--- title: PanelPainter-Project canonical_url: "https://www.modelscope.ai/models/kokoboy/PanelPainter-Project" md_url: "https://www.modelscope.ai/models/kokoboy/PanelPainter-Project.md" repository: kokoboy/PanelPainter-Project last_updated: 2025-12-31 license: apache-2.0 pipeline_tag: image-colorization tasks: - image-colorization library_name: - lora - diffusers - safetensors - pytorch frameworks: - Pytorch downloads: 166 stars: 2 tags: - lora - manga - coloring - anime - qwen - dataset - diffusers - image-to-image --- # PanelPainter-Project > PanelPainter-Project - An open-source model by kokoboy on ModelScope. Qwen-based LoRA for automating the coloring of black-and-white manga panels. Includes V1, V2, and V3 training iterations. kokoboy/PanelPainter-Project is a image-colorization model on ModelScope. licensed under apache-2.0. - **Repository**: kokoboy/PanelPainter-Project - **License**: apache-2.0 - **Tasks**: image-colorization - **Tags**: lora, manga, coloring, anime, qwen, dataset, diffusers, image-to-image - **Downloads**: 166 - **Stars**: 2 - **Last updated**: 2025-12-31 Source: https://www.modelscope.ai/models/kokoboy/PanelPainter-Project ---

PanelPainter Logo

# PanelPainter-Project **PanelPainter-Project** is an open-source initiative to automate black-and-white manga coloring using fine-tuned LoRAs. This project is dedicated to training LoRAs to automate the coloring of black-and-white manga panels. I am releasing all the files here, including datasets, logs, and experimental versions, so others can see exactly how it was trained. ## Showcase Here are some examples comparing the original panel, the base Qwen Image Edit model, and the result with the PanelPainter V3 LoRA. > [!NOTE] > **Showcase Generation Settings:** > * **LoRAs:** PanelPainter V3 (Weight: 1.0) + 4-Step Lighting (Weight: 1.0) > * **Steps:** 4 > * **Sampler:** Euler > * **Scheduler:** Simple > * **Seed:** 1000 > * **CFG:** 1.0

Chainsaw Man Showcase
Chainsaw Man

Frieren Showcase
Frieren

Komi Showcase
Komi Can't Communicate

Oshi no Ko Showcase
Oshi no Ko

## Project Structure This repository contains everything used to create the models: ### 1. LoRA Models (`/loras`) This directory contains the model weights for all iterations of the project: > [!TIP] > **Trigger Word:** `Color this panelpainter` (Applicable for both V2 and V3) * **V3 (Latest Release):** `PanelPainter_v3_Qwen2511.safetensors` * **Base:** Qwen Image Edit 2511 * **Note:** The latest model trained on the expanded 903-image dataset. * **V2 (Stable):** `PanelPainter_v2_Qwen2509.safetensors` * **Base:** Qwen Image Edit 2509 (Compatible with 2511). * **Note:** Standard release (High quality, low variety). * **V1 (Legacy):** `PanelPainter_v1_Legacy.safetensors` * **Base:** Qwen Image Edit 2509 * **Note:** Archived experimental version (synthetic data). ### 2. Training Logs (`/logs`) **Content:** Tensorboard logs and charts from my training runs. You can check these to see how the loss converged and how the model learned over time for each version. ### 3. Workflows (`/workflows`) **Content:** ComfyUI workflow JSON files to help you get started with PanelPainter. ### 4. Training Dataset The datasets used for this project are hosted separately: * **PanelPainter-Dataset** > [!NOTE] > **Coming Soon:** The V3 dataset was a good learning step for captioning, but it was randomly picked without any streamlined curation roughly 50% doujin and 50% mainstream colored manga. We're refining it further. Expect handpicked panels, better captions, and reduced doujin content. Release coming once quality standards are met. --- ## Version History & Development Log ### Version 3.0 (Current Release) * **Status:** Released. * **Base Architecture:** Qwen 2511. * **Strategy:** Scaling Up High-Quality Data. * **Dataset:** Expanded to 903 images. Recreated from scratch, comprising 50% doujin and 50% SFW panels. * **Summary:** This version combines the correct "real line art" training method discovered in V2 with a significantly larger dataset. This improves the model's ability to generalize across different manga styles while maintaining the color quality of V2. ### Version 2.0 * **Status:** Released / Stable. * **Base Model:** Trained on Qwen Image Edit 2509, also it works on Qwen 2511 as well. * **The Breakthrough:** After V1 failed, this version switched to training on real line art instead of synthetic grayscale. * **Dataset:** A tiny, hyper-curated set of 150 images (70% Doujin / 30% SFW). * **Outcome:** Despite the small size, it proved that high-quality real line art outperforms massive synthetic datasets. It produces good colors but lacks variety due to the small sample size. ### Version 1.0 * **Status:** Archived / Deprecated. * **Base Model:** Qwen Image Edit 2509. * **The Mistake:** Trained on 7,000 images generated by simply desaturating colored pages (synthetic grayscale). * **Outcome:** The model learned to color "perfect gray" inputs but failed on real, imperfect ink lines. * **Lesson:** Quantity does not matter if the data distribution doesn't match real usage. --- ## Training Configuration (V3) **Hardware:** Trained on an A40 GPU on Runpod. Below is the exact accelerate command used to train the V3 model on Musubi Tuner: ```bash accelerate launch --num_cpu_threads_per_process 1 --mixed_precision bf16 \ /workspace/musubi-tuner/src/musubi_tuner/qwen_image_train_network.py \ --dataset_config dataset_edit.toml \ --dit /workspace/Training_Models_Qwen/Qwen_Image_Edit_2511_BF16.safetensors \ --vae /workspace/Training_Models_Qwen/qwen_train_vae.safetensors \ --text_encoder /workspace/Training_Models_Qwen/qwen_2.5_vl_7b_bf16.safetensors \ --model_version edit-2511 \ --network_module networks.lora_qwen_image \ --output_dir /workspace/output_panelpainter \ --output_name panelpainter_v3_part1 \ --mixed_precision bf16 \ --max_data_loader_n_workers 0 \ --learning_rate 3e-4 \ --network_dim 128 \ --network_alpha 128 \ --optimizer_type adafactor \ --optimizer_args "scale_parameter=False" "relative_step=False" "warmup_init=False" "weight_decay=0.01" \ --lr_scheduler cosine \ --lr_warmup_steps 150 \ --timestep_sampling qinglong_qwen \ --discrete_flow_shift 2.2 \ --max_train_epochs 8 \ --save_every_n_epochs 1 \ --save_state \ --gradient_checkpointing \ --gradient_checkpointing_cpu_offload \ --gradient_accumulation_steps 4 \ --blocks_to_swap 20 \ --sdpa ``` **Dataset Settings:** Use a resolution of **1328x1328** with bucketing enabled to handle varying aspect ratios (no upscaling). The training ran with a batch size of 1 and enabled `qwen_image_edit_no_resize_control` to preserve the original dimensions of the control images during processing. ## License * **Project:** Apache 2.0 * **Dataset:** Hosted separately, contains copyrighted manga panels. * **Copyright:** Original art belongs to the respective creators and publishers. ## Acknowledgements Trained on Musubi Tuner. Thanks to kohya-ss. **Dataset Contributors:** Thanks to @Rox_Jr & @lucifer_brine04 for their help with the dataset. ## External Links * **Public Model Page:** [Civitai: PanelPainter](https://civitai.com/models/2103847/panelpainter-manga-coloring)