train_dreambooth.py 54.4 KB
Newer Older
1
2
#!/usr/bin/env python
# coding=utf-8
Patrick von Platen's avatar
Patrick von Platen committed
3
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
4
5
6
7
8
9
10
11
12
13
14
15
#
# 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

16
import argparse
17
import copy
18
import gc
19
import hashlib
20
import itertools
Suraj Patil's avatar
Suraj Patil committed
21
import logging
22
23
import math
import os
24
import shutil
25
import warnings
26
27
from pathlib import Path

28
import numpy as np
29
30
31
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
Suraj Patil's avatar
Suraj Patil committed
32
import transformers
33
34
from accelerate import Accelerator
from accelerate.logging import get_logger
35
from accelerate.utils import ProjectConfiguration, set_seed
Patrick von Platen's avatar
Patrick von Platen committed
36
37
38
39
40
41
42
43
44
45
from huggingface_hub import create_repo, model_info, upload_folder
from packaging import version
from PIL import Image
from PIL.ImageOps import exif_transpose
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig

import diffusers
46
47
48
49
50
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
51
    StableDiffusionPipeline,
52
53
    UNet2DConditionModel,
)
54
from diffusers.optimization import get_scheduler
55
from diffusers.utils import check_min_version, is_wandb_available
56
57
from diffusers.utils.import_utils import is_xformers_available

58

59
60
61
if is_wandb_available():
    import wandb

62
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
Sayak Paul's avatar
Sayak Paul committed
63
check_min_version("0.21.0.dev0")
64

65
66
67
logger = get_logger(__name__)


68
69
70
71
72
73
74
75
76
def save_model_card(
    repo_id: str,
    images=None,
    base_model=str,
    train_text_encoder=False,
    prompt=str,
    repo_folder=None,
    pipeline: DiffusionPipeline = None,
):
77
78
79
80
81
82
83
84
85
86
87
    img_str = ""
    for i, image in enumerate(images):
        image.save(os.path.join(repo_folder, f"image_{i}.png"))
        img_str += f"![img_{i}](./image_{i}.png)\n"

    yaml = f"""
---
license: creativeml-openrail-m
base_model: {base_model}
instance_prompt: {prompt}
tags:
88
89
- {'stable-diffusion' if isinstance(pipeline, StableDiffusionPipeline) else 'if'}
- {'stable-diffusion-diffusers' if isinstance(pipeline, StableDiffusionPipeline) else 'if-diffusers'}
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
- text-to-image
- diffusers
- dreambooth
inference: true
---
    """
    model_card = f"""
# DreamBooth - {repo_id}

This is a dreambooth model derived from {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following. \n
{img_str}

DreamBooth for the text encoder was enabled: {train_text_encoder}.
"""
    with open(os.path.join(repo_folder, "README.md"), "w") as f:
        f.write(yaml + model_card)


109
def log_validation(
110
111
112
113
114
115
116
117
118
119
    text_encoder,
    tokenizer,
    unet,
    vae,
    args,
    accelerator,
    weight_dtype,
    global_step,
    prompt_embeds,
    negative_prompt_embeds,
120
):
121
122
123
124
    logger.info(
        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
        f" {args.validation_prompt}."
    )
125
126
127
128
129
130

    pipeline_args = {}

    if vae is not None:
        pipeline_args["vae"] = vae

131
132
133
    if text_encoder is not None:
        text_encoder = accelerator.unwrap_model(text_encoder)

134
135
136
137
    # create pipeline (note: unet and vae are loaded again in float32)
    pipeline = DiffusionPipeline.from_pretrained(
        args.pretrained_model_name_or_path,
        tokenizer=tokenizer,
138
        text_encoder=text_encoder,
139
140
141
        unet=accelerator.unwrap_model(unet),
        revision=args.revision,
        torch_dtype=weight_dtype,
142
        **pipeline_args,
143
    )
144
145
146
147
148
149
150
151
152
153
154
155
156

    # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
    scheduler_args = {}

    if "variance_type" in pipeline.scheduler.config:
        variance_type = pipeline.scheduler.config.variance_type

        if variance_type in ["learned", "learned_range"]:
            variance_type = "fixed_small"

        scheduler_args["variance_type"] = variance_type

    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
157
158
159
    pipeline = pipeline.to(accelerator.device)
    pipeline.set_progress_bar_config(disable=True)

