forked from mindspore-lab/mindone
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_controlnet.py
431 lines (382 loc) · 16.4 KB
/
train_controlnet.py
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
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
import argparse
import ast
import os
import sys
import time
from functools import partial
__dir__ = os.path.dirname(os.path.abspath(__file__))
mindone_lib_path = os.path.abspath(os.path.join(__dir__, "../../"))
sys.path.insert(0, mindone_lib_path)
from gm.data.loader import create_loader
from gm.helpers import (
EMA,
SD_XL_BASE_RATIOS,
VERSION2SPECS,
create_model,
get_grad_reducer,
get_learning_rate,
get_loss_scaler,
get_optimizer,
load_checkpoint,
save_checkpoint,
set_default,
)
from omegaconf import OmegaConf
import mindspore as ms
from mindspore import Tensor, nn
from mindone.utils.amp import auto_mixed_precision
def count_params(model, verbose=False):
total_params = sum([param.size for param in model.get_parameters()])
trainable_params = sum([param.size for param in model.get_parameters() if param.requires_grad])
if verbose:
print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
return total_params, trainable_params
def get_parser_train():
parser = argparse.ArgumentParser(description="train with sd-xl")
parser.add_argument("--version", type=str, default="SDXL-base-1.0", choices=["SDXL-base-1.0", "SDXL-refiner-1.0"])
parser.add_argument("--config", type=str, default="configs/training/sd_xl_base_finetune_controlnet_910b.yaml")
parser.add_argument(
"--task",
type=str,
default="txt2img",
choices=[
"txt2img",
],
)
parser.add_argument(
"--group_lr_scaler", default=10.0, type=float, help="scaler for lr of a particular group of params"
)
parser.add_argument("--gradient_accumulation_steps", default=1, type=int, help="gradient accumulation steps")
parser.add_argument("--clip_grad", default=False, type=ast.literal_eval, help="whether apply gradient clipping")
parser.add_argument(
"--max_grad_norm",
default=1.0,
type=float,
help="max gradient norm for clipping, effective when `clip_grad` enabled.",
)
parser.add_argument("--use_ema", action="store_true", help="whether use ema")
parser.add_argument("--weight", type=str, default="checkpoints/sd_xl_base_1.0_ms_controlnet_init.ckpt")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--sd_xl_base_ratios", type=str, default="1.0")
parser.add_argument("--data_path", type=str, default="")
parser.add_argument("--save_path", type=str, default="./runs")
parser.add_argument("--save_path_with_time", type=ast.literal_eval, default=True)
parser.add_argument("--log_interval", type=int, default=1, help="log interval")
parser.add_argument("--save_ckpt_interval", type=int, default=10000, help="save ckpt interval")
parser.add_argument(
"--max_num_ckpt",
type=int,
default=None,
help="Max number of ckpts saved. If exceeds, delete the oldest one. Set None: keep all ckpts.",
)
parser.add_argument("--optimizer_weight", type=str, default=None, help="load optimizer weight")
parser.add_argument("--save_optimizer", type=ast.literal_eval, default=False, help="enable save optimizer")
parser.add_argument("--data_sink", type=ast.literal_eval, default=False)
parser.add_argument("--sink_size", type=int, default=1000)
parser.add_argument(
"--dataset_load_tokenizer", type=ast.literal_eval, default=True, help="create dataset with tokenizer"
)
parser.add_argument(
"--total_step",
type=int,
default=None,
