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TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT
Tencent HunyuanVideo-I2V Release Date: March 5, 2025
THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION, UNITED KINGDOM AND SOUTH KOREA AND IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW.
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EXHIBIT A
ACCEPTABLE USE POLICY
Tencent reserves the right to update this Acceptable Use Policy from time to time.
Last modified: November 5, 2024
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Usage and Legal Notices:
Tencent is pleased to support the open source community by making HunyuanVideo-I2V available.
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# HunyuanVideo-I2V
## 论文
`HunyuanVideo: A Systematic Framework For Large Video Generative Models`
* https://arxiv.org/abs/2412.03603
## 模型结构
模型采用主流`Latent Diffusion`架构，包含用于数据压缩/恢复的`3D VAE`，去噪模块`DiT`，文本使用`CLIP`以及多模态语言模型(MLLM)编码器处理。
![alt text](readme_imgs/arch.png)
## 算法原理
采用Flow matching算法训练模型。
![alt text](readme_imgs/alg.png)
## 环境配置
### Docker（方法一）
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10
docker run --shm-size 100g --network=host --name=hunyuanvideo_i2v --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install -r requirements.txt
pip install distvae yunchang
pip install "xfuser>=0.4" --no-deps torch
bash modified/fix.sh
### Dockerfile（方法二）
docker build -t <IMAGE_NAME>:<TAG> .
docker run --shm-size 100g --network=host --name=hunyuanvideo_i2v --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
pip install -r requirements.txt
pip install distvae yunchang
pip install "xfuser>=0.4" --no-deps torch
bash modified/fix.sh
### Anaconda（方法三）
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
```
DTK驱动:dtk24.04.3
python:python3.10
torch:2.3.0
torchvision:0.18.1
torchaudio:2.1.2
triton:2.1.0
flash-attn:2.6.1
deepspeed:0.14.2
apex:1.3.0
xformers:0.0.25
transformers:4.48.0
```
2、其他非特殊库直接按照requirements.txt安装
```
pip install -r requirements.txt
pip install distvae yunchang
pip install "xfuser>=0.4" --no-deps torch
# 需要参考 modified/fix.sh中的命令修改相应位置的代码
```
## 数据集
无
## 训练
无
## 推理
### 多卡
```bash
torchrun --nproc_per_node=5 sample_image2video.py \
--model HYVideo-T/2 \
--prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
--i2v-resolution 720p \
--i2v-stability \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 7.0 \
--seed 0 \
--embedded-cfg-scale 6.0 \
--save-path ./results \
--ulysses-degree 1 \
--ring-degree 5 \
--video-size 720 720 \
--xdit-adaptive-size
```
参数说明
| --video-size | --video-length | --ulysses-degree x --ring-degree | --nproc_per_node |
|----------------------|----------------|----------------------------------|------------------|
| 1280 720 or 720 1280 | 129 | 8x1,4x2,2x4,1x8 | 8 |
| 1280 720 or 720 1280 | 129 | 1x5 | 5 |
| 1280 720 or 720 1280 | 129 | 4x1,2x2,1x4 | 4 |
| 1280 720 or 720 1280 | 129 | 3x1,1x3 | 3 |
| 1280 720 or 720 1280 | 129 | 2x1,1x2 | 2 |
| 1104 832 or 832 1104 | 129 | 4x1,2x2,1x4 | 4 |
| 1104 832 or 832 1104 | 129 | 3x1,1x3 | 3 |
| 1104 832 or 832 1104 | 129 | 2x1,1x2 | 2 |
| 960 960 | 129 | 6x1,3x2,2x3,1x6 | 6 |
| 960 960 | 129 | 4x1,2x2,1x4 | 4 |
| 960 960 | 129 | 3x1,1x3 | 3 |
| 960 960 | 129 | 1x2,2x1 | 2 |
| 960 544 or 544 960 | 129 | 6x1,3x2,2x3,1x6 | 6 |
| 960 544 or 544 960 | 129 | 4x1,2x2,1x4 | 4 |
| 960 544 or 544 960 | 129 | 3x1,1x3 | 3 |
| 960 544 or 544 960 | 129 | 1x2,2x1 | 2 |
| 832 624 or 624 832 | 129 | 4x1,2x2,1x4 | 4 |
| 624 832 or 624 832 | 129 | 3x1,1x3 | 3 |
| 832 624 or 624 832 | 129 | 2x1,1x2 | 2 |
