code: def main(args):
      logging_dir = Path(args.output_dir, args.logging_dir)
  
      accelerator = Accelerator(
          gradient_accumulation_steps=args.gradient_accumulation_steps,
          mixed_precision=args.mixed_precision,
          log_with=args.report_to,
          project_dir=logging_dir,
      )
      if args.report_to == "wandb":
          import wandb
  
          wandb.login(key=args.wandb_key)
          wandb.init(project=args.wandb_project_name)
      # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
      # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
      # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
      if (
          args.train_text_encoder
          and args.gradient_accumulation_steps > 1
          and accelerator.num_processes > 1
      ):
          raise ValueError(
              "Gradient accumulation is not supported when training the text encoder in distributed training. "
              "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
          )
  
      # 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)
  
      # 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.push_to_hub:
              if args.hub_model_id is None:
                  repo_name = get_full_repo_name(
                      Path(args.output_dir).name, token=args.hub_token
                  )
              else:
                  repo_name = args.hub_model_id
              repo = Repository(
                  args.output_dir, clone_from=repo_name
              )  # noqa: F841
  
              with open(
                  os.path.join(args.output_dir, ".gitignore"), "w+"
              ) as gitignore:
                  if "step_*" not in gitignore:
                      gitignore.write("step_*\n")
                  if "epoch_*" not in gitignore:
                      gitignore.write("epoch_*\n")
          elif args.output_dir is not None:
              os.makedirs(args.output_dir, exist_ok=True)
  
      # 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(
          beta_start=0.00085,
          beta_end=0.012,
          beta_schedule="scaled_linear",
          num_train_timesteps=1000,
      )  # 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 = UNet2DConditionModel.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="unet",
          revision=args.revision,
      )
  
      if args.use_oft:
          config = OFTConfig(
              r=args.oft_r,
              alpha=args.oft_alpha,
              target_modules=UNET_TARGET_MODULES,
              module_dropout=args.oft_dropout,
              init_weights=True,
              coft=args.oft_use_coft,
              eps=args.oft_eps,
          )
          unet = get_peft_model(unet, config)
          unet.print_trainable_parameters()
          print(unet)
  
      vae.requires_grad_(False)
      if not args.train_text_encoder:
          text_encoder.requires_grad_(False)
      elif args.train_text_encoder and args.use_oft:
          config = OFTConfig(
              r=args.oft_text_encoder_r,
              alpha=args.oft_text_encoder_alpha,
              target_modules=TEXT_ENCODER_TARGET_MODULES,
              module_dropout=args.oft_text_encoder_dropout,
              init_weights=True,
              coft=args.oft_text_encoder_use_coft,
              eps=args.oft_text_encoder_eps,
          )
          text_encoder = get_peft_model(text_encoder, config)
          text_encoder.print_trainable_parameters()
          print(text_encoder)
  
      if args.enable_xformers_memory_efficient_attention:
          if is_xformers_available():
              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()
          # below fails when using oft so commenting it out
          if args.train_text_encoder and not args.use_oft:
              text_encoder.gradient_checkpointing_enable()
  
      # Enable TF32 for faster training on Ampere GPUs,
      # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
      if args.allow_tf32:
          torch.backends.cuda.matmul.allow_tf32 = True
  
      if args.scale_lr:
          args.learning_rate = (
              args.learning_rate
              * args.gradient_accumulation_steps
              * args.train_batch_size
              * accelerator.num_processes
          )
  
      # 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.parameters(), text_encoder.parameters())
          if args.train_text_encoder
          else unet.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,
      )
  
      # 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,
          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.num_dataloader_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,
              text_encoder,
              optimizer,
              train_dataloader,
              lr_scheduler,
          ) = accelerator.prepare(
              unet,
              text_encoder,
              optimizer,
              train_dataloader,
              lr_scheduler,
          )
      else:
          (
              unet,
              optimizer,
              train_dataloader,
              lr_scheduler,
          ) = accelerator.prepare(
              unet, optimizer, train_dataloader, lr_scheduler
          )
  
      # 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 vae and text_encoder to device and cast to weight_dtype
      vae.to(accelerator.device, dtype=weight_dtype)
      if not args.train_text_encoder:
          text_encoder.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:
          accelerator.init_trackers("dreambooth", 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]
          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 = resume_global_step // num_update_steps_per_epoch
          resume_step = resume_global_step % num_update_steps_per_epoch
  
      # 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()
          with TorchTracemalloc() if not args.no_tracemalloc else nullcontext() as tracemalloc:
              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)
                          if args.report_to == "wandb":
                              accelerator.print(progress_bar)
                      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 * 0.18215
  
