✨ UWU_XL: V.2 ✨

(This model is crafted with passion, merging unique aesthetics to bring your visions to life. It's built to impress, designed for creators who demand the extraordinary.)

🌟 WHAT'S NEW IN V.2?🌟

+ Realism! (dpmpp_2m_sde_gpu seems to be best for realism)

+ Enhanced Img2Img Support!

+ Seamless Natural Language Integration!

+ More Custom Characters!

+ Unique Stylistic Horizons! (Euler_Ancestral can guide us towards stunning 2D+3D fusions)

+ Illustrious & Pony LoRA Support! (Work in Progress – some connections are stronger than others.)

⚠️ IMPORTANT!⚠️

This version fixes a lot of broken things in V1 but also suffers from keeping generations with very similar elements. If you don't change up how you prompt something, you'll tend to get very similar results (which can be good and bad).

💡OPTIMIZING YOUR GENERATIONS💡

CFG Guidance:

To remedy consistency, adjust your CFG occasionally.

* 3-7 is generally its sweet spot.

* 3-4 for initial generations.

* 5-7 for upscaling or refiners.

Prompting Strategy:

Tags work wonderfully, but try things that aren't the usual. The more unique the better. During training, I used a combination of natural language and tags, applying three methods: AI-generated descriptions, traditional tagging, and describing scenes in my own words.

Structure your prompts for optimal results:

1. Start with your subject description.

2. Describe the scene or background.

3. Finally, apply any specific tags.

Avoiding the same person/people:

1. Add an ethnicity.

2. Describe the face "X shaped eyes, narrow nose, etc."

3. Avoid vague descriptions "Handsome, Pretty"

4. Negatives come in clutch here, try adding characters or names. This creates a strong negative bias and the model will push towards different looking people.

😈 CONTENT & CAPABILITIES 😈

* This model can and will generate nudity. If you don't specify the subject's attire, they will default to wearing nothing.

* Explicit scenes are something this model does struggle with; it works sometimes but not reliably.

* Solo subjects work really well, duo encounters are iffy.

* I strongly encourage using your favorite LoRAs to depict any acts you wish to see. (However, if you're shooting for realism, things might not go as expected; if you use a stylized LoRA, stick to a stylized prompt, and vice versa.)

🧬 MODEL DNA & TRAINING (UWU_XL's Core) 🧬

This model is named UWU_XL because it was heavily trained on a diverse dataset of goth variants (pastel, cyber, etc.) and egirl aesthetics, among others.

The dataset contains:

* Publicly available images.

* My personal generations.

* My own photographs.

* My own art.

This model also features base model merges of SDXL, Pony, and Illustrious, leveraging their unique tags and dataset dimensions for enhanced results.

💖 SUGGESTIONS & FEEDBACK💖

If you'd like to suggest styles or datasets, please message me, and I will try to add features or subjects to the dataset.

✨ V.1 (The Foundation)✨

My first XL checkpoint, used for all my initial generations. When paired with my LoRAs at low strengths, it generally yields something resembling my stylistic examples.

It can do both realistic and semi-realistic generations, but by default might yield Pony-like results (it was trained on real and animated images). Due to this, some tags will lean certain directions. Natural language and tags both seem to work well in most cases.

V.1 Realism Tags (Still Useful for V.2!)

in the year 1995, analog film, kodak snapshot, candid photography, high resolution, 100mp, filmic, natural face, proportional facial features, imperfect skin, low contrast, modern color grade, aged, film grain, unique angle, instagram, profile pic, screen grab, captured in 4k, photo of (insert subject), (fake name of a person), technicolor, digital cinema, movie-like