code: def main(args):
      logging_dir = Path(args.output_dir, args.logging_dir)
  
      accelerator_project_config = ProjectConfiguration(
          project_dir=args.output_dir, logging_dir=logging_dir
      )
  
      accelerator = Accelerator(
          gradient_accumulation_steps=args.gradient_accumulation_steps,
          mixed_precision=args.mixed_precision,
          log_with=args.report_to,
          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:
          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)
  
      # 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,
                  private=True,
              ).repo_id
  
      # Load the tokenizers
      tokenizer_one = AutoTokenizer.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="tokenizer",
          revision=args.revision,
          use_fast=False,
      )
      tokenizer_two = AutoTokenizer.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="tokenizer_2",
          revision=args.revision,
          use_fast=False,
      )
  
      # import correct text encoder classes
      text_encoder_cls_one = import_model_class_from_model_name_or_path(
          args.pretrained_model_name_or_path, args.revision
      )
      text_encoder_cls_two = import_model_class_from_model_name_or_path(
          args.pretrained_model_name_or_path,
          args.revision,
          subfolder="text_encoder_2",
      )
  
      # Load scheduler and models
      noise_scheduler = EulerDiscreteScheduler.from_pretrained(
          args.pretrained_model_name_or_path, subfolder="scheduler"
      )
      text_encoder_one = text_encoder_cls_one.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="text_encoder",
          revision=args.revision,
      )
      text_encoder_two = text_encoder_cls_two.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="text_encoder_2",
          revision=args.revision,
      )
      vae_path = (
          args.pretrained_model_name_or_path
          if args.pretrained_vae_model_name_or_path is None
          else args.pretrained_vae_model_name_or_path
      )
      vae = AutoencoderKL.from_pretrained(
          vae_path,
          subfolder="vae"
          if args.pretrained_vae_model_name_or_path is None
          else None,
          revision=args.revision,
      )
      unet = UNet2DConditionModel.from_pretrained(
          args.pretrained_model_name_or_path,
          subfolder="unet",
          revision=args.revision,
      )
  
      if args.adapter_model_name_or_path:
          logger.info("Loading existing adapter weights.")
          t2iadapter = T2IAdapter.from_pretrained(
              args.adapter_model_name_or_path
          )
      else:
          logger.info("Initializing t2iadapter weights.")
          t2iadapter = T2IAdapter(
              in_channels=3,
              channels=(320, 640, 1280, 1280),
              num_res_blocks=2,
              downscale_factor=16,
              adapter_type="full_adapter_xl",
          )
  
      # `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):
              i = len(weights) - 1
  
              while len(weights) > 0:
                  weights.pop()
                  model = models[i]
  
                  sub_dir = "t2iadapter"
                  model.save_pretrained(
                      os.path.join(output_dir, sub_dir)
                  )
  
                  i -= 1
  
          def load_model_hook(models, input_dir):
              while len(models) > 0:
                  # pop models so that they are not loaded again
                  model = models.pop()
  
                  # load diffusers style into model
                  load_model = T2IAdapter.from_pretrained(
                      os.path.join(input_dir, "t2iadapter")
                  )
  
                  if args.control_type != "style":
                      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)
  
      vae.requires_grad_(False)
      text_encoder_one.requires_grad_(False)
      text_encoder_two.requires_grad_(False)
      t2iadapter.train()
      unet.train()
  
      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()
  
      # Check that all trainable models are in full precision
      low_precision_error_string = (
          " Please make sure to always have all model weights in full float32 precision when starting training - even if"
          " doing mixed precision training, copy of the weights should still be float32."
      )
  
      if accelerator.unwrap_model(t2iadapter).dtype != torch.float32:
          raise ValueError(
              f"Controlnet loaded as datatype {accelerator.unwrap_model(t2iadapter).dtype}. {low_precision_error_string}"
          )
  
      # 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 = t2iadapter.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,
      )
  
      # 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, unet and text_encoder to device and cast to weight_dtype
      # The VAE is in float32 to avoid NaN losses.
      if args.pretrained_vae_model_name_or_path is not None:
          vae.to(accelerator.device, dtype=weight_dtype)
      else:
          vae.to(accelerator.device, dtype=torch.float32)
      unet.to(accelerator.device, dtype=weight_dtype)
      text_encoder_one.to(accelerator.device, dtype=weight_dtype)
      text_encoder_two.to(accelerator.device, dtype=weight_dtype)
  
      # Here, we compute not just the text embeddings but also the additional embeddings
      # needed for the SD XL UNet to operate.
      def compute_embeddings(
          batch,
          proportion_empty_prompts,
          text_encoders,
          tokenizers,
          is_train=True,
      ):
          original_size = (args.resolution, args.resolution)
          target_size = (args.resolution, args.resolution)
          crops_coords_top_left = (
              args.crops_coords_top_left_h,
              args.crops_coords_top_left_w,
          )
          prompt_batch = batch[args.caption_column]
  
          prompt_embeds, pooled_prompt_embeds = encode_prompt(
              prompt_batch,
              text_encoders,
              tokenizers,
              proportion_empty_prompts,
              is_train,
          )
          add_text_embeds = pooled_prompt_embeds
  
          # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
          add_time_ids = list(
              original_size + crops_coords_top_left + target_size
          )
          add_time_ids = torch.tensor([add_time_ids])
  
          prompt_embeds = prompt_embeds.to(accelerator.device)
          add_text_embeds = add_text_embeds.to(accelerator.device)
          add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)
          add_time_ids = add_time_ids.to(
              accelerator.device, dtype=prompt_embeds.dtype
          )
          unet_added_cond_kwargs = {
              "text_embeds": add_text_embeds,
              "time_ids": add_time_ids,
          }
  
          return {
              "prompt_embeds": prompt_embeds,
              **unet_added_cond_kwargs,
          }
  
      def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
          sigmas = noise_scheduler.sigmas.to(
              device=accelerator.device, dtype=dtype
          )
          schedule_timesteps = noise_scheduler.timesteps.to(
              accelerator.device
          )
          timesteps = timesteps.to(accelerator.device)
  
          step_indices = [
              (schedule_timesteps == t).nonzero().item()
              for t in timesteps
          ]
  
          sigma = sigmas[step_indices].flatten()
          while len(sigma.shape) < n_dim:
              sigma = sigma.unsqueeze(-1)
          return sigma
  
      # Let's first compute all the embeddings so that we can free up the text encoders
      # from memory.
      text_encoders = [text_encoder_one, text_encoder_two]
      tokenizers = [tokenizer_one, tokenizer_two]
      train_dataset = get_train_dataset(args, accelerator)
      compute_embeddings_fn = functools.partial(
          compute_embeddings,
          proportion_empty_prompts=args.proportion_empty_prompts,
          text_encoders=text_encoders,
          tokenizers=tokenizers,
      )
      with accelerator.main_process_first():
          from datasets.fingerprint import Hasher
  
          # fingerprint used by the cache for the other processes to load the result
          # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
          new_fingerprint = Hasher.hash(args)
          train_dataset = train_dataset.map(
              compute_embeddings_fn,
              batched=True,
              new_fingerprint=new_fingerprint,
          )
  
      # Then get the training dataset ready to be passed to the dataloader.
      train_dataset = prepare_train_dataset(train_dataset, accelerator)
  
      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,
          num_training_steps=args.max_train_steps,
          num_cycles=args.lr_num_cycles,
          power=args.lr_power,
      )
  
      # Prepare everything with our `accelerator`.
      (
          t2iadapter,
          optimizer,
          train_dataloader,
          lr_scheduler,
      ) = accelerator.prepare(
          t2iadapter, 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:
          tracker_config = dict(vars(args))
  
          # tensorboard cannot handle list types for config
          tracker_config.pop("validation_prompt")
          tracker_config.pop("validation_image")
  
          accelerator.init_trackers(
              args.tracker_project_name, config=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 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 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
              initial_global_step = 0
          else:
              accelerator.print(f"Resuming from checkpoint {path}")
              accelerator.load_state(
                  os.path.join(args.output_dir, path)
              )
              global_step = int(path.split("-")[1])
  
              initial_global_step = global_step
              first_epoch = global_step // num_update_steps_per_epoch
      else:
          initial_global_step = 0
  
      progress_bar = tqdm(
          range(0, args.max_train_steps),
          initial=initial_global_step,
          desc="Steps",
          # Only show the progress bar once on each machine.
          disable=not accelerator.is_local_main_process,
      )
  
      image_logs = None
      for epoch in range(first_epoch, args.num_train_epochs):
          for step, batch in enumerate(train_dataloader):
              with accelerator.accumulate(t2iadapter):
                  if args.pretrained_vae_model_name_or_path is not None:
                      pixel_values = batch["pixel_values"].to(
                          dtype=weight_dtype
                      )
                  else:
                      pixel_values = batch["pixel_values"]
  
                  # encode pixel values with batch size of at most 8 to avoid OOM
                  latents = []
                  for i in range(0, pixel_values.shape[0], 8):
                      latents.append(
                          vae.encode(
                              pixel_values[i : i + 8]
                          ).latent_dist.sample()
                      )
                  latents = torch.cat(latents, dim=0)
                  latents = latents * vae.config.scaling_factor
                  if args.pretrained_vae_model_name_or_path is None:
                      latents = latents.to(weight_dtype)
  
                  # Sample noise that we'll add to the latents
                  noise = torch.randn_like(latents)
                  bsz = latents.shape[0]
  
                  # Cubic sampling to sample a random timestep for each image.
                  # For more details about why cubic sampling is used, refer to section 3.4 of https://arxiv.org/abs/2302.08453
                  timesteps = torch.rand((bsz,), device=latents.device)
                  timesteps = (
                      1 - timesteps**3
                  ) * noise_scheduler.config.num_train_timesteps
                  timesteps = timesteps.long().to(
                      noise_scheduler.timesteps.dtype
                  )
                  timesteps = timesteps.clamp(
                      0, noise_scheduler.config.num_train_timesteps - 1
                  )
  
