code: def main():
      args = parse_args()
  
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      # Setup logging, we only want one process per machine to log things on the screen.
      logger.setLevel(
          logging.INFO if jax.process_index() == 0 else logging.ERROR
      )
      if jax.process_index() == 0:
          transformers.utils.logging.set_verbosity_info()
      else:
          transformers.utils.logging.set_verbosity_error()
  
      if args.seed is not None:
          set_seed(args.seed)
  
      # Handle the repository creation
      if jax.process_index() == 0:
          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,
              ).repo_id
  
      # 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,
              data_dir=args.train_data_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.
      # We need to tokenize inputs and targets.
      column_names = dataset["train"].column_names
  
      # 6. 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.
      # We need to tokenize input captions and transform the images.
      def tokenize_captions(examples, is_train=True):
          captions = []
          for caption in examples[caption_column]:
              if 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="do_not_pad",
              truncation=True,
          )
          input_ids = inputs.input_ids
          return input_ids
  
      train_transforms = transforms.Compose(
          [
              transforms.Resize(
                  args.resolution,
                  interpolation=transforms.InterpolationMode.BILINEAR,
              ),
              transforms.CenterCrop(args.resolution)
              if args.center_crop
              else transforms.RandomCrop(args.resolution),
              transforms.RandomHorizontalFlip()
              if args.random_flip
              else transforms.Lambda(lambda x: x),
              transforms.ToTensor(),
              transforms.Normalize([0.5], [0.5]),
          ]
      )
  
      def preprocess_train(examples):
          images = [
              image.convert("RGB") for image in examples[image_column]
          ]
          examples["pixel_values"] = [
              train_transforms(image) for image in images
          ]
          examples["input_ids"] = tokenize_captions(examples)
  
          return examples
  
      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()
          input_ids = [example["input_ids"] for example in examples]
  
          padded_tokens = tokenizer.pad(
              {"input_ids": input_ids},
              padding="max_length",
              max_length=tokenizer.model_max_length,
              return_tensors="pt",
          )
          batch = {
              "pixel_values": pixel_values,
              "input_ids": padded_tokens.input_ids,
          }
          batch = {k: v.numpy() for k, v in batch.items()}
  
          return batch
  
      total_train_batch_size = (
          args.train_batch_size * jax.local_device_count()
      )
      train_dataloader = torch.utils.data.DataLoader(
          train_dataset,
          shuffle=True,
          collate_fn=collate_fn,
          batch_size=total_train_batch_size,
          drop_last=True,
      )
  
      weight_dtype = jnp.float32
      if args.mixed_precision == "fp16":
          weight_dtype = jnp.float16
      elif args.mixed_precision == "bf16":
          weight_dtype = jnp.bfloat16
  
      # Load models and create wrapper for stable diffusion
      tokenizer = CLIPTokenizer.from_pretrained(
          args.pretrained_model_name_or_path,
          from_pt=args.from_pt,
          revision=args.revision,
          subfolder="tokenizer",
      )
      text_encoder = FlaxCLIPTextModel.from_pretrained(
          args.pretrained_model_name_or_path,
          from_pt=args.from_pt,
          revision=args.revision,
          subfolder="text_encoder",
          dtype=weight_dtype,
      )
      vae, vae_params = FlaxAutoencoderKL.from_pretrained(
          args.pretrained_model_name_or_path,
          from_pt=args.from_pt,
          revision=args.revision,
          subfolder="vae",
          dtype=weight_dtype,
      )
      unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
          args.pretrained_model_name_or_path,
          from_pt=args.from_pt,
          revision=args.revision,
          subfolder="unet",
          dtype=weight_dtype,
      )
  
      # Optimization
      if args.scale_lr:
          args.learning_rate = (
              args.learning_rate * total_train_batch_size
          )
  
      constant_scheduler = optax.constant_schedule(args.learning_rate)
  
      adamw = optax.adamw(
          learning_rate=constant_scheduler,
          b1=args.adam_beta1,
          b2=args.adam_beta2,
          eps=args.adam_epsilon,
          weight_decay=args.adam_weight_decay,
      )
  
      optimizer = optax.chain(
          optax.clip_by_global_norm(args.max_grad_norm),
          adamw,
      )
  
      state = train_state.TrainState.create(
          apply_fn=unet.__call__, params=unet_params, tx=optimizer
      )
  
      noise_scheduler = FlaxDDPMScheduler(
          beta_start=0.00085,
          beta_end=0.012,
          beta_schedule="scaled_linear",
          num_train_timesteps=1000,
      )
      noise_scheduler_state = noise_scheduler.create_state()
  
      # Initialize our training
      rng = jax.random.PRNGKey(args.seed)
      train_rngs = jax.random.split(rng, jax.local_device_count())
  
      def train_step(
          state, text_encoder_params, vae_params, batch, train_rng
      ):
          dropout_rng, sample_rng, new_train_rng = jax.random.split(
              train_rng, 3
          )
  
          def compute_loss(params):
              # Convert images to latent space
              vae_outputs = vae.apply(
                  {"params": vae_params},
                  batch["pixel_values"],
                  deterministic=True,
                  method=vae.encode,
              )
              latents = vae_outputs.latent_dist.sample(sample_rng)
              # (NHWC) -> (NCHW)
              latents = jnp.transpose(latents, (0, 3, 1, 2))
              latents = latents * vae.config.scaling_factor
  
