LongCat-Image: 6B Model With Chinese Text

What is LongCat-Image?

LongCat-Image is a groundbreaking 6-billion parameter image generation and editing model developed by Meituan's LongCat team. Despite its relatively small size compared to competing models, LongCat-Image punches far above its weight class with capabilities that rival or exceed much larger models.

The defining characteristic of LongCat-Image is its exceptional bilingual support (Chinese and English) with particular excellence in rendering complex Chinese text - a capability that most competing models struggle with or fail at entirely. Additionally, the model features advanced image editing capabilities allowing single-prompt edits to modify multiple scene elements simultaneously.

Why LongCat-Image Stands Out

Compared to Traditional 6B Models:

• Professional quality images rivaling 20-30B models
• Chinese text rendering that matches native understanding
• Advanced editing with spatial control
• Runs on consumer cards with quantised or offloaded checkpoints (upstream publishes no universal VRAM minimum, so measure on your own machine)
• One hosted image here costs 80 credits, quoted before you submit

Compared to commercial platforms:

• The LongCat-Image weights are Apache-2.0, so a local install carries no per-image or subscription fee; you pay in GPU time instead
• Complete privacy (local generation)
• No per-image billing locally; the hosted generator on this site charges 80 credits per image
• Full control over parameters
• Professional results without API waitlists

Key Capabilities

1. Photorealistic Text-to-Image Generation

LongCat-Image generates high-fidelity images from text descriptions with exceptional detail and consistency.

Strengths:

• Accurate composition and framing
• Rich texture and material details
• Natural color gradients and lighting
• Realistic human figures with proper anatomy
• Consistent multi-person scenes

Typical Generation Quality:

• Resolution: Up to 1024x1024
• Detail level: Professional commercial quality
• Style adaptability: Photorealistic to illustrated
• Consistency: High across multiple generations

2. Chinese Text Rendering Excellence

LongCat-Image uniquely excels at rendering text in Chinese characters with remarkable accuracy.

Text Rendering Capabilities:

• Simple Chinese characters: 99%+ accuracy
• Complex traditional Chinese: 95%+ accuracy
• Mixed Chinese/English text: Highly accurate
• Calligraphy-style text: Excellent reproduction
• Multiple lines of text: Proper layout and spacing

Real-World Impact: Most other image generators completely fail at Chinese text, either rendering gibberish or refusing to attempt it. LongCat-Image understands Chinese semantically and renders it properly.

3. Poster and Design Capability

LongCat-Image features professional-grade poster generation rivaling specialized design tools like Ovis Image.

Design Features:

• Multi-layer composition
• Typography integration
• Color psychology application
• Brand-aligned styling
• Product showcase layouts

Typical Use Cases:

• Marketing posters (e-commerce, events, promotions)
• Book covers with embedded text
• Album artwork with titles
• Product packaging designs
• Social media graphics with overlays

4. Advanced Image Editing

The breakthrough feature: AI-powered image editing with spatial control through reference frames.

Editing Capabilities:

• Object removal (erase unwanted elements)
• Object insertion (add new elements to scenes)
• Color/texture modification (change materials, colors, textures)
• Multi-element simultaneous editing (combine operations)
• Spatial control with "red frame" masking technique

Editing Accuracy:

• Precise spatial control using reference masks
• Natural integration of edits into surrounding scene
• Consistent lighting and shadows on edited elements
• Seamless blend between original and edited regions

System Requirements

Minimum Hardware

CPU:

• Intel Core i7-8700K or equivalent
• AMD Ryzen 7 2700X or equivalent
• 8+ CPU cores recommended

RAM:

• 16GB system memory minimum
• 32GB recommended for comfortable operation

GPU:

• NVIDIA RTX 4070 Ti (12GB VRAM minimum for comfortable use)
• RTX 4080 (16GB VRAM, recommended)
• RTX 4090 (24GB VRAM, ideal)
• RTX A6000 (48GB VRAM, professional grade)

Storage:

• 100GB free space on NVMe SSD
• Model files: ~15GB (models + dependencies)
• Generated images cache: Variable

OS:

• Windows 10/11 (Recommended)
• Linux (Ubuntu 20.04+)
• macOS (Apple Silicon or Intel, untested officially)

Performance by GPU

Installation Guide

Step 1: Install ComfyUI

If not already installed, set up ComfyUI first.

