From 2abd89acc66419abf2eee9b03fd093f2737670de Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 28 Jan 2023 20:04:35 +0300 Subject: [PATCH] index on master: 91c8d0d Merge pull request #7231 from EllangoK/master --- extensions-builtin/Lora/lora.py | 21 +++++++++++++++++-- .../Lora/scripts/lora_script.py | 5 +++++ 2 files changed, 24 insertions(+), 2 deletions(-) diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index cb8f1d36..568a7675 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -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')}" + elif 'self_attn': + 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() diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index 2e860160..a385ae94 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -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)