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,
          split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
      )
  
      # 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
  
      # 1. Create the noise scheduler and the desired noise schedule.
      noise_scheduler = DDPMScheduler.from_pretrained(
          args.pretrained_teacher_model,
          subfolder="scheduler",
          revision=args.teacher_revision,
      )
  
      # DDPMScheduler calculates the alpha and sigma noise schedules (based on the alpha bars) for us
      alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)
      sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)
      # Initialize the DDIM ODE solver for distillation.
      solver = DDIMSolver(
          noise_scheduler.alphas_cumprod.numpy(),
          timesteps=noise_scheduler.config.num_train_timesteps,
          ddim_timesteps=args.num_ddim_timesteps,
      )
  
      # 2. Load tokenizers from SDXL checkpoint.
      tokenizer_one = AutoTokenizer.from_pretrained(
          args.pretrained_teacher_model,
          subfolder="tokenizer",
          revision=args.teacher_revision,
          use_fast=False,
      )
      tokenizer_two = AutoTokenizer.from_pretrained(
          args.pretrained_teacher_model,
          subfolder="tokenizer_2",
          revision=args.teacher_revision,
          use_fast=False,
      )
  
      # 3. Load text encoders from SDXL checkpoint.
      # import correct text encoder classes
      text_encoder_cls_one = import_model_class_from_model_name_or_path(
          args.pretrained_teacher_model, args.teacher_revision
      )
      text_encoder_cls_two = import_model_class_from_model_name_or_path(
          args.pretrained_teacher_model,
          args.teacher_revision,
          subfolder="text_encoder_2",
      )
  
      text_encoder_one = text_encoder_cls_one.from_pretrained(
          args.pretrained_teacher_model,
          subfolder="text_encoder",
          revision=args.teacher_revision,
      )
      text_encoder_two = text_encoder_cls_two.from_pretrained(
          args.pretrained_teacher_model,
          subfolder="text_encoder_2",
          revision=args.teacher_revision,
      )
  
      # 4. Load VAE from SDXL checkpoint (or more stable VAE)
      vae_path = (
          args.pretrained_teacher_model
          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.teacher_revision,
      )
  
      # 6. Freeze teacher vae, text_encoders.
      vae.requires_grad_(False)
      text_encoder_one.requires_grad_(False)
      text_encoder_two.requires_grad_(False)
  
      # 7. Create online student U-Net.
      unet = UNet2DConditionModel.from_pretrained(
          args.pretrained_teacher_model,
          subfolder="unet",
          revision=args.teacher_revision,
      )
      unet.requires_grad_(False)
  
      # 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(unet).dtype != torch.float32:
          raise ValueError(
              f"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
          )
  
      # 8. Handle mixed precision and device placement
      # For mixed precision training we cast all non-trainable weigths to half-precision
      # as these weights are only used for inference, keeping weights in full precision is not required.
      weight_dtype = torch.float32
      if accelerator.mixed_precision == "fp16":
          weight_dtype = torch.float16
      elif accelerator.mixed_precision == "bf16":
          weight_dtype = torch.bfloat16
  
      # Move unet, vae and text_encoder to device and cast to weight_dtype
      # The VAE is in float32 to avoid NaN losses.
      unet.to(accelerator.device, dtype=weight_dtype)
      if args.pretrained_vae_model_name_or_path is None:
          vae.to(accelerator.device, dtype=torch.float32)
      else:
          vae.to(accelerator.device, dtype=weight_dtype)
      text_encoder_one.to(accelerator.device, dtype=weight_dtype)
      text_encoder_two.to(accelerator.device, dtype=weight_dtype)
  
      # 9. Add LoRA to the student U-Net, only the LoRA projection matrix will be updated by the optimizer.
      if args.lora_target_modules is not None:
          lora_target_modules = [
              module_key.strip()
              for module_key in args.lora_target_modules.split(",")
          ]
      else:
          lora_target_modules = [
              "to_q",
              "to_k",
              "to_v",
              "to_out.0",
              "proj_in",
              "proj_out",
              "ff.net.0.proj",
              "ff.net.2",
              "conv1",
              "conv2",
              "conv_shortcut",
              "downsamplers.0.conv",
              "upsamplers.0.conv",
              "time_emb_proj",
          ]
      lora_config = LoraConfig(
          r=args.lora_rank,
          target_modules=lora_target_modules,
          lora_alpha=args.lora_alpha,
          lora_dropout=args.lora_dropout,
      )
      unet.add_adapter(lora_config)
  
