wassname/denoising-diffusion-pytorch
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fix sampling ddpm tqdm

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This commit is contained in:
Phil Wang
2022-08-10 12:02:56 -07:00
parent 689593a579
commit f0d59acdfd
2 changed files with 5 additions and 5 deletions
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denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+4 -4
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@@ -542,7 +542,7 @@ class GaussianDiffusion(nn.Module):
x_start = None
for t in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step'):
for t in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
self_cond = x_start if self.self_condition else None
img, x_start = self.p_sample(img, t, self_cond)
@@ -599,11 +599,11 @@ class GaussianDiffusion(nn.Module):
assert x1.shape == x2.shape
t_batched = torch.stack([torch.tensor(t, device=device)] * b)
xt1, xt2 = map(lambda x: self.q_sample(x, t=t_batched), (x1, x2))
t_batched = torch.stack([torch.tensor(t, device = device)] * b)
xt1, xt2 = map(lambda x: self.q_sample(x, t = t_batched), (x1, x2))
img = (1 - lam) * xt1 + lam * xt2
for i in tqdm(reversed(range(0, t)), desc='interpolation sample time step', total=t):
for i in tqdm(reversed(range(0, t)), desc = 'interpolation sample time step', total = t):
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long))
return img
setup.py
+1 -1
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@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
setup(
name = 'denoising-diffusion-pytorch',
packages = find_packages(),
version = '0.27.0',
version = '0.27.1',
license='MIT',
description = 'Denoising Diffusion Probabilistic Models - Pytorch',
author = 'Phil Wang',