Model reference · open weights

ERNIE Image Turbo

On the AxForge API eu-se-1 · Stockholm Image baidu Text→image 2 builds Open weights 3k dl/mo

ERNIE Image Turbo is an open-weight image model from Baidu, listed on the AxForge serverless API, OpenAI-compatible and hosted in the EU.

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AxForge offerings: ERNIE Image Turbo

ERNIE-Image-Turbo is an open text-to-image generation model developed by Baidu, released under the Apache 2.0 license.
It is a distilled version of ERNIE-Image with 8.0B parameters, designed for fast generation in only 8 inference steps.
The model supports complex instruction following, text rendering, and structured image generation, and can run on consumer GPUs with 24G VRAM.

Summary of the baidu/ERNIE-Image-Turbo model card, 2026-10-01. The estimate below is for another build of the family.

Hugging Face ↗GitHub ↗

What it is

Released by	Baidu
Released	2026-04-02
Parameters	8.0B
VRAM	10.8 GB for the weights

What it runs on

Memory and cards for ERNIE-Image-Turbo-GGUF (GGUF)

10.8 GBweights, file size

5.0 GBworking memory, one 1024² image

537 MBruntime overhead

Card	One image	Memory
RTX 3060 12 GB … RTX 4060 Ti 16 GB	does not fit
RTX 3090 24 GB	fits	23.4 GB
RTX 4090 24 GB	fits	23.4 GB
RTX 5090 32 GB	fits	31.0 GB
L40S 48 GB	fits	44.0 GB
A100 80 GB	fits	78.2 GB
H100 80 GB	fits	78.1 GB
RTX PRO 6000 Blackwell 96 GB	fits	93.8 GB
DGX Spark (GB10) 128 GB unified	fits	107 GB
H200 141 GB	fits	138 GB
B200 180 GB	fits	176 GB

Builds

Sizes, precisions & builds

Build	Params	Precision	Weights	Smallest setup
ERNIE-Image-Turbo ↗ On the API	8.0B	FP8	31.6 GB	RTX 4090 24 GB
ERNIE-Image-Turbo-GGUF (above) ↗ packaged by unsloth 15 builds: Q3_K_S 3.8 GB … F16 16.1 GB the transformer alone, to use with the text encoders and VAE		GGUF	10.8 GB	RTX 4090 24 GB

From the model card

What Baidu says about ERNIE Image Turbo

Read the model card

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
ERN

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

Using it via the API

Call it like any OpenAI endpoint

ERNIE Image Turbo answers on the OpenAI-compatible API today as ernie-image-turbo, with the same base URL and keys as every other model.

# your API key, from API Keys
export AXFORGE_API_KEY="YOUR_KEY"

curl --max-time 300 https://api.axforge.ai/v1/images/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"ernie-image-turbo","prompt":"a red bicycle","size":"1024x1024"}' \
  | sed -n 's/.*"b64_json":"\([^"]*\)".*/\1/p' | base64 -d > image.png

# your API key, from API Keys
$env:AXFORGE_API_KEY = "YOUR_KEY"

$call = @{
  Method      = "Post"
  Uri         = "https://api.axforge.ai/v1/images/generations"
  Headers     = @{ Authorization = "Bearer $env:AXFORGE_API_KEY" }
  ContentType = "application/json"
  Body        = '{"model":"ernie-image-turbo","prompt":"a red bicycle","size":"1024x1024"}'
  TimeoutSec  = 300
}
$r = Invoke-RestMethod @call
[IO.File]::WriteAllBytes("$PWD\image.png", [Convert]::FromBase64String($r.data[0].b64_json))

Images: sizes, seeds, editing

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Details

Type	Image models
Task	Text→image
Runs with	diffusers
Popularity	3k downloads / month
Weights	10.8 GB, ERNIE-Image-Turbo-GGUF (GGUF, file size)