      # Make sure the trainable params are in float32.
      if args.mixed_precision == "fp16":
          # only upcast trainable parameters (LoRA) into fp32
          cast_training_params(unet, dtype=torch.float32)
  
      # Also move the alpha and sigma noise schedules to accelerator.device.
      alpha_schedule = alpha_schedule.to(accelerator.device)
      sigma_schedule = sigma_schedule.to(accelerator.device)
      solver = solver.to(accelerator.device)
  
      # 10. Handle saving and loading of checkpoints
      # `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 accelerator.is_main_process:
                  unet_ = accelerator.unwrap_model(unet)
                  # also save the checkpoints in native `diffusers` format so that it can be easily
                  # be independently loaded via `load_lora_weights()`.
                  state_dict = convert_state_dict_to_diffusers(
                      get_peft_model_state_dict(unet_)
                  )
                  StableDiffusionXLPipeline.save_lora_weights(
                      output_dir, unet_lora_layers=state_dict
                  )
  
                  for _, model in enumerate(models):
                      # make sure to pop weight so that corresponding model is not saved again
                      weights.pop()
  
          def load_model_hook(models, input_dir):
              # load the LoRA into the model
              unet_ = accelerator.unwrap_model(unet)
              (
                  lora_state_dict,
                  network_alphas,
              ) = StableDiffusionXLPipeline.lora_state_dict(input_dir)
              StableDiffusionXLPipeline.load_lora_into_unet(
                  lora_state_dict,
                  network_alphas=network_alphas,
                  unet=unet_,
              )
  
              for _ in range(len(models)):
                  # pop models so that they are not loaded again
                  models.pop()
  
          accelerator.register_save_state_pre_hook(save_model_hook)
          accelerator.register_load_state_pre_hook(load_model_hook)
  
      # 11. Enable optimizations
      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"
              )
  
      # 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.gradient_checkpointing:
          unet.enable_gradient_checkpointing()
  
      # 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
  
      # 12. Optimizer creation
      params_to_optimize = filter(
          lambda p: p.requires_grad, 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,
      )
  
      # 13. Dataset creation and data processing
      # 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.
      column_names = dataset["train"].column_names
  
      # 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.
      interpolation_mode = resolve_interpolation_mode(
          args.interpolation_type
      )
      train_resize = transforms.Resize(
          args.resolution, interpolation=interpolation_mode
      )
      train_crop = (
          transforms.CenterCrop(args.resolution)
          if args.center_crop
          else transforms.RandomCrop(args.resolution)
      )
      train_flip = transforms.RandomHorizontalFlip(p=1.0)
      train_transforms = transforms.Compose(
          [transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
      )
  
      def preprocess_train(examples):
          images = [
              image.convert("RGB") for image in examples[image_column]
          ]
          # image aug
          original_sizes = []
          all_images = []
          crop_top_lefts = []
          for image in images:
              original_sizes.append((image.height, image.width))
              image = train_resize(image)
              if args.center_crop:
                  y1 = max(
                      0,
                      int(
                          round((image.height - args.resolution) / 2.0)
                      ),
                  )
                  x1 = max(
                      0,
                      int(round((image.width - args.resolution) / 2.0)),
                  )
                  image = train_crop(image)
              else:
                  y1, x1, h, w = train_crop.get_params(
                      image, (args.resolution, args.resolution)
                  )
                  image = crop(image, y1, x1, h, w)
              if args.random_flip and random.random() < 0.5:
                  # flip
                  x1 = image.width - x1
                  image = train_flip(image)
              crop_top_left = (y1, x1)
              crop_top_lefts.append(crop_top_left)
              image = train_transforms(image)
              all_images.append(image)
  
          examples["original_sizes"] = original_sizes
          examples["crop_top_lefts"] = crop_top_lefts
          examples["pixel_values"] = all_images
          examples["captions"] = list(examples[caption_column])
          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()
          original_sizes = [
              example["original_sizes"] for example in examples
          ]
          crop_top_lefts = [
              example["crop_top_lefts"] for example in examples
          ]
          captions = [example["captions"] for example in examples]
  
          return {
              "pixel_values": pixel_values,
              "captions": captions,
              "original_sizes": original_sizes,
              "crop_top_lefts": crop_top_lefts,
          }
  
