Testing VAE until it works - v1

generator/src/models/vae.py

@@ -22,17 +22,21 @@ def _init_weights(m):
         nn.init.normal_(m.weight, 0.0, 0.02)
         if m.bias is not None:
             nn.init.zeros_(m.bias)
-    elif isinstance(m, nn.BatchNorm2d) and m.weight is not None:
+    elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)) and m.weight is not None:
         nn.init.normal_(m.weight, 1.0, 0.02)
         nn.init.zeros_(m.bias)
 
 
+def _norm(channels: int) -> nn.GroupNorm:
+    return nn.GroupNorm(8, channels)
+
+
 def _upsample_block(in_ch: int, out_ch: int) -> nn.Sequential:
     """Nearest-neighbour upsample followed by a 3×3 conv — no checkerboard."""
     return nn.Sequential(
         nn.Upsample(scale_factor=2, mode="nearest"),
         nn.Conv2d(in_ch, out_ch, 3, padding=1, bias=False),
-        nn.BatchNorm2d(out_ch),
+        _norm(out_ch),
         nn.ReLU(inplace=True),
     )
 
@@ -65,7 +69,7 @@ class VAE(nn.Module):
         for _ in range(n_down - 1):
             enc_layers += [
                 nn.Conv2d(ch, ch * 2, 4, stride=2, padding=1, bias=False),
-                nn.BatchNorm2d(ch * 2),
+                _norm(ch * 2),
                 nn.LeakyReLU(0.2, inplace=True),
             ]
             ch *= 2
@@ -102,6 +106,8 @@ class VAE(nn.Module):
         return self.fc_mu(h), log_var
 
     def reparameterize(self, mu: torch.Tensor, log_var: torch.Tensor) -> torch.Tensor:
+        if not self.training:
+            return mu
         std = torch.exp(0.5 * log_var)
         return mu + std * torch.randn_like(std)
 

generator/src/training/ema.py

@@ -20,3 +20,5 @@ class EMA:
     def update(self, model: nn.Module) -> None:
         for p_ema, p in zip(self.model.parameters(), model.parameters()):
             p_ema.data.mul_(self.decay).add_(p.data, alpha=1.0 - self.decay)
+        for b_ema, b in zip(self.model.buffers(), model.buffers()):
+            b_ema.copy_(b)