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SoReal! - Portraits

|| SoReal! - POV || SoReal! - Natural Bodies || Vacation Photos ||

Overview

Capture the range of human beauty! This model is the next model in a series in Z-Image LORAs - aimed to bring diversity in both concepts and humanity itself to Z-Image.

Compatibility & Usage

Due to it's small rank and layer filter, the model should have a minimal influence on the base models actual weights, further improving compatibility with other LORAs across Base/Turbo and indeed other checkpoints.

'Trigger words' aren't real - don't ask for one, just prompt normally! 'Portrait' or 'Portrait photography' appears very frequently in the captions so this may help you get closer to a more portrait-style image if your prompt is not very portrait-esque.

When using Z-Image Turbo, I use a strength of 1.

Limitations

This isn't explicitly an NSFW model - it was trained on both male & female anatomy, but there was nothing special done to it to improve adherence to NSFW concepts.

Future

I am planning on finetuning Z-Image considerably with a model called 'SoReal!' (Or, alternatively, ZoReal!). However, I want it to be the best possible amateur finetune possible, to achieve this, I have:

• 1. Trained a custom quality model.

• 2. Trained a custom one-shot demographic model (height, weight, skin tone, ethnicity, age in years, body shape) with an average accuracy of 89% for top-confidence prediction using ConvNext-XL.

• 3. Finetuned wd-tagger-large-v3 on a large sample dataset of 50k hand-tagged images with human-assisted active learning.

• 4. Fed those tagged images (with quality, demographics and general labels) with the image metadata (incl. EXIF & Camera Metadata) to Gemini 3 Flash for generating captions.

• No over-trained LORAs baked in, no dramatic loss of generalisation, just a good, all-round, NSFW-ready, finetuned model.

I am now severely limited, however, by my compute and financial situation, so if you'd like to help make SoReal!, well, so real, then you can follow me on Patreon!

Dataset & Training

Dataset of 3000 sourced from a variety of sources. Deduplication and Quality Scoring (through MANIQA) lowered the dataset to around 1200.

This model was trained on a dataset of 1200 images at a batch size minimum of 16.

Validation loss was used with 10% of the dataset size to prevent overfitting while still maintaining strong concept adherence and generalisation.

Model was trained with AdamW through the Python adv-optm package.

Licensing

If you'd like to release a merge of this model, please contact me.

Made with <3 By BitcrushedHeart