ERNIE-Image

ERNIE-Image Brand Visual Design Complete Guide: From Brand Consistency to Multilingual Marketing Materials

Jun 12, 2026

ERNIE-Image Brand Visual Design Complete Guide: From Brand Consistency to Multilingual Marketing Materials

Summary: ERNIE-Image's precise text rendering and multilingual support make it an ideal AI tool for brand visual design. This article provides a complete brand design workflow from scratch: brand color management, bilingual Chinese-English poster generation, product catalog layout, social media adaptation, and LoRA training for brand style consistency. Includes complete prompt templates and real-world application cases.

1. Why ERNIE-Image for Brand Design?

Traditional AI image generation models face three major challenges in brand design scenarios:

  1. Inaccurate text rendering: Brand names, slogans, and promotional text often appear garbled
  2. Weak multilingual support: CJK languages (Chinese, Japanese) are nearly impossible to render correctly
  3. Imprecise brand colors: Specified brand colors (HEX/Pantone) deviate significantly

ERNIE-Image directly solves these three core problems:

  • ✅ High-precision text rendering: Chinese, English, and Japanese all rendered accurately, with complex layout support
  • ✅ Precise brand color control: Community tests show ERNIE-Image performs best in brand color matching
  • ✅ Structured layout capability: Grid and column layouts for posters, infographics, and product catalogs

2. Brand Color Management

2.1 Specifying Brand Colors in Prompts

ERNIE-Image supports specifying brand colors through prompts. Best practice is to use HEX codes or Pantone numbers:

Prompt Example:
"A brand promotional poster, main color brand blue #1E88E5,
background white #FFFFFF,
title text dark gray #424242,
brand logo in upper right corner,
modern minimalist style overall, 16:9 aspect ratio"

2.2 Brand Color Workflow

  1. Define brand palette: Prepare HEX codes for primary, secondary, and neutral colors
  2. Create base templates: Generate 3-5 base poster templates with ERNIE-Image
  3. Iterate and optimize: Refine color descriptions in prompts based on results
  4. Batch production: Fix prompt templates, swap products and copy

2.3 Brand Color Prompt Tips

Tip Description Example
Use HEX codes Most precise color specification #1E88E5
Describe color relationships Primary, secondary, background Primary #1E88E5, accent #FFC107
Specify color regions Which elements use which colors Title #1E88E5, body #424242, button #FF5722
Style anchoring Reference well-known brands Style similar to Apple event posters

3. Bilingual Chinese-English Poster Generation

3.1 Bilingual Poster Core Prompt Structure

Prompt Template:
"A professional promotional poster,
[brand element description],
Main title: '[Chinese title]' (large bold font, [color]),
Subtitle: '[English title]' (medium font, [color]),
Body text: '[Chinese description]' / '[English description]',
[visual element description],
Overall style: [style description],
Aspect ratio: 16:9, Resolution: 1024×1024"

3.2 Real Case: Tech Company Product Launch Poster

Prompt:

"A tech product launch poster,
deep blue gradient background #0D47A1 to #1565C0,
Main title: 'Next-Gen AI Vision Engine' (white large bold font, upper center),
Subtitle: '全新 AI 视觉引擎' (light gray medium font),
Body text: 'Faster · Smarter · More Accurate' / '更快 · 更准 · 更智能',
product rendering at bottom,
brand logo in lower right corner,
modern tech style, 16:9 aspect ratio"

3.3 Multilingual Poster Best Practices

Practice Description
Chinese primary, English secondary Large font for Chinese, medium for English
Avoid long text Keep under 15 Chinese characters per line
Consistent font style Describe "sans-serif" or "modern font"
Clear text positioning Specify "upper left", "centered", "bottom center"
Color contrast Ensure clear contrast between text and background

4. Product Catalogs and Infographic Design

4.1 Structured Infographics

ERNIE-Image excels at structured layout generation. Product catalogs and infographics are typical use cases:

Prompt Example:
"A product information graphic,
top title: '2026 Product Collection',
divided into three sections:
Left section title: 'Flagship Series', with 3 product thumbnails and names,
Center section title: 'Standard Series', with 3 product thumbnails and names,
Right section title: 'Entry Series', with 3 product thumbnails and names,
each product has price and brief description below,
brand colors #1E88E5 and #424242,
white background, professional layout, 16:9 ratio"

4.2 Product Catalog Design Tips

  1. Grid layout: Specify "3×3 grid" or "four-panel" clear structure
  2. Product consistency: Maintain consistent product style across catalog
  3. Text hierarchy: Title > Product name > Description > Price font sizes
  4. White space: Describe "generous white space" to avoid clutter