160
161
162
163
164
165
166
167
    if args.pre_compute_text_embeddings:
        pipeline_args = {
            "prompt_embeds": prompt_embeds,
            "negative_prompt_embeds": negative_prompt_embeds,
        }
    else:
        pipeline_args = {"prompt": args.validation_prompt}

168
169
170
    # run inference
    generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
    images = []
171
172
173
174
175
176
177
178
179
180
    if args.validation_images is None:
        for _ in range(args.num_validation_images):
            with torch.autocast("cuda"):
                image = pipeline(**pipeline_args, num_inference_steps=25, generator=generator).images[0]
            images.append(image)
    else:
        for image in args.validation_images:
            image = Image.open(image)
            image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
            images.append(image)
181
182
183
184

    for tracker in accelerator.trackers:
        if tracker.name == "tensorboard":
            np_images = np.stack([np.asarray(img) for img in images])
185
            tracker.writer.add_images("validation", np_images, global_step, dataformats="NHWC")
186
187
188
189
190
191
192
193
194
195
196
197
        if tracker.name == "wandb":
            tracker.log(
                {
                    "validation": [
                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
                    ]
                }
            )

    del pipeline
    torch.cuda.empty_cache()

198
199
    return images

200

201
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
202
203
204
    text_encoder_config = PretrainedConfig.from_pretrained(
        pretrained_model_name_or_path,
        subfolder="text_encoder",
205
        revision=revision,
206
207
208
209
210
211
212
213
214
215
216
    )
    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
217
218
219
220
    elif model_class == "T5EncoderModel":
        from transformers import T5EncoderModel

        return T5EncoderModel
221
222
223
224
    else:
        raise ValueError(f"{model_class} is not supported.")


Suraj Patil's avatar
Suraj Patil committed
225
def parse_args(input_args=None):
226
227
228
229
230
231
232
233
    parser = argparse.ArgumentParser(description="Simple example of a 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.",
    )
234
235
236
237
238
    parser.add_argument(
        "--revision",
        type=str,
        default=None,
        required=False,
239
240
241
242
        help=(
            "Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
            " float32 precision."
        ),
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
    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,
        required=True,
        help="A folder containing the training data of instance images.",
    )
    parser.add_argument(
        "--class_data_dir",
        type=str,
        default=None,
        required=False,
        help="A folder containing the training data of class images.",
    )
    parser.add_argument(
        "--instance_prompt",
        type=str,
        default=None,
268
        required=True,
Yuta Hayashibe's avatar
Yuta Hayashibe committed
269
        help="The prompt with identifier specifying the instance",
270
271
272
273
274
    )
    parser.add_argument(
        "--class_prompt",
        type=str,
        default=None,
Yuta Hayashibe's avatar
Yuta Hayashibe committed
275
        help="The prompt to specify images in the same class as provided instance images.",
276
277
278
279
280
    )
    parser.add_argument(
        "--with_prior_preservation",
        default=False,
        action="store_true",
Yuta Hayashibe's avatar
Yuta Hayashibe committed
281
        help="Flag to add prior preservation loss.",
282
283
284
285
286
287
288
    )
    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=100,
        help=(
289
290
            "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."
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
        ),
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="text-inversion-model",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument("--seed", type=int, default=None, 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(
310
311
312
        "--center_crop",
        default=False,
        action="store_true",
patil-suraj's avatar
patil-suraj committed
313
314
315
316
        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."
        ),
317
    )
318
319
320
321
322
    parser.add_argument(
        "--train_text_encoder",
        action="store_true",
        help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
    )
323
324
325
326
327
328
329
330
331
332
333
334
335
    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.",
    )
336
337
338
339
340
    parser.add_argument(
        "--checkpointing_steps",
        type=int,
        default=500,
        help=(
341
342
343
344
345
            "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
            "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
            "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
            "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
            "instructions."
346
347
        ),
    )
348
    parser.add_argument(
349
        "--checkpoints_total_limit",
350
351
352
353
354
355
356
357
        type=int,
        default=None,
        help=(
            "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
            " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
            " for more details"
        ),
    )
358
359
360
361
362
363
364
365
366
    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.'
        ),
    )
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
    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=5e-6,
        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(
        "--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."
    )
402
403
404
405
406
407
408
    parser.add_argument(
        "--lr_num_cycles",
        type=int,
        default=1,
        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
    )
    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
409
410
411
    parser.add_argument(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
412
413
414
415
416
417
418
419
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=0,
        help=(
            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
        ),
    )
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
    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***."
        ),
    )
Suraj Patil's avatar
Suraj Patil committed
442
443
444
445
446
447
448
449
450
451
452
453
454
    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=(
455
456
            '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.'
Suraj Patil's avatar
Suraj Patil committed
457
458
        ),
    )
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
    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=4,
        help="Number of images that should be generated during validation with `validation_prompt`.",
    )
    parser.add_argument(
        "--validation_steps",
        type=int,
        default=100,
        help=(
            "Run validation every X steps. Validation consists of running the prompt"
            " `args.validation_prompt` multiple times: `args.num_validation_images`"
            " and logging the images."
        ),
    )
481
482
483
    parser.add_argument(
        "--mixed_precision",
        type=str,
484
        default=None,
485
486
        choices=["no", "fp16", "bf16"],
        help=(
487
488
489
            "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."
490
491
        ),
    )
492
493
494
495
496
497
498
499
500
501
    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."
        ),
    )
502
    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
503
504
505
    parser.add_argument(
        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
    )
506
507
508
509
510
511
512
513
514
    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"
        ),
    )
515

