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The paper introduces HunyuanVideo 1.5, an 8.3B parameter open-source video generation model achieving state-of-the-art visual quality and motion coherence. This is accomplished through data curation, a DiT architecture with selective and sliding tile attention (SSTA), glyph-aware text encoding for improved bilingual understanding, progressive pre-training and post-training, and an efficient video super-resolution network. The model supports both text-to-video and image-to-video generation across various durations and resolutions, demonstrating superior performance compared to existing open-source alternatives.
An 8.3B parameter model now rivals the video quality of much larger models, thanks to innovations in attention and training.
We present HunyuanVideo 1.5, a lightweight yet powerful open-source video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs. This achievement is built upon several key components, including meticulous data curation, an advanced DiT architecture featuring selective and sliding tile attention (SSTA), enhanced bilingual understanding through glyph-aware text encoding, progressive pre-training and post-training, and an efficient video super-resolution network. Leveraging these designs, we developed a unified framework capable of high-quality text-to-video and image-to-video generation across multiple durations and resolutions. Extensive experiments demonstrate that this compact and proficient model establishes a new state-of-the-art among open-source video generation models. By releasing the code and model weights, we provide the community with a high-performance foundation that lowers the barrier to video creation and research, making advanced video generation accessible to a broader audience. All open-source assets are publicly available at https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5.