This is my seventh major publicly posted LoRA. This is my first Z-Image Turbo LoRA and using AI-Toolkit.

This LoRA was an experiment to test the "wisdom" that a dataset should be "small and high quality", by training a LoRA on a large dataset of low quality images. The source images used in this LoRA were mostly from DVD frames from 2004-2011. Approximately 25% of images were from 2011 and high definition frames of that era. A total of 10956 images were used in the data set.

It is okay. It struggles in resolving a glans next to lips. Please continue reading for setting recommendations:

Summary for LoRA:

This is a concept LoRA that produces two men engaging in oral sex. The goals of this LoRA were to:

• generate two men engaged in oral sex.

• test whether using many low quality images instead of few high quality images was viable.

I consider this an alpha because like TwinkCockXL and with TwinCockFlux, it works most of the time, but still is not perfect..

• The tags were generated by joy-caption-beta-one.

• Tags were manually edited.

The primary activation tag "TheFutonEra" was added to all images. A secondary tag "1face" or "2face" was added depending on whether the image was a closeup only showing one face, or a slightly wider shot showing two faces.

Sampler, Scheduling, Clip notes:

• Initial testing was done on the Stability Matrix implementation of Forge UI neo. Testing was done using the z_image_turbo_bf16 model.

• Recommended Shift is between 18 and 24.

• Recommended Sampling steps is 18

• Recommended LoRA strength is between 0.44 and 0.66.

• The LoRA was tested mainly on Euler a and Linear Quadratic, Beta, or DDIM. Euler and Laplace also was tested.

• SeedVarianceEnhancer Integrated was set to 2 steps with a Cosine decay.

Other notes:

• Ethnic and age representation will likely rely on the main Z-image turbo model.

• The length or size of the penis, and the circumcision status of the penis was not consistently tagged in this LoRA. Z-Image Turbo by default seems to favor distorted penises.

• Women were not included in the training data, and I have no clear idea what will happen if a woman is specified, One likely result may be a penis being added to any figure in the generation. I do not plan on including women in the future as I do not have source images.

• Regularization images were used. Extensive testing on style flexibility beyond photorealistic has not yet been conducted.