| Property | Value |
|---|---|
| License | FLUX.1-dev Non-Commercial License |
| Training Infrastructure | 16×A800 GPUs |
| Architecture | 4 FluxTransformerBlock + 1 FluxSingleTransformerBlock |
| Framework | Diffusers |
What is FLUX.1-dev-ControlNet-Depth?
FLUX.1-dev-ControlNet-Depth is a sophisticated depth-aware image generation model developed collaboratively by InstantX Team and Shakker Labs. It represents a specialized implementation of ControlNet architecture integrated with the FLUX.1-dev base model, designed specifically for depth-controlled image generation.
Implementation Details
The model features a robust architecture trained over 70K steps with a significant batch size of 64 at 1024 resolution. It utilizes Depth-Anything-V2 for depth map extraction and operates with a recommended controlnet_conditioning_scale of 0.3-0.7. The training process employed a learning rate of 5e-6 and leveraged both real and generated image datasets.
- Advanced architecture with 4 FluxTransformerBlocks and 1 FluxSingleTransformerBlock
- Comprehensive training on diverse datasets
- Optimized for high-resolution output (1024px)
- Integration with Depth-Anything-V2 for precise depth mapping
Core Capabilities
- High-quality depth-aware image generation
- Flexible conditioning scale adjustment
- Support for multi-ControlNet operations
- Compatible with the FLUX.1-dev ecosystem
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized depth control capabilities and extensive training on both real and synthetic data, making it particularly effective for depth-aware image generation tasks. The integration with Depth-Anything-V2 ensures high-quality depth map processing.
Q: What are the recommended use cases?
The model excels in scenarios requiring precise depth control in image generation, such as architectural visualization, character positioning in scenes, and depth-aware content creation. It's particularly useful when working with the FLUX.1-dev ecosystem and can be combined with other ControlNet models for enhanced results.