Merge pull request #12543 from AUTOMATIC1111/extra-norm-module (3a4bee10) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/Lora/networks.py

+30 −9

Original line number	Diff line number	Diff line
		@@ -277,7 +277,15 @@ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Li
		self.weight.copy_(weights_backup)

		if bias_backup is not None:
		if isinstance(self, torch.nn.MultiheadAttention):
		self.out_proj.bias.copy_(bias_backup)
		else:
		self.bias.copy_(bias_backup)
		else:
		if isinstance(self, torch.nn.MultiheadAttention):
		self.out_proj.bias = None
		else:
		self.bias = None


		def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
		@@ -304,8 +312,13 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
		self.network_weights_backup = weights_backup

		bias_backup = getattr(self, "network_bias_backup", None)
		if bias_backup is None and getattr(self, 'bias', None) is not None:
		if bias_backup is None:
		if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
		bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
		elif getattr(self, 'bias', None) is not None:
		bias_backup = self.bias.to(devices.cpu, copy=True)
		else:
		bias_backup = None
		self.network_bias_backup = bias_backup

		if current_names != wanted_names:
		@@ -323,7 +336,10 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
		updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))

		self.weight += updown
		if ex_bias is not None and getattr(self, 'bias', None) is not None:
		if ex_bias is not None and hasattr(self, 'bias'):
		if self.bias is None:
		self.bias = torch.nn.Parameter(ex_bias)
		else:
		self.bias += ex_bias
		except RuntimeError as e:
		logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
		@@ -339,14 +355,19 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
		if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
		try:
		with torch.no_grad():
		updown_q = module_q.calc_updown(self.in_proj_weight)
		updown_k = module_k.calc_updown(self.in_proj_weight)
		updown_v = module_v.calc_updown(self.in_proj_weight)
		updown_q, _ = module_q.calc_updown(self.in_proj_weight)
		updown_k, _ = module_k.calc_updown(self.in_proj_weight)
		updown_v, _ = module_v.calc_updown(self.in_proj_weight)
		updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
		updown_out = module_out.calc_updown(self.out_proj.weight)
		updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)

		self.in_proj_weight += updown_qkv
		self.out_proj.weight += updown_out
		if ex_bias is not None:
		if self.out_proj.bias is None:
		self.out_proj.bias = torch.nn.Parameter(ex_bias)
		else:
		self.out_proj.bias += ex_bias

		except RuntimeError as e:
		logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")

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