LyCORIS VS Text-To-Video-Finetuning

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

I used this repo for the finetuning: https://github.com/ExponentialML/Text-To-Video-Finetuning

Has anyone successfully done this? I walked through the steps and did not find what I wanted so wanting to know if anyone has a tutorial about fine-tuning Modelscope with https://github.com/ExponentialML/Text-To-Video-Finetuning

Well, actually today all I have is ModelScope txt2video, SadTalker and an understanding of how this technology works, but pretty soon I'll have this https://github.com/ExponentialML/Text-To-Video-Finetuning/pull/27 too. Then whatever advancements things like https://ai.facebook.com/blog/dino-v2-computer-vision-self-supervised-learning/ unlock will filter down to me too and so on it will go. My understanding of the tech will continue to deepen as I continue to retrain from traditional software engineering to machine learning. Things like Culitho (https://www.anandtech.com/show/18792/nvidias-culitho-to-speed-up-computational-lithography-for-2nm-and-beyond) and AlphaTensor (https://www.deepmind.com/blog/discovering-novel-algorithms-with-alphatensor) will continue to make compute faster and more affordable driving the cost of training/inference down and massively increasing the accessibility. Increasingly more functions will continue to be approximated closer and closer (https://www.youtube.com/watch?v=0QczhVg5HaI).

Made by strangeman3107 via https://github.com/ExponentialML/Text-To-Video-Finetuning. The original Diffusers weights https://huggingface.co/datasets/strangeman3107/animov-0.1

Just as one of Deforum Discord's server members linked me it, I was so inspired that I quickly wrote the Diffusers->pth (ModelScope original format) conversion script

Yes, there's a Diffusers based repo https://github.com/ExponentialML/Text-To-Video-Finetuning.