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ashim-hq
f67a03bb36
- Add 8 new E2E specs for AI tools (upscale, enhance-faces, colorize, restore-photo, erase-object, smart-crop, passport-photo, red-eye-removal) closing all HIGH/MEDIUM coverage gaps from the test matrix audit - Fix ensureAiDirs() crash on non-Docker environments by gating on isDockerEnvironment() — prevents ENOENT when /data doesn't exist - Bump torch 2.6.0→2.7.0 and torchvision 0.21.0→0.22.0 in feature manifest for broader Python version compatibility - Add Python 3.14 version guard warning in install_feature.py - Remove duplicate torchvision shims from upscale.py and enhance_faces.py (dispatcher.py already handles this at startup) - Remove orphaned tools.batch i18n key and dead pipeline-builder filter - Regenerate 4 visual regression baselines for current UI state - Add data-testid to passport-photo generate button for E2E testability
Key Features
- 45+ image tools - Resize, crop, compress, convert, watermark, color adjust, vectorize, create GIFs, find duplicates, generate passport photos, and more
- Local AI - Remove backgrounds, upscale images, restore and colorize old photos, erase objects, blur faces, enhance faces, extract text (OCR). All on your hardware - no internet required
- Pipelines - Chain tools into reusable workflows. Batch process up to 200 images at once
- REST API - Every tool available via API with API key auth. Interactive docs at
/api/docs - Single container - One
docker run, no Redis, no Postgres, no external services - Multi-arch - Runs on AMD64 and ARM64 (Intel, Apple Silicon, Raspberry Pi)
- Your data stays yours - No telemetry, no tracking, no external calls. Images never leave your machine
Quick Start
docker run -d --name ashim -p 1349:1349 -v ashim-data:/data ghcr.io/ashim-hq/ashim:latest
Open http://localhost:1349 in your browser.
Have an NVIDIA GPU? Click here for GPU acceleration.
Add --gpus all for GPU-accelerated background removal, upscaling, and OCR:
docker run -d --name ashim -p 1349:1349 --gpus all -v ashim-data:/data ghcr.io/ashim-hq/ashim:latest
Requires an NVIDIA GPU and Container Toolkit. Falls back to CPU if no GPU is found. See Docker Tags for benchmarks and Docker Compose examples.
Default credentials:
| Field | Value |
|---|---|
| Username | admin |
| Password | admin |
You will be asked to change your password on first login.
For Docker Compose, persistent storage, and other setup options, see the Getting Started Guide. For GPU acceleration and tag details, see Docker Tags.
Documentation
Feedback
Found a bug or have a feature idea? Open a GitHub Issue. We don't accept pull requests, but your feedback directly shapes the project. See CONTRIBUTING.md for details.
License
This project is dual-licensed under the AGPLv3 and a commercial license.
- AGPLv3 (free): You may use, modify, and distribute this software under the AGPLv3. If you run a modified version as a network service, you must make your source code available under the AGPLv3. This applies to personal use, open-source projects, and any use that complies with AGPLv3 terms.
- Commercial license (paid): For use in proprietary software or SaaS products where AGPLv3 source-disclosure is not suitable, a commercial license is available. Contact us for pricing and terms.