Add 'low_vram' flag to optimize VRAM usage in rb-modulation.ipynb and fix typo in README.md

[ChoYongchae]

Thanks for the research and code sharing. I’m submitting a PR to help the notebook run on GPUs with lower VRAM.

Changes:

1. Add 'low_vram' flag to rb-modulation.ipynb:

   • Introduce flag to optimize the notebook for systems with 24GB of VRAM.
   • Approaches:
     • Offload models.generator to CPU during the plugin RB-modulation stage
     • Offload all modules except generator and previewer during the VRAM-intensive sampling stage

2. (Minor) Fix typo in README.md:

   • Correct ffty to ftfy.

Benefits:

• Improved compatibility of the notebook on systems with lower VRAM.
• Enhanced documentation accuracy.

Disclaimer:

• Setting low_vram to True resolves out-of-memory (OOM) issues and ensures stable operation on GPUs like the RTX 4090, which have 24GB of VRAM.
• For GPUs with less than 24GB of VRAM, an advanced offloading strategy will be required.

Please review the changes and let me know if any further adjustments are needed.

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[tema7707]

Thank you, that's very helpful! Could you also move the SAM model in the Composition block to the CPU? It doesn't need to be stored on the CUDA, and I'm encountering an OOM error without this change.

if low_vram:
    # The sampling process uses more vram, so we offload everything except two modules to the cpu.
    models_to(sam_model, device="cpu")
    models_to(sam_model.sam, device="cpu")
    models_to(models_rbm, device="cpu", excepts=["generator", "previewer"])

[ChoYongchae]

Thank you, @tema7707! Your suggestion has helped confirm that an additional 3GB of VRAM used by SAM can be saved. I have included this change in the fix.

[LituRout]

Thanks for the PR.