      # 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,
      )
  
      # 14. Embeddings for the UNet.
      # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
      def compute_embeddings(
          prompt_batch,
          original_sizes,
          crop_coords,
          text_encoders,
          tokenizers,
          is_train=True,
      ):
          def compute_time_ids(original_size, crops_coords_top_left):
              target_size = (args.resolution, args.resolution)
              add_time_ids = list(
                  original_size + crops_coords_top_left + target_size
              )
              add_time_ids = torch.tensor([add_time_ids])
              add_time_ids = add_time_ids.to(
                  accelerator.device, dtype=weight_dtype
              )
              return add_time_ids
  
          prompt_embeds, pooled_prompt_embeds = encode_prompt(
              prompt_batch, text_encoders, tokenizers, is_train
          )
          add_text_embeds = pooled_prompt_embeds
  
          add_time_ids = torch.cat(
              [
                  compute_time_ids(s, c)
                  for s, c in zip(original_sizes, crop_coords)
              ]
          )
  
          prompt_embeds = prompt_embeds.to(accelerator.device)
          add_text_embeds = add_text_embeds.to(accelerator.device)
          unet_added_cond_kwargs = {
              "text_embeds": add_text_embeds,
              "time_ids": add_time_ids,
          }
  
          return {
              "prompt_embeds": prompt_embeds,
              **unet_added_cond_kwargs,
          }
  
      text_encoders = [text_encoder_one, text_encoder_two]
      tokenizers = [tokenizer_one, tokenizer_two]
  
      compute_embeddings_fn = functools.partial(
          compute_embeddings,
          text_encoders=text_encoders,
          tokenizers=tokenizers,
      )
  
      # 15. LR Scheduler creation
      # 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
  
      if args.scale_lr:
          args.learning_rate = (
              args.learning_rate
              * args.gradient_accumulation_steps
              * args.train_batch_size
              * accelerator.num_processes
          )
  
      lr_scheduler = get_scheduler(
          args.lr_scheduler,
          optimizer=optimizer,
          num_warmup_steps=args.lr_warmup_steps
          * accelerator.num_processes,
          num_training_steps=args.max_train_steps
          * accelerator.num_processes,
      )
  
      # 16. Prepare for training
      # Prepare everything with our `accelerator`.
      (
          unet,
          optimizer,
          train_dataloader,
          lr_scheduler,
      ) = accelerator.prepare(
          unet, optimizer, train_dataloader, lr_scheduler
      )
  
      # We need to recalculate our total training steps as the size of the training dataloader may have changed.
      num_update_steps_per_epoch = math.ceil(
          len(train_dataloader) / args.gradient_accumulation_steps
      )
      if overrode_max_train_steps:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
      # Afterwards we recalculate our number of training epochs
      args.num_train_epochs = math.ceil(
          args.max_train_steps / num_update_steps_per_epoch
      )
  
      # We need to initialize the trackers we use, and also store our configuration.
      # The trackers initializes automatically on the main process.
      if accelerator.is_main_process:
          tracker_config = dict(vars(args))
          accelerator.init_trackers(
              args.tracker_project_name, config=tracker_config
          )
  
      # 17. 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
              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,
      )
  
      unet.train()
      for epoch in range(first_epoch, args.num_train_epochs):
          for step, batch in enumerate(train_dataloader):
              with accelerator.accumulate(unet):
                  # 1. Load and process the image and text conditioning
                  pixel_values, text, orig_size, crop_coords = (
                      batch["pixel_values"],
                      batch["captions"],
                      batch["original_sizes"],
                      batch["crop_top_lefts"],
                  )
  
                  encoded_text = compute_embeddings_fn(
                      text, orig_size, crop_coords
                  )
  
                  # encode pixel values with batch size of at most args.vae_encode_batch_size
                  pixel_values = pixel_values.to(dtype=vae.dtype)
                  latents = []
                  for i in range(
                      0,
                      pixel_values.shape[0],
                      args.vae_encode_batch_size,
                  ):
                      latents.append(
                          vae.encode(
                              pixel_values[
                                  i : i + args.vae_encode_batch_size
                              ]
                          ).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)
  
