Forgery

Build, remix, and re-style characters with AI — extract equipment from images, store them in a library, and apply equipment + pose/expression to generate fresh looks.

Forge characters, not chaos. forgery helps you extract equipments (props, clothing, accessories) from character images, save them as reusable items in a DB, and then generate new character looks by applying selected equipments + pose/expression controls using AI image generation.

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Why Forgery?

• Modular Assets: Turn screenshots, cosplay photos, or movie stills into modular assets.
• Rapid Prototyping: Quickly prototype many character looks by mixing and matching equipments.
• Fine-Grained Control: Add expression & pose control to generated looks for richer results.
• Local-First & Private: All data, including your image library and generated results, is stored locally on your machine.
• Flexible AI Integration: Seamlessly switch between Google Gemini and OpenAI models for different stages of the pipeline.

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Highlights

• 🔍 Equipment extraction — detect & crop equipment regions, normalize and store metadata.
• 🧩 Equipment library — structured DB with visual preview, masks, tags, anchors, and compatibility hints.
• 🖼️ Apply equipments — generate a new image of a character wearing selected equipments.
• 🎭 Pose & expression controls — specify expression and pose priors to steer generation.
• 🔌 Model-agnostic — plug in your favorite segmentation or image-generation models (OpenAI, Gemini supported out-of-the-box).
• ♻️ Open-source — clear data model & contribution guidelines.

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How It Works

1. Extract: Upload a character image. Forgery analyzes it to identify and extract equipment (outfits, weapons, accessories).
2. Refine: Review the extracted assets, refine their masks, and save them to your library.
3. Create Character: Upload a base portrait for your character.
4. Fitting Room: Combine your character with selected equipment, choose a pose, and set an expression.
5. Generate: Watch as Forgery generates a new Look for your character.

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Core Concepts

• Equipment — any wearable or attachable asset extracted from images (hat, glasses, sword, jacket). Stored with mask, anchor points, metadata.
• Character — a user-created profile representing a base person/portrait used as the generation canvas.
• Look — a generated image (or variant) that applies a set of equipments to a character, optionally altered by pose/expression.
• Extraction — computer vision process that detects equipment regions and outputs masks, crops, and suggested anchors.
• Application/Generation — image generation pipeline that composes character + selected equipments + conditioning (pose/expression) into a final render.

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Getting Started

Requirements

• Bun (v1.0.0 or later)
• OR Docker
• API Keys for OpenAI or Google Gemini (for AI generation)

Local Quickstart

git clone https://github.com/yourname/forgery.git
cd forgery
bun install
bun run prisma migrate dev  # Initialize local SQLite DB
bun run dev
# server available at http://localhost:3000

Docker Quickstart

docker compose up -d
# server available at http://localhost:3000

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Configuration

Forgery requires API keys for AI generation services (Google Gemini and/or OpenAI).

1. Launch the application and go to the Settings page (accessed via the sidebar).
2. Enter your Google API Key and/or OpenAI API Key.
3. Configure your preferred models for text and image generation.
4. (Optional) Customize the "Extraction Flow" to choose specific models for different steps of the asset analysis and generation process.

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Architecture Overview

A modular pipeline built on a modern stack:

1. Frontend (React + Shadcn UI + Tailwind)
   • Upload UI, equipment library browser, look composer (Fitting Room).
2. API Server (Hono on Bun)
   • REST API for extraction, equipment CRUD, character CRUD, generation.
3. Extraction Service
   • Orchestrates AI models (OpenAI/Gemini) to analyze images and generate masks.
4. Asset Store
   • Local file system (data/files) for storing optimized WebP images.
5. Generation Service
   • Composes prompts and conditioning for image generation models.
6. DB (SQLite + Prisma)
   • Stores metadata, relationships, settings, and generation history.

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Contributing

Love forks, fixes, and wild experiments.

• Run tests: bun test
• Lint code: bun run lint
• Create clear PRs with reproducible steps and small commits.

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License

MIT