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	# *************************************************************************
	# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
	# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
	# ytedance Inc..
	# *************************************************************************

	import argparse
	import gc
	import hashlib
	import itertools
	import logging
	import math
	import os
	import warnings
	from pathlib import Path

	import numpy as np
	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 create_repo, 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
	from diffusers import (
	AutoencoderKL,
	DDPMScheduler,
	DiffusionPipeline,
	DPMSolverMultistepScheduler,
	StableDiffusionPipeline,
	UNet2DConditionModel,
	)
	from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
	from diffusers.models.attention_processor import (
	AttnAddedKVProcessor,
	AttnAddedKVProcessor2_0,
	LoRAAttnAddedKVProcessor,
	LoRAAttnProcessor,
	LoRAAttnProcessor2_0,
	SlicedAttnAddedKVProcessor,
	)
	from diffusers.optimization import get_scheduler
	from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, check_min_version, is_wandb_available
	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("0.17.0")

	logger = get_logger(__name__)


	def save_model_card(
	repo_id: str,
	images=None,
	base_model=str,
	train_text_encoder=False,
	prompt=str,
	repo_folder=None,
	pipeline: DiffusionPipeline = None,
	):
	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:
	- {'stable-diffusion' if isinstance(pipeline, StableDiffusionPipeline) else 'if'}
	- {'stable-diffusion-diffusers' if isinstance(pipeline, StableDiffusionPipeline) else 'if-diffusers'}
	- text-to-image
	- diffusers
	- lora
	inference: true
	---
	"""
	model_card = f"""
	# LoRA DreamBooth - {repo_id}

	These are LoRA adaption weights for {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}

	LoRA 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)


	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
	elif model_class == "T5EncoderModel":
	from transformers import T5EncoderModel

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


	def parse_args(input_args=None):
	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.",
	)
	parser.add_argument(
	"--revision",
	type=str,
	default=None,
	required=False,
	help="Revision of pretrained model identifier from huggingface.co/models.",
	)
	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,
	required=True,
	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=4,
	help="Number of images that should be generated during validation with `validation_prompt`.",
	)
	parser.add_argument(
	"--validation_epochs",
	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("--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=(
	"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="lora-dreambooth-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(
	"--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_text_encoder",
	action="store_true",
	help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
	)
	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=500,
	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. 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 docs"
	),
	)
	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=5e-4,
	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."
	)
	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.")
	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."
	),
	)
	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("--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(
	"--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(
	"--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`.",
	)
	parser.add_argument(
	"--lora_rank",
	type=int,
	default=4,
	help="rank of lora."
	)


	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.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.")

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

	return args


	class DreamBoothDataset(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,
	instance_data_root,
	instance_prompt,
	tokenizer,
	class_data_root=None,
	class_prompt=None,
	class_num=None,
	size=512,
	center_crop=False,
	encoder_hidden_states=None,
	instance_prompt_encoder_hidden_states=None,
	tokenizer_max_length=None,
	):
	self.size = size
	self.center_crop = center_crop
	self.tokenizer = tokenizer
	self.encoder_hidden_states = encoder_hidden_states
	self.instance_prompt_encoder_hidden_states = instance_prompt_encoder_hidden_states
	self.tokenizer_max_length = tokenizer_max_length

	self.instance_data_root = Path(instance_data_root)
	if not self.instance_data_root.exists():
	raise ValueError("Instance images root doesn't exists.")

	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)
	self.class_images_path = list(self.class_data_root.iterdir())
	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)
	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])
	instance_image = exif_transpose(instance_image)

	if not instance_image.mode == "RGB":
	instance_image = instance_image.convert("RGB")
	example["instance_images"] = self.image_transforms(instance_image)

	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

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

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

	if self.instance_prompt_encoder_hidden_states is not None:
	example["class_prompt_ids"] = self.instance_prompt_encoder_hidden_states
	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

	return example


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

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

	if has_attention_mask:
	attention_mask = [example["instance_attention_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]
	if has_attention_mask:
	attention_mask += [example["class_attention_mask"] for example in examples]

	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,
	}

	if has_attention_mask:
	batch["attention_mask"] = attention_mask

	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


	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


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

	accelerator_project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit)

	accelerator = Accelerator(
	gradient_accumulation_steps=args.gradient_accumulation_steps,
	mixed_precision=args.mixed_precision,
	log_with=args.report_to,
	logging_dir=logging_dir,
	project_config=accelerator_project_config,
	)

	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 (sayakpaul): 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."
	)

	# 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.
	if args.seed is not None:
	set_seed(args.seed)

	# Generate class images if prior preservation is enabled.
	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
	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,
	)
	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
	):
	images = pipeline(example["prompt"]).images

	for i, image in enumerate(images):
	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)

	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
	)
	try:
	vae = AutoencoderKL.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
	)
	except OSError:
	# IF does not have a VAE so let's just set it to None
	# We don't have to error out here
	vae = None

	unet = UNet2DConditionModel.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
	)

	# We only train the additional adapter LoRA layers
	if vae is not None:
	vae.requires_grad_(False)
	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
	unet.to(accelerator.device, dtype=weight_dtype)
	if vae is not None:
	vae.to(accelerator.device, dtype=weight_dtype)
	text_encoder.to(accelerator.device, dtype=weight_dtype)

