Baidu releases ERNIE-Image-Turbo, a distilled text-to-image model generating in 8 inference steps

Baidu has released ERNIE-Image-Turbo, a distilled version of its ERNIE-Image text-to-image model that generates images in 8 inference steps. The model is built on a single-stream Diffusion Transformer (DiT) architecture and runs on consumer GPUs with 24GB VRAM.

Technical specifications

ERNIE-Image-Turbo supports multiple resolutions: 1024×1024, 848×1264, 1264×848, 768×1376, 896×1200, 1376×768, and 1200×896. The model uses a guidance scale of 1.0 and operates with bfloat16 precision. Pricing has not been disclosed.

The distillation process used Distribution Matching Distillation (DMD) and reinforcement learning to reduce the 50-step inference requirement of the base ERNIE-Image model to 8 steps while maintaining generation quality, according to Baidu.

Benchmark performance

On GENEval, ERNIE-Image-Turbo with prompt enhancement scored 0.8510 overall, compared to 0.8728 for the base ERNIE-Image model and 0.8481 for FLUX.2-klein-9B. The model achieved 0.9938 on single object detection and 0.8375 on counting tasks.

For text rendering measured on LongTextBench, ERNIE-Image-Turbo scored 0.9655 average across English and Chinese benchmarks, trailing Seedream 4.5 (0.9882) and the base ERNIE-Image model (0.9733) but outperforming FLUX.2-klein-9B (0.5413).

On the OneIG-EN benchmark measuring alignment, text, reasoning, style, and diversity, ERNIE-Image-Turbo scored 0.5656 overall. Nano Banana 2.0 led with 0.5780, while the base ERNIE-Image achieved 0.5750.

Implementation details

The model is available through Hugging Face's diffusers library and SGLang for deployment. Baidu states the model is designed for "posters, comics, multi-panel layouts, and other content creation tasks" requiring text rendering and structured generation.

Two versions are available: ERNIE-Image-Turbo with and without prompt enhancement (PE). The PE version generally shows higher benchmark scores across most metrics.

What this means

ERNIE-Image-Turbo represents Baidu's entry into fast text-to-image generation, prioritizing deployment efficiency over maximum quality. The 8-step generation and 24GB VRAM requirement make it accessible for consumer hardware, though benchmark scores indicate trade-offs compared to the base model. The focus on text rendering and structured layouts positions it for specific use cases like poster and comic generation rather than general-purpose image synthesis. Whether the speed gains justify the quality reduction will depend on application requirements.