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
metadata
pipeline_tag: depth-estimation
library_name: diffusers
GeoNeXt
Video Generative Models as Geometry Learner
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.
- Paper: Video Generative Models as Geometry Learner
- Project page: https://happy-hsy.github.io/projects/GeoNeXt/
- GitHub: https://github.com/Creative-Intelligence-Studio/GeoNeXt
- Model checkpoints: happy0612/GeoNeXt
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.