Instructions to use zeyuren2002/EvalMDE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zeyuren2002/EvalMDE with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zeyuren2002/EvalMDE", 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
| import os | |
| os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1' | |
| from pathlib import Path | |
| import sys | |
| if (_package_root := str(Path(__file__).absolute().parents[2])) not in sys.path: | |
| sys.path.insert(0, _package_root) | |
| import click | |
| def cli(): | |
| pass | |
| def main(): | |
| from moge.scripts import app, infer, infer_baseline, infer_panorama, eval_baseline, vis_data | |
| cli.add_command(app.main, name='app') | |
| cli.add_command(infer.main, name='infer') | |
| cli.add_command(infer_baseline.main, name='infer_baseline') | |
| cli.add_command(infer_panorama.main, name='infer_panorama') | |
| cli.add_command(eval_baseline.main, name='eval_baseline') | |
| cli.add_command(vis_data.main, name='vis_data') | |
| cli() | |
| if __name__ == '__main__': | |
| main() |