Character Consistency

The Consistency Challenge

One of the biggest challenges in AI image generation is maintaining character consistency across multiple images. By default, each generation produces a different result, even with the same prompt.

Technique 1: IP-Adapter (Image Prompt Adapter)

IP-Adapter uses an image as a prompt alongside text. It analyzes visual features from a reference image and injects them into the generation process.

Key Parameters

  • weight (0.0-2.0): reference image influence strength
  • IP-Adapter FaceID: specialized for facial identity preservation

Technique 2: Character LoRA

Train a LoRA on 10-20 images of your character using tools like Kohya_ss, then reuse it in any scene.

LoRA Parameters

  • strength_model (0.6-0.9): influence on the diffusion model
  • strength_clip (0.6-0.9): influence on text encoding

Technique 3: Fixed Seed + Detailed Prompt

The simplest method: fix the seed and use a detailed character description prompt. Limited but effective for minor variations.

Technique 4: Reactor (Face Swap)

Post-processing face swap that replaces generated faces with a reference photo.

Recommended Strategy: Combine Techniques

  • Basic: Fixed seed + detailed prompt
  • Good: IP-Adapter with reference image (weight 0.7-0.8)
  • Excellent: IP-Adapter FaceID + Character LoRA + Reactor post-processing

Best Practices

  1. Create a "character sheet" with detailed prompt + reference image + base seed
  2. Test at low resolution first
  3. Keep a reference folder for each character
  4. Document your parameters
  5. Always use the same base model for a given project