                      # 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 = (
                              itertools.chain(
                                  unet.parameters(),
                                  text_encoder.parameters(),
                              )
                              if args.train_text_encoder
                              else unet.parameters()
                          )
                          accelerator.clip_grad_norm_(
                              params_to_clip, args.max_grad_norm
                          )
                      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)
                      if args.report_to == "wandb":
                          accelerator.print(progress_bar)
                      global_step += 1
  
                  logs = {
                      "loss": loss.detach().item(),
                      "lr": lr_scheduler.get_last_lr()[0],
                  }
                  progress_bar.set_postfix(**logs)
                  accelerator.log(logs, step=global_step)
  
                  if (
                      args.validation_prompt is not None
                      and (step + num_update_steps_per_epoch * epoch)
                      % args.validation_steps
                      == 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,
                          safety_checker=None,
                          revision=args.revision,
                      )
                      # set `keep_fp32_wrapper` to True because we do not want to remove
                      # mixed precision hooks while we are still training
                      pipeline.unet = accelerator.unwrap_model(
                          unet, keep_fp32_wrapper=True
                      )
                      pipeline.text_encoder = accelerator.unwrap_model(
                          text_encoder, keep_fp32_wrapper=True
                      )
                      pipeline.scheduler = (
                          DPMSolverMultistepScheduler.from_config(
                              pipeline.scheduler.config
                          )
                      )
                      pipeline = pipeline.to(accelerator.device)
                      pipeline.set_progress_bar_config(disable=True)
  
                      # run inference
                      if args.seed is not None:
                          generator = torch.Generator(
                              device=accelerator.device
                          ).manual_seed(args.seed)
                      else:
                          generator = None
                      images = []
                      for _ in range(args.num_validation_images):
                          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":
                              import 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()
  
                  if global_step >= args.max_train_steps:
                      break
          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  
          if not args.no_tracemalloc:
              accelerator.print(
                  "GPU Memory before entering the train : {}".format(
                      b2mb(tracemalloc.begin)
                  )
              )
              accelerator.print(
                  "GPU Memory consumed at the end of the train (end-begin): {}".format(
                      tracemalloc.used
                  )
              )
              accelerator.print(
                  "GPU Peak Memory consumed during the train (max-begin): {}".format(
                      tracemalloc.peaked
                  )
              )
              accelerator.print(
                  "GPU Total Peak Memory consumed during the train (max): {}".format(
                      tracemalloc.peaked + b2mb(tracemalloc.begin)
                  )
              )
  
              accelerator.print(
                  "CPU Memory before entering the train : {}".format(
                      b2mb(tracemalloc.cpu_begin)
                  )
              )
              accelerator.print(
                  "CPU Memory consumed at the end of the train (end-begin): {}".format(
                      tracemalloc.cpu_used
                  )
              )
              accelerator.print(
                  "CPU Peak Memory consumed during the train (max-begin): {}".format(
                      tracemalloc.cpu_peaked
                  )
              )
              accelerator.print(
                  "CPU Total Peak Memory consumed during the train (max): {}".format(
                      tracemalloc.cpu_peaked
                      + b2mb(tracemalloc.cpu_begin)
                  )
              )
  
      # Create the pipeline using using the trained modules and save it.
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
          if args.use_oft:
              unwarpped_unet = accelerator.unwrap_model(unet)
              unwarpped_unet.save_pretrained(
                  os.path.join(args.output_dir, "unet"),
                  state_dict=accelerator.get_state_dict(unet),
              )
              if args.train_text_encoder:
                  unwarpped_text_encoder = accelerator.unwrap_model(
                      text_encoder
                  )
                  unwarpped_text_encoder.save_pretrained(
                      os.path.join(args.output_dir, "text_encoder"),
                      state_dict=accelerator.get_state_dict(
                          text_encoder
                      ),
                  )
          else:
              pipeline = DiffusionPipeline.from_pretrained(
                  args.pretrained_model_name_or_path,
                  unet=accelerator.unwrap_model(unet),
                  text_encoder=accelerator.unwrap_model(text_encoder),
                  revision=args.revision,
              )
              pipeline.save_pretrained(args.output_dir)
  
          if args.push_to_hub:
              repo.push_to_hub(
                  commit_message="End of training",
                  blocking=False,
                  auto_lfs_prune=True,
              )
  
      accelerator.end_training()
  
imports: import argparse
  import gc
  import hashlib
  import itertools
  import logging
  import math
  import os
  import threading
  import warnings
  from contextlib import nullcontext
  from pathlib import Path
  from typing import Optional
  import datasets
  import diffusers
  import numpy
  import psutil
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version
  from diffusers.utils.import_utils import is_xformers_available
  from huggingface_hub import HfFolder, Repository, whoami
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  from peft import get_peft_model
  from peft.tuners.oft.config import OFTConfig
label_desc: Use image node_id 6 for calling a global function with example usage: main(args) without return types