metadata
license: apache-2.0
pipeline_tag: depth-estimation
library_name: diffusers
NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors
NormalCrafter generates temporally consistent normal sequences with fine-grained details from open-world videos of arbitrary lengths. This model is based on the paper NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors.
🚀 Quick Start
🤖 Gradio Demo
- Online demo: NormalCrafter
- Local demo:
gradio app.py
🛠️ Installation
- Clone this repo:
git clone git@github.com:Binyr/NormalCrafter.git
- Install dependencies (please refer to requirements.txt):
pip install -r requirements.txt
🤗 Model Zoo
NormalCrafter is available in the Hugging Face Model Hub.
🏃♂️ Inference
1. High-resolution inference, requires a GPU with ~20GB memory for 1024x576 resolution:
python run.py --video-path examples/example_01.mp4
2. Low-resolution inference requires a GPU with ~6GB memory for 512x256 resolution:
python run.py --video-path examples/example_01.mp4 --max-res 512