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 SD-XL 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 SD-XL 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 SD-XL 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,
      )
  
      # 5. Load teacher U-Net from SD-XL checkpoint
      teacher_unet = UNet2DConditionModel.from_pretrained(
          args.pretrained_teacher_model,
          subfolder="unet",
          revision=args.teacher_revision,
      )
  
      # 6. Freeze teacher vae, text_encoders, and teacher_unet
      vae.requires_grad_(False)
      text_encoder_one.requires_grad_(False)
      text_encoder_two.requires_grad_(False)
      teacher_unet.requires_grad_(False)
  
      # 7. Create online student U-Net. This will be updated by the optimizer (e.g. via backpropagation.)
      # Add `time_cond_proj_dim` to the student U-Net if `teacher_unet.config.time_cond_proj_dim` is None
      time_cond_proj_dim = (
          teacher_unet.config.time_cond_proj_dim
          if teacher_unet.config.time_cond_proj_dim is not None
          else args.unet_time_cond_proj_dim
      )
      unet = UNet2DConditionModel.from_config(
          teacher_unet.config, time_cond_proj_dim=time_cond_proj_dim
      )
      # load teacher_unet weights into unet
      unet.load_state_dict(teacher_unet.state_dict(), strict=False)
      unet.train()
  
      # 8. Create target student U-Net. This will be updated via EMA updates (polyak averaging).
      # Initialize from (online) unet
      target_unet = UNet2DConditionModel(**teacher_unet.config)
      target_unet.load_state_dict(unet.state_dict())
      target_unet.train()
      target_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}"
          )
  
      # 9. 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.
      vae.to(accelerator.device)
      if args.pretrained_vae_model_name_or_path is not None:
          vae.to(dtype=weight_dtype)
      text_encoder_one.to(accelerator.device, dtype=weight_dtype)
      text_encoder_two.to(accelerator.device, dtype=weight_dtype)
      target_unet.to(accelerator.device)
      # Move teacher_unet to device, optionally cast to weight_dtype
      teacher_unet.to(accelerator.device)
      if args.cast_teacher_unet:
          teacher_unet.to(dtype=weight_dtype)
  
      # 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)
      # Move the ODE solver 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:
                  target_unet.save_pretrained(
                      os.path.join(output_dir, "unet_target")
                  )
  
                  for i, model in enumerate(models):
                      model.save_pretrained(
                          os.path.join(output_dir, "unet")
                      )
  
                      # make sure to pop weight so that corresponding model is not saved again
                      weights.pop()
  
          def load_model_hook(models, input_dir):
              load_model = UNet2DConditionModel.from_pretrained(
                  os.path.join(input_dir, "unet_target")
              )
              target_unet.load_state_dict(load_model.state_dict())
              target_unet.to(accelerator.device)
              del load_model
  
              for i in range(len(models)):
                  # pop models so that they are not loaded again
                  model = models.pop()
  
                  # load diffusers style into model
                  load_model = UNet2DConditionModel.from_pretrained(
                      input_dir, subfolder="unet"
                  )
                  model.register_to_config(**load_model.config)
  
                  model.load_state_dict(load_model.state_dict())
                  del load_model
  
          accelerator.register_save_state_pre_hook(save_model_hook)
          accelerator.register_load_state_pre_hook(load_model_hook)
  
      # 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()
              teacher_unet.enable_xformers_memory_efficient_attention()
              target_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
      optimizer = optimizer_class(
          unet.parameters(),
          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
      # Here, we compute not just the text embeddings but also the additional embeddings
      # needed for the SD XL UNet to operate.
      def compute_embeddings(
          prompt_batch,
          original_sizes,
          crop_coords,
          proportion_empty_prompts,
          text_encoders,
          tokenizers,
          is_train=True,
      ):
          target_size = (args.resolution, args.resolution)
          original_sizes = list(map(list, zip(*original_sizes)))
          crops_coords_top_left = list(map(list, zip(*crop_coords)))
  
          original_sizes = torch.tensor(
              original_sizes, dtype=torch.long
          )
          crops_coords_top_left = torch.tensor(
              crops_coords_top_left, dtype=torch.long
          )
  
          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(target_size)
          add_time_ids = torch.tensor([add_time_ids])
          add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)
          add_time_ids = torch.cat(
              [original_sizes, crops_coords_top_left, add_time_ids],
              dim=-1,
          )
          add_time_ids = add_time_ids.to(
              accelerator.device, dtype=prompt_embeds.dtype
          )
  
          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,
          }
  
      dataset = SDXLText2ImageDataset(
          train_shards_path_or_url=args.train_shards_path_or_url,
          num_train_examples=args.max_train_samples,
          per_gpu_batch_size=args.train_batch_size,
          global_batch_size=args.train_batch_size
          * accelerator.num_processes,
          num_workers=args.dataloader_num_workers,
          resolution=args.resolution,
          interpolation_type=args.interpolation_type,
          shuffle_buffer_size=1000,
          pin_memory=True,
          persistent_workers=True,
          use_fix_crop_and_size=args.use_fix_crop_and_size,
      )
      train_dataloader = dataset.train_dataloader
  
      # 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]
  
      compute_embeddings_fn = functools.partial(
          compute_embeddings,
          proportion_empty_prompts=0,
          text_encoders=text_encoders,
          tokenizers=tokenizers,
      )
  
