docs: add README for wan22.

docs/src/content/docs/en/getting_started/wan.md

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+# Wan2.2 Deployment
+
+This document describes how to deploy the Wan2.2 video generation service using xLLM in an Ascend NPU environment.
+
+## 1. Pull the Docker Image
+
+First, pull the xLLM-provided image:
+
+```bash
+# A2 x86
+docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-a2-x86-cann9-20260605
+# A2 arm
+docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-a2-arm-cann9-20260605
+# A3 arm
+docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-a3-arm-cann9-20260605
+```
+
+Then create the corresponding container:
+
+```bash
+sudo docker run -it --ipc=host -u 0 --privileged --name mydocker --network=host \
+ -v /var/queue_schedule:/var/queue_schedule \
+ -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+ -v /usr/local/Ascend/add-ons/:/usr/local/Ascend/add-ons/ \
+ -v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi \
+ -v /var/log/npu/conf/slog/slog.conf:/var/log/npu/conf/slog/slog.conf \
+ -v /var/log/npu/slog/:/var/log/npu/slog \
+ -v ~/.ssh:/root/.ssh  \
+ -v /var/log/npu/profiling/:/var/log/npu/profiling \
+ -v /var/log/npu/dump/:/var/log/npu/dump \
+ -v /runtime/:/runtime/ -v /etc/hccn.conf:/etc/hccn.conf \
+ -v /export/home:/export/home \
+ -v /home/:/home/  \
+ -w /export/home \
+ quay.io/jd_xllm/xllm-ai:xllm-dev-a3-arm-cann9-20260605
+```
+
+## 2. Clone the Source Code and Build
+
+Clone the official repository and module dependencies:
+
+```bash
+git clone https://github.com/xLLM-AI/xllm.git
+cd xllm
+git submodule update --init --update
+```
+
+Install dependencies:
+
+```bash
+pip install --upgrade pre-commit
+```
+
+Build the project to generate the executable `build/xllm/core/server/xllm` under the `build/` directory:
+
+```bash
+python setup.py build --device npu
+```
+
+## 3. Model Weight Preparation
+
+The model root directory should contain the component directories required by the DiT service. A typical directory structure is as follows:
+
+```text
+Wan2.2-I2V/
+├── model_index.json
+├── processor/
+├── text_encoder/
+├── tokenizer/
+├── transformer/
+├── transformer_2/
+└── vae/
+```
+
+## 4. Start the Model and Expose the `/v1/video/generation` API
+
+### Kill Previous xLLM Service Processes
+
+```bash
+pkill -9 xllm
+```
+
+### Environment Variables
+
+```bash
+# 0. Load Ascend environment (must be executed before python3)
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+source /usr/local/Ascend/nnal/atb/set_env.sh
+export LD_LIBRARY_PATH=/usr/local/Ascend/driver/lib64/driver:$LD_LIBRARY_PATH
+
+# 1. Configure dependency path environment variables
+export PYTHON_INCLUDE_PATH="$(python3 -c 'from sysconfig import get_paths; print(get_paths()["include"])')"
+export PYTHON_LIB_PATH="$(python3 -c 'from sysconfig import get_paths; print(get_paths()["include"])')"
+export PYTORCH_NPU_INSTALL_PATH=/usr/local/libtorch_npu/
+export PYTORCH_INSTALL_PATH="$(python3 -c 'import torch, os; print(os.path.dirname(os.path.abspath(torch.__file__)))')"
+export LIBTORCH_ROOT="$PYTORCH_INSTALL_PATH"
+export LD_LIBRARY_PATH=/usr/local/libtorch_npu/lib:$LD_LIBRARY_PATH
+
+# 2. NPU-device environment variables
+export ASDOPS_LOG_TO_STDOUT=1
+export ASDOPS_LOG_LEVEL=ERROR
+export ASDOPS_LOG_TO_FILE=1
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export NPU_MEMORY_FRACTION=0.98