help="The number of training steps. If not provided, will use the `total_step` in training yaml file.",
)
parser.add_argument(
"--per_batch_size",
type=int,
default=None,
help="The batch size for training. If not provided, will use `per_batch_size` in training yaml file.",
)
# args for infer
parser.add_argument("--infer_during_train", type=ast.literal_eval, default=False)
parser.add_argument("--infer_interval", type=int, default=1, help="log interval")
# args for env
parser.add_argument("--device_target", type=str, default="Ascend", help="device target, Ascend/GPU/CPU")
parser.add_argument(
"--ms_mode", type=int, default=0, help="Running in GRAPH_MODE(0) or PYNATIVE_MODE(1) (default=1)"
)
parser.add_argument("--ms_amp_level", type=str, default="O2")
parser.add_argument(
"--ms_enable_graph_kernel", type=ast.literal_eval, default=False, help="use enable_graph_kernel or not"
)
parser.add_argument("--param_fp16", type=ast.literal_eval, default=False)
parser.add_argument("--overflow_still_update", type=ast.literal_eval, default=True)
parser.add_argument("--max_device_memory", type=str, default=None)
parser.add_argument("--is_parallel", type=ast.literal_eval, default=False)
# args for ModelArts
parser.add_argument("--enable_modelarts", type=ast.literal_eval, default=False, help="enable modelarts")
parser.add_argument(
"--ckpt_url", type=str, default="", help="ModelArts: obs path to pretrain model checkpoint file"
)
parser.add_argument("--train_url", type=str, default="", help="ModelArts: obs path to output folder")
parser.add_argument(
"--multi_data_url", type=str, default="", help="ModelArts: list of obs paths to multi-dataset folders"
)
parser.add_argument(
"--pretrain_url", type=str, default="", help="ModelArts: list of obs paths to multi-pretrain model files"
)
parser.add_argument(
"--ckpt_dir",
type=str,
default="/cache/pretrain_ckpt/",
help="ModelArts: local device path to checkpoint folder",
)
return parser
def train(args):
# 1. Init Env
args = set_default(args)
# 2. Create LDM Engine
config = OmegaConf.load(args.config)
model, _ = create_model(
config,
checkpoints=args.weight,
freeze=False,
load_filter=False,
param_fp16=args.param_fp16,
amp_level=args.ms_amp_level,
)
assert isinstance(model.model, nn.Cell)
if config.model.params.network_config.params.sd_locked:
model.model.set_train(False)
model.model.diffusion_model.controlnet.set_train(True)
else:
model.model.set_train(True)
# 3. Create dataloader
assert "data" in config
if args.total_step is not None:
config.data["total_step"] = args.total_step
if args.per_batch_size is not None:
config.data["per_batch_size"] = args.per_batch_size
dataloader = create_loader(
data_path=args.data_path,
rank=args.rank,
rank_size=args.rank_size,
tokenizer=model.conditioner.tokenize if args.dataset_load_tokenizer else None,
token_nums=len(model.conditioner.embedders) if args.dataset_load_tokenizer else None,
**config.data,
)
# 4. Create train step func
assert "optim" in config
lr = get_learning_rate(config.optim, config.data.total_step)
scaler = get_loss_scaler(ms_loss_scaler="static", scale_value=1024)
optimizer = get_optimizer(
config.optim,
lr,
params=model.model.trainable_params() + model.conditioner.trainable_params(),
group_lr_scaler=args.group_lr_scaler,
)
reducer = get_grad_reducer(is_parallel=args.is_parallel, parameters=optimizer.parameters)
if args.optimizer_weight:
print(f"Loading optimizer from {args.optimizer_weight}")
load_checkpoint(optimizer, args.optimizer_weight, remove_prefix="ldm_with_loss_grad.optimizer.")
