* docs: rebrand from image-only to multi-modality across docs and metadata SnapOtter expanded from image-only to 157 tools across 5 modalities (image, video, audio, document/PDF, data). Update all product-level copy, metadata, and i18n that still framed it as an image-only tool. - README, package.json, root llms.txt: multi-modality framing, 157 tools - OpenAPI info + tags, generated /llms.txt tagline (docs.ts) - VitePress docs site: hero, getting-started, architecture, security, deployment, configuration, developer, supported-formats - i18n: 10 product keys across all 21 locales (hero, app description, privacy notes, AI features, progress messages, getting-started) - web/demo/landing meta + privacy copy, COMMUNITY_GUIDE, .env.example Stale tool counts (53/50+/52/70+/35) corrected to 157 throughout. Database/container deployment claims left unchanged (out of scope). * docs: fix stale post-rebrand test assertions and README language list - tests/e2e-docs/homepage.spec.ts: assert the current docs homepage (file toolkit, 157 tools, 5 modalities) instead of the old image-only strings - tests/unit/api/docs-route.test.ts: sync the reproduced llms.txt tagline with docs.ts - README.md: 21 languages with the correct list (add Swedish and Chinese Traditional, drop Czech which is not supported) * docs: correct 2.0 architecture references (Postgres 17 + Redis 8, 3-container stack) The docs and metadata still described the 1.x stack (SQLite, single container, p-queue). Update them to the current 2.0 reality. - README: replace the broken single-container `docker run` quick-start with the real Docker Compose stack (app + Postgres 17 + Redis 8); fix the "no Redis, no Postgres" feature bullet - package.json: description no longer claims a single container - apps/docs: rewrite database.md for Postgres; configuration.md DB_PATH -> DATABASE_URL + REDIS_URL; architecture.md SQLite/p-queue/better-sqlite3 -> Postgres/BullMQ/pg and add media-engine + doc-engine; developer/security/deployment/docker-tags/getting-started/contributing compose examples now include postgres + redis; index.md + api/ai.md AI count 16 -> 19 - SECURITY.md: Drizzle (SQLite) -> (PostgreSQL) - landing: enterprise/FeatureHighlights single-container wording; TrustSignals/ToolGrid 150+ -> 157 (dynamic); Pricing/FAQ 15 -> 19 AI tools * docs(api): document all video, audio, document, and data tool endpoints in OpenAPI The spec covered only image tools; the Scalar UI and the generated /llms.txt and /llms-full.txt inherited that gap. Add the 104 missing tool endpoints so the API docs match the code. - Video: 29 endpoints (most long/async; auto-subtitles is AI) - Audio: 17 (transcribe-audio is AI) - Document/PDF: 36 (ocr-pdf is AI; conversions are long/async) - Data: 10 - Image: 12 newer tools (background-replace, blur-background AI; histogram/lqip-placeholder/sprite-sheet custom responses; barcode-generate uses a JSON body) Each schema is derived from the tool's Zod validator and executionHint (fast -> 200, long -> 202+SSE, AI adds 501 FeatureNotInstalledError, multi-file inputs as arrays), referencing the existing shared schemas. Tool path entries: 64 -> 168. Spec parses as valid YAML with no duplicate paths and only known $refs.
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description
| description |
|---|
| AI engine reference with all local ML tools. Background removal, upscaling, OCR, face detection, photo restoration, and more. |
AI Engine Reference
The @snapotter/ai package bridges Node.js to a persistent Python sidecar for all ML operations. The dispatcher process stays alive between requests for fast warm-start performance. GPU is auto-detected at startup and used when available.
19 AI tool routes. All models run locally - no internet required after initial model download.
Architecture
Node.js Tool Route
│
▼
@snapotter/ai bridge.ts
│ (stdin/stdout JSON + stderr progress events)
▼
Python dispatcher (persistent process)
│
├─ remove_bg.py (rembg / BiRefNet)
├─ upscale.py (RealESRGAN)
├─ inpaint.py (LaMa ONNX)
├─ ocr.py (PaddleOCR / Tesseract)
├─ detect_faces.py (MediaPipe)
├─ face_landmarks.py (MediaPipe landmarks)
├─ enhance_faces.py (GFPGAN / CodeFormer)
├─ colorize.py (DDColor)
├─ noise_removal.py (tiered denoising)
├─ red_eye_removal.py (landmark + color analysis)
├─ restore.py (scratch repair + enhancement + denoising)
├─ transparency_fix.py (BiRefNet HR-matting + defringe)
└─ seam_carving (Go caire binary - not Python)
Timeouts: 300 s default; OCR and BiRefNet background removal get 600 s.
