hmmm... uses notebook. i don't use that
Kohoya ss is a web ui specifically for training different types of models. It has dreambooth, lora, ti etc. Some useful tools for the preparation process such as making the captions. I assume that you are doing the training in A1111.
Kohya ss is far superior as far as I remember. I have only trained one lora that I consider a success, though I have done many practice runs and experimentation runs etc.
For just trying out training a lora I suppose A1111 is fine for this but if you are serious about it I think kohya ss is the better option.
Aitrepeneur kohyas ss lora tutorial:
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The basic settings in this video is only to get you started, you need to figure out the best settings for your own scenario yourself.
This rentry guide is very useful:
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Which ckpt model you choose to train your lora on is very important, it's best with a model that is responsive and consistent.
Don't use an ancestral sampler, you want consistency. Choose one of the well established classics, Euler, DPM++ 2m Karras, DPM++ SDE Karras etc.
The next thing is which optimizer you use, AdamW8bit or AdamW is good as a start.
Next is the learning rate, don't use a too fast setting as it tend to make an overtrained lora.
Then "Net dim (Rank)" settings, 128 for both is a good base but you can try lowering it slightly depending on what type of lora you are training such as style or character etc.
Learn about the concept or topic of dampening. It refers to settings that has the secondary effect of slowing down the learning rate.
There is a section about it in the rentry guide.
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Something to try is a bit of "denoise offset", it makes the image sharper and more colorful but if you overdo it, the image can look "burnt". I used a very low setting (0.1) for my lora with good result but it's not strictly necessary. Consider using clipskip 2 as it might give better results.
There are some general recommendations in the section " Starting settings and "optimal settings" .
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Something to keep in mind is that if you use bad images to start with you will not get a good result so be very selective in choosing your images for the data set. I think it's best to not use too many, 20-30 ish is a good number.
The captions are very important as well. It's fine to use the tool in kohya for auto captions as a starting point but it's well worth the time to go through them manually to adjust them. If your gpu can handle it go with 768 instead of 512 resolution.
It's not necessary to use 1:1 images it can be either portrait or lanscape just make sure to upscale and crop etc manually with photoshop or similar so you don't have a bunch of variations. you can have a some variations ofc, just not too much. Make sure to enable "photobuckets", this will take care of the variations for you.
Good luck.
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