+export ATB_WORKSPACE_MEM_ALLOC_ALG_TYPE=3
+export ATB_WORKSPACE_MEM_ALLOC_GLOBAL=1
+export OMP_NUM_THREADS=12
+export HCCL_CONNECT_TIMEOUT=7200
+export INF_NAN_MODE_ENABLE=0
+export INF_NAN_MODE_FORCE_DISABLE=1
+export HCCL_IF_BASE_PORT=41433
+
+# 3. Clean up old logs
+\rm -rf core.*
+\rm -rf log/node_*.log
+```
+
+## Startup Command — Single-Machine Example
+
+```bash
+# 4. Inference configuration
+XLLM_PATH="./build/xllm/core/server/xllm"
+MODEL_PATH="/export/home/models/wan2_2"
+MASTER_NODE_ADDR="127.0.0.1:17372"
+START_PORT=18013
+START_DEVICE=8
+LOG_DIR="log"
+NNODES=8
+
+for (( i=0; i<$NNODES; i++ ))
+do
+  PORT=$((START_PORT + i))
+  DEVICE=$((START_DEVICE + i))
+  LOG_FILE="$LOG_DIR/node_$i.log"
+
+  ${XLLM_PATH} \
+    --model="$MODEL_PATH" \
+    --max_memory_utilization=0.98 \
+    --backend="dit" \
+    --tp_size=1 \
+    --cfg_size=2 \
+    --sp_size=4 \
+    --vae_size=4 \
+    --output_shm_size=1024 \
+    --devices="npu:$DEVICE" \
+    --master_node_addr=$MASTER_NODE_ADDR \
+    --nnodes=$NNODES \
+    --port $PORT \
+    --communication_backend="hccl" \
+    --enable_prefix_cache=false \
+    --enable_chunked_prefill=false \
+    --enable_schedule_overlap=false \
+    --use_contiguous_input_buffer=false \
+    --enable_rolling_load=true \
+    --rolling_load_num_rolling_slots=2 \
+    --dit_laser_attention_enabled=true \
+    --dit_sparse_attention_enabled=false \
+    --dit_sparse_attention_sparsity=0.8 \
+    --dit_sparse_attention_sparse_start_step=15 \
+    --enable-shm=true \
+    --node_rank=$i > $LOG_FILE 2>&1 &
+done
+```
+
+When "Brpc Server Started" appears in the log, the service has started successfully.
+
+## DiT Parameter Reference
+
+| Parameter | Description | Default | Values |
+| --------- | ----------- | ------- | ------ |
+| `--sp_size` | Sequence Parallel degree | `1` | Positive integer, e.g. `1`, `2`, `4`, `8` |
+| `--cfg_size` | Classifier-Free Guidance parallel degree | `1` | `1` or `2` |
+| `--vae_size` | VAE Spatial Parallel degree | `1` | Positive integer, e.g. `1`, `2`, `4`; can be set to the same value as `sp_size` |
+| `--tp_size` | Tensor Parallel degree | `1` | Positive integer, e.g. `2`, `4`; it is recommended not to enable this and use dynamic weight loading instead |
+| `--enable_rolling_load` | Enable dynamic weight loading | `false` | Bool, `true` or `false` |
+| `--rolling_load_num_rolling_slot` | Number of slots for dynamic weight loading | `2` | Positive integer, e.g. `2`, `3` |
+| `--dit_laser_attention_enable` | Enable Laser Attention | `false` | Bool, `true` or `false` |
+| `--dit_distill_enable` | Enable distilled model | `false` | Bool, `true` or `false` |
+| `--dit_sparse_attention_enabled` | Enable sparse attention | `false` | Bool, `true` or `false`; mutually exclusive with `laser_attention` |
+| `--dit_sparse_attention_sparsity` | Sparsity ratio for sparse attention | `0.5` | Float, e.g. `0.5`, `0.6`; depends on `dit_sparse_attention_enabled` |
+| `--dit_sparse_attention_sparse_start_step` | Step at which sparsification starts | `0` | Integer, e.g. `5`, `10`; depends on `dit_sparse_attention_enabled` |
+| `--dit_sparse_attention_version` | Sparse attention version | `rain_fusion` | String, `rain_fusion` or `sparse_attention`; depends on `dit_sparse_attention_enabled` |
+
+`NNODES` must equal `sp_size * cfg_size * tp_size`.
