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Update.
[pytorch.git]
/
minidiffusion.py
diff --git
a/minidiffusion.py
b/minidiffusion.py
index
7327522
..
e7be8c1
100755
(executable)
--- a/
minidiffusion.py
+++ b/
minidiffusion.py
@@
-289,6
+289,9
@@
model.eval()
if train_input.dim() == 2 and train_input.size(1) == 1:
fig = plt.figure()
if train_input.dim() == 2 and train_input.size(1) == 1:
fig = plt.figure()
+ fig.set_figheight(5)
+ fig.set_figwidth(8)
+
ax = fig.add_subplot(1, 1, 1)
x = generate((10000, 1), T, alpha, alpha_bar, sigma,
ax = fig.add_subplot(1, 1, 1)
x = generate((10000, 1), T, alpha, alpha_bar, sigma,
@@
-310,12
+313,12
@@
if train_input.dim() == 2 and train_input.size(1) == 1:
ax.legend(frameon = False, loc = 2)
ax.legend(frameon = False, loc = 2)
- filename = f'diffusion_{args.data}.pdf'
+ filename = f'
mini
diffusion_{args.data}.pdf'
print(f'saving {filename}')
fig.savefig(filename, bbox_inches='tight')
if not args.no_window and hasattr(plt.get_current_fig_manager(), 'window'):
print(f'saving {filename}')
fig.savefig(filename, bbox_inches='tight')
if not args.no_window and hasattr(plt.get_current_fig_manager(), 'window'):
- plt.get_current_fig_manager().window.setGeometry(2, 2,
2048
, 768)
+ plt.get_current_fig_manager().window.setGeometry(2, 2,
1024
, 768)
plt.show()
########################################
plt.show()
########################################
@@
-323,6
+326,9
@@
if train_input.dim() == 2 and train_input.size(1) == 1:
elif train_input.dim() == 2 and train_input.size(1) == 2:
fig = plt.figure()
elif train_input.dim() == 2 and train_input.size(1) == 2:
fig = plt.figure()
+ fig.set_figheight(6)
+ fig.set_figwidth(6)
+
ax = fig.add_subplot(1, 1, 1)
x = generate((1000, 2), T, alpha, alpha_bar, sigma,
ax = fig.add_subplot(1, 1, 1)
x = generate((1000, 2), T, alpha, alpha_bar, sigma,
@@
-344,7
+350,7
@@
elif train_input.dim() == 2 and train_input.size(1) == 2:
ax.legend(frameon = False, loc = 2)
ax.legend(frameon = False, loc = 2)
- filename = f'diffusion_{args.data}.pdf'
+ filename = f'
mini
diffusion_{args.data}.pdf'
print(f'saving {filename}')
fig.savefig(filename, bbox_inches='tight')
print(f'saving {filename}')
fig.savefig(filename, bbox_inches='tight')
@@
-369,7
+375,7
@@
elif train_input.dim() == 4:
result = 1 - torch.cat((t, x), 2) / 255
result = 1 - torch.cat((t, x), 2) / 255
- filename = f'diffusion_{args.data}.png'
+ filename = f'
mini
diffusion_{args.data}.png'
print(f'saving {filename}')
torchvision.utils.save_image(result, filename)
print(f'saving {filename}')
torchvision.utils.save_image(result, filename)