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/da3/config.yaml from phi-lab-rice/GRADE: direct link, hf CLI and curl.
- Browser
- Download file 303 Bytes
-
https://huggingface.co/phi-lab-rice/GRADE/resolve/main/src/models/da3/config.yaml
- Command line
-
hf download hf://phi-lab-rice/GRADE/src/models/da3/config.yaml
-
curl -L -o config.yaml https://huggingface.co/phi-lab-rice/GRADE/resolve/main/src/models/da3/config.yaml
303 Bytes
| # Runtime metadata for the DA3 direct backend. The runner passes the data, | |
| # output, and checkpoint paths explicitly because the upstream-style CLI | |
| # requires them. | |
| data: | |
| smoke_eval_root: ../../../evaluation_dataset/Smoke-Eval | |
| runtime: | |
| seed: 42 | |
| mixed_precision: fp16 | |
| model_name: da3metric-large | |