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See the list [here](https://github.com/cloud-py-api/visionatrix/blob/main/ex_app/lib/supported_flows.py)\n\n\u003cpicture\u003e\n  \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"./screenshots/visionatrix_3_dark.jpg\"\u003e\n  \u003cimg alt=\"Visionatrix in Assistant UI\" src=\"./screenshots/visionatrix_3_light.jpg\"\u003e\n\u003c/picture\u003e\n\n\u003cpicture\u003e\n  \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"./screenshots/visionatrix_4_dark.jpg\"\u003e\n  \u003cimg alt=\"Visionatrix in Assistant UI\" src=\"./screenshots/visionatrix_4_light.jpg\"\u003e\n\u003c/picture\u003e\n\n## Installation\n\n\u003e [!NOTE]\n\u003e The `AppAPI` application needs to be enabled to install and use this application.\n\n### Installation\n\n1. Configure [AppAPI](https://github.com/nextcloud/app_api).\n2. Once AppAPI is set up and the deployment daemon is configured, install the Nextcloud Visionatrix.\n3. Enjoy Nextcloud Visionatrix from the Top Menu.\n\nWe offer two types of installation:\n\n1. **Standard Installation**: Includes both the Visionatrix server for task management and a built-in worker for task processing.\n\n\t\u003e For this installation, you'll need either a powerful CPU with at least 32 GB of RAM or a GPU with at least 8 GB of VRAM.\n\t\u003e _(Some flows, like background removal, are fast even on CPU and don’t require high-end hardware.)_\n\n2. **Advanced Installation**: Starting from Nextcloud version `31`, you can install only the server component by setting `DISABLE_WORKER=1` in the environment variables during application installation. In this mode, the server will manage tasks, while external workers (installed elsewhere) will process them.\n\n\u003e [!NOTE]\n\u003e Worker authentication uses Nextcloud credentials. If 2FA is enabled, you will need to use an App Password to connect external workers.\n\nFor more information, please refer to the original [Visionatrix documentation](https://visionatrix.github.io/VixFlowsDocs/).\n\n## Freeing GPU Memory After Execution\n\nBy default, models remain resident in GPU memory after each task. To control this behavior, we’ve added two options on the **Settings → Workers** page:\n\n1. **Smart Memory** (enable/disable)\n2. **Cache Type** (select caching strategy)\n\nIf you need GPU memory to be released after task execution, we recommend:\n\n1. **For systems with \u003e 64 GB RAM**\n   Disable **Smart Memory**. This will offload GPU memory to your system RAM, freeing up VRAM.\n\n2. **For systems with ≤ 64 GB RAM**\n   a. Disable **Smart Memory**.\n   b. Set **Cache Type** to **None**.\n\n## Questions\n\nFeel free to ask questions or report issues.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloud-py-api%2Fvisionatrix","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcloud-py-api%2Fvisionatrix","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloud-py-api%2Fvisionatrix/lists"}