Final Version of this series (probably) :D

ℹ️CLIP SKIP 1ℹ️

Fine-tuned vanilla SD1.5 that trained to mimic my art style [My Instagram: https://www.instagram.com/ray_vietii ]

I did UNet of effective_total_steps = ( 2500 steps + ( 500 dataset_image_count )) iteration count ) to make the model understood what the style is without specifying some sort of "trigger word". And then i merge my LoRa which is based on the same art style,,my art style, but my LoRa has already learned the context and tags and or text_encoder_lr.

What exactly the recipe is?

What i did is making 2 variants, base1 is having high unet_lr (6e-5)+LoRa, and base2 is lower unet_lr (2e-5)+LoRa, and then base0 is another 2e-5 without LoRa.

And next thing i did was merging these bases: base1[0.4] + base2[0.6] = base1+2.

And then base1+2 [0.8] + base0[0.2], and so on, which represents as "iterations".

I did that recipe with slightly different and keep merging the variants to itself. With that, despite only have X images training, it now have pretty much broad variations.

Just like any other basemodel, it's cohesive and stable, no more SD1.5 vanilla leaking, just pure style distillation, and Mean Average Emergent or " MaE is my method, which allows me to "sculpt" the base vanilla SD1.5 with only 43 image of dataset(as of iteration 1).

It is expected to be murky and muddy because of this method, but with the right prompting, it will generate some decent good images.

MaE is way much efficient since if you're having bare minimum dataset.