Marigold Depth v1-1 Cool Depth Estimation Model - Install Locally
AI Summary
This video is a tutorial by Fahd Mirza on installing and testing Marigold Depth v1.1, a monocular depth estimation model that leverages latent diffusion model (LDM) technology for computer vision tasks.
Key Concepts Explained
Latent Diffusion Models (LDMs): The presenter explains these as AI models (like Stable Diffusion) that work like artists studying billions of images, starting with noisy canvases and gradually refining them into clear images based on text descriptions. They’re called “latent” because they work with compressed versions of images, and “diffusion” describes the noise-to-clarity process.
Marigold’s Innovation: Rather than creating new images, Marigold repurposes the visual understanding of LDMs for analysis tasks. It teaches these powerful models to analyze existing images for depth estimation - determining how far away objects are in a 2D photo to create 3D understanding.
Installation Process
System Setup:
- Ubuntu system with Nvidia RTX A6000 (48GB VRAM)
- GPU rental sponsored by Mast Compute (50% discount available)
- Creates conda virtual environment
- Clones the Marigold repository
- Installs prerequisites using pip
Demo Launch: The model downloads automatically on first run and launches a web-based interface accessible through a browser.
Performance Testing
Test Results:
- Bee Image: Excellent depth estimation with fine detail capture, including wing contours, edges, and hair details
- Portrait Image: Outstanding performance on earrings and hair edges, described as “sublime”
- Kangaroo Scene: Successfully estimated depth for three kangaroos and tree, though struggled with birds in the background
- Various Benchmarks: Generally impressive depth mapping across different image types
Technical Performance:
- VRAM usage: ~5.5GB when loaded
- Processing speed: Very fast inference times
- Model size: Slightly higher VRAM consumption than previous versions but with significantly improved quality
Assessment
The presenter, having covered Marigold models for over a year, expresses high enthusiasm for this v1.1 release. The model shows substantial improvements over previous versions, particularly in edge detection and fine detail preservation. While there’s still room for improvement (noted issues with bird detection), the overall performance is described as “amazing” and “impressive.”
Context
This video is part of Fahd Mirza’s ongoing coverage of AI models, sponsored by Camel AI (focused on multi-agent infrastructures and world simulation). The tutorial provides both technical implementation guidance and conceptual understanding of how diffusion models can be adapted for computer vision tasks beyond image generation.