4o-ghibli-at-home is currently deeply optimized specifically for Linux systems, demonstrating significant cross-device compatibility features. The project documentation details test data on Raspberry Pi 4B (ARM architecture) to high performance workstations (x86 architecture):
- Stable 15 sec/sheet processing speed on mobile devices with NVIDIA Jetson Nano
- Desktop-class RTX 3060 Graphics Card Boosts Real-Time Processing Power to 2 Seconds Per Image
- Virtual environments built with UV tools ensure accurate version control of dependent libraries
This design makes it an ideal sample for studying AI applications in edge computing scenarios. Developers can utilize the tool:
- Testing the efficiency of model inference on different hardware platforms
- Hands-on Python Virtual Environment Deployment Program
- Investigating CUDA acceleration implementations for ARM architectures
The project roadmap shows that the upcoming supported version of Windows will be in WSL2 compatibility mode, which will then reach a wider group of developers.
This answer comes from the article4o-ghibli-at-home: locally run Ghibli-style image conversion tool》
May not be reproduced without permission:AI productivity tools » 4o-ghibli-at-home's Linux-compatible design makes it an ideal lab platform for embedded AI developers
