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Stable_diffusion_v1.4 / train_text_to_image.py

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	#!/usr/bin/env python
	# coding=utf-8
	# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and

	import argparse
	import logging
	import math
	import os
	import random
	from pathlib import Path

	import accelerate
	import datasets
	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.state import AcceleratorState
	from accelerate.utils import ProjectConfiguration, set_seed
	from datasets import load_dataset
	from huggingface_hub import create_repo, upload_folder
	from packaging import version
	from torchvision import transforms
	from tqdm.auto import tqdm
	from transformers import CLIPTextModel, CLIPTokenizer
	from transformers.utils import ContextManagers

	import diffusers
	from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
	from diffusers.optimization import get_scheduler
	from diffusers.training_utils import EMAModel
	from diffusers.utils import check_min_version, deprecate, is_wandb_available
	from diffusers.utils.import_utils import is_xformers_available


	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("0.17.0.dev0")

	logger = get_logger(__name__, log_level="INFO")

	DATASET_NAME_MAPPING = {
	"lambdalabs/pokemon-blip-captions": ("image", "text"),
	}


	def log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, epoch):
	logger.info("Running validation... ")

	pipeline = StableDiffusionPipeline.from_pretrained(
	args.pretrained_model_name_or_path,
	vae=accelerator.unwrap_model(vae),
	text_encoder=accelerator.unwrap_model(text_encoder),
	tokenizer=tokenizer,
	unet=accelerator.unwrap_model(unet),
	safety_checker=None,
	revision=args.revision,
	torch_dtype=weight_dtype,
	)
	pipeline = pipeline.to(accelerator.device)
	pipeline.set_progress_bar_config(disable=True)

	if args.enable_xformers_memory_efficient_attention:
	pipeline.enable_xformers_memory_efficient_attention()

	if args.seed is None:
	generator = None
	else:
	generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)

	images = []
	for i in range(len(args.validation_prompts)):
	with torch.autocast("cuda"):
	image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]

	images.append(image)

	for tracker in accelerator.trackers:
	breakpoint()
	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")
	elif tracker.name == "wandb":
	tracker.log(
	{
	"validation": [
	wandb.Image(image, caption=f"{i}: {args.validation_prompts[i]}")
	for i, image in enumerate(images)
	]
	}
	)
	else:
	logger.warn(f"image logging not implemented for {tracker.name}")

	del pipeline
	torch.cuda.empty_cache()


