# meituan-longcat/LongCat-Image-Edit — vLLM serving recipe

> Bilingual (Chinese-English) image editing model from Meituan LongCat, served via vLLM-Omni

Configuration: hardware `h200` (8× H200) · variant `default` (BF16) · task `i2i`

- Interactive page: https://vllm-recipes-git-preview-vllm-pr-64586-inferact-inc.vercel.app/meituan-longcat/LongCat-Image-Edit
- This configuration as JSON: https://vllm-recipes-git-preview-vllm-pr-64586-inferact-inc.vercel.app/meituan-longcat/LongCat-Image-Edit.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`: `i2i`

## Install

Requires vLLM >= 0.12.0.

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

Extra dependencies:

- vLLM-Omni must be installed from source with vllm==0.33.0 for LongCat-Image-Edit

```bash
git clone https://github.com/vllm-project/vllm-omni.git && cd vllm-omni && uv pip install --torch-backend=cu130 -e . vllm==0.33.0
```

## Serve

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

```bash
vllm serve meituan-longcat/LongCat-Image-Edit \
  --omni
```

## Guide

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

### Overview

LongCat-Image-Edit is the image-editing variant of LongCat-Image by Meituan. It supports
bilingual (Chinese-English) editing instructions and is served via **vLLM-Omni** (not
standard vLLM). It achieves SOTA performance among open-source image editing models.

### Prerequisites

- Hardware: 1x GPU with >=40 GB VRAM
- vLLM-Omni with final vLLM 0.33.0 and its CUDA 13 / PyTorch 2.14 runtime
- diffusers 0.40.0 (installed by the source checkout)

### Installation

```bash
# Clone and install vllm-omni
git clone https://github.com/vllm-project/vllm-omni.git
cd vllm-omni
uv venv
source .venv/bin/activate
uv pip install --torch-backend=cu130 -e . vllm==0.33.0
uv pip check
```

### Usage

```bash
cd vllm-omni
python3 ./examples/offline_inference/image_to_image/image_edit.py \
    --image qwen_bear.png \
    --prompt "Add a white art board written with colorful text 'vLLM-Omni' on grassland. Add a paintbrush in the bear's hands. Position the bear standing in front of the art board as if painting." \
    --output output_image_edit.png \
    --num_inference_steps 50 \
    --guidance_scale 4.5 \
    --seed 42 \
    --model meituan-longcat/LongCat-Image-Edit \
    --cache_backend cache_dit \
    --cache_dit_max_continuous_cached_steps 2
```

### References

- [LongCat-Image-Edit on Hugging Face](https://huggingface.co/meituan-longcat/LongCat-Image-Edit)
- [vLLM-Omni](https://github.com/vllm-project/vllm-omni)