Hardware Performance Compromise Program
Optimization strategies for devices with different configurations:
- Base configuration (4GB video memory)
- 1. Add -low_vram mode startup
2. Adjust -resolution to 256 x 256
3. Fast reasoning with -quick_inference - GPU-free environment program
- CPU Inference via ONNX Runtime
- Calling on Google Colab's free GPU resources
- Using the HuggingFace Inference API Cloud Service
- Memory Optimization Tips
Key parameter adjustments:- -batch_size is set to 1 to reduce video memory usage.
- -precision=fp16Enable half-precision calculation
- -cache_dir specifies the SSD storage path
Measured to achieve 5 minutes/model on GTX1060 with optimization. It is recommended to use -clean_memory to free up video memory immediately after generation, and for long-term use it is recommended to configure an 8GB+ video memory device.
This answer comes from the articlePartCrafter: Generating Editable 3D Part Models from a Single ImageThe
May not be reproduced without permission:AI productivity tools " How to optimize PartCrafter's performance on low-profile hardware?
