|
''' |
|
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) |
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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(.+)") |
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re_unet_time_embed = re.compile(r"lora_unet_time_embedding_linear_(\d+)(.+)") |
|
|
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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+)_(.+)") |
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re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)") |
|
|
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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+)_(.+)") |
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re_unet_up_blocks_res = re.compile(r"lora_unet_up_blocks_(\d+)_resnets_(\d+)_(.+)") |
|
|
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re_unet_downsample = re.compile(r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv(.+)") |
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re_unet_upsample = re.compile(r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv(.+)") |
|
|
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re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)") |
|
|
|
|
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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'):]}" |
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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') |
|
|