	def parse_args():
	parser = argparse.ArgumentParser(description="Simple example of a training script.")
	parser.add_argument(
	"--input_pertubation", type=float, default=0, help="The scale of input pretubation. Recommended 0.1."
	)
	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(
	"--dataset_name",
	type=str,
	default=None,
	help=(
	"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
	" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
	" or to a folder containing files that 🤗 Datasets can understand."
	),
	)
	parser.add_argument(
	"--dataset_config_name",
	type=str,
	default=None,
	help="The config of the Dataset, leave as None if there's only one config.",
	)
	parser.add_argument(
	"--train_data_dir",
	type=str,
	default=None,
	help=(
	"A folder containing the training data. Folder contents must follow the structure described in"
	" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
	" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
	),
	)
	parser.add_argument(
	"--image_column", type=str, default="image", help="The column of the dataset containing an image."
	)
	parser.add_argument(
	"--caption_column",
	type=str,
	default="text",
	help="The column of the dataset containing a caption or a list of captions.",
	)
	parser.add_argument(
	"--max_train_samples",
	type=int,
	default=None,
	help=(
	"For debugging purposes or quicker training, truncate the number of training examples to this "
	"value if set."
	),
	)
	parser.add_argument(
	"--validation_prompts",
	type=str,
	default=None,
	nargs="+",
	help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
	)
	parser.add_argument(
	"--output_dir",
	type=str,
	default="sd-model-finetuned",
	help="The output directory where the model predictions and checkpoints will be written.",
	)
	parser.add_argument(
	"--cache_dir",
	type=str,
	default=None,
	help="The directory where the downloaded models and datasets will be stored.",
	)
	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(
	"--random_flip",
	action="store_true",
	help="whether to randomly flip images horizontally",
	)
	parser.add_argument(
	"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
	)
	parser.add_argument("--num_train_epochs", type=int, default=100)
	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(
	"--gradient_accumulation_steps",
	type=int,
	default=1,
	help="Number of updates steps to accumulate before performing a backward/update pass.",
	)
	parser.add_argument(
	"--gradient_checkpointing",
	action="store_true",
	help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
	)
	parser.add_argument(
	"--learning_rate",
	type=float,
	default=1e-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(
	"--snr_gamma",
	type=float,
	default=None,
	help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
	"More details here: https://arxiv.org/abs/2303.09556.",
	)
	parser.add_argument(
	"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
	)
	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("--use_ema", action="store_true", help="Whether to use EMA model.")
	parser.add_argument(
	"--non_ema_revision",
	type=str,
	default=None,
	required=False,
	help=(
	"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
	" remote repository specified with --pretrained_model_name_or_path."
	),
	)
	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("--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(
	"--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(
	"--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("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
	parser.add_argument(
	"--checkpointing_steps",
	type=int,
	default=500,
	help=(
	"Save a checkpoint of the training state every X updates. These checkpoints are only 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(
	"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
	)
	parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
	parser.add_argument(
	"--validation_epochs",
	type=int,
	default=5,
	help="Run validation every X epochs.",
	)
	parser.add_argument(
	"--tracker_project_name",
	type=str,
	default="text2image-fine-tune",
	help=(
	"The `project_name` argument passed to Accelerator.init_trackers for"
	" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
	),
	)

	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

	# Sanity checks
	if args.dataset_name is None and args.train_data_dir is None:
	raise ValueError("Need either a dataset name or a training folder.")

	# default to using the same revision for the non-ema model if not specified
	if args.non_ema_revision is None:
	args.non_ema_revision = args.revision

	return args


	def main():
	args = parse_args()

	if args.non_ema_revision is not None:
	deprecate(
	"non_ema_revision!=None",
	"0.15.0",
	message=(
	"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"
	" use `--variant=non_ema` instead."
	),
	)
	logging_dir = os.path.join(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,
	)

	# 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:
	datasets.utils.logging.set_verbosity_warning()
	transformers.utils.logging.set_verbosity_warning()
	diffusers.utils.logging.set_verbosity_info()
	else:
	datasets.utils.logging.set_verbosity_error()
	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)

	# 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 scheduler, tokenizer and models.
	noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
	tokenizer = CLIPTokenizer.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
	)

	def deepspeed_zero_init_disabled_context_manager():
	"""
	returns either a context list that includes one that will disable zero.Init or an empty context list
	"""
	deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None
	if deepspeed_plugin is None:
	return []

	return [deepspeed_plugin.zero3_init_context_manager(enable=False)]

	# Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.
	# For this to work properly all models must be run through `accelerate.prepare`. But accelerate
	# will try to assign the same optimizer with the same weights to all models during
	# `deepspeed.initialize`, which of course doesn't work.
	#
	# For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2
	# frozen models from being partitioned during `zero.Init` which gets called during
	# `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding
	# across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.
	with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
	text_encoder = CLIPTextModel.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 = UNet2DConditionModel.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision
	)

	# Freeze vae and text_encoder
	vae.requires_grad_(False)
	text_encoder.requires_grad_(False)

	# Create EMA for the unet.
	if args.use_ema:
	ema_unet = UNet2DConditionModel.from_pretrained(
	args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
	)
	ema_unet = EMAModel(ema_unet.parameters(), model_cls=UNet2DConditionModel, model_config=ema_unet.config)

