ERNIE-Image Delivers Accurate Text-inclusive Image Generation

Baidu published ERNIE-Image, an open-source, single-stream Diffusion Transformer with 8B DiT parameters designed for high-fidelity text-in-image generation, layout fidelity, and bilingual prompts. The release includes a distilled 8-step ERNIE-Image-Turbo for draft-speed generation and a 50-step SFT model for final renders. Core benchmark numbers reported include GENEval 0.8856 and LongTextBench 0.9733, and the project is released under Apache-2.0 so weights and outputs are commercially usable.

ERNIE-Image is built on a single-stream DiT architecture and pairs the generator with a lightweight Prompt Enhancer that expands terse user inputs into structured descriptions. The team ships two runtime points: the distilled ERNIE-Image-Turbo (8 steps) for quick iteration and a full 50-step SFT model for quality. Supported resolutions include 1024x1024, and the codebase exposes generation, edit, composite, and upscale primitives so designers can centralize an asset pipeline.

The model is explicitly trained to solve three failure modes common to diffusion-based image models: accurate long-form glyph rendering, instruction-faithful multi-object composition, and page-level layout reasoning. Independent and reproducible benchmark claims include LongTextBench 0.9733 for text rendering and GENEval 0.8856 for instruction fidelity, positioning ERNIE-Image ahead of several open competitors on text and layout-centric tasks.

• •Distilled 8-step ERNIE-Image-Turbo for rapid drafts and a 50-step SFT model for production frames
• •Built-in Prompt Enhancer to reduce manual prompt engineering and expand short prompts into structured instructions
• •Pipeline features: generate, edit, composite, upscale, and export in a single surface
• •Apache-2.0 licensing, enabling commercial use, fine-tuning, and self-hosting

ERNIE-Image addresses a persistent pain point for practitioners: the inability of many diffusion models to render legible, layout-sensitive text and strict compositions. Where Stable Diffusion and some proprietary models produce blurred or hallucinated glyphs, ERNIE-Image targets fidelity with dedicated training and evaluation on bilingual prompts. The Apache-2.0 license is strategically important for design teams and agencies that need clear commercial rights and the option to self-host or fine-tune. This release also reflects continued maturation of the Chinese foundation-model ecosystem, building on lineage work such as ERNIE-ViLG and signaling stronger competition in the open-weight image model space.

If you operate creative pipelines, ERNIE-Image lowers the integration cost for generating posters, comics, storyboards, UI mockups, and ad frames that require accurate typography and layout constraints. Teams can self-host the weights, plug the Prompt Enhancer into existing prompt flows, and use the Turbo variant for iterative authoring followed by the SFT model for high-quality outputs.

Adoption by the open-source community, forks that add safety filters or domain-specific fine-tunes, and real-world asset pipelines that replace stock shoots. Also monitor compute costs for self-hosting, model safety and copyright moderation in downstream tooling, and how competitors respond on the text-in-image benchmark front.