Add Wan I2V/T2V example.

examples/pytorch/diffusion_model/diffusers/wan/README.md

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+# Step-by-Step
+
+This example provides a unified Wan entry for quantization and evaluation, with both t2v and i2v support.
+
+# Prerequisite
+
+## 1. Environment
+
+```shell
+pip install -r requirements.txt
+# Use latest dev branch if needed before release
+# INC_PT_ONLY=1 pip install git+https://github.com/intel/neural-compressor.git@master
+# pip install git+https://github.com/intel/auto-round.git@main
+pip install neural-compressor-pt
+pip install auto-round
+```
+
+## 2. Prepare Model
+
+Use a local Wan diffusers model path, for example:
+
+- Wan2.2-T2V-A14B-Diffusers
+- Wan2.2-I2V-A14B-Diffusers
+
+Download example (from Hugging Face):
+
+```bash
+pip install -U "huggingface_hub[cli]"
+
+# t2v model
+huggingface-cli download Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --local-dir /path/to/Wan2.2-T2V-A14B-Diffusers
+
+# i2v model
+huggingface-cli download Wan-AI/Wan2.2-I2V-A14B-Diffusers \
+  --local-dir /path/to/Wan2.2-I2V-A14B-Diffusers
+```
+
+## 3. Prepare Dataset
+Clone VBench to prepare the required dataset, then download i2v data:
+
+```bash
+# recommended: install VBench from pip
+python3 -m pip install VBench
+
+# required for dataset preparation
+git clone https://github.com/Vchitect/VBench.git
+cd VBench
+bash vbench2_beta_i2v/download_data.sh
+```
+
+- t2v: pass prompt folder with --prompt_folder, and set --dimension to select `${prompt_folder}/${dimension}.txt`
+- t2v/i2v: pass comma-separated values in `--dimension` to run multiple dimensions in one command (e.g., `subject_consistency,overall_consistency`)
+- t2v: can pass --dimension for evaluation filtering (validated dimensions include `subject_consistency` and `overall_consistency`)
+- i2v: pass --image_folder, --info_json, and --dimension (validated dimensions include `i2v_subject`, `i2v_background`, `subject_consistency`, `background_consistency`, and `motion_smoothness`)
+
+# Run
+
+## Quantization
+
+```bash
+# topology supports wan_mxfp8 or wan_fp8
+bash run_quant.sh \
+  --topology=wan_mxfp8 \
+  --input_model=/path/to/Wan2.2-T2V-A14B-Diffusers \
+  --task=t2v \
+  --output_model=wan_mxfp8_model
+```
+
+## Inference + Evaluation
+
+### t2v bf16
+
+```bash
+bash run_benchmark.sh \
+  --topology=wan_bf16 \
+  --input_model=/path/to/Wan2.2-T2V-A14B-Diffusers \
+  --task=t2v \
+  --dimension=subject_consistency,overall_consistency \
+  --prompt_folder=/path/to/VBench/prompts/prompts_per_dimension/ \
+  --output_video_path=wan_t2v_bf16_video \
+  --accuracy
+```
+
+### t2v mxfp8/fp8
+
+```bash
+# topology supports wan_mxfp8 or wan_fp8
+bash run_benchmark.sh \
+  --topology=wan_mxfp8 \
+  --input_model=/path/to/Wan2.2-T2V-A14B-Diffusers \
+  --quantized_model=wan_mxfp8_model \
+  --task=t2v \
+  --dimension=subject_consistency,overall_consistency \
+  --prompt_folder=/path/to/VBench/prompts/prompts_per_dimension/ \
+  --output_video_path=wan_t2v_mxfp8_video \
+  --accuracy
+```
+
+### i2v bf16
+
+```bash
+bash run_benchmark.sh \
+  --topology=wan_bf16 \
+  --input_model=/path/to/Wan2.2-I2V-A14B-Diffusers \
+  --task=i2v \
+  --dimension=i2v_background,i2v_subject \
+  --image_folder=/path/to/VBench/vbench2_beta_i2v/data/crop/16-9 \
+  --info_json=/path/to/VBench/vbench2_beta_i2v/vbench2_i2v_full_info.json \
+  --output_video_path=wan_i2v_bf16_video \
+  --accuracy
+```
+
+### i2v mxfp8/fp8
+
+```bash
+# topology supports wan_mxfp8 or wan_fp8
+bash run_benchmark.sh \
+  --topology=wan_mxfp8 \
+  --input_model=/path/to/Wan2.2-I2V-A14B-Diffusers \
+  --quantized_model=wan_mxfp8_model \
+  --task=i2v \
+  --dimension=i2v_background,i2v_subject \
+  --image_folder=/path/to/VBench/vbench2_beta_i2v/data/crop/16-9 \
+  --info_json=/path/to/VBench/vbench2_beta_i2v/vbench2_i2v_full_info.json \
+  --output_video_path=wan_i2v_mxfp8_video \
+  --accuracy
+```
+
+Note: For sharding and multi-GPU execution, set `--gpu_ids` (for example `--gpu_ids=0,1,2,3`) or set `CUDA_VISIBLE_DEVICES` before running `run_benchmark.sh`.
+
+### Standalone Accuracy Evaluation (Optional)
+
+If you already use `--accuracy` in `run_benchmark.sh`, you can skip this section.
+Use this section when you want to evaluate existing videos without re-running generation.
+
+```bash
+# t2v accuracy on generated videos
+cd /path/to/VBench
+python evaluate.py \
+  --dimension subject_consistency motion_smoothness aesthetic_quality overall_consistency imaging_quality \
+  --videos_path /path/to/wan_t2v_bf16_video \
+  --mode vbench_standard
+
+# i2v accuracy on generated videos
+cd /path/to/VBench
+python evaluate_i2v.py \
+  --dimension i2v_background i2v_subject subject_consistency background_consistency motion_smoothness \
+  --videos_path /path/to/wan_i2v_bf16_video \
+  --ratio 16-9 \
+  --mode vbench_standard
+```
+
+# Notes
+
+- Quantized weights are saved under:
+  - <output_model>/transformer
+  - <output_model>/transformer_2
+