--- title: Wan2_1-HuMo_17B-GGUF canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Wan2_1-HuMo_17B-GGUF" md_url: "https://www.modelscope.cn/models/AI-ModelScope/Wan2_1-HuMo_17B-GGUF.md" repository: AI-ModelScope/Wan2_1-HuMo_17B-GGUF last_updated: 2025-10-17 license: apache-2.0 pipeline_tag: image-to-video tasks: - image-to-video library_name: - gguf - pytorch frameworks: - pytorch downloads: 402 stars: 0 tags: - gguf --- # Wan2_1-HuMo_17B-GGUF > Wan2_1-HuMo_17B-GGUF - AI-ModelScope 在 ModelScope 开源的模型。Quantization of Kijai's FP16 Release HuMo AI-ModelScope/Wan2_1-HuMo_17B-GGUF 是 ModelScope 魔搭社区上的image-to-video模型，采用 apache-2.0 许可。 - **Repository**: AI-ModelScope/Wan2_1-HuMo_17B-GGUF - **License**: apache-2.0 - **Tasks**: image-to-video - **Tags**: gguf - **Downloads**: 402 - **Stars**: 0 - **Last updated**: 2025-10-17 Source: https://www.modelscope.cn/models/AI-ModelScope/Wan2_1-HuMo_17B-GGUF --- Quantization of Kijai's FP16 Release [HuMo](https://huggingface.co/Kijai/WanVideo_comfy/tree/main/HuMo) # HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning
[![arXiv](https://img.shields.io/badge/arXiv%20paper-2509.08519-b31b1b.svg)](https://arxiv.org/abs/2509.08519)  [![project page](https://img.shields.io/badge/Project_page-More_visualizations-green)](https://phantom-video.github.io/HuMo/) 
> [**HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning**](https://arxiv.org/abs/2509.08519)
> [Liyang Chen](https://scholar.google.com.hk/citations?user=jk6jWXgAAAAJ&hl) * , [Tianxiang Ma](https://tianxiangma.github.io/) * , [Jiawei Liu](https://scholar.google.com/citations?user=X21Fz-EAAAAJ), [Bingchuan Li](https://scholar.google.com/citations?user=ac5Se6QAAAAJ)†, [Zhuowei Chen](https://scholar.google.com/citations?user=ow1jGJkAAAAJ), [Lijie Liu](https://liulj13.github.io/), [Xu He](https://scholar.google.com.hk/citations?user=KMrFk2MAAAAJ&hl), [Gen Li](https://scholar.google.com/citations?user=wqA7EIoAAAAJ), [Qian He](https://scholar.google.com/citations?user=9rWWCgUAAAAJ), [Zhiyong Wu](https://scholar.google.com.hk/citations?hl=zh-CN&user=7Xl6KdkAAAAJ&) § >
* Equal contribution, † Project lead, § Corresponding author >
Tsinghua University | Intelligent Creation Team, ByteDance

## ✨ Key Features HuMo is a unified, human-centric video generation framework designed to produce high-quality, fine-grained, and controllable human videos from multimodal inputs—including text, images, and audio. It supports strong text prompt following, consistent subject preservation, synchronized audio-driven motion. > - **​​VideoGen from Text-Image**​​ - Customize character appearance, clothing, makeup, props, and scenes using text prompts combined with reference images. > - **​​VideoGen from Text-Audio**​​ - Generate audio-synchronized videos solely from text and audio inputs, removing the need for image references and enabling greater creative freedom. > - **​​VideoGen from Text-Image-Audio**​​ - Achieve the higher level of customization and control by combining text, image, and audio guidance. ## 📑 Todo List - [x] Release Paper - [x] Checkpoint of HuMo-17B - [x] Inference Codes - [ ] Text-Image Input - [x] Text-Audio Input - [x] Text-Image-Audio Input - [x] Multi-GPU Inference - [ ] Release Prompts to Generate Demo of ***Faceless Thrones*** - [ ] HuMo-1.7B ## ⚡️ Quickstart ### Installation ``` conda create -n humo python=3.11 conda activate humo pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124 pip install flash_attn==2.6.3 pip install -r requirements.txt conda install -c conda-forge ffmpeg ``` ### Model Preparation | Models | Download Link | Notes | |--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------| | HuMo-17B | 🤗 [Huggingface](https://huggingface.co/bytedance-research/HuMo/tree/main) | Released before September 15 | HuMo-1.7B | 🤗 [Huggingface](https://huggingface.co/bytedance-research/HuMo/tree/main) | To be released soon | Wan-2.1 | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) | VAE & Text encoder | Whisper-large-v3 | 🤗 [Huggingface](https://huggingface.co/openai/whisper-large-v3) | Audio encoder | Audio separator | 🤗 [Huggingface](https://huggingface.co/huangjackson/Kim_Vocal_2) | Remove background noise (optional) Download models using huggingface-cli: ``` sh huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./weights/Wan2.1-T2V-1.3B huggingface-cli download bytedance-research/HuMo --local-dir ./weights/HuMo huggingface-cli download openai/whisper-large-v3 --local-dir ./weights/whisper-large-v3 huggingface-cli download huangjackson/Kim_Vocal_2 --local-dir ./weights/audio_separator ``` ### Run Multimodal-Condition-to-Video Generation Our model is compatible with both 480P and 720P resolutions. 720P inference will achieve much better quality. > Some tips > - Please prepare your text, reference images and audio as described in [test_case.json](./examples/test_case.json). > - We support Multi-GPU inference using FSDP + Sequence Parallel. > - ​The model is trained on 97-frame videos at 25 FPS. Generating video longer than 97 frames may degrade the performance. We will provide a new checkpoint for longer generation. #### Configure HuMo HuMo’s behavior and output can be customized by modifying [generate.yaml](humo/configs/inference/generate.yaml) configuration file. The following parameters control generation length, video resolution, and how text, image, and audio inputs are balanced: ```yaml generation: frames: # Number of frames for the generated video. scale_a: # Strength of audio guidance. Higher = better audio-motion sync. scale_t: # Strength of text guidance. Higher = better adherence to text prompts. mode: "TA" # Input mode: "TA" for text+audio; "TIA" for text+image+audio. height: 720 # Video height (e.g., 720 or 480). width: 1280 # Video width (e.g., 1280 or 832). diffusion: timesteps: sampling: steps: 50 # Number of denoising steps. Lower (30–40) = faster generation. ``` #### 1. Text-Audio Input ``` sh bash infer_ta.sh ``` #### 2. Text-Image-Audio Input ``` sh bash infer_tia.sh ``` ## Acknowledgements Our work builds upon and is greatly inspired by several outstanding open-source projects, including [Phantom](https://github.com/Phantom-video/Phantom), [SeedVR](https://github.com/IceClear/SeedVR?tab=readme-ov-file), [MEMO](https://github.com/memoavatar/memo), [Hallo3](https://github.com/fudan-generative-vision/hallo3), [OpenHumanVid](https://github.com/fudan-generative-vision/OpenHumanVid), and [Whisper](https://github.com/openai/whisper). We sincerely thank the authors and contributors of these projects for generously sharing their excellent codes and ideas. ## ⭐ Citation If HuMo is helpful, please help to ⭐ the repo. If you find this project useful for your research, please consider citing our [paper](https://arxiv.org/abs/2509.08519). ### BibTeX ```bibtex @misc{chen2025humo, title={HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning}, author={Liyang Chen and Tianxiang Ma and Jiawei Liu and Bingchuan Li and Zhuowei Chen and Lijie Liu and Xu He and Gen Li and Qian He and Zhiyong Wu}, year={2025}, eprint={2509.08519}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2509.08519}, } ``` ## 📧 Contact If you have any comments or questions regarding this open-source project, please open a new issue or contact [Liyang Chen](lyangchen@outlook.com) and [Tianxiang Ma](https://tianxiangma.github.io/).