Lora support for SD2 (650ddc9d) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/Lora/lora.py

+116 −39

Original line number	Diff line number	Diff line
		@@ -8,14 +8,27 @@ from modules import shared, devices, sd_models, errors
		metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}

		re_digits = re.compile(r"\d+")
		re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
		re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
		re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
		re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
		re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
		re_compiled = {}

		suffix_conversion = {
		"attentions": {},
		"resnets": {
		"conv1": "in_layers_2",
		"conv2": "out_layers_3",
		"time_emb_proj": "emb_layers_1",
		"conv_shortcut": "skip_connection",
		}
		}


		def convert_diffusers_name_to_compvis(key, is_sd2):
		def match(match_list, regex):
		def match(match_list, regex_text):
		regex = re_compiled.get(regex_text)
		if regex is None:
		regex = re.compile(regex_text)
		re_compiled[regex_text] = regex

		r = re.match(regex, key)
		if not r:
		return False
		@@ -26,16 +39,25 @@ def convert_diffusers_name_to_compvis(key, is_sd2):

		m = []

		if match(m, re_unet_down_blocks):
		return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
		if match(m, r"lora_unet_down_blocks_(\d+)_(attentions\|resnets)_(\d+)_(.+)"):
		suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
		return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"

		if match(m, r"lora_unet_mid_block_(attentions\|resnets)_(\d+)_(.+)"):
		suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
		return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"

		if match(m, r"lora_unet_up_blocks_(\d+)_(attentions\|resnets)_(\d+)_(.+)"):
		suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
		return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"

		if match(m, re_unet_mid_blocks):
		return f"diffusion_model_middle_block_1_{m[1]}"
		if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
		return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"

		if match(m, re_unet_up_blocks):
		return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
		if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
		return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"

		if match(m, re_text_block):
		if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
		if is_sd2:
		if 'mlp_fc1' in m[1]:
		return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
		@@ -109,16 +131,22 @@ def load_lora(name, filename):

		sd = sd_models.read_state_dict(filename)

		keys_failed_to_match = []
		keys_failed_to_match = {}
		is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping

		for key_diffusers, weight in sd.items():
		fullkey = convert_diffusers_name_to_compvis(key_diffusers, is_sd2)
		key, lora_key = fullkey.split(".", 1)
		key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
		key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)

		sd_module = shared.sd_model.lora_layer_mapping.get(key, None)

		if sd_module is None:
		m = re_x_proj.match(key)
		if m:
		sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)

		if sd_module is None:
		keys_failed_to_match.append(key_diffusers)
		keys_failed_to_match[key_diffusers] = key
		continue

		lora_module = lora.modules.get(key, None)
		@@ -133,7 +161,9 @@ def load_lora(name, filename):
		if type(sd_module) == torch.nn.Linear:
		module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
		elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
		module = torch.nn.modules.linear.NonDynamicallyQuantizableLinear(weight.shape[1], weight.shape[0], bias=False)
		module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
		elif type(sd_module) == torch.nn.MultiheadAttention:
		module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
		elif type(sd_module) == torch.nn.Conv2d:
		module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
		else:
		@@ -190,54 +220,94 @@ def load_loras(names, multipliers=None):
		loaded_loras.append(lora)


		def lora_apply_weights(self: torch.nn.Conv2d \| torch.nn.Linear):
		def lora_calc_updown(lora, module, target):
		with torch.no_grad():
		up = module.up.weight.to(target.device, dtype=target.dtype)
		down = module.down.weight.to(target.device, dtype=target.dtype)

		if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
		updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
		else:
		updown = up @ down

		updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)

		return updown


		def lora_apply_weights(self: torch.nn.Conv2d \| torch.nn.Linear \| torch.nn.MultiheadAttention):
		"""
		Applies the currently selected set of Loras to the weight of torch layer self.
		Applies the currently selected set of Loras to the weights of torch layer self.
		If weights already have this particular set of loras applied, does nothing.
		If not, restores orginal weights from backup and alters weights according to loras.
		"""

		lora_layer_name = getattr(self, 'lora_layer_name', None)
		if lora_layer_name is None:
		return