516
517
518
519
520
521
522
523
524
    parser.add_argument(
        "--offset_noise",
        action="store_true",
        default=False,
        help=(
            "Fine-tuning against a modified noise"
            " See: https://www.crosslabs.org//blog/diffusion-with-offset-noise for more information."
        ),
    )
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
    parser.add_argument(
        "--pre_compute_text_embeddings",
        action="store_true",
        help="Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.",
    )
    parser.add_argument(
        "--tokenizer_max_length",
        type=int,
        default=None,
        required=False,
        help="The maximum length of the tokenizer. If not set, will default to the tokenizer's max length.",
    )
    parser.add_argument(
        "--text_encoder_use_attention_mask",
        action="store_true",
        required=False,
        help="Whether to use attention mask for the text encoder",
    )
    parser.add_argument(
        "--skip_save_text_encoder", action="store_true", required=False, help="Set to not save text encoder"
    )
546
547
548
549
550
551
552
553
554
555
556
557
558
    parser.add_argument(
        "--validation_images",
        required=False,
        default=None,
        nargs="+",
        help="Optional set of images to use for validation. Used when the target pipeline takes an initial image as input such as when training image variation or superresolution.",
    )
    parser.add_argument(
        "--class_labels_conditioning",
        required=False,
        default=None,
        help="The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.",
    )
559

560
561
562
563
564
    if input_args is not None:
        args = parser.parse_args(input_args)
    else:
        args = parser.parse_args()

565
566
567
568
569
570
571
572
573
    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.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.")
574
    else:
575
        # logger is not available yet
576
        if args.class_data_dir is not None:
577
            warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
578
        if args.class_prompt is not None:
579
            warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
580

581
582
583
    if args.train_text_encoder and args.pre_compute_text_embeddings:
        raise ValueError("`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`")

584
585
586
587
588
    return args


class DreamBoothDataset(Dataset):
    """
Yuta Hayashibe's avatar
Yuta Hayashibe committed
589
    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
590
591
592
593
594
595
596
597
598
599
    It pre-processes the images and the tokenizes prompts.
    """

    def __init__(
        self,
        instance_data_root,
        instance_prompt,
        tokenizer,
        class_data_root=None,
        class_prompt=None,
600
        class_num=None,
601
602
        size=512,
        center_crop=False,
603
        encoder_hidden_states=None,
604
        class_prompt_encoder_hidden_states=None,
605
        tokenizer_max_length=None,
606
607
608
609
    ):
        self.size = size
        self.center_crop = center_crop
        self.tokenizer = tokenizer
610
        self.encoder_hidden_states = encoder_hidden_states
611
        self.class_prompt_encoder_hidden_states = class_prompt_encoder_hidden_states
612
        self.tokenizer_max_length = tokenizer_max_length
613
614
615

        self.instance_data_root = Path(instance_data_root)
        if not self.instance_data_root.exists():
616
            raise ValueError(f"Instance {self.instance_data_root} images root doesn't exists.")
617
618
619
620
621
622
623
624
625

        self.instance_images_path = list(Path(instance_data_root).iterdir())
        self.num_instance_images = len(self.instance_images_path)
        self.instance_prompt = instance_prompt
        self._length = self.num_instance_images

        if class_data_root is not None:
            self.class_data_root = Path(class_data_root)
            self.class_data_root.mkdir(parents=True, exist_ok=True)
626
            self.class_images_path = list(self.class_data_root.iterdir())
627
628
629
630
            if class_num is not None:
                self.num_class_images = min(len(self.class_images_path), class_num)
            else:
                self.num_class_images = len(self.class_images_path)
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
            self._length = max(self.num_class_images, self.num_instance_images)
            self.class_prompt = class_prompt
        else:
            self.class_data_root = None

        self.image_transforms = transforms.Compose(
            [
                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 __getitem__(self, index):
        example = {}
        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
651
652
        instance_image = exif_transpose(instance_image)

