---
title: LongCat-Image
metatags:
    description: "Deploy LongCat-Image with SGLang Diffusion and its native in-process Qwen2.5-VL prompt rewriter."
---

import { DiffusionModelTags } from '/src/snippets/diffusion/model-tags.jsx';

<DiffusionModelTags tags={["image", "text-to-image", "prompt rewriting", "Qwen2.5-VL"]} />

## 1. Model Introduction

[LongCat-Image](https://huggingface.co/meituan-longcat/LongCat-Image) is a
text-to-image model from Meituan. SGLang runs its Qwen2.5-VL prompt rewriter
in process with the native SGLang runtime before text encoding and denoising.

The native pipeline keeps prompt rewriting and diffusion behind one OpenAI-compatible
image endpoint. Rewriting is enabled by default for stronger prompt expansion, but
each request can disable it when lower latency matters more than the rewritten prompt.

## 2. Installation

Install SGLang with the diffusion dependencies:

```bash Command
pip install -e "python[diffusion]"
```

For other installation options, see the
[SGLang Diffusion installation guide](/docs/sglang-diffusion/installation).

## 3. Serve the model

```bash Command
sglang serve \
  --model-path meituan-longcat/LongCat-Image \
  --performance-mode auto \
  --port 30010
```

Prompt rewriting is enabled by default for LongCat-Image. It adds an
autoregressive Qwen2.5-VL pass before diffusion; set
`enable_prompt_rewrite=false` on a request when lower latency is more important
than rewritten prompt quality.

## 4. Generate an image

```python Python
import base64
from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://127.0.0.1:30010/v1")

response = client.images.generate(
    model="meituan-longcat/LongCat-Image",
    prompt="A quiet bookshop on a rainy evening, warm light in the windows",
    n=1,
    response_format="b64_json",
)

image_bytes = base64.b64decode(response.data[0].b64_json)
with open("longcat_image.png", "wb") as f:
    f.write(image_bytes)
```

To skip prompt rewriting with the OpenAI client, pass the model-specific request
field through `extra_body`:

```python Python
response = client.images.generate(
    model="meituan-longcat/LongCat-Image",
    prompt="A quiet bookshop on a rainy evening",
    extra_body={"enable_prompt_rewrite": False},
)
```

## 5. Memory placement

Use the unified component-residency selector when the complete pipeline does
not fit on the accelerator. For example, keep the repeatedly used DiT resident
while moving auxiliary components to CPU between stages:

```bash Command
sglang serve \
  --model-path meituan-longcat/LongCat-Image \
  --component-residency dit=resident text_encoder=component-offload vae=component-offload \
  --pin-cpu-memory \
  --port 30010
```

See [Component Residency](/docs/sglang-diffusion/api/cli#component-residency)
for mode semantics and compatibility with the existing CPU-offload flags.