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.
docstr: Return the results of the HF pipeline's StableDiffusionPipeline __call__ method. Only
  accepts keyword arguments. See the HF docs for information on all available args.
docstr_tok: ["Return","the","results","of","the","HF","pipeline","'s","StableDiffusionPipeline","__call__","method",".","Only","accepts","keyword","arguments",".","See","the","HF","docs","for","information","on","all","available","args","."]
code: def __call__(self, **kwargs) -> StableDiffusionPipelineOutput:
      """Return the results of the HF pipeline's StableDiffusionPipeline __call__ method. Only
      accepts keyword arguments. See the HF docs for information on all available args.
      """
      if isinstance(kwargs["prompt"], str):
          kwargs["prompt"] = self._replace_concepts_with_dummies