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 cv2 | |
| from evalmde.utils.common import pathlib_file | |
| def imread_rgb(img_f): | |
| return cv2.imread(str(pathlib_file(img_f)))[..., ::-1].copy() | |
| def imwrite_rgb(img_f, img, verbose=False): | |
| img_f = pathlib_file(img_f) | |
| img_f.parent.mkdir(parents=True, exist_ok=True) | |
| cv2.imwrite(str(img_f), img[..., ::-1]) | |
| if verbose: | |
| print(f'Saved to {img_f.resolve()}') | |
| def resize(img, H=None, W=None, interpolation=cv2.INTER_NEAREST, return_sc=False): | |
| ''' | |
| if both H and W are specified, resize to smaller one while keeping aspect ratio | |
| :param img: | |
| :param H: | |
| :param W: | |
| :param interpolation: | |
| :param return_sc: | |
| :return: | |
| ''' | |
| cur_H, cur_W = img.shape[:2] | |
| if (H is not None) and (W is not None): | |
| H = int(H) | |
| W = int(W) | |
| if H / cur_H < W / cur_W: | |
| W = None | |
| else: | |
| H = None | |
| if H is not None: | |
| H = int(H) | |
| img = cv2.resize(img, (int(img.shape[1] / img.shape[0] * H), H), interpolation=interpolation) | |
| if W is not None: | |
| W = int(W) | |
| img = cv2.resize(img, (W, int(img.shape[0] / img.shape[1] * W)), interpolation=interpolation) | |
| if return_sc: | |
| sc = img.shape[0] / cur_H | |
| return img, sc | |
| return img | |