For the best experience, use it together with a futanari friendly checkpoint like Futanari Factor

This is an "improved" version of the FutaVeinyV6 that has more penis and testicles sizes. The adapter was trained using LoKr and was tested together with models that are already able to draw futanaris without much effort like abyssFuta, bb95-furry-mix, fluffyrock and their merges.

How to use

Edit the "SIZE" to the size you want to use.

Penis sizes:

• small penis (WIP)

• penis

• large penis

• huge penis

• gigantic penis

Testicles sizes:

• testicles

• large testicles

• huge testicles

• gigantic testicles (WIP)

If the size slider doesn't seem to be working well with your prompt, try adding some weight to the token:

futanari, veiny penis, (gigantic penis:1.4), (huge testicles:1.2)

Optional:

You can add "erection" or "flaccid" to your positive or negative prompts to control the dong status.

(WIP) If you like some saggers, add "sagging testicles" to your positive prompt.

(WIP) Add "foreskin, phimosis" to your positive or negative prompts to change the dong sheath.

Your feedback is appreciated! Its going to help a lot with tracking what checkpoints are working the best to produce the best dongs and what I need to change in the next iterations of this lora.

Development notes:

The training images were tagged using conv2 and swinv2 at the same time and then merged together and the duplicate tags removed.

Every caption was revised by me and the concept tokens were reordered to be used with keep_n_tokens.

Every image was cleaned of signatures and watermarks using lama cleaner and photoshop.

Censored images were redraw using deepcreampy and then manually retouched using manga studio.

5%~10% of the dataset is reserved for AI generated images made by me that were upscaled and manually retouched using manga studio and a drawing tablet. It helps to augment some concepts that don't have enough good pictures and achieve that specific futanari aesthetic I'm looking for.

I took 1 month to assemble, clean, redraw and tag the dataset, but the training and testing process is even more time consuming. To help with that, I'm also trying to code a tool to help me evaluate the testing grids, find the best epochs and track what worked or not between batches.