Proposal: Optional Integration of FLUX.1 Models for Advanced Inpainting

[D-Ogi]

Background

The FLUX.1 model suite by Black Forest Labs, particularly the [dev] and [schnell] variants, introduces cutting-edge inpainting capabilities optimized for different use cases:

• FLUX.1 [dev]: A guidance-distilled model designed for non-commercial use, delivering high-quality, efficient inpainting.
• FLUX.1 [schnell]: A fast and lightweight model optimized for local development and high-speed processing.

These models excel in handling complex and irregular areas in images, making them a promising addition to our AI Watermark Remover project. While FLUX.1 is capable of prompt-guided inpainting, I propose integrating it for use without prompts, focusing solely on its inpainting strengths.

Why FLUX.1?

The current LaMa implementation is robust, but FLUX.1 offers:

1. Speed Optimization: The [schnell] variant is designed for fast local inference, making it ideal for users prioritizing speed.
2. Flexibility: FLUX.1’s architecture supports precise inpainting for challenging cases, such as irregular watermarks or complex image structures.
3. Non-Commercial Accessibility: The [dev] variant provides state-of-the-art results while being freely available for non-commercial use.

By introducing FLUX.1 as an optional backend, we can empower users with more choices, tailored to their specific performance and quality requirements.

Proposed Implementation

1. Backend Integration:

   • Add FLUX.1 as an alternative inpainting backend in both CLI and GUI (remwm.py and remwmgui.py).
   • Support both FLUX.1 [dev] and [schnell] variants, allowing users to switch between them based on their needs.

2. Dependencies:

   • Update environment.yml to include FLUX.1’s dependencies, such as the diffusers library.
   • Ensure CUDA compatibility to leverage FLUX.1’s full performance potential.

3. User Options:

   • Add a dropdown in the GUI for selecting the inpainting backend (LaMa, FLUX.1 [dev], or FLUX.1 [schnell]).
   • Keep the settings simple, with no need for prompts or complex configurations.

4. Fallback Mechanism:

   • Provide clear guidance for users on selecting the right backend for their hardware and use case.
   • Retain LaMa as the default backend to ensure compatibility for all users.

Questions for Discussion

• User Needs: Would you find FLUX.1 useful for specific scenarios, such as handling irregular watermarks or requiring faster processing?
• Configuration Simplicity: Should I include any user-adjustable parameters for FLUX.1, or keep the integration streamlined with fixed defaults?
• Performance Testing: Are there specific image types or challenges where you feel FLUX.1 would significantly outperform LaMa?

I believe FLUX.1 has the potential to greatly enhance the capabilities of the AI Watermark Remover project. Your feedback will be invaluable in shaping how we approach this integration.