| license: apache-2.0 | |
| pipeline_tag: depth-estimation | |
| library_name: diffusers | |
| # NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors | |
| [NormalCrafter](https://normalcrafter.github.io/) 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](https://huggingface.co/papers/2504.11427). | |
| ## 🚀 Quick Start | |
| ### 🤖 Gradio Demo | |
| - Online demo: [NormalCrafter](https://huggingface.co/spaces/Yanrui95/NormalCrafter) | |
| - Local demo: | |
| ```bash | |
| gradio app.py | |
| ``` | |
| ### 🛠️ Installation | |
| 1. Clone this repo: | |
| ```bash | |
| git clone git@github.com:Binyr/NormalCrafter.git | |
| ``` | |
| 2. Install dependencies (please refer to [requirements.txt](requirements.txt)): | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### 🤗 Model Zoo | |
| [NormalCrafter](https://huggingface.co/Yanrui95/NormalCrafter) is available in the Hugging Face Model Hub. | |
| ### 🏃♂️ Inference | |
| #### 1. High-resolution inference, requires a GPU with ~20GB memory for 1024x576 resolution: | |
| ```bash | |
| python run.py --video-path examples/example_01.mp4 | |
| ``` | |
| #### 2. Low-resolution inference requires a GPU with ~6GB memory for 512x256 resolution: | |
| ```bash | |
| python run.py --video-path examples/example_01.mp4 --max-res 512 | |
| ``` |