                  # 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
                  )
  
                  # Scale the noisy latents for the UNet
                  sigmas = get_sigmas(
                      timesteps,
                      len(noisy_latents.shape),
                      noisy_latents.dtype,
                  )
                  inp_noisy_latents = noisy_latents / (
                      (sigmas**2 + 1) ** 0.5
                  )
  
                  # Adapter conditioning.
                  t2iadapter_image = batch[
                      "conditioning_pixel_values"
                  ].to(dtype=weight_dtype)
                  down_block_additional_residuals = t2iadapter(
                      t2iadapter_image
                  )
                  down_block_additional_residuals = [
                      sample.to(dtype=weight_dtype)
                      for sample in down_block_additional_residuals
                  ]
  
                  # Predict the noise residual
                  model_pred = unet(
                      inp_noisy_latents,
                      timesteps,
                      encoder_hidden_states=batch["prompt_ids"],
                      added_cond_kwargs=batch["unet_added_conditions"],
                      down_block_additional_residuals=down_block_additional_residuals,
                  ).sample
  
                  # Denoise the latents
                  denoised_latents = (
                      model_pred * (-sigmas) + noisy_latents
                  )
                  weighing = sigmas**-2.0
  
                  # Get the target for loss depending on the prediction type
                  if (
                      noise_scheduler.config.prediction_type
                      == "epsilon"
                  ):
                      target = latents  # we are computing loss against denoise latents
                  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}"
                      )
  
                  # MSE loss
                  loss = torch.mean(
                      (
                          weighing.float()
                          * (denoised_latents.float() - target.float())
                          ** 2
                      ).reshape(target.shape[0], -1),
                      dim=1,
                  )
                  loss = loss.mean()
  
                  accelerator.backward(loss)
                  if accelerator.sync_gradients:
                      params_to_clip = t2iadapter.parameters()
                      accelerator.clip_grad_norm_(
                          params_to_clip, args.max_grad_norm
                      )
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad(
                      set_to_none=args.set_grads_to_none
                  )
  
              # Checks if the accelerator has performed an optimization step behind the scenes
              if accelerator.sync_gradients:
                  progress_bar.update(1)
                  global_step += 1
  
                  if accelerator.is_main_process:
                      if global_step % args.checkpointing_steps == 0:
                          # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
                          if args.checkpoints_total_limit is not None:
                              checkpoints = os.listdir(args.output_dir)
                              checkpoints = [
                                  d
                                  for d in checkpoints
                                  if d.startswith("checkpoint")
                              ]
                              checkpoints = sorted(
                                  checkpoints,
                                  key=lambda x: int(x.split("-")[1]),
                              )
  
                              # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
                              if (
                                  len(checkpoints)
                                  >= args.checkpoints_total_limit
                              ):
                                  num_to_remove = (
                                      len(checkpoints)
                                      - args.checkpoints_total_limit
                                      + 1
                                  )
                                  removing_checkpoints = checkpoints[
                                      0:num_to_remove
                                  ]
  
                                  logger.info(
                                      f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
                                  )
                                  logger.info(
                                      f"removing checkpoints: {', '.join(removing_checkpoints)}"
                                  )
  
                                  for (
                                      removing_checkpoint
                                  ) in removing_checkpoints:
                                      removing_checkpoint = (
                                          os.path.join(
                                              args.output_dir,
                                              removing_checkpoint,
                                          )
                                      )
                                      shutil.rmtree(removing_checkpoint)
  
                          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}")
  
                      if (
                          args.validation_prompt is not None
                          and global_step % args.validation_steps == 0
                      ):
                          image_logs = log_validation(
                              vae,
                              unet,
                              t2iadapter,
                              args,
                              accelerator,
                              weight_dtype,
                              global_step,
                          )
  
              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
  
      # Create the pipeline using using the trained modules and save it.
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
          t2iadapter = accelerator.unwrap_model(t2iadapter)
          t2iadapter.save_pretrained(args.output_dir)
  
          if args.push_to_hub:
              save_model_card(
                  repo_id,
                  image_logs=image_logs,
                  base_model=args.pretrained_model_name_or_path,
                  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()
  
imports: import argparse
  import functools
  import gc
  import logging
  import math
  import os
  import random
  import shutil
  from pathlib import Path
  import accelerate
  import numpy
  import torch
  import torch.utils.checkpoint
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  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 PIL import Image
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  import diffusers
  from diffusers import AutoencoderKL, EulerDiscreteScheduler, StableDiffusionXLAdapterPipeline, T2IAdapter, UNet2DConditionModel
  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
label_desc: Use image node_id 10 for calling a global function with example usage: main(args) and returns: dict, sigma