if args.use_ema:
ema = EMA(model, ema_decay=0.9999)
else:
ema = None
if args.ms_mode == 1:
# Pynative Mode
train_step_fn = partial(
model.train_step_pynative,
grad_func=model.get_grad_func(
optimizer, reducer, scaler, jit=True, overflow_still_update=args.overflow_still_update
),
)
model = auto_mixed_precision(model, args.ms_amp_level)
jit_config = None
elif args.ms_mode == 0:
# Graph Mode
from gm.models.trainer_factory import TrainOneStepCellControlNet
train_step_fn = TrainOneStepCellControlNet(
model,
optimizer,
reducer,
scaler,
overflow_still_update=args.overflow_still_update,
gradient_accumulation_steps=args.gradient_accumulation_steps,
clip_grad=args.clip_grad,
clip_norm=args.max_grad_norm,
ema=ema,
)
train_step_fn = auto_mixed_precision(train_step_fn, amp_level=args.ms_amp_level)
if model.disable_first_stage_amp:
train_step_fn.first_stage_model.to_float(ms.float32)
jit_config = ms.JitConfig()
else:
raise ValueError("args.ms_mode value must in [0, 1]")
num_params, num_trainable_params = count_params(model)
print(f"Total number of parameters: {num_params:,}")
print(f"Total number of trainable parameters: {num_trainable_params:,}")
# 5. Start Training
if args.max_num_ckpt is not None and args.max_num_ckpt <= 0:
raise ValueError("args.max_num_ckpt must be None or a positive integer!")
if args.task == "txt2img":
train_fn = train_txt2img if not args.data_sink else train_txt2img_datasink
train_fn(
args, train_step_fn, dataloader=dataloader, optimizer=optimizer, model=model, jit_config=jit_config, ema=ema
)
elif args.task == "img2img":
raise NotImplementedError
else:
raise ValueError(f"Unknown task {args.task}")
def train_txt2img(
args, train_step_fn, dataloader, optimizer=None, model=None, ema=None, **kwargs
): # for print # for infer/ckpt
dtype = ms.float32 if args.ms_amp_level not in ("O2", "O3") else ms.float16
total_step = dataloader.get_dataset_size()
loader = dataloader.create_tuple_iterator(output_numpy=True, num_epochs=1)
s_time = time.time()
ckpt_queue = []
for i, data in enumerate(loader):
if not args.dataset_load_tokenizer:
# Get data, image and tokens, to tensor
data = data[0]
data = {k: (Tensor(v, dtype) if k != "txt" else v.tolist()) for k, v in data.items()}
image = data[model.input_key]
tokens, _ = model.conditioner.tokenize(data)
tokens = [Tensor(t) for t in tokens]
else:
image, tokens = data[0], data[1:]
image, tokens = Tensor(image), [Tensor(t) for t in tokens]
# Train a step
if i == 0:
print(
"The first step will be compiled for the graph, which may take a long time; "
"You can come back later :)",
flush=True,
)
loss, overflow = train_step_fn(image, *tokens)
# Print meg
if (i + 1) % args.log_interval == 0 and args.rank % 8 == 0:
print(
f"Step {i + 1}/{total_step}, size: {image.shape[:]}, loss: {loss.asnumpy():.6f}"
f", time cost: {(time.time()-s_time) * 1000 / args.log_interval:.2f} ms",
flush=True,
)
s_time = time.time()
# Save checkpoint
if (i + 1) % args.save_ckpt_interval == 0 and args.rank % 8 == 0:
save_ckpt_dir = os.path.join(args.save_path, "weights", args.version + f"_{(i + 1)}.ckpt")
if isinstance(model.model, nn.Cell):
model.model.set_train(False) # only unet
save_checkpoint(
model if not ema else ema,
save_ckpt_dir,
ckpt_queue,
args.max_num_ckpt,
only_save_lora=False
if not hasattr(model.model.diffusion_model, "only_save_lora")
else model.model.diffusion_model.only_save_lora,
)
model.model.set_train(True) # only unet
else:
model.save_checkpoint(save_ckpt_dir)
ckpt_queue.append(save_ckpt_dir)
if args.save_optimizer:
save_optimizer_dir = os.path.join(args.save_path, "optimizer.ckpt")
ms.save_checkpoint(optimizer, save_optimizer_dir)
print(f"save optimizer weight to {save_optimizer_dir}")
# Infer during train
if (i + 1) % args.infer_interval == 0 and args.infer_during_train:
print(f"Step {i + 1}/{total_step}, infer starting...")
