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__pycache__

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# a1111-sd-webui-lycoris
An extension for loading lycoris model in sd-webui.
I made this stand alone extension (Use sd-webui's extra networks api) to avoid some conflict with other loras extensions.
### LyCORIS
https://github.com/KohakuBlueleaf/LyCORIS
### usage
Install it and restart the webui
**Don't use "Apply and restart UI", please restart the webui process**
And you will find "LyCORIS" tab in the extra networks page

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from modules import extra_networks, shared
import lycoris
class ExtraNetworkLyCORIS(extra_networks.ExtraNetwork):
def __init__(self):
super().__init__('lyco')
def activate(self, p, params_list):
additional = shared.opts.sd_lyco
if additional != "" and additional in lycoris.available_lycos and len([x for x in params_list if x.items[0] == additional]) == 0:
p.all_prompts = [x + f"<lyco:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
names = []
multipliers = []
for params in params_list:
assert len(params.items) > 0
names.append(params.items[0])
multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
lycoris.load_lycos(names, multipliers)
def deactivate(self, p):
pass

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from typing import *
import os
import re
import glob
import torch
import torch.nn as nn
import torch.nn.functional as F
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_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):
# I don't know why but some state dict has this kind of thing
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
def assign_lyco_names_to_compvis_modules(sd_model):
lyco_layer_mapping = {}
for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
lyco_name = name.replace(".", "_")
lyco_layer_mapping[lyco_name] = module
module.lyco_layer_name = lyco_name
for name, module in shared.sd_model.model.named_modules():
lyco_name = name.replace(".", "_")
lyco_layer_mapping[lyco_name] = module
module.lyco_layer_name = lyco_name
sd_model.lyco_layer_mapping = lyco_layer_mapping
class LycoOnDisk:
def __init__(self, name, filename):
self.name = name
self.filename = filename
self.metadata = {}
_, ext = os.path.splitext(filename)
if ext.lower() == ".safetensors":
try:
self.metadata = sd_models.read_metadata_from_safetensors(filename)
except Exception as e:
errors.display(e, f"reading lora {filename}")
if self.metadata:
m = {}
for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
m[k] = v
self.metadata = m
self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None) # those are cover images and they are too big to display in UI as text
class LycoModule:
def __init__(self, name):
self.name = name
self.multiplier = 1.0
self.modules = {}
self.mtime = None
class FullModule:
def __init__(self):
self.weight = None
self.alpha = None
self.dim = None
self.shape = None
class LycoUpDownModule:
def __init__(self):
self.up_model = None
self.mid_model = None
self.down_model = None
self.alpha = None
self.dim = None
self.shape = None
self.bias = None
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 LycoHadaModule:
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.shape = None
self.bias = None
class IA3Module:
def __init__(self):
self.w = None
self.alpha = None
self.dim = 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 LycoKronModule:
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.shape = None
self.bias = 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
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_lyco(name, filename):
print('locon load lyco method')
lyco = LycoModule(name)
lyco.mtime = os.path.getmtime(filename)
sd = sd_models.read_state_dict(filename)
is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lyco_layer_mapping
keys_failed_to_match = []
for key_diffusers, weight in sd.items():
fullkey = convert_diffusers_name_to_compvis(key_diffusers, is_sd2)
key, lyco_key = fullkey.split(".", 1)
sd_module = shared.sd_model.lyco_layer_mapping.get(key, None)
if sd_module is None:
m = re_x_proj.match(key)
if m:
sd_module = shared.sd_model.lyco_layer_mapping.get(m.group(1), None)
if sd_module is None:
print(key)
keys_failed_to_match.append(key_diffusers)
continue
lyco_module = lyco.modules.get(key, None)
if lyco_module is None:
lyco_module = LycoUpDownModule()
lyco.modules[key] = lyco_module
if lyco_key == "alpha":
lyco_module.alpha = weight.item()
continue
if lyco_key == "diff":
weight = weight.to(device=devices.device, dtype=devices.dtype)
weight.requires_grad_(False)
lyco_module = FullModule()
lyco.modules[key] = lyco_module
lyco_module.weight = weight
continue
if 'bias_' in lyco_key:
if lyco_module.bias is None:
lyco_module.bias = [None, None, None]
if 'bias_indices' == lyco_key:
lyco_module.bias[0] = weight
elif 'bias_values' == lyco_key:
lyco_module.bias[1] = weight
elif 'bias_size' == lyco_key:
lyco_module.bias[2] = weight
