Fix up loop-binding issues in ImageTransformerV2

Author: akx

k_diffusion/models/image_transformer_v2.py

@@ -653,6 +653,18 @@ class MappingSpec:
     dropout: float
 
 
+def make_layer_factory(spec, mapping):
+    if isinstance(spec.self_attn, GlobalAttentionSpec):
+        return lambda _: GlobalTransformerLayer(spec.width, spec.d_ff, spec.self_attn.d_head, mapping.width, dropout=spec.dropout)
+    elif isinstance(spec.self_attn, NeighborhoodAttentionSpec):
+        return lambda _: NeighborhoodTransformerLayer(spec.width, spec.d_ff, spec.self_attn.d_head, mapping.width, spec.self_attn.kernel_size, dropout=spec.dropout)
+    elif isinstance(spec.self_attn, ShiftedWindowAttentionSpec):
+        return lambda i: ShiftedWindowTransformerLayer(spec.width, spec.d_ff, spec.self_attn.d_head, mapping.width, spec.self_attn.window_size, i, dropout=spec.dropout)
+    elif isinstance(spec.self_attn, NoAttentionSpec):
+        return lambda _: NoAttentionTransformerLayer(spec.width, spec.d_ff, mapping.width, dropout=spec.dropout)
+    raise ValueError(f"unsupported self attention spec {spec.self_attn}")
+
+
 # Model class
 
 class ImageTransformerDenoiserModelV2(nn.Module):
@@ -672,16 +684,7 @@ def __init__(self, levels, mapping, in_channels, out_channels, patch_size, num_c
 
         self.down_levels, self.up_levels = nn.ModuleList(), nn.ModuleList()
         for i, spec in enumerate(levels):
-            if isinstance(spec.self_attn, GlobalAttentionSpec):
-                layer_factory = lambda _: GlobalTransformerLayer(spec.width, spec.d_ff, spec.self_attn.d_head, mapping.width, dropout=spec.dropout)
-            elif isinstance(spec.self_attn, NeighborhoodAttentionSpec):
-                layer_factory = lambda _: NeighborhoodTransformerLayer(spec.width, spec.d_ff, spec.self_attn.d_head, mapping.width, spec.self_attn.kernel_size, dropout=spec.dropout)
-            elif isinstance(spec.self_attn, ShiftedWindowAttentionSpec):
-                layer_factory = lambda i: ShiftedWindowTransformerLayer(spec.width, spec.d_ff, spec.self_attn.d_head, mapping.width, spec.self_attn.window_size, i, dropout=spec.dropout)
-            elif isinstance(spec.self_attn, NoAttentionSpec):
-                layer_factory = lambda _: NoAttentionTransformerLayer(spec.width, spec.d_ff, mapping.width, dropout=spec.dropout)
-            else:
-                raise ValueError(f"unsupported self attention spec {spec.self_attn}")
+            layer_factory = self.make_layer_factory(spec, mapping)
 
             if i < len(levels) - 1:
                 self.down_levels.append(Level([layer_factory(i) for i in range(spec.depth)]))