Phokus

A local-first desktop media library for browsing, filtering, and curating image and video folders.

Features

Library

• Add and remove media folders; background indexing with live progress
• Live file tracking — a filesystem watcher keeps the library in sync as files are added, changed, or removed; renames and moves are handled in place, preserving thumbnails and embeddings
• Browse all media or filter by folder, type (image/video), favorites, or star rating
• Sort by date added, date taken (EXIF), name, size, rating, or duration
• Grid density controls (compact / comfortable / detail)
• Desktop notifications, batched per folder, with per-folder mute and a global pause

Search & discovery

• Filename search, semantic search (/s query), and tag search (/t tag)
• Similar image search — find visually similar media by image or a selected region
• Explore view — visual cluster map and tag cloud for browsing by theme
• Timeline view — media grouped chronologically by capture date (EXIF)
• Duplicate finder — three-phase exact-duplicate scan (size → sample hash → full hash) with live progress and bulk delete
• Explore, Timeline, and Duplicates are folder-scopable directly from their headers — no need to bounce through the sidebar

Viewing & curation

• Lightbox with keyboard navigation, zoom, inline tag editing, and rating controls
• Custom video player — immersive edge-to-edge playback with scrubbing, volume, playback speed, loop, and fullscreen; auto-hiding controls and full keyboard support
• AI tagging via WD tagger (ONNX, CPU/DirectML) with confidence threshold, batch size, and per-folder queue targeting

Maintenance

• Database compaction and orphaned-thumbnail cleanup from Settings, with live size/reclaimable stats
• Per-folder pausing of background work (thumbnails, metadata, embeddings, tagging)

Supported formats

Images	Videos
jpg, jpeg, png, gif, bmp	mp4, mov, m4v
tiff, tif, webp, avif	webm

Installation

Phokus is a Windows desktop app. Download the latest installer from the Releases page and run it.

Requirements: Windows 10 or 11. The installer will fetch the WebView2 runtime automatically if it isn't already present (it ships with Windows 11).

A note on the unsigned build

Phokus 0.1.0 installers are not code-signed. Code-signing certificates are an ongoing expense that's hard to justify for an unfunded open-source project, so signing is on the roadmap rather than in place today.

In practice this means Windows SmartScreen will show a blue "Windows protected your PC" warning the first time you run the installer. To proceed: click More info → Run anyway. If a release publishes a SHA-256 checksum, you can verify the download against it first.

First run

On first launch Phokus downloads a few tools and models — all one-time, and all processed on your machine:

• FFmpeg (~tens of MB) for video thumbnails and metadata — downloaded in the background; the guided first-run tour shows progress.
• WD tagger model (~1.3 GB) — optional, only if you enable AI tagging.
• CLIP embedding model (~580 MB) — downloaded automatically the first time visual embeddings run (powers semantic search and similar images).

Privacy & local-first

Phokus is local-first by design. Your media and everything derived from it — thumbnails, embeddings, tags, ratings — never leave your machine. There is no account, no telemetry, and no cloud sync.

The only network activity is:

• One-time tool/model downloads on first use (FFmpeg, the CLIP and WD models, and the ONNX runtime), fetched from their official sources (FFmpeg builds, Hugging Face, NuGet). These pull tools to your machine — none of your images are uploaded.
• Update checks against the GitHub Releases page, so the app can offer new versions. This can be ignored if you never update.

Everything Phokus stores lives in its app-data directory (gallery.db, thumbnails/, models/, settings/). Removing that directory resets the app completely; your original media folders are never modified.

Stack

• Tauri 2 + Rust backend
• React 19 + TypeScript + Zustand
• SQLite + sqlite-vec (vector search) + HNSW index
• ONNX Runtime (ort) for AI tagging
• Candle (Rust ML) for CLIP visual embeddings
• mozjpeg for fast scaled JPEG decoding
• FFmpeg sidecar for video thumbnails and metadata
• Vite + Tailwind CSS v4

Development

Prerequisites: Node.js 20+, pnpm, Rust toolchain, Tauri system prerequisites for Windows.

pnpm install

# Run with hot-reload (frontend + Rust)
pnpm dev:app

# Frontend only
pnpm dev:vite

# Production build
pnpm build:app

# Type-check the frontend
pnpm build:vite

How it works

1. Add a folder from the sidebar — the Rust indexer walks it recursively.
2. Supported files are written to SQLite with metadata (path, dimensions, media type, EXIF capture date, etc.).
3. Background workers process the queue as a strict priority pipeline — thumbnails first, then video metadata, then visual embeddings, then AI tags — so each stage runs at full speed instead of competing for CPU, disk, and the database.
4. Progress events stream back to the UI while the gallery updates incrementally.
5. A filesystem watcher picks up later changes (new files, edits, deletions, renames) and keeps the index current without rescans.
6. Embeddings power semantic search and the similar-images feature via an HNSW index.