initial work on SD2 Lora support (7cb31a27) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/Lora/lora.py

+19 −2

Original line number	Diff line number	Diff line
		@@ -12,7 +12,7 @@ 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+)_(.+)")


		def convert_diffusers_name_to_compvis(key):
		def convert_diffusers_name_to_compvis(key, is_sd2):
		def match(match_list, regex):
		r = re.match(regex, key)
		if not r:
		@@ -34,6 +34,14 @@ def convert_diffusers_name_to_compvis(key):
		return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"

		if match(m, re_text_block):
		if is_sd2:
		if 'mlp_fc1' in m[1]:
		return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
		elif 'mlp_fc2' in m[1]:
		return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
		else:
		return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"

		return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"

		return key
		@@ -83,9 +91,10 @@ def load_lora(name, filename):
		sd = sd_models.read_state_dict(filename)

		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)
		fullkey = convert_diffusers_name_to_compvis(key_diffusers, is_sd2)
		key, lora_key = fullkey.split(".", 1)

		sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
		@@ -104,9 +113,13 @@ 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)
		elif type(sd_module) == torch.nn.Conv2d:
		module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
		else:
		print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
		continue
		assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'

		with torch.no_grad():
		@@ -182,6 +195,10 @@ def lora_Conv2d_forward(self, input):
		return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))


		def lora_NonDynamicallyQuantizableLinear_forward(self, input):
		return lora_forward(self, input, torch.nn.NonDynamicallyQuantizableLinear_forward_before_lora(self, input))


		def list_available_loras():
		available_loras.clear()

extensions-builtin/Lora/scripts/lora_script.py

+5 −0

Original line number	Diff line number	Diff line
		@@ -10,6 +10,7 @@ from modules import script_callbacks, ui_extra_networks, extra_networks, shared
		def unload():
		torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
		torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
		torch.nn.modules.linear.NonDynamicallyQuantizableLinear.forward = torch.nn.NonDynamicallyQuantizableLinear_forward_before_lora


		def before_ui():
		@@ -23,8 +24,12 @@ if not hasattr(torch.nn, 'Linear_forward_before_lora'):
		if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
		torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward

		if not hasattr(torch.nn, 'NonDynamicallyQuantizableLinear_forward_before_lora'):
		torch.nn.NonDynamicallyQuantizableLinear_forward_before_lora = torch.nn.modules.linear.NonDynamicallyQuantizableLinear.forward

		torch.nn.Linear.forward = lora.lora_Linear_forward
		torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
		torch.nn.modules.linear.NonDynamicallyQuantizableLinear.forward = lora.lora_NonDynamicallyQuantizableLinear_forward

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

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