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
File size: 1,255 Bytes
d547008 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | 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
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