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)