| ''' |
| Modified version for full net lora |
| (Lora for ResBlock and up/down sample block) |
| ''' |
| import os, sys |
| import re |
| import torch |
|
|
| from modules import shared, devices, sd_models |
| now_dir = os.path.dirname(os.path.abspath(__file__)) |
| lora_path = os.path.join(now_dir, '..', '..', '..', 'extensions-builtin/Lora') |
| sys.path.insert(0, lora_path) |
| import lora |
| new_lora = 'lora_calc_updown' in dir(lora) |
|
|
| from locon_compvis import LoConModule, LoConNetworkCompvis, create_network_and_apply_compvis |
|
|
|
|
| try: |
| ''' |
| Hijack Additional Network extension |
| ''' |
| |
| raise |
| now_dir = os.path.dirname(os.path.abspath(__file__)) |
| addnet_path = os.path.join(now_dir, '..', '..', 'sd-webui-additional-networks/scripts') |
| sys.path.append(addnet_path) |
| import lora_compvis |
| import scripts |
| scripts.lora_compvis = lora_compvis |
| scripts.lora_compvis.LoRAModule = LoConModule |
| scripts.lora_compvis.LoRANetworkCompvis = LoConNetworkCompvis |
| scripts.lora_compvis.create_network_and_apply_compvis = create_network_and_apply_compvis |
| print('LoCon Extension hijack addnet extension successfully') |
| except: |
| print('Additional Network extension not installed, Only hijack built-in lora') |
|
|
|
|
| ''' |
| Hijack sd-webui LoRA |
| ''' |
| re_digits = re.compile(r"\d+") |
| re_x_proj = re.compile(r"(.*)_([qkv]_proj)$") |
|
|
| re_unet_conv_in = re.compile(r"lora_unet_conv_in(.+)") |
| re_unet_conv_out = re.compile(r"lora_unet_conv_out(.+)") |
| re_unet_time_embed = re.compile(r"lora_unet_time_embedding_linear_(\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_unet_down_blocks_res = re.compile(r"lora_unet_down_blocks_(\d+)_resnets_(\d+)_(.+)") |
| re_unet_mid_blocks_res = re.compile(r"lora_unet_mid_block_resnets_(\d+)_(.+)") |
| re_unet_up_blocks_res = re.compile(r"lora_unet_up_blocks_(\d+)_resnets_(\d+)_(.+)") |
|
|
| re_unet_downsample = re.compile(r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv(.+)") |
| re_unet_upsample = re.compile(r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv(.+)") |
|
|
| re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)") |
|
|
|
|
| def convert_diffusers_name_to_compvis(key, is_sd2): |
| |
| key = key.replace('text_model_text_model', 'text_model') |
| def match(match_list, regex): |
| r = re.match(regex, key) |
| if not r: |
| return False |
|
|
| match_list.clear() |
| match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()]) |
| return True |
|
|
| m = [] |
| |
| if match(m, re_unet_conv_in): |
| return f'diffusion_model_input_blocks_0_0{m[0]}' |
| |
| if match(m, re_unet_conv_out): |
| return f'diffusion_model_out_2{m[0]}' |
| |
| if match(m, re_unet_time_embed): |
| return f"diffusion_model_time_embed_{m[0]*2-2}{m[1]}" |
| |
| 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, re_unet_mid_blocks): |
| return f"diffusion_model_middle_block_1_{m[1]}" |
|
|
| if match(m, re_unet_up_blocks): |
| return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}" |
|
|
| if match(m, re_unet_down_blocks_res): |
| block = f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_0_" |
| if m[2].startswith('conv1'): |
| return f"{block}in_layers_2{m[2][len('conv1'):]}" |
| elif m[2].startswith('conv2'): |
| return f"{block}out_layers_3{m[2][len('conv2'):]}" |
| elif m[2].startswith('time_emb_proj'): |
| return f"{block}emb_layers_1{m[2][len('time_emb_proj'):]}" |
| elif m[2].startswith('conv_shortcut'): |
| return f"{block}skip_connection{m[2][len('conv_shortcut'):]}" |
|
|
| if match(m, re_unet_mid_blocks_res): |
| block = f"diffusion_model_middle_block_{m[0]*2}_" |
| if m[1].startswith('conv1'): |
| return f"{block}in_layers_2{m[1][len('conv1'):]}" |
| elif m[1].startswith('conv2'): |
| return f"{block}out_layers_3{m[1][len('conv2'):]}" |