infer_during_train(
model=model,
prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
save_path=os.path.join(args.save_path, "txt2img/", f"step_{i+1}_rank_{args.rank}"),
)
print(f"Step {i + 1}/{total_step}, infer done.", flush=True)
def train_txt2img_datasink(
args, train_step_fn, dataloader, optimizer=None, model=None, jit_config=None, ema=None, **kwargs
): # for print # for infer/ckpt
total_step = dataloader.get_dataset_size()
epochs = total_step // args.sink_size
assert args.dataset_load_tokenizer
train_fn_sink = ms.data_sink(fn=train_step_fn, dataset=dataloader, sink_size=args.sink_size, jit_config=jit_config)
ckpt_queue = []
for epoch in range(epochs):
cur_step = args.sink_size * (epoch + 1)
if epoch == 0:
print(
"The first epoch will be compiled for the graph, which may take a long time; "
"You can come back later :)",
flush=True,
)
s_time = time.time()
loss, _ = train_fn_sink()
e_time = time.time()
# Print meg
if cur_step % args.log_interval == 0 and args.rank % 8 == 0:
if optimizer.dynamic_lr:
cur_lr = optimizer.learning_rate(Tensor((cur_step - 1), ms.int32)).asnumpy().item()
else:
cur_lr = optimizer.learning_rate.asnumpy().item()
print(
f"Step {cur_step}/{total_step}, lr: {cur_lr}, loss: {loss.asnumpy():.6f}"
f", per step time: {(e_time - s_time) * 1000 / args.sink_size:.2f} ms",
flush=True,
)
# Save checkpoint
if cur_step % args.save_ckpt_interval == 0 and args.rank % 8 == 0:
save_ckpt_dir = os.path.join(args.save_path, "weights", args.version + f"_{cur_step}.ckpt")
if isinstance(model.model, nn.Cell):
model.model.set_train(False) # only unet
save_checkpoint(
model if not ema else ema,
save_ckpt_dir,
ckpt_queue,
args.max_num_ckpt,
only_save_lora=False
if not hasattr(model.model.diffusion_model, "only_save_lora")
else model.model.diffusion_model.only_save_lora,
)
model.model.set_train(True) # only unet
else:
model.save_checkpoint(save_ckpt_dir)
ckpt_queue.append(save_ckpt_dir)
# Infer during train
if cur_step % args.infer_interval == 0 and args.infer_during_train:
print(f"Step {cur_step}/{total_step}, infer starting...")
infer_during_train(
model=model,
prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
save_path=os.path.join(args.save_path, "txt2img/", f"step_{cur_step}_rank_{args.rank}"),
)
print(f"Step {cur_step}/{total_step}, infer done.", flush=True)
def infer_during_train(model, prompt, save_path):
from gm.helpers import init_sampling, perform_save_locally
version_dict = VERSION2SPECS.get(args.version)
W, H = SD_XL_BASE_RATIOS[args.sd_xl_base_ratios]
C = version_dict["C"]
F = version_dict["f"]
is_legacy = version_dict["is_legacy"]
value_dict = {
"prompt": prompt,
"negative_prompt": "",
"orig_width": W,
"orig_height": H,
"target_width": W,
"target_height": H,
"crop_coords_top": 0,
"crop_coords_left": 0,
"aesthetic_score": 6.0,
"negative_aesthetic_score": 2.5,
}
sampler, num_rows, num_cols = init_sampling(steps=40, num_cols=1)
out = model.do_sample(
sampler,
value_dict,
num_rows * num_cols,
H,
W,
C,
F,
force_uc_zero_embeddings=["txt"] if not is_legacy else [],
return_latents=False,
filter=None,
amp_level="O2",
)
perform_save_locally(save_path, out)
if __name__ == "__main__":
parser = get_parser_train()
args, _ = parser.parse_known_args()
train(args)