653
654
655
        if not instance_image.mode == "RGB":
            instance_image = instance_image.convert("RGB")
        example["instance_images"] = self.image_transforms(instance_image)
656
657
658
659
660
661
662
663
664

        if self.encoder_hidden_states is not None:
            example["instance_prompt_ids"] = self.encoder_hidden_states
        else:
            text_inputs = tokenize_prompt(
                self.tokenizer, self.instance_prompt, tokenizer_max_length=self.tokenizer_max_length
            )
            example["instance_prompt_ids"] = text_inputs.input_ids
            example["instance_attention_mask"] = text_inputs.attention_mask
665
666
667

        if self.class_data_root:
            class_image = Image.open(self.class_images_path[index % self.num_class_images])
668
669
            class_image = exif_transpose(class_image)

670
671
672
            if not class_image.mode == "RGB":
                class_image = class_image.convert("RGB")
            example["class_images"] = self.image_transforms(class_image)
673

674
675
            if self.class_prompt_encoder_hidden_states is not None:
                example["class_prompt_ids"] = self.class_prompt_encoder_hidden_states
676
677
678
679
680
681
            else:
                class_text_inputs = tokenize_prompt(
                    self.tokenizer, self.class_prompt, tokenizer_max_length=self.tokenizer_max_length
                )
                example["class_prompt_ids"] = class_text_inputs.input_ids
                example["class_attention_mask"] = class_text_inputs.attention_mask
682
683
684
685

        return example


686
def collate_fn(examples, with_prior_preservation=False):
687
688
    has_attention_mask = "instance_attention_mask" in examples[0]

689
690
691
    input_ids = [example["instance_prompt_ids"] for example in examples]
    pixel_values = [example["instance_images"] for example in examples]

692
693
694
    if has_attention_mask:
        attention_mask = [example["instance_attention_mask"] for example in examples]

695
696
697
698
699
700
    # 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]

701
702
703
        if has_attention_mask:
            attention_mask += [example["class_attention_mask"] for example in examples]

704
705
706
707
708
709
710
711
712
    pixel_values = torch.stack(pixel_values)
    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()

    input_ids = torch.cat(input_ids, dim=0)

    batch = {
        "input_ids": input_ids,
        "pixel_values": pixel_values,
    }
713
714

    if has_attention_mask:
715
        attention_mask = torch.cat(attention_mask, dim=0)
716
717
        batch["attention_mask"] = attention_mask

718
719
720
    return batch


721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
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


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
def model_has_vae(args):
    config_file_name = os.path.join("vae", AutoencoderKL.config_name)
    if os.path.isdir(args.pretrained_model_name_or_path):
        config_file_name = os.path.join(args.pretrained_model_name_or_path, config_file_name)
        return os.path.isfile(config_file_name)
    else:
        files_in_repo = model_info(args.pretrained_model_name_or_path, revision=args.revision).siblings
        return any(file.rfilename == config_file_name for file in files_in_repo)


def tokenize_prompt(tokenizer, prompt, tokenizer_max_length=None):
    if tokenizer_max_length is not None:
        max_length = tokenizer_max_length
    else:
        max_length = tokenizer.model_max_length

    text_inputs = tokenizer(
        prompt,
        truncation=True,
        padding="max_length",
        max_length=max_length,
        return_tensors="pt",
    )

    return text_inputs


def encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_attention_mask=None):
    text_input_ids = input_ids.to(text_encoder.device)

    if text_encoder_use_attention_mask:
        attention_mask = attention_mask.to(text_encoder.device)
    else:
        attention_mask = None

    prompt_embeds = text_encoder(
        text_input_ids,
        attention_mask=attention_mask,
    )
    prompt_embeds = prompt_embeds[0]

    return prompt_embeds


782
def main(args):
783
784
    logging_dir = Path(args.output_dir, args.logging_dir)

785
    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
786

787
788
789
    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
Suraj Patil's avatar
Suraj Patil committed
790
        log_with=args.report_to,
791
        project_config=accelerator_project_config,
792
793
    )

794
795
796
797
    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.")