if all((i is not None) for i in lyco_module.bias):
print('build bias')
lyco_module.bias = torch.sparse_coo_tensor(
lyco_module.bias[0],
lyco_module.bias[1],
tuple(lyco_module.bias[2]),
).to(device=devices.cpu, dtype=devices.dtype)
lyco_module.bias.requires_grad_(False)
continue
if lyco_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)
elif type(sd_module) == torch.nn.Conv2d:
if lyco_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 lyco_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 lyco_key == "lora_up.weight":
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
else:
assert False, f'Lyco layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
if hasattr(sd_module, 'weight'):
lyco_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, dtype=devices.dtype)
module.requires_grad_(False)
if lyco_key == "lora_up.weight":
lyco_module.up_model = module
elif lyco_key == "lora_mid.weight":
lyco_module.mid_model = module
elif lyco_key == "lora_down.weight":
lyco_module.down_model = module
lyco_module.dim = weight.shape[0]
else:
print(lyco_key)
elif lyco_key in HADA_KEY:
if type(lyco_module) != LycoHadaModule:
alpha = lyco_module.alpha
bias = lyco_module.bias
lyco_module = LycoHadaModule()
lyco_module.alpha = alpha
lyco_module.bias = bias
lyco.modules[key] = lyco_module
if hasattr(sd_module, 'weight'):
lyco_module.shape = sd_module.weight.shape
weight = weight.to(device=devices.cpu, dtype=devices.dtype)
weight.requires_grad_(False)
if lyco_key == 'hada_w1_a':
lyco_module.w1a = weight
elif lyco_key == 'hada_w1_b':
lyco_module.w1b = weight
lyco_module.dim = weight.shape[0]
elif lyco_key == 'hada_w2_a':
lyco_module.w2a = weight
elif lyco_key == 'hada_w2_b':
lyco_module.w2b = weight
lyco_module.dim = weight.shape[0]
elif lyco_key == 'hada_t1':
lyco_module.t1 = weight
elif lyco_key == 'hada_t2':
lyco_module.t2 = weight
elif lyco_key in IA3_KEY:
if type(lyco_module) != IA3Module:
lyco_module = IA3Module()
lyco.modules[key] = lyco_module
if lyco_key == "weight":
lyco_module.w = weight.to(devices.device, dtype=devices.dtype)
elif lyco_key == "on_input":
lyco_module.on_input = weight
elif lyco_key in KRON_KEY:
if not isinstance(lyco_module, LycoKronModule):
alpha = lyco_module.alpha
bias = lyco_module.bias
lyco_module = LycoKronModule()
lyco_module.alpha = alpha
lyco_module.bias = bias
lyco.modules[key] = lyco_module
if hasattr(sd_module, 'weight'):
lyco_module.shape = sd_module.weight.shape
weight = weight.to(device=devices.cpu, dtype=devices.dtype)
weight.requires_grad_(False)
if lyco_key == 'lokr_w1':
lyco_module.w1 = weight
elif lyco_key == 'lokr_w1_a':
lyco_module.w1a = weight
elif lyco_key == 'lokr_w1_b':
lyco_module.w1b = weight
lyco_module.dim = weight.shape[0]
elif lyco_key == 'lokr_w2':
lyco_module.w2 = weight
elif lyco_key == 'lokr_w2_a':
lyco_module.w2a = weight
elif lyco_key == 'lokr_w2_b':
lyco_module.w2b = weight
lyco_module.dim = weight.shape[0]
elif lyco_key == 'lokr_t2':
lyco_module.t2 = weight
else:
assert False, f'Bad Lyco layer name: {key_diffusers} - must end in lyco_up.weight, lyco_down.weight or alpha'
if len(keys_failed_to_match) > 0:
print(shared.sd_model.lyco_layer_mapping)
print(f"Failed to match keys when loading Lyco {filename}: {keys_failed_to_match}")
return lyco
def load_lycos(names, multipliers=None):
already_loaded = {}
for lyco in loaded_lycos:
if lyco.name in names:
already_loaded[lyco.name] = lyco
loaded_lycos.clear()
lycos_on_disk = [available_lycos.get(name, None) for name in names]
if any([x is None for x in lycos_on_disk]):
list_available_lycos()
lycos_on_disk = [available_lycos.get(name, None) for name in names]
for i, name in enumerate(names):
lyco = already_loaded.get(name, None)
lyco_on_disk = lycos_on_disk[i]
if lyco_on_disk is not None:
if lyco is None or os.path.getmtime(lyco_on_disk.filename) > lyco.mtime:
lyco = load_lyco(name, lyco_on_disk.filename)
if lyco is None:
print(f"Couldn't find Lora with name {name}")
continue
lyco.multiplier = multipliers[i] if multipliers else 1.0
loaded_lycos.append(lyco)
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__ == 'LycoUpDownModule':
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:
# cp-decomposition
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__ == 'LycoHadaModule':
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__ == 'LycoKronModule':
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 "
"'LycoUpDownModule', 'LycoHadaModule', 'FullModule', 'IA3Module', 'LycoKronModule'"
"You may have other lyco 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)
# print(torch.sum(updown))
return updown
def lyco_calc_updown(lyco, module, target):
with torch.no_grad():
updown = rebuild_weight(module, target)
updown = updown * lyco.multiplier * (module.alpha / module.dim if module.alpha and module.dim else 1.0)
return updown
def lyco_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
"""
Applies the currently selected set of Lycos to the weights of torch layer self.
