---
title: ERNIE-Image
metatags:
    description: "Deploy ERNIE-Image and ERNIE-Image-Turbo with SGLang Diffusion."
---

## 1. Model introduction

[ERNIE-Image](https://huggingface.co/baidu/ERNIE-Image) is Baidu's text-to-image diffusion model family. SGLang Diffusion supports both the regular and Turbo checkpoints with the native `ErnieImagePipeline`.

| Model | Hugging Face model ID | Notes |
| --- | --- | --- |
| ERNIE-Image | `baidu/ERNIE-Image` | Regular text-to-image checkpoint |
| ERNIE-Image-Turbo | `baidu/ERNIE-Image-Turbo` | Turbo text-to-image checkpoint |

## 2. Installation

Install SGLang with the diffusion dependencies:

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

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

## 3. Serve the model

The commands below target a single supported NVIDIA CUDA or AMD ROCm GPU. Start with `--performance-mode auto`; use `speed` only when the full pipeline fits comfortably on the selected GPU(s), and use `memory` when you need lower peak GPU memory.

Serve ERNIE-Image:

```bash Command
sglang serve \
  --model-path baidu/ERNIE-Image \
  --num-gpus 1 \
  --performance-mode auto \
  --port 30010
```

Serve ERNIE-Image-Turbo:

```bash Command
sglang serve \
  --model-path baidu/ERNIE-Image-Turbo \
  --num-gpus 1 \
  --performance-mode auto \
  --port 30010
```

## 4. Generate an image

Use the OpenAI-compatible image generation API after the server starts:

```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="baidu/ERNIE-Image-Turbo",
    prompt="A cinematic photo of a quiet lakeside cabin at sunrise",
    n=1,
    response_format="b64_json",
)

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

## 5. Configuration tips

- ERNIE-Image is a text-to-image pipeline; do not pass `--image-path`.
- `--performance-mode auto` keeps conservative defaults while preserving explicit user flags.
- If the checkpoint includes a PE component, SGLang loads it automatically from `model_index.json`.
- Treat FSDP, SP/Ulysses/Ring, and TP as explicit benchmark knobs. Measure the target resolution, step count, and GPU type before making them production defaults.
