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SVDiff-Training-UI / train_svdiff.py

abhishek-kumar

Duplicate from svdiff-library/SVDiff-Training-UI

b59d5de about 1 year ago

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	import argparse
	import hashlib
	import logging
	import math
	import os
	import warnings
	from pathlib import Path
	from typing import Optional
	from packaging import version

	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 torch.utils.data import Dataset
	from torchvision import transforms
	from tqdm.auto import tqdm
	from transformers import AutoTokenizer, PretrainedConfig

	import diffusers
	from diffusers import __version__
	from diffusers import (
	AutoencoderKL,
	DDPMScheduler,
	DiffusionPipeline,
	StableDiffusionPipeline,
	DPMSolverMultistepScheduler,
	)
	from svdiff_pytorch import load_unet_for_svdiff, SCHEDULER_MAPPING
	from diffusers.loaders import AttnProcsLayers
	from diffusers.optimization import get_scheduler
	from diffusers.utils import check_min_version, is_wandb_available
	from diffusers.utils.import_utils import is_xformers_available
	from safetensors import safe_open
	from safetensors.torch import save_file
	if is_wandb_available():
	import wandb


	# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
	# check_min_version("0.15.0.dev0")
	diffusers_version = "0.14.0"
	if version.parse(__version__) != version.parse(diffusers_version):
	error_message = f"This example requires a version of {diffusers_version},"
	error_message += f" but the version found is {__version__}.\n"
	raise ImportError(error_message)

	logger = get_logger(__name__)


	def save_model_card(repo_id: str, base_model=str, prompt=str, repo_folder=None):
	yaml = f"""
	---
	license: creativeml-openrail-m
	base_model: {base_model}
	instance_prompt: {prompt}
	tags:
	- stable-diffusion
	- stable-diffusion-diffusers
	- text-to-image
	- diffusers
	- svdiff
	inference: true
	---
	"""
	model_card = f"""
	# SVDiff-pytorch - {repo_id}
	These are SVDiff weights for {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/).
	"""
	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
	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(
	"--pretrained_vae_name_or_path",
	type=str,
	default=None,
	help="Path to pretrained vae or vae identifier from huggingface.co/models. This will be used in prior generation",
	)
	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=42, help="A seed for reproducible training.")
	parser.add_argument(
	"--resolution",
	type=int,
	default=512,
	help=(
	"The resolution for input images, all the images in the train/validation dataset will be resized to this"
	" resolution"
	),
	)
	parser.add_argument(
	"--center_crop",
	default=False,
	action="store_true",
	help=(
	"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
	" cropped. The images will be resized to the resolution first before cropping."
	),
	)
	parser.add_argument(
	"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
	)
	parser.add_argument(
	"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
	)
	parser.add_argument("--num_train_epochs", type=int, default=1)
	parser.add_argument(
	"--max_train_steps",
	type=int,
	default=None,
	help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
	)
	parser.add_argument(
	"--checkpointing_steps",
	type=int,
	default=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("--prior_generation_scheduler_type", type=str, choices=["ddim", "plms", "lms", "euler", "euler_ancestral", "dpm_solver++"], default="ddim", help="diffusion scheduler type")
	parser.add_argument("--prior_generation_num_inference_steps", type=int, default=50, help="number of sampling steps")

	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(
	"--enable_token_merging", action="store_true", help="Whether or not to use tomesd on prior generation"
	)
	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.")

	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,
	):
	self.size = size
	self.center_crop = center_crop
	self.tokenizer = tokenizer

	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])
	if not instance_image.mode == "RGB":
	instance_image = instance_image.convert("RGB")
	example["instance_images"] = self.image_transforms(instance_image)
	example["instance_prompt_ids"] = self.tokenizer(
	self.instance_prompt,
	truncation=True,
	padding="max_length",
	max_length=self.tokenizer.model_max_length,
	return_tensors="pt",
	).input_ids

	if self.class_data_root:
	class_image = Image.open(self.class_images_path[index % self.num_class_images])
	if not class_image.mode == "RGB":
	class_image = class_image.convert("RGB")
	example["class_images"] = self.image_transforms(class_image)
	example["class_prompt_ids"] = self.tokenizer(
	self.class_prompt,
	truncation=True,
	padding="max_length",
	max_length=self.tokenizer.model_max_length,
	return_tensors="pt",
	).input_ids

	return example


	def collate_fn(examples, with_prior_preservation=False):
	input_ids = [example["instance_prompt_ids"] for example in examples]
	pixel_values = [example["instance_images"] 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]

	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,
	}
	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 log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch):
	logger.info(
	f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
	f" {args.validation_prompt}."
	)
	# create pipeline (note: unet and vae are loaded again in float32)
	pipeline = DiffusionPipeline.from_pretrained(
	args.pretrained_model_name_or_path,
	text_encoder=text_encoder,
	tokenizer=tokenizer,
	unet=accelerator.unwrap_model(unet),
	vae=vae,
	revision=args.revision,
	torch_dtype=weight_dtype,
	)
	pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
	pipeline = pipeline.to(accelerator.device)
	pipeline.set_progress_bar_config(disable=True)

