Introduction: When Text-to-Image Meets Two Languages

Creative teams often find that English prompts look great, but Chinese (or mixed) prompts don’t. That gap has blocked cross-border campaigns, product visuals, and classroom content for years. ERNIE-ViLG, Baidu’s multilingual diffusion model, aims to fix it.

This ERNIE-ViLG review and ERNIE-ViLG tutorial shows how Baidu’s text-to-image AI handles bilingual prompts, where it shines vs. Midjourney and Stable Diffusion, how to access the ERNIE-ViLG API, and realistic API pricing expectations in 2025. If you’ve been searching for a Chinese AI image generator with credible English support, this guide is for you.

For readers exploring broader prompt-engineering systems, see Nano Banana — Turbocharge Your AI Prompts for hands-on workflows built around Gemini’s image diffusion models.

What Is ERNIE-ViLG? (Baidu ERNIE-ViLG in 2025)

ERNIE-ViLG (Vision-Language Generation) sits inside Baidu’s ERNIE family—short for Enhanced Representation through kNowledge IntEgration. It’s a Baidu diffusion model trained with large-scale bilingual image-text data to generate images directly from prompts. In plain English: it turns your words into pictures and understands both Chinese and English.

2025 Snapshot: What’s Improved

• Higher resolution tiers suitable for production use.
• Faster inference on ERNIE-ViLG Baidu Cloud infrastructure.
• Better multilingual text-to-image alignment for mixed prompts.
• Style presets that span ink-wash, anime, cinematic realism, and product photography.

You’ll see the benefits in this ERNIE-ViLG review: stronger composition, better attribute control, and far fewer mistranslations when prompts mix languages.

For the official research details, check the CVPR 2023 ERNIE-ViLG 2.0 paper

How ERNIE-ViLG Works (A Quick Tour of Diffusion)

Diffusion models start with pure noise and iteratively denoise toward an image guided by your text. ERNIE-ViLG uses a text encoder (to understand language) plus a denoising network (to turn that understanding into pixels). Because it’s a multilingual AI generation model, the text encoder maps both Chinese and English into a shared space so the visuals reflect the same meaning.

For those who want to learn how diffusion systems fuse multiple visuals or prompts, read Multi-Image Fusion in Nano Banana: Merging Photos with One Prompt. It explains how similar AI pipelines blend imagery in creative production.

Think of it as a bilingual art director. You provide the brief—“product photo, glossy black keyboard, neon rim light — 产品摄影 黑色 键盘 霓虹 边缘光”—and the model composes a scene that respects both halves of your prompt.

To see an independent walkthrough, you can also watch this short YouTube demo of ERNIE-ViLG image synthesis

Getting Started: ERNIE-ViLG Login, Access, and Interface

Step-by-Step Access (Web + API)

1. Sign up / Login: Create a Baidu account and complete ERNIE-ViLG login on the Wenxin platform.
2. Find the image generator: Look for ERNIE-ViLG under AI art / image creation.
3. Choose a plan: Free trials are often capped; paid tiers unlock higher resolution and faster queues.
4. Get API keys (Developers): In Baidu Cloud / Qianfan, generate an ERNIE-ViLG API key and secret.
5. Start prompting: Enter text, pick style/aspect ratio, and generate. For apps, call the text-to-image endpoint with your payload.

For developers integrating similar image APIs, check Getting Started with the Nano Banana API in AI Studio and Vertex AI, it mirrors Baidu’s process through Google’s Gemini ecosystem.

Interface at a Glance

• Web UI: Text box, style picker, size options, history. Ideal for marketers, designers, and students.
• API: JSON parameters such as prompt, style, size, guidance strength; returns an image URL/Base64. Great for pipelines and automation.

Tip: If you operate outside Mainland China, check regional availability and any access restrictions before committing toolchains. For a free alternative setup, see How to Run ERNIE-ViLG AI Art Generator in Google Colab (Free)

ERNIE-ViLG API and Pricing (2025)

Public English documentation on exact ERNIE-ViLG API pricing is limited. Based on visible tiers and comparable platforms, here’s a planning-grade view (verify before purchase):

API & Membership Overview

Indicative, subject to region/plan. Always confirm in your account console.

Baidu has also published API cost comparisons in their ecosystem; see https://apidog.com/blog/baidu-ernie-4-5-x1-2/?utm_source=chatgpt.comfor reference.

Prompt Strategies: Getting the Best From ERNIE-ViLG

Use bilingual prompts that pair technical English descriptors with Chinese cultural context.
For example:

“Ultra-lightweight RGB gaming mouse hovering above carbon-fibre surface — 超轻量化 RGB 游戏鼠标 悬浮 于 碳纤维 表面。”

For more prompt-design inspiration, see Nano Banana Guide for Beginners | Create Like a Pro (No Code), its section on “prompt tone and modifiers” applies equally well here.

Performance Review: Quality, Speed, and Reliability

According to benchmarks and the ERNIE-ViLG 2.0 CVPR paper, the model delivers a zero-shot FID of 6.75 on MS-COCO—competitive with global diffusion systems.
Average generation latency ranges from 6–20 seconds (1024×1024 images).

For context on how Google’s diffusion-transformer hybrids achieve similar speeds, explore Behind the Scenes: How Gemini 2.5 Flash Image Processes Multi-Prompt Edits.

Use Cases: Creative and Business Wins

Advertising & Brand

• Bilingual campaign art and hero banners.
• Localized assets for different markets.
• AI-generated test visuals for A/B experimentation.

Product & E-Commerce

• Consistent catalog renders and lifestyle scenes.
• Variant thumbnails without reshoots.
• Automated mock-ups for marketing.

These workflows parallel how retailers adopt AI visualization—see Virtual Try-On with Nano Banana: How to Test Fashion Items Before Buying to understand similar diffusion-based product rendering.

Strengths and Limitations

Developer Notes: ERNIE-ViLG API Key Setup Guide

1. Log into Baidu Cloud → Qianfan.
2. Request API access, generate keys.
3. Test a simple POST with prompt + size.
4. Add retries and rate-limit handling.
5. Automate batch generations via n8n/Zapier.

For developers building their own creative editors, see Building a Prompt-Driven Image Editor with Nano Banana Templates for practical guidance on text-to-image UI design.

Frequently Asked Questions

1. How to use ERNIE-ViLG for text-to-image generation?

Log into Wenxin, enter a bilingual prompt, and generate. See YouTube walkthrough for live demo.

2. ERNIE-ViLG API key setup guide?

Covered above; similar to other AI image synthesis tools like Nano Banana.

3. ERNIE-ViLG vs DALL·E vs Stable Diffusion?

ERNIE-ViLG excels at bilingual accuracy; DALL·E leads in prompt reasoning; Stable Diffusion offers customization.

Conclusion: Why ERNIE-ViLG Deserves a Spot in Your Stack

If your creative workflow crosses languages, Baidu ERNIE-ViLG is a practical, cost-efficient option. Its multilingual diffusion architecture ensures semantic fidelity across Chinese and English prompts—ideal for global marketing, education, and design.

To explore how multilingual AI complements Google’s ecosystem, read What Is Nano Banana? A Complete Guide to Google’s Gemini 2.5 Flash Image Model. Together, they frame where diffusion models are headed in 2025.