Instructions to use phi-lab-rice/GRADE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use phi-lab-rice/GRADE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("phi-lab-rice/GRADE", 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
Download src/Baselines/radarcam-depth/modules/midas/base_model.py from phi-lab-rice/GRADE: direct link, hf CLI and curl.
- Browser
- Download file 282 Bytes
-
https://huggingface.co/phi-lab-rice/GRADE/resolve/main/src/Baselines/radarcam-depth/modules/midas/base_model.py
- Command line
-
hf download hf://phi-lab-rice/GRADE/src/Baselines/radarcam-depth/modules/midas/base_model.py
-
curl -L -o base_model.py https://huggingface.co/phi-lab-rice/GRADE/resolve/main/src/Baselines/radarcam-depth/modules/midas/base_model.py
282 Bytes
| import torch | |
| from safetensors.torch import load_file | |
| class BaseModel(torch.nn.Module): | |
| def load(self, path): | |
| """Load model from file. | |
| Args: | |
| path (str): file path | |
| """ | |
| self.load_state_dict(load_file(path, device="cpu"), strict=True) | |