EDIT: Published!
https://github.com/dgracey01/SDXL-LoRA-Training-Suite-2.0.git
What am I looking for here?
Community feedback. I've been working on what was supposed to be a small project that kept on growing. This project is not published yet, but it is a working prototype. Maybe I am just expecting to be told this is crap and shelf it. 🙈
It all began with the need to facilitate the LoRA training process, a calculator, nothing more than a spreadsheet. Then I had the opportunity to assist as "Crash Test Dummy" for someone else's project (@ktiseos_nyx)
I was introduced to Claude and here I am 😁
This section allows to prepare the dataset. The red box is a marker to crop the image inside to the appropriate aspect ratios, the natural buckets SDXL is trained on. On process, it uses either of the models shown in the dropdown to enlarge if necessary. The sliders allow adjustment in X, Y and zoom. Since this process is destructive, meaning you can't un-crop the images. There is the option to create a Zip backup of your dataset before it is modified. It also allows to convert the images to either PNG or JPG and to take into account those images that may end up converted with the same same (photo.png, photo.webp, photo.jpg...). Also, you notice the images in that dataset are all very similar? I can zoom different parts of each picture and make them unique, as in zoom the face, midriff, legs...
This section allows images to be tagged, "Tags" is configured to do a double pass, it uses two separate models one after the other, and produces a single .txt file per image. If "Hybrid" is chosen, it will do the double pass and run a third model to create a combined tags + caption in a single file. I already had a set of negative tags that I normally use, the list is now part of the code and can be used, modified or not used based on the needs of the user, it even allows to create custom new lists. There is a negative set for each Lora type.
This section allows tag management, standard stuff. Allows handling of hybrid tags as well.
This section is kind of miscellaneous stuff, still debating what am I going to do with it. It does have the CVS file with the tag list and prepare the final dataset zip file for archive.
The Calculator, The very entrance to the rabbit hole. In short, it determines the appropriate settings for the trainer based on the number of images. I tried to optimize three different trainers. AI-Toolkit section can output to clipboard the entire config file, so I just need to paste the thing into ai-toolkit and I'm good to go. This follows a mathematical model I believe to be unique since there was no literature about the concept and I had to come up with the math for it. It is based on community feedback. I call it "TOS" or Target Optimization Signal. It defines how many effective gradient updates each image in the dataset receives across the entire training run. It is the core exposure metric that drives steps, epochs and repeats calculations across all three trainers. I included a manual override to adjust TOS values to suit personal taste and possibly new data.
TOS controls.
I also added a Randomizer. After the appropriate models are downloaded, it allows to remove the background of your image. the list on the left is the dataset. The images on the right, there is a folder you can drop your own images to be applied against the dataset image, like so:
Did I mentioned it also serves as launch pad for the WebUI's I use?
Ok...Let me have it. What do you all think? I have no idea how to use a Linux, or Mac based systems, so this is a Windows only prototype, at least for now.