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Scale decoding architectures to lower parameter counts and to fit on smaller GPUs #6

@reesekneeland

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@reesekneeland

MindEye 1 and 2 in their default training/inference configurations require an A100 to use. There has been other recent work exploring a reduction in parameter counts that could be valuable to implement, in service of our tertiary goal of making these decoding algorithms more scalable and easier to use. This is also a good item for people with limited compute (no A100s) to work on.

Lite-Mind paper: https://arxiv.org/html/2312.03781v1

Other easy things:

  • Don't load all of the images onto the CPU
  • Smaller batch sizes
  • Disable unnecessary modules (captioning module, etc)

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    limited-gpuIssues that can be addressed with access to limited GPUs (less than an A100)

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