LongCat Video Avatar ComfyUI GGUF

# LongCat-Video-Avatar ## 🚀 Model Introduction We are excited to announce the release of LongCat-Video-Avatar, a unified model that delivers expressive and highly dynamic audio-driven character animation, supporting native tasks including Audio-Text-to-Video, Audio-Text-Image-to-Video, and Video Continuation with seamless compatibility for both single-stream and multi-stream audio inputs. ### Key Features - 🌟 : One unified model can be used for generation, generation, and . - 🌟 : The disentangled unconditional guidance is designed to effectively decouple speech signals from motion dynamics for natural behavior. - 🌟 : The reference skip attention is adopted to​ strategically incorporates reference cues to preserve identity while preventing excessive conditional image leakage. - 🌟 : Cross-Chunk Latent Stitching is designed to eliminates redundant VAE decode-encode cycles to reduce pixel degradation in long sequences. For more detail, please refer to the comprehensive . ## 🌀 Preview Gallery --> The following videos showcase example generations from our model and have been compressed for easier viewing. ## 📊 Human Evaluation Human evaluation on naturalness and realism of the synthesized videos. The benchmark EvalTalker [1] contains more than 400 testing samples with different difficulty levels for evaluating the single and multiple human video generation. Reference: [1] Zhou Y, Zhu X, Ren S, et al. EvalTalker: Learning to Evaluate Real-Portrait-Driven Multi-Subject Talking Humans[J]. arXiv preprint arXiv:2512.01340, 2025. ## ⚖️ License Agreement The are released under the . Any contributions to this repository are licensed under the MIT License, unless otherwise stated. This license does not grant any rights to use Meituan trademarks or patents. See the file for the full license text. ## 🧠 Usage Considerations This model has not been specifically designed or comprehensively evaluated for every possible downstream application. Developers should take into account the known limitations of large language models, including performance variations across different languages, and carefully assess accuracy, safety, and fairness before deploying the model in sensitive or high-risk scenarios. It is the responsibility of developers and downstream users to understand and comply with all applicable laws and regulations relevant to their use case, including but not limited to data protection, privacy, and content safety requirements. Nothing in this Model Card should be interpreted as altering or restricting the terms of the MIT License under which the model is released. ## 📖 Citation We kindly encourage citation of our work if you find it useful. ## 🙏 Acknowledgements We would like to thank the contributors to the , , and repositories, for their open research. ## 📞 Contact Please contact us at or join our WeChat Group if you have any questions.