Marigold Normals LCM v0.1
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Paper | View Research Paper |
| Downloads | 2,359,798 |
| Primary Task | Surface Normals Estimation |
What is marigold-normals-lcm-v0-1?
Marigold Normals LCM is an innovative diffusion-based model designed specifically for surface normals estimation in computer vision tasks. Built upon the foundation of Stable Diffusion, this model leverages Latent Consistency Models (LCM) technology to provide fast and accurate surface normal predictions from single images.
Implementation Details
The model employs a sophisticated architecture that combines the visual knowledge from Stable Diffusion with specialized training for normals estimation. It's trained using synthetic data and further enhanced with LCM fine-tuning for improved performance.
- Zero-shot capability for in-the-wild images
- Monocular estimation architecture
- Integration with Latent Consistency Models for faster processing
- Built on Stable Diffusion's rich visual understanding
Core Capabilities
- Single-image surface normals estimation
- Real-time processing with LCM optimization
- Robust performance on various scene types
- Zero-shot inference without additional training
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its innovative use of diffusion-based architecture combined with LCM technology, enabling fast and accurate surface normals estimation without requiring specific training for new scenarios.
Q: What are the recommended use cases?
The model is ideal for computer vision applications requiring surface normal estimation, including 3D reconstruction, scene understanding, and augmented reality applications. It's particularly useful in scenarios where real-time processing is needed.
