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Kohaku-Blueleaf
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__pycache__
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@ -0,0 +1,13 @@
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# a1111-sd-webui-lycoris
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||||
|
||||
An extension for loading lycoris model in sd-webui.
|
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I made this stand alone extension (Use sd-webui's extra networks api) to avoid some conflict with other loras extensions.
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||||
|
||||
### LyCORIS
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https://github.com/KohakuBlueleaf/LyCORIS
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### usage
|
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Install it and restart the webui
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**Don't use "Apply and restart UI", please restart the webui process**
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||||
|
||||
And you will find "LyCORIS" tab in the extra networks page
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@ -0,0 +1,26 @@
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from modules import extra_networks, shared
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import lycoris
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class ExtraNetworkLyCORIS(extra_networks.ExtraNetwork):
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||||
def __init__(self):
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||||
super().__init__('lyco')
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|
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def activate(self, p, params_list):
|
||||
additional = shared.opts.sd_lyco
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||||
|
||||
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]
|
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params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
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|
||||
names = []
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multipliers = []
|
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for params in params_list:
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assert len(params.items) > 0
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|
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names.append(params.items[0])
|
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multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
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|
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lycoris.load_lycos(names, multipliers)
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|
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def deactivate(self, p):
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pass
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@ -0,0 +1,724 @@
|
|||
from typing import *
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||||
import os
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import re
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import glob
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|
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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|
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from modules import shared, devices, sd_models, errors
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||||
|
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metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
|
||||
|
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|
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re_digits = re.compile(r"\d+")
|
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re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
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|
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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+)_(.+)")
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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+)_(.+)")
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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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|
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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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|
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def convert_diffusers_name_to_compvis(key, is_sd2):
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# I don't know why but some state dict has this kind of thing
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key = key.replace('text_model_text_model', 'text_model')
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def match(match_list, regex):
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r = re.match(regex, key)
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if not r:
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return False
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match_list.clear()
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match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
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return True
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m = []
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if match(m, re_unet_conv_in):
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return f'diffusion_model_input_blocks_0_0{m[0]}'
|
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|
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if match(m, re_unet_conv_out):
|
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return f'diffusion_model_out_2{m[0]}'
|
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|
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if match(m, re_unet_time_embed):
|
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return f"diffusion_model_time_embed_{m[0]*2-2}{m[1]}"
|
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|
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if match(m, re_unet_down_blocks):
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return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
|
||||
|
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if match(m, re_unet_mid_blocks):
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return f"diffusion_model_middle_block_1_{m[1]}"
|
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|
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if match(m, re_unet_up_blocks):
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||||
return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
|
||||
|
||||
if match(m, re_unet_down_blocks_res):
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block = f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_0_"
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if m[2].startswith('conv1'):
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return f"{block}in_layers_2{m[2][len('conv1'):]}"
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elif m[2].startswith('conv2'):
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return f"{block}out_layers_3{m[2][len('conv2'):]}"
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elif m[2].startswith('time_emb_proj'):
|
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return f"{block}emb_layers_1{m[2][len('time_emb_proj'):]}"
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||||
elif m[2].startswith('conv_shortcut'):
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return f"{block}skip_connection{m[2][len('conv_shortcut'):]}"
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||||
|
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if match(m, re_unet_mid_blocks_res):
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block = f"diffusion_model_middle_block_{m[0]*2}_"
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||||
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()
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
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'))
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
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),
|
||||
}))
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
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]
|
||||
|
||||
Loading…
Reference in New Issue