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Merge pull request #14726 from v0xie/fix-oft-device

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Fix kohya-ss OFT network wrong device for eye and constraint
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AUTOMATIC1111 2024-01-23 22:34:24 +03:00 committed by GitHub
commit 358e9e2847
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1 changed files with 2 additions and 2 deletions
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extensions-builtin/Lora/network_oft.py
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@ -57,12 +57,12 @@ class NetworkModuleOFT(network.NetworkModule):
def calc_updown(self, orig_weight): def calc_updown(self, orig_weight):
oft_blocks = self.oft_blocks.to(orig_weight.device) oft_blocks = self.oft_blocks.to(orig_weight.device)
eye = torch.eye(self.block_size, device=self.oft_blocks.device) eye = torch.eye(self.block_size, device=oft_blocks.device)
if self.is_kohya: if self.is_kohya:
block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
norm_Q = torch.norm(block_Q.flatten()) norm_Q = torch.norm(block_Q.flatten())
new_norm_Q = torch.clamp(norm_Q, max=self.constraint) new_norm_Q = torch.clamp(norm_Q, max=self.constraint.to(oft_blocks.device))
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8)) 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()) oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())