Practical methods to improve image processing efficiency
For different hardware configurations, the conversion speed can be optimized in the following ways:
- Hardware accelerated configuration:
Verify that the CUDA environment is properly installed (NVIDIA graphics card):
nvidia-smi
GPU acceleration is enabled through the .env file:
DEVICE_TYPE=cuda - Parameter tuning:
Modify key parameters in config.yml:
resolution: 512 x 512 (reduced resolution)
steps: 20 (reduced number of iterations)
batch_size: 1 (reduce video memory usage) - Model Cache:
The model is automatically cached after the first run, and it is recommended that the $HOME/.cache/huggingface directory be mounted to SSD storage - Back-office processing:
Avoid reloading models by keeping the service running via nohup:
nohup python3.12 app.py & - Resource monitoring:
Use htop and nvtop tools to monitor CPU/GPU load and adjust the number of concurrent tasks according to the actual situation
For lower-end devices, try the developer-supplied lightweight DFloat11-Micro model branch, which sacrifices some image quality but significantly improves speed.
This answer comes from the article4o-ghibli-at-home: locally run Ghibli-style image conversion tool》
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