ERNIE-Image

1. Model introduction

ERNIE-Image is Baidu's text-to-image diffusion model family. SGLang Diffusion supports both the regular and Turbo checkpoints with the native ErnieImagePipeline.

2. Installation

Install SGLang with the diffusion dependencies:

pip install -e "python[diffusion]"

For full installation options, see the SGLang Diffusion installation guide.

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:

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

Serve ERNIE-Image-Turbo:

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:

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.