StableNormal

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StableNormal is an Image Generation tool that reduces diffusion variance for stable and sharp normal map estimation. It offers both a standard and a turbo version for faster inference.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is StableNormal?

StableNormal is an open-source AI tool designed to enhance monocular normal estimation by reducing the inherent stochasticity of diffusion models. This approach leads to "Stable-and-Sharp" normal maps, outperforming various baselines in terms of accuracy and stability. The tool is presented as a research project from SIGGRAPH Asia 2024 and provides a Python-based pipeline for installation and usage. It includes a faster inference option, StableNormal-turbo, which is 10 times quicker. Users can compute metrics on datasets like DIODE, IBims-1, Scannet, and NYUv2 to evaluate performance, making it suitable for researchers and developers in computer vision and generative AI.

Best used for

Ideal for professors and researchers who need to generate stable and sharp normal maps, reduce diffusion variance in AI models, and evaluate model performance on various datasets. Especially valuable for those working on computer vision and 3D reconstruction applications requiring high-quality surface normal estimation.

Common actions

generate normal maps

reduce diffusion variance

evaluate model performance

enhance image stability

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Capabilities

Key features

Reduce diffusion variance
Stable normal estimation
Sharp normal estimation
Faster inference (turbo)
Compute evaluation metrics

Target Audience

professorresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary advantage of StableNormal over other diffusion-based normal estimation methods?

StableNormal focuses on reducing the inherent stochasticity of diffusion models, leading to more stable and sharper normal estimations. This approach directly addresses variance issues, resulting in higher quality outputs compared to many existing baselines, as demonstrated by its performance metrics.

How can I achieve faster inference with StableNormal?

For significantly faster inference, you can utilize StableNormal-turbo. This optimized version is reported to be 10 times faster than the standard StableNormal model, making it suitable for applications requiring quicker processing times without significant loss in quality.

What datasets can be used to evaluate StableNormal's performance?

StableNormal provides scripts to compute metrics on several standard datasets, including DIODE-indoor, IBims-1, Scannet, and NYUv2. This allows users to rigorously evaluate the normal predictor's performance against established benchmarks.

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Research & Education › Academic Research Coding & Development › Open Source & Models

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