SD fine-tuning methods compared: a benchmark

This page summarizes the projects mentioned and recommended in the original post on /r/StableDiffusion
finetune stable-diffusion

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  • LyCORIS

    Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.

    • You might want to expand LoRA to include LoCon and LoHa, (and also add a column for VRAM requirements) (Think of it as a more complete LoRA that works for the kernels in the convolutional units rather than just the weights for the feed-forward network), support is still quite limited, but it's starting to pick up steam https://github.com/KohakuBlueleaf/LyCORIS

  • StableTuner

    Discontinued Finetuning SD in style.

  • Scout Monitoring

    Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.

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  • sd-scripts

    • Kohya's GUI has a way to do it, or you can just the script. https://github.com/kohya-ss/sd-scripts/blob/main/networks/extract_lora_from_models.py

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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