ERNIE-Image-Turbo

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ERNIE-Image-Turbo is an open text-to-image generation model developed by the ERNIE-Image team at Baidu. It is the distilled release of ERNIE-Image, built on the same single-stream Diffusion Transformer (DiT) family and designed for fast generation with strong fidelity in only 8 inference steps. The model retains strong controllability in practical generation scenarios where accurate content realization matters as much as aesthetics. In particular, ERNIE-Image-Turbo remains strong on complex instruction following, text rendering, and structured image generation, making it well suited for posters, comics, multi-panel layouts, and other content creation tasks that require both visual quality and efficiency. It also supports a broad range of visual styles, including realistic photography, design-oriented imagery, and stylized aesthetic outputs.

Highlights:

• Fast and efficient: As the distilled checkpoint of ERNIE-Image, ERNIE-Image-Turbo delivers strong generation quality with only 8 inference steps, making it suitable for latency-sensitive applications.
• Text rendering: ERNIE-Image-Turbo performs well on dense, long-form, and layout-sensitive text, making it a strong choice for posters, infographics, UI-like images, and other text-heavy visual content.
• Instruction following: The model is able to follow complex prompts involving multiple objects, detailed relationships, and knowledge-intensive descriptions with strong reliability.
• Structured generation: ERNIE-Image-Turbo is effective for structured visual tasks such as posters, comics, storyboards, and multi-panel compositions, where layout and organization are critical.
• Style coverage: In addition to clean and readable design-oriented outputs, the model also supports realistic photography and distinctive stylized aesthetics, including softer and more cinematic visual tones.
• Practical deployment: Thanks to its compact size, ERNIE-Image-Turbo can run on consumer GPUs with 24G VRAM, which lowers the barrier for research, downstream use, and model adaptation.

Released Versions

ERNIE-Image: Our SFT model, delivers stronger general-purpose capability and instruction fidelity in typically 50 inference steps.

ERNIE-Image-Turbo: Our Turbo model, optimized by DMD and RL, achieves faster speed and higher aesthetics in only 8 inference steps.

Benchmark

GENEval

Model	Single Object	Two Object	Counting	Colors	Position	Attribute Binding	Overall
ERNIE-Image (w/o PE)	1.0000	0.9596	0.7781	0.9282	0.8550	0.7925	0.8856
ERNIE-Image (w/ PE)	0.9906	0.9596	0.8187	0.8830	0.8625	0.7225	0.8728
Qwen-Image	0.9900	0.9200	0.8900	0.8800	0.7600	0.7700	0.8683
ERNIE-Image-Turbo (w/o PE)	1.0000	0.9621	0.7906	0.9202	0.7975	0.7300	0.8667
ERNIE-Image-Turbo (w/ PE)	0.9938	0.9419	0.8375	0.8351	0.7950	0.7025	0.8510
FLUX.2-klein-9B	0.9313	0.9571	0.8281	0.9149	0.7175	0.7400	0.8481
Z-Image	1.0000	0.9400	0.7800	0.9300	0.6200	0.7700	0.8400
Z-Image-Turbo	1.0000	0.9500	0.7700	0.8900	0.6500	0.6800	0.8233

OneIG-EN

Model	Alignment	Text	Reasoning	Style	Diversity	Overall
Nano Banana 2.0	0.8880	0.9440	0.3340	0.4810	0.2450	0.5780
Seedream 4.5	0.8910	0.9980	0.3500	0.4340	0.2070	0.5760
ERNIE-Image (w/ PE)	0.8678	0.9788	0.3566	0.4309	0.2411	0.5750
Seedream 4.0	0.8920	0.9830	0.3470	0.4530	0.1910	0.5730
ERNIE-Image-Turbo (w/ PE)	0.8676	0.9666	0.3537	0.4191	0.2212	0.5656
ERNIE-Image (w/o PE)	0.8909	0.9668	0.2950	0.4471	0.1687	0.5537
Z-Image	0.8810	0.9870	0.2800	0.3870	0.1940	0.5460
Qwen-Image	0.8820	0.8910	0.3060	0.4180	0.1970	0.5390
ERNIE-Image-Turbo (w/o PE)	0.8795	0.9488	0.2913	0.4277	0.1232	0.5341
FLUX.2-klein-9B	0.8871	0.8657	0.3117	0.4417	0.1560	0.5324
Qwen-Image-2512	0.8760	0.9900	0.2920	0.3380	0.1510	0.5300
GLM-Image	0.8050	0.9690	0.2980	0.3530	0.2130	0.5280
Z-Image-Turbo	0.8400	0.9940	0.2980	0.3680	0.1390	0.5280

