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Completed2026ai-data

2D Animation Character LoRA Training Pipeline

A 2D animation LoRA data pipeline packaged as a screenshot-ready, recordable portfolio demo

A data engineering and training pipeline for 2D animation character LoRAs. It covers frame extraction, YOLO/ByteTrack multi-character tracking, ToonOut-style segmentation, identity clustering, DWpose conditioning, captioned datasets, LoRA training configs, and a public mock-safe demo.

Live DemoView GitHubDemo RecordingRead README

Verified Demo Recording 1

Copied by the portfolio quality pass from existing project demo media.

18 screenshots3 demo videos

Media overview

Browse the project screenshots and recorded walkthroughs.

Project links and demo readiness

live

Live Demo

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github

View GitHub

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video

Demo Recording

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documentation

Read README

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Project overview

## Overview This project is an end-to-end data and training pipeline for 2D animation character LoRAs. It turns video footage into reproducible training artifacts: frames, character detections and tracks, foreground masks, identity clusters, pose conditioning data, captions, and LoRA training configs. ## Demo Strategy The public demo does not use private footage or named characters. It uses synthetic 2D characters to simulate production outputs. The first screen shows a pipeline dashboard, stage readiness, character dataset sheet, and result metrics; later sections show transformation visuals, training metrics, evaluation matrix, motion strip, screenshots, and the demo video. ## Architecture The backend is not a hosted service. It is a Python CLI and file-based artifact pipeline. `anime_pipeline/` contains the core modules, `configs/` owns global/stage/project configuration, `portfolio-web/` is the GitHub Pages static showcase, and `tests/` provides CPU-safe smoke tests. The public page only fetches a static manifest JSON; real LoRA training still requires a local GPU, model warehouse, and dataset warehouse. ## Interview Focus The value of this project is not only the AI vocabulary. It shows how to turn a GPU-heavy, media-heavy research pipeline into a public, testable, deployable, and understandable portfolio project.

My role

Solo developer: pipeline architecture, 2D demo packaging, test verification, GitHub Pages showcase, and portfolio media

Problem

2D animation LoRA pipelines are hard to show publicly. Real footage, model weights, GPU dependencies, captioning services, and generated artifacts cannot simply be pushed to a portfolio. Reviewers need a version that is easy to understand, screenshot-ready, and honest about what runs publicly versus what requires a local ML workstation.

Solution

I split the project into a real workstation workflow and a public demo layer. The real path keeps YOLO, ToonOut, DWpose, HDBSCAN, kohya/diffusers, and local model/data warehouses. The public path adds a static mock-safe demo with deterministic synthetic assets: stage dashboard, character dataset sheet, frame-to-sample transformation, training metrics, evaluation matrix, motion strip, screenshots, and a WebM walkthrough.

Current outcome

Delivered a public showcase version: the GitHub Pages demo site presents the product-style review flow, the main portfolio page includes cover art, screenshots, video, GitHub, and README links, and the repo verifies the demo with a generator, CPU-safe pytest smoke suite, static HTTP checks, and Docker/Nginx build.

Highlights

  • First viewport shows the product itself: pipeline readiness, stage metrics, synthetic character sheet, and demo results
  • Mock-safe demo mode runs without API keys, model weights, private footage, or GPU access
  • 2D pipeline focus: YOLO/ByteTrack tracking, ToonOut-style masks, HDBSCAN identity merging, and DWpose conditioning
  • README includes Mermaid architecture, data flow, deployment, module organization, tech stack, and interview walkthrough diagrams
  • Portfolio media package: cover, desktop screenshot, mobile screenshot, results screenshot, and WebM walkthrough

Engineering challenges

  • The public demo needed a clear boundary between static mock-safe presentation and full GPU/model execution
  • The original repo includes many research and batch scripts, so the review path had to be narrowed into stable commands and evidence

Target users

  • Portfolio reviewers and interviewers
  • Technical readers who need a quick view of purpose, stack, and maturity

Technical highlights

  • Detected technical signals: Python, PyTorch, Diffusers, kohya-ss, PEFT (LoRA), Stable Diffusion / SDXL, ControlNet, Ultralytics YOLO, ByteTrack, DWpose, HDBSCAN, OmegaConf, GitHub Pages, Docker
  • README evidence exists and can support a fuller reviewed case study
  • A public GitHub repository is linked for source traceability

Architecture

This case study is generated from the portfolio catalog pipeline using README, Git metadata, package/build configuration, and media signals. The final architecture narrative still needs source-level review. Current detected technology signals include: Python, PyTorch, Diffusers, kohya-ss, PEFT (LoRA), Stable Diffusion / SDXL, ControlNet, Ultralytics YOLO, ByteTrack, DWpose, HDBSCAN, OmegaConf, GitHub Pages, Docker.

Data flow

A public data-flow narrative is not fully reviewed yet. If the project includes data processing, AI pipelines, or backend APIs, the next pass should document input, processing, storage, and UI/output flow end to end.

Project structure

2d-animation-lora-pipeline/
  README.md              # project documentation, when available
  source files           # implementation reviewed by local audit
  package/build config   # detected capability signals

Setup / Run guide

This project does not expose a verified runnable web command yet. Review the README/source tree and add exact install, run, test, and build commands before interview use.
No verified build command was detected. Treat the current portfolio page as a case-study placeholder until build steps are reviewed.

Future improvements

  • Complete the production-quality README, screenshots, and demo recording
  • Add architecture diagrams, data-flow notes, and key technical decisions
  • Verify build/test status and update the portfolio release report

Interview notes

  • State the current maturity and demonstrable scope first
  • Focus on verified stack, source structure, and completed behavior
  • Do not claim unverified deployment, video, or test coverage as finished

Next steps

  • Add more anonymous demo scenarios such as quality review, cluster review, and checkpoint comparison
  • Publish more aggregate metrics from real training runs without exposing private media or model artifacts

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