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
|
from typing import Optional
from functools import partial
from contextlib import contextmanager, nullcontext
from pathlib import Path
import itertools
import torch
from torch.utils.data import DataLoader
from accelerate import Accelerator
from transformers import CLIPTextModel
from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler
from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
from models.clip.tokenizer import MultiCLIPTokenizer
from training.util import EMAModel
from training.functional import TrainingStrategy, TrainingCallbacks, save_samples
def dreambooth_strategy_callbacks(
accelerator: Accelerator,
unet: UNet2DConditionModel,
text_encoder: CLIPTextModel,
tokenizer: MultiCLIPTokenizer,
vae: AutoencoderKL,
sample_scheduler: DPMSolverMultistepScheduler,
train_dataloader: DataLoader,
val_dataloader: Optional[DataLoader],
sample_output_dir: Path,
checkpoint_output_dir: Path,
seed: int,
train_text_encoder_epochs: int,
max_grad_norm: float = 1.0,
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,
):
if accelerator.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
raise ValueError(
"Gradient accumulation is not supported when training the text encoder in distributed training. "
"Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
)
sample_output_dir.mkdir(parents=True, exist_ok=True)
checkpoint_output_dir.mkdir(parents=True, exist_ok=True)
weight_dtype = torch.float32
if accelerator.state.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.state.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
save_samples_ = partial(
save_samples,
accelerator=accelerator,
tokenizer=tokenizer,
vae=vae,
sample_scheduler=sample_scheduler,
train_dataloader=train_dataloader,
val_dataloader=val_dataloader,
output_dir=sample_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_unet = EMAModel(
unet.parameters(),
inv_gamma=ema_inv_gamma,
power=ema_power,
max_value=ema_max_decay,
)
else:
ema_unet = None
def ema_context():
if ema_unet is not None:
return ema_unet.apply_temporary(unet.parameters())
else:
return nullcontext()
def on_accum_model():
return unet
def on_prepare():
unet.requires_grad_(True)
text_encoder.requires_grad_(True)
text_encoder.text_model.embeddings.requires_grad_(False)
if ema_unet is not None:
ema_unet.to(accelerator.device)
@contextmanager
def on_train(epoch: int):
tokenizer.train()
if epoch < train_text_encoder_epochs:
text_encoder.train()
elif epoch == train_text_encoder_epochs:
text_encoder.requires_grad_(False)
text_encoder.eval()
yield
@contextmanager
def on_eval():
tokenizer.eval()
text_encoder.eval()
with ema_context():
yield
def on_before_optimize(lr: float, epoch: int):
if accelerator.sync_gradients:
params_to_clip = [unet.parameters()]
if epoch < train_text_encoder_epochs:
params_to_clip.append(text_encoder.parameters())
accelerator.clip_grad_norm_(itertools.chain(*params_to_clip), max_grad_norm)
@torch.no_grad()
def on_after_optimize(lr: float):
if ema_unet is not None:
ema_unet.step(unet.parameters())
def on_log():
if ema_unet is not None:
return {"ema_decay": ema_unet.decay}
return {}
@torch.no_grad()
def on_checkpoint(step, postfix):
if postfix != "end":
return
print("Saving model...")
unet_ = accelerator.unwrap_model(unet)
text_encoder_ = accelerator.unwrap_model(text_encoder)
with ema_context():
pipeline = VlpnStableDiffusion(
text_encoder=text_encoder_,
vae=vae,
unet=unet_,
tokenizer=tokenizer,
scheduler=sample_scheduler,
)
pipeline.save_pretrained(checkpoint_output_dir)
del unet_
del text_encoder_
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
@torch.no_grad()
def on_sample(step):
with ema_context():
unet_ = accelerator.unwrap_model(unet)
text_encoder_ = accelerator.unwrap_model(text_encoder)
orig_unet_dtype = unet_.dtype
orig_text_encoder_dtype = text_encoder_.dtype
unet_.to(dtype=weight_dtype)
text_encoder_.to(dtype=weight_dtype)
save_samples_(step=step, unet=unet_, text_encoder=text_encoder_)
unet_.to(dtype=orig_unet_dtype)
text_encoder_.to(dtype=orig_text_encoder_dtype)
del unet_
del text_encoder_
if torch.cuda.is_available():
torch.cuda.empty_cache()
return TrainingCallbacks(
on_prepare=on_prepare,
on_accum_model=on_accum_model,
on_train=on_train,
on_eval=on_eval,
on_before_optimize=on_before_optimize,
on_after_optimize=on_after_optimize,
on_log=on_log,
on_checkpoint=on_checkpoint,
on_sample=on_sample,
)
def dreambooth_prepare(
accelerator: Accelerator,
text_encoder: CLIPTextModel,
unet: UNet2DConditionModel,
optimizer: torch.optim.Optimizer,
train_dataloader: DataLoader,
val_dataloader: Optional[DataLoader],
lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
**kwargs
):
return accelerator.prepare(text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler) + ({})
dreambooth_strategy = TrainingStrategy(
callbacks=dreambooth_strategy_callbacks,
prepare=dreambooth_prepare
)
|
