from text prompts, enabled by three key innovations: (1) A minimalist architecture that maximizes reuse of pre-trained image generation models for video generation; (2) A systematic multi-stage training strategy leveraging flow matching for enhanced efficiency; and (3) A cost-effective reinforcement learning with human feedback framework that improves generation quality without requiring additional human annotations. All the code and models are available at: https://contentv.github.io.