README.md

---
base_model: black-forest-labs/FLUX.1-dev
widget:
  - text: yearbook portrait of student, plain background, smiling, teeth, braces
    output:
      url: images/7-1.png
  - text: yearbook portrait of student, plain background, smiling, teeth, braces
    output:
      url: images/7-2.png
  - text: yearbook portrait of student, plain background, smiling, teeth, braces
    output:
      url: images/7-3.png
  - text: yearbook portrait of student, plain background, smiling, teeth, braces
    output:
      url: images/7-4.png
instance_prompt: braces
---
# Better Braces LoRA

<Gallery />

## Model description 

This LoRA model specializes in generating portraits with braces, adding a realistic and distinctive touch to character images. Trained to excel at producing yearbook-style portraits with various styles of dental braces, it creates diverse and authentic representations.

The model aims to:
- Create diverse representations of braces on teeth
- Maintain natural facial expressions, particularly smiles

This LoRA is particularly useful for:
- Character designers seeking to add distinctive dental features
- Artists looking to create more diverse and realistic portraits
- Projects requiring specific orthodontic characteristics in images

![A photo of a man with braces. He is wearing a brown jacket and a white shirt. The man has short brown hair and is wearing glasses. The background is blurred and contains a few objects.](images/portrait.png)

While primarily focused on braces, the model maintains overall image quality and facial structure integrity.

## Trigger words

To activate this LoRA's specific features, include `braces` in your prompts.

## Download model

The weights for this model are available in Safetensors format.

You can [download](https://huggingface.co/Rejekts/Better-Braces/resolve/main/better-braces.safetensors) them in the Files & versions tab.

## Usage Tips

- Experiment with different strength settings to control the intensity of the braces effect
- Combine with other LoRAs or models for more diverse results
- Use negative prompts to fine-tune unwanted features if necessary

Remember, results may vary based on the base model and other parameters used in your generation pipeline.