forked from huggingface/diffusers
-
Notifications
You must be signed in to change notification settings - Fork 2
Expand file tree
/
Copy pathtrain_custom_diffusion.py
More file actions
1381 lines (1240 loc) 路 59.3 KB
/
Copy pathtrain_custom_diffusion.py
File metadata and controls
1381 lines (1240 loc) 路 59.3 KB
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
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 Custom Diffusion authors and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import itertools
import json
import logging
import math
import os
import random
import shutil
import warnings
from pathlib import Path
import numpy as np
import safetensors
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from huggingface_hub import HfApi, create_repo
from huggingface_hub.utils import insecure_hashlib
from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
)
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import (
CustomDiffusionAttnProcessor,
CustomDiffusionAttnProcessor2_0,
CustomDiffusionXFormersAttnProcessor,
)
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.36.0.dev0")
logger = get_logger(__name__)
def freeze_params(params):
for param in params:
param.requires_grad = False
def save_model_card(repo_id: str, images=None, base_model=str, prompt=str, repo_folder=None):
img_str = ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"\n"
model_description = f"""
# Custom Diffusion - {repo_id}
These are Custom Diffusion adaption weights for {base_model}. The weights were trained on {prompt} using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following. \n
{img_str}
\nFor more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
"""
model_card = load_or_create_model_card(
repo_id_or_path=repo_id,
from_training=True,
license="creativeml-openrail-m",
base_model=base_model,
prompt=prompt,
model_description=model_description,
inference=True,
)
tags = [
"text-to-image",
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"custom-diffusion",
"diffusers-training",
]
model_card = populate_model_card(model_card, tags=tags)
model_card.save(os.path.join(repo_folder, "README.md"))
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
revision=revision,
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "RobertaSeriesModelWithTransformation":
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
return RobertaSeriesModelWithTransformation
else:
raise ValueError(f"{model_class} is not supported.")
def collate_fn(examples, with_prior_preservation):
input_ids = [example["instance_prompt_ids"] for example in examples]
pixel_values = [example["instance_images"] for example in examples]
mask = [example["mask"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
input_ids += [example["class_prompt_ids"] for example in examples]
pixel_values += [example["class_images"] for example in examples]
mask += [example["class_mask"] for example in examples]
input_ids = torch.cat(input_ids, dim=0)
pixel_values = torch.stack(pixel_values)
mask = torch.stack(mask)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
mask = mask.to(memory_format=torch.contiguous_format).float()
batch = {"input_ids": input_ids, "pixel_values": pixel_values, "mask": mask.unsqueeze(1)}
return batch
class PromptDataset(Dataset):
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
class CustomDiffusionDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images and the tokenizes prompts.
"""
def __init__(
self,
concepts_list,
tokenizer,
size=512,
mask_size=64,
center_crop=False,
with_prior_preservation=False,
num_class_images=200,
hflip=False,
aug=True,
):
self.size = size
self.mask_size = mask_size
self.center_crop = center_crop
self.tokenizer = tokenizer
self.interpolation = Image.BILINEAR
self.aug = aug
self.instance_images_path = []
self.class_images_path = []
self.with_prior_preservation = with_prior_preservation
for concept in concepts_list:
inst_img_path = [
(x, concept["instance_prompt"]) for x in Path(concept["instance_data_dir"]).iterdir() if x.is_file()
]
self.instance_images_path.extend(inst_img_path)
if with_prior_preservation:
class_data_root = Path(concept["class_data_dir"])
if os.path.isdir(class_data_root):
class_images_path = list(class_data_root.iterdir())
class_prompt = [concept["class_prompt"] for _ in range(len(class_images_path))]
else:
with open(class_data_root, "r") as f:
class_images_path = f.read().splitlines()
with open(concept["class_prompt"], "r") as f:
class_prompt = f.read().splitlines()
class_img_path = list(zip(class_images_path, class_prompt))
self.class_images_path.extend(class_img_path[:num_class_images])
random.shuffle(self.instance_images_path)
self.num_instance_images = len(self.instance_images_path)
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
self.flip = transforms.RandomHorizontalFlip(0.5 * hflip)
self.image_transforms = transforms.Compose(
[
self.flip,
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def preprocess(self, image, scale, resample):
outer, inner = self.size, scale
factor = self.size // self.mask_size
if scale > self.size:
outer, inner = scale, self.size
top, left = np.random.randint(0, outer - inner + 1), np.random.randint(0, outer - inner + 1)
image = image.resize((scale, scale), resample=resample)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
instance_image = np.zeros((self.size, self.size, 3), dtype=np.float32)
mask = np.zeros((self.size // factor, self.size // factor))
if scale > self.size:
instance_image = image[top : top + inner, left : left + inner, :]
mask = np.ones((self.size // factor, self.size // factor))
else:
instance_image[top : top + inner, left : left + inner, :] = image
mask[
top // factor + 1 : (top + scale) // factor - 1, left // factor + 1 : (left + scale) // factor - 1
] = 1.0
return instance_image, mask
def __getitem__(self, index):
example = {}
instance_image, instance_prompt = self.instance_images_path[index % self.num_instance_images]
instance_image = Image.open(instance_image)
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
instance_image = self.flip(instance_image)
# apply resize augmentation and create a valid image region mask
random_scale = self.size
if self.aug:
random_scale = (
np.random.randint(self.size // 3, self.size + 1)
if np.random.uniform() < 0.66
else np.random.randint(int(1.2 * self.size), int(1.4 * self.size))
)
instance_image, mask = self.preprocess(instance_image, random_scale, self.interpolation)
if random_scale < 0.6 * self.size:
instance_prompt = np.random.choice(["a far away ", "very small "]) + instance_prompt
elif random_scale > self.size:
instance_prompt = np.random.choice(["zoomed in ", "close up "]) + instance_prompt
example["instance_images"] = torch.from_numpy(instance_image).permute(2, 0, 1)
example["mask"] = torch.from_numpy(mask)
example["instance_prompt_ids"] = self.tokenizer(
instance_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
if self.with_prior_preservation:
class_image, class_prompt = self.class_images_path[index % self.num_class_images]