	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")

	def compute_snr(timesteps):
	"""
	Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
	"""
	alphas_cumprod = noise_scheduler.alphas_cumprod
	sqrt_alphas_cumprod = alphas_cumprod**0.5
	sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5

	# Expand the tensors.
	# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
	sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
	while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
	sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
	alpha = sqrt_alphas_cumprod.expand(timesteps.shape)

	sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
	while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
	sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
	sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)

	# Compute SNR.
	snr = (alpha / sigma) ** 2
	return snr

	# `accelerate` 0.16.0 will have better support for customized saving
	if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
	# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
	def save_model_hook(models, weights, output_dir):
	if args.use_ema:
	ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))

	for i, model in enumerate(models):
	model.save_pretrained(os.path.join(output_dir, "unet"))

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

	def load_model_hook(models, input_dir):
	if args.use_ema:
	load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DConditionModel)
	ema_unet.load_state_dict(load_model.state_dict())
	ema_unet.to(accelerator.device)
	del load_model

	for i in range(len(models)):
	# pop models so that they are not loaded again
	model = models.pop()

	# 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)

	if args.gradient_checkpointing:
	unet.enable_gradient_checkpointing()

	# 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
	)

	# Initialize the optimizer
	if args.use_8bit_adam:
	try:
	import bitsandbytes as bnb
	except ImportError:
	raise ImportError(
	"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
	)

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

	optimizer = optimizer_cls(
	unet.parameters(),
	lr=args.learning_rate,
	betas=(args.adam_beta1, args.adam_beta2),
	weight_decay=args.adam_weight_decay,
	eps=args.adam_epsilon,
	)

	# Get the datasets: you can either provide your own training and evaluation files (see below)
	# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).

	# In distributed training, the load_dataset function guarantees that only one local process can concurrently
	# download the dataset.
	if args.dataset_name is not None:
	# Downloading and loading a dataset from the hub.
	dataset = load_dataset(
	args.dataset_name,
	args.dataset_config_name,
	cache_dir=args.cache_dir,
	)
	else:
	data_files = {}
	if args.train_data_dir is not None:
	data_files["train"] = os.path.join(args.train_data_dir, "**")
	dataset = load_dataset(
	"imagefolder",
	data_files=data_files,
	cache_dir=args.cache_dir,
	)
	# See more about loading custom images at
	# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder

	# Preprocessing the datasets.
	# We need to tokenize inputs and targets.
	column_names = dataset["train"].column_names

	# 6. Get the column names for input/target.
	dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
	if args.image_column is None:
	image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
	else:
	image_column = args.image_column
	if image_column not in column_names:
	raise ValueError(
	f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
	)
	if args.caption_column is None:
	caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
	else:
	caption_column = args.caption_column
	if caption_column not in column_names:
	raise ValueError(
	f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
	)

	# Preprocessing the datasets.
	# We need to tokenize input captions and transform the images.
	def tokenize_captions(examples, is_train=True):
	captions = []
	for caption in examples[caption_column]:
	if isinstance(caption, str):
	captions.append(caption)
	elif isinstance(caption, (list, np.ndarray)):
	# take a random caption if there are multiple
	captions.append(random.choice(caption) if is_train else caption[0])
	else:
	raise ValueError(
	f"Caption column `{caption_column}` should contain either strings or lists of strings."
	)
	inputs = tokenizer(
	captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
	)
	return inputs.input_ids

	# Preprocessing the datasets.
	train_transforms = transforms.Compose(
	[
	transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
	transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
	transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
	transforms.ToTensor(),
	transforms.Normalize([0.5], [0.5]),
	]
	)

	def preprocess_train(examples):
	images = [image.convert("RGB") for image in examples[image_column]]
	examples["pixel_values"] = [train_transforms(image) for image in images]
	examples["input_ids"] = tokenize_captions(examples)
	return examples

	with accelerator.main_process_first():
	if args.max_train_samples is not None:
	dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
	# Set the training transforms
	train_dataset = dataset["train"].with_transform(preprocess_train)

	def collate_fn(examples):
	pixel_values = torch.stack([example["pixel_values"] for example in examples])
	pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
	input_ids = torch.stack([example["input_ids"] for example in examples])
	return {"pixel_values": pixel_values, "input_ids": input_ids}

