SD-ControlNet-HED
| Property | Value |
|---|---|
| Base Model | Stable Diffusion v1-5 |
| License | OpenRAIL |
| Training Data | 3M edge-image pairs |
| Training Duration | 600 GPU-hours (Nvidia A100 80G) |
| Paper | Adding Conditional Control to Text-to-Image Diffusion Models |
What is sd-controlnet-hed?
SD-ControlNet-HED is a specialized neural network architecture designed to enhance Stable Diffusion's image generation capabilities by incorporating HED (Holistically-nested Edge Detection) boundary information. This model enables precise control over image generation by using soft edge detection as conditioning input, allowing for more accurate and controlled image synthesis.
Implementation Details
The model was trained as part of the ControlNet framework, specifically optimized for edge-based image control. It processes input images through HED edge detection, creating soft boundary maps that guide the image generation process. The training involved 3 million edge-image pairs and utilized Stable Diffusion 1.5 as its foundation.
- Integrates seamlessly with Stable Diffusion v1-5
- Utilizes HED edge detection for soft boundary detection
- Supports end-to-end training with small datasets
- Optimized for efficient processing on personal devices
Core Capabilities
- Generation of images based on edge map conditioning
- Precise control over structural elements in generated images
- Support for various input conditions including sketches and edge maps
- Compatible with different diffusion models, especially SD v1-5
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in soft edge detection-based image generation control, offering more natural and nuanced edge guidance compared to traditional edge detection methods. It's particularly effective for artistic and realistic image generation where precise structural control is needed.
Q: What are the recommended use cases?
The model is ideal for applications requiring precise control over image structure, such as artistic rendering, architectural visualization, and photo editing where maintaining specific edge constraints is crucial. It's particularly useful when working with sketches or edge-based input conditions.
