Baidu's Open-Source ERNIE-Image-Turbo 8B: The Most Comprehensive Multi-Model Horizontal Comparison

Baidu just open-sourced ERNIE-Image, an 8B-parameter text-to-image model. When comparing text-to-image models, we can't help but compare parameter counts. Parameter count doesn't directly show a model's capabilities or strengths, but we still believe that "bigger is generally better." However, parameter count is just potential — real capability depends on training quality. We won't make subjective judgments; we'll leave it all to the benchmarks.

First, let's list the open-source text-to-image models available as of today, ranked by parameter count.

Open-Source Text-to-Image Model Parameter Rankings

HunyuanImage-3.0 (Tencent Hunyuan) — 80B total parameters (MoE, 64 experts, ~13B active)
- Currently the largest open-source T2I MoE model, native multimodal autoregressive architecture, excels at complex prompts, world knowledge reasoning, Chinese/English text rendering, and knowledge-dense generation. Suitable for high-quality, professional scenarios, but requires high-end hardware for inference (datacenter-grade, optimizable with quantization). Open-sourced September 2025, weights available on HF.
FLUX.2 [dev] (Black Forest Labs) — 32B parameters
- Open-source version of FLUX.2 series, supporting T2I, image editing, and multi-reference image fusion. Rectified flow transformer architecture with extremely strong prompt following, detail, and coherence. Runs on consumer GPUs (with quantization/FP8 optimization), one of the 2026 open-source flagship models.
FLUX.1 [dev/schnell] — ~12B parameters
- (Early FLUX.1 series, Kontext Pro at ~12B) Classic DiT/flow matching model with top-tier prompt following and text rendering. Schnell is the distilled fast version. Largest community ecosystem, well-supported by ComfyUI.
Stable Diffusion 3.5 Large (Stability AI) — 8.1B parameters (MMDiT)
- SD series' latest flagship, significantly improved prompt following and typography, supports 1MP+ resolution. Medium variant at 2.5B parameters, more suitable for consumer hardware. Largest open-source community, mature LoRA/fine-tuning ecosystem.
Qwen-Image / Qwen-Image-2.0 (Alibaba Qwen) — ~7B–20B parameters
- (2.0 unified at ~7B, lightweight and efficient) Strong Chinese/multilingual text rendering, professional typography (supports 1K token long prompts), native 2K resolution. Version 2.0 unified generation + editing, high efficiency, ideal for Asian languages and infographics.
Z-Image-Turbo / Other small efficient models — ~6B parameters
- Efficient real-time/edge deployment models, fast speed, suitable for low-resource environments.

So in terms of parameter count, Baidu's ERNIE-Image is actually not that large — it sits in the middle. As for visual performance, let the images speak.

ERNIE-Image Introduction

ERNIE-Image is an open text-to-image model developed by Baidu's ERNIE-Image team. Based on a single-stream Diffusion Transformer (DiT) with 8B parameters, it uses a Latent Diffusion Model (LDM) framework equipped with a lightweight Prompt Enhancer that expands short inputs into richer, more structured prompts to better unlock model capabilities. With just 8B DiT parameters, ERNIE-Image achieves state-of-the-art performance among open-weight text-to-image models — it cares not only about visual appeal but also controllability: accurate content representation is as important as aesthetics. In practice, it excels at complex instruction following, precise text rendering, and structured image generation — areas where many existing open-weight models still fall short.

Model Benchmark Horizontal Comparison (Official)

Built-in Prompt Enhancement

ERNIE-Image performs best with verbose, detailed, and well-structured prompts — richer descriptions generally bring better generation quality, tighter instruction fidelity, and more faithful reproduction of complex layouts or narrative content. But in reality, users typically input short sentences rather than detailed prompts that leverage the model's strengths.

To bridge this gap, we released a built-in 3B Prompt Enhancer that expands short user inputs into more detailed, structured prompts better suited for ERNIE-Image. The goal is not to change the user's intent, but to transform concise requests into a form that better unlocks the model's value — especially in posters, anime, web layouts, game screenshots, and other structured visual tasks.

The examples below illustrate this effect. Without prompt enhancement, the model tends to interpret short prompts literally and incompletely. With our 3B Prompt Enhancer, prompts become more descriptive and structured, significantly improving results in many scenarios. We also found that stronger LLMs can push this goal further — suggesting that prompt enhancement is a practical lever for leveraging ERNIE-Image's long-prompt generation capabilities.

Official Prompt Reproduction Test

For fairness, we reproduced results using the official prompts from the online demo at https://huggingface.co/spaces/baidu/ERNIE-Image-Turbo

Prompt (Oreo Miniature Scene)

This prompt is quite complex:

A studio macro photograph showcasing a handcrafted polymer clay-textured miniature diorama. Centered vertically is a small Oreo-themed shop. The roof is constructed from oversized Oreo cookie pieces interlocked together — dark black with thick white cream filling, classic embossed texture, and clear 'OREO' lettering with handmade clay roundness and subtle fingerprint marks for realistic tactile feel. The shop front has an open wooden serving window with a prominent blue rectangular signboard above it displaying the brand name 'OREO' in bold 3D white letters. At the window, a miniature clay shopkeeper wearing a blue apron and white work cap leans forward, holding a small Oreo cookie outward. Outside stands a customer — a miniature clay figure with a yellow backpack and red casual jacket, looking up slightly with both hands extended to receive the cookie. The ground around the shop is densely scattered with Oreos of various sizes — some whole, some broken revealing white filling — covering the entire foreground and surrounding ground. Soft studio lighting, even and gentle, highlighting the polymer clay's characteristic semi-gloss texture. Extremely shallow depth of field, precise focus on the shopkeeper, customer, and sign area, while foreground cookies and background edges show strong smooth blur, perfectly creating the miniature world's scale and dreamy atmosphere.

Official Result

Baidu ERNIE-Image Reproduction

Z-Image

FireRed Qwen-Image-2512

Flux2

Nanobana 2 pro

Jimeng (即梦)

Multi-Text Combination Test

Chinese text can be 100% reproduced by Z-image