Initial implementation

extensions-builtin/Lora/network.py

@@ -146,6 +146,9 @@ def __init__(self, net: Network, weights: NetworkWeights):
         self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
         self.scale = weights.w["scale"].item() if "scale" in weights.w else None
 
+        self.dora_scale = weights.w["dora_scale"] if "dora_scale" in weights.w else None
+        self.dora_mean_dim = tuple(i for i in range(len(self.shape)) if i != 1)
+
     def multiplier(self):
         if 'transformer' in self.sd_key[:20]:
             return self.network.te_multiplier
@@ -160,6 +163,15 @@ def calc_scale(self):
 
         return 1.0
 
+    def apply_weight_decompose(self, updown, orig_weight):
+        orig_weight = orig_weight.to(updown)
+        merged_scale1 = updown + orig_weight
+        dora_merged = (
+            merged_scale1 / merged_scale1(dim=self.dora_mean_dim, keepdim=True) * self.dora_scale
+        )
+        final_updown = dora_merged - orig_weight
+        return final_updown
+
     def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
         if self.bias is not None:
             updown = updown.reshape(self.bias.shape)
@@ -175,6 +187,9 @@ def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
         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)
+
         return updown * self.calc_scale() * self.multiplier(), ex_bias
 
     def calc_updown(self, target):