StableTuner VS LyCORIS

Of all dreamboothing/finetuning implementations I tried I liked StableTuner the most. Might be worth giving it a shot to compare as well.

Largest I've gone is about 100 images for objects or people. I don't think it matters though, it can be a hassle setting up and resuming the training session each time if your doing small sessions. Stable Tuner can simplify all of this by helping you set everything up through their client installed locally. You can then easily do your training locally in short sessions or have it automatically packed up to be exported to colab or another gpu hosting service, also with the ability to train in short sessions. Its a smart way to manage large training projects like yours. It requires a bit of time setting up but most folks who have already played around with dreambooth should be able to navigate their way through easily enough. It has all the other training methods built into it too, including proper fine tuning https://github.com/devilismyfriend/StableTuner

Some people also like StableTuner: https://github.com/devilismyfriend/StableTuner

Stable Tuner seems like it's setup to allow training on regular/inpaint/depth models. https://github.com/devilismyfriend/StableTuner

StableTuner is an alternative to the sd_dreambooth plugin. It can do Dreambooth and Fine Tuning (I haven't tried this but I think it's embeddings) It uses diffusers but will convert between that and ckpt files, is for Windows/Nvidia, and uses a local app instead of a webapp. This is the only successful local Dreambooth I've done. You'll need to go to their discord for help but it's not hard to use.

Dreambooth is has better results in older commits. StableTuner is better for training : https://github.com/devilismyfriend/StableTuner

Lycoris is another implementation of LoRA done by KohakuBlueleaf: https://github.com/KohakuBlueleaf/LyCORIS

Most guides focus on LoRa training as that has been around for longer. But I think LoHa can give better results. But the training is about half as fas it/s and it requires different training settings.

I've read in some tutorials that it is best that the value should be 64 or below, also here they suggest to not go over 64 ( https://github.com/KohakuBlueleaf/LyCORIS )

Does anyone know how to make LyCORIS models (https://github.com/KohakuBlueleaf/LyCORIS) work with inpainting models?

I think it was linked already but this is also relevant for LoRa: https://github.com/KohakuBlueleaf/LyCORIS Nice work!

There are some WIP evolutions of SD Lora in the works, like locon and lycoris.

https://github.com/KohakuBlueleaf/LyCORIS

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