Low-configuration equipment optimization solutions
For devices with less than 24GB of video memory, the following options are available:
- Memory Offload Technology: Adding the -offload True parameter, together with -chunk-size 16 to split the computation into multiple chunks, can reduce the video memory usage by 42% in real life.
- Low Precision Mode: Modify dtype=float16 in the config file, some Ampere architecture GPUs can also try -amp-mode bf16
- Resolution Classification: use the 144p base model (torchrun .../144px.py), and enhance the image quality later with supersegmentation tools like ESRGAN
- Time Slice GenerationMr. Mister: 8 frames, then spliced with the -concat-mode parameter, suitable for slideshow style videos.
- Cloud Collaboration Solutions
Setting up HuggingFace's Inference API endpoint to shunt rendering tasks to the cloud
This answer comes from the articleOpen Sora: An Open Source Video Generation Tool for Optimizing Face ConsistencyThe
May not be reproduced without permission:AI productivity tools " How to achieve efficient operation of Open-Sora on low graphics memory devices?
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