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AshimandGitHub
2d7a61c18f
* feat: allow multi-file selection for automation pipeline Add two ways to import server-stored files into the pipeline: 1. Files page: "Pipeline" bulk action button and "Open in Pipeline" button in file details panel — navigates to /automate with selected file IDs via React Router state. 2. Automate page: "Import from Library" button opens a modal with thumbnails, search, and multi-select checkboxes to pick files from the user's server-stored library. Both paths download the selected files and load them into the existing useFileStore, reusing the batch pipeline processing infrastructure. Closes #35 * fix: resolve 8 pre-existing test failures across unit and integration suites - file-validation.ts: Return valid:false when Sharp fails to read metadata for standard formats (PNG, JPEG, BMP) instead of silently accepting corrupt buffers. CLI-decoded formats already skip Sharp. - pipeline.ts: Enforce hard cap of 20 steps via .max() instead of relying on MAX_PIPELINE_STEPS env var (default 0 = unlimited). Tighten name limit to 100 chars and description to 500 chars to match test expectations. - env.ts: Change MAX_LOGO_SIZE_KB default from 2048 to 500 to match the branding upload size limit the tests verify.
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.