Baidu releases ERNIE-Image, an 8B parameter text-to-image model with strong text rendering capabilities

Baidu has released ERNIE-Image, an 8B parameter text-to-image generation model that claims state-of-the-art performance among open-weight models in its size class. The model is built on a single-stream Diffusion Transformer (DiT) architecture and includes a lightweight Prompt Enhancer component that expands user inputs into structured descriptions.

Technical specifications

ERNIE-Image uses 8 billion DiT parameters and can generate images at multiple resolutions including 1024x1024, 848x1264, and 1264x848 pixels. The model requires 50 inference steps with a guidance scale of 4.0 for the base version. According to Baidu, the model can run on consumer GPUs with 24GB VRAM.

Baidu has also released ERNIE-Image-Turbo, a faster variant optimized with Distribution Matching Distillation (DMD) and reinforcement learning that generates images in 8 inference steps.

Benchmark performance

On the GENEval benchmark, ERNIE-Image with Prompt Enhancer scored 0.8728 overall, outperforming FLUX.2-klein-9B (0.8481) and Z-Image (0.8400). The model scored particularly well on single object generation (0.9906) and two object generation (0.9596).

For text rendering specifically, ERNIE-Image achieved 0.9733 average score on LongTextBench across English and Chinese, trailing only Seedream 4.5 (0.9882) but ahead of GLM-Image (0.9656) and Nano Banana 2.0 (0.9650).

On the OneIG-EN benchmark measuring alignment, text, reasoning, style, and diversity, ERNIE-Image with Prompt Enhancer scored 0.5750 overall, ranking third behind Nano Banana 2.0 (0.5780) and Seedream 4.5 (0.5760).

Intended use cases

Baidu positions ERNIE-Image for commercial applications requiring precise control over generated content, including posters, comics, multi-panel layouts, infographics, and UI mockups. The model supports multiple visual styles including realistic photography, design-oriented imagery, and stylized aesthetic outputs.

The model is available on Hugging Face with both Diffusers and SGLang inference support. Baidu has not disclosed pricing for commercial API access.

What this means

ERNIE-Image represents a strategic release from Baidu targeting practical commercial applications rather than purely aesthetic generation. The 8B parameter count makes it computationally accessible while the benchmark scores suggest competitive performance with larger models. The emphasis on text rendering and instruction following addresses specific pain points in text-to-image generation where models often struggle with accurate text and complex layouts. The availability of a turbo variant with 8-step inference indicates Baidu's focus on deployment efficiency alongside quality.