798
799
800
801
802
803
804
805
806
    # 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.
    if args.train_text_encoder and args.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."
        )

Suraj Patil's avatar
Suraj Patil committed
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
    # 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()

    # If passed along, set the training seed now.
822
823
824
    if args.seed is not None:
        set_seed(args.seed)

Suraj Patil's avatar
Suraj Patil committed
825
    # Generate class images if prior preservation is enabled.
826
827
828
829
830
831
832
833
    if args.with_prior_preservation:
        class_images_dir = Path(args.class_data_dir)
        if not class_images_dir.exists():
            class_images_dir.mkdir(parents=True)
        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
834
835
836
837
838
839
            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
840
            pipeline = DiffusionPipeline.from_pretrained(
841
842
843
844
                args.pretrained_model_name_or_path,
                torch_dtype=torch_dtype,
                safety_checker=None,
                revision=args.revision,
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
            )
            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(args.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
            ):
860
                images = pipeline(example["prompt"]).images
861
862

                for i, image in enumerate(images):
863
864
865
                    hash_image = hashlib.sha1(image.tobytes()).hexdigest()
                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
                    image.save(image_filename)
866
867
868
869
870
871
872

            del pipeline
            if torch.cuda.is_available():
                torch.cuda.empty_cache()

    # Handle the repository creation
    if accelerator.is_main_process:
873
        if args.output_dir is not None:
874
875
            os.makedirs(args.output_dir, exist_ok=True)

876
877
878
879
880
        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

881
882
    # Load the tokenizer
    if args.tokenizer_name:
Suraj Patil's avatar
Suraj Patil committed
883
        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
884
    elif args.pretrained_model_name_or_path:
885
        tokenizer = AutoTokenizer.from_pretrained(
886
887
888
            args.pretrained_model_name_or_path,
            subfolder="tokenizer",
            revision=args.revision,
889
            use_fast=False,
890
        )
891

892
    # import correct text encoder class
893
    text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
894

Suraj Patil's avatar
Suraj Patil committed
895
896
    # Load scheduler and models
    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
897
    text_encoder = text_encoder_cls.from_pretrained(
Suraj Patil's avatar
Suraj Patil committed
898
        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
899
    )
900
901
902
903
904
905
906
907

    if model_has_vae(args):
        vae = AutoencoderKL.from_pretrained(
            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
        )
    else:
        vae = None

908
    unet = UNet2DConditionModel.from_pretrained(
Suraj Patil's avatar
Suraj Patil committed
909
        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
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
    # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
    def save_model_hook(models, weights, output_dir):
        for model in models:
            sub_dir = "unet" if isinstance(model, type(accelerator.unwrap_model(unet))) else "text_encoder"
            model.save_pretrained(os.path.join(output_dir, sub_dir))

            # make sure to pop weight so that corresponding model is not saved again
            weights.pop()

    def load_model_hook(models, input_dir):
        while len(models) > 0:
            # pop models so that they are not loaded again
            model = models.pop()

            if isinstance(model, type(accelerator.unwrap_model(text_encoder))):
                # load transformers style into model
                load_model = text_encoder_cls.from_pretrained(input_dir, subfolder="text_encoder")
                model.config = load_model.config
            else:
                # load diffusers style into model
                load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
                model.register_to_config(**load_model.config)

            model.load_state_dict(load_model.state_dict())
            del load_model

    accelerator.register_save_state_pre_hook(save_model_hook)
    accelerator.register_load_state_pre_hook(load_model_hook)
940

941
942
943
    if vae is not None:
        vae.requires_grad_(False)

Suraj Patil's avatar
Suraj Patil committed
944
945
946
    if not args.train_text_encoder:
        text_encoder.requires_grad_(False)

947
948
    if args.enable_xformers_memory_efficient_attention:
        if is_xformers_available():
949
950
951
952
953
954
955
            import xformers

            xformers_version = version.parse(xformers.__version__)
            if xformers_version == version.parse("0.0.16"):
                logger.warn(
                    "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."
                )
956
            unet.enable_xformers_memory_efficient_attention()
957
958
        else:
            raise ValueError("xformers is not available. Make sure it is installed correctly")
959

