Flux.1-dev-Controlnet-Depth
| Property | Value |
|---|---|
| Author | jasperai |
| License | Flux.1-dev Non-Commercial License |
| Framework | Diffusers |
| Downloads | 3,323 |
What is Flux.1-dev-Controlnet-Depth?
Flux.1-dev-Controlnet-Depth is a specialized ControlNet model designed for depth-aware image generation. Built on the foundation of Flux.1-dev, this model enables precise control over image generation using depth maps, allowing for accurate spatial relationships and 3D-aware image synthesis.
Implementation Details
The model is implemented using the Diffusers library and supports bfloat16 precision for efficient inference. It was trained using a combination of Clipdrop's depth estimator model and open-source depth estimation models like Midas and Leres. The model can be easily integrated into existing pipelines using the FluxControlNetModel and FluxControlNetPipeline classes.
- Supports custom height and width parameters
- Configurable conditioning scale for depth control
- Compatible with various depth map generation tools
- Optimized for 28 inference steps
Core Capabilities
- Depth-aware image generation
- Precise spatial control in generated images
- Integration with MidasDetector for depth map computation
- Support for both image-to-image and controlled generation workflows
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in depth-aware image generation, allowing for precise control over spatial relationships in generated images using depth maps as conditioning input. It's built on the powerful Flux.1-dev architecture and optimized for practical applications.
Q: What are the recommended use cases?
The model is ideal for scenarios requiring precise control over spatial composition, such as architectural visualization, product photography simulation, and creative artwork requiring specific depth arrangements. It works particularly well with the MidasDetector for generating depth maps from existing images.
