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
|
from contextlib import nullcontext
from typing import Optional
from functools import partial
from contextlib import contextmanager, nullcontext
from pathlib import Path
import torch
from torch.utils.data import DataLoader
from accelerate import Accelerator
from transformers import CLIPTextModel
from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler
from slugify import slugify
from models.clip.tokenizer import MultiCLIPTokenizer
from training.util import EMAModel
from training.functional import TrainingCallbacks, save_samples
def textual_inversion_strategy(
accelerator: Accelerator,
unet: UNet2DConditionModel,
text_encoder: CLIPTextModel,
tokenizer: MultiCLIPTokenizer,
vae: AutoencoderKL,
sample_scheduler: DPMSolverMultistepScheduler,
train_dataloader: DataLoader,
val_dataloader: DataLoader,
dtype: torch.dtype,
output_dir: Path,
seed: int,
placeholder_tokens: list[str],
placeholder_token_ids: list[list[int]],
learning_rate: float,
gradient_checkpointing: bool = False,
use_emb_decay: bool = False,
emb_decay_target: float = 0.4,
emb_decay_factor: float = 1,
emb_decay_start: float = 1e-4,
use_ema: bool = False,
ema_inv_gamma: float = 1.0,
ema_power: int = 1,
ema_max_decay: float = 0.9999,
sample_batch_size: int = 1,
sample_num_batches: int = 1,
sample_num_steps: int = 20,
sample_guidance_scale: float = 7.5,
sample_image_size: Optional[int] = None,
):
save_samples_ = partial(
save_samples,
accelerator=accelerator,
unet=unet,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
sample_scheduler=sample_scheduler,
train_dataloader=train_dataloader,
val_dataloader=val_dataloader,
dtype=dtype,
output_dir=output_dir,
seed=seed,
batch_size=sample_batch_size,
num_batches=sample_num_batches,
num_steps=sample_num_steps,
guidance_scale=sample_guidance_scale,
image_size=sample_image_size,
)
if use_ema:
ema_embeddings = EMAModel(
text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
inv_gamma=ema_inv_gamma,
power=ema_power,
max_value=ema_max_decay,
)
else:
ema_embeddings = None
def on_prepare():
text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(True)
if use_ema:
ema_embeddings.to(accelerator.device)
if gradient_checkpointing:
unet.train()
@contextmanager
def on_train(epoch: int):
try:
tokenizer.train()
yield
finally:
pass
@contextmanager
def on_eval():
try:
tokenizer.eval()
ema_context = ema_embeddings.apply_temporary(
text_encoder.text_model.embeddings.temp_token_embedding.parameters()) if use_ema else nullcontext()
with ema_context:
yield
finally:
pass
@torch.no_grad()
def on_after_optimize(lr: float):
if use_emb_decay:
text_encoder.text_model.embeddings.normalize(
emb_decay_target,
min(1.0, max(0.0, emb_decay_factor * ((lr - emb_decay_start) / (learning_rate - emb_decay_start))))
)
if use_ema:
ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters())
def on_log():
if use_ema:
return {"ema_decay": ema_embeddings.decay}
return {}
@torch.no_grad()
def on_checkpoint(step, postfix):
print(f"Saving checkpoint for step {step}...")
checkpoints_path = output_dir.joinpath("checkpoints")
checkpoints_path.mkdir(parents=True, exist_ok=True)
text_encoder = accelerator.unwrap_model(text_encoder)
ema_context = ema_embeddings.apply_temporary(
text_encoder.text_model.embeddings.temp_token_embedding.parameters()
) if ema_embeddings is not None else nullcontext()
with ema_context:
for (token, ids) in zip(placeholder_tokens, placeholder_token_ids):
text_encoder.text_model.embeddings.save_embed(
ids,
checkpoints_path.joinpath(f"{slugify(token)}_{step}_{postfix}.bin")
)
@torch.no_grad()
def on_sample(step):
ema_context = ema_embeddings.apply_temporary(
text_encoder.text_model.embeddings.temp_token_embedding.parameters()
) if ema_embeddings is not None else nullcontext()
with ema_context:
save_samples_(step=step)
return TrainingCallbacks(
on_prepare=on_prepare,
on_train=on_train,
on_eval=on_eval,
on_after_optimize=on_after_optimize,
on_log=on_log,
on_checkpoint=on_checkpoint,
on_sample=on_sample,
)
|
