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="tensorboard",
          logging_dir=logging_dir,
      )
  
      # 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."
          )
  
      if args.seed is not None:
          set_seed(args.seed)
  
      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
              )
              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)
  
              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
      )
  
      # Load models and create wrapper for stable diffusion
      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,
      )
  
      vae.requires_grad_(False)
      if not args.train_text_encoder:
          text_encoder.requires_grad_(False)
  
      if args.gradient_checkpointing:
          unet.enable_gradient_checkpointing()
          if args.train_text_encoder:
              text_encoder.gradient_checkpointing_enable()
  
      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
  
      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,
      )
  
      noise_scheduler = DDPMScheduler.from_config(
          args.pretrained_model_name_or_path, subfolder="scheduler"
      )
  
      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,
      )
  
      def collate_fn(examples):
          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 args.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 = tokenizer.pad(
              {"input_ids": input_ids},
              padding="max_length",
              max_length=tokenizer.model_max_length,
              return_tensors="pt",
          ).input_ids
  
          batch = {
              "input_ids": input_ids,
              "pixel_values": pixel_values,
          }
          return batch
  
      train_dataloader = torch.utils.data.DataLoader(
          train_dataset,
          batch_size=args.train_batch_size,
          shuffle=True,
          collate_fn=collate_fn,
          num_workers=1,
      )
  
      # 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,
      )
  
      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
          )
  
      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.
      # 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.
      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}"
      )
      # Only show the progress bar once on each machine.
      progress_bar = tqdm(
          range(args.max_train_steps),
          disable=not accelerator.is_local_main_process,
      )
      progress_bar.set_description("Steps")
      global_step = 0
  
      for epoch in range(args.num_train_epochs):
          unet.train()
          if args.train_text_encoder:
              text_encoder.train()
          for step, batch in enumerate(train_dataloader):
              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="none",
                          )
                          .mean([1, 2, 3])
                          .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)
                  global_step += 1
  
                  if global_step % args.save_steps == 0:
                      if accelerator.is_main_process:
                          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,
                          )
                          save_path = os.path.join(
                              args.output_dir,
                              f"checkpoint-{global_step}",
                          )
                          pipeline.save_pretrained(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
  
          accelerator.wait_for_everyone()
  
      # Create the pipeline using using the trained modules and save it.
      if accelerator.is_main_process:
          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 hashlib
  import itertools
  import math
  import os
  from pathlib import Path
  from typing import Optional
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  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
label_desc: Use image node_id 4 for calling a global function with example usage: main(args) and returns: batch