Solving the AI Character Consistency Problem: A Complete Workflow with LoRA + ControlNet + ComfyUI

This project combines a custom-trained Personal LoRA model, ControlNet pose control, and the ComfyUI visual workflow to achieve consistent image generation of a single character across multiple scenarios and poses. It provides a practical solution to the character coherence problem in AI painting, suitable for scenarios such as comic creation, virtual idols, and brand IPs.

In the field of AI painting, models like Stable Diffusion can generate high-quality images, but character consistency remains a tricky issue. When creators want the same character to maintain coherence across different scenarios, actions, and expressions, traditional methods often lead to a sense of disharmony, severely limiting the practicality of AI in scenarios requiring character coherence such as comics and virtual idols.

The project addresses the character consistency problem using a three-layer tech stack: 1. Personal LoRA Model: Custom training allows the model to remember the character's facial features and overall image; 2. ControlNet Pose Control: Precisely controls the character's posture, actions, and composition; 3. ComfyUI Workflow: Integrates the above capabilities into a reusable automated process.

LoRA: Lightweight Character Memory

LoRA is a parameter-efficient fine-tuning technique that trains only a small number of additional parameters (about 1% of the original model). By training a Personal LoRA using reference images of the target character, it stably reproduces key visual elements such as the character's facial features, hairstyle, and clothing style.

ControlNet: Precise Pose Control

It controls diffusion model generation through additional conditional inputs, using multiple variants: Canny edge detection (ensures consistent composition), OpenPose (precise pose transfer), and Depth (controls spatial relationships and occlusion), enabling pose specification while maintaining the character's identity.

ComfyUI: Visual Workflow Orchestration

The node-based design breaks down the generation process into key steps: reference image input, pose image input, ControlNet processing, LoRA loading, text prompts (scene/lighting/style), diffusion sampling, and SwinIR 4x super-resolution upscaling.

This workflow can achieve: 1. Multi-pose consistency: The character maintains the same image across actions like standing, running, and sitting; 2. Multi-scenario adaptability: The character is placed in different backgrounds and lighting conditions; 3. Expression change control: Adjusts facial expressions while maintaining the character's identity; 4. High-resolution output: Obtains 4K-level images through the super-resolution module.

Limitations of the current solution: 1. Training cost: LoRA training requires a certain number of reference images and computing resources; 2. Angle limitations: Consistency in extreme angles (pure side view, top-down view) remains challenging; 3. Style binding: LoRA may be bound to a specific artistic style, and switching styles affects consistency. In the future, new technologies like InstantID can be integrated to lower the generation threshold.

The Advanced-Image-Generation-Techniques project provides a practical technical solution to the character consistency problem in AI painting. Through the combination of LoRA, ControlNet, and ComfyUI, creators can make AI characters appear in various scenarios as needed, which has important practical value for fields such as comic creation, virtual image design, and advertising production.