Kohaku is a cute dragon girl.

Kohaku-XL gamma

By kblueleaf on Jan 19, 2024
Image post

Kohaku XL Gamma

A SDXL anime base model aims to create unique artworks.


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.


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)

The metadata database can be downloaded here: KBlueLeaf/danbooru2023-sqlite · Datasets at Hugging Face

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)


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)

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