Create README.md

README.md

@@ -0,0 +1,53 @@
+---
+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
+
+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.