class_image = Image.open(class_image)
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_mask"] = torch.ones_like(example["mask"])
example["class_prompt_ids"] = self.tokenizer(
class_prompt,
truncation=True,
padding="max_length",
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids
return example
def save_new_embed(text_encoder, modifier_token_id, accelerator, args, output_dir, safe_serialization=True):
"""Saves the new token embeddings from the text encoder."""
logger.info("Saving embeddings")
learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight
for x, y in zip(modifier_token_id, args.modifier_token):
learned_embeds_dict = {}
learned_embeds_dict[y] = learned_embeds[x]
if safe_serialization:
filename = f"{output_dir}/{y}.safetensors"
safetensors.torch.save_file(learned_embeds_dict, filename, metadata={"format": "pt"})
else:
filename = f"{output_dir}/{y}.bin"
torch.save(learned_embeds_dict, filename)
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Custom Diffusion training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
help="A folder containing the training data of instance images.",
)
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
help="The prompt with identifier specifying the instance",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=2,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=50,
help=(
"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument(
"--real_prior",
default=False,
action="store_true",
help="real images as prior.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=200,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="custom-diffusion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
parser.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=250,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-5,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=2,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--freeze_model",
type=str,
default="crossattn_kv",
choices=["crossattn_kv", "crossattn"],
help="crossattn to enable fine-tuning of all params in the cross attention",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument(
"--concepts_list",
type=str,
default=None,
help="Path to json containing multiple concepts, will overwrite parameters like instance_prompt, class_prompt, etc.",
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--set_grads_to_none",
action="store_true",
help=(
"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
" behaviors, so disable this argument if it causes any problems. More info:"
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
),
)
parser.add_argument(
"--modifier_token",
type=str,
default=None,
help="A token to use as a modifier for the concept.",
)
parser.add_argument(
"--initializer_token", type=str, default="ktn+pll+ucd", help="A token to use as initializer word."
)
parser.add_argument("--hflip", action="store_true", help="Apply horizontal flip data augmentation.")
parser.add_argument(
"--noaug",
action="store_true",
help="Dont apply augmentation during data augmentation when this flag is enabled.",
)
parser.add_argument(
"--no_safe_serialization",
action="store_true",
help="If specified save the checkpoint not in `safetensors` format, but in original PyTorch format instead.",
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if args.concepts_list is None:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
def main(args):
if args.report_to == "wandb" and args.hub_token is not None:
raise ValueError(
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
" Please use `hf auth login` to authenticate with the Hub."
)
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
# Disable AMP for MPS.
if torch.backends.mps.is_available():
accelerator.native_amp = False
if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
import wandb
# Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
# This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
# TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("custom-diffusion", config=vars(args))
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
if args.concepts_list is None:
args.concepts_list = [
{
"instance_prompt": args.instance_prompt,
"class_prompt": args.class_prompt,
"instance_data_dir": args.instance_data_dir,
"class_data_dir": args.class_data_dir,
}
]
else:
with open(args.concepts_list, "r") as f:
args.concepts_list = json.load(f)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
for i, concept in enumerate(args.concepts_list):
class_images_dir = Path(concept["class_data_dir"])
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True, exist_ok=True)
if args.real_prior:
assert (class_images_dir / "images").exists(), (
f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
)
assert len(list((class_images_dir / "images").iterdir())) == args.num_class_images, (
f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
)
assert (class_images_dir / "caption.txt").exists(), (
f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
)
assert (class_images_dir / "images.txt").exists(), (
f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
)
concept["class_prompt"] = os.path.join(class_images_dir, "caption.txt")
concept["class_data_dir"] = os.path.join(class_images_dir, "images.txt")
args.concepts_list[i] = concept
accelerator.wait_for_everyone()
else:
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
if args.prior_generation_precision == "fp32":
torch_dtype = torch.float32
elif args.prior_generation_precision == "fp16":
torch_dtype = torch.float16
elif args.prior_generation_precision == "bf16":
torch_dtype = torch.bfloat16
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
safety_checker=None,
revision=args.revision,
variant=args.variant,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(concept["class_prompt"], num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader,
desc="Generating class images",
disable=not accelerator.is_local_main_process,
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
image_filename = (
class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
)
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizer
if args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name,
revision=args.revision,
use_fast=False,
)
elif args.pretrained_model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
# import correct text encoder class
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder = text_encoder_cls.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
vae = AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
)
# Adding a modifier token which is optimized ####
# Code taken from https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py
modifier_token_id = []
initializer_token_id = []
if args.modifier_token is not None:
args.modifier_token = args.modifier_token.split("+")
args.initializer_token = args.initializer_token.split("+")
if len(args.modifier_token) > len(args.initializer_token):
raise ValueError("You must specify + separated initializer token for each modifier token.")