Windows:

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt
python main.py

Linux/macOS:

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt
python main.py

Step 2: Install LongCat-Image Custom Node

Navigate to ComfyUI's custom_nodes directory:

cd ComfyUI/custom_nodes
git clone https://github.com/sooxt98/comfyui_longcat_image.git
cd comfyui_longcat_image
pip install -r requirements.txt

Or using ComfyUI Manager:

1. Open ComfyUI interface
2. Click "Manager" button
3. Search "LongCat-Image"
4. Click "Install"
5. Restart ComfyUI

Step 3: Download Model Files

Download these files from Hugging Face:

Main Model:

• longcat-image-dev (6B parameters, ~12GB)
  • Format: Diffusers format
  • Location: Download from https://huggingface.co/meituan-longcat/LongCat-Image-Dev

Installation Directory:

ComfyUI/models/diffusion_models/longcat-image/

Optional Components:

• Text encoder (usually included)
• VAE (included in model)

Step 4: Verify Installation

1. Launch ComfyUI
2. In the node menu, search for "LongCat"
3. You should see:
   • LongCat Image Loader
   • LongCat Generate
   • LongCat Edit
4. If nodes appear, installation successful

Basic Workflow: Text-to-Image Generation

Creating Your First Image

Step 1: Load the Model

1. Right-click in the empty canvas area
2. Select "Add Node" → "LongCat" → "LongCat Image Loader"
3. Select model: "longcat-image-dev" from dropdown
4. Connect the output to subsequent nodes

Step 2: Set Generation Parameters

Add a "LongCat Generate" node:

• Resolution: 1024x1024 (square) or 1280x720 (landscape)
• Sampling Steps: 50 (standard), 30 (fast), 100 (maximum quality)
• Guidance Scale: 4.5 (default, controls prompt adherence)
• Seed: -1 (random), or specific number for reproducibility

Step 3: Write Your Prompt

In the "Prompt" text box:

For Photorealistic Images:

A professional product photograph of luxury leather handbag,
shot on white background with studio lighting,
sharp focus, shallow depth of field, product photography style,
4K quality, magazine cover style, premium aesthetic

For Chinese Text Integration:

Modern restaurant interior design, wooden tables and warm lighting,
with large sign reading "品茶馆" (tea house) in red calligraphy,
customers enjoying tea, professional interior photography,
warm color palette, luxury ambiance, 4K quality

For Poster Design:

Movie poster for sci-fi thriller, bold typography with film title,
dramatic lighting with deep shadows, cosmic background,
lead actor's face prominent, professional movie poster design,
cinema-quality color grading, IMAX poster aesthetic

Key Prompt Elements:

• Subject: Clear description of main content
• Style: Photorealistic, illustrated, artistic, etc.
• Technical: Resolution, lighting, camera angle
• Quality: 4K, magazine-quality, professional, etc.
• Medium: Photography, painting, poster, etc.

Step 4: Generate

1. Click "Queue" button
2. Watch the progress indicator
3. Typical generation: 2-4 minutes on RTX 4080
4. Image appears in preview once complete

Step 5: Download

1. Right-click on generated image
2. Select "Save Image" or "Send to Output"
3. Image saves to ComfyUI output folder

Prompt Engineering for LongCat-Image

Framework for Highly Effective Prompts

Structure prompts using this proven framework:

[MAIN SUBJECT and ACTION],
in [DETAILED ENVIRONMENT with SPECIFIC DETAILS],
[CAMERA ANGLE and PERSPECTIVE],
[LIGHTING SETUP with COLOR DESCRIPTION],
[STYLE and MEDIUM],
[TECHNICAL QUALITY SPECIFICATIONS]

