These models makes everything look like it's made out of wool and having been hand-knitted.
I have trained 4 versions of this model to learn more about Flux training. All models are trained with the same training settings (read more about it in my training article), using the same images, but with different captions.
Training Diary Article part 1 (lots of images)
Training Diary Article part 2 (even more images)
âš¡âš¡âš¡ Please support further training and information sharing by downloading this model during early access. âš¡âš¡âš¡
Usage
Based on which model you use, you will either have to use a trigger word, or not.
Example prompt: wizard casting a spell
W00lyW0rldFlux-SingleWord.safetensors: Trigger word is "W00lyW0rld"
W00lyW0rld a wizard casting a spell <lora:W00lyW0rldFlux-SingleWord:1.0>
W00lyW0rldFlux-WD14.safetensors: Trigger word is "W00lyW0rld"
W00lyW0rld a wizard casting a spell <lora:W00lyW0rldFlux-WD14:1.0>
All other models do not require a trigger word, but you still need to "unlock" the knowledge of the model by describing the effect in natural language. For this, I recommend that you use "Made out of wool".
W00lyW0rldFlux-NoCaption.safetensors: No trigger word, but use natural language like:
Made out of wool, a wizard casting a spell <lora:W00lyW0rldFlux-NoCaption:1.0>
I've found that most settings work well with this model. No special samplers or CFG-scale.
Read the training diary to learn more about this Flux training test. (link coming after I've posted)
Note:
I may be using the following extensions in the reference images: FreeU or ADetailer / Face Editor. I may also be using a specific Hires Checkpoint (swaps to another model when doing the hires steps). If your results don't match mine exactly, this may explain why.
Tips and tricks
Below are some tips and tricks for how to get even more out of the model.
You can always add some extra keywords to enhance the style.
made out of wool
knittedLike it?
Description
From the training diary: https://civitai.com/articles/6792
This version uses long and complex captions to describe the training images in very high detail. The captions are generated using the JoyCaption tool. I also created a script to let you run this on all images in an /input/-folder, for easy quick tagging without needing to open Kohya or anything else. Here's the Joy-Caption-Batch Github page.
Captions are very long (perhaps overly so), and I trained them unedited, without adding any trigger word. Here's a caption example: