Background and core programs
Lumina-mGPT-2.0 requires 80GB of video memory by default, which poses a challenge for ordinary devices. According to official test data, resource requirements can be significantly reduced through quantization techniques and speculative decoding.
Specific steps
- Enable quantization compression: add
--quantparameter, which reduces the video memory footprint from 80GB to 33.8GB - Combined with speculative decoding: simultaneous use of
--speculative_jacobiParameters, measured memory footprint on A100 is only 79.2GB - Adjusting the output resolution: by
--widthcap (a poem)--heightReduce generation size, e.g. to 512 x 512 - Adopt chunk generation: refer to the chunk generation mode in the project documentation, large size images can be processed in batches.
Options
- Cloud deployment: leasing A100 instances using platforms such as Colab Pro
- Model distillation: lightweight fine-tuning of the original model according to TRAIN.md guidelines
This answer comes from the articleLumina-mGPT-2.0: an autoregressive image generation model for handling multiple image generation tasksThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of Lumina-mGPT-2.0 running on low graphics memory devices?
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