Real-World Examples

Example 1: Product Photography

High-end luxury watch, rose gold case with diamond complications,
placed on black marble surface with soft reflection,
professional product photography lighting with specular highlights,
close-up macro shot showing intricate dial details,
luxury watch catalog style, pristine condition,
museum quality photography, 8K ultra-high resolution

Example 2: Chinese Text Marketing

Modern Chinese restaurant storefront at night,
red neon sign reading "老北京烤鸭" (Peking Duck) in bold characters,
warm golden light spilling onto street from windows,
street-level evening photography, cinematic urban scene,
professional commercial photography, 4K quality

Example 3: Architectural Design

Modern minimalist villa with large glass windows,
white concrete and natural wood materials,
mountain landscape visible through floor-to-ceiling windows,
afternoon warm sunlight creating dramatic shadows,
architectural digest photography style,
professional architectural rendering, 4K resolution

Example 4: Poster Design

Movie poster for romantic drama, titled "远方的你" in elegant script,
lead couple in soft focus background, rain-soaked urban street,
neon signs and city lights creating romantic atmosphere,
professional cinema poster design with text integration,
premium movie poster style, theatrical release quality

Advanced Technique: Image Editing with LongCat-Image

The Red Frame Masking Technique

LongCat-Image's most powerful feature is spatial editing using reference masks - dubbed the "red frame" technique.

How It Works:

1. You create or upload a reference image
2. Draw red frames around elements you want to modify
3. Write an edit prompt describing the changes
4. Model edits only within red-framed regions
5. Rest of image remains unchanged with perfect blending

Why This Technique is Powerful:

Traditional image editing requires separate edit commands for each modification:

• Remove object → Generate
• Change color → Generate
• Add element → Generate
• (3 separate generations required)

With red frames:

• "Remove the person, change wall color to yellow, add a vase"
• (Single generation, all changes applied)

Setting Up the Editing Workflow

Step 1: Prepare Your Workflow

Add these nodes:

• Image Input (your reference image)
• Mask Editor (for drawing red frames)
• LongCat Edit node
• Image output

Step 2: Create the Reference Mask

1. Click on the "Mask Editor" node
2. An image editing interface opens
3. Select the "Red Pen" tool
4. Draw boxes around areas you want to edit
5. Save/confirm the mask

Drawing Red Frames:

• Draw rectangular or irregular shapes
• Enclose the element you want to modify
• Don't need to be perfect - interior of frame matters
• Can draw multiple overlapping frames for multiple edits

Step 3: Write Your Edit Prompt

Structure edit prompts clearly:

Format:

[REMOVE/CHANGE/ADD] the [ELEMENT] [HOW TO CHANGE IT]
[AND] [ADDITIONAL EDITS]
Do NOT change [WHAT TO KEEP UNCHANGED]

Example 1: Single Edit

Remove the person standing in the room. Keep the background, furniture, and lighting exactly the same.

Example 2: Multiple Edits

Remove the laptop from the desk, change the wall color to soft blue, add a framed painting above the desk.
Keep the furniture and floor unchanged.

Example 3: Complex Edit

In the red frame region: replace this wine bottle with an expensive champagne bottle.
Change the bottle label to read "Cristal 1995" in elegant gold text.
Keep the lighting and background completely unchanged.

Step 4: Generate the Edit

1. Configure LongCat Edit node:
   • Image: Your source image
   • Mask: Your red frame mask
   • Prompt: Your edit description
   • Steps: 50 (standard), 75 (high quality)
   • Strength: 1.0 (full edit) to 0.5 (subtle)
2. Click "Queue" to generate
3. Result shows edited image

Editing Tips and Tricks

Achieving Photorealistic Edits

Include These in Every Edit Prompt:

• Exact lighting description ("warm afternoon sunlight")
• Shadow placement ("shadows on left side")
• Material properties ("glossy surface" or "matte finish")
• Texture details ("fabric weave visible")
• Color matching ("same tone as surrounding area")

Example with Details:

Remove the old brown chair from the red frame area.
Replace with a modern black leather office chair.
Chair should have the same warm afternoon lighting with shadows on the right side.
Maintain exact perspective and floor positioning.

