PanelPainter - Manga Coloring
詳細
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モデル説明
PanelPainter-Project is an open-source initiative to automate black-and-white manga coloring using fine-tuned LoRAs. This project is dedicated to training models that maintain clean line art while achieving smooth, anime-style color fills.
Update – V3.0 Major Release
PanelPainter V3 is a major milestone. Unlike previous "helper" versions, V3 is a standalone coloring lora trained on Qwen Image Edit 2511.
Training Data: Trained on 903 hand-picked panels using Natural Language Captions.
Methodology: Combines the "real line art" training method discovered in V2 with a significantly larger dataset to generalize across different manga styles.
Trigger Word:
Color this panelpainter
Workflows & Resources
For the best results, use the dedicated workflows on RunningHub.
PanelPainter V3 (Qwen 2511)
RunningHub: V3 BF16 Workflow (Fast, Balanced)
RunningHub: V3 AIO Workflow (All-In-One)
PanelPainter V2.5 (Qwen 2511)
RunningHub: V2.5 AIO App (With VL Prompting) – Includes Vision Language prompting for better adherence.
Usage Guide
V3 has been tested on various styles including Chainsaw Man, Frieren, Komi Can't Communicate, and Oshi no Ko.
Recommended Generation Settings
LoRA: PanelPainter V3 (Weight: 1.0)
Helper LoRA: 4-Step Lighting (Weight: 1.0)
Steps: 4
Sampler: Euler
Scheduler: Simple
CFG: 1.0
Prompting Use the trigger word in your prompt:
Color this panelpainter
You can also add specific lighting or atmospheric tags:
Color this panelpainter, sunset lighting, warm tones
Project History & Development
Version 3.0 (Current)
Base: Qwen 2511
Summary: Scaled up to 903 images using the "real line art" training method. Solves the generalization issues of V2 while maintaining color quality.
Version 2.0 (Stable)
Base: Qwen 2509 (Compatible with 2511)
Summary: The breakthrough version. Switched from synthetic data to real line art (150 images). Proved that small, high-quality datasets outperform massive synthetic ones.
Version 1.0 (Legacy)
Summary: Trained on 7,000 synthetic grayscale images. Failed to handle real ink imperfections. Kept for archival purposes.
Credits & Acknowledgements
Training: Trained on Musubi Tuner (Thanks to kohya-ss).
Dataset Contributors: Special thanks to @Rox_Jr & @lucifer_brine04 for their help with dataset curation.