Instructions to use happy0612/GeoNeXt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use happy0612/GeoNeXt with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("happy0612/GeoNeXt", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add model card (#1)
Browse files- Add model card (44300e892187db0d25033b1ddbddf9297ec71384)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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---
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pipeline_tag: depth-estimation
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library_name: diffusers
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---
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# GeoNeXt
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**Video Generative Models as Geometry Learner**
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GeoNeXt is a unified framework for monocular depth and surface normal estimation, repurposing pretrained video generative models for geometry prediction through a next-frames formulation.
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- **Paper**: [Video Generative Models as Geometry Learner](https://huggingface.co/papers/2608.28549)
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- **Project page**: [https://happy-hsy.github.io/projects/GeoNeXt/](https://happy-hsy.github.io/projects/GeoNeXt/)
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- **GitHub**: [https://github.com/Creative-Intelligence-Studio/GeoNeXt](https://github.com/Creative-Intelligence-Studio/GeoNeXt)
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- **Model checkpoints**: [happy0612/GeoNeXt](https://huggingface.co/happy0612/GeoNeXt)
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The repository contains checkpoints for GeoNeXt-Wan and GeoNeXt-SVD. Follow the instructions in the GitHub repository to run inference for depth and normal estimation.
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