+
+| `sp_size` | `cfg_size` | `vae_size` | `tp_size` | `NNODES` | Description |
+| --------- | ---------- | ---------- | --------- | -------- | ----------- |
+| `1` | `1` | `1` | `1` | `1` | Single-card deployment |
+| `2` | `1` | `1` | `1` | `2` | SP only |
+| `1` | `2` | `1` | `1` | `2` | CFG parallel only |
+| `2` | `2` | `2` | `1` | `4` | SP + CFG + VAE parallel |
+| `4` | `2` | `2` | `1` | `8` | 8-card deployment |

docs/src/content/docs/zh/getting_started/wan.md

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+# Wan2.2 部署
+
+本文档介绍如何基于 xLLM 在 Ascend NPU 环境中部署 Wan2.2 视频生成服务。
+
+
+## 1.拉取镜像环境
+
+首先下载xLLM提供的镜像：
+
+```bash
+# A2 x86
+docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-a2-x86-cann9-20260605
+# A2 arm
+docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-a2-arm-cann9-20260605
+# A3 arm
+docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-a3-arm-cann9-20260605
+```
+
+然后创建对应的容器
+
+```bash
+sudo docker run -it --ipc=host -u 0 --privileged --name mydocker --network=host \
+ -v /var/queue_schedule:/var/queue_schedule \
+ -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
+ -v /usr/local/Ascend/add-ons/:/usr/local/Ascend/add-ons/ \
+ -v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi \
+ -v /var/log/npu/conf/slog/slog.conf:/var/log/npu/conf/slog/slog.conf \
+ -v /var/log/npu/slog/:/var/log/npu/slog \
+ -v ~/.ssh:/root/.ssh  \
+ -v /var/log/npu/profiling/:/var/log/npu/profiling \
+ -v /var/log/npu/dump/:/var/log/npu/dump \
+ -v /runtime/:/runtime/ -v /etc/hccn.conf:/etc/hccn.conf \
+ -v /export/home:/export/home \
+ -v /home/:/home/  \
+ -w /export/home \
+ quay.io/jd_xllm/xllm-ai:xllm-dev-a3-arm-cann9-20260605
+```
+
+## 2.拉取源码并编译
+
+下载官方仓库与模块依赖：
+
+```bash
+git clone https://github.com/xLLM-AI/xllm.git
+cd xllm 
+git submodule update --init --update
+```
+
+下载安装依赖:
+
+```bash
+pip install --upgrade pre-commit
+```
+
+执行编译，在`build/`下生成可执行文件`build/xllm/core/server/xllm`：
+
+```bash
+python setup.py build --device npu
+```
+
+## 3. 权重准备
+
+
+模型根目录需要包含 DiT 服务加载所需的组件目录。典型目录结构如下：
+
+```text
+Wan2.2-I2V/
+├── model_index.json
+├── processor/
+├── text_encoder/
+├── tokenizer/
+├── transformer/
+├── transformer_2/
+└── vae/
+```
+
+## 4.启动模型，对外提供 `/v1/video/generation` 接口。
+
+### 杀掉之前的xllm服务化进程
+
+```bash
+pkill -9 xllm
+```
+
+### 环境变量
+
+```bash
+# 0. 加载 Ascend 环境（必须先于 python3 调用）
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+source /usr/local/Ascend/nnal/atb/set_env.sh
+export LD_LIBRARY_PATH=/usr/local/Ascend/driver/lib64/driver:$LD_LIBRARY_PATH
+
+# 1. 环境变量设置
+export PYTHON_INCLUDE_PATH="$(python3 -c 'from sysconfig import get_paths; print(get_paths()["include"])')"
+export PYTHON_LIB_PATH="$(python3 -c 'from sysconfig import get_paths; print(get_paths()["include"])')"
+export PYTORCH_NPU_INSTALL_PATH=/usr/local/libtorch_npu/
+export PYTORCH_INSTALL_PATH="$(python3 -c 'import torch, os; print(os.path.dirname(os.path.abspath(torch.__file__)))')"
+export LIBTORCH_ROOT="$PYTORCH_INSTALL_PATH"
+export LD_LIBRARY_PATH=/usr/local/libtorch_npu/lib:$LD_LIBRARY_PATH
+
+# 2. NPU-device 环境变量
+export ASDOPS_LOG_TO_STDOUT=1
+export ASDOPS_LOG_LEVEL=ERROR
+export ASDOPS_LOG_TO_FILE=1
