Update README.md
Browse files# ⚠️ 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:
```bash
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