	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.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."
	)
	unet.enable_xformers_memory_efficient_attention()
	else:
	raise ValueError("xformers is not available. Make sure it is installed correctly")

	# now we will add new LoRA 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

	# Set correct lora layers
	unet_lora_attn_procs = {}
	for name, attn_processor 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]

	if isinstance(attn_processor, (AttnAddedKVProcessor, SlicedAttnAddedKVProcessor, AttnAddedKVProcessor2_0)):
	lora_attn_processor_class = LoRAAttnAddedKVProcessor
	else:
	lora_attn_processor_class = (
	LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
	)
	unet_lora_attn_procs[name] = lora_attn_processor_class(
	hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=args.lora_rank
	)

	unet.set_attn_processor(unet_lora_attn_procs)
	unet_lora_layers = AttnProcsLayers(unet.attn_processors)

	# The text encoder comes from 🤗 transformers, so we cannot directly modify it.
	# So, instead, we monkey-patch the forward calls of its attention-blocks. For this,
	# we first load a dummy pipeline with the text encoder and then do the monkey-patching.
	text_encoder_lora_layers = None
	if args.train_text_encoder:
	text_lora_attn_procs = {}
	for name, module in text_encoder.named_modules():
	if name.endswith(TEXT_ENCODER_ATTN_MODULE):
	text_lora_attn_procs[name] = LoRAAttnProcessor(
	hidden_size=module.out_proj.out_features, cross_attention_dim=None
	)
	text_encoder_lora_layers = AttnProcsLayers(text_lora_attn_procs)
	temp_pipeline = DiffusionPipeline.from_pretrained(
	args.pretrained_model_name_or_path, text_encoder=text_encoder
	)
	temp_pipeline._modify_text_encoder(text_lora_attn_procs)
	text_encoder = temp_pipeline.text_encoder
	del temp_pipeline

	# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
	def save_model_hook(models, weights, output_dir):
	# there are only two options here. Either are just the unet attn processor layers
	# or there are the unet and text encoder atten layers
	unet_lora_layers_to_save = None
	text_encoder_lora_layers_to_save = None

	if args.train_text_encoder:
	text_encoder_keys = accelerator.unwrap_model(text_encoder_lora_layers).state_dict().keys()
	unet_keys = accelerator.unwrap_model(unet_lora_layers).state_dict().keys()

	for model in models:
	state_dict = model.state_dict()

	if (
	text_encoder_lora_layers is not None
	and text_encoder_keys is not None
	and state_dict.keys() == text_encoder_keys
	):
	# text encoder
	text_encoder_lora_layers_to_save = state_dict
	elif state_dict.keys() == unet_keys:
	# unet
	unet_lora_layers_to_save = state_dict

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

	LoraLoaderMixin.save_lora_weights(
	output_dir,
	unet_lora_layers=unet_lora_layers_to_save,
	text_encoder_lora_layers=text_encoder_lora_layers_to_save,
	)

	def load_model_hook(models, input_dir):
	# Note we DON'T pass the unet and text encoder here an purpose
	# so that the we don't accidentally override the LoRA layers of
	# unet_lora_layers and text_encoder_lora_layers which are stored in `models`
	# with new torch.nn.Modules / weights. We simply use the pipeline class as
	# an easy way to load the lora checkpoints
	temp_pipeline = DiffusionPipeline.from_pretrained(
	args.pretrained_model_name_or_path,
	revision=args.revision,
	torch_dtype=weight_dtype,
	)
	temp_pipeline.load_lora_weights(input_dir)

	# load lora weights into models
	models[0].load_state_dict(AttnProcsLayers(temp_pipeline.unet.attn_processors).state_dict())
	if len(models) > 1:
	models[1].load_state_dict(AttnProcsLayers(temp_pipeline.text_encoder_lora_attn_procs).state_dict())

	# delete temporary pipeline and pop models
	del temp_pipeline
	for _ in range(len(models)):
	models.pop()

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

	# 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
	)

	# 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

	# Optimizer creation
	params_to_optimize = (
	itertools.chain(unet_lora_layers.parameters(), text_encoder_lora_layers.parameters())
	if args.train_text_encoder
	else unet_lora_layers.parameters()
	)
	optimizer = optimizer_class(
	params_to_optimize,
	lr=args.learning_rate,
	betas=(args.adam_beta1, args.adam_beta2),
	weight_decay=args.adam_weight_decay,
	eps=args.adam_epsilon,
	)

	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

	if args.instance_prompt is not None:
	pre_computed_instance_prompt_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)
	else:
	pre_computed_instance_prompt_encoder_hidden_states = None

	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
	pre_computed_instance_prompt_encoder_hidden_states = None

