> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Diffusion Models with Prompt Enhancement (PE)

> Run ERNIE-Image with built-in prompt enhancement or a separate SGLang-served PE model.

## Quick Start

By default, the PE model is loaded by the diffusion model server, which may not provide optimal performance. For higher performance, the PE model can be deployed as a separate SGLang server. This document uses `baidu/ERNIE-Image` as an example.

Run the model with the built-in Transformers PE implementation (default):

```bash theme={null}
# Terminal 1: launch server
sglang serve --model-path baidu/ERNIE-Image --port ${PORT}
```

```bash theme={null}
# Terminal 2: launch client
curl -X POST http://${HOST}:${PORT}/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "This is a photograph depicting an urban street scene. Shot at eye level, it shows a covered pedestrian or commercial street. Slightly below the center of the frame, a cyclist rides away from the camera toward the background, appearing as a dark silhouette against backlighting with indistinct details. The ground is paved with regular square tiles, bisected by a prominent tactile paving strip running through the scene, whose raised textures are clearly visible under the light. Light streams in diagonally from the right side of the frame, creating a strong backlight effect with a distinct Tyndall effect—visible light beams illuminating dust or vapor in the air and casting long shadows across the street. Several pedestrians appear on the left side and in the distance, some with their backs to the camera and others walking sideways, all rendered as silhouettes or semi-silhouettes. The overall color palette is warm, dominated by golden yellows and dark browns, evoking the atmosphere of dusk or early morning.",
    "height": 1264,
    "width": 848,
    "num_inference_steps":   50,
    "guidance_scale": 4.0,
    "use_pe": true
  }'
```

Run the model with an SGLang-served PE model (high performance):

```bash theme={null}
# Terminal 1: launch SGLang PE model server
sglang serve --model-path /path/to/baidu/ERNIE-Image/pe/ --port ${PE_PORT}
```

```bash theme={null}
# Terminal 2: launch diffusion model server with PE server
sglang serve --model-path /path/to/baidu/ERNIE-Image/ \
  --pe-server-url "http://${HOST}:${PE_PORT}" \
  --port ${PORT}
```

```bash theme={null}
# Terminal 3: launch client
curl -X POST http://${HOST}:${PORT}/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "This is a photograph depicting an urban street scene. Shot at eye level, it shows a covered pedestrian or commercial street. Slightly below the center of the frame, a cyclist rides away from the camera toward the background, appearing as a dark silhouette against backlighting with indistinct details. The ground is paved with regular square tiles, bisected by a prominent tactile paving strip running through the scene, whose raised textures are clearly visible under the light. Light streams in diagonally from the right side of the frame, creating a strong backlight effect with a distinct Tyndall effect—visible light beams illuminating dust or vapor in the air and casting long shadows across the street. Several pedestrians appear on the left side and in the distance, some with their backs to the camera and others walking sideways, all rendered as silhouettes or semi-silhouettes. The overall color palette is warm, dominated by golden yellows and dark browns, evoking the atmosphere of dusk or early morning.",
    "height": 1264,
    "width": 848,
    "num_inference_steps": 50,
    "guidance_scale": 4.0,
    "use_pe": true
  }'
```

## Support matrix

| Model       | Built-in PE | SGLang PE Server |
| ----------- | ----------- | ---------------- |
| ERNIE-Image | ✅           | ✅                |

## Ascend NPU Environment

See [Diffusion models with AR stage like GLM-Image](/docs/sglang-diffusion/models_with_ar#ascend-npu-env).