With a LongTextBench score of 0.9733, ERNIE-Image has become the open-source model with the strongest text rendering capability. Deep dive into its text rendering principles, prompt techniques, and practical tutorials.
# 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 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 ``` 1. **PE Enhancer Stage**: Expands brief text instructions (e.g., "poster title 'Happy New Year'") into detailed layout descriptions 2. **DiT Text Understanding Stage**: Identifies text content, font style, and positioning requirements 3. **Pixel-Level Text Generation Stage**: Gradually generates clear text pixels during the diffusion denoising process ### 1.3 Key Parameters Affecting Text Rendering ```python # 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 1. **Wrap text in quotes**: `"Hello World"` is more accurate than `Hello World` 2. **Specify position and style explicitly**: Don't just write "has text", write "top center in bold red text" 3. **Control text length**: Keep text under 30 characters (English) or 15 Chinese characters per generation 4. **Use standard font descriptors**: "sans-serif", "serif", "handwritten", "gothic" 5. **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 `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 ```python 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: 1. **Model Loading**: Use the `Ernie Image: Text to Image` template 2. **PE Settings**: Enable `use_pe` (recommended) 3. **Sampler**: Recommended `Euler` or `DPM++ 2M Karras` 4. **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 1. Baidu ERNIE-Image Team. (2026). *ERNIE-Image: Open Text-to-Image Generation Model*. HuggingFace. https://huggingface.co/baidu/ERNIE-Image 2. 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 3. Baidu AI Studio. (2026). *Introducing ERNIE-Image*. https://ernie.baidu.com/blog/posts/ernie-image/ 4. Gradually AI. (2026). *The 9 Best AI Image Generation Models in 2026*. https://www.gradually.ai/en/ai-image-models/ 5. GitHub - baidu/ernie-image. https://github.com/baidu/ernie-image