	# DataLoaders creation:
	train_dataloader = torch.utils.data.DataLoader(
	train_dataset,
	shuffle=True,
	collate_fn=collate_fn,
	batch_size=args.train_batch_size,
	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,
	)

	# Prepare everything with our `accelerator`.
	unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
	unet, optimizer, train_dataloader, lr_scheduler
	)

	if args.use_ema:
	ema_unet.to(accelerator.device)

	# 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 text_encode and vae to gpu and cast to weight_dtype
	text_encoder.to(accelerator.device, dtype=weight_dtype)
	vae.to(accelerator.device, dtype=weight_dtype)

	# 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:
	tracker_config = dict(vars(args))
	tracker_config.pop("validation_prompts")
	accelerator.init_trackers(args.tracker_project_name, tracker_config)

	# 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 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 most 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):
	breakpoint()
	unet.train()
	train_loss = 0.0
	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(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)
	if args.noise_offset:
	# https://www.crosslabs.org//blog/diffusion-with-offset-noise
	noise += args.noise_offset * torch.randn(
	(latents.shape[0], latents.shape[1], 1, 1), device=latents.device
	)
	if args.input_pertubation:
	new_noise = noise + args.input_pertubation * torch.randn_like(noise)
	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)
	if args.input_pertubation:
	noisy_latents = noise_scheduler.add_noise(latents, new_noise, timesteps)
	else:
	noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

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

	# 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}")

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

	if args.snr_gamma is None:
	loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
	else:
	# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
	# Since we predict the noise instead of x_0, the original formulation is slightly changed.
	# This is discussed in Section 4.2 of the same paper.
	snr = compute_snr(timesteps)
	mse_loss_weights = (
	torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
	)
	# We first calculate the original loss. Then we mean over the non-batch dimensions and
	# rebalance the sample-wise losses with their respective loss weights.
	# Finally, we take the mean of the rebalanced loss.
	loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
	loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
	loss = loss.mean()

	# Gather the losses across all processes for logging (if we use distributed training).
	avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
	train_loss += avg_loss.item() / args.gradient_accumulation_steps

	# Backpropagate
	accelerator.backward(loss)
	if accelerator.sync_gradients:
	accelerator.clip_grad_norm_(unet.parameters(), 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:
	if args.use_ema:
	ema_unet.step(unet.parameters())
	progress_bar.update(1)
	global_step += 1
	accelerator.log({"train_loss": train_loss}, step=global_step)
	train_loss = 0.0

	if global_step % args.checkpointing_steps == 0:
	if accelerator.is_main_process:
	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 = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
	progress_bar.set_postfix(**logs)

	if global_step >= args.max_train_steps:
	break

	if accelerator.is_main_process:
	if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
	if args.use_ema:
	# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
	ema_unet.store(unet.parameters())
	ema_unet.copy_to(unet.parameters())
	log_validation(
	vae,
	text_encoder,
	tokenizer,
	unet,
	args,
	accelerator,
	weight_dtype,
	global_step,
	)
	if args.use_ema:
	# Switch back to the original UNet parameters.
	ema_unet.restore(unet.parameters())

	# Create the pipeline using the trained modules and save it.
	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
	unet = accelerator.unwrap_model(unet)
	if args.use_ema:
	ema_unet.copy_to(unet.parameters())

	pipeline = StableDiffusionPipeline.from_pretrained(
	args.pretrained_model_name_or_path,
	text_encoder=text_encoder,
	vae=vae,
	unet=unet,
	revision=args.revision,
	)
	pipeline.save_pretrained(args.output_dir)

	if args.push_to_hub:
	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__":
	main()