Update README.md

README.md

@@ -4,4 +4,72 @@ datasets:
 - SubMaroon/danbooru-lineart
 base_model:
 - cagliostrolab/animagine-xl-3.0
----
+---
+
+# 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.
+
+---
+
+## 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**
+
+---
+
+## Base Model
+
+- **Base model:** `cagliostrolab/animagine-xl-3.0`
+- **Architecture:** ControlNet SDXL
+- **Training framework:** 🤗 Diffusers
+- **Precision:** `bf16`
+
+---
+
+## 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.
+
+---
+
+## 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
+
+---
+
+## 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