inference working but SLOW (1c6efdbb) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/Lora/network_oft.py

+36 −37

Original line number	Diff line number	Diff line
		@@ -12,6 +12,7 @@ class ModuleTypeOFT(network.ModuleType):
		# adapted from https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
		class NetworkModuleOFT(network.NetworkModule):
		def __init__(self, net: network.Network, weights: network.NetworkWeights):

		super().__init__(net, weights)

		self.oft_blocks = weights.w["oft_blocks"]
		@@ -20,24 +21,29 @@ class NetworkModuleOFT(network.NetworkModule):
		self.dim = self.oft_blocks.shape[0]
		self.num_blocks = self.dim

		#if type(self.alpha) == torch.Tensor:
		# self.alpha = self.alpha.detach().numpy()

		if "Linear" in self.sd_module.__class__.__name__:
		self.out_dim = self.sd_module.out_features
		elif "Conv" in self.sd_module.__class__.__name__:
		self.out_dim = self.sd_module.out_channels

		self.constraint = self.alpha * self.out_dim
		self.constraint = self.alpha
		#self.constraint = self.alpha * self.out_dim
		self.block_size = self.out_dim // self.num_blocks

		self.oft_multiplier = self.multiplier()
		self.org_module: list[torch.Module] = [self.sd_module]

		self.R = self.get_weight()

		self.apply_to()

		# replace forward method of original linear rather than replacing the module
		# self.org_forward = self.sd_module.forward
		# self.sd_module.forward = self.forward
		def apply_to(self):
		self.org_forward = self.org_module[0].forward
		self.org_module[0].forward = self.forward

		def get_weight(self):
		def get_weight(self, multiplier=None):
		if not multiplier:
		multiplier = self.multiplier()
		block_Q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
		norm_Q = torch.norm(block_Q.flatten())
		new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
		@@ -45,38 +51,31 @@ class NetworkModuleOFT(network.NetworkModule):
		I = torch.eye(self.block_size, device=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
		block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())

		block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
		block_R_weighted = multiplier * block_R + (1 - multiplier) * I
		R = torch.block_diag(*block_R_weighted)

		return R

		def calc_updown(self, orig_weight):
		oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
		block_Q = oft_blocks - oft_blocks.transpose(1, 2)
		norm_Q = torch.norm(block_Q.flatten())
		new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
		block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
		I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
		block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())

		block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
		R = torch.block_diag(*block_R_weighted)
		#R = self.get_weight().to(orig_weight.device, dtype=orig_weight.dtype)
		# W = R*W_0
		updown = orig_weight + R
		output_shape = [R.size(0), orig_weight.size(1)]
		R = self.R
		if orig_weight.dim() == 4:
		weight = torch.einsum("oihw, op -> pihw", orig_weight, R)
		else:
		weight = torch.einsum("oi, op -> pi", orig_weight, R)
		updown = orig_weight @ R
		output_shape = [orig_weight.size(0), R.size(1)]
		#output_shape = [R.size(0), orig_weight.size(1)]
		return self.finalize_updown(updown, orig_weight, output_shape)

		# def forward(self, x, y=None):
		# x = self.org_forward(x)
		# if self.oft_multiplier == 0.0:
		# return x

		# R = self.get_weight().to(x.device, dtype=x.dtype)
		# if x.dim() == 4:
		# x = x.permute(0, 2, 3, 1)
		# x = torch.matmul(x, R)
		# x = x.permute(0, 3, 1, 2)
		# else:
		# x = torch.matmul(x, R)
		# return x
		def forward(self, x, y=None):
		x = self.org_forward(x)
		if self.multiplier() == 0.0:
		return x
		R = self.get_weight().to(x.device, dtype=x.dtype)
		if x.dim() == 4:
		x = x.permute(0, 2, 3, 1)
		x = torch.matmul(x, R)
		x = x.permute(0, 3, 1, 2)
		else:
		x = torch.matmul(x, R)
		return x

extensions-builtin/Lora/networks.py

+39 −3

Original line number	Diff line number	Diff line
		@@ -170,6 +170,10 @@ def load_network(name, network_on_disk):
		emb_dict[vec_name] = weight
		bundle_embeddings[emb_name] = emb_dict

		#if key_network_without_network_parts == "oft_unet":
		# print(key_network_without_network_parts)
		# pass

		key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
		sd_module = shared.sd_model.network_layer_mapping.get(key, None)

		@@ -185,15 +189,39 @@ def load_network(name, network_on_disk):
		elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
		key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
		sd_module = shared.sd_model.network_layer_mapping.get(key, None)
		elif sd_module is None and "oft_unet" in key_network_without_network_parts:
		key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
		sd_module = shared.sd_model.network_layer_mapping.get(key, None)

		# some SD1 Loras also have correct compvis keys
		if sd_module is None:
		key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
		sd_module = shared.sd_model.network_layer_mapping.get(key, None)

		elif sd_module is None and "oft_unet" in key_network_without_network_parts:
		# UNET_TARGET_REPLACE_MODULE_ALL_LINEAR = ["Transformer2DModel"]
		# UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
		UNET_TARGET_REPLACE_MODULE_ATTN_ONLY = ["CrossAttention"]
		# TODO: Change matchedm odules based on whether all linear, conv, etc

		key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
		sd_module = shared.sd_model.network_layer_mapping.get(key, None)
		#key_no_suffix = key.rsplit("_to_", 1)[0]
		## Match all modules of class CrossAttention
		#replace_module_list = []
		#for module_type in UNET_TARGET_REPLACE_MODULE_ATTN_ONLY:
		# replace_module_list += [module for k, module in shared.sd_model.network_layer_mapping.items() if module_type in module.__class__.__name__]

		#matched_module = replace_module_list.get(key_no_suffix, None)
		#if key.endswith('to_q'):
		# sd_module = matched_module.to_q or None
		#if key.endswith('to_k'):
		# sd_module = matched_module.to_k or None
		#if key.endswith('to_v'):
		# sd_module = matched_module.to_v or None
		#if key.endswith('to_out_0'):
		# sd_module = matched_module.to_out[0] or None
		#if key.endswith('to_out_1'):
		# sd_module = matched_module.to_out[1] or None


		if sd_module is None:
		keys_failed_to_match[key_network] = key
		continue
		@@ -215,6 +243,14 @@ def load_network(name, network_on_disk):

		net.modules[key] = net_module

		# replaces forward method of original Linear
		# applied_to_count = 0
		#for key, created_module in net.modules.items():
		# if isinstance(created_module, network_oft.NetworkModuleOFT):
		# net_module.apply_to()
		#applied_to_count += 1
		# print(f'Applied OFT modules: {applied_to_count}')

		embeddings = {}
		for emb_name, data in bundle_embeddings.items():
		embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)

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