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Dinamus/
thr3shr

Name: thr3shr
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THR3SHR

Local FastAPI + React workflow that threshes a media dump into destination bins — anime/GIF/video tagging, review, and folder migration (ML-Danbooru / WD taggers).

This repo includes:

Tag vocabulary (tags.csv)
FastAPI backend for scan → tag → review → migrate
React SPA with sectioned settings, run dashboard, and review table
Multi-model inference via dghs-imgutils (ML-Danbooru ONNX and WD14 taggers)

ONNX weights are not stored in this git tree (too large for GitHub). They are downloaded into the Hugging Face Hub cache on first use. See Model weights for direct links.

What The Web UI Does

Settings — paths, tagger model, thresholds, workers; sticky unsaved save bar
Tag selection — search chips from tags.csv (auto-persisted)
Preview scan — eligible image counts before starting a run
Run dashboard — status, totals, failed/review counts, progress bar, cancel
Review table — preview, primary (or “needs review”), secondary suggestions, global top-5 tags, final tag, approve/migrate

Tagger Models

Choose in Settings (persisted in SQLite as tagger_model):

Setting value	Backend call	Notes
`wd_swinv2_v3` (default)	`get_wd14_tags(..., model_name="SwinV2_v3")`	Recommended accuracy baseline
`wd_eva02_large`	`get_wd14_tags(..., model_name="EVA02_Large")`	Larger / slower WD tagger
`ml_danbooru`	`get_mldanbooru_tags(..., threshold=0.0, size=448)`	Original ML-Danbooru path

Related settings:

confidence_threshold (default 0.6) — assignment gate for selected tags
wd_general_threshold (default 0.35) — WD14 general-tag cutoff at inference (WD models only)
max_inference_workers (default 2) — keep low; ORT session is serialized under a lock
force_cpu_inference — force CPU path

Classification Rules

Scores are produced for the whole vocabulary (model-dependent).
Folder assignment ranks only user-selected tags that map to tags.csv.
Empty scores → failed / needs review.
Best selected score < noise floor max(0.15, confidence_threshold * 0.5) → no primary (weak noise winner discarded; kept as secondary suggestion).
Best selected score < confidence_threshold` → no primary; suggestion in secondary.
Review payload always includes global top-5 tags so mis-assignments are diagnosable.
Each run stores tagger_model for provenance.

Quick Start

1) Create Python environment and install backend dependencies

bash

cd /path/to/thr3shr
python3 -m venv .venv
. .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r backend/requirements.txt

2) Start backend API (port 8000)

bash

cd /path/to/thr3shr/backend
../.venv/bin/uvicorn app.main:app --host 127.0.0.1 --port 8000 --reload

Health check:

curl http://127.0.0.1:8000/health

3) Start frontend Web UI (port 5173)

bash

cd /path/to/thr3shr/frontend
npm install
npm run dev

Open http://localhost:5173.

Frontend Offline Mode

The frontend falls back to local mock mode when the backend is unreachable. Preview/migration are mock-only in that mode.

Review Workflow

Set root repository (unsorted images) and categories root (destination folders — not tags.csv)
Pick a tagger model and thresholds → Save settings
Optionally Preview scan
Select destination tags → Start run
Watch the run dashboard; cancel if needed
Review: primary / secondary / global tops → approve → migrate (copy or move)

Supported And Ignored Files

Supported image extensions: .jpg, .jpeg, .jfif, .png, .bmp, .webp, .tiff
Extensionless / odd extensions are included when PIL can decode them
By default ignored: .gif and common video formats
Optional experimental media: quality-filtered multi-frame sampling with presence pooling (require ≥2 corroborating frames; tagged budget up to 48; black/blank frames rejected) when enabled in settings

GPU Acceleration

Inference uses ONNX Runtime CUDA when the pip CUDA/cuDNN wheels are installed and discoverable.

bash

pip install -r backend/requirements.txt
# includes: onnxruntime-gpu[cuda,cudnn]==1.26.0

On Windows the API prepends site-packages/nvidia/*/bin to the DLL search path before creating sessions. Without that, ORT may list CUDA then fall back to CPU on the first Conv.

CUDA usability is independent of which tagger model is selected; both ML-Danbooru and WD14 share the ORT runtime path.