5. Social Media Content Adaptation

5.1 Platform Sizes and Styles

Platform Recommended Size Style Characteristics Prompt Key Points
Instagram 1080×1080 (1:1) Strong visual impact Vibrant colors, moderate white space
Xiaohongshu 1080×1440 (3:4) Lifestyle, approachable People scenes, text descriptions
LinkedIn 1200×627 (1.91:1) Professional, clean Data charts, professional tones
Twitter/X 1200×675 (16:9) High info density Prominent titles, clean layout
Facebook 1200×627 (1.91:1) Social interaction People + text, emotional expression

5.2 Social Media Prompt Templates

Instagram Post:

"An Instagram post image,
[product/scene description],
brand text at bottom: '[brand name]' (brand color #XXXXXX),
overall style [style description],
1:1 ratio, 1080×1080 pixels"

Xiaohongshu Cover:

"A Xiaohongshu post cover image,
[lifestyle scene description],
top title text: '[Chinese title]' (eye-catching color),
bottom subtitle: '[brief description]',
lifestyle, approachable style,
3:4 ratio, 1080×1440 pixels"

6. Achieving Brand Style Consistency with LoRA

6.1 Training Brand LoRA

ERNIE-Image supports LoRA fine-tuning for custom brand styles:

  1. Collect training data: 15-30 brand style reference images
  2. Define trigger word: e.g., brand_style_v1
  3. Training parameters:
    • Learning rate: 1e-4
    • Training steps: 1000-2000
    • Batch size: 1-4
  4. Validate: Test LoRA with trigger word + various scene descriptions

6.2 Cloud LoRA Training

fal.ai provides ERNIE-Image LoRA cloud training API:

# fal.ai ERNIE-Image LoRA training example
import fal_client

result = fal_client.submit(

"fal-ai/ernie-image-lora-training",

arguments={

"base_model": "baidu/ERNIE-Image",

"training_images": ["url1", "url2", "url3"],

"trigger_word": "brand_style_v1",

"num_steps": 1500,

"learning_rate": 1e-4,

}

)

6.3 LoRA Inference

After training, load LoRA in ComfyUI or Diffusers:

# Load LoRA in ComfyUI
# Use LoadLoRA node, set trigger word to brand_style_v1
# Add trigger word to prompt: brand_style_v1, [scene description]

7. Complete Brand Visual Workflow

7.1 From Zero to Brand Visual System

Step Action Output
1 Define brand palette and style Brand guidelines doc
2 Train brand LoRA (optional) Brand LoRA file
3 Generate base poster templates 3-5 templates
4 Create multilingual variants CN+EN versions
5 Adapt platform sizes Social media assets
6 Batch produce series content Monthly content library

7.2 Batch Production Prompt Management

Use prompt templates for efficient batch production:

# Prompt template management
BRAND_PROMPTS = {
    "poster": "A brand promotional poster, {brand_colors}, main title: '{title}', subtitle: '{subtitle}', {visual_elements}, style {style}, {aspect_ratio}",
    "social_ig": "An Instagram post, {brand_colors}, {product_description}, brand text at bottom: '{brand_name}', 1:1 ratio",
    "infographic": "A product information graphic, {brand_colors}, title: '{title}', divided into {sections} sections, each with {elements}, white background professional layout",
}

Batch generation


for product in products:

prompt = BRAND_PROMPTS["poster"].format(

brand_colors="#1E88E5, #424242",

title=product["title_cn"],

subtitle=product["title_en"],

visual_elements=product["visual_desc"],

style="modern minimalist",

aspect_ratio="16:9"

)

generate_image(prompt)

8. Common Issues and Solutions

Q1: Generated brand colors are inaccurate?

Solution:

  • Emphasize brand colors multiple times in prompts
  • Use "overall color scheme #XXXXXX" instead of single element colors
  • Fine-tune colors in Photoshop/Figma after generation

Q2: Chinese text still has garbled characters?

Solution:

  • Shorten text length (per line < 10 characters)
  • Use common words, avoid rare characters
  • Enable Prompt Enhancer (PE) mode
  • Lower guidance_scale to 3.0-4.0

Q3: Multilingual mixed layout looks bad?

Solution:

  • Arrange Chinese and English on separate lines, not mixed in same line
  • Large font for Chinese, medium-small font for English
  • Describe clear hierarchy: "Chinese main title, English subtitle"

9. Conclusion

ERNIE-Image provides unprecedented AI tools for brand visual design:

  • ✅ Precise brand color control: HEX/Pantone color specification
  • ✅ Multilingual text rendering: Accurate display in Chinese, English, and Japanese
  • ✅ Structured layout generation: Posters, infographics, product catalogs
  • ✅ LoRA brand style consistency: Train custom brand models
  • ✅ Apache 2.0 commercial license: No legal risk

From basic poster design to complex brand visual systems, ERNIE-Image handles it all. Combined with ComfyUI workflows and LoRA fine-tuning, you can build a complete brand visual production pipeline.

This article is based on ERNIE-Image official documentation, HuggingFace community discussions, and practical application testing. All prompt templates are ready to use.

ERNIE-Image Team