prs-eth/Marigold

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3198 stars · 208 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Marigold is a family of diffusion-based models and a fine-tuning protocol that adapts pretrained latent diffusion models like Stable Diffusion for dense image analysis tasks. It delivers state-of-the-art zero-shot monocular depth estimation, surface normal prediction, and intrinsic image decomposition.

Use cases

• estimate depth maps from a single image
• predict surface normals from photos
• decompose an image into albedo and shading
• run zero-shot depth estimation on in-the-wild images
• fine-tune stable diffusion for dense prediction tasks
• generate high-resolution depth maps for 3d reconstruction

When to choose

• you need high-quality monocular depth estimation with strong zero-shot generalization
• you want to repurpose pretrained diffusion models for dense image analysis
• you need surface normals or intrinsic decomposition from single images
• you want a model trainable on small synthetic datasets with a single GPU

When to avoid

• you need real-time depth estimation on low-power or CPU-only hardware
• you need metric depth with calibrated scale rather than affine-invariant depth
• you want a lightweight classical depth estimator without diffusion inference overhead

Facets

library · maturity active

computer-vision image-processing machine-learning deep-learning computer-vision image-processing deep-learning machine-learning python cross-platform monocular-depth-estimation diffusion-models stable-diffusion surface-normals intrinsic-decomposition zero-shot cvpr-2024 huggingface gpu

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