SD-ControlNet-Normal
| Property | Value |
|---|---|
| Base Model | Stable Diffusion v1-5 |
| License | OpenRAIL |
| Authors | Lvmin Zhang, Maneesh Agrawala |
| Paper | Adding Conditional Control to Text-to-Image Diffusion Models |
What is sd-controlnet-normal?
SD-ControlNet-Normal is a specialized version of ControlNet designed to work with normal map estimation. It enables precise control over image generation by utilizing surface normal information derived from depth maps, allowing for more accurate 3D-aware image generation. The model was trained on the Stable Diffusion v1-5 base and can interpret normal mapped images to generate corresponding detailed outputs.
Implementation Details
The model underwent a two-phase training process: initial training on 25,452 normal-image pairs from DIODE with BLIP-generated captions (100 GPU-hours), followed by extended training on coarse normal maps generated using Midas depth estimation (200 GPU-hours on Nvidia A100 80G). The model leverages normal-from-distance techniques to enhance its understanding of 3D surface properties.
- Integrates with Stable Diffusion v1-5 architecture
- Supports normal map conditioning for enhanced 3D awareness
- Utilizes depth-to-normal conversion pipeline
- Implements efficient GPU memory management
Core Capabilities
- Normal map interpretation and generation
- Depth-aware image synthesis
- 3D-structure-preserving image generation
- Integration with existing Stable Diffusion pipelines
Frequently Asked Questions
Q: What makes this model unique?
This model specifically handles normal maps, which contain information about surface orientation and depth, allowing for more precise control over the 3D aspects of generated images. It's particularly useful for maintaining consistent surface geometry in image generation.
Q: What are the recommended use cases?
The model is ideal for applications requiring precise control over surface geometry, such as architectural visualization, character design with specific pose requirements, and 3D-aware image editing. It works best when paired with Stable Diffusion v1-5 and can be used in conjunction with depth estimation tools.
