support specifying te and unet weights separately

update lora code
support full module

1 files changed, 39 insertions, 1 deletions

diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py
index 4ac63722..fe42dbdd 100644
--- a/extensions-builtin/Lora/network.py
+++ b/extensions-builtin/Lora/network.py
@@ -68,7 +68,9 @@ class Network:  # LoraModule
     def __init__(self, name, network_on_disk: NetworkOnDisk):

         self.name = name

         self.network_on_disk = network_on_disk

-        self.multiplier = 1.0

+        self.te_multiplier = 1.0

+        self.unet_multiplier = 1.0

+        self.dyn_dim = None

         self.modules = {}

         self.mtime = None

 

@@ -88,6 +90,42 @@ class NetworkModule:
         self.sd_key = weights.sd_key

         self.sd_module = weights.sd_module

 

+        if hasattr(self.sd_module, 'weight'):

+            self.shape = self.sd_module.weight.shape

+

+        self.dim = None

+        self.bias = weights.w.get("bias")

+        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

+

+    def multiplier(self):

+        if 'transformer' in self.sd_key[:20]:

+            return self.network.te_multiplier

+        else:

+            return self.network.unet_multiplier

+

+    def calc_scale(self):

+        if self.scale is not None:

+            return self.scale

+        if self.dim is not None and self.alpha is not None:

+            return self.alpha / self.dim

+

+        return 1.0

+

+    def finalize_updown(self, updown, orig_weight, output_shape):

+        if self.bias is not None:

+            updown = updown.reshape(self.bias.shape)

+            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)

+            updown = updown.reshape(output_shape)

+

+        if len(output_shape) == 4:

+            updown = updown.reshape(output_shape)

+

+        if orig_weight.size().numel() == updown.size().numel():

+            updown = updown.reshape(orig_weight.shape)

+

+        return updown * self.calc_scale() * self.multiplier()

+

     def calc_updown(self, target):

         raise NotImplementedError()