Spaces:
Configuration error
Configuration error
File size: 11,928 Bytes
fd74f7e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 |
import math
from typing import Callable, Dict, List, Optional, Tuple
import numpy as np
import PIL
import torch
import torch.nn.functional as F
import torch.nn as nn
class LoraInjectedLinear(nn.Module):
def __init__(self, in_features, out_features, bias=False, r=4):
super().__init__()
if r > min(in_features, out_features):
raise ValueError(
f"LoRA rank {r} must be less or equal than {min(in_features, out_features)}"
)
self.linear = nn.Linear(in_features, out_features, bias)
self.lora_down = nn.Linear(in_features, r, bias=False)
self.lora_up = nn.Linear(r, out_features, bias=False)
self.scale = 1.0
nn.init.normal_(self.lora_down.weight, std=1 / r**2)
nn.init.zeros_(self.lora_up.weight)
def forward(self, input):
return self.linear(input) + self.lora_up(self.lora_down(input)) * self.scale
def inject_trainable_lora(
model: nn.Module,
target_replace_module: List[str] = ["CrossAttention", "Attention"],
r: int = 4,
loras=None, # path to lora .pt
):
"""
inject lora into model, and returns lora parameter groups.
"""
require_grad_params = []
names = []
if loras != None:
loras = torch.load(loras)
for _module in model.modules():
if _module.__class__.__name__ in target_replace_module:
for name, _child_module in _module.named_modules():
if _child_module.__class__.__name__ == "Linear":
weight = _child_module.weight
bias = _child_module.bias
_tmp = LoraInjectedLinear(
_child_module.in_features,
_child_module.out_features,
_child_module.bias is not None,
r,
)
_tmp.linear.weight = weight
if bias is not None:
_tmp.linear.bias = bias
# switch the module
_module._modules[name] = _tmp
require_grad_params.append(
_module._modules[name].lora_up.parameters()
)
require_grad_params.append(
_module._modules[name].lora_down.parameters()
)
if loras != None:
_module._modules[name].lora_up.weight = loras.pop(0)
_module._modules[name].lora_down.weight = loras.pop(0)
_module._modules[name].lora_up.weight.requires_grad = True
_module._modules[name].lora_down.weight.requires_grad = True
names.append(name)
return require_grad_params, names
def extract_lora_ups_down(model, target_replace_module=["CrossAttention", "Attention"]):
loras = []
for _module in model.modules():
if _module.__class__.__name__ in target_replace_module:
for _child_module in _module.modules():
if _child_module.__class__.__name__ == "LoraInjectedLinear":
loras.append((_child_module.lora_up, _child_module.lora_down))
if len(loras) == 0:
raise ValueError("No lora injected.")
return loras
def save_lora_weight(
model, path="./lora.pt", target_replace_module=["CrossAttention", "Attention"]
):
weights = []
for _up, _down in extract_lora_ups_down(
model, target_replace_module=target_replace_module
):
weights.append(_up.weight)
weights.append(_down.weight)
torch.save(weights, path)
def save_lora_as_json(model, path="./lora.json"):
weights = []
for _up, _down in extract_lora_ups_down(model):
weights.append(_up.weight.detach().cpu().numpy().tolist())
weights.append(_down.weight.detach().cpu().numpy().tolist())
import json
with open(path, "w") as f:
json.dump(weights, f)
def weight_apply_lora(
model, loras, target_replace_module=["CrossAttention", "Attention"], alpha=1.0
):
for _module in model.modules():
if _module.__class__.__name__ in target_replace_module:
for _child_module in _module.modules():
if _child_module.__class__.__name__ == "Linear":
weight = _child_module.weight
up_weight = loras.pop(0).detach().to(weight.device)
down_weight = loras.pop(0).detach().to(weight.device)
# W <- W + U * D
weight = weight + alpha * (up_weight @ down_weight).type(
weight.dtype
)
_child_module.weight = nn.Parameter(weight)
def monkeypatch_lora(
model, loras, target_replace_module=["CrossAttention", "Attention"], r: int = 4
):
for _module in model.modules():
if _module.__class__.__name__ in target_replace_module:
for name, _child_module in _module.named_modules():
if _child_module.__class__.__name__ == "Linear":
weight = _child_module.weight
bias = _child_module.bias
_tmp = LoraInjectedLinear(
_child_module.in_features,
_child_module.out_features,
_child_module.bias is not None,
r=r,
)
_tmp.linear.weight = weight
if bias is not None:
_tmp.linear.bias = bias
# switch the module
_module._modules[name] = _tmp
up_weight = loras.pop(0)
down_weight = loras.pop(0)
_module._modules[name].lora_up.weight = nn.Parameter(
up_weight.type(weight.dtype)
