Update README.md

README.md

@@ -16,33 +16,9 @@ I'm including EVERYTHING I used to create my onegirl200 model:
 I've been playing around with the thousands of images I've filtered so far from danbooro, at
 https://huggingface.co/datasets/ppbrown/danbooru-cleaned
 So, the images here are a strict subset of those images. 
-I also used the tagging ALMOST as-is: I only added one tag:  "anime"
+I also used their tagging ALMOST as-is. I only added one tag:  "anime"
 
-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. But, 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..
-I 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 :)
+See [METHODOLOGY-adamw.md] for a detailed description of what I personally did to coax a model out
+of this dataset.
 
+I also plan to try other training methods.