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update README (batch 1/1)
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README_CN.md
61
README_CN.md
@ -24,10 +24,10 @@ HunyuanVideo-1.5作为一款轻量级视频生成模型，仅需83亿参数即
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<a href="https://hunyuan.tencent.com/video/zh?tabIndex=0" target="_blank"><img src=https://img.shields.io/badge/Official%20Site-333399.svg?logo=homepage height=22px></a>
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<a href=https://huggingface.co/tencent/HunyuanVideo-1.5 target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a>
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<a href=https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5 target="_blank"><img src= https://img.shields.io/badge/Page-bb8a2e.svg?logo=github height=22px></a>
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<a href="https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/assets/HunyuanVideo_1_5.pdf" target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>
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<a href="https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/report/HunyuanVideo_1_5.pdf" target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>
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<a href=https://x.com/TencentHunyuan target="_blank"><img src=https://img.shields.io/badge/Hunyuan-black.svg?logo=x height=22px></a>
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<a href="https://doc.weixin.qq.com/doc/w3_AXcAcwZSAGgCNACVygLxeQjyn4FYS?scode=AJEAIQdfAAoSfXnTj0AAkA-gaeACk" target="_blank"><img src=https://img.shields.io/badge/📚-PromptHandBook-blue.svg?logo=book height=22px></a> <br/>
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<a href="https://github.com/comfyanonymous/ComfyUI" target="_blank"><img src=https://img.shields.io/badge/ComfyUI-blue.svg?logo=book height=22px></a>
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<a href="./ComfyUI/README.md" target="_blank"><img src=https://img.shields.io/badge/ComfyUI-blue.svg?logo=book height=22px></a>
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<a href="https://github.com/ModelTC/LightX2V" target="_blank"><img src=https://img.shields.io/badge/LightX2V-yellow.svg?logo=book height=22px></a>
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</div>
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@ -51,6 +51,8 @@ HunyuanVideo-1.5作为一款轻量级视频生成模型，仅需83亿参数即
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如果您在项目中使用或开发了 HunyuanVideo-1.5，欢迎告知我们。
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- **ComfyUI** - [ComfyUI](https://github.com/comfyanonymous/ComfyUI): 一个强大且模块化的扩散模型图形界面，采用节点式工作流。ComfyUI 支持 HunyuanVideo-1.5，并提供多种工程加速优化以实现快速推理。
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我们提供了一个 [ComfyUI 使用指南](./ComfyUI/README.md) 用于 HunyuanVideo-1.5。
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- **社区实现的 ComfyUI 插件** - [comfyui_hunyuanvideo_1.5_plugin](https://github.com/yuanyuan-spec/comfyui_hunyuanvideo_1.5_plugin): 社区实现的 HunyuanVideo-1.5 ComfyUI 插件，提供简化版和完整版节点集，支持快速使用或深度工作流定制，内置自动模型下载功能。
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- **LightX2V** - [LightX2V](https://github.com/ModelTC/LightX2V): 一个轻量级高效的视频生成框架，集成了 HunyuanVideo-1.5，支持多种工程加速技术以实现快速推理。
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@ -77,6 +79,7 @@ HunyuanVideo-1.5作为一款轻量级视频生成模型，仅需83亿参数即
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- [🔑 使用方法](#-使用方法)
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- [视频生成](#视频生成)
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- [命令行参数](#命令行参数)
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- [最优推理配置](#最优推理配置)
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- [🧱 模型卡片](#-模型卡片)
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- [🎬 更多示例](#-更多示例)
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- [📊 性能评估](#-性能评估)
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@ -140,7 +143,7 @@ pip install -i https://mirrors.tencent.com/pypi/simple/ --upgrade tencentcloud-s
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### 步骤 3：安装注意力库
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* Flash Attention:
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建议安装 Flash Attention 以实现更快的推理速度和更低的 GPU 内存消耗。
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安装 Flash Attention 以实现更快的推理速度和更低的 GPU 内存消耗。
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详细安装说明请参考 [Flash Attention](https://github.com/Dao-AILab/flash-attention)。
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* Flex-Block-Attention:
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@ -152,7 +155,8 @@ pip install -i https://mirrors.tencent.com/pypi/simple/ --upgrade tencentcloud-s
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```
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* SageAttention:
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要启用 SageAttention 以实现更快的推理，您需要通过以下命令安装：
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> **注意**: 启用 SageAttention 将自动禁用 Flex-Block-Attention。
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```bash
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git clone https://github.com/cooper1637/SageAttention.git
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cd SageAttention
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@ -211,10 +215,11 @@ OUTPUT_PATH=./outputs/output.mp4
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# 配置
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N_INFERENCE_GPU=8 # 并行推理 GPU 数量
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CFG_DISTILLED=true # 使用 CFG 蒸馏模型进行推理，2倍加速
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SPARSE_ATTN=true # 使用稀疏注意力进行推理
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SPARSE_ATTN=false # 使用稀疏注意力进行推理（仅 720p 模型配备了稀疏注意力）。请确保 flex-block-attn 已安装
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SAGE_ATTN=false # 使用 SageAttention 进行推理
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REWRITE=true # 启用提示词重写。请确保 rewrite vLLM server 已部署和配置。
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OVERLAP_GROUP_OFFLOADING=true # 仅在组卸载启用时有效，会显著增加 CPU 内存占用，但能够提速
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MODEL_PATH=ckpts # 预训练模型路径
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REWRITE=true # 启用提示词重写
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torchrun --nproc_per_node=$N_INFERENCE_GPU generate.py \
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--prompt "$PROMPT" \
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@ -227,10 +232,16 @@ torchrun --nproc_per_node=$N_INFERENCE_GPU generate.py \
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--use_sageattn $SAGE_ATTN \
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--rewrite $REWRITE \
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--output_path $OUTPUT_PATH \
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--overlap_group_offloading $OVERLAP_GROUP_OFFLOADING \
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--save_pre_sr_video \
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--model_path $MODEL_PATH
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```
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> **Tips:** 如果您的 GPU 内存 > 14GB 但您在生成过程中遇到 OOM (Out of Memory) 错误，可以尝试在运行前设置以下环境变量：
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> ```bash
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> export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True,max_split_size_mb:128
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> ```
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### 命令行参数
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| 参数 | 类型 | 是否必需 | 默认值 | 描述 |
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@ -252,6 +263,7 @@ torchrun --nproc_per_node=$N_INFERENCE_GPU generate.py \
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| `--sparse_attn` | bool | 否 | `false` | 启用稀疏注意力以加速推理（约 1.5-2 倍加速，需要 H 系列 GPU，会自动启用 CFG 蒸馏，使用 `--sparse_attn` 或 `--sparse_attn true` 来启用） |
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| `--offloading` | bool | 否 | `true` | 启用 CPU 卸载（使用 `--offloading false` 或 `--offloading 0` 来禁用，如果 GPU 内存允许，禁用后速度会更快） |
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| `--group_offloading` | bool | 否 | `None` | 启用组卸载（默认：None，如果启用了 offloading 则自动启用。使用 `--group_offloading` 或 `--group_offloading true/1` 来启用，`--group_offloading false/0` 来禁用） |
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| `--overlap_group_offloading` | bool | 否 | `true` | 启用重叠组卸载（默认：true）。会显著增加 CPU 内存占用，但能够提速。使用 `--overlap_group_offloading` 或 `--overlap_group_offloading true/1` 来启用，`--overlap_group_offloading false/0` 来禁用 |
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| `--dtype` | str | 否 | `bf16` | Transformer 的数据类型：`bf16`（更快，内存占用更低）或 `fp32`（质量更好，速度更慢，内存占用更高） |
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| `--use_sageattn` | bool | 否 | `false` | 启用 SageAttention（使用 `--use_sageattn` 或 `--use_sageattn true/1` 来启用，`--use_sageattn false/0` 来禁用） |
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| `--sage_blocks_range` | str | 否 | `0-53` | SageAttention 块范围（例如：`0-5` 或 `0,1,2,3,4,5`） |
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@ -259,22 +271,43 @@ torchrun --nproc_per_node=$N_INFERENCE_GPU generate.py \
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**注意：** 使用 `--nproc_per_node` 指定使用的 GPU 数量。例如，`--nproc_per_node=8` 表示使用 8 个 GPU。
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### 最优推理配置
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下表提供了每个模型的最优推理配置（CFG 缩放、嵌入 CFG 缩放、流偏移和推理步数），以获得最佳生成质量：
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| 模型 | CFG 缩放 | 嵌入 CFG 缩放 | 流偏移 | 推理步数 |