                  # 2. Sample a random timestep for each image t_n from the ODE solver timesteps without bias.
                  # For the DDIM solver, the timestep schedule is [T - 1, T - k - 1, T - 2 * k - 1, ...]
                  bsz = latents.shape[0]
                  topk = (
                      noise_scheduler.config.num_train_timesteps
                      // args.num_ddim_timesteps
                  )
                  index = torch.randint(
                      0,
                      args.num_ddim_timesteps,
                      (bsz,),
                      device=latents.device,
                  ).long()
                  start_timesteps = solver.ddim_timesteps[index]
                  timesteps = start_timesteps - topk
                  timesteps = torch.where(
                      timesteps < 0,
                      torch.zeros_like(timesteps),
                      timesteps,
                  )
  
                  # 3. Get boundary scalings for start_timesteps and (end) timesteps.
                  (
                      c_skip_start,
                      c_out_start,
                  ) = scalings_for_boundary_conditions(
                      start_timesteps,
                      timestep_scaling=args.timestep_scaling_factor,
                  )
                  c_skip_start, c_out_start = [
                      append_dims(x, latents.ndim)
                      for x in [c_skip_start, c_out_start]
                  ]
                  c_skip, c_out = scalings_for_boundary_conditions(
                      timesteps,
                      timestep_scaling=args.timestep_scaling_factor,
                  )
                  c_skip, c_out = [
                      append_dims(x, latents.ndim)
                      for x in [c_skip, c_out]
                  ]
  
                  # 4. Sample noise from the prior and add it to the latents according to the noise magnitude at each
                  # timestep (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]
                  noise = torch.randn_like(latents)
                  noisy_model_input = noise_scheduler.add_noise(
                      latents, noise, start_timesteps
                  )
  
                  # 5. Sample a random guidance scale w from U[w_min, w_max]
                  # Note that for LCM-LoRA distillation it is not necessary to use a guidance scale embedding
                  w = (args.w_max - args.w_min) * torch.rand(
                      (bsz,)
                  ) + args.w_min
                  w = w.reshape(bsz, 1, 1, 1)
                  w = w.to(device=latents.device, dtype=latents.dtype)
  
                  # 6. Prepare prompt embeds and unet_added_conditions
                  prompt_embeds = encoded_text.pop("prompt_embeds")
  
                  # 7. Get online LCM prediction on z_{t_{n + k}} (noisy_model_input), w, c, t_{n + k} (start_timesteps)
                  noise_pred = unet(
                      noisy_model_input,
                      start_timesteps,
                      encoder_hidden_states=prompt_embeds,
                      added_cond_kwargs=encoded_text,
                  ).sample
                  pred_x_0 = get_predicted_original_sample(
                      noise_pred,
                      start_timesteps,
                      noisy_model_input,
                      noise_scheduler.config.prediction_type,
                      alpha_schedule,
                      sigma_schedule,
                  )
                  model_pred = (
                      c_skip_start * noisy_model_input
                      + c_out_start * pred_x_0
                  )
  
                  # 8. Compute the conditional and unconditional teacher model predictions to get CFG estimates of the
                  # predicted noise eps_0 and predicted original sample x_0, then run the ODE solver using these
                  # estimates to predict the data point in the augmented PF-ODE trajectory corresponding to the next ODE
                  # solver timestep.
  
                  # With the adapters disabled, the `unet` is the regular teacher model.
                  accelerator.unwrap_model(unet).disable_adapters()
                  with torch.no_grad():
                      # 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c
                      cond_teacher_output = unet(
                          noisy_model_input,
                          start_timesteps,
                          encoder_hidden_states=prompt_embeds,
                          added_cond_kwargs={
                              k: v.to(weight_dtype)
                              for k, v in encoded_text.items()
                          },
                      ).sample
                      cond_pred_x0 = get_predicted_original_sample(
                          cond_teacher_output,
                          start_timesteps,
                          noisy_model_input,
                          noise_scheduler.config.prediction_type,
                          alpha_schedule,
                          sigma_schedule,
                      )
                      cond_pred_noise = get_predicted_noise(
                          cond_teacher_output,
                          start_timesteps,
                          noisy_model_input,
                          noise_scheduler.config.prediction_type,
                          alpha_schedule,
                          sigma_schedule,
                      )
  