960
961
    if args.gradient_checkpointing:
        unet.enable_gradient_checkpointing()
962
963
        if args.train_text_encoder:
            text_encoder.gradient_checkpointing_enable()
964

965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
    # Check that all trainable models are in full precision
    low_precision_error_string = (
        "Please make sure to always have all model weights in full float32 precision when starting training - even if"
        " doing mixed precision training. copy of the weights should still be float32."
    )

    if accelerator.unwrap_model(unet).dtype != torch.float32:
        raise ValueError(
            f"Unet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
        )

    if args.train_text_encoder and accelerator.unwrap_model(text_encoder).dtype != torch.float32:
        raise ValueError(
            f"Text encoder loaded as datatype {accelerator.unwrap_model(text_encoder).dtype}."
            f" {low_precision_error_string}"
        )

Suraj Patil's avatar
Suraj Patil committed
982
983
984
985
986
    # 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

987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
    if args.scale_lr:
        args.learning_rate = (
            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
        )

    # 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(
                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
            )

        optimizer_class = bnb.optim.AdamW8bit
    else:
        optimizer_class = torch.optim.AdamW

Suraj Patil's avatar
Suraj Patil committed
1005
    # Optimizer creation
1006
1007
1008
    params_to_optimize = (
        itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
    )
1009
    optimizer = optimizer_class(
1010
        params_to_optimize,
1011
1012
1013
1014
1015
1016
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
    if args.pre_compute_text_embeddings:

        def compute_text_embeddings(prompt):
            with torch.no_grad():
                text_inputs = tokenize_prompt(tokenizer, prompt, tokenizer_max_length=args.tokenizer_max_length)
                prompt_embeds = encode_prompt(
                    text_encoder,
                    text_inputs.input_ids,
                    text_inputs.attention_mask,
                    text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
                )

            return prompt_embeds

        pre_computed_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)
        validation_prompt_negative_prompt_embeds = compute_text_embeddings("")

        if args.validation_prompt is not None:
            validation_prompt_encoder_hidden_states = compute_text_embeddings(args.validation_prompt)
        else:
            validation_prompt_encoder_hidden_states = None

1039
1040
        if args.class_prompt is not None:
            pre_computed_class_prompt_encoder_hidden_states = compute_text_embeddings(args.class_prompt)
1041
        else:
1042
            pre_computed_class_prompt_encoder_hidden_states = None
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052

        text_encoder = None
        tokenizer = None

        gc.collect()
        torch.cuda.empty_cache()
    else:
        pre_computed_encoder_hidden_states = None
        validation_prompt_encoder_hidden_states = None
        validation_prompt_negative_prompt_embeds = None
1053
        pre_computed_class_prompt_encoder_hidden_states = None
1054

Suraj Patil's avatar
Suraj Patil committed
1055
    # Dataset and DataLoaders creation:
1056
1057
1058
1059
1060
    train_dataset = DreamBoothDataset(
        instance_data_root=args.instance_data_dir,
        instance_prompt=args.instance_prompt,
        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
        class_prompt=args.class_prompt,
1061
        class_num=args.num_class_images,
1062
1063
1064
        tokenizer=tokenizer,
        size=args.resolution,
        center_crop=args.center_crop,
1065
        encoder_hidden_states=pre_computed_encoder_hidden_states,
1066
        class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states,
1067
        tokenizer_max_length=args.tokenizer_max_length,
1068
1069
1070
    )

    train_dataloader = torch.utils.data.DataLoader(
1071
1072
1073
1074
        train_dataset,
        batch_size=args.train_batch_size,
        shuffle=True,
        collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
1075
        num_workers=args.dataloader_num_workers,
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
    )

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
1088
1089
        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
        num_training_steps=args.max_train_steps * accelerator.num_processes,
1090
1091
        num_cycles=args.lr_num_cycles,
        power=args.lr_power,
1092
1093
    )

Suraj Patil's avatar
Suraj Patil committed
1094
    # Prepare everything with our `accelerator`.
1095
1096
1097
1098
1099
1100
1101
1102
    if args.train_text_encoder:
        unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
            unet, text_encoder, optimizer, train_dataloader, lr_scheduler
        )
    else:
        unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
            unet, optimizer, train_dataloader, lr_scheduler
        )
1103

