sd-webui-additional-networks VS alpaca-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.

Here is a example: https://github.com/tloen/alpaca-lora/blob/main/finetune.py

In this blog post, I want to make it as simple as possible to fine-tune the LLaMA 2 - 7B model, using as little code as possible. We will be using the Alpaca Lora Training script, which automates the process of fine-tuning the model and for GPU we will be using Beam.

Implement the code in Llama LoRA repo in a script we can run locally

Hi all - relatively new to GitHub / programming in general, and I wanted to try to set up Alpaca Lora locally. Following the guide here: https://github.com/tloen/alpaca-lora

Follow up the popular work of u/tloen alpaca-lora, I wrapped the setup of alpaca_lora_4bit to add support for GPTQ training in form of installable pip packages. You can perform training and inference with multiple quantizations method to compare the results.

If instead you want to apply a LoRa to a pytorch model, a lot of people use this script to apply to LoRa to the 16 bit model and then quantize it with a GPTQ program afterwards https://github.com/tloen/alpaca-lora/blob/main/export_hf_checkpoint.py

There are a few papers on watermarking LLM output, but from what I have seen they all use complex methods of detection to allow the watermark to go unseen by the end user, only to be detected by algorithm. I believe that a more overt system of watermarking might also be beneficial. One simple method that I have tried is character substitution. For this model, I LORA finetuned openlm-research/open_llama_3b on the alpaca_data_cleaned_archive.json dataset from https://github.com/tloen/alpaca-lora/ modified by replacing all instances of the "." character in the outputs with a "ι" The results are pretty good, with the correct the correct substitutions being generated by the model in most cases. It doesn't always work, but this was only a LORA training and for two epochs of 400 steps each, and 100% substitution isn't really required.

I am kind of new to finetuning LLM's and am not able to understand what this option exactly refers to. I guess it has the same meaning as the "train_on_inputs" parameter of alpacalora though.

Read the paper and also: https://github.com/tloen/alpaca-lora