      # 14. LR Scheduler creation
      # Scheduler and math around the number of training steps.
      overrode_max_train_steps = False
      num_update_steps_per_epoch = math.ceil(
          train_dataloader.num_batches
          / 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,
          num_training_steps=args.max_train_steps,
      )
  
      # 15. Prepare for training
      # Prepare everything with our `accelerator`.
      unet, optimizer, lr_scheduler = accelerator.prepare(
          unet, optimizer, 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(
          train_dataloader.num_batches
          / 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
          )
  
      # Create uncond embeds for classifier free guidance
      uncond_prompt_embeds = torch.zeros(
          args.train_batch_size, 77, 2048
      ).to(accelerator.device)
      uncond_pooled_prompt_embeds = torch.zeros(
          args.train_batch_size, 1280
      ).to(accelerator.device)
  
      # 16. Train!
      total_batch_size = (
          args.train_batch_size
          * accelerator.num_processes
          * args.gradient_accumulation_steps
      )
  
      logger.info("***** Running training *****")
      logger.info(
          f"  Num batches each epoch = {train_dataloader.num_batches}"
      )
      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,
      )
  
      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, text, and micro-conditioning (original image size, crop coordinates)
                  image, text, orig_size, crop_coords = batch
  
                  image = image.to(
                      accelerator.device, non_blocking=True
                  )
                  encoded_text = compute_embeddings_fn(
                      text, orig_size, crop_coords
                  )
  
                  if args.pretrained_vae_model_name_or_path is not None:
                      pixel_values = image.to(dtype=weight_dtype)
                      if vae.dtype != weight_dtype:
                          vae.to(dtype=weight_dtype)
                  else:
                      pixel_values = image
  
                  # encode pixel values with batch size of at most args.vae_encode_batch_size
                  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)
                  bsz = latents.shape[0]
  
                  # 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, ...]
                  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] and embed it
                  w = (args.w_max - args.w_min) * torch.rand(
                      (bsz,)
                  ) + args.w_min
                  w_embedding = guidance_scale_embedding(
                      w, embedding_dim=time_cond_proj_dim
                  )
                  w = w.reshape(bsz, 1, 1, 1)
                  # Move to U-Net device and dtype
                  w = w.to(device=latents.device, dtype=latents.dtype)
                  w_embedding = w_embedding.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,
                      timestep_cond=w_embedding,
                      encoder_hidden_states=prompt_embeds.float(),
                      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 torch.no_grad():
                      with torch.autocast("cuda"):
                          # 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c
                          cond_teacher_output = teacher_unet(
                              noisy_model_input.to(weight_dtype),
                              start_timesteps,
                              encoder_hidden_states=prompt_embeds.to(
                                  weight_dtype
                              ),
                              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_added_conditions = copy.deepcopy(
                              encoded_text
                          )
                          uncond_added_conditions[
                              "text_embeds"
                          ] = uncond_pooled_prompt_embeds
                          uncond_teacher_output = teacher_unet(
                              noisy_model_input.to(weight_dtype),
                              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
                          )
  
                  # 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)
                  with torch.no_grad():
                      with torch.autocast("cuda", dtype=weight_dtype):
                          target_noise_pred = target_unet(
                              x_prev.float(),
                              timesteps,
                              timestep_cond=w_embedding,
                              encoder_hidden_states=prompt_embeds.float(),
                              added_cond_kwargs=encoded_text,
                          ).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`)
                  accelerator.backward(loss)
                  if accelerator.sync_gradients:
                      accelerator.clip_grad_norm_(
                          unet.parameters(), args.max_grad_norm
                      )
                  optimizer.step()
                  lr_scheduler.step()
                  optimizer.zero_grad(set_to_none=True)
  
              # Checks if the accelerator has performed an optimization step behind the scenes
              if accelerator.sync_gradients:
                  # 12. Make EMA update to target student model parameters (`target_unet`)
                  update_ema(
                      target_unet.parameters(),
                      unet.parameters(),
                      args.ema_decay,
                  )
                  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,
                              target_unet,
                              args,
                              accelerator,
                              weight_dtype,
                              global_step,
                              "target",
                          )
                          log_validation(
                              vae,
                              unet,
                              args,
                              accelerator,
                              weight_dtype,
                              global_step,
                              "online",
                          )
  
              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.save_pretrained(os.path.join(args.output_dir, "unet"))
  
          target_unet = accelerator.unwrap_model(target_unet)
          target_unet.save_pretrained(
              os.path.join(args.output_dir, "unet_target")
          )
  
          if args.push_to_hub:
              upload_folder(
                  repo_id=repo_id,
                  folder_path=args.output_dir,
                  commit_message="End of training",
                  ignore_patterns=["step_*", "epoch_*"],
              )
  
      accelerator.end_training()
  
imports: import argparse
  import copy
  import functools
  import gc
  import itertools
  import json
  import logging
  import math
  import os
  import random
  import shutil
  from pathlib import Path
  from typing import List, Union
  import accelerate
  import numpy
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  import torchvision.transforms.functional
  import transformers
  import webdataset
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration, set_seed
  from braceexpand import braceexpand
  from huggingface_hub import create_repo, upload_folder
  from packaging import version
  from torch.utils.data import default_collate
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  from webdataset.tariterators import base_plus_ext, tar_file_expander, url_opener, valid_sample
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, LCMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import resolve_interpolation_mode
  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 15 for calling a global function with example usage: main(args) and returns: dict