|
This describes my first experiments with this dataset. |
|
I will also be tweaking it with adafactor |
|
|
|
I was initially playing around with loras, to see which combination of images did best. |
|
Previously, I spent a LOT of time experimenting with training and getting nowhere, so I decided to stick with |
|
adaptive ones. Sadly, even a 4090 cant do a finetune of SDXL with an adaptive optimizer, which is why I was |
|
training Loras. |
|
|
|
My results were very mixed. Eventually, I figured out that stable diffusion, while seemingly magic, |
|
cannot TRUELY figure out "good" anime style, if you throw a whole bunch of MIXED styles at it. So I |
|
decided to drastically change my strategy, and throw out everything that was not strictly in one style. |
|
|
|
Once I got down to <200 images images, and had a reasonable lora, I decided to give a full finetune a try, |
|
"the hard way" (ie: no adaptive optimizer) |
|
|
|
So, I set EMA=CPU (because not enough VRAM to fit in GPU) played with the learning rate a little, and... |
|
that was it? Nope! |
|
|
|
I was training with 100 epoch, and having onetrainer do an image sample every few epochs. |
|
ut, due to disk space, I was only doing saves every 20 or so. |
|
|
|
When I looked back at the image samples, I noticed that the one I liked best, was actually around epoch 72. |
|
didnt know what e71,72, or 73 looked like... and I didnt even have a save for 72 either! |
|
|
|
What I did have, was a save at 70. So I configured OneTrainer to do a new run, starting with my saved model at |
|
70. This time, however, I disabled warmup, and also EMA. I then set OneTrainer to make a save every epoch, and a preview every epoch. |
|
|
|
The previews looked kind of like my original series! Thus encouraged, I decided to try out all 10 of the models, |
|
just in case. It turns out that I liked epoch 78/100 the best.. so here we are :) |