	# run inference
	generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
	images = []
	for _ in range(args.num_validation_images):
	with torch.autocast("cuda"):
	image = pipeline(args.validation_prompt, num_inference_steps=25, 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()



	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 (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
	# Make one log on every process with the configuration for debugging.
	logging.basicConfig(
	format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
	datefmt="%m/%d/%Y %H:%M:%S",
	level=logging.INFO,
	)
	logger.info(accelerator.state, main_process_only=False)
	if accelerator.is_local_main_process:
	transformers.utils.logging.set_verbosity_warning()
	diffusers.utils.logging.set_verbosity_info()
	else:
	transformers.utils.logging.set_verbosity_error()
	diffusers.utils.logging.set_verbosity_error()

	# 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 = StableDiffusionPipeline.from_pretrained(
	args.pretrained_model_name_or_path,
	vae=AutoencoderKL.from_pretrained(
	args.pretrained_vae_name_or_path or args.pretrained_model_name_or_path,
	subfolder=None if args.pretrained_vae_name_or_path else "vae",
	revision=None if args.pretrained_vae_name_or_path else args.revision,
	torch_dtype=torch_dtype
	),
	torch_dtype=torch_dtype,
	safety_checker=None,
	revision=args.revision,
	)
	pipeline.scheduler = SCHEDULER_MAPPING[args.prior_generation_scheduler_type].from_config(pipeline.scheduler.config)
	if is_xformers_available():
	pipeline.enable_xformers_memory_efficient_attention()
	if args.enable_token_merging:
	try:
	import tomesd
	except ImportError:
	raise ImportError(
	"To use token merging (ToMe), please install the tomesd library: `pip install tomesd`."
	)
	tomesd.apply_patch(pipeline, ratio=0.5)

	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"],
	num_inference_steps=args.prior_generation_num_inference_steps,
	).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
	)
	vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
	unet = load_unet_for_svdiff(args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, low_cpu_mem_usage=True)

	# We only train the additional spectral shifts
	vae.requires_grad_(False)
	text_encoder.requires_grad_(False)
	unet.requires_grad_(False)
	optim_params = []
	for n, p in unet.named_parameters():
	if "delta" in n:
	p.requires_grad = True
	optim_params.append(p)
	total_params = sum(p.numel() for p in optim_params)
	print(f"Number of Trainable Parameters: {total_params * 1.e-6:.2f} M")

	# 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)
	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")

	if args.gradient_checkpointing:
	unet.enable_gradient_checkpointing()

	if args.scale_lr:
	args.learning_rate = (
	args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
	)

	# 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
	optimizer = optimizer_class(
	optim_params,
	lr=args.learning_rate,
	betas=(args.adam_beta1, args.adam_beta2),
	weight_decay=args.adam_weight_decay,
	eps=args.adam_epsilon,
	)

	# 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,
	)

	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`.
	unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
	unet, 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("svdiff-pytorch", config=vars(args))

	def save_weights(step):
	# Create the pipeline using using the trained modules and save it.
	if accelerator.is_main_process:
	save_path = os.path.join(args.output_dir, f"checkpoint-{step}")
	os.makedirs(save_path, exist_ok=True)
	unet_model = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
	state_dict = {k: v for k, v in unet_model.state_dict().items() if "delta" in k}
	save_file(state_dict, os.path.join(save_path, "spectral_shifts.safetensors"))
	print(f"[*] Weights saved at {save_path}")

	# 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()
	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):
	# Convert images to latent space
	latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
	latents = latents * vae.config.scaling_factor

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

	# Add noise to the latents according to the noise magnitude at each timestep
	# (this is the forward diffusion process)
	noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

	# Get the text embedding for conditioning
	encoder_hidden_states = text_encoder(batch["input_ids"])[0]

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

	# 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(latents, 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 = unet.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 global_step % args.checkpointing_steps == 0:
	if accelerator.is_main_process:
	save_weights(global_step)
	# 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:
	log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch)

	accelerator.wait_for_everyone()
	save_weights(global_step)
	# put the latest checkpoint to output-dir
	save_path = args.output_dir
	unet_model = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
	state_dict = {k: v for k, v in unet_model.state_dict().items() if "delta" in k}
	save_file(state_dict, os.path.join(save_path, "spectral_shifts.safetensors"))
	print(f"[*] Weights saved at {save_path}")

	if accelerator.is_main_process:
	if args.push_to_hub:
	save_model_card(
	repo_id,
	base_model=args.pretrained_model_name_or_path,
	prompt=args.instance_prompt,
	repo_folder=args.output_dir,
	)
	upload_folder(
	repo_id=repo_id,
	folder_path=args.output_dir,
	commit_message="End of training",
	ignore_patterns=["step_", "epoch_"],
	)

	accelerator.end_training()


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