[Model] Adapt Wan2.2-I2V-A14B via LightX2V offline conversion path (#2134)

Signed-off-by: Celeste-jq <591998922@qq.com>
Co-authored-by: Canlin Guo <canlinguosdu@gmail.com>

docs/user_guide/diffusion/lora.md

@@ -56,6 +56,92 @@ outputs = omni.generate(
 !!! note "Server-side Path Requirement"
     The LoRA adapter path (`local_path`) must be readable on the **server** machine. If your client and server are on different machines, ensure the LoRA adapter is accessible via a shared mount or copied to the server.
 
+## Wan2.2 LightX2V Offline Assembly
+
+This workflow is LoRA-adjacent: it uses external LightX2V conversion plus
+`Wan2.2-Distill-Loras` to bake converted Wan2.2 I2V checkpoints into a local
+Diffusers directory, instead of loading LoRA adapters at runtime.
+
+### Required assets
+
+- Base model: `Wan-AI/Wan2.2-I2V-A14B`
+- Diffusers skeleton: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`
+- Optional external converter from the LightX2V project (not shipped in this repository)
+- Optional LoRA weights: `lightx2v/Wan2.2-Distill-Loras`
+
+### Step 1: Optional - convert high/low-noise DiT weights with LightX2V
+
+Install or clone LightX2V from the upstream repository
+(`https://github.com/ModelTC/LightX2V`). After cloning, the converter used
+below is available at `<lightx2v_root>/tools/convert/converter.py`.
+
+```bash
+python /path/to/lightx2v/tools/convert/converter.py \
+  --source /path/to/Wan2.2-I2V-A14B/high_noise_model \
+  --output /tmp/wan22_lightx2v/high_noise_out \
+  --output_ext .safetensors \
+  --output_name diffusion_pytorch_model \
+  --model_type wan_dit \
+  --direction forward \
+  --lora_path /path/to/wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_1022.safetensors \
+  --lora_key_convert auto \
+  --single_file
+
+python /path/to/lightx2v/tools/convert/converter.py \
+  --source /path/to/Wan2.2-I2V-A14B/low_noise_model \
+  --output /tmp/wan22_lightx2v/low_noise_out \
+  --output_ext .safetensors \
+  --output_name diffusion_pytorch_model \
+  --model_type wan_dit \
+  --direction forward \
+  --lora_path /path/to/wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_1022.safetensors \
+  --lora_key_convert auto \
+  --single_file
+```
+
+If you are not using LightX2V, skip this step and either keep the original
+Diffusers weights from the skeleton or point Step 2 at any other converted
+`transformer/` and `transformer_2/` checkpoints.
+
+### Step 2: Assemble a final Diffusers-style directory
+
+```bash
+python tools/wan22/assemble_wan22_i2v_diffusers.py \
+  --diffusers-skeleton /path/to/Wan2.2-I2V-A14B-Diffusers \
+  --transformer-weight /tmp/wan22_lightx2v/high_noise_out \
+  --transformer-2-weight /tmp/wan22_lightx2v/low_noise_out \
+  --output-dir /path/to/Wan2.2-I2V-A14B-Custom-Diffusers \
+  --asset-mode symlink \
+  --overwrite
+```
+
+`--transformer-weight` and `--transformer-2-weight` are optional. If you omit
+them, the tool keeps the original weights from the Diffusers skeleton.
+
+### Step 3: Run offline inference
+
+```bash
+python examples/offline_inference/image_to_video/image_to_video.py \
+  --model /path/to/Wan2.2-I2V-A14B-Custom-Diffusers \
+  --image /path/to/input.jpg \
+  --prompt "A cat playing with yarn" \
+  --num-frames 81 \
+  --num-inference-steps 4 \
+  --tensor-parallel-size 4 \
+  --height 480 \
+  --width 832 \
+  --flow-shift 12 \
+  --sample-solver euler \
+  --guidance-scale 1.0 \
+  --guidance-scale-high 1.0 \
+  --boundary-ratio 0.875
+```
+
+Notes:
+
+- This route avoids runtime LoRA loading changes in vLLM-Omni when you choose to bake converted weights into a local Diffusers directory.
+- Output quality and speed depend on the replacement checkpoints and sampling params you choose.
+
 
 ## See Also
 

docs/user_guide/examples/offline_inference/image_to_video.md

@@ -62,12 +62,13 @@ Key arguments:
 - `--negative-prompt`: Optional list of artifacts to suppress.
 - `--boundary-ratio`: Boundary split ratio for two-stage MoE models.
 - `--flow-shift`: Scheduler flow shift (5.0 for 720p, 12.0 for 480p).
+- `--sample-solver`: Wan2.2 sampling solver. Use `unipc` for the default multistep solver, or `euler` for Lightning/Distill checkpoints.
 - `--num-inference-steps`: Number of denoising steps (default 50).
 - `--fps`: Frames per second for the saved MP4 (requires `diffusers` export_to_video).
 - `--output`: Path to save the generated video.
 - `--vae-use-slicing`: Enable VAE slicing for memory optimization.
 - `--vae-use-tiling`: Enable VAE tiling for memory optimization.
-- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism_acceleration.md#cfg-parallel).
+- `--cfg-parallel-size`: set it to 2 to enable CFG Parallel. See more examples in [`user_guide`](https://github.com/vllm-project/vllm-omni/tree/main/docs/user_guide/diffusion/parallelism/cfg_parallel.md).
 - `--tensor-parallel-size`: tensor parallel size (effective for models that support TP, e.g. LTX2).
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 - `--use-hsdp`: Enable Hybrid Sharded Data Parallel to shard model weights across GPUs.
@@ -78,6 +79,9 @@ Key arguments:
 
 > ℹ️ If you encounter OOM errors, try using `--vae-use-slicing` and `--vae-use-tiling` to reduce memory usage.
 
+For Wan2.2 LightX2V-converted local Diffusers directories and related LoRA
+assets, see the [LoRA guide](../../diffusion/lora.md#wan22-lightx2v-offline-assembly).
+
 ## Example materials
 
 ??? abstract "image_to_video.py"