Example Without Details (Bad):

Replace the chair with a different chair

The detailed version generates photorealistic results; the vague version often fails.

Precise Object Placement

When adding objects:

1. Describe position: "left edge," "center," "upper right corner"
2. Describe scale: "should be half the height of the person"
3. Describe perspective: "same viewing angle as camera"
4. Describe alignment: "aligned with floor, casting shadows"

Color Consistency

When changing colors:

1. Include RGB values if possible: "change to RGB(255,182,193) - light pink"
2. Reference surroundings: "match the color of the wooden floor"
3. Specify finish: "matte finish" or "glossy with reflections"
4. Describe lighting effect: "warm sunlight making it appear golden"

Multi-Element Editing

When modifying multiple elements, group by region:

In the left red frame: remove the old lamp, add a modern desk lamp.
In the center red frame: change wall color from beige to forest green.
In the right red frame: add two framed landscape photographs.
Maintain consistent professional studio lighting throughout.

Specialized Use Cases

E-Commerce Product Images

Typical Workflow:

1. Generate product on white background
2. Use editing to change product color variations
3. Add shadows and reflections for realism
4. Generate lifestyle shots (product in use)

Generation Example:

Professional product photo of athletic sneakers, white background,
studio lighting with subtle shadows, side-angle view,
sharp focus on shoe details, catalog quality,
color: sleek black with white accents, rubber sole visible,
professional shoe photography style, 4K resolution

Editing Example:

In the red frame, change the shoe color to red instead of black.
Keep all lighting, shadows, and background exactly the same.
Sneaker should look new and pristine with same studio lighting.

Chinese Text Design

When Text Rendering Works Best:

• Larger text (avoid tiny characters)
• Traditional calligraphy fonts (not modern sans-serif)
• Single-line text (multiple lines may have spacing issues)
• Common characters (rare characters may fail)

Effective Chinese Text Prompt:

Luxury tea package design with elegant calligraphy reading "龙井茶" (Longjing Tea),
premium packaging aesthetic with gold accents,
traditional Chinese design motifs, professional packaging photography,
museum-quality presentation, 4K resolution

Interior Design Mockups

Generate Initial Space:

Modern minimalist living room, white walls, wooden flooring,
large window with natural light, empty space ready for furniture,
architectural interior photography, bright and airy aesthetic,
4K professional rendering, daylight illumination

Edit to Add Furniture:

In the red frame area, add a modern gray sectional sofa.
Sofa should be positioned against the left wall.
Add matching throw pillows in muted earth tones.
Maintain all existing lighting and perspective.

Poster and Marketing Graphics

Generate Base Design:

Movie poster for action thriller, dramatic composition,
dark atmospheric background with city lights,
bold typography space for title, professional cinema poster,
premium theatrical release quality, 4K resolution

Edit to Add Text:

In the red frame region, add title text "RETURN OF THE GUARDIAN" in bold sans-serif,
text should be white with slight shadow effect for depth,
positioned centrally, matching cinematic poster style,
text should be prominent but not overwhelming the image

Comparing LongCat-Image to Competitors

LongCat-Image vs Nano Banana

Verdict: LongCat-Image excels for Chinese content and editing; Nano Banana better for general English prompts.

LongCat-Image vs Qwen-Image

Verdict: Tie for image generation; LongCat-Image wins on editing capabilities.

Troubleshooting and Common Issues

Issue 1: Chinese Text Renders as Gibberish

Problem: Chinese characters appear corrupted or incorrect.

Solutions:

1. Simplify the characters (use simplified vs traditional)
2. Include more context in prompt: "elegant calligraphy reading '明月'"
3. Use proper Unicode characters (verify text encoding)
4. Try fewer characters (single word/phrase works better)
5. Specify font style explicitly: "bold sans-serif text" or "elegant calligraphy"

Issue 2: Edit Doesn't Apply Correctly

Problem: Red frame region doesn't get edited as expected.

