code: def make_train_dataset(args, tokenizer, accelerator):
      # Get the datasets: you can either provide your own training and evaluation files (see below)
      # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
  
      # 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:
          if args.train_data_dir is not None:
              dataset = load_dataset(
                  args.train_data_dir,
                  cache_dir=args.cache_dir,
              )
          # See more about loading custom images at
          # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
  
      # Preprocessing the datasets.
      # We need to tokenize inputs and targets.
      column_names = dataset["train"].column_names
  
      # 6. Get the column names for input/target.
      if args.image_column is None:
          image_column = column_names[0]
          logger.info(f"image column defaulting to {image_column}")
      else:
          image_column = args.image_column
          if image_column not in column_names:
              raise ValueError(
                  f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
              )
  
      if args.caption_column is None:
          caption_column = column_names[1]
          logger.info(f"caption column defaulting to {caption_column}")
      else:
          caption_column = args.caption_column
          if caption_column not in column_names:
              raise ValueError(
                  f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
              )
  
      if args.conditioning_image_column is None:
          conditioning_image_column = column_names[2]
          logger.info(
              f"conditioning image column defaulting to {conditioning_image_column}"
          )
      else:
          conditioning_image_column = args.conditioning_image_column
          if conditioning_image_column not in column_names:
              raise ValueError(
                  f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
              )
  
      def tokenize_captions(examples, is_train=True):
          captions = []
          for caption in examples[caption_column]:
              if random.random() < args.proportion_empty_prompts:
                  captions.append("")
              elif isinstance(caption, str):
                  captions.append(caption)
              elif isinstance(caption, (list, np.ndarray)):
                  # take a random caption if there are multiple
                  captions.append(
                      random.choice(caption) if is_train else caption[0]
                  )
              else:
                  raise ValueError(
                      f"Caption column `{caption_column}` should contain either strings or lists of strings."
                  )
          inputs = tokenizer(
              captions,
              max_length=tokenizer.model_max_length,
              padding="max_length",
              truncation=True,
              return_tensors="pt",
          )
          return inputs.input_ids
  
      image_transforms = transforms.Compose(
          [
              transforms.Resize(
                  args.resolution,
                  interpolation=transforms.InterpolationMode.BILINEAR,
              ),
              transforms.CenterCrop(args.resolution),
              transforms.ToTensor(),
              transforms.Normalize([0.5], [0.5]),
          ]
      )
  
      conditioning_image_transforms = transforms.Compose(
          [
              transforms.Resize(
                  args.resolution,
                  interpolation=transforms.InterpolationMode.BILINEAR,
              ),
              transforms.CenterCrop(args.resolution),
              transforms.ToTensor(),
          ]
      )
  
      def preprocess_train(examples):
          images = [
              image.convert("RGB") for image in examples[image_column]
          ]
          images = [image_transforms(image) for image in images]
  
          conditioning_images = [
              image.convert("RGB")
              for image in examples[conditioning_image_column]
          ]
          conditioning_images = [
              conditioning_image_transforms(image)
              for image in conditioning_images
          ]
  
          examples["pixel_values"] = images
          examples["conditioning_pixel_values"] = conditioning_images
          examples["input_ids"] = tokenize_captions(examples)
  
          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
          )
  
      return train_dataset
  
imports: import argparse
  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 PIL import Image
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  import diffusers
  from diffusers import AutoencoderKL, ControlNetModel, DDPMScheduler, StableDiffusionControlNetPipeline, UNet2DConditionModel, UniPCMultistepScheduler
  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 6 for calling a global function with example usage: make_train_dataset(args, tokenizer, accelerator) and returns: train_dataset, inputs, examples