1104
1105
    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
    # as these weights are only used for inference, keeping weights in full precision is not required.
1106
    weight_dtype = torch.float32
1107
    if accelerator.mixed_precision == "fp16":
1108
        weight_dtype = torch.float16
1109
    elif accelerator.mixed_precision == "bf16":
1110
1111
        weight_dtype = torch.bfloat16

Suraj Patil's avatar
Suraj Patil committed
1112
    # Move vae and text_encoder to device and cast to weight_dtype
1113
1114
1115
1116
    if vae is not None:
        vae.to(accelerator.device, dtype=weight_dtype)

    if not args.train_text_encoder and text_encoder is not None:
1117
        text_encoder.to(accelerator.device, dtype=weight_dtype)
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # 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:
1129
        tracker_config = vars(copy.deepcopy(args))
1130
1131
        tracker_config.pop("validation_images")
        accelerator.init_trackers("dreambooth", config=tracker_config)
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143

    # Train!
    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
1144
1145
1146
    global_step = 0
    first_epoch = 0

Suraj Patil's avatar
Suraj Patil committed
1147
    # Potentially load in the weights and states from a previous save
1148
1149
1150
1151
1152
1153
1154
1155
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint != "latest":
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the mos recent checkpoint
            dirs = os.listdir(args.output_dir)
            dirs = [d for d in dirs if d.startswith("checkpoint")]
            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
            path = dirs[-1] if len(dirs) > 0 else None

        if path is None:
            accelerator.print(
                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
            )
            args.resume_from_checkpoint = None
        else:
            accelerator.print(f"Resuming from checkpoint {path}")
            accelerator.load_state(os.path.join(args.output_dir, path))
            global_step = int(path.split("-")[1])

            resume_global_step = global_step * args.gradient_accumulation_steps
            first_epoch = global_step // num_update_steps_per_epoch
            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
1171

1172
    # Only show the progress bar once on each machine.
1173
    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
1174
1175
    progress_bar.set_description("Steps")

1176
    for epoch in range(first_epoch, args.num_train_epochs):
1177
        unet.train()
1178
1179
        if args.train_text_encoder:
            text_encoder.train()
1180
        for step, batch in enumerate(train_dataloader):
1181
1182
1183
1184
1185
1186
            # Skip steps until we reach the resumed step
            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
                if step % args.gradient_accumulation_steps == 0:
                    progress_bar.update(1)
                continue

1187
            with accelerator.accumulate(unet):
1188
                pixel_values = batch["pixel_values"].to(dtype=weight_dtype)
1189

1190
1191
1192
1193
1194
1195
1196
1197
                if vae is not None:
                    # Convert images to latent space
                    model_input = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
                    model_input = model_input * vae.config.scaling_factor
                else:
                    model_input = pixel_values

                # Sample noise that we'll add to the model input
1198
                if args.offset_noise:
1199
1200
                    noise = torch.randn_like(model_input) + 0.1 * torch.randn(
                        model_input.shape[0], model_input.shape[1], 1, 1, device=model_input.device
1201
1202
                    )
                else:
1203
                    noise = torch.randn_like(model_input)
1204
                bsz, channels, height, width = model_input.shape
1205
                # Sample a random timestep for each image
1206
1207
1208
                timesteps = torch.randint(
                    0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
                )
1209
1210
                timesteps = timesteps.long()

1211
                # Add noise to the model input according to the noise magnitude at each timestep
1212
                # (this is the forward diffusion process)
1213
                noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
1214
1215

                # Get the text embedding for conditioning
1216
1217
1218
1219
1220
1221
1222
1223
1224
                if args.pre_compute_text_embeddings:
                    encoder_hidden_states = batch["input_ids"]
                else:
                    encoder_hidden_states = encode_prompt(
                        text_encoder,
                        batch["input_ids"],
                        batch["attention_mask"],
                        text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
                    )
1225

1226
                if accelerator.unwrap_model(unet).config.in_channels == channels * 2:
1227
                    noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)
1228
1229
1230
1231
1232
1233

                if args.class_labels_conditioning == "timesteps":
                    class_labels = timesteps
                else:
                    class_labels = None

1234
                # Predict the noise residual
1235
1236
1237
                model_pred = unet(
                    noisy_model_input, timesteps, encoder_hidden_states, class_labels=class_labels
                ).sample
1238
1239
1240

                if model_pred.shape[1] == 6:
                    model_pred, _ = torch.chunk(model_pred, 2, dim=1)
1241
1242
1243
1244
1245