| 720 720 | 129 | 1x5 | 5 |
| 720 720 | 129 | 3x1,1x3 | 3 |
### 单卡（不推荐）
```bash
python3 sample_image2video.py \
--model HYVideo-T/2 \
--prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
--i2v-resolution 720p \
--i2v-stability \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 7.0 \
--seed 0 \
--embedded-cfg-scale 6.0 \
--use-cpu-offload \
--save-path ./results
```
参数说明：
| 参数 | 默认 | 描述 |
|:----------------------:|:-----------------------------:|:----------------------------------------------------------------------------------------------------------------------------------:|
| `--prompt` | None | 用于视频生成的文本提示。 |
| `--model` | HYVideo-T/2-cfgdistill | 这里我们使用 HYVideo-T/2 用于 I2V，HYVideo-T/2-cfgdistill 用于 T2V 模式。 |
| `--i2v-mode` | False | 是否开启 I2V 模式。 |
| `--i2v-image-path` | ./assets/demo/i2v/imgs/0.png | 用于视频生成的参考图像。 |
| `--i2v-resolution` | 720p | 生成视频的分辨率。 |
| `--i2v-stability` | False | 是否使用稳定模式进行 i2v 推理。 |
| `--video-length` | 129 | 生成视频的长度。 |
| `--infer-steps` | 50 | 采样步骤的数量。 |
| `--flow-shift` | 7.0 | 流匹配调度器的偏移因子。我们建议将`--i2v-stability`设置为 7，以获得更稳定的视频；将`--i2v-stability`设置为 17，以获得更动态的视频 |
| `--flow-reverse` | False | 如果反转，从 t=1 学习/采样到 t=0。 |
| `--seed` | None | 生成视频的随机种子，如果为 None，则初始化一个随机种子。 |
| `--use-cpu-offload` | False | 使用 CPU 卸载模型加载以节省更多内存，对于高分辨率视频生成是必要的。 |
| `--save-path` | ./results | 保存生成视频的路径。 |
## result
![](readme_imgs/hy_i2v.gif)
### 精度
无
## 应用场景
### 算法类别
`视频生成`
### 热点应用行业
`电商,教育,广媒`
## 预训练权重
|model|save_path|链接|
|:---:|:---:|:---:|
|HunyuanVideo-I2V|ckpts/hunyuan-video-i2v-720p/|[hf](https://hf-mirror.com/tencent/HunyuanVideo-I2V) \| [SCNet](http://113.200.138.88:18080/aimodels/tencent/HunyuanVideo-I2V)|
|llava-llama-3-8b-v1_1-transformers|ckpts/text_encoder_i2v/|[hf](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers) \| [SCNet](http://113.200.138.88:18080/aimodels/xtuner/llava-llama-3-8b-v1_1-transformers) |
|clip-vit-large-patch14 |ckpts/text_encoder_2| [hf](https://huggingface.co/openai/clip-vit-large-patch14) \| [SCNet](http://113.200.138.88:18080/aimodels/findsource-dependency/clip-vit-large-patch14) |
## 源码仓库及问题反馈
* https://developer.sourcefind.cn/codes/modelzoo/hunyuanvideo-i2v_pytorch
## 参考资料
* https://github.com/Tencent/HunyuanVideo-I2V
assets/WECHAT.md 0 → 100644
View file @ 1da75ff3
<div align="center">
<img src=wechat.jpg width="60%"/>
<p> 扫码关注混元系列工作，加入「 Hunyuan Video 交流群」 </p>
<p> Scan the QR code to join the "Hunyuan Discussion Group" </p>
</div>
assets/demo/i2v_lora/train_dataset/meta_data.json 0 → 100644
View file @ 1da75ff3
{
"video_path": "/path/to/video.mp4",
"raw_caption": {
"long caption": "Detailed description text of the video"
}
}
\ No newline at end of file
assets/demo/i2v_lora/train_dataset/meta_file.list 0 → 100644
View file @ 1da75ff3
./assets/demo/i2v_lora/train_dataset/meta_data.json
\ No newline at end of file
assets/demo/i2v_lora/train_dataset/processed_data/json_path/embrace.json 0 → 100644
View file @ 1da75ff3
{"video_id": "embrace", "latent_shape": [1, 16, 33, 52, 68], "video_path": ".", "prompt": "Two people hugged tightly, In the video, two people are standing apart from each other. They then move closer to each other and begin to hug tightly. The hug is very affectionate, with the two people holding each other tightly and looking into each other's eyes. The interaction is very emotional and heartwarming, with the two people expressing their love and affection for each other.", "npy_save_path": "./assets/demo/i2v_lora/train_dataset/processed_data/embrace.npy"}
\ No newline at end of file
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