AI Painting Resources

Awesome AI Painting

Repository: https://github.com/hua1995116/awesome-ai-painting

Introduction: This is a carefully curated list of AI painting resources, containing various AI painting tools, models, tutorials, and community resources, helping users quickly get started and advance in AI painting.

Main Categories

1. AI Painting Platforms

Online Platforms:

Platform	Features	Price	Use Cases
Midjourney	Artistic, active community	$10+/month	Art creation, creative design
Stable Diffusion	Open-source, customizable	Free	Open-source applications, customization needs
DALL-E 3	High quality, easy to use	Pay-per-use	Commercial applications, quick generation
Leonardo AI	Feature-rich, easy to use	Free+Paid	Quick prototyping, multiple styles
Adobe Firefly	Adobe integrated, safe	Pay-per-use	Adobe users, commercial applications

Local Deployment:

Platform	Features	Use Cases
Stable Diffusion WebUI	Comprehensive features, rich plugins	Local use, advanced features
ComfyUI	Node-based workflow	Professional users, complex workflows
InvokeAI	Easy to use, friendly interface	Beginners, quick start
Fooocus	Simplified Stable Diffusion	Quick generation, simple needs
Automatic1111	Most popular WebUI	General scenarios, large community support

Usage Suggestions:

• Art creation: Use Midjourney
• Open-source needs: Use Stable Diffusion
• Commercial applications: Use DALL-E 3 or Adobe Firefly
• Quick start: Use Fooocus or InvokeAI
• Professional needs: Use ComfyUI

2. Model Resources

Base Models:

Model	Features	Use Cases
SD 1.5	Classic, many plugins	General scenarios, plugin applications
SD 2.1	Improved version, high quality	High quality needs
SDXL	High resolution, high quality	High resolution, professional needs
SD 3	Latest, multilingual	Latest technology, multilingual

Fine-Tuned Models:

Model	Features	Use Cases
DreamShaper	Strong artistic style	Art creation
Realistic Vision	Strong realism	Realistic photos

• Anime Pastel Dream: Anime style
• Deliberate: General high quality
• ChilloutMix: Asian portraits
• CounterfeitV: Anime style
• GhostMix: Artistic style
• Rev Animated: Animation style
• Majo Mix: Magic style
• Protogen: General style
• OpenJourney: Midjourney style

Usage Suggestions:

• General scenarios: Use SD 1.5 or SDXL
• Art creation: Use DreamShaper
• Realistic photos: Use Realistic Vision
• Anime style: Use Anime Pastel Dream or CounterfeitV
• Asian portraits: Use ChilloutMix

3. Prompt Engineering

Prompt Structure:

[Subject] [Style] [Details] [Quality Words] [Negative Prompts]

Common Positive Prompts:

Quality Words:

masterpiece, best quality, high quality, ultra-detailed,
highres, 8k, 4k, highly detailed

Style Words:

photorealistic, realistic, anime, illustration,
painting, drawing, sketch, 3d render, digital art

Lighting Words:

cinematic lighting, dramatic lighting, soft lighting,
natural lighting, volumetric lighting, studio lighting

Composition Words:

rule of thirds, golden ratio, wide angle, close up,
portrait, landscape, aerial view, bird's eye view

Common Negative Prompts:

lowres, bad anatomy, bad hands, text, error, missing fingers,
extra digit, fewer digits, cropped, worst quality, low quality,
normal quality, jpeg artifacts, signature, watermark, username,
blurry, ugly, duplicate, morbid, mutilated, out of frame, extra fingers

Prompt Techniques:

1. Weight Control

   • Use (word) to increase weight
   • Use [word] to decrease weight
   • Use (word:1.5) to specify weight

2. Combination Techniques

   • Use | to separate options
   • Use BREAK to separate sections
   • Use AND to combine elements

3. Iterative Optimization

   • Start with simple prompts
   • Gradually add details
   • Test different combinations
   • Record effective prompts

4. ControlNet

ControlNet Types:

Type	Function	Use Cases
Canny	Edge detection	Outline control
Depth	Depth map	Spatial control
OpenPose	Pose control	Character poses
Segmentation	Segmentation control	Regional control
Normal	Normal map	Surface control
Lineart	Line art control	Line art coloring
Shuffle	Color control	Color transfer
IP-Adapter	Image reference	Style transfer

Usage Scenarios:

1. Canny

   • Preserve outlines
   • Change style
   • Line art coloring

2. Depth

   • Control space
   • Maintain perspective
   • Depth editing

3. OpenPose

   • Pose control
   • Action generation
   • Character arrangement

4. Segmentation

   • Regional control
   • Local editing
   • Scene construction

Usage Suggestions:

• Line art coloring: Use Canny or Lineart
• Character poses: Use OpenPose
• Spatial control: Use Depth
• Style transfer: Use IP-Adapter

5. LoRA

LoRA Types:

Type	Function	Examples
Character LoRA	Specific characters	Celebrities, OC
Style LoRA	Specific styles	Artists, styles
Concept LoRA	Specific concepts	Objects, scenes
Action LoRA	Specific actions	Poses, actions

Common LoRAs:

Character LoRA:

• Celebrity characters
• OC characters
• Anime characters

Style LoRA:

• Artist styles
• Painting styles
• Photography styles

Concept LoRA:

• Clothing
• Scenes
• Objects

Usage Techniques:

1. Weight Adjustment

   • Start from 0.5
   • Gradually adjust
   • Test effects

2. Combined Use

   • Combine multiple LoRAs
   • Pay attention to weight distribution
   • Avoid conflicts

3. Train LoRA

   • Prepare dataset
   • Choose appropriate parameters
   • Test and optimize

6. Workflows

Simple Workflow:

1. Write prompts
2. Select model
3. Generate images
4. Adjust parameters
5. Iterate and optimize

Advanced Workflow:

1. Prepare reference images
2. Use ControlNet for control
3. Apply LoRA
4. Generate initial images
5. Use img2img for optimization
6. Local repainting
7. Final adjustments

Professional Workflow:

1. Requirement analysis
2. Collect references
3. Design workflow
4. Prepare models and LoRAs
5. Configure ControlNet
6. Batch generation
7. Filter and optimize
8. Post-processing
9. Delivery

7. Tutorial Resources

Beginner Tutorials:

1. Basic Concepts

   • What is AI painting
   • Main platforms introduction
   • Basic terminology

2. Quick Start

   • Installation and deployment
   • Basic operations
   • Prompt writing

3. Advanced Techniques

   • Prompt engineering
   • ControlNet usage
   • LoRA application

Advanced Tutorials:

1. Model Training

   • Data preparation
   • Training methods
   • Optimization techniques

2. Workflow Design

   • Node workflows
   • Automation
   • Batch processing

3. Advanced Applications

   • Commercial applications
   • Creative projects
   • Art creation

Recommended Resources:

• YouTube Channels:

  • AI painting tutorials
  • Stable Diffusion tutorials
  • Midjourney tutorials

• Online Courses:

  • Coursera AI painting courses
  • Udemy AI painting courses
  • Bilibili AI painting tutorials

• Community Resources:

  • Civitai
  • Hugging Face
  • Reddit communities

8. Community Resources

Model Sharing:

Platform	Features	Content
Civitai	Largest model community	Models, LoRAs, Embeddings
Hugging Face	Open-source models	Models, datasets
LiblibAI	Chinese community	Models, tutorials, resources

Tutorial Sharing:

• YouTube
• Bilibili
• Medium
• Zhihu

Community Discussions:

• Reddit
• Discord
• Telegram
• WeChat groups

Inspiration Sources:

• Pinterest
• ArtStation
• Behance
• Dribbble

Integration with This Project

1. Tool Selection

This Project's Tool Guides:

• AI tool comparisons
• Usage suggestions
• Best practices

Integration with AI Painting:

• Understand painting platforms
• Choose appropriate tools
• Optimize workflows
• Improve creation efficiency

2. Prompt Engineering

This Project's Prompt Library:

• Prompt techniques
• Scenario applications
• Best practices

Integration with AI Painting:

• Apply prompt techniques
• Optimize painting prompts
• Improve generation quality
• Explore creative possibilities

3. Creative Applications

This Project's Creative Applications:

• Creative scenarios
• Practical cases
• Best practices

Integration with AI Painting:

• Explore creative scenarios
• Apply practical cases
• Learn best practices
• Innovate application methods

Learning Path Recommendations

Beginner Path

Week 1: Understand Basics

• Understand AI painting concepts
• Browse main platforms
• Try online tools
• Record usage experience

Weeks 2-3: Practical Application

• Choose one platform
• Learn prompts
• Generate images
• Optimize effects

Week 4: Advanced Learning

• Learn ControlNet
• Try LoRA
• Optimize workflows
• Share works

Advanced Path

Weeks 1-2: Deep Learning

• Local deployment
• Learn advanced features
• Research models
• Explore workflows

Weeks 3-4: Professional Application

• Train LoRA
• Design workflows
• Commercial applications
• Creative projects

Weeks 5-6: Innovation and Sharing

• Innovative applications
• Share experiences
• Contribute to community
• Continuous learning

Frequently Asked Questions

Q1: How to choose an AI painting platform?

A:

1. Clarify needs
2. Evaluate costs
3. Consider technical barriers
4. Test effects

Q2: How to choose between online platforms and local deployment?

A:

• Online platforms: Quick start, no configuration needed
• Local deployment: Powerful features, privacy and security

Q3: How to improve generation quality?

A:

1. Optimize prompts
2. Choose appropriate models
3. Use ControlNet
4. Apply LoRA

Q4: How to train my own LoRA?

A:

1. Prepare dataset
2. Choose training tools
3. Configure parameters
4. Test and optimize

Summary

AI painting resources are important references for learning and applying AI painting:

Core Resources:

• ✅ AI painting platforms
• ✅ Model resources
• ✅ Prompt engineering
• ✅ ControlNet
• ✅ LoRA
• ✅ Workflows
• ✅ Tutorial resources
• ✅ Community resources

Best Practices:

1. Start simple
2. Progress gradually
3. Practice is primary
4. Continuous learning
5. Participate in community

Remember:

• Technology is a tool
• Creativity is core
• Practice is key
• Community is wealth

Next Steps

• AI Application Resources - Learn more AI applications
• Modern Generative AI Resources - Learn about generative AI
• Prompt Library - Learn prompt techniques