Solutions:

1. Ensure red mask is properly created (fully enclosed)
2. Draw larger red frame (avoid too-small regions)
3. Use clear, specific edit prompt (include lighting details)
4. Lower strength parameter to 0.7-0.8 (try gradual changes)
5. Increase sampling steps to 75 for better quality

Issue 3: Generated Images Have Artifacts

Problem: Visible distortions, weird objects, or errors.

Solutions:

1. Increase sampling steps to 75-100
2. Improve prompt specificity (be more detailed)
3. Reduce guidance scale to 3.5-4.0 (try lower values)
4. Regenerate (sometimes variance produces better results)
5. Clear cache: Restart ComfyUI between generations

Issue 4: Out of Memory Errors

Problem: CUDA out of memory crash.

Solutions:

1. Reduce resolution to 768x768 (from 1024x1024)
2. Enable attention optimization in node settings
3. Use lower guidance scale (3.5 instead of 4.5)
4. Close other applications using GPU
5. Restart ComfyUI completely

Issue 5: Slow Generation Speed

Problem: Taking 5-10+ minutes per image.

Solutions:

1. Reduce sampling steps from 50 to 30 (still high quality)
2. Lower resolution to 768x768 or 512x512 for testing
3. Check GPU isn't thermal throttling (monitor temps)
4. Update to latest ComfyUI and node versions
5. Upgrade GPU if regularly generating at high volume

Best Practices for Professional Results

1. Prompt Structure and Clarity

Always Include:

• What is being created (subject, object, scene)
• Where it is (environment, context, setting)
• How it looks (style, appearance, mood)
• How it's captured (camera angle, perspective)
• Quality indicators (4K, professional, magazine-quality)

Example of Complete Prompt:

Professional photograph of luxury leather briefcase positioned on oak desk,
in a modern executive office with large windows showing city skyline,
side-angle close-up showing craftsmanship and details,
warm afternoon sunlight creating soft shadows,
high-end business photography style, premium aesthetic,
4K quality, sharp focus, shallow depth of field

2. Chinese Text Optimization

For Best Text Rendering:

• Use characters that appear commonly in images (place names, brands)
• Shorter text (2-4 characters works best)
• Specify font style (calligraphy, modern, elegant)
• Include context (e.g., "wine bottle label")
• Add color guidance (e.g., "red characters on gold background")

3. Iterative Refinement

Workflow for Excellence:

1. Generation 1: Basic concept (10-20 generations)
   • Identify winner
   • Note what works/doesn't
2. Generation 2: Refine top picks (5-10 generations)
   • Improve prompt based on results
   • Add specific details
3. Generation 3: Finalize (1-3 generations)
   • Highest steps (75-100)
   • Highest quality settings
   • Select final version

Time Investment: 30-60 minutes total, 1-2 final quality images

4. Version Comparison

Always generate multiple versions:

1. Same prompt, different seeds (3-5 versions)
2. Slightly different prompts (2-3 variations)
3. Different parameters (try different guidance scales)

This ensures you capture the best result rather than settling for "good enough."

Cloud Usage Option: RunningHub

If your local GPU is insufficient or you prefer cloud generation:

RunningHub for LongCat-Image:

• Pre-installed models and workflows
• Pay-per-generation (cheap, usually $0.01-0.05 per image)
• No local setup required
• Faster generation on high-end GPUs
• Web-based interface

Link: https://www.runninghub.ai/

Related Resources

1. LongCat-Image: Next-Level Editing - Advanced image editing techniques with LongCat-Image
2. LongCat-Image Edit vs Nano Banana - Detailed comparison of image generation models
3. LongCat-Image Technical Report Deep Dive - Understand the architecture and training methodology
4. LongCat Image AI Review: Red Line Trick - Master the red line editing technique