Background Removal
Function: removeBackground
Tool route: remove-background
Model: rembg with BiRefNet (default) or U2-Net variants
| Parameter | Type | Default | Description |
|---|---|---|---|
model |
string | birefnet-general |
Model variant - see table below |
alphaMattingForeground |
number (1–255) | 240 | Foreground threshold for alpha matting |
alphaMattingBackground |
number (1–255) | 10 | Background threshold for alpha matting |
returnMask |
boolean | false | Return the mask instead of the cutout |
backgroundColor |
string | - | Fill removed area (hex color or "transparent") |
Available models:
| Model ID | Best for |
|---|---|
birefnet-general |
General purpose (default) |
birefnet-portrait |
People / portraits |
birefnet-dis |
Dichotomous Image Segmentation |
birefnet-hrsod |
High-resolution salient objects |
birefnet-cod |
Camouflaged objects |
u2net |
Fast general purpose |
u2net_human_seg |
Human segmentation |
isnet-general-use |
High quality general |
Image Upscaling
Function: upscale
Tool route: upscale
Model: RealESRGAN (with Lanczos fallback on CPU-constrained systems)
| Parameter | Type | Default | Description |
|---|---|---|---|
scale |
2 | 4 | 4 | Upscale factor |
model |
string | realesrgan-x4plus |
Model variant |
faceEnhance |
boolean | false | Apply GFPGAN face enhancement pass |
denoise |
number (0–1) | 0.5 | Denoising strength |
format |
string | - | Output format override |
quality |
number | 95 | Output quality (for JPEG/WebP) |
OCR / Text Extraction
Function: extractText
Tool route: ocr
Models: Tesseract (fast), PaddleOCR PP-OCRv5 (balanced), PaddleOCR-VL 1.5 (best)
| Parameter | Type | Default | Description |
|---|---|---|---|
quality |
fast | balanced | best |
balanced |
Processing tier |
language |
string | en |
Language code (ISO 639-1) |
enhance |
boolean | false | Pre-process image to improve OCR accuracy |
Returns structured results with bounding boxes, confidence scores, and extracted text blocks.
Face / PII Blur
Function: blurFaces
Tool route: blur-faces
Model: MediaPipe face detection
| Parameter | Type | Default | Description |
|---|---|---|---|
blurRadius |
number | 30 | Gaussian blur radius |
sensitivity |
number (0–1) | 0.5 | Detection confidence threshold |
Face Enhancement
Function: enhanceFaces
Tool route: enhance-faces
Models: GFPGAN, CodeFormer
| Parameter | Type | Default | Description |
|---|---|---|---|
model |
gfpgan | codeformer |
gfpgan |
Enhancement model |
strength |
number (0–1) | 0.7 | Enhancement strength |
sensitivity |
number (0–1) | 0.5 | Face detection threshold |
centerFace |
boolean | false | Focus enhancement on center face only |
AI Colorization
Function: colorize
Tool route: colorize
Model: DDColor (with OpenCV DNN fallback)
Converts black-and-white or grayscale photos to full color.
| Parameter | Type | Default | Description |
|---|---|---|---|
intensity |
number (0–1) | 0.85 | Color saturation strength |
model |
string | ddcolor |
Model variant |
Noise Removal
Function: noiseRemoval
Tool route: noise-removal
Three-tier denoising pipeline (fast: OpenCV bilateral filter; balanced: frequency-domain; best: deep learning model).
| Parameter | Type | Default | Description |
|---|---|---|---|
quality |
fast | balanced | best |
balanced |
Processing tier |
strength |
number (0–1) | 0.5 | Denoising strength |
preserveDetail |
boolean | true | Edge-preserving mode |
colorNoise |
boolean | false | Target color noise specifically |
Red Eye Removal
Function: removeRedEye
Tool route: red-eye-removal
Detects face landmarks, locates eye regions, and corrects red-channel oversaturation.
| Parameter | Type | Default | Description |
|---|---|---|---|
sensitivity |
number (0–1) | 0.5 | Red pixel detection threshold |
strength |
number (0–1) | 0.9 | Correction strength |
Photo Restoration
Function: restorePhoto
Tool route: restore-photo
Multi-step pipeline for old or damaged photos: scratch/tear detection and repair → face enhancement → denoising → optional colorization.