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+export NPU_MEMORY_FRACTION=0.98
+export ATB_WORKSPACE_MEM_ALLOC_ALG_TYPE=3
+export ATB_WORKSPACE_MEM_ALLOC_GLOBAL=1
+export OMP_NUM_THREADS=12
+export HCCL_CONNECT_TIMEOUT=7200
+export INF_NAN_MODE_ENABLE=0
+export INF_NAN_MODE_FORCE_DISABLE=1
+export HCCL_IF_BASE_PORT=41433
+
+# 3. 清理旧日志
+\rm -rf core.*
+\rm -rf log/node_*.log
+```
+
+## 启动命令 - 单机拉起样例
+
+```bash
+# 4. 推理配置
+XLLM_PATH="./build/xllm/core/server/xllm"
+MODEL_PATH="/export/home/models/wan2_2"
+MASTER_NODE_ADDR="127.0.0.1:17372"
+START_PORT=18013
+START_DEVICE=8
+LOG_DIR="log"
+NNODES=8
+
+for (( i=0; i<$NNODES; i++ ))
+do
+  PORT=$((START_PORT + i))
+  DEVICE=$((START_DEVICE + i))
+  LOG_FILE="$LOG_DIR/node_$i.log"
+
+  ${XLLM_PATH} \
+    --model="$MODEL_PATH" \
+    --max_memory_utilization=0.98 \
+    --backend="dit" \
+    --tp_size=1 \
+    --cfg_size=2 \
+    --sp_size=4 \
+    --vae_size=4 \
+    --output_shm_size=1024 \
+    --devices="npu:$DEVICE" \
+    --master_node_addr=$MASTER_NODE_ADDR \
+    --nnodes=$NNODES \
+    --port $PORT \
+    --communication_backend="hccl" \
+    --enable_prefix_cache=false \
+    --enable_chunked_prefill=false \
+    --enable_schedule_overlap=false \
+    --use_contiguous_input_buffer=false \
+    --enable_rolling_load=true \
+    --rolling_load_num_rolling_slots=2 \
+    --dit_laser_attention_enabled=true \
+    --dit_sparse_attention_enabled=false \
+    --dit_sparse_attention_sparsity=0.8 \
+    --dit_sparse_attention_sparse_start_step=15 \
+    --enable-shm=true \
+    --node_rank=$i > $LOG_FILE 2>&1 &
+done
+
+
+日志出现"Brpc Server Started"表示服务成功拉起。
+
+
+
+
+## DiT 参数说明
+
+| 参数 | 说明 | 默认值 | 取值 |
+| ---- | ---- | ------ | ---- |
+| `--sp_size` | Sequence Parallel 并行度 | `1` | 正整数，如 `1`、`2`、`4`、`8` |
+| `--cfg_size` | Classifier-Free Guidance 并行度 | `1` | `1` 或 `2` |
+| `--vae_size` | Vae Sparital Parallel 并行度 | `1` | 正整数, 如 `1`、`2`、`4`,  可以与sp_size保持一致|
+| `--tp_size` | Tensor Parallel 并行度 | `1` | 正整数, 如 `2`、`4`,  建议不开启，使用动态权重加载|
+| `--enable_rolling_load` | 是否启动动态权重加载 | `false` | bool值, true 或者 false|
+| `--rolling_load_num_rolling_slot` | 动态权重加载分配槽数 | `2` | 正整数, , 如 `2`、`3`|
+| `--dit_laser_attention_enable` | 是否使能laser_attention | `false` | bool值, 如 true，false|
+| `--dit_distill_enable` | 是否使能蒸馏模型| `false` | bool值, 如 true，false|
+| `--dit_sparse_attention_enabled` | 是否使能稀疏attention| `false` | bool值, 如 true，false；与laser_attention互斥|
+| `--dit_sparse_attention_sparsity` | 稀疏attention的稀疏度| `0.5` | float, 如 0.5，0.6；依赖dit_sparse_attention_enabled|
+| `--dit_sparse_attention_sparse_start_step` | 开始稀疏的step| `0` | int, 如 5, 10；依赖dit_sparse_attention_enabled|
+| `--dit_sparse_attention_version` | 稀疏attention的版本| `rain_fusion` | string, rain_fusion, sparse_attention；依赖dit_sparse_attention_enabled|
+
+`NNODES` 必须等于 `sp_size * cfg_size * tp_size`。
+
+| `sp_size` | `cfg_size` | `vae_size` |`tp_size` |`NNODES` | 说明 |
+| --------- | ---------- | -------- | -------- | -------- | ---- |
+| `1` | `1` |  `1` | `1` |`1` | 单卡部署 |
+| `2` | `1` | `1` | `1` | `2` | 仅开启 SP |
+| `1` | `2` | `1` | `1` | `2` | 仅开启 CFG 并行 |
+| `2` | `2` |  `2` | `1` |`4` | 同时开启 SP 和 CFG和vae并行 |
+ `4` | `2` |  `2` | `1` |`8` | 8 卡部署 |