Recently, the DeepBeepMeep team released Wan2.1GP on GitHub, a video generation model optimized for low-end GPU users. Based on Alibaba's Wan2.1, the model is designed to provide strong video generation capabilities for users who lack high-performance GPU resources. The launch of Wan2.1GP marks an important advancement in video generation technology, especially in the open source field.
The main features of Wan2.1GP include its excellent performance and wide applicability. The model continues to surpass existing open source models and some commercial solutions in multiple benchmarks, showing strong competitiveness. In addition, the T2V-1.3B model requires only 8.19GB of video memory, which makes almost all consumer-grade GPUs run. With an RTX4090 graphics card, users can generate a 5-second 480P video in about 4 minutes, and their performance is even comparable to some closed source models.
Wan2.1GP not only supports text to video, image to video, video editing and other tasks, but is also the first video model that can generate Chinese and English text at the same time. This feature brings more possibilities to users' practical applications. In addition, the model is equipped with a powerful video variational autoencoder (VAE), which can efficiently encode and decode 1080P videos of any length, retaining time information intact, laying a solid foundation for video and image generation.
To improve the user experience, Wan2.1GP has made several optimizations, including significantly reducing memory and video memory requirements, and supports multiple configurations to suit devices with different performance. Users can quickly get started with this tool through a simplified installation process. With the continuous version updates, Wan2.1GP has gradually added more practical functions, such as Tea Cache support, Gradio interface improvement, etc., further improving the generation speed and convenience of use.
Project entrance: https://github.com/deepbeepmeep/Wan2GP
Key points:
SOTA Performance: Wan2.1GP performs well in multiple benchmarks, surpassing existing open source and commercial solutions.
Strong compatibility: only 8.19GB of video memory is required, it supports almost all consumer-grade GPUs, suitable for low-end users.
Multi-task support: supports multiple generation tasks such as text to video, image to video, and has the ability to generate Chinese and English texts.