vantagewithai/LongCat-Video-Avatar-1.5-GGUF-ComfyUI

Quantized GGUFs of LongCat-Video-Avatar 1.5 for ComfyUI + WanVideoWrapper

Original model Link: https://huggingface.co/meituan-longcat/LongCat-Video-Avatar-1.5

Watch us at Youtube: @VantageWithAI

LongCat-Video-Avatar-1.5

---

🚀 Model Introduction

We are excited to announce the release of LongCat-Video-Avatar 1.5, an upgraded open-source framework that prioritizes extreme empirical optimization and production-readiness for audio-driven human video generation. Built upon the LongCat-Video foundation model, v1.5 delivers highly stable, commercial-grade avatar video synthesis supporting native tasks including Audio-Text-to-Video (AT2V), Audio-Text-Image-to-Video (ATI2V), and Video Continuation, with seamless compatibility for both single-stream and multi-stream audio inputs.

Key Features

• 🌟 Upgraded Audio Encoder (Whisper-Large):: Replaces Wav2Vec2 with Whisper-Large, yielding significantly smoother and more natural lip dynamics.
• 🌟 Production-Ready Stability: Achieves accurate lip-synchronization, full-body temporal stability, and robust long-video generation with strict identity consistency.
• 🌟 Stylized Domain Generalization: Robustly generalizes to anime, animals, and complex real-world conditions such as multi-person interactions and object handling.
• 🌟 Efficient 8-Step Inference: Advanced DMD2-based step distillation accelerates inference to 8 NFE, balancing cost-effective serving with exceptional visual fidelity.

For more detail, please refer to the comprehensive LongCat-Video-Avatar-1.5 Technical Report.

🌀 Preview Gallery

LongCat-Video-Avatar 1.5 supports diverse application scenarios including broadcasting, acting, singing, e-commerce marketing, multi-person conversation, animation, and animal characters.

📊 Human Evaluation

We introduce a comprehensive human evaluation benchmark specifically tailored for audio-driven digital human generation. The benchmark encompasses 6 application scenarios (News Broadcasting, Knowledge Education, Daily Life, Entertainment, Singing, Commercial Promotion), 2 languages (Chinese/English), and 2 visual styles (Realistic/Animated), yielding a total of 508 image-audio source pairs. Evaluation Methodology:(1)Subjective Track: 770 crowdsourced evaluators rated each generated video on a 1–5 human-likeness scale, yielding 13,240 judgments. (2) Objective Track: 10 domain experts conducted structured quality analysis across four dimensions: Physical Rationality, Harmony (Audio-Visual Coordination), Temporal Stability, and Identity Consistency.

The results are in the following figure: (a) Expert-level objective quality evaluation across four dimensions (b)Subjective human-likeness comparison with leading commercial models.

⚖️ License Agreement

The model weights are released under the MIT License.

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 LICENSE 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.

@misc{meituanlongcatteam2025longcatvideoavatar15technicalreport,
      title={LongCat-Video-Avatar 1.5 Technical Report}, 
      author={Meituan LongCat Team},
      year={2026},
      eprint={},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={}, 
}

🙏 Acknowledgements

We would like to thank the contributors to the Wan, UMT5-XXL, Diffusers and HuggingFace repositories, for their open research.

📞 Contact

Please contact us at longcat-team@meituan.com or join our WeChat Group if you have any questions.