| elif m[1].startswith('time_emb_proj'): |
| return f"{block}emb_layers_1{m[1][len('time_emb_proj'):]}" |
| elif m[1].startswith('conv_shortcut'): |
| return f"{block}skip_connection{m[1][len('conv_shortcut'):]}" |
|
|
| if match(m, re_unet_up_blocks_res): |
| block = f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_0_" |
| if m[2].startswith('conv1'): |
| return f"{block}in_layers_2{m[2][len('conv1'):]}" |
| elif m[2].startswith('conv2'): |
| return f"{block}out_layers_3{m[2][len('conv2'):]}" |
| elif m[2].startswith('time_emb_proj'): |
| return f"{block}emb_layers_1{m[2][len('time_emb_proj'):]}" |
| elif m[2].startswith('conv_shortcut'): |
| return f"{block}skip_connection{m[2][len('conv_shortcut'):]}" |
|
|
| if match(m, re_unet_downsample): |
| return f"diffusion_model_input_blocks_{m[0]*3+3}_0_op{m[1]}" |
|
|
| if match(m, re_unet_upsample): |
| return f"diffusion_model_output_blocks_{m[0]*3 + 2}_{1+(m[0]!=0)}_conv{m[1]}" |
|
|
| 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')}" |
| 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 |
|
|
|
|
| class LoraOnDisk: |
| def __init__(self, name, filename): |
| self.name = name |
| self.filename = filename |
|
|
|
|
| class LoraModule: |
| def __init__(self, name): |
| self.name = name |
| self.multiplier = 1.0 |
| self.modules = {} |
| self.mtime = None |
|
|
|
|
| class FakeModule(torch.nn.Module): |
| def __init__(self, weight, func): |
| super().__init__() |
| self.weight = weight |
| self.func = func |
| |
| def forward(self, x): |
| return self.func(x) |
|
|
|
|
| class FullModule: |
| def __init__(self): |
| self.weight = None |
| self.alpha = None |
| self.op = None |
| self.extra_args = {} |
| self.shape = None |
| self.up = None |
| |
| def down(self, x): |
| return x |
| |
| def inference(self, x): |
| return self.op(x, self.weight, **self.extra_args) |
|
|
|
|
| class LoraUpDownModule: |
| def __init__(self): |
| self.up_model = None |
| self.mid_model = None |
| self.down_model = None |
| self.alpha = None |
| self.dim = None |
| self.op = None |
| self.extra_args = {} |
| self.shape = None |
| self.bias = None |
| self.up = None |
| |
| def down(self, x): |
| return x |
| |
| def inference(self, x): |
| if hasattr(self, 'bias') and isinstance(self.bias, torch.Tensor): |
| out_dim = self.up_model.weight.size(0) |
| rank = self.down_model.weight.size(0) |
| rebuild_weight = ( |
| self.up_model.weight.reshape(out_dim, -1) @ self.down_model.weight.reshape(rank, -1) |
| + self.bias |
| ).reshape(self.shape) |
| return self.op( |
| x, rebuild_weight, |
| bias=None, |
| **self.extra_args |
| ) |
| else: |
| if self.mid_model is None: |
| return self.up_model(self.down_model(x)) |
| else: |
| return self.up_model(self.mid_model(self.down_model(x))) |
|
|
|
|
| def make_weight_cp(t, wa, wb): |
| temp = torch.einsum('i j k l, j r -> i r k l', t, wb) |
| return torch.einsum('i j k l, i r -> r j k l', temp, wa) |
|
|
|
|
| class LoraHadaModule: |
| def __init__(self): |
| self.t1 = None |
| self.w1a = None |
| self.w1b = None |
| self.t2 = None |
| self.w2a = None |
| self.w2b = None |
| self.alpha = None |
| self.dim = None |
| self.op = None |
| self.extra_args = {} |
| self.shape = None |
| self.bias = None |
| self.up = None |
| |
| def down(self, x): |
| return x |
| |
| def inference(self, x): |
| if hasattr(self, 'bias') and isinstance(self.bias, torch.Tensor): |
| bias = self.bias |
| else: |
| bias = 0 |
| |
| if self.t1 is None: |
| return self.op( |
| x, |
| ((self.w1a @ self.w1b) * (self.w2a @ self.w2b) + bias).view(self.shape), |
| bias=None, |
| **self.extra_args |
| ) |
| else: |
| return self.op( |
| x, |
| (make_weight_cp(self.t1, self.w1a, self.w1b) |
| * make_weight_cp(self.t2, self.w2a, self.w2b) + bias).view(self.shape), |
| bias=None, |
| **self.extra_args |
| ) |
|
|
|
|
| class IA3Module: |
