Merge pull request #14546 from AUTOMATIC1111/fix-oft-dtype

Fix dtype casting in OFT module

Author: AUTOMATIC1111

extensions-builtin/Lora/network_oft.py

@@ -56,7 +56,7 @@ def __init__(self,  net: network.Network, weights: network.NetworkWeights):
             self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
 
     def calc_updown(self, orig_weight):
-        oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
+        oft_blocks = self.oft_blocks.to(orig_weight.device)
         eye = torch.eye(self.block_size, device=self.oft_blocks.device)
 
         if self.is_kohya:
@@ -66,7 +66,7 @@ def calc_updown(self, orig_weight):
             block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
             oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
 
-        R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
+        R = oft_blocks.to(orig_weight.device)
 
         # This errors out for MultiheadAttention, might need to be handled up-stream
         merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
@@ -77,6 +77,6 @@ def calc_updown(self, orig_weight):
         )
         merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
 
-        updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
+        updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype)
         output_shape = orig_weight.shape
         return self.finalize_updown(updown, orig_weight, output_shape)