code: def train(self):
      progress_bar = tqdm(
          range(self.global_step, self.config.num_steps),
          disable=not self.accelerator.is_local_main_process,
      )
      progress_bar.set_description("Steps")
  
      for epoch in range(self.first_epoch, self.config.epochs):
          self.unet.train()
  
          if self.config.train_text_encoder:
              self.text_encoder1.train()
              if self.config.xl:
                  self.text_encoder2.train()
  
          for step, batch in enumerate(self.train_dataloader):
              # Skip steps until we reach the resumed step
              if (
                  self.config.resume_from_checkpoint
                  and epoch == self.first_epoch
                  and step < self.resume_step
              ):
                  if step % self.config.gradient_accumulation == 0:
                      progress_bar.update(1)
                  continue
  
              with self.accelerator.accumulate(self.unet):
                  if self.config.xl:
                      pixel_values = batch["pixel_values"]
                  else:
                      pixel_values = batch["pixel_values"].to(
                          dtype=self.weight_dtype
                      )
  
                  if self.vae is not None:
                      # Convert images to latent space
                      model_input = self.vae.encode(
                          pixel_values
                      ).latent_dist.sample()
                      model_input = (
                          model_input * self.vae.config.scaling_factor
                      )
                      model_input = model_input.to(
                          dtype=self.weight_dtype
                      )
                  else:
                      model_input = pixel_values
  
                  # Sample noise that we'll add to the latents
                  noise = torch.randn_like(model_input)
                  bsz, channels, height, width = model_input.shape
                  # Sample a random timestep for each image
                  timesteps = torch.randint(
                      0,
                      self.noise_scheduler.config.num_train_timesteps,
                      (bsz,),
                      device=model_input.device,
                  )
                  timesteps = timesteps.long()
  
                  # Add noise to the model input according to the noise magnitude at each timestep
                  # (this is the forward diffusion process)
                  noisy_model_input = self.noise_scheduler.add_noise(
                      model_input, noise, timesteps
                  )
                  model_pred = self._get_model_pred(
                      batch, channels, noisy_model_input, timesteps, bsz
                  )
                  loss = self._calculate_loss(
                      model_pred, noise, model_input, timesteps
                  )
                  self.accelerator.backward(loss)
  
                  self._clip_gradients()
                  self.optimizer.step()
                  self.scheduler.step()
                  self.optimizer.zero_grad()
  
              if self.accelerator.sync_gradients:
                  progress_bar.update(1)
                  self.global_step += 1
                  self._save_checkpoint()
  
              logs = {
                  "loss": loss.detach().item(),
                  "lr": self.scheduler.get_last_lr()[0],
              }
              progress_bar.set_postfix(**logs)
              self.accelerator.log(logs, step=self.global_step)
  
              if self.global_step >= self.config.num_steps:
                  break
  
      self.accelerator.wait_for_everyone()
      if self.accelerator.is_main_process:
          self.unet = self.accelerator.unwrap_model(self.unet)
          self.unet = self.unet.to(torch.float32)
          unet_lora_layers = utils.unet_attn_processors_state_dict(
              self.unet
          )
          text_encoder_lora_layers_1 = None
          text_encoder_lora_layers_2 = None
  
          if (
              self.text_encoder1 is not None
              and self.config.train_text_encoder
          ):
              text_encoder1 = self.accelerator.unwrap_model(
                  self.text_encoder1
              )
              text_encoder1 = text_encoder1.to(torch.float32)
              text_encoder_lora_layers_1 = text_encoder_lora_state_dict(
                  text_encoder1
              )
  
          if (
              self.text_encoder2 is not None
              and self.config.train_text_encoder
          ):
              text_encoder2 = self.accelerator.unwrap_model(
                  self.text_encoder2
              )
              text_encoder2 = text_encoder2.to(torch.float32)
              text_encoder_lora_layers_2 = text_encoder_lora_state_dict(
                  text_encoder2
              )
  
          if self.config.xl:
              StableDiffusionXLPipeline.save_lora_weights(
                  save_directory=self.config.project_name,
                  unet_lora_layers=unet_lora_layers,
                  text_encoder_lora_layers=text_encoder_lora_layers_1,
                  text_encoder_2_lora_layers=text_encoder_lora_layers_2,
                  safe_serialization=True,
              )
          else:
              LoraLoaderMixin.save_lora_weights(
                  save_directory=self.config.project_name,
                  unet_lora_layers=unet_lora_layers,
                  text_encoder_lora_layers=text_encoder_lora_layers_1,
                  safe_serialization=True,
              )
      self.accelerator.end_training()
  
imports: import itertools
  import math
  import os
  import shutil
  import torch
  import torch.nn.functional
  from diffusers import StableDiffusionXLPipeline
  from diffusers.loaders import LoraLoaderMixin, text_encoder_lora_state_dict
  from diffusers.optimization import get_scheduler
  from huggingface_hub import create_repo, upload_folder
  from tqdm import tqdm
  from autotrain import logger
  from autotrain.trainers.dreambooth import utils
label_desc: Use image node_id 10 for calling the Trainer obj's underlying member method code with example usage: obj.train() without return types