SubMaroon/ControlNet-anime-colorize

Experimental ControlNet (Low Quality / Research Prototype)

Experimental model. Low quality. Not intended for production use.
This ControlNet was trained as a research experiment to explore line-based conditioning and colorization behavior in SDXL anime models.

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Model Summary

This repository contains an experimental ControlNet for SDXL, trained on anime-style images.
The model is not stable, shows inconsistent color behavior, and should be treated as a research prototype rather than a finished or polished solution.

The goal of this experiment was to understand:

• How SDXL ControlNet learns colorization from line-based conditioning
• How different conditioning types (Canny vs Lineart) affect color consistency

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Base Model

• Base model: cagliostrolab/animagine-xl-3.0
• Architecture: ControlNet SDXL
• Training framework: 🤗 Diffusers
• Precision: bf16

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Conditioning Type

• Primary conditioning: Lineart / Canny-like edges
• Backgrounds are mostly white
• Line quality varies (mostly clean, some noisy samples)

Important limitation:
Lineart / Canny does not contain color information, which leads to unstable and drifting color predictions.

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Dataset

• Size: ~14,000 image pairs
• Format:
  • Original image (color)
  • Conditioning image (lineart / canny)
  • Prompt (caption)

Known dataset issues

• Some lineart images are noisy or inconsistent
• Images are resized to square resolution (possible cropping artifacts)
• No explicit color supervision
• No palette or region-level color constraints

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Training Configuration

Typical training setup:

resolution: 768
train_batch_size: 2
gradient_accumulation_steps: 2
effective_batch_size: 4
learning_rate: 2e-5
lr_scheduler: cosine
max_train_steps: 6000–8000
mixed_precision: bf16