Update.

diff --git a/minidiffusion.py b/minidiffusion.py
index 42dff7c..879b796 100755 (executable)
--- a/minidiffusion.py
+++ b/minidiffusion.py
@@ -14,6 +14,8 @@ from torch import nn
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
+print(f'device {device}')
+
 ######################################################################
 
 def sample_gaussian_mixture(nb):
@@ -205,6 +207,24 @@ model.to(device)
 
 print(f'nb_parameters {sum([ p.numel() for p in model.parameters() ])}')
 
+######################################################################
+# Generate
+
+def generate(size, alpha, alpha_bar, sigma, model):
+    with torch.no_grad():
+        x = torch.randn(size, device = device)
+
+        for t in range(T-1, -1, -1):
+            z = torch.zeros_like(x) if t == 0 else torch.randn_like(x)
+            input = torch.cat((x, torch.full_like(x[:,:1], t / (T - 1) - 0.5)), 1)
+            x = 1/torch.sqrt(alpha[t]) \
+                * (x - (1-alpha[t]) / torch.sqrt(1-alpha_bar[t]) * model(input)) \
+                + sigma[t] * z
+
+        x = x * train_std + train_mean
+
+        return x
+
 ######################################################################
 # Train
 
@@ -240,36 +260,19 @@ for k in range(args.nb_epochs):
 
 ema.copy()
 
-######################################################################
-# Generate
-
-def generate(size, model):
-    with torch.no_grad():
-        x = torch.randn(size, device = device)
-
-        for t in range(T-1, -1, -1):
-            z = torch.zeros_like(x) if t == 0 else torch.randn_like(x)
-            input = torch.cat((x, torch.full_like(x[:,:1], t / (T - 1) - 0.5)), 1)
-            x = 1/torch.sqrt(alpha[t]) \
-                * (x - (1-alpha[t]) / torch.sqrt(1-alpha_bar[t]) * model(input)) \
-                + sigma[t] * z
-
-        x = x * train_std + train_mean
-
-        return x
-
 ######################################################################
 # Plot
 
 model.eval()
 
 if train_input.dim() == 2:
+
     fig = plt.figure()
     ax = fig.add_subplot(1, 1, 1)
 
     if train_input.size(1) == 1:
 
-        x = generate((10000, 1), model)
+        x = generate((10000, 1), alpha, alpha_bar, sigma, model)
 
         ax.set_xlim(-1.25, 1.25)
         ax.spines.right.set_visible(False)
@@ -289,7 +292,7 @@ if train_input.dim() == 2:
 
     elif train_input.size(1) == 2:
 
-        x = generate((1000, 2), model)
+        x = generate((1000, 2), alpha, alpha_bar, sigma, model)
 
         ax.set_xlim(-1.5, 1.5)
         ax.set_ylim(-1.5, 1.5)
@@ -316,7 +319,8 @@ if train_input.dim() == 2:
         plt.show()
 
 elif train_input.dim() == 4:
-    x = generate((128,) + train_input.size()[1:], model)
+
+    x = generate((128,) + train_input.size()[1:], alpha, alpha_bar, sigma, model)
     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)