[Docs] Add Wan2.2 image-to-video recipe for Ascend NPU (A2/A3) (#2919)

Signed-off-by: gcanlin <canlinguosdu@gmail.com>

pyproject.toml

@@ -237,3 +237,4 @@ ue = "ue"
 semantics = "semantics"
 fullset = "fullset"
 Vai = "Vai"
+CANN = "CANN"

recipes/README.md

@@ -27,6 +27,8 @@ recipes/
 
 - [`Qwen/Qwen3-Omni.md`](./Qwen/Qwen3-Omni.md): online serving recipe for
   multimodal chat on `1x A100 80GB`
+- [`Wan-AI/Wan2.2-I2V.md`](./Wan-AI/Wan2.2-I2V.md): image-to-video serving
+  recipe for Wan2.2 14B on `8x Ascend NPU (A2/A3)`
 
 Within a single recipe file, include different hardware support sections such
 as `GPU`, `ROCm`, and `NPU`, and add concrete tested configurations like

recipes/Wan-AI/Wan2.2-I2V.md

@@ -0,0 +1,136 @@
+# Wan2.2 Image To Video
+
+## Summary
+
+- Vendor: Wan-AI
+- Model: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`
+- Task: Image-to-video generation
+- Mode: Online serving with the OpenAI-compatible API
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe when you want to deploy the Wan2.2 14B image-to-video model
+with vLLM-Omni using multi-card parallelism. Two configurations are provided:
+
+1. **Distilled model (no negative-prompt / CFG computation)** — higher
+   throughput, recommended when using a distilled checkpoint that does not
+   require classifier-free guidance.
+2. **Official open-source model (with CFG)** — uses `--cfg 2` to run negative
+   and positive samples in parallel for the original released weights.
+
+## References
+
+- Upstream model card: <https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers>
+
+## Hardware Support
+
+## NPU
+
+### 8x Ascend A2 / A3
+
+#### Environment
+
+- OS: Linux
+- Python: 3.10+
+- Driver / runtime: Ascend NPU driver with CANN toolkit
+- Recommended operator library: **mindie-sd** (Ascend high-performance fused
+  operators — enables `adalayernorm` and other fused kernels automatically upon
+  installation)
+- vLLM version: Match the repository requirements for your checkout
+- vLLM-Omni version or commit: Use the commit you are deploying from
+
+#### Prerequisites
+
+Install the **mindie-sd** operator library to enable Ascend-optimized fused
+operators (`adalayernorm`, etc.):
+
+```bash
+git clone https://gitcode.com/Ascend/MindIE-SD.git && cd MindIE-SD
+
+# Comment out the tik_ops build step (not needed for this use case)
+sed -i 's|^\(\s*\)source ${current_script_dir}/build_tik_ops.sh|\1# source ${current_script_dir}/build_tik_ops.sh|' build/build_ops.sh
+
+python setup.py bdist_wheel
+cd dist
+pip install mindiesd-*.whl
+```
+
+After installation, enable the Laser Attention kernel for significant
+long-sequence speedups (up to ~40% at 720p in tested workloads):
+
+```bash
+export MINDIE_SD_FA_TYPE=ascend_laser_attention
+```
+
+When using HSDP with FSDP2, set the following environment variable to work
+around a PyTorch NPU multi-stream memory reuse issue
+([pytorch/pytorch#147168](https://github.com/pytorch/pytorch/issues/147168)).
+This issue has been fixed on CUDA but still applies to NPU:
+
+```bash
+export MULTI_STREAM_MEMORY_REUSE=2
+```
+
+#### Command
+
+**Distilled model (no CFG, recommended for distilled checkpoints):**
+
+```bash
+export MINDIE_SD_FA_TYPE=ascend_laser_attention
+export MULTI_STREAM_MEMORY_REUSE=2
+
+vllm serve \
+  --omni Wan-AI/Wan2.2-I2V-A14B-Diffusers \
+  --use-hsdp \
+  --usp 8 \
+  --vae-patch-parallel-size 8 \
+  --vae-use-tiling
+```
+
+**Official open-source model (with CFG):**
+
+```bash
+export MINDIE_SD_FA_TYPE=ascend_laser_attention
+export MULTI_STREAM_MEMORY_REUSE=2
+
+vllm serve \
+  --omni Wan-AI/Wan2.2-I2V-A14B-Diffusers \
+  --use-hsdp \
+  --usp 4 \
+  --cfg 2 \
+  --vae-patch-parallel-size 8 \
+  --vae-use-tiling
+```
+
+> **Why the difference?** With `--cfg 2`, two copies of the input (positive and
+> negative prompts) are processed in parallel, effectively doubling the compute
+> for the DiT backbone. USP is therefore halved from 8 to 4 so that the total
+> parallelism across the 8 cards remains balanced (`usp * cfg = 8`).
+
+#### Verification
+
+After the server is ready, see
+[`examples/online_serving/image_to_video/README.md`](../../examples/online_serving/image_to_video/README.md)
+for complete client examples and request formats.
+
+#### Notes
+
+- **Key flags:**
+  - `--omni` — enables vLLM-Omni diffusion serving.
+  - `--use-hsdp` — enables Hybrid Sharded Data Parallelism for the DiT model
+    weights.
+  - `--usp <N>` — Unified Sequence Parallelism degree.
+  - `--cfg <N>` — Classifier-Free Guidance parallelism; set to 2 for models
+    that require negative-prompt computation, omit for distilled models.
+  - `--vae-patch-parallel-size 8` — parallelizes VAE decoding across all 8
+    cards.
+  - `--vae-use-tiling` — enables tiled VAE decoding to reduce peak memory.
+- **Performance tips:**
+  - Installing mindie-sd and enabling Laser Attention
+    (`MINDIE_SD_FA_TYPE=ascend_laser_attention`) provides up to ~40%
+    performance improvement at 720p resolution due to long-sequence attention
+    optimization.
+- **Known limitations:**
+  - `MULTI_STREAM_MEMORY_REUSE=2` is required on NPU when using HSDP/FSDP2
+    due to a multi-stream memory reuse bug. This is not needed on CUDA.