                      # 2. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and unconditional embedding 0
                      uncond_prompt_embeds = torch.zeros_like(
                          prompt_embeds
                      )
                      uncond_pooled_prompt_embeds = torch.zeros_like(
                          encoded_text["text_embeds"]
                      )
                      uncond_added_conditions = copy.deepcopy(
                          encoded_text
                      )
                      uncond_added_conditions[
                          "text_embeds"
                      ] = uncond_pooled_prompt_embeds
                      uncond_teacher_output = unet(
                          noisy_model_input,
                          start_timesteps,
                          encoder_hidden_states=uncond_prompt_embeds.to(
                              weight_dtype
                          ),
                          added_cond_kwargs={
                              k: v.to(weight_dtype)
                              for k, v in uncond_added_conditions.items()
                          },
                      ).sample
                      uncond_pred_x0 = get_predicted_original_sample(
                          uncond_teacher_output,
                          start_timesteps,
                          noisy_model_input,
                          noise_scheduler.config.prediction_type,
                          alpha_schedule,
                          sigma_schedule,
                      )
                      uncond_pred_noise = get_predicted_noise(
                          uncond_teacher_output,
                          start_timesteps,
                          noisy_model_input,
                          noise_scheduler.config.prediction_type,
                          alpha_schedule,
                          sigma_schedule,
                      )
  
                      # 3. Calculate the CFG estimate of x_0 (pred_x0) and eps_0 (pred_noise)
                      # Note that this uses the LCM paper's CFG formulation rather than the Imagen CFG formulation
                      pred_x0 = cond_pred_x0 + w * (
                          cond_pred_x0 - uncond_pred_x0
                      )
                      pred_noise = cond_pred_noise + w * (
                          cond_pred_noise - uncond_pred_noise
                      )
                      # 4. Run one step of the ODE solver to estimate the next point x_prev on the
                      # augmented PF-ODE trajectory (solving backward in time)
                      # Note that the DDIM step depends on both the predicted x_0 and source noise eps_0.
                      x_prev = solver.ddim_step(
                          pred_x0, pred_noise, index
                      ).to(unet.dtype)
  
                  # re-enable unet adapters to turn the `unet` into a student unet.
                  accelerator.unwrap_model(unet).enable_adapters()
  
                  # 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)
                  # Note that we do not use a separate target network for LCM-LoRA distillation.
                  with torch.no_grad():
                      target_noise_pred = unet(
                          x_prev,
                          timesteps,
                          encoder_hidden_states=prompt_embeds,
                          added_cond_kwargs={
                              k: v.to(weight_dtype)
                              for k, v in encoded_text.items()
                          },
                      ).sample
                      pred_x_0 = get_predicted_original_sample(
                          target_noise_pred,
                          timesteps,
                          x_prev,
                          noise_scheduler.config.prediction_type,
                          alpha_schedule,
                          sigma_schedule,
                      )
                      target = c_skip * x_prev + c_out * pred_x_0
  
                  # 10. Calculate loss
                  if args.loss_type == "l2":
                      loss = F.mse_loss(
                          model_pred.float(),
                          target.float(),
                          reduction="mean",
                      )
                  elif args.loss_type == "huber":
                      loss = torch.mean(
                          torch.sqrt(
                              (model_pred.float() - target.float()) ** 2
                              + args.huber_c**2
                          )
                          - args.huber_c
                      )
  
                  # 11. Backpropagate on the online student model (`unet`) (only LoRA)
                  accelerator.backward(loss)
                  if accelerator.sync_gradients:
                      accelerator.clip_grad_norm_(
                          params_to_optimize, args.max_grad_norm
                      )
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad(set_to_none=True)
  
              # 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 global_step % args.validation_steps == 0:
                          log_validation(
                              vae,
                              args,
                              accelerator,
                              weight_dtype,
                              global_step,
                              unet=unet,
                              is_final_validation=False,
                          )
  
              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:
          unet = accelerator.unwrap_model(unet)
          unet_lora_state_dict = convert_state_dict_to_diffusers(
              get_peft_model_state_dict(unet)
          )
          StableDiffusionXLPipeline.save_lora_weights(
              args.output_dir, unet_lora_layers=unet_lora_state_dict
          )
  
          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_*"],
              )
  
          del unet
          torch.cuda.empty_cache()
  
          # Final inference.
          if args.validation_steps is not None:
              log_validation(
                  vae,
                  args,
                  accelerator,
                  weight_dtype,
                  step=global_step,
                  unet=None,
                  is_final_validation=True,
              )
  
      accelerator.end_training()
  
imports: import argparse
  import copy
  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.nn.functional
  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 peft import LoraConfig, get_peft_model_state_dict
  from torchvision import transforms
  from torchvision.transforms.functional import crop
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, LCMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import cast_training_params, resolve_interpolation_mode
  from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, 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: examples, dict, dict, add_time_ids