for modifier_token, initializer_token in zip(
args.modifier_token, args.initializer_token[: len(args.modifier_token)]
):
# Add the placeholder token in tokenizer
num_added_tokens = tokenizer.add_tokens(modifier_token)
if num_added_tokens == 0:
raise ValueError(
f"The tokenizer already contains the token {modifier_token}. Please pass a different"
" `modifier_token` that is not already in the tokenizer."
)
# Convert the initializer_token, placeholder_token to ids
token_ids = tokenizer.encode([initializer_token], add_special_tokens=False)
print(token_ids)
# Check if initializer_token is a single token or a sequence of tokens
if len(token_ids) > 1:
raise ValueError("The initializer token must be a single token.")
initializer_token_id.append(token_ids[0])
modifier_token_id.append(tokenizer.convert_tokens_to_ids(modifier_token))
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder.resize_token_embeddings(len(tokenizer))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = text_encoder.get_input_embeddings().weight.data
for x, y in zip(modifier_token_id, initializer_token_id):
token_embeds[x] = token_embeds[y]
# Freeze all parameters except for the token embeddings in text encoder
params_to_freeze = itertools.chain(
text_encoder.text_model.encoder.parameters(),
text_encoder.text_model.final_layer_norm.parameters(),
text_encoder.text_model.embeddings.position_embedding.parameters(),
)
freeze_params(params_to_freeze)
########################################################
########################################################
vae.requires_grad_(False)
if args.modifier_token is None:
text_encoder.requires_grad_(False)
unet.requires_grad_(False)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
if accelerator.mixed_precision != "fp16" and args.modifier_token is not None:
text_encoder.to(accelerator.device, dtype=weight_dtype)
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
attention_class = (
CustomDiffusionAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else CustomDiffusionAttnProcessor
)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warning(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
attention_class = CustomDiffusionXFormersAttnProcessor
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# now we will add new Custom Diffusion weights to the attention layers
# It's important to realize here how many attention weights will be added and of which sizes
# The sizes of the attention layers consist only of two different variables:
# 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
# 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
# Let's first see how many attention processors we will have to set.
# For Stable Diffusion, it should be equal to:
# - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
# - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
# - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
# => 32 layers
# Only train key, value projection layers if freeze_model = 'crossattn_kv' else train all params in the cross attention layer
train_kv = True
train_q_out = False if args.freeze_model == "crossattn_kv" else True
custom_diffusion_attn_procs = {}
st = unet.state_dict()
for name, _ in unet.attn_processors.items():
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
layer_name = name.split(".processor")[0]
weights = {
"to_k_custom_diffusion.weight": st[layer_name + ".to_k.weight"],
"to_v_custom_diffusion.weight": st[layer_name + ".to_v.weight"],
}
if train_q_out:
weights["to_q_custom_diffusion.weight"] = st[layer_name + ".to_q.weight"]
weights["to_out_custom_diffusion.0.weight"] = st[layer_name + ".to_out.0.weight"]
weights["to_out_custom_diffusion.0.bias"] = st[layer_name + ".to_out.0.bias"]
if cross_attention_dim is not None:
custom_diffusion_attn_procs[name] = attention_class(
train_kv=train_kv,
train_q_out=train_q_out,
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
).to(unet.device)
custom_diffusion_attn_procs[name].load_state_dict(weights)
else:
custom_diffusion_attn_procs[name] = attention_class(
train_kv=False,
train_q_out=False,
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
)
del st
unet.set_attn_processor(custom_diffusion_attn_procs)
custom_diffusion_layers = AttnProcsLayers(unet.attn_processors)
accelerator.register_for_checkpointing(custom_diffusion_layers)
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.modifier_token is not None:
text_encoder.gradient_checkpointing_enable()
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
if args.with_prior_preservation:
args.learning_rate = args.learning_rate * 2.0
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(