class_docstr: Class for generating images from a Stable Diffusion checkpoint trained using Determined
  AI. Initialize with no arguments in order to run plain Stable Diffusion without any trained
  textual inversion embeddings. Can optionally be run on a Determined cluster through the
  .generate_on_cluster() method for large-scale generation. Only intended for use with a GPU.
code: def __repr__(self) -> str:
      attr_dict = {
          "scheduler_name": self.scheduler_name,
          "beta_start": self.beta_start,
          "beta_end": self.beta_end,
          "beta_schedule": self.beta_schedule,
          "other_scheduler_kwargs": self.other_scheduler_kwargs,
          "pretrained_model_name_or_path": self.pretrained_model_name_or_path,
          "device": self.device,
          "use_fp16": self.use_fp16,
          "all_added_concepts": self.all_added_concepts,
      }
      attr_dict_str = ", ".join(
          [f"{key}={value}" for key, value in attr_dict.items()]
      )
      return f"{self.__class__.__name__}({attr_dict_str})"
  
imports: import json
  import logging
  import os
  import pathlib
  from typing import Any, Dict, List, Literal, Optional, Sequence, Tuple, Union
  import torch
  from detsd import defaults, utils
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline, StableDiffusionPipelineOutput
  from torch.utils.tensorboard import SummaryWriter
  from torchvision.transforms.functional import pil_to_tensor
  import determined
  from determined.experimental import client
label_desc: Use image node_id 8 for calling the DetSDTextualInversionPipeline obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, attr_dict_str, str