Skip to content

Dev Project README

Jump to bottom
github-actions[bot] edited this page Aug 19, 2026 · 12 revisions

Project README

Auto-generated from README.md. Do not edit this page manually.

🌟 LrGeniusAI

A smart Lightroom Classic plugin for AI-powered tagging, describing, semantic search, and develop edits.

Lua Rust Website Downloads

ko-fi

📖 About the Project

LrGeniusAI brings the power of modern Large Language Models (LLMs) directly into Adobe Lightroom Classic. It analyzes your photos, automatically generates accurate tags and detailed descriptions, creates AI-guided Lightroom develop edit recipes, and lets you rediscover your library with semantic free-text search using natural language.

Whether you prefer running local models to ensure maximum privacy or want to leverage powerful cloud APIs, LrGeniusAI seamlessly adapts to your photography workflow.

✨ Core Features

  • 🤖 AI-Powered Tagging & Describing: Uses advanced LLMs to accurately recognize image content, generate metadata, and provide detailed descriptions of your photos.
  • 🎛️ AI Lightroom Edit (Develop) (beta): Generates a Lightroom edit recipe per photo and applies it in Develop, either to the photo itself or to a new virtual copy. No language model is involved: the recipe is interpolated from the edits you saved yourself (see Style Training), so at least five training examples are required. Per-photo review is on by default.
  • 🔍 Semantic Free-Text Search (Advanced Search): Find images naturally through descriptive queries (e.g., "Red sports car parked in front of a garage" or "Sunset over the mountains"). LrGeniusAI automatically creates a relevance-sorted Collection in Lightroom based on your prompt.
  • 📸 Image Culling (beta): Group similar photos into bursts or near-duplicate stacks, automatically pick the strongest frames, and create Lightroom collections for picks, alternates, reject candidates, and optional duplicates.
  • 👥 People & Faces: Detect and cluster faces, assign names to persons, browse person collections, and find similar faces across your catalog.
  • 🦋 Species Identification (on-device): Identify animals, plants and fungi down to the species with BioCLIP 2, running entirely on your machine — no LLM, so no invented binomials. Results land in searchable metadata fields (kingdom → species) and, optionally, as a keyword branch under Species. An uncertain call stops at the rank it is sure of rather than guessing. Enable it in Analyze & Index Photos after downloading the model in the Plug-in Manager.
  • 🔎 Find Similar Images: Find near-duplicate or visually similar photos for any selected image using perceptual hash or semantic CLIP comparison.
  • 🏠 Built-In Local AI (no external app): The backend runs vision models itself, using whichever engine suits the platform — MLX on macOS (Apple silicon, via a small Metal helper process) and llama.cpp in-process on Windows (GGUF, Vulkan). Pick a model in the Plug-in Manager, click Download, and analysis runs entirely on your machine. Models you already have in LM Studio (or the Hugging Face cache) are picked up without a second copy.
  • ☁️ Local & Cloud Models: Also supports local AI models via Ollama and LM Studio, as well as integration with cloud providers like ChatGPT/OpenAI and Google Gemini. (Vertex AI — removed, see below.)
  • 🎨 Customizable Prompts & Temperature Control: System prompts for the AI can be added, edited, and deleted directly within the Lightroom Plug-In Manager. Use the temperature slider to control whether the AI should be highly creative or strictly consistent.
  • 📝 Photo Context (Contextual Info): Provide manual hints to the AI before analysis (e.g., names of people or specific background details) that aren't immediately obvious from the image itself. This can be done via a popup dialog or directly in Lightroom's metadata panel.
  • 🗂️ Keyword Management: Interactive synonym deduplication and automatic de-clutter during indexing to keep your keyword catalog clean.
  • 🎓 Style Training: Save your own Lightroom edits as AI training examples to teach the AI your personal editing style.
  • 🗄️ Custom Backend & Database: The plugin utilizes a high-performance local server (geniusai-server), written in Rust for low memory overhead. Existing metadata from your Lightroom catalog can easily be imported prior to the first AI analysis.

