Generate AI images locally through a node-based visual workflow instead of a single prompt box.
Deliver an image generation tool to users through a Telegram Mini App.
Manage and organize a personal library of prompts and generated image assets.
Sync curated prompt collections from public repositories into a local prompt library.
| dreamlovebetty/canvashub | azurecertprep/azurecertprep.github.io | cheiineeey/always-here | |
|---|---|---|---|
| Stars | 12 | 12 | 12 |
| Language | JavaScript | JavaScript | JavaScript |
| Setup difficulty | moderate | moderate | hard |
| Complexity | 3/5 | 3/5 | — |
| Audience | vibe coder | ops devops | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires a Python virtual environment and, for full features, a Codex login or third-party image API key.
CanvasHub is a local-first console for generating and managing AI-created images. It runs on your own computer through a small Python server that serves both a desktop-style web interface and the JSON APIs behind it, and it can also be delivered as a Telegram Mini App so people can generate images from within Telegram. Generated images are saved locally, and the Telegram integration syncs those saved files. The tool is built around node-based image generation, meaning you connect pieces like prompts, image imports, and generation steps rather than filling in a single form. Two image generation paths are supported: one called Image2, which needs a Codex login or account credentials imported through an account-pool system, and one called Banana generation, which needs a separate third-party API. Beyond generation itself, CanvasHub includes prompt management tools and an optional feature that syncs example prompts and images from several public, curated prompt repositories into your local library. Security is opt-in by design. The server only listens on your own machine by default, and turning on public mode, which is needed to expose the app to the internet or use the Telegram Mini App remotely, requires you to explicitly set an access password in the settings. The README is upfront that this password gate is basic protection, not a full multi-user permission system, so anyone deploying it publicly is expected to add their own HTTPS, reverse proxy, and firewall rules. Setup uses a standard Python virtual environment and a requirements file, with one-click start scripts provided for Windows, macOS, and Linux. A separate desktop app build is also available through GitHub Releases. The project's own code is released under the MIT License, though some bundled third-party components carry their own separate licenses.
CanvasHub is a local-first, node-based console for generating and organizing AI images, with optional delivery through a Telegram Mini App.
Mainly JavaScript. The stack also includes JavaScript, Python.
MIT license for the project's own code: free to use, modify, and distribute, including commercially, as long as the license text is kept.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly vibe coder.
This repo across BitVibe Labs
Verify against the repo before relying on details.