The standardized deployment process provided by UniPic allows for a systematic solution:
- environmental isolation: Creating a dedicated Python 3.10.14 environment with conda
- dependency lock: By
requirements.txtFixed library version - route management: Settings
PYTHONPATH=./Avoiding module import conflicts - Hardware Verification: Run
nvidia-smiConfirm CUDA version matches PyTorch - troubleshooting: The GitHub Issues page provides a repository of solutions to common errors.
This answer comes from the articleSkyworkUniPic: An Open Source Model for Unified Processing Image Understanding and GenerationThe
May not be reproduced without permission:AI productivity tools " What are the key steps to improve the problem of dependency conflicts in the local deployment of multimodal models?
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