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|-------|-----------|-------------------|------------|-----------------|
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| 480p T2V | 6 | None | 5 | 50 |
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| 480p I2V | 6 | None | 5 | 50 |
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| 720p T2V | 6 | None | 9 | 50 |
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| 720p I2V | 6 | None | 7 | 50 |
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| 480p T2V cfg 蒸馏 | 1 | None | 5 | 50 |
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| 480p I2V cfg 蒸馏 | 1 | None | 5 | 50 |
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| 720p T2V cfg 蒸馏 | 1 | None | 9 | 50 |
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| 720p I2V cfg 蒸馏 | 1 | None | 7 | 50 |
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| 720p T2V cfg 蒸馏稀疏 | 1 | None | 7 | 50 |
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| 720p I2V cfg 蒸馏稀疏 | 1 | None | 9 | 50 |
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| 480→720 超分 步数蒸馏 | 1 | None | 2 | 6 |
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| 720→1080 超分 步数蒸馏 | 1 | None | 2 | 8 |
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**请注意我们提供的cfg蒸馏模型，需要50步的推理步数来获得正确的结果.**
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## 🧱 模型卡片
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|模型名称| 下载链接 |
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|-|---------------------------|
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|HunyuanVideo-1.5-480P-T2V|[480P-T2V](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/480p_t2v) |
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|HunyuanVideo-1.5-480P-I2V |[480P-I2V](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/480p_i2v) |
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|HunyuanVideo-1.5-480P-T2V-distill | [480P-T2V-distill](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/480p_t2v_distilled) |
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|HunyuanVideo-1.5-480P-I2V-distill |[480P-I2V-distill](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/480p_i2v_distilled) |
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|HunyuanVideo-1.5-480P-T2V-cfg-distill | [480P-T2V-cfg-distill](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/480p_t2v_distilled) |
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|HunyuanVideo-1.5-480P-I2V-cfg-distill |[480P-I2V-cfg-distill](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/480p_i2v_distilled) |
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|HunyuanVideo-1.5-720P-T2V|[720P-T2V](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/720p_t2v) |
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|HunyuanVideo-1.5-720P-I2V |[720P-I2V](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/720p_i2v) |
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|HunyuanVideo-1.5-720P-T2V-distiill| Comming soon |
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|HunyuanVideo-1.5-720P-I2V-distiill |[720P-I2V-distiill](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/720p_i2v_distilled) |
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|HunyuanVideo-1.5-720P-T2V-sparse-distiill| Comming soon |
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|HunyuanVideo-1.5-720P-I2V-sparse-distiill |[720P-I2V-sparse-distiill](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/720p_i2v_distilled_sparse) |
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|HunyuanVideo-1.5-720P-sr |[720P-sr](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/720p_sr_distilled) |
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|HunyuanVideo-1.5-1080P-sr |[1080P-sr](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/1080p_sr_distilled) |
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|HunyuanVideo-1.5-720P-T2V-cfg-distill| Comming soon |
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|HunyuanVideo-1.5-720P-I2V-cfg-distill |[720P-I2V-cfg-distill](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/720p_i2v_distilled) |
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|HunyuanVideo-1.5-720P-T2V-sparse-cfg-distill| Comming soon |
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|HunyuanVideo-1.5-720P-I2V-sparse-cfg-distill |[720P-I2V-sparse-cfg-distill](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/720p_i2v_distilled_sparse) |
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|HunyuanVideo-1.5-720P-sr-step-distill |[720P-sr](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/720p_sr_distilled) |
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|HunyuanVideo-1.5-1080P-sr-step-distill |[1080P-sr](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/1080p_sr_distilled) |
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