              # Sample noise that we'll add to the latents
              noise_rng, timestep_rng = jax.random.split(sample_rng)
              noise = jax.random.normal(noise_rng, latents.shape)
              # Sample a random timestep for each image
              bsz = latents.shape[0]
              timesteps = jax.random.randint(
                  timestep_rng,
                  (bsz,),
                  0,
                  noise_scheduler.config.num_train_timesteps,
              )
  
              # 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(
                  noise_scheduler_state, latents, noise, timesteps
              )
  
              # Get the text embedding for conditioning
              encoder_hidden_states = text_encoder(
                  batch["input_ids"],
                  params=text_encoder_params,
                  train=False,
              )[0]
  
              # Predict the noise residual and compute loss
              model_pred = unet.apply(
                  {"params": params},
                  noisy_latents,
                  timesteps,
                  encoder_hidden_states,
                  train=True,
              ).sample
  
              # Get the target for loss depending on the prediction type
              if noise_scheduler.config.prediction_type == "epsilon":
                  target = noise
              elif (
                  noise_scheduler.config.prediction_type
                  == "v_prediction"
              ):
                  target = noise_scheduler.get_velocity(
                      noise_scheduler_state, latents, noise, timesteps
                  )
              else:
                  raise ValueError(
                      f"Unknown prediction type {noise_scheduler.config.prediction_type}"
                  )
  
              loss = (target - model_pred) ** 2
              loss = loss.mean()
  
              return loss
  
          grad_fn = jax.value_and_grad(compute_loss)
          loss, grad = grad_fn(state.params)
          grad = jax.lax.pmean(grad, "batch")
  
          new_state = state.apply_gradients(grads=grad)
  
          metrics = {"loss": loss}
          metrics = jax.lax.pmean(metrics, axis_name="batch")
  
          return new_state, metrics, new_train_rng
  
      # Create parallel version of the train step
      p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
  
      # Replicate the train state on each device
      state = jax_utils.replicate(state)
      text_encoder_params = jax_utils.replicate(text_encoder.params)
      vae_params = jax_utils.replicate(vae_params)
  
      # Train!
      num_update_steps_per_epoch = math.ceil(len(train_dataloader))
  
      # Scheduler and math around the number of training steps.
      if args.max_train_steps is None:
          args.max_train_steps = (
              args.num_train_epochs * num_update_steps_per_epoch
          )
  
      args.num_train_epochs = math.ceil(
          args.max_train_steps / num_update_steps_per_epoch
      )
  
      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) = {total_train_batch_size}"
      )
      logger.info(
          f"  Total optimization steps = {args.max_train_steps}"
      )
  
      global_step = 0
  
      epochs = tqdm(
          range(args.num_train_epochs), desc="Epoch ... ", position=0
      )
      for epoch in epochs:
          # ======================== Training ================================
  
          train_metrics = []
  
          steps_per_epoch = len(train_dataset) // total_train_batch_size
          train_step_progress_bar = tqdm(
              total=steps_per_epoch,
              desc="Training...",
              position=1,
              leave=False,
          )
          # train
          for batch in train_dataloader:
              batch = shard(batch)
              state, train_metric, train_rngs = p_train_step(
                  state,
                  text_encoder_params,
                  vae_params,
                  batch,
                  train_rngs,
              )
              train_metrics.append(train_metric)
  
              train_step_progress_bar.update(1)
  
              global_step += 1
              if global_step >= args.max_train_steps:
                  break
  
          train_metric = jax_utils.unreplicate(train_metric)
  
          train_step_progress_bar.close()
          epochs.write(
              f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})"
          )
  
      # Create the pipeline using using the trained modules and save it.
      if jax.process_index() == 0:
          scheduler = FlaxPNDMScheduler(
              beta_start=0.00085,
              beta_end=0.012,
              beta_schedule="scaled_linear",
              skip_prk_steps=True,
          )
          safety_checker = (
              FlaxStableDiffusionSafetyChecker.from_pretrained(
                  "CompVis/stable-diffusion-safety-checker",
                  from_pt=True,
              )
          )
          pipeline = FlaxStableDiffusionPipeline(
              text_encoder=text_encoder,
              vae=vae,
              unet=unet,
              tokenizer=tokenizer,
              scheduler=scheduler,
              safety_checker=safety_checker,
              feature_extractor=CLIPImageProcessor.from_pretrained(
                  "openai/clip-vit-base-patch32"
              ),
          )
  
          pipeline.save_pretrained(
              args.output_dir,
              params={
                  "text_encoder": get_params_to_save(
                      text_encoder_params
                  ),
                  "vae": get_params_to_save(vae_params),
                  "unet": get_params_to_save(state.params),
                  "safety_checker": safety_checker.params,
              },
          )
  
          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_*"],
              )
  
imports: import argparse
  import logging
  import math
  import os
  import random
  from pathlib import Path
  import jax
  import jax.numpy
  import numpy
  import optax
  import torch
  import torch.utils.checkpoint
  import transformers
  from datasets import load_dataset
  from flax import jax_utils
  from flax.training import train_state
  from flax.training.common_utils import shard
  from huggingface_hub import create_repo, upload_folder
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
  from diffusers import FlaxAutoencoderKL, FlaxDDPMScheduler, FlaxPNDMScheduler, FlaxStableDiffusionPipeline, FlaxUNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
  from diffusers.utils import check_min_version
label_desc: Use image node_id 3 for calling a global function with example usage: main() and returns: input_ids, examples, batch, new_state,  metrics,  new_train_rng, loss