Merge pull request #2 from v0xie/network-oft-change-impl (7edd50f3) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/Lora/network_oft.py

+27 −17

Original line number	Diff line number	Diff line
		@@ -24,12 +24,14 @@ class NetworkModuleOFT(network.NetworkModule):
		# kohya-ss
		if "oft_blocks" in weights.w.keys():
		self.is_kohya = True
		self.oft_blocks = weights.w["oft_blocks"]
		self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
		self.alpha = weights.w["alpha"]
		self.dim = self.oft_blocks.shape[0]
		self.dim = self.oft_blocks.shape[0] # lora dim
		#self.oft_blocks = rearrange(self.oft_blocks, 'k m ... -> (k m) ...')
		elif "oft_diag" in weights.w.keys():
		self.is_kohya = False
		self.oft_blocks = weights.w["oft_diag"]
		self.oft_blocks = weights.w["oft_diag"] # (num_blocks, block_size, block_size)

		# alpha is rank if alpha is 0 or None
		if self.alpha is None:
		pass
		@@ -51,12 +53,11 @@ class NetworkModuleOFT(network.NetworkModule):
		raise ValueError("sd_module must be Linear or Conv")

		if self.is_kohya:
		self.num_blocks = self.dim
		self.block_size = self.out_dim // self.num_blocks
		self.constraint = self.alpha * self.out_dim
		self.num_blocks, self.block_size = factorization(self.out_dim, self.dim)
		else:
		self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
		self.constraint = None
		self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)

		def merge_weight(self, R_weight, org_weight):
		R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)
		@@ -77,7 +78,8 @@ class NetworkModuleOFT(network.NetworkModule):
		else:
		new_norm_Q = norm_Q
		block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
		m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
		m_I = torch.eye(self.num_blocks, device=oft_blocks.device).unsqueeze(0).repeat(self.block_size, 1, 1)
		#m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
		block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())

		block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
		@@ -97,25 +99,33 @@ class NetworkModuleOFT(network.NetworkModule):
		is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention]

		if not is_other_linear:
		if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
		orig_weight=orig_weight.permute(1, 0)
		#if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
		# orig_weight=orig_weight.permute(1, 0)

		oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)

		# without this line the results are significantly worse / less accurate
		oft_blocks = oft_blocks - oft_blocks.transpose(1, 2)

		R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
		R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)

		R = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
		merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
		merged_weight = torch.einsum(
		'k n m, k n ... -> k m ...',
		R * multiplier + torch.eye(self.block_size, device=orig_weight.device),
		R,
		merged_weight
		)
		merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')

		if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
		orig_weight=orig_weight.permute(1, 0)
		#if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
		# orig_weight=orig_weight.permute(1, 0)

		updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
		output_shape = orig_weight.shape
		else:
		# FIXME: skip MultiheadAttention for now
		#up = self.lin_module.weight.to(orig_weight.device, dtype=orig_weight.dtype)
		updown = torch.zeros([orig_weight.shape[1], orig_weight.shape[1]], device=orig_weight.device, dtype=orig_weight.dtype)
		output_shape = (orig_weight.shape[1], orig_weight.shape[1])

		@@ -123,9 +133,9 @@ class NetworkModuleOFT(network.NetworkModule):

		def calc_updown(self, orig_weight):
		multiplier = self.multiplier() * self.calc_scale()
		if self.is_kohya:
		return self.calc_updown_kohya(orig_weight, multiplier)
		else:
		#if self.is_kohya:
		# return self.calc_updown_kohya(orig_weight, multiplier)
		#else:
		return self.calc_updown_kb(orig_weight, multiplier)

		# override to remove the multiplier/scale factor; it's already multiplied in get_weight

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