try to keep the same state_dict structure

flux/modules/autoencoder.py

@@ -105,6 +105,16 @@ def forward(self, x: Tensor):
         x = self.conv(x)
         return x
 
+class DownBlock(nn.Module):
+    def __init__(self, block: list, downsample: nn.Module) -> None:
+        super().__init__()
+        # we're doing this instead of a flat nn.Sequential to preserve the keys "block" "downsample"
+        self.block = nn.Sequential(*block)
+        self.downsample = downsample
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.downsample(self.block(x))
+
 
 class Encoder(nn.Module):
     def __init__(
@@ -127,20 +137,25 @@ def __init__(
 
         curr_res = resolution
         in_ch_mult = (1,) + tuple(ch_mult)
-        self.in_ch_mult: tuple[int] = in_ch_mult
+        self.in_ch_mult = in_ch_mult
         down_layers = []
         block_in = self.ch
+        # ideally, this would all append to a single flat nn.Sequential
+        # we cannot do this due to the existing state dict keys
         for i_level in range(self.num_resolutions):
-            attn = nn.ModuleList()
             block_in = ch * in_ch_mult[i_level]
             block_out = ch * ch_mult[i_level]
+            block_layers = []
             for _ in range(self.num_res_blocks):
-                down_layers.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_layers.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
                 block_in = block_out # ?
             # originally this provided for attn layers, but those are never actually created
             if i_level != self.num_resolutions - 1:
-                down_layers.append(Downsample(block_in))
+                downsample = Downsample(block_in)
                 curr_res = curr_res // 2
+            else:
+                downsample = nn.Identity()
+            down_layers.append(DownBlock(block_layers, downsample))
         self.down = nn.Sequential(*down_layers)
 
         # middle
@@ -168,6 +183,15 @@ def forward(self, x: Tensor) -> Tensor:
         h = self.conv_out(h)
         return h
 
+class UpBlock(nn.Module):
+    def __init__(self, block: list, upsample: nn.Module) -> None:
+        super().__init__()
+        self.block = nn.Sequential(*block)
+        self.upsample = upsample
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.upsample(self.block(x))
+
 
 class Decoder(nn.Module):
     def __init__(
@@ -203,19 +227,22 @@ def __init__(
         self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
 
         # upsampling
-        up_layers = []
+        up_blocks = []
+        # 3, 2, 1, 0, descending order
         for i_level in reversed(range(self.num_resolutions)):
-            blocks = []
+            level_blocks = []
             block_out = ch * ch_mult[i_level]
             for _ in range(self.num_res_blocks + 1):
-                blocks.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                level_blocks.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
                 block_in = block_out
             if i_level != 0:
-                blocks.append(Upsample(block_in))
+                upsample = Upsample(block_in)
                 curr_res = curr_res * 2
-            # ??? gross
-            up_layers = blocks + up_layers  # prepend to get consistent order
-        self.up = nn.Sequential(*up_layers)
+            else:
+                upsample = nn.Identity()
+            # 0, 1, 2, 3, ascending order
+            up_blocks.insert(0, UpBlock(level_blocks, upsample)) # prepend to get consistent order
+        self.up = nn.Sequential(*up_blocks)
 
         # end
         self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)