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Merge pull request #15283 from AUTOMATIC1111/dora-weight-decompose
Use correct DoRA implementation
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commit
df8c09bcb3
@ -153,7 +153,7 @@ class NetworkModule:
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self.scale = weights.w["scale"].item() if "scale" in weights.w else None
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self.dora_scale = weights.w.get("dora_scale", None)
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self.dora_mean_dim = tuple(i for i in range(len(self.shape)) if i != 1)
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self.dora_norm_dims = len(self.shape) - 1
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def multiplier(self):
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if 'transformer' in self.sd_key[:20]:
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@ -170,10 +170,22 @@ class NetworkModule:
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return 1.0
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def apply_weight_decompose(self, updown, orig_weight):
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orig_weight = orig_weight.to(updown)
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# Match the device/dtype
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orig_weight = orig_weight.to(updown.dtype)
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dora_scale = self.dora_scale.to(device=orig_weight.device, dtype=updown.dtype)
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updown = updown.to(orig_weight.device)
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merged_scale1 = updown + orig_weight
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merged_scale1_norm = (
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merged_scale1.transpose(0, 1)
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.reshape(merged_scale1.shape[1], -1)
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.norm(dim=1, keepdim=True)
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.reshape(merged_scale1.shape[1], *[1] * self.dora_norm_dims)
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.transpose(0, 1)
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)
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dora_merged = (
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merged_scale1 / merged_scale1.mean(dim=self.dora_mean_dim, keepdim=True) * self.dora_scale
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merged_scale1 * (dora_scale / merged_scale1_norm)
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)
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final_updown = dora_merged - orig_weight
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return final_updown
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