X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=minidiffusion.py;h=e7be8c1c8651a3cc4ee4a578586e2e4dd3c29bcf;hb=8d71d2f43cec159c7ca368c1dc4fa76f061d13b7;hp=7327522e1f13b08965cff1ddb91cad75c68e22fd;hpb=e8500a3f0cec4be59442e2b3bdbe692b04a9524a;p=pytorch.git

diff --git a/minidiffusion.py b/minidiffusion.py
index 7327522..e7be8c1 100755
--- 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()
+    fig.set_figheight(5)
+    fig.set_figwidth(8)
+
     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)
 
-    filename = f'diffusion_{args.data}.pdf'
+    filename = f'minidiffusion_{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'):
-        plt.get_current_fig_manager().window.setGeometry(2, 2, 2048, 768)
+        plt.get_current_fig_manager().window.setGeometry(2, 2, 1024, 768)
         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()
+    fig.set_figheight(6)
+    fig.set_figwidth(6)
+
     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)
 
-    filename = f'diffusion_{args.data}.pdf'
+    filename = f'minidiffusion_{args.data}.pdf'
     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
 
-    filename = f'diffusion_{args.data}.png'
+    filename = f'minidiffusion_{args.data}.png'
     print(f'saving {filename}')
     torchvision.utils.save_image(result, filename)