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Merge pull request #15283 from AUTOMATIC1111/dora-weight-decompose

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Use correct DoRA implementation
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AUTOMATIC1111 2024-03-16 20:20:08 +03:00 committed by GitHub
commit df8c09bcb3
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1 changed files with 15 additions and 3 deletions
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extensions-builtin/Lora/network.py
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@ -153,7 +153,7 @@ class NetworkModule:
self.scale = weights.w["scale"].item() if "scale" in weights.w else None self.scale = weights.w["scale"].item() if "scale" in weights.w else None
self.dora_scale = weights.w.get("dora_scale", None) self.dora_scale = weights.w.get("dora_scale", None)
self.dora_mean_dim = tuple(i for i in range(len(self.shape)) if i != 1) self.dora_norm_dims = len(self.shape) - 1
def multiplier(self): def multiplier(self):
if 'transformer' in self.sd_key[:20]: if 'transformer' in self.sd_key[:20]:
@ -170,10 +170,22 @@ class NetworkModule:
return 1.0 return 1.0
def apply_weight_decompose(self, updown, orig_weight): def apply_weight_decompose(self, updown, orig_weight):
orig_weight = orig_weight.to(updown) # Match the device/dtype
orig_weight = orig_weight.to(updown.dtype)
dora_scale = self.dora_scale.to(device=orig_weight.device, dtype=updown.dtype)
updown = updown.to(orig_weight.device)
merged_scale1 = updown + orig_weight merged_scale1 = updown + orig_weight
merged_scale1_norm = (
merged_scale1.transpose(0, 1)
.reshape(merged_scale1.shape[1], -1)
.norm(dim=1, keepdim=True)
.reshape(merged_scale1.shape[1], *[1] * self.dora_norm_dims)
.transpose(0, 1)
)
dora_merged = ( dora_merged = (
merged_scale1 / merged_scale1.mean(dim=self.dora_mean_dim, keepdim=True) * self.dora_scale merged_scale1 * (dora_scale / merged_scale1_norm)
) )
final_updown = dora_merged - orig_weight final_updown = dora_merged - orig_weight
return final_updown return final_updown