	# Dataset and DataLoaders creation:
	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,
	class_num=args.num_class_images,
	tokenizer=tokenizer,
	size=args.resolution,
	center_crop=args.center_crop,
	encoder_hidden_states=pre_computed_encoder_hidden_states,
	instance_prompt_encoder_hidden_states=pre_computed_instance_prompt_encoder_hidden_states,
	tokenizer_max_length=args.tokenizer_max_length,
	)

	train_dataloader = torch.utils.data.DataLoader(
	train_dataset,
	batch_size=args.train_batch_size,
	shuffle=True,
	collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
	num_workers=args.dataloader_num_workers,
	)

	# 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,
	num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
	num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
	num_cycles=args.lr_num_cycles,
	power=args.lr_power,
	)

	# Prepare everything with our `accelerator`.
	if args.train_text_encoder:
	unet_lora_layers, text_encoder_lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
	unet_lora_layers, text_encoder_lora_layers, optimizer, train_dataloader, lr_scheduler
	)
	else:
	unet_lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
	unet_lora_layers, optimizer, train_dataloader, lr_scheduler
	)

	# 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:
	accelerator.init_trackers("dreambooth-lora", config=vars(args))

	# 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}")
	global_step = 0
	first_epoch = 0

	# Potentially load in the weights and states from a previous save
	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]))
	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)

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

	for epoch in range(first_epoch, args.num_train_epochs):
	unet.train()
	if args.train_text_encoder:
	text_encoder.train()
	for step, batch in enumerate(train_dataloader):
	# 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

	with accelerator.accumulate(unet):
	pixel_values = batch["pixel_values"].to(dtype=weight_dtype)
	if vae is not None:
	# Convert images to latent space
	model_input = vae.encode(pixel_values).latent_dist
	model_input = model_input.sample() * vae.config.scaling_factor
	else:
	model_input = pixel_values

	# Sample noise that we'll add to the latents
	noise = torch.randn_like(model_input)
	bsz, channels, height, width = model_input.shape
	# Sample a random timestep for each image
	timesteps = torch.randint(
	0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
	)
	timesteps = timesteps.long()

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

	# Get the text embedding for conditioning
	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,
	)

	if accelerator.unwrap_model(unet).config.in_channels == channels * 2:
	noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)

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

	# Predict the noise residual
	model_pred = unet(
	noisy_model_input, timesteps, encoder_hidden_states, class_labels=class_labels
	).sample

	# if model predicts variance, throw away the prediction. we will only train on the
	# simplified training objective. This means that all schedulers using the fine tuned
	# model must be configured to use one of the fixed variance variance types.
	if model_pred.shape[1] == 6:
	model_pred, _ = torch.chunk(model_pred, 2, dim=1)

	# 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":
	target = noise_scheduler.get_velocity(model_input, noise, timesteps)
	else:
	raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")

	if args.with_prior_preservation:
	# 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)

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

	# Compute prior loss
	prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")

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

	accelerator.backward(loss)
	if accelerator.sync_gradients:
	params_to_clip = (
	itertools.chain(unet_lora_layers.parameters(), text_encoder_lora_layers.parameters())
	if args.train_text_encoder
	else unet_lora_layers.parameters()
	)
	accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
	optimizer.step()
	lr_scheduler.step()
	optimizer.zero_grad()

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

	if accelerator.is_main_process:
	if global_step % args.checkpointing_steps == 0:
	save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
	accelerator.save_state(save_path)
	logger.info(f"Saved state to {save_path}")

	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

	if accelerator.is_main_process:
	if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
	logger.info(
	f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
	f" {args.validation_prompt}."
	)
	# create pipeline
	pipeline = DiffusionPipeline.from_pretrained(
	args.pretrained_model_name_or_path,
	unet=accelerator.unwrap_model(unet),
	text_encoder=None if args.pre_compute_text_embeddings else accelerator.unwrap_model(text_encoder),
	revision=args.revision,
	torch_dtype=weight_dtype,
	)

	# 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
	)

	pipeline = pipeline.to(accelerator.device)
	pipeline.set_progress_bar_config(disable=True)

	# run inference
	generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
	if args.pre_compute_text_embeddings:
	pipeline_args = {
	"prompt_embeds": validation_prompt_encoder_hidden_states,
	"negative_prompt_embeds": validation_prompt_negative_prompt_embeds,
	}
	else:
	pipeline_args = {"prompt": args.validation_prompt}

	if args.validation_images is None:
	images = [
	pipeline(**pipeline_args, generator=generator).images[0]
	for _ in range(args.num_validation_images)
	]
	else:
	images = []
	for image in args.validation_images:
	image = Image.open(image)
	image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
	images.append(image)

	for tracker in accelerator.trackers:
	if tracker.name == "tensorboard":
	np_images = np.stack([np.asarray(img) for img in images])
	tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
	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()

	# Save the lora layers
	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
	unet = unet.to(torch.float32)
	unet_lora_layers = accelerator.unwrap_model(unet_lora_layers)

	if text_encoder is not None:
	text_encoder = text_encoder.to(torch.float32)
	text_encoder_lora_layers = accelerator.unwrap_model(text_encoder_lora_layers)

	LoraLoaderMixin.save_lora_weights(
	save_directory=args.output_dir,
	unet_lora_layers=unet_lora_layers,
	text_encoder_lora_layers=text_encoder_lora_layers,
	)

	accelerator.end_training()


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