| def __init__(self): |
| self.w = None |
| self.alpha = None |
| self.on_input = None |
|
|
|
|
| def make_kron(orig_shape, w1, w2): |
| if len(w2.shape) == 4: |
| w1 = w1.unsqueeze(2).unsqueeze(2) |
| w2 = w2.contiguous() |
| return torch.kron(w1, w2).reshape(orig_shape) |
|
|
|
|
| class LoraKronModule: |
| def __init__(self): |
| self.w1 = None |
| self.w1a = None |
| self.w1b = None |
| self.w2 = None |
| self.t2 = None |
| self.w2a = None |
| self.w2b = None |
| self._alpha = None |
| self.dim = None |
| self.op = None |
| self.extra_args = {} |
| self.shape = None |
| self.bias = None |
| self.up = None |
| |
| @property |
| def alpha(self): |
| if self.w1a is None and self.w2a is None: |
| return None |
| else: |
| return self._alpha |
| |
| @alpha.setter |
| def alpha(self, x): |
| self._alpha = x |
| |
| def down(self, x): |
| return x |
| |
| def inference(self, x): |
| if hasattr(self, 'bias') and isinstance(self.bias, torch.Tensor): |
| bias = self.bias |
| else: |
| bias = 0 |
| |
| if self.t2 is None: |
| return self.op( |
| x, |
| (torch.kron(self.w1, self.w2a@self.w2b) + bias).view(self.shape), |
| **self.extra_args |
| ) |
| else: |
| |
| return self.op( |
| x, |
| (torch.kron(self.w1, make_weight_cp(self.t2, self.w2a, self.w2b)) + bias).view(self.shape), |
| **self.extra_args |
| ) |
|
|
|
|
| CON_KEY = { |
| "lora_up.weight", |
| "lora_down.weight", |
| "lora_mid.weight" |
| } |
| HADA_KEY = { |
| "hada_t1", |
| "hada_w1_a", |
| "hada_w1_b", |
| "hada_t2", |
| "hada_w2_a", |
| "hada_w2_b", |
| } |
| IA3_KEY = { |
| "weight", |
| "on_input" |
| } |
| KRON_KEY = { |
| "lokr_w1", |
| "lokr_w1_a", |
| "lokr_w1_b", |
| "lokr_t2", |
| "lokr_w2", |
| "lokr_w2_a", |
| "lokr_w2_b", |
| } |
|
|
| def load_lora(name, filename): |
| print('locon load lora method') |
| lora = LoraModule(name) |
| lora.mtime = os.path.getmtime(filename) |
|
|
| sd = sd_models.read_state_dict(filename) |
| is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping |
|
|
| keys_failed_to_match = [] |
|
|
| for key_diffusers, weight in sd.items(): |
| 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) |
| |
| 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: |
| print(key) |
| keys_failed_to_match.append(key_diffusers) |
| continue |
|
|
| lora_module = lora.modules.get(key, None) |
| if lora_module is None: |
| lora_module = LoraUpDownModule() |
| lora.modules[key] = lora_module |
|
|
| if lora_key == "alpha": |
| lora_module.alpha = weight.item() |
| continue |
| |
| if lora_key == "diff": |
| weight = weight.to(device=devices.device, dtype=devices.dtype) |
| weight.requires_grad_(False) |
| lora_module = FullModule() |
| lora.modules[key] = lora_module |
| lora_module.weight = weight |
| lora_module.alpha = weight.size(1) |
| lora_module.up = FakeModule( |
| weight, |
| lora_module.inference |
| ) |
| lora_module.up.to(device=devices.cpu if new_lora else devices.device, dtype=devices.dtype) |
| if len(weight.shape)==2: |
| lora_module.op = torch.nn.functional.linear |
| lora_module.extra_args = { |
| 'bias': None |
| } |
| else: |
| lora_module.op = torch.nn.functional.conv2d |
| lora_module.extra_args = { |
| 'stride': sd_module.stride, |
| 'padding': sd_module.padding, |
| 'bias': None |
| } |
| continue |
| |
| if 'bias_' in lora_key: |
| if lora_module.bias is None: |
| lora_module.bias = [None, None, None] |
| if 'bias_indices' == lora_key: |
| lora_module.bias[0] = weight |
| elif 'bias_values' == lora_key: |
| lora_module.bias[1] = weight |
| elif 'bias_size' == lora_key: |
| lora_module.bias[2] = weight |
| |
| if all((i is not None) for i in lora_module.bias): |
| print('build bias') |
| lora_module.bias = torch.sparse_coo_tensor( |
| lora_module.bias[0], |
| lora_module.bias[1], |
| tuple(lora_module.bias[2]), |
| ).to(device=devices.cpu if new_lora else devices.device, dtype=devices.dtype) |
