Instructions to use Cccccz/HY with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Cccccz/HY with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Cccccz/HY", 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 tools/generate_predictor_disca_train_case_eval.py from Cccccz/HY: direct link, hf CLI and curl.
- Browser
- Download file 449 Bytes
-
https://huggingface.co/Cccccz/HY/resolve/main/tools/generate_predictor_disca_train_case_eval.py
- Command line
-
hf download hf://Cccccz/HY/tools/generate_predictor_disca_train_case_eval.py
-
curl -L -o generate_predictor_disca_train_case_eval.py https://huggingface.co/Cccccz/HY/resolve/main/tools/generate_predictor_disca_train_case_eval.py
449 Bytes
| #!/usr/bin/env python3 | |
| """Online train-case evaluation entry point for Predictor-DisCa.""" | |
| from __future__ import annotations | |
| import sys | |
| from generate_predictor_v4_train_case_eval import main | |
| if __name__ == "__main__": | |
| if not any( | |
| argument == "--predictor_variant" | |
| or argument.startswith("--predictor_variant=") | |
| for argument in sys.argv[1:] | |
| ): | |
| sys.argv[1:1] = ["--predictor_variant", "disca"] | |
| main() | |