If weights already have this particular set of lycos applied, does nothing.
If not, restores orginal weights from backup and alters weights according to lycos.
"""
lyco_layer_name = getattr(self, 'lyco_layer_name', None)
if lyco_layer_name is None:
return
current_names = getattr(self, "lyco_current_names", ())
lora_names = getattr(self, "lora_current_names", ())
wanted_names = tuple((x.name, x.multiplier) for x in loaded_lycos)
weights_backup = getattr(self, "lora_backup_weights", None)
if current_names != wanted_names:
if weights_backup is not None and lora_names == ():
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)
for lyco in loaded_lycos:
module = lyco.modules.get(lyco_layer_name, None)
if module is not None and hasattr(self, 'weight'):
self.weight += lyco_calc_updown(lyco, module, self.weight)
continue
module_q = lyco.modules.get(lyco_layer_name + "_q_proj", None)
module_k = lyco.modules.get(lyco_layer_name + "_k_proj", None)
module_v = lyco.modules.get(lyco_layer_name + "_v_proj", None)
module_out = lyco.modules.get(lyco_layer_name + "_out_proj", None)
if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
updown_q = lyco_calc_updown(lyco, module_q, self.in_proj_weight)
updown_k = lyco_calc_updown(lyco, module_k, self.in_proj_weight)
updown_v = lyco_calc_updown(lyco, module_v, self.in_proj_weight)
updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
self.in_proj_weight += updown_qkv
self.out_proj.weight += lyco_calc_updown(lyco, module_out, self.out_proj.weight)
continue
if module is None:
continue
print(f'failed to calculate lyco weights for layer {lyco_layer_name}')
setattr(self, "lyco_current_names", wanted_names)
def lyco_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
setattr(self, "lyco_current_names", ())
setattr(self, "lora_weights_backup", None)
def lyco_Linear_forward(self, input):
lyco_apply_weights(self)
return torch.nn.Linear_forward_before_lyco(self, input)
def lyco_Linear_load_state_dict(self, *args, **kwargs):
lyco_reset_cached_weight(self)
return torch.nn.Linear_load_state_dict_before_lyco(self, *args, **kwargs)
def lyco_Conv2d_forward(self, input):
lyco_apply_weights(self)
return torch.nn.Conv2d_forward_before_lyco(self, input)
def lyco_Conv2d_load_state_dict(self, *args, **kwargs):
lyco_reset_cached_weight(self)
return torch.nn.Conv2d_load_state_dict_before_lyco(self, *args, **kwargs)
def lyco_MultiheadAttention_forward(self, *args, **kwargs):
lyco_apply_weights(self)
return torch.nn.MultiheadAttention_forward_before_lyco(self, *args, **kwargs)
def lyco_MultiheadAttention_load_state_dict(self, *args, **kwargs):
lyco_reset_cached_weight(self)
return torch.nn.MultiheadAttention_load_state_dict_before_lyco(self, *args, **kwargs)
def list_available_lycos():
available_lycos.clear()
os.makedirs(shared.cmd_opts.lyco_dir, exist_ok=True)
candidates = \
glob.glob(os.path.join(shared.cmd_opts.lyco_dir, '**/*.pt'), recursive=True) + \
glob.glob(os.path.join(shared.cmd_opts.lyco_dir, '**/*.safetensors'), recursive=True) + \
glob.glob(os.path.join(shared.cmd_opts.lyco_dir, '**/*.ckpt'), recursive=True)
for filename in sorted(candidates, key=str.lower):
if os.path.isdir(filename):
continue
name = os.path.splitext(os.path.basename(filename))[0]
available_lycos[name] = LycoOnDisk(name, filename)
available_lycos = {}
loaded_lycos = []
list_available_lycos()

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preload.py Normal file
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import os
from modules import paths
def preload(parser):
parser.add_argument("--lyco-dir", type=str, help="Path to directory with LyCORIS networks.", default=os.path.join(paths.models_path, 'LyCORIS'))

56
scripts/lycoris_script.py Normal file
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import torch
import gradio as gr
import lycoris
import extra_networks_lyco
import ui_extra_networks_lyco
from modules import script_callbacks, ui_extra_networks, extra_networks, shared
def unload():
torch.nn.Linear.forward = torch.nn.Linear_forward_before_lyco
torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lyco
torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lyco
torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lyco
torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lyco
torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lyco
def before_ui():
ui_extra_networks.register_page(ui_extra_networks_lyco.ExtraNetworksPageLyCORIS())
extra_networks.register_extra_network(extra_networks_lyco.ExtraNetworkLyCORIS())
if not hasattr(torch.nn, 'Linear_forward_before_lyco'):
torch.nn.Linear_forward_before_lyco = torch.nn.Linear.forward
if not hasattr(torch.nn, 'Linear_load_state_dict_before_lyco'):
torch.nn.Linear_load_state_dict_before_lyco = torch.nn.Linear._load_from_state_dict
if not hasattr(torch.nn, 'Conv2d_forward_before_lyco'):
torch.nn.Conv2d_forward_before_lyco = torch.nn.Conv2d.forward
if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lyco'):
torch.nn.Conv2d_load_state_dict_before_lyco = torch.nn.Conv2d._load_from_state_dict
if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lyco'):
torch.nn.MultiheadAttention_forward_before_lyco = torch.nn.MultiheadAttention.forward
if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lyco'):
torch.nn.MultiheadAttention_load_state_dict_before_lyco = torch.nn.MultiheadAttention._load_from_state_dict
torch.nn.Linear.forward = lycoris.lyco_Linear_forward
torch.nn.Linear._load_from_state_dict = lycoris.lyco_Linear_load_state_dict
torch.nn.Conv2d.forward = lycoris.lyco_Conv2d_forward
torch.nn.Conv2d._load_from_state_dict = lycoris.lyco_Conv2d_load_state_dict
torch.nn.MultiheadAttention.forward = lycoris.lyco_MultiheadAttention_forward
torch.nn.MultiheadAttention._load_from_state_dict = lycoris.lyco_MultiheadAttention_load_state_dict
script_callbacks.on_model_loaded(lycoris.assign_lyco_names_to_compvis_modules)
script_callbacks.on_script_unloaded(unload)
script_callbacks.on_before_ui(before_ui)
shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
"sd_lyco": shared.OptionInfo("None", "Add LyCORIS to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lycoris.available_lycos]}, refresh=lycoris.list_available_lycos),
}))

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ui_extra_networks_lyco.py Normal file
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import json
import os
import lycoris
from modules import shared, ui_extra_networks
class ExtraNetworksPageLyCORIS(ui_extra_networks.ExtraNetworksPage):
def __init__(self):
super().__init__('LyCORIS')
def refresh(self):
lycoris.list_available_lycos()
def list_items(self):
for name, lyco_on_disk in lycoris.available_lycos.items():
path, ext = os.path.splitext(lyco_on_disk.filename)
yield {
"name": name,
"filename": path,
"preview": self.find_preview(path),
"description": self.find_description(path),
"search_term": self.search_terms_from_path(lyco_on_disk.filename),
"prompt": json.dumps(f"<lyco:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
"local_preview": f"{path}.{shared.opts.samples_format}",
"metadata": json.dumps(lyco_on_disk.metadata, indent=4) if lyco_on_disk.metadata else None,
}
def allowed_directories_for_previews(self):
return [shared.cmd_opts.lyco_dir]