| lora_module.bias.requires_grad_(False) |
| continue |
| |
| if lora_key in CON_KEY: |
| if (type(sd_module) == torch.nn.Linear |
| or type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear |
| or type(sd_module) == torch.nn.MultiheadAttention): |
| weight = weight.reshape(weight.shape[0], -1) |
| module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) |
| lora_module.op = torch.nn.functional.linear |
| elif type(sd_module) == torch.nn.Conv2d: |
| if lora_key == "lora_down.weight": |
| if len(weight.shape) == 2: |
| weight = weight.reshape(weight.shape[0], -1, 1, 1) |
| if weight.shape[2] != 1 or weight.shape[3] != 1: |
| module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], sd_module.kernel_size, sd_module.stride, sd_module.padding, bias=False) |
| else: |
| module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) |
| elif lora_key == "lora_mid.weight": |
| module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], sd_module.kernel_size, sd_module.stride, sd_module.padding, bias=False) |
| elif lora_key == "lora_up.weight": |
| module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) |
| lora_module.op = torch.nn.functional.conv2d |
| lora_module.extra_args = { |
| 'stride': sd_module.stride, |
| 'padding': sd_module.padding |
| } |
| else: |
| assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}' |
| |
| if hasattr(sd_module, 'weight'): |
| lora_module.shape = sd_module.weight.shape |
| with torch.no_grad(): |
| if weight.shape != module.weight.shape: |
| weight = weight.reshape(module.weight.shape) |
| module.weight.copy_(weight) |
|
|
| module.to(device=devices.cpu if new_lora else devices.device, dtype=devices.dtype) |
| module.requires_grad_(False) |
|
|
| if lora_key == "lora_up.weight": |
| lora_module.up_model = module |
| lora_module.up = FakeModule( |
| lora_module.up_model.weight, |
| lora_module.inference |
| ) |
| elif lora_key == "lora_mid.weight": |
| lora_module.mid_model = module |
| elif lora_key == "lora_down.weight": |
| lora_module.down_model = module |
| lora_module.dim = weight.shape[0] |
| else: |
| print(lora_key) |
| elif lora_key in HADA_KEY: |
| if type(lora_module) != LoraHadaModule: |
| alpha = lora_module.alpha |
| bias = lora_module.bias |
| lora_module = LoraHadaModule() |
| lora_module.alpha = alpha |
| lora_module.bias = bias |
| lora.modules[key] = lora_module |
| if hasattr(sd_module, 'weight'): |
| lora_module.shape = sd_module.weight.shape |
| |
| weight = weight.to(device=devices.cpu if new_lora else devices.device, dtype=devices.dtype) |
| weight.requires_grad_(False) |
| |
| if lora_key == 'hada_w1_a': |
| lora_module.w1a = weight |
| if lora_module.up is None: |
| lora_module.up = FakeModule( |
| lora_module.w1a, |
| lora_module.inference |
| ) |
| elif lora_key == 'hada_w1_b': |
| lora_module.w1b = weight |
| lora_module.dim = weight.shape[0] |
| elif lora_key == 'hada_w2_a': |
| lora_module.w2a = weight |
| elif lora_key == 'hada_w2_b': |
| lora_module.w2b = weight |
| elif lora_key == 'hada_t1': |
| lora_module.t1 = weight |
| lora_module.up = FakeModule( |
| lora_module.t1, |
| lora_module.inference |
| ) |
| elif lora_key == 'hada_t2': |
| lora_module.t2 = weight |
| |
| if (type(sd_module) == torch.nn.Linear |
| or type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear |
| or type(sd_module) == torch.nn.MultiheadAttention): |
| lora_module.op = torch.nn.functional.linear |
| elif type(sd_module) == torch.nn.Conv2d: |
| lora_module.op = torch.nn.functional.conv2d |
| lora_module.extra_args = { |
| 'stride': sd_module.stride, |
| 'padding': sd_module.padding |
| } |
| else: |
| assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}' |
| elif lora_key in IA3_KEY: |
| if type(lora_module) != IA3Module: |
| lora_module = IA3Module() |
