Your own self-hosted and lightweight booru.
Designed for organizing your local media collection, including AI-generated images (Stable Diffusion, ComfyUI, A1111/Forge). Works fully offline and with no telemetry. Supports ONNX models for local auto-tagging of your collection (WD14, JoyTag...).

• Tag-based gallery with folder tree, favorites, saved searches, related-image suggestions
• A watcher that picks up new, moved, and deleted files in the gallery within a few seconds; and support for multi-file browser upload
• Stable Diffusion metadata extraction from A1111/Forge and ComfyUI (prompts, models, seeds, full workflow)
• Optional auto-tagging with local ONNX models (WD14, JoyTag, or any compatible model), CPU or GPU (CUDA)
• Search with wildcards, OR, exclusions, plus filters on folder, date, size, dimensions, category, generation recipe...
• Batch operations: bulk delete, bulk move, bulk auto-tag, delete-all-search-results, tag-all-search-results / tag-selected (add or remove tags in one go)
• Create multiple galleries with their own filesystem and switch between them
• Per-gallery export and import: full backup (.db, .json, .zip with images), or a light format (.json or .zip with images) with only images<->tags map. See docs/MIGRATING.md for migrating from another supported booru-style app.
• REST API for third-party integrations (e.g. adding images to the gallery from an external app)
• Daily maintenance schedule: scheduled sync, auto-tag, recompute counts, merge general tags, and vacuum databases...
• Fully offline, no inbound or outbound internet connection.

Edit the volume paths in docker/docker-compose.yml, then docker compose up -d. The app is available at http://localhost:8080.

Tags separated by spaces means AND. Everything else stacks on top:

Autocomplete is combination-aware: the count next to each suggestion is for the full query, and suggestions that would return zero results are hidden.

Sort options: newest, file size, random shuffle. Any query can be saved from the sidebar for one-click access later.

You can use autotaggers to tag your images automatically.
Instructions are available in the settings page to download support autotaggers. You can also place other ONNX models in the models/ volume. Each tagger lives in its own subfolder with two files:

• model.onnx, the weights
• tags.csv in WD14 format (tag_id,name,category_id), or tags.txt with one label per line (all labels are assigned to general)

Reload the settings page after adding a model, then enable it in Settings → Auto-Tagger. Multiple taggers can run at the same time, and each tag records which tagger produced it. Auto-tags can be removed per tagger or all at once without touching manual tags.

If no tagger is enabled, auto-tagging is simply disabled. The rest of the app works normally. The model stays loaded in memory across back-to-back jobs and is unloaded automatically after 30 minutes without a tagger run. Tune the window via tagger.idle_release_after_minutes in the TOML; set it to 0 to release on every run.

The default image is CPU-only (~210 MB). For GPU inference, switch to the -cuda image (~2.3 GB), pass the GPU into the container the usual way, then enable Settings → Auto-Tagger → Use GPU (CUDA) (or set MONBOORU_TAGGER_USE_CUDA=true).

The current mode (GPU or CPU) is shown as a badge in the Auto-Tagger section. The -cuda image also runs on CPU when the toggle is off, so switching between the two does not require a rebuild. Worker count is configurable in Settings (default 4); increase it on GPU if preprocessing becomes the bottleneck.

All of these override the TOML config. Pattern: MONBOORU_{SECTION}_{KEY}.

Per-tagger settings (enable flags, confidence thresholds, worker count) live in the Settings UI, not in env vars.

Disabled by default. Generate a token in Settings → Authentication to enable it.

• HTML reference: /api/v1/docs (also linked in the footer)
• OpenAPI spec: /api/v1/openapi.json

Covers search, tag add/remove, upload, delete.

# CPU only, no auto-tagger
go build -o monbooru ./cmd/monbooru

# With auto-tagger (requires the ONNX Runtime shared library on the system)
CGO_ENABLED=1 go build -tags tagger -o monbooru ./cmd/monbooru

./monbooru -config /path/to/monbooru.toml

If the watcher reports an inotify limit error (logged as no space left on device from inotify_add_watch), raise fs.inotify.max_user_watches on the host (not inside the container) and restart. If you see too many open files, raise fs.inotify.max_user_instances. Alternatively, disable the watcher in Settings and use the manual Sync button when adding new files.

Intended for local network use. This project is not designed for direct exposure to the public internet.