fix(lora): scale the dora diff before the decompose norm

finalize_updown ran apply_weight_decompose on the unscaled delta and
multiplied the result by alpha/rank afterward. LyCORIS and ComfyUI both
bake alpha/rank into the diff before computing the row norms, so any
DoRA with alpha != rank renormalized against the wrong merged weight
(64% relative delta error for kohya-style alpha=1 rank=8; exact only
when alpha == rank, which full-matrix LoKR forces).

- scale updown by calc_scale() before apply_weight_decompose; apply
  only the multiplier afterward
- multiplier lerps the full merged delta (0 disables, 1 equals the
  trainer output); LyCORIS weight-mode ratio interpolation leaves the
  diff applied at multiplier 0 and is not used
- add a numeric regression test mirroring the LyCORIS forward reference

modules/lora/network.py

@@ -268,7 +268,13 @@ class NetworkModule:
         if ex_bias is not None:
             ex_bias = ex_bias * self.multiplier()
         if self.dora_scale is not None:
-            updown = self.apply_weight_decompose(updown, orig_weight)
+            # LyCORIS/ComfyUI convention: alpha/rank is baked into the diff
+            # before the decompose norm. The multiplier then lerps the full
+            # merged delta (ComfyUI semantics: 0 disables, 1 equals the
+            # trainer's output; LyCORIS weight-mode ratio interpolation is
+            # not used since it leaves the diff applied at multiplier 0).
+            updown = self.apply_weight_decompose(updown * self.calc_scale(), orig_weight)
+            return updown * self.multiplier(), ex_bias
         return updown * self.calc_scale() * self.multiplier(), ex_bias
 
     def calc_updown(self, target):