OpenComic AI Training is an ethical dataset generation pipeline that procedurally synthesizes paired clean and degraded comic image datasets inside Krita. It powers the OpenComic AI models for artifact removal, halftone descreening, and 2×/3×/4× upscaling — all trained exclusively on procedurally generated, copyright-free images.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/ollm/opencomic-ai-training/llms.txt
Use this file to discover all available pages before exploring further.
Quickstart
Install dependencies, build the project, and generate your first dataset in minutes.
Requirements
Everything you need: Linux, Krita 5.3+, the kra-remote plugin, and Node.js.
Pipeline Overview
Understand how Krita, Node.js, and YAML options work together to produce training data.
Configuration Reference
Full schema for options files — seeds, drawings, degradations, and output paths.
Model Reference
Browse all pre-trained ESRGAN, Lite, and Compact model weights in NCNN format.
CLI Reference
Every flag for the generate command and the dataset validation utility.
How it works
Install and build
Clone the repository, run
npm install, then npm run prepare to compile TypeScript and bundle the output.Choose or write an options file
Pick one of the pre-built YAML presets in
options/ — such as opencomic-ai-upscale-2x.yml — or create your own by composing common building blocks.Run the generator
Point the generator at your options file and your Krita AppImage. It launches Krita, draws procedural comic art, applies synthetic degradations, and saves paired
clean/ and degraded/ images.Dataset tasks
Artifact Removal
Remove JPEG, WebP, AVIF, and JXL compression artifacts from scanned or re-encoded comic images.
Descreen
Eliminate halftone dot patterns from printed comics scanned at various resolutions and angles.
Upscale
Super-resolve comic pages at 2×, 3×, or 4× scale while preserving fine linework detail.