sd-webui-additional-networks VS LyCORIS

In the input field labeled "URL for extension's git repository," paste the following link: https://github.com/kohya-ss/sd-webui-additional-networks.git

Yes, this one: https://github.com/kohya-ss/sd-webui-additional-networks

Seems that calling Loras from the prompt is bugged right now. Install "Additional Networks" (https://github.com/kohya-ss/sd-webui-additional-networks) and use them from the GUI, without mentioning it in the prompt at all:

Use the additional networks extension Download your lora and place it into "stable-diffusion-webui\extensions\sd-webui-additional-networks\models\lora". Now you can select the lora in the additonal networks tab in your web ui and set a weight. Afterwards you can just generate your image.

sd-webui-additional-networks: wouldn't be necessary, but it has more functions than the basic web-ui integration.

📷 If you are getting the error "failed to match keys when loading Lora" it means you are either on the native Lora integration of Automatic1111 which may not support LoCon, please install the https://github.com/kohya-ss/sd-webui-additional-networks extension to solve the issue.

if you use civitai, you can use this extension, if you have this Lora extension, you can edit the meta data and load it from there.

I have both https://github.com/kohya-ss/sd-webui-additional-networks and https://github.com/KohakuBlueleaf/a1111-sd-webui-lycoris because i was told these are the ones i needed for LyCORIS to work. IT did for a while but since recently it has stopped working altogether.

This does not require the use of the Latent Couple(Two-Shot) extension. This requires the Additional Networks Extension, which may already be in your Stable Diffusion install or can be installed from https://github.com/kohya-ss/sd-webui-additional-networks through the extensions tab as shown below.

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