		current_names = getattr(self, "lora_current_names", ())
		wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)

		weights_backup = getattr(self, "lora_weights_backup", None)
		if weights_backup is None:
		if isinstance(self, torch.nn.MultiheadAttention):
		weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
		else:
		weights_backup = self.weight.to(devices.cpu, copy=True)

		self.lora_weights_backup = weights_backup

		if current_names != wanted_names:
		if weights_backup is not None:
		if isinstance(self, torch.nn.MultiheadAttention):
		self.in_proj_weight.copy_(weights_backup[0])
		self.out_proj.weight.copy_(weights_backup[1])
		else:
		self.weight.copy_(weights_backup)

		lora_layer_name = getattr(self, 'lora_layer_name', None)
		for lora in loaded_loras:
		module = lora.modules.get(lora_layer_name, None)
		if module is None:
		if module is not None and hasattr(self, 'weight'):
		self.weight += lora_calc_updown(lora, module, self.weight)
		continue

		with torch.no_grad():
		up = module.up.weight.to(self.weight.device, dtype=self.weight.dtype)
		down = module.down.weight.to(self.weight.device, dtype=self.weight.dtype)
		module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
		module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
		module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
		module_out = lora.modules.get(lora_layer_name + "_out_proj", None)

		if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
		updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
		else:
		updown = up @ down
		if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
		updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
		updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
		updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
		updown_qkv = torch.vstack([updown_q, updown_k, updown_v])

		self.weight += updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
		self.in_proj_weight += updown_qkv
		self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
		continue

		if module is None:
		continue

		print(f'failed to calculate lora weights for layer {lora_layer_name}')

		setattr(self, "lora_current_names", wanted_names)


		def lora_reset_cached_weight(self: torch.nn.Conv2d \| torch.nn.Linear):
		setattr(self, "lora_current_names", ())
		setattr(self, "lora_weights_backup", None)


		def lora_Linear_forward(self, input):
		lora_apply_weights(self)

		return torch.nn.Linear_forward_before_lora(self, input)


		def lora_Linear_load_state_dict(self: torch.nn.Linear, args, *kwargs):
		setattr(self, "lora_current_names", ())
		setattr(self, "lora_weights_backup", None)
		def lora_Linear_load_state_dict(self, args, *kwargs):
		lora_reset_cached_weight(self)

		return torch.nn.Linear_load_state_dict_before_lora(self, args, *kwargs)

		@@ -248,15 +318,22 @@ def lora_Conv2d_forward(self, input):
		return torch.nn.Conv2d_forward_before_lora(self, input)


		def lora_Conv2d_load_state_dict(self: torch.nn.Conv2d, args, *kwargs):
		setattr(self, "lora_current_names", ())
		setattr(self, "lora_weights_backup", None)
		def lora_Conv2d_load_state_dict(self, args, *kwargs):
		lora_reset_cached_weight(self)

		return torch.nn.Conv2d_load_state_dict_before_lora(self, args, *kwargs)


		def lora_NonDynamicallyQuantizableLinear_forward(self, input):
		return lora_forward(self, input, torch.nn.NonDynamicallyQuantizableLinear_forward_before_lora(self, input))
		def lora_MultiheadAttention_forward(self, args, *kwargs):
		lora_apply_weights(self)

		return torch.nn.MultiheadAttention_forward_before_lora(self, args, *kwargs)


		def lora_MultiheadAttention_load_state_dict(self, args, *kwargs):
		lora_reset_cached_weight(self)

		return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, args, *kwargs)


		def list_available_loras():

extensions-builtin/Lora/scripts/lora_script.py

+10 −0

Original line number	Diff line number	Diff line
		@@ -12,6 +12,8 @@ def unload():
		torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
		torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
		torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
		torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
		torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora


		def before_ui():
		@@ -31,10 +33,18 @@ if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
		if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
		torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict

		if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
		torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward

		if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
		torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict

		torch.nn.Linear.forward = lora.lora_Linear_forward
		torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
		torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
		torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
		torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
		torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict

		script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
		script_callbacks.on_script_unloaded(unload)

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