                # Get the target for loss depending on the prediction type
                if noise_scheduler.config.prediction_type == "epsilon":
                    target = noise
                elif noise_scheduler.config.prediction_type == "v_prediction":
1246
                    target = noise_scheduler.get_velocity(model_input, noise, timesteps)
1247
1248
                else:
                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
1249
1250

                if args.with_prior_preservation:
1251
1252
1253
                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                    model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
                    target, target_prior = torch.chunk(target, 2, dim=0)
1254
1255

                    # Compute instance loss
1256
                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
1257
1258

                    # Compute prior loss
1259
                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
1260
1261
1262
1263

                    # Add the prior loss to the instance loss.
                    loss = loss + args.prior_loss_weight * prior_loss
                else:
1264
                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
1265
1266

                accelerator.backward(loss)
1267
                if accelerator.sync_gradients:
1268
1269
1270
1271
1272
1273
                    params_to_clip = (
                        itertools.chain(unet.parameters(), text_encoder.parameters())
                        if args.train_text_encoder
                        else unet.parameters()
                    )
                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
1274
1275
                optimizer.step()
                lr_scheduler.step()
1276
                optimizer.zero_grad(set_to_none=args.set_grads_to_none)
1277
1278
1279
1280
1281
1282

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                global_step += 1

1283
1284
                if accelerator.is_main_process:
                    if global_step % args.checkpointing_steps == 0:
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
                        if args.checkpoints_total_limit is not None:
                            checkpoints = os.listdir(args.output_dir)
                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))

                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
                            if len(checkpoints) >= args.checkpoints_total_limit:
                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
                                removing_checkpoints = checkpoints[0:num_to_remove]

                                logger.info(
                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
                                )
                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")

                                for removing_checkpoint in removing_checkpoints:
                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
                                    shutil.rmtree(removing_checkpoint)

1305
                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
1306
1307
                        accelerator.save_state(save_path)
                        logger.info(f"Saved state to {save_path}")
1308

1309
1310
                    images = []

1311
                    if args.validation_prompt is not None and global_step % args.validation_steps == 0:
1312
                        images = log_validation(
1313
1314
1315
1316
1317
1318
1319
                            text_encoder,
                            tokenizer,
                            unet,
                            vae,
                            args,
                            accelerator,
                            weight_dtype,
1320
                            global_step,
1321
1322
                            validation_prompt_encoder_hidden_states,
                            validation_prompt_negative_prompt_embeds,
1323
                        )
1324

1325
1326
1327
1328
1329
1330
1331
1332
            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)

            if global_step >= args.max_train_steps:
                break

    # Create the pipeline using using the trained modules and save it.
Suraj Patil's avatar
Suraj Patil committed
1333
    accelerator.wait_for_everyone()
1334
    if accelerator.is_main_process:
1335
1336
1337
1338
1339
1340
1341
1342
        pipeline_args = {}

        if text_encoder is not None:
            pipeline_args["text_encoder"] = accelerator.unwrap_model(text_encoder)

        if args.skip_save_text_encoder:
            pipeline_args["text_encoder"] = None

1343
        pipeline = DiffusionPipeline.from_pretrained(
1344
1345
            args.pretrained_model_name_or_path,
            unet=accelerator.unwrap_model(unet),
1346
            revision=args.revision,
1347
            **pipeline_args,
1348
        )
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362

        # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
        scheduler_args = {}

        if "variance_type" in pipeline.scheduler.config:
            variance_type = pipeline.scheduler.config.variance_type

            if variance_type in ["learned", "learned_range"]:
                variance_type = "fixed_small"

            scheduler_args["variance_type"] = variance_type

        pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)

1363
1364
1365
        pipeline.save_pretrained(args.output_dir)

        if args.push_to_hub:
1366
1367
1368
1369
1370
1371
1372
            save_model_card(
                repo_id,
                images=images,
                base_model=args.pretrained_model_name_or_path,
                train_text_encoder=args.train_text_encoder,
                prompt=args.instance_prompt,
                repo_folder=args.output_dir,
1373
                pipeline=pipeline,
1374
            )
1375
1376
1377
1378
1379
1380
            upload_folder(
                repo_id=repo_id,
                folder_path=args.output_dir,
                commit_message="End of training",
                ignore_patterns=["step_*", "epoch_*"],
            )
1381
1382
1383
1384
1385

    accelerator.end_training()


if __name__ == "__main__":
1386
1387
    args = parse_args()
    main(args)