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/models/ours_diffusion/config.yaml from phi-lab-rice/GRADE: direct link, hf CLI and curl.
- Browser
- Download file 464 Bytes
-
https://huggingface.co/phi-lab-rice/GRADE/resolve/main/src/models/ours_diffusion/config.yaml
- Command line
-
hf download hf://phi-lab-rice/GRADE/src/models/ours_diffusion/config.yaml
-
curl -L -o config.yaml https://huggingface.co/phi-lab-rice/GRADE/resolve/main/src/models/ours_diffusion/config.yaml
464 Bytes
| training: {batch_size: 1, mixed_precision: fp16, seed: 42} | |
| data: | |
| smoke_eval_root: ../../../evaluation_dataset/Smoke-Eval | |
| resolution: {height: 288, width: 512} | |
| scale_factor: 0.001 | |
| max_depth_m: 11.2 | |
| num_frames: 1 | |
| num_workers: 0 | |
| pretrained: | |
| radar_model: ../../../checkpoints/grade/radar.safetensors | |
| unet: ../../../checkpoints/grade/diffusion.safetensors | |
| diffusion: {num_train_timesteps: 1000} | |
| inference: {frame_skip: 1, batch_size: 1, num_workers: 0} | |