🚀 Installation & Getting Started

  1. Download the latest release from the GitHub Releases page.
  2. Extract the ZIP file and add the plugin via the Plug-in Manager in Lightroom Classic.
  3. Backend Server Setup (First Launch):
    • The backend starts automatically from Lightroom.
    • Bypassing Security Warnings: Because the installers are currently not code-signed, you will see warnings from Windows SmartScreen or macOS Gatekeeper.
      • Windows: Click More info -> Run anyway.
      • macOS: Right-click the .pkg -> Open -> Open anyway.
    • Optional troubleshooting: if you want to start it manually, run lrgenius-server/lrgenius-server.cmd on Windows or lrgenius-server/lrgenius-server on macOS.
  4. Pick an AI model. Either enter a cloud API key in the Plug-in Manager, or stay fully local: the Local AI Model section offers a curated list of vision models (Gemma 4, Ministral 3, Qwen3-VL, Qwen2.5-VL) — choose one and click Download. The section shows the engine your platform ships: MLX on macOS, llama.cpp on Windows. See Local AI Models.
  5. Select photos in the library and choose one of the AI actions from Library -> Plug-in Extras:
    • Analyze & Index Photos... — AI tagging, descriptions, search index, faces, and optional species identification
    • AI Edit Photos... (beta) — generate and apply Lightroom develop edits learned from your own edits
    • Advanced Search... — semantic free-text search
    • Cull Similar Photos... (beta) — burst grouping and auto-ranking
    • People... — face clusters and named person collections
    • Find Similar Images... — find near-duplicates or visually similar photos
    • Deduplicate Keyword Synonyms... — clean up synonym sprawl in your catalog
  6. For AI Edit, first teach it your style: edit a few photos by hand and run Save Edits as AI Training Examples.... AI Edit builds every recipe from those examples and needs at least five of them — it calls no LLM. Keep Review each proposed edit before applying it enabled while you validate the results.

For comprehensive details, model setup guides, and tips, please visit lrgenius.com/help.

⚠️ Google Vertex AI has been removed (August 2026). The Vertex AI controls are gone from the Lightroom plugin: no project ID / location settings, no Vertex embeddings during Analyze & Index, and no Semantic (Vertex AI) search option. Existing Vertex embeddings in your database stay untouched but are no longer created or queried. The old setup instructions are kept for reference on the Google Vertex AI Login Wiki Page.

⚖️ License

The LrGeniusAI core, plugin, and backend are released under the GNU Affero General Public License v3 (AGPL-3.0).

This project is built on the belief that AI tooling for creatives should remain open, transparent, and community-driven. See the LICENSE file for the full license text.

🛠️ Tech Stack

  • Frontend / Lightroom Plugin: Lua (Lightroom SDK)
  • Backend / Server: geniusai-server — Rust (axum) for deterministic memory behavior
  • AI & Embedding: SigLIP2 via ONNX Runtime (ort crate)
  • Identity & Faces: YuNet + FaceNet (ONNX)
  • Species: BioCLIP 2 (ONNX) with a pruned TreeOfLife taxonomy head
  • Local Inference: an MLX Swift helper (lrgenius-mlx) on macOS; llama.cpp compiled into the backend (Vulkan / CPU) on Windows
  • Database: LanceDB
  • Supported Interfaces: built-in MLX (macOS), built-in llama.cpp (Windows), Google Gemini, ChatGPT/OpenAI, Ollama, LM-Studio (Vertex AI — removed)

🛠️ Development

For more detailed information on how to contribute, please see our CONTRIBUTING.md.

🤝 Credits

Developed with a passion for photography and IT by:

  • Bastian Machek (LrGenius / Fokuspunk) – Creator & Lead Developer
  • Community – Special thanks to all contributors and testers for your valuable input and support.
  • Various AI agents - For the great support in developing this project.

This project leverages many incredible open-source libraries and models, including YuNet, FaceNet, BioCLIP 2 / TreeOfLife-200M, OpenCLIP, ONNX Runtime, LanceDB, llama.cpp, and MLX / mlx-swift-lm. See the Credits wiki page for the full list and licences.

A huge thank you to the open-source community and the developers of the underlying AI frameworks that make this integration possible!

Clone this wiki locally