| lora.modules[key] = lora_module |
| |
| if lora_key == "weight": |
| lora_module.w = weight.to(devices.device, dtype=devices.dtype) |
| elif lora_key == "on_input": |
| lora_module.on_input = weight |
| elif lora_key in KRON_KEY: |
| if not isinstance(lora_module, LoraKronModule): |
| alpha = lora_module.alpha |
| bias = lora_module.bias |
| lora_module = LoraKronModule() |
| lora_module.alpha = alpha |
| lora_module.bias = bias |
| lora.modules[key] = lora_module |
| if hasattr(sd_module, 'weight'): |
| lora_module.shape = sd_module.weight.shape |
| |
| weight = weight.to(device=devices.cpu if new_lora else devices.device, dtype=devices.dtype) |
| weight.requires_grad_(False) |
| |
| if lora_key == 'lokr_w1': |
| lora_module.w1 = weight |
| elif lora_key == 'lokr_w1_a': |
| lora_module.w1a = weight |
| if lora_module.up is None: |
| lora_module.up = FakeModule( |
| lora_module.w1a, |
| lora_module.inference |
| ) |
| elif lora_key == 'lokr_w1_b': |
| lora_module.w1b = weight |
| elif lora_key == 'lokr_w2': |
| lora_module.w2 = weight |
| elif lora_key == 'lokr_w2_a': |
| lora_module.w2a = weight |
| lora_module.dim = weight.shape[0] |
| if lora_module.up is None: |
| lora_module.up = FakeModule( |
| lora_module.w2a, |
| lora_module.inference |
| ) |
| elif lora_key == 'lokr_w2_b': |
| lora_module.w2b = weight |
| elif lora_key == 'lokr_t2': |
| lora_module.t2 = weight |
| |
| if (any(isinstance(sd_module, torch_layer) for torch_layer in |
| [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention])): |
| lora_module.op = torch.nn.functional.linear |
| elif isinstance(sd_module, torch.nn.Conv2d): |
| lora_module.op = torch.nn.functional.conv2d |
| lora_module.extra_args = { |
| 'stride': sd_module.stride, |
| 'padding': sd_module.padding |
| } |
| else: |
| assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha' |
|
|
| if len(keys_failed_to_match) > 0: |
| print(shared.sd_model.lora_layer_mapping) |
| print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}") |
|
|
| return lora |
|
|
|
|
| def lora_forward(module, input, res): |
| if len(lora.loaded_loras) == 0: |
| return res |
| |
| lora_layer_name = getattr(module, 'lora_layer_name', None) |
| for lora_m in lora.loaded_loras: |
| module = lora_m.modules.get(lora_layer_name, None) |
| if module is not None and lora_m.multiplier: |
| if hasattr(module, 'up'): |
| scale = lora_m.multiplier * (module.alpha / module.up.weight.size(1) if module.alpha else 1.0) |
| else: |
| scale = lora_m.multiplier * (module.alpha / module.dim if module.alpha else 1.0) |
| |
| if shared.opts.lora_apply_to_outputs and res.shape == input.shape: |
| x = res |
| else: |
| x = input |
| |
| if hasattr(module, 'inference'): |
| res = res + module.inference(x) * scale |
| elif hasattr(module, 'up'): |
| res = res + module.up(module.down(x)) * scale |
| else: |
| raise NotImplementedError( |
| "Your settings, extensions or models are not compatible with each other." |
| ) |
| return res |
|
|
|
|
| def _rebuild_conventional(up, down, shape): |
| return (up.reshape(up.size(0), -1) @ down.reshape(down.size(0), -1)).reshape(shape) |
|
|
|
|
| def _rebuild_cp_decomposition(up, down, mid): |
| up = up.reshape(up.size(0), -1) |
| down = down.reshape(down.size(0), -1) |
| return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down) |
|
|
|
|
| def rebuild_weight(module, orig_weight: torch.Tensor) -> torch.Tensor: |
| if module.__class__.__name__ == 'LoraUpDownModule': |
| up = module.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
| down = module.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
| |
| output_shape = [up.size(0), down.size(1)] |
| if (mid:=module.mid_model) is not None: |
| |
| mid = mid.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
| updown = _rebuild_cp_decomposition(up, down, mid) |
