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import numpy as np
import torch
def torch_samps_to_imgs(imgs, uncenter=True):
if uncenter:
imgs = (imgs + 1) / 2 # [-1, 1] -> [0, 1]
imgs = (imgs * 255).clamp(0, 255)
imgs = imgs.to(torch.uint8)
imgs = imgs.permute(0, 2, 3, 1)
imgs = imgs.cpu().numpy()
return imgs
def imgs_to_torch(imgs):
assert imgs.dtype == np.uint8
assert len(imgs.shape) == 4 and imgs.shape[-1] == 3, "expect (N, H, W, C)"
_, H, W, _ = imgs.shape
imgs = imgs.transpose(0, 3, 1, 2)
imgs = (imgs / 255).astype(np.float32)
imgs = (imgs * 2) - 1
imgs = torch.as_tensor(imgs)
H, W = [_l - (_l % 32) for _l in (H, W)]
imgs = torch.nn.functional.interpolate(imgs, (H, W), mode="bilinear")
return imgs
def test_encode_decode():
import imageio
from run_img_sampling import ScoreAdapter, SD
from vis import _draw
fname = "~/clean.png"
raw = imageio.imread(fname)
raw = imgs_to_torch(raw[np.newaxis, ...])
model: ScoreAdapter = SD().run()
raw = raw.to(model.device)
zs = model.encode(raw)
img = model.decode(zs)
img = torch_samps_to_imgs(img)
_draw(
[imageio.imread(fname), img.squeeze(0)],
)
def test():
test_encode_decode()
if __name__ == "__main__":
test()
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