OneIG-ZH

Model	Alignment	Text	Reasoning	Style	Diversity	Overall
Nano Banana 2.0	0.8430	0.9830	0.3110	0.4610	0.2360	0.5670
ERNIE-Image (w/ PE)	0.8299	0.9539	0.3056	0.4342	0.2478	0.5543
Seedream 4.0	0.8360	0.9860	0.3040	0.4430	0.2000	0.5540
Seedream 4.5	0.8320	0.9860	0.3000	0.4260	0.2130	0.5510
Qwen-Image	0.8250	0.9630	0.2670	0.4050	0.2790	0.5480
ERNIE-Image-Turbo (w/ PE)	0.8258	0.9386	0.3043	0.4208	0.2281	0.5435
Z-Image	0.7930	0.9880	0.2660	0.3860	0.2430	0.5350
ERNIE-Image (w/o PE)	0.8421	0.8979	0.2656	0.4212	0.1772	0.5208
Qwen-Image-2512	0.8230	0.9830	0.2720	0.3420	0.1570	0.5150
GLM-Image	0.7380	0.9760	0.2840	0.3350	0.2210	0.5110
Z-Image-Turbo	0.7820	0.9820	0.2760	0.3610	0.1340	0.5070
ERNIE-Image-Turbo (w/o PE)	0.8326	0.9086	0.2580	0.4002	0.1316	0.5062
FLUX.2-klein-9B	0.8201	0.4920	0.2599	0.4166	0.1625	0.4302

LongTextBench

Model	LongText-Bench-EN	LongText-Bench-ZH	Avg
Seedream 4.5	0.9890	0.9873	0.9882
ERNIE-Image (w/ PE)	0.9804	0.9661	0.9733
GLM-Image	0.9524	0.9788	0.9656
ERNIE-Image-Turbo (w/ PE)	0.9675	0.9636	0.9655
Nano Banana 2.0	0.9808	0.9491	0.9650
ERNIE-Image-Turbo (w/o PE)	0.9602	0.9675	0.9639
ERNIE-Image (w/o PE)	0.9679	0.9594	0.9636
Qwen-Image-2512	0.9561	0.9647	0.9604
Qwen-Image	0.9430	0.9460	0.9445
Z-Image	0.9350	0.9360	0.9355
Seedream 4.0	0.9214	0.9261	0.9238
Z-Image-Turbo	0.9170	0.9260	0.9215
FLUX.2-klein-9B	0.8642	0.2183	0.5413

Quick Start

Recommended Parameters

• Resolution:
  • 1024x1024
  • 848x1264
  • 1264x848
  • 768x1376
  • 896x1200
  • 1376x768
  • 1200x896
• Guidance scale: 1.0
• Inference steps: 8

Diffusers

Install the latest version of diffusers:

pip install git+https://github.com/huggingface/diffusers

import torch
from diffusers import ErnieImagePipeline

pipe = ErnieImagePipeline.from_pretrained(
    "Baidu/ERNIE-Image-Turbo",
    torch_dtype=torch.bfloat16,
).to("cuda")

image = pipe(
    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=8,
    guidance_scale=1.0,
    use_pe=True # use prompt enhancer
).images[0]

image.save("output.png")

SGLang

Install the latest version of sglang:

git clone https://github.com/sgl-project/sglang.git

Start the server:

sglang serve --model-path baidu/ERNIE-Image-Turbo

Send a generation request:

curl -X POST http://localhost:30000/generate \
  -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": 8,
    "guidance_scale": 1.0,
    "use_pe": true
  }' \
  --output output.png