| Parameter | Type | Default | Description |
|---|---|---|---|
mode |
auto | light | heavy |
auto |
Restoration intensity |
scratchRemoval |
boolean | true | Detect and repair scratches, tears |
faceEnhancement |
boolean | true | Apply face enhancement pass |
fidelity |
number (0–1) | 0.7 | Face enhancement strength |
denoise |
boolean | true | Apply denoising pass |
denoiseStrength |
number (0–100) | 40 | Denoising strength |
colorize |
boolean | false | Colorize after restoration |
Passport Photo
Function: Uses detectFaceLandmarks + removeBackground
Tool route: passport-photo
Model: MediaPipe face landmarks
Generates government-compliant ID photos. Supports 37 countries across 6 regions (Americas, Europe, Asia, Africa, Oceania, Middle East). Each spec includes physical dimensions, DPI, head-height ratio, eye-line position, and background color requirements.
| Parameter | Type | Default | Description |
|---|---|---|---|
country |
string | us |
ISO country code (see list in UI) |
printLayout |
4x6 | A4 | none |
none |
Output as print sheet or standalone |
backgroundColor |
string | country default | Background fill color |
Object Erasing (Inpainting)
Function: inpaint
Tool route: erase-object
Model: LaMa via ONNX Runtime
| Parameter | Type | Required | Description |
|---|---|---|---|
maskData |
string | Yes | Base64-encoded PNG mask (white = erase) |
maskThreshold |
number (0–255) | No | Threshold for mask binarization |
GPU-accelerated when an NVIDIA GPU is available.
Smart Crop
Function: Uses MediaPipe + Sharp attention/entropy
Tool route: smart-crop
Model: MediaPipe face detection
| Parameter | Type | Default | Description |
|---|---|---|---|
mode |
subject | face | trim |
subject |
Crop strategy |
width |
number | - | Output width |
height |
number | - | Output height |
facePreset |
string | - | Preset framing when mode=face |
Face presets:
| Preset | Head ratio | Best for |
|---|---|---|
close-up |
1.8× face | Headshots |
head-and-shoulders |
2.8× face | Profile photos |
upper-body |
4.5× face | LinkedIn / formal |
half-body |
7.0× face | Full upper body |
Image Enhancement
Function: analyzeImage + applyCorrections
Tool route: image-enhancement
Engine: Analysis-based (Sharp histogram and statistics)
Analyzes the image and applies automatic corrections for exposure, contrast, white balance, saturation, sharpness, and noise. Supports scene-specific modes.
| Parameter | Type | Default | Description |
|---|---|---|---|
mode |
auto | portrait | landscape | low-light | food | document |
auto |
Scene mode for tuning corrections |
intensity |
number (0-100) | 50 | Overall correction strength |
corrections.exposure |
boolean | true | Apply exposure correction |
corrections.contrast |
boolean | true | Apply contrast correction |
corrections.whiteBalance |
boolean | true | Apply white balance correction |
corrections.saturation |
boolean | true | Apply saturation correction |
corrections.sharpness |
boolean | true | Apply sharpness correction |
corrections.denoise |
boolean | true | Apply denoising |
An additional analysis endpoint is available at POST /api/v1/tools/image-enhancement/analyze which returns the detected corrections without applying them.
Content-Aware Resize (Seam Carving)
Function: seamCarve
Tool route: content-aware-resize
Engine: Go caire binary (not Python - no GPU benefit)
Intelligently resizes images by removing or adding low-energy seams, preserving important content.
| Parameter | Type | Default | Description |
|---|---|---|---|
width |
number | - | Target width |
height |
number | - | Target height |
protectFaces |
boolean | true | Protect detected face regions from seam removal |
blurRadius |
number | 0 | Pre-blur to reduce noise sensitivity |
sobelThreshold |
number | 10 | Edge sensitivity threshold |
square |
boolean | false | Force square output |
Max input edge before auto-downscaling: 1200 px.
PNG Transparency Fixer
Function: fixTransparency
Tool route: transparency-fixer
Model: BiRefNet HR-matting (2048x2048 resolution)
Fixes "fake transparent" PNGs where the background was removed but left behind fringing, halos, or semi-transparent artifacts. Uses BiRefNet's high-resolution matting model to produce a clean alpha channel, then applies configurable defringe processing to remove color contamination along edges.
OOM fallback chain: If BiRefNet HR-matting exceeds available memory, the tool automatically falls back to birefnet-general, then to u2net.
Feature bundle: Background Removal (shared with Remove Background and Passport Photo).
| Parameter | Type | Default | Description |
|---|---|---|---|
defringe |
number (0-100) | 30 | Edge defringe strength to remove color contamination |
outputFormat |
"png" | "webp" |
"png" |
Output image format |
curl -X POST http://localhost:1349/api/v1/tools/transparency-fixer \
-H "Authorization: Bearer <token>" \
-F "file=@fake-transparent.png" \
-F 'settings={"defringe":30,"outputFormat":"png"}'