sd-webui-additional-networks VS lora

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.

Diffusion unet has an "extended" version nowadays that applies to the resnet part as well as the cross-attention: https://github.com/cloneofsimo/lora

Absolutely. But that doesn't matter because you only have to train it at scale, once. There are papers released already that show it's possible to update weights in small sections. You won't have to wait for the next monolithic LLM to drop to get up to date information. It will start to learn in bits and pieces.

No, it's a deep learning thing

Low Rank Adaptation of Large Language Models.

Recently, I have seen the LoRA technique (Low-Rank Adaptation of Large Language Models) as a popular method for fine-tuning LLMs and other models.

Enter LoRA, a method proposed for adapting pre-trained models to specific tasks[2]. By freezing pre-trained model weights and injecting trainable rank decomposition matrices into the transformer architecture, LoRA can reduce the number of trainable parameters and the GPU memory requirement, making the adaptation of LLMs for downstream tasks more feasible.

Open-source LLM projects have largely solved this using Low-Rank Adaptation of Large Language Models (LoRA): https://arxiv.org/abs/2106.09685

Apparently an RTX 4090 running overnight is sufficient to produce a fine-tuned model that can spit out new Harry Potter stories, or whatever...

Alpaca was trained for $600 ($100 for the smaller model) and offers outputs competitive with ChatGTP. https://arxiv.org/abs/2106.09685