baidu/ERNIE-Image-Turbo

ERNIE-Image-Turbo

🤗 ERNIE-Image | 🤗 ERNIE-Image-Turbo | 🤖 ERNIE-Image | 🤖 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…