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Flux Inpainting with LoRA

This project implements an AI-powered image inpainting model using the Flux architecture with LoRA (Low-Rank Adaptation) fine-tuning. It's designed to run on Replicate, allowing users to easily perform image inpainting tasks with custom prompts and LoRA models.

Overview

The Flux Inpainting model can intelligently fill in masked areas of an image based on the surrounding context and a text prompt. This implementation also supports the use of custom LoRA models for fine-tuned results.

Features

  • Image inpainting using the Flux architecture
  • Support for custom LoRA models
  • Adjustable parameters for fine-tuned control
  • Easy deployment on Replicate

Usage

To use this model on Replicate, you'll need to provide the following inputs:

  • hf_token: Your Hugging Face API token for accessing the model
  • image: The input image for inpainting
  • mask: A mask image indicating the area to be inpainted
  • prompt: A text prompt describing the desired inpainting result
  • lora_path: Path to the LoRA model (default: "XLabs-AI/flux-RealismLora")
  • lora_weights: Name of the LoRA weights file (default: "lora.safetensors")
  • lora_scale: Scale factor for LoRA (default: 0.9, range: 0-1)
  • trigger_word: LoRA trigger word (default: "a photo of TOK")
  • seed: Random seed for reproducibility (default: 42)
  • strength: Strength of the inpainting effect (default: 0.85, range: 0-1)
  • num_inference_steps: Number of inference steps (default: 28, range: 1-100)

The model will return an output image with the inpainted result.

Development

To modify or extend this project:

  1. Update the predict.py file to change the model's behavior or add new features.
  2. Modify the cog.yaml file if you need to change the build configuration or add new dependencies.
  3. Test your changes locally using the Cog CLI before deploying to Replicate.

Deployment

To deploy this model to Replicate:

  1. Ensure you have the Cog CLI installed and configured.
  2. Run cog push r8.im/your-username/your-model-name to build and push the model.

Acknowledgements

This project is based on the work by jiuface on Hugging Face Spaces.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.