Verify GPU is actually usable

bash

curl http://127.0.0.1:8000/health/providers
curl http://127.0.0.1:8000/api/providers

Look for "cuda_usable": true, "likely_device": "gpu", and active_providers containing CUDAExecutionProvider. Listing CUDA alone is not enough. The providers payload also echoes the active tagger_model.

Runtime controls

Settings UI values are mirrored into env knobs used by the run executor:

MAX_INFERENCE_WORKERS (default 2, clamped 1..16)
FORCE_CPU_INFERENCE=true
INFERENCE_MODE=batch|single (default single in practice for imgutils; batch falls back to per-image)
INFERENCE_BATCH_SIZE (default 1, clamped 1..64)
QUEUE_SHUFFLE_ENABLED=true|false (default true)
QUEUE_SHUFFLE_SEED=<int> (default run_id)

Examples:

bash

# CPU-safe baseline
FORCE_CPU_INFERENCE=true MAX_INFERENCE_WORKERS=1 ../.venv/bin/uvicorn app.main:app --host 127.0.0.1 --port 8000 --reload

# Conservative GPU (recommended)
MAX_INFERENCE_WORKERS=2 ../.venv/bin/uvicorn app.main:app --host 127.0.0.1 --port 8000 --reload

API Surface (High Level)

GET /health
GET /health/providers
GET /api/providers
GET/PUT /api/settings — includes tagger_model, wd_general_threshold, selected_tags, workers, etc.
GET /api/tags
GET /api/scan/preview — discovery stats without inference
POST /api/runs/start
GET /api/runs/{run_id}/status — includes tagger_model, progress telemetry
POST /api/runs/{run_id}/cancel
GET /api/runs/{run_id}/items — includes global_top_tags, secondary suggestions
PATCH /api/items/{item_id}
GET /api/items/{item_id}/preview
GET /api/items/{item_id}/scores
POST /api/runs/{run_id}/batch
POST /api/runs/{run_id}/migrate

Tests

bash

cd backend
../.venv/bin/python -m pytest tests -q

Model weights

Tagger ONNX files are pulled automatically via huggingface_hub on first inference. Hosted copies (not in this repo):

Setting value	Weights / labels	Size note
`wd_swinv2_v3` (default)	SmilingWolf/wd-swinv2-tagger-v3 · mirror deepghs/wd14_tagger_with_embeddings (`…/model.onnx`)	~hundreds of MB
`wd_eva02_large`	SmilingWolf/wd-eva02-large-tagger-v3 · same deepghs mirror path	larger / slower
`ml_danbooru`	deepghs/ml-danbooru-onnx (`ml_caformer_m36_dec-5-97527.onnx`) · tags deepghs/imgutils-models (`mldanbooru/mldanbooru_tags.csv`)	ONNX + CSV

Cache location (typical): ~/.cache/huggingface/hub/.

License and attribution

What	Owner	License
THR3SHR source code	Dinamush	MIT
THR3SHR brand, creative docs, curated taxonomies	Dinamush	CC BY 4.0
WD / ML-Danbooru / style / optional VLM weights	Upstream authors (SmilingWolf, deepghs, bartowski, porntech, …)	See ATTRIBUTION.md

Model weights are not shipped in this repository. Preserve LICENSE, CREATIVE_COMMONS.md, and ATTRIBUTION.md when redistributing.

Remotes

Remote	URL
`origin` (Hugging Face Hub)	https://huggingface.co/Dinamus/thr3shr
`github`	https://github.com/Dinamush/thr3shr

bash

git push origin main    # Hugging Face
git push github main    # GitHub

The Gradio app.py entry in the YAML front matter is a legacy Space config; the primary workflow documented here is the FastAPI + React app.

Dinamus/thr3shr

THR3SHR

What The Web UI Does

Tagger Models

Classification Rules

Quick Start

1) Create Python environment and install backend dependencies

2) Start backend API (port 8000)

3) Start frontend Web UI (port 5173)

Frontend Offline Mode

Review Workflow

Supported And Ignored Files

GPU Acceleration

Verify GPU is actually usable

Runtime controls

API Surface (High Level)

Tests

Model weights

License and attribution

Remotes

Dinamus/
thr3shr