Kohaku XL beta
An anime SDXL model trained on 1.5M images.
Introduction
This model is resumed from [Kohaku-XL alpha](Kohaku-XL alpha - nyan | Stable Diffusion Checkpoint | Civitai) with 1.5M images and then merged with other models.
Usage Details
This model is very flexible on resolution, you can use the resolution you used in sd1.x/2.x to get normal result(like 512x768), you can also use the resolution that is more native for sdxl(like 896*1280) or even bigger (1024x1536 also ok for t2i).
recommended negative prompt for anime style:
photorealistic, 3d model, bad, worse, worst, ugly, bad anatomy, blurry, close-up, disembodied limb
photorealistic, text, icon, artist name, signature, twitter username, naked, nude, monochrome, blurry, bad anatomy, watercolor, oil painting
watercolor, oil painting, photo, deformed, realism, disfigured, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
Training Details
Kohaku-XL beta5
This model is trained on new-danbooru (danbooru images with id from 5,000,000~6,600,000) which have 1.48M images. This model is resumed from kohaku-xl alpha7 and then merged with NekoRayXL.
Kohaku-XL base4 (haven't publish yet)
This model is trained on new-danbooru (danbooru images with id from 5,000,000~6,600,000) which have 1.48M images. Thie model is resumed from sdxl-0.9 (due to some bad property in sdxl-1.0 which will affect finetuning). In the plan this model will be trained with 2epoch (about 94.5k steps).
I haven't published this pretrained model yet.
Kohaku-XL beta7
This model is merged with base4 and beta 5, the formula is:
beta(5+n) = beta5 + (n/4) * (base4 - sdxl0.9)
So beta7 is beta5 + 0.5 * (base4 - sdxl0.9)
Kohaku-XL beta7.1
as same as beta7 but use the finished base4 and 0.25 weight.
Note: the base4 here is the 50k step version!!
Future Plan
I will run training on base4(after it finish) in Mynefactory dataset or CyberMeow(alea31415)/Narugo1992's reg dataset.
Acknowledgements
Models
