Merge branch 'main' of github.com:ModelTC/lightx2v into main

docs/EN/source/method_tutorials/attention.md

-# 注意力机制
+# 🎯 Attention Type Configuration in DiT Model
 
-xxx
+The DiT model in `LightX2V` currently uses three types of attention mechanisms. Each type of attention can be configured with a specific backend library.
+
+---
+
+## Attention Usage Locations
+
+1. **Self-Attention on the image**
+   - Configuration key: `self_attn_1_type`
+
+2. **Cross-Attention between image and prompt text**
+   - Configuration key: `cross_attn_1_type`
+
+3. **Cross-Attention between image and reference image (in I2V mode)**
+   - Configuration key: `cross_attn_2_type`
+
+---
+
+## 🚀 Supported Attention Backends
+
+| Name               | Type Identifier   | GitHub Link |
+|--------------------|-------------------|-------------|
+| Flash Attention 2  | `flash_attn2`     | [flash-attention v2](https://github.com/Dao-AILab/flash-attention) |
+| Flash Attention 3  | `flash_attn3`     | [flash-attention v3](https://github.com/Dao-AILab/flash-attention) |
+| Sage Attention 2   | `sage_attn2`      | [SageAttention](https://github.com/thu-ml/SageAttention) |
+| Radial Attention   | `radial_attn`     | [Radial Attention](https://github.com/mit-han-lab/radial-attention) |
+| Sparge Attention   | `sparge_ckpt`     | [Sparge Attention](https://github.com/thu-ml/SpargeAttn) |
+
+---
+
+## 🛠️ Configuration Example
+
+In the `wan_i2v.json` configuration file, you can specify the attention types as follows:
+
+```json
+{
+  "self_attn_1_type": "radial_attn",
+  "cross_attn_1_type": "flash_attn3",
+  "cross_attn_2_type": "flash_attn3"
+}
+```
+
+To use other attention backends, simply replace the values with the appropriate type identifiers listed above.
+
+Tip: Due to the limitations of the sparse algorithm's principle, radial_attn can only be used in self-attention.
+
+---
+
+For Sparge Attention like `wan_t2v_sparge.json` configuration file:
+
+   Sparge Attention need PostTrain weight path
+
+```json
+{
+  "self_attn_1_type": "flash_attn3",
+  "cross_attn_1_type": "flash_attn3",
+  "cross_attn_2_type": "flash_attn3"
+  "sparge": true,
+  "sparge_ckpt": "/path/to/sparge_wan2.1_t2v_1.3B.pt"
+}
+```
+
+---
+
+For further customization or behavior tuning, please refer to the official documentation of the respective attention libraries.

docs/ZH_CN/source/method_tutorials/attention.md

-# 注意力机制
+# 🎯 DiT 模型中的注意力类型配置说明
 
-xxx
+当前 DiT 模型在 `LightX2V` 中三个地方使用到了注意力，每个注意力可以分别配置底层注意力库类型。
+
+---
+
+## 使用注意力的位置
+
+1. **图像的自注意力（Self-Attention）**
+   - 配置参数：`self_attn_1_type`
+
+2. **图像与提示词（Text）之间的交叉注意力（Cross-Attention）**
+   - 配置参数：`cross_attn_1_type`
+
+3. **I2V 模式下图像与参考图（Reference）之间的交叉注意力**
+   - 配置参数：`cross_attn_2_type`
+
+---
+
+## 🚀 支持的注意力库（Backend）
+
+| 名称               | 类型名称         | GitHub 链接 |
+|--------------------|------------------|-------------|
+| Flash Attention 2  | `flash_attn2`    | [flash-attention v2](https://github.com/Dao-AILab/flash-attention) |
+| Flash Attention 3  | `flash_attn3`    | [flash-attention v3](https://github.com/Dao-AILab/flash-attention) |
+| Sage Attention 2   | `sage_attn2`     | [SageAttention](https://github.com/thu-ml/SageAttention) |
+| Radial Attention   | `radial_attn`    | [Radial Attention](https://github.com/mit-han-lab/radial-attention) |
+| Sparge Attention   | `sparge_ckpt`     | [Sparge Attention](https://github.com/thu-ml/SpargeAttn) |
+
+---
+
+## 🛠️ 配置示例
+
+在 `wan_i2v.json` 配置文件中，可以通过如下方式指定使用的注意力类型：
+
+```json
+{
+  "self_attn_1_type": "radial_attn",
+  "cross_attn_1_type": "flash_attn3",
+  "cross_attn_2_type": "flash_attn3"
+}
+```
+
+如需更换为其他类型，只需将对应值替换为上述表格中的类型名称即可。
+
+tips: radial_attn因为稀疏算法原理的限制只能用在self attention
+
+---
+
+对于 Sparge Attention 配置参考 `wan_t2v_sparge.json` 文件:
+
+    Sparge Attention是需要后一个训练的权重
+
+```json
+{
+  "self_attn_1_type": "flash_attn3",
+  "cross_attn_1_type": "flash_attn3",
+  "cross_attn_2_type": "flash_attn3"
+  "sparge": true,
+  "sparge_ckpt": "/path/to/sparge_wan2.1_t2v_1.3B.pt"
+}
+```
+
+---
+
+如需进一步定制注意力机制的行为，请参考各注意力库的官方文档或实现代码。