LoRA Model: swimsuit_fun_flux_d_1

This LoRA model allows for more dynamic control over both swimsuit design and fabric texture, providing users with versatile options for customizing their renders. There is the potential to apply the fabric properties to other attires, such as stockings, like in the example of a woman wearing rubber1zed stockings provided in the dataset.

Style Options

swimsui1: A bodysuit with straps and an open back. You can further refine the style using prompts such as:
- "raceback"
- "high neckline"
- "scoop neckline"
- "ruched neckline"
- "thin spaghetti straps"
bodysui1: A more covered design, always featuring a high neckline, a closed back, and a sleeveless cut.

Fabric Options

ma11e: A stretchy, smooth fabric with a subtle shine, perfect for traditional swimwear.
semi-g1oss: A thicker, less stretchy material with a mild sheen, similar to neoprene but with a slightly shinier finish.
wet1ook: A very smooth, stretchy fabric with a glossy, liquid-like sheen, giving off a "wet" appearance.
meta11iclook: Semi-stretchy fabric with a metallic shine, adding a bold, futuristic touch to designs.
rubber1zed: A thicker, almost non-stretch fabric with a highly glossy finish, reminiscent of latex or rubber.

Prompting Tips

Consistency is key: Always stick to the specific trigger words (e.g., "swimsui1", "bodysui1") in your prompts, and avoid mixing them with the correct spellings (e.g., don’t mix "swimsui1" and "swimsuit").
Use trigger words naturally: Apply the trigger words as you would use regular adjectives or descriptors. For example: "She wears a red, wet1ook swimsui1."
One style, one fabric per person: Keep each prompt consistent by selecting one style and one fabric per person. Example of what NOT to do: "She wears a smoo1h swimsui1. The bodysui1 has a sem1-gloss shine."
Multiple styles for multiple people: You can prompt different styles and fabrics for different people in the same image. For example, one figure could wear a "meta11iclook swimsui1" while another wears a "smoo1h bodysui1". Note that FLUX may occasionally mix things up, but the LoRA should not worsen this.
Detailed Descriptions for Specific Results: Don't rely solely on keywords. Adding descriptive details improves your chances of getting the desired outcome. Example: "The woman is wearing a sleek, navy blue one-piece swimsui1 made from a glossy, rubber1zed material. The suit reflects the sunlight, creating a shiny, almost wet appearance. It features a high neckline and racerback, exposing her shoulders and upper back. The high-cut leg openings accentuate her athletic build."
LoRA value makes a difference: from my experience, often lower values around 0.6-0.4 work better with this LoRA.

Why This Model Stands Out

This is my third LoRA model and by far the best. The results are stunning—be sure to check out the dataset for some examples! I trained the model using 100+ high-quality, manually optimized images featuring women of various ages and ethnicities to ensure diversity and avoid bias.

Support the Project

The training process took significant time and effort. If you enjoy the model, I kindly ask for a small donation—every contribution will go toward expanding the model to include additional attires, such as leggings, catsuits, or leotards. Your feedback is invaluable, so please leave comments below and share your creations to help improve this project!

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