ERNIE-Image Text Rendering Deep Dive: Posters, Infographics, and Multilingual Layout Practice Guide

Abstract: With a LongTextBench score of 0.9733, ERNIE-Image has become the open-source model with the strongest text rendering capability. This article provides an in-depth analysis of its text rendering principles, prompt engineering techniques, and hands-on tutorials across three practical scenarios: posters, infographics, and multilingual layouts.

Published: 2026-05-11
Reading Time: ~12 minutes
Difficulty: Intermediate

Why Text Rendering is the "Ultimate Test" for Text-to-Image Models

In 2026, most text-to-image models (including Midjourney v7, DALL-E 3, and Stable Diffusion 3.5) still struggle with text rendering—generated text is either blurry, misspelled, or produces "hallucinated text" (looks like text but is completely unreadable).

ERNIE-Image has achieved a breakthrough on this problem.

According to the authoritative LongTextBench benchmark, ERNIE-Image scores 0.9733, ranking #1 among open-source models globally, and even surpassing closed-source commercial models in certain dimensions.

ERNIE-Image text rendering capability

ERNIE-Image generated poster example: Title text is clear and legible, with accurate bilingual Chinese-English presentation.

1. Technical Principles of ERNIE-Image Text Rendering

1.1 DiT Architecture: Why It Can "Understand" Text

ERNIE-Image is built on a single-stream Diffusion Transformer (DiT) architecture, which is fundamentally different from traditional U-Net-based diffusion models:

Dimension	Traditional U-Net Diffusion	ERNIE-Image DiT
Text Processing	Pixel-level noise denoising	Token-level semantic understanding + pixel generation
Text Rendering	Blurry, misspelled	Clear, precise strokes
Multilingual Support	Limited	Chinese, English, Japanese, Korean, etc.
Long Text Support	Difficult	LongTextBench 0.9733

The core advantage of the DiT architecture lies in using Transformer as the backbone network, enabling it to understand text semantics like a language model, rather than merely treating text as a pixel pattern to denoise.

1.2 Three Stages of Text Rendering

The text rendering process in ERNIE-Image can be divided into three stages:

Prompt → [PE Enhancer] → [DiT Text Understanding] → [Pixel-Level Text Generation] → Output Image
             ↓                 ↓                          ↓
        Expand text         Identify text          Generate clear
        instructions       content and            characters and
        and layout         layout requirements    stroke details

PE Enhancer Stage: Expands brief text instructions (e.g., "poster title 'Happy New Year'") into detailed layout descriptions
DiT Text Understanding Stage: Identifies text content, font style, and positioning requirements
Pixel-Level Text Generation Stage: Gradually generates clear text pixels during the diffusion denoising process

1.3 Key Parameters Affecting Text Rendering

# Recommended parameters for text rendering
image = pipe(
    prompt="A poster with the title 'AI 2026' in bold white text...",
    height=1024,       # 1024×1024 recommended
    width=1024,        # Best text rendering quality
    num_inference_steps=50,  # Standard 50 steps
    guidance_scale=4.0,      # Recommended value
    use_pe=True              # Enable PE Enhancer
).images[0]

Rule of Thumb: use_pe=True improves text rendering accuracy by ~5-8%. For extremely long text (over 50 characters), use use_pe=False with a detailed manual prompt to avoid PE-induced hallucinations.

2. Practical Scenario 1: Commercial Poster Design

2.1 Promotional Poster

Requirement: Generate an e-commerce promotional poster with sale information and product names.

A promotional poster for a summer sale event, with the text 
"SUMMER SALE 50% OFF" in large bold red characters at the top center, 
a vibrant beach scene background with palm trees and waves, 
bright yellow and orange color scheme, commercial photography style, 
high resolution, 1024x1024

Key Points:

Use quotation marks to wrap text content that needs precise rendering
Specify text position ("top center"), color ("red"), and size ("large bold")
Describe the background scene to complement the text content

2.2 Brand Event Poster

Requirement: Tech launch event poster with bilingual English-Chinese text.

A technology launch event poster, minimalist dark blue background, 
with the text "ERNIE-IMAGE" in large white sans-serif font 
at the top, subtitle "AI Image Generation" below it in smaller 
characters, a subtle gradient glow effect, professional design style, 
centered composition, high quality

Tips:

When mixing languages, explicitly specify the content and style for each language
Use font descriptors like "sans-serif", "serif", "bold"
"Centered composition" ensures text is properly centered

3. Practical Scenario 2: Infographics

3.1 Data Visualization Infographic

Requirement: Generate an infographic showing AI development trends.

An information infographic about AI trends in 2026, clean modern 
design with blue and white color scheme, featuring three sections: 
top section with the title "AI 2026 TRENDS" in bold text, middle 
section with bar charts labeled "LLM" "Image Gen" "Robotics", 
bottom section with the text "Data Source: Industry Report 2026", 
flat design style, professional layout, 1024x1024

3.2 Multi-Step Process Guide

Requirement: Generate an airport security check process infographic.

An information design poster showing airport security check process, 
with the title "Security Check Process" at the top center in bold 
black text, English subtitle "STEP-BY-STEP GUIDE" below in smaller text, 
four pictographic icons arranged horizontally from left to right 
showing: (1) document check, (2) X-ray scanning, (3) metal detector, 
(4) boarding, clean white background, instructional design style, 
1024x1024

Tip: ERNIE-Image excels at structured infographic generation, handling multiple text labels and icon layouts simultaneously. This is one of the core advantages of its DiT architecture.

4. Practical Scenario 3: Multilingual Layout

4.1 Japanese-English Magazine Cover

Requirement: Generate a fashion magazine cover with Japanese-English text.

A fashion magazine cover page, featuring a fashion model in a 
modern outfit against a city skyline background, with the magazine 
title "VOGUE" in elegant serif font at the top center, Japanese 
subtitle "ファッション" on the right side, tagline "Fashion Forward" 
on the left side, professional magazine layout, high-end photography 
style, 1024x1024

4.2 Korean Product Label

Requirement: Generate a Korean product packaging label.

A product packaging label design, white background, with the Korean 
text "프리미엄 커피" in elegant serif font at the top, English 
text "PREMIUM COFFEE" below it, a coffee bean illustration in 
the center, gold and brown color scheme, premium product design 
style, clean and minimal layout, 1024x1024

5. Advanced Tips and Pitfall Guide

5.1 Five Golden Rules for Text Rendering

Wrap text in quotes: "Hello World" is more accurate than Hello World
Specify position and style explicitly: Don't just write "has text", write "top center in bold red text"
Control text length: Keep text under 30 characters (English) or 15 Chinese characters per generation
Use standard font descriptors: "sans-serif", "serif", "handwritten", "gothic"
Resolution choice: 1024×1024 is optimal for text rendering

5.2 Common Failure Cases and Solutions

Problem	Cause	Solution
Blurry text	Too few inference steps	Use 50-step standard mode, not Turbo
Misspelling	Text too long	Split long text into shorter segments
Wrong position	Position not specified	Use "top center", "bottom left", etc.
Inconsistent fonts	Font style not specified	Use "same font throughout" or "consistent typography"
PE hallucination	Long text + PE	use_pe=False + detailed manual prompt

5.3 ERNIE-Image Turbo vs Standard Mode

Dimension	Turbo (8 steps)	Standard (50 steps)
Speed	~6x faster	Baseline
Text Clarity	Good	Excellent
Text Accuracy	~92%	~97%
Recommended Use	Quick iteration / drafts	Final output

Workflow Tip: Use Turbo mode for rapid prompt iteration, then switch to standard mode for final output.

6. Complete Code Examples

Python Diffusers Implementation

import torch
from diffusers import ErnieImagePipeline

# Load model
pipe = ErnieImagePipeline.from_pretrained(
    "Baidu/ERNIE-Image",
    torch_dtype=torch.bfloat16,
).to("cuda")

# Generate poster with text
prompt = """
A movie poster for a sci-fi film, with the title 
"STELLAR QUEST" in large golden bold characters at the center, 
English subtitle "A Journey Beyond the Stars" below it in white 
sans-serif font, a spaceship flying toward a nebula in the background, 
cinematic lighting, dramatic composition, 1024x1024
"""

image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    num_inference_steps=50,
    guidance_scale=4.0,
    use_pe=True
).images[0]

image.save("movie_poster.png")

ComfyUI Workflow Essentials

When using ERNIE-Image for text rendering in ComfyUI:

Model Loading: Use the Ernie Image: Text to Image template
PE Settings: Enable use_pe (recommended)
Sampler: Recommended Euler or DPM++ 2M Karras
Resolution: 1024×1024 or custom aspect ratios (e.g., 1024×1536 for vertical posters)

7. Summary

ERNIE-Image's text rendering capability is its biggest differentiator in the open-source text-to-image landscape:

✅ LongTextBench 0.9733: #1 among open-source models globally
✅ Multilingual Support: Chinese, English, Japanese, Korean with clear, precise characters
✅ Structured Layout Capability: Posters, infographics, magazine covers with complex layouts
✅ Apache 2.0 License: No commercial restrictions
✅ Low Deployment Cost: 8B parameters, runs on 12GB VRAM

If you need accurate, legible text in your generated images, ERNIE-Image is currently the best choice in the open-source ecosystem.

References

Baidu ERNIE-Image Team. (2026). ERNIE-Image: Open Text-to-Image Generation Model. HuggingFace. https://huggingface.co/baidu/ERNIE-Image
Let's Data Science. (2026). ERNIE-Image Delivers Accurate Text-inclusive Image Generation. https://letsdatascience.com/news/ernie-image-delivers-accurate-text-inclusive-image-generatio-d45de927
Baidu AI Studio. (2026). Introducing ERNIE-Image. https://ernie.baidu.com/blog/posts/ernie-image/
Gradually AI. (2026). The 9 Best AI Image Generation Models in 2026. https://www.gradually.ai/en/ai-image-models/
GitHub - baidu/ernie-image. https://github.com/baidu/ernie-image

ERNIE-Image Text Rendering Deep Dive: Posters, Infographics, and Multilingual Layout Practice Guide

Table of Contents

ERNIE-Image Text Rendering Deep Dive: Posters, Infographics, and Multilingual Layout Practice Guide

Why Text Rendering is the "Ultimate Test" for Text-to-Image Models

1. Technical Principles of ERNIE-Image Text Rendering

1.1 DiT Architecture: Why It Can "Understand" Text

1.2 Three Stages of Text Rendering

1.3 Key Parameters Affecting Text Rendering

2. Practical Scenario 1: Commercial Poster Design

2.1 Promotional Poster

2.2 Brand Event Poster

3. Practical Scenario 2: Infographics

3.1 Data Visualization Infographic

3.2 Multi-Step Process Guide

4. Practical Scenario 3: Multilingual Layout

4.1 Japanese-English Magazine Cover

4.2 Korean Product Label

5. Advanced Tips and Pitfall Guide

5.1 Five Golden Rules for Text Rendering

5.2 Common Failure Cases and Solutions

5.3 ERNIE-Image Turbo vs Standard Mode

6. Complete Code Examples

Python Diffusers Implementation

ComfyUI Workflow Essentials

7. Summary

References