| output_shape += mid.shape[2:] |
| else: |
| if len(down.shape) == 4: |
| output_shape += down.shape[2:] |
| updown = _rebuild_conventional(up, down, output_shape) |
| |
| elif module.__class__.__name__ == 'LoraHadaModule': |
| w1a = module.w1a.to(orig_weight.device, dtype=orig_weight.dtype) |
| w1b = module.w1b.to(orig_weight.device, dtype=orig_weight.dtype) |
| w2a = module.w2a.to(orig_weight.device, dtype=orig_weight.dtype) |
| w2b = module.w2b.to(orig_weight.device, dtype=orig_weight.dtype) |
| |
| output_shape = [w1a.size(0), w1b.size(1)] |
| |
| if module.t1 is not None: |
| output_shape = [w1a.size(1), w1b.size(1)] |
| t1 = module.t1.to(orig_weight.device, dtype=orig_weight.dtype) |
| updown1 = make_weight_cp(t1, w1a, w1b) |
| output_shape += t1.shape[2:] |
| else: |
| if len(w1b.shape) == 4: |
| output_shape += w1b.shape[2:] |
| updown1 = _rebuild_conventional(w1a, w1b, output_shape) |
| |
| if module.t2 is not None: |
| t2 = module.t2.to(orig_weight.device, dtype=orig_weight.dtype) |
| updown2 = make_weight_cp(t2, w2a, w2b) |
| else: |
| updown2 = _rebuild_conventional(w2a, w2b, output_shape) |
| |
| updown = updown1 * updown2 |
| |
| elif module.__class__.__name__ == 'FullModule': |
| output_shape = module.weight.shape |
| updown = module.weight.to(orig_weight.device, dtype=orig_weight.dtype) |
| |
| elif module.__class__.__name__ == 'IA3Module': |
| output_shape = [module.w.size(0), orig_weight.size(1)] |
| if module.on_input: |
| output_shape.reverse() |
| else: |
| module.w = module.w.reshape(-1, 1) |
| updown = orig_weight * module.w |
| |
| elif module.__class__.__name__ == 'LoraKronModule': |
| if module.w1 is not None: |
| w1 = module.w1.to(orig_weight.device, dtype=orig_weight.dtype) |
| else: |
| w1a = module.w1a.to(orig_weight.device, dtype=orig_weight.dtype) |
| w1b = module.w1b.to(orig_weight.device, dtype=orig_weight.dtype) |
| w1 = w1a @ w1b |
| |
| if module.w2 is not None: |
| w2 = module.w2.to(orig_weight.device, dtype=orig_weight.dtype) |
| elif module.t2 is None: |
| w2a = module.w2a.to(orig_weight.device, dtype=orig_weight.dtype) |
| w2b = module.w2b.to(orig_weight.device, dtype=orig_weight.dtype) |
| w2 = w2a @ w2b |
| else: |
| t2 = module.t2.to(orig_weight.device, dtype=orig_weight.dtype) |
| w2a = module.w2a.to(orig_weight.device, dtype=orig_weight.dtype) |
| w2b = module.w2b.to(orig_weight.device, dtype=orig_weight.dtype) |
| w2 = make_weight_cp(t2, w2a, w2b) |
| |
| output_shape = [w1.size(0)*w2.size(0), w1.size(1)*w2.size(1)] |
| if len(orig_weight.shape) == 4: |
| output_shape = orig_weight.shape |
| |
| updown = make_kron( |
| output_shape, w1, w2 |
| ) |
| |
| else: |
| raise NotImplementedError( |
| f"Unknown module type: {module.__class__.__name__}\n" |
| "If the type is one of " |
| "'LoraUpDownModule', 'LoraHadaModule', 'FullModule', 'IA3Module', 'LoraKronModule'" |
| "You may have other lora extension that conflict with locon extension." |
| ) |
| |
| if hasattr(module, 'bias') and module.bias != None: |
| updown = updown.reshape(module.bias.shape) |
| updown += module.bias.to(orig_weight.device, dtype=orig_weight.dtype) |
| updown = updown.reshape(output_shape) |
| |
| if len(output_shape) == 4: |
| updown = updown.reshape(output_shape) |
| |
| if orig_weight.size().numel() == updown.size().numel(): |
| updown = updown.reshape(orig_weight.shape) |
| |
| return updown |
|
|
|
|
| def lora_calc_updown(lora, module, target): |
| with torch.no_grad(): |
| updown = rebuild_weight(module, target) |
| updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0) |
| return updown |
|
|
|
|
| lora.convert_diffusers_name_to_compvis = convert_diffusers_name_to_compvis |
| lora.load_lora = load_lora |
| lora.lora_forward = lora_forward |
| lora.lora_calc_updown = lora_calc_updown |
| print('LoCon Extension hijack built-in lora successfully') |
|
|
