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Update.
author
Francois Fleuret
<francois@fleuret.org>
Sun, 14 Aug 2022 13:59:51 +0000
(15:59 +0200)
committer
Francois Fleuret
<francois@fleuret.org>
Sun, 14 Aug 2022 13:59:51 +0000
(15:59 +0200)
minidiffusion.py
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diff --git
a/minidiffusion.py
b/minidiffusion.py
index
e1f6abd
..
841dd2a
100755
(executable)
--- a/
minidiffusion.py
+++ b/
minidiffusion.py
@@
-207,8
+207,10
@@
print(f'nb_parameters {sum([ p.numel() for p in model.parameters() ])}')
######################################################################
# Generate
######################################################################
# Generate
-def generate(size, alpha, alpha_bar, sigma, model):
+def generate(size, alpha, alpha_bar, sigma, model, train_mean, train_std):
+
with torch.no_grad():
with torch.no_grad():
+
x = torch.randn(size, device = device)
for t in range(T-1, -1, -1):
x = torch.randn(size, device = device)
for t in range(T-1, -1, -1):
@@
-269,7
+271,8
@@
if train_input.dim() == 2:
if train_input.size(1) == 1:
if train_input.size(1) == 1:
- x = generate((10000, 1), alpha, alpha_bar, sigma, model)
+ x = generate((10000, 1), alpha, alpha_bar, sigma,
+ model, train_mean, train_std)
ax.set_xlim(-1.25, 1.25)
ax.spines.right.set_visible(False)
ax.set_xlim(-1.25, 1.25)
ax.spines.right.set_visible(False)
@@
-289,7
+292,8
@@
if train_input.dim() == 2:
elif train_input.size(1) == 2:
elif train_input.size(1) == 2:
- x = generate((1000, 2), alpha, alpha_bar, sigma, model)
+ x = generate((1000, 2), alpha, alpha_bar, sigma,
+ model, train_mean, train_std)
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
@@
-317,7
+321,8
@@
if train_input.dim() == 2:
elif train_input.dim() == 4:
elif train_input.dim() == 4:
- x = generate((128,) + train_input.size()[1:], alpha, alpha_bar, sigma, model)
+ x = generate((128,) + train_input.size()[1:], alpha, alpha_bar, sigma,
+ model, train_mean, train_std)
x = 1 - x.clamp(min = 0, max = 255) / 255
torchvision.utils.save_image(x, f'diffusion_{args.data}.png', nrow = 16, pad_value = 0.8)
x = 1 - x.clamp(min = 0, max = 255) / 255
torchvision.utils.save_image(x, f'diffusion_{args.data}.png', nrow = 16, pad_value = 0.8)