# Wan-AI/Wan2.2-T2V-A14B-Diffusers — vLLM serving recipe

> Wan2.2 video generation models — T2V/I2V MoE (14B active) and unified TI2V (5B dense), served via vLLM-Omni

Configuration: hardware `h200` (8× H200) · variant `default` (BF16) · task `t2v`

- Interactive page: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/Wan-AI/Wan2.2-T2V-A14B-Diffusers
- This configuration as JSON: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/Wan-AI/Wan2.2-T2V-A14B-Diffusers.json?hardware=h200

The commands below were rendered for exactly this configuration and take precedence over any example in the guide.

## Other configurations

Change the query string (the same parameters as the page URL) and fetch this URL again. Unknown or incompatible values fall back to the recipe default, so check the Configuration line.

- `hardware`: `h100`, `h200`, `b200`, `gb200`, `b300`, `gb300`, `mi300x`, `mi325x`, `mi350x`, `mi355x`
- `variant`: `default`
- `task`: `t2v`, `i2v`, `ti2v`

## Install

Requires vLLM >= 0.12.0.

```bash
uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend auto
```

Extra dependencies:

- Pin vllm==0.12.0 for Wan2.2

```bash
uv pip install vllm==0.12.0
```

- vllm-omni pinned to a specific commit that includes Wan2.2 text-to-video support

```bash
uv pip install git+https://github.com/vllm-project/vllm-omni.git@ef01223c42be10ee260b9f6e5ec31894cd09d86e
```

## Serve

vLLM-Omni recipe — the generation endpoints and request examples are in the guide below.

```bash
vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers \
  --omni
```

## Guide

> Background reference. Commands in it are examples and may target another configuration — use the Serve section above.

### Overview

Wan2.2 is a video generation family served via **vLLM-Omni** with optional **Cache-DiT**
acceleration:

- `Wan-AI/Wan2.2-T2V-A14B-Diffusers` — Text-to-Video (MoE, 14B active)
- `Wan-AI/Wan2.2-I2V-A14B-Diffusers` — Image-to-Video (MoE, 14B active)
- `Wan-AI/Wan2.2-TI2V-5B-Diffusers` — Unified Text+Image-to-Video (dense 5B)

### Prerequisites

- vLLM-Omni on top of vLLM 0.12.0
- diffusers (bundled in vLLM-Omni CLI scripts)

### Installation

```bash
uv venv
source .venv/bin/activate
uv pip install vllm==0.12.0
uv pip install git+https://github.com/vllm-project/vllm-omni.git@ef01223c42be10ee260b9f6e5ec31894cd09d86e
```

#### AMD ROCm (MI300X/MI325X/MI355X)

```bash
uv venv
source .venv/bin/activate
uv pip install vllm==0.12.0 --extra-index-url https://wheels.vllm.ai/rocm/
uv pip install git+https://github.com/vllm-project/vllm-omni.git@ef01223c42be10ee260b9f6e5ec31894cd09d86e
```

> ⚠️ The vLLM ROCm wheel requires Python 3.12, ROCm 7.0, and glibc >= 2.35.

### Text-to-Video (T2V)

```python
from vllm_omni.entrypoints.omni import Omni

omni = Omni(model="Wan-AI/Wan2.2-T2V-A14B-Diffusers")
frames = omni.generate(
    "Two anthropomorphic cats in comfy boxing gear fight on a spotlighted stage.",
    height=720, width=1280,
    num_frames=81,
    num_inference_steps=40,
    guidance_scale=4.0,
)
```

CLI:

```bash
python examples/offline_inference/text_to_video/text_to_video.py \
  --model Wan-AI/Wan2.2-T2V-A14B-Diffusers \
  --prompt "A serene lakeside sunrise with mist over the water." \
  --height 720 --width 1280 \
  --num_frames 81 --num_inference_steps 40 \
  --guidance_scale 4.0 --fps 24 \
  --output t2v_output.mp4
```

#### Running T2V on MI300X/MI325X/MI355X GPUs

```bash
VLLM_ROCM_USE_AITER=1 \
SAFETENSORS_FAST_GPU=1 \
python examples/offline_inference/text_to_video/text_to_video.py \
  --model Wan-AI/Wan2.2-T2V-A14B-Diffusers \
  --prompt "A serene lakeside sunrise with mist over the water." \
  --height 720 --width 1280 \
  --num_frames 81 --num_inference_steps 40 \
  --guidance_scale 4.0 --fps 24 \
  --output t2v_output.mp4
```

### Image-to-Video (I2V)

```python
import PIL.Image
from vllm_omni.entrypoints.omni import Omni

omni = Omni(model="Wan-AI/Wan2.2-I2V-A14B-Diffusers")
image = PIL.Image.open("input.jpg").convert("RGB")

frames = omni.generate(
    "A cat playing with yarn",
    pil_image=image,
    height=480, width=832,
    num_frames=81,
    num_inference_steps=50,
    guidance_scale=5.0,
)
```

#### Running I2V on MI300X/MI325X/MI355X GPUs

```bash
VLLM_ROCM_USE_AITER=1 \
SAFETENSORS_FAST_GPU=1 \
python examples/offline_inference/image_to_video/image_to_video.py \
  --model Wan-AI/Wan2.2-I2V-A14B-Diffusers \
  --image input.jpg --prompt "A cat playing with yarn" \
  --num_frames 81 --num_inference_steps 50 \
  --guidance_scale 5.0 --fps 16 --output i2v_output.mp4
```

TI2V CLI:

```bash
python examples/offline_inference/image_to_video/image_to_video.py \
  --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
  --image input.jpg --prompt "A cat playing with yarn" \
  --num_frames 81 --num_inference_steps 50 \
  --guidance_scale 5.0 --fps 16 --output ti2v_output.mp4
```

#### Running TI2V on MI300X/MI325X/MI355X GPUs

```bash
VLLM_ROCM_USE_AITER=1 \
SAFETENSORS_FAST_GPU=1 \
python examples/offline_inference/image_to_video/image_to_video.py \
  --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
  --image input.jpg --prompt "A cat playing with yarn" \
  --num_frames 81 --num_inference_steps 50 \
  --guidance_scale 5.0 --fps 16 --output ti2v_output.mp4
```

### Cache-DiT Acceleration

```python
omni = Omni(
    model="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
    cache_backend="cache_dit",
    cache_config={
        "Fn_compute_blocks": 8,
        "Bn_compute_blocks": 0,
        "max_warmup_steps": 4,
        "residual_diff_threshold": 0.12,
    },
)
```

### Key Parameters

| Parameter | Default | Description |
|-----------|---------|-------------|
| `height` | 720 (T2V) / auto (I2V) | Video height (multiples of 16) |
| `width` | 1280 (T2V) / auto (I2V) | Video width (multiples of 16) |
| `num_frames` | 81 | Frames to generate |
| `num_inference_steps` | 40–50 | Denoising steps |
| `guidance_scale` | 4.0–5.0 | Classifier-free guidance scale |
| `boundary_ratio` | 0.875 | MoE boundary split ratio |
| `flow_shift` | 5.0 (720p) / 12.0 (480p) | Scheduler flow shift |

### References

- [Wan2.2-T2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers)
- [Wan2.2-I2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers)
- [Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers)
- [Cache-DiT Acceleration](https://github.com/vipshop/cache-dit)