)
_module._modules[name].lora_down.weight = nn.Parameter(
down_weight.type(weight.dtype)
)
_module._modules[name].to(weight.device)
def monkeypatch_replace_lora(
model, loras, target_replace_module=["CrossAttention", "Attention"], r: int = 4
):
for _module in model.modules():
if _module.__class__.__name__ in target_replace_module:
for name, _child_module in _module.named_modules():
if _child_module.__class__.__name__ == "LoraInjectedLinear":
weight = _child_module.linear.weight
bias = _child_module.linear.bias
_tmp = LoraInjectedLinear(
_child_module.linear.in_features,
_child_module.linear.out_features,
_child_module.linear.bias is not None,
r=r,
)
_tmp.linear.weight = weight
if bias is not None:
_tmp.linear.bias = bias
# switch the module
_module._modules[name] = _tmp
up_weight = loras.pop(0)
down_weight = loras.pop(0)
_module._modules[name].lora_up.weight = nn.Parameter(
up_weight.type(weight.dtype)
)
_module._modules[name].lora_down.weight = nn.Parameter(
down_weight.type(weight.dtype)
)
_module._modules[name].to(weight.device)
def monkeypatch_add_lora(
model,
loras,
target_replace_module=["CrossAttention", "Attention"],
alpha: float = 1.0,
beta: float = 1.0,
):
for _module in model.modules():
if _module.__class__.__name__ in target_replace_module:
for name, _child_module in _module.named_modules():
if _child_module.__class__.__name__ == "LoraInjectedLinear":
weight = _child_module.linear.weight
up_weight = loras.pop(0)
down_weight = loras.pop(0)
_module._modules[name].lora_up.weight = nn.Parameter(
up_weight.type(weight.dtype).to(weight.device) * alpha
+ _module._modules[name].lora_up.weight.to(weight.device) * beta
)
_module._modules[name].lora_down.weight = nn.Parameter(
down_weight.type(weight.dtype).to(weight.device) * alpha
+ _module._modules[name].lora_down.weight.to(weight.device)
* beta
)
_module._modules[name].to(weight.device)
def tune_lora_scale(model, alpha: float = 1.0):
for _module in model.modules():
if _module.__class__.__name__ == "LoraInjectedLinear":
_module.scale = alpha
def _text_lora_path(path: str) -> str:
assert path.endswith(".pt"), "Only .pt files are supported"
return ".".join(path.split(".")[:-1] + ["text_encoder", "pt"])
def _ti_lora_path(path: str) -> str:
assert path.endswith(".pt"), "Only .pt files are supported"
return ".".join(path.split(".")[:-1] + ["ti", "pt"])
def load_learned_embed_in_clip(
learned_embeds_path, text_encoder, tokenizer, token=None, idempotent=False
):
loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
# separate token and the embeds
trained_token = list(loaded_learned_embeds.keys())[0]
embeds = loaded_learned_embeds[trained_token]
# cast to dtype of text_encoder
dtype = text_encoder.get_input_embeddings().weight.dtype
# add the token in tokenizer
token = token if token is not None else trained_token
num_added_tokens = tokenizer.add_tokens(token)
i = 1
if num_added_tokens == 0 and idempotent:
return token
while num_added_tokens == 0:
print(f"The tokenizer already contains the token {token}.")
token = f"{token[:-1]}-{i}>"
print(f"Attempting to add the token {token}.")
num_added_tokens = tokenizer.add_tokens(token)
i += 1
# resize the token embeddings
text_encoder.resize_token_embeddings(len(tokenizer))
# get the id for the token and assign the embeds
token_id = tokenizer.convert_tokens_to_ids(token)
text_encoder.get_input_embeddings().weight.data[token_id] = embeds
return token
def patch_pipe(
pipe,
unet_path,
token,
alpha: float = 1.0,
r: int = 4,
patch_text=False,
patch_ti=False,
idempotent_token=True,
):
ti_path = _ti_lora_path(unet_path)
text_path = _text_lora_path(unet_path)
unet_has_lora = False
text_encoder_has_lora = False
for _module in pipe.unet.modules():
if _module.__class__.__name__ == "LoraInjectedLinear":
unet_has_lora = True
for _module in pipe.text_encoder.modules():
if _module.__class__.__name__ == "LoraInjectedLinear":
text_encoder_has_lora = True
if not unet_has_lora:
monkeypatch_lora(pipe.unet, torch.load(unet_path), r=r)
else:
monkeypatch_replace_lora(pipe.unet, torch.load(unet_path), r=r)
if patch_text:
if not text_encoder_has_lora:
monkeypatch_lora(
pipe.text_encoder,
torch.load(text_path),
target_replace_module=["CLIPAttention"],
r=r,
)
else:
monkeypatch_replace_lora(
pipe.text_encoder,
torch.load(text_path),
target_replace_module=["CLIPAttention"],
r=r,
)
if patch_ti:
token = load_learned_embed_in_clip(
ti_path,
pipe.text_encoder,
pipe.tokenizer,
token,
idempotent=idempotent_token,
)
|