(HoJ) - High on Juice - Semi-realistic IllustriousXL

Short Story

HoJ is a brand new checkpoint focused on my personal tastes: high-constrasted, vividly colored, semi-realistic anime-like pictures.

Recommended usage:

• Sample/Scheduler:

  • DPM++ 2M SDE Heun & SGM Uniform (what i used for showcase)

  • Euler A & Beta

  • DPM++ 2M & Karras works too, but it is not the best

• Steps: between 30 and 40

• CFG: 6 (this is my preference, feel free to experiment)

• VAE: Standard SDXL VAE is baked in, no need for an "extra colored VAE"

• Upscaler: 4xNMKDSuperscale 20 steps, denoising max 0.3

• Adetailer: 0.15 denoising maximum, same number of step, same VAE (or None)

• Positive prompt: Quality tags are not needed (masterpiece and so on)

• Negative prompt: IllusN3g

• Artist tags: I don't recommend using any of those, they will not respond correctly

• Clip Skip: 2 (but 1 can works fine too)

• 🚩 NSFW Warning: Be sure to include model age or some measure of security in this direction or you may land with far too young characters. That's something i noticed and that i'll need to fix in a future version.

Permissions: Feel free to use this model in a merge, just make sure to mention the source 😉

Don't upload this model in other website,

i'll handle myself the upload on TA and SA.

@GZees kindly added HoJ on https://socialdiff.net also if you want to give it a try 🥰

To comply with source resources, commercial use of pictures generated is forbidden.

The (famous) long story

It was build on top of my own Illustrious merge checkpoint, with some elaborate mixing of JunkJuice Psychonex (a Pony model) without losing the Illustrious goodness.

For the sake of openess, the resources i leveraged to do so are:

• Featureless 2.5D

• Coco Illustrious 5.5 (-)

• (Psychonex - Pony) as an "add difference" merge

• realistic filter [IL]

• Painted_World_IllustriousXL

• Smooth Booster

• Pino_Art_Style_by_UOC

• Hands 5.5

And then manually trained on a subset of "best pictures" for a few epoch as explained in this article.

The resulting trained model was carefully merged in the original checkpoint to fix its defaults. To do so, using merge block, i created two versions (the similarity of blocks is here):

• Original + Training, similar looking blocks only (IN/OUT pairs 02,03,05,06 + MID, at strength 1.0)

• Original + Training, different looking blocks only (IN/OUT pairs 00,01,04,07,08, at strength 1.0, except for the 01 pair at 0.25 to avoid over-saturation)

And then, using Supermerger and cosine calculation mode, these two versions were mixed for a finished checkpoint (Similar + 0.25 different, CosineA).

Thanks for reading! 💖

EDIT: the dataset used to fine-tune HoJ is available here: https://huggingface.co/datasets/n-Arno/juicy