Kohaku XL Gamma

A SDXL anime base model aims to create unique artworks.

Introduction

This model can be seen as a derivative of Animagine XL 3.0 project.
Basically I'm collaborating with Linaqruf for making better Anime base model (and it is obvious that we have different goal/target)
We share our models and technique to improve our models' quality.
And that is also how this model been created.

Base7

Kohaku-XL base7 is resumed from beta7 and use same dataset that beta series have used. But this time I use my own metadata system to create captions. (Can be taken as advanced version of what linaqruf used, will open source it soon)

Trainin details:
LR: 8e-6/2e-6
Scheduler: constant with warmup
Batch size: 128 (batch size 4 * grad acc 16 * gpu count 2)

Gamma rev1

Kohaku-XL Gamma rev1 is a merged model which combine the learned diff from anxl3 and kohaku xl base 7. With this forumla:

gamma rev1 = beta7 + 0.8 * (anxl3 - anxl2) + 0.5 * (base7 - beta7)

Usage

Parameter

This model is trained under 768x1024 to 1024x1024 ARB. It is recommended to use pixel count within 786432 ~ 1310720.
Recommended CFG scale is 4~7.
Sampler should not matter.

Tagging

This model use my own system for quality tags or something like that.
So although this model combine the diff weight from anxl3, I will still recommend user to use mine (or both) tagging system.

The format of prompt is as same as anxl3. (You can check the sample images I post)

Rating tags:

General: safe
Sensitive: sensitive
Questionable: nsfw
Explicit: explicit, nsfw

Quality tags (Better to worse):

Masterpiece
best quality
great quality
good quality
normal quality
low quality
worst quality

Year tags (New to Old):

newest
recent
mid
early
old

You may meet some subtle mosaic-like artifact, that may be caused by high-lr or bad resizing/image encoding.
I will try to fix it in next version. For now, try to use R-ESRGAN anime6b or SCUNet models for fixing it.

Future plan

Since my dataset have some resize/webp artifacts that will harm the models. I will recreate my dataset based on my new system (and opensource it once I done it).

The next plan is to train model on larger (3M-6M) dataset with better configuration (which will require A100s and I plan to spend about 2000-10000 USD on it, if you like my works, consider to sponsor me via buy-me-a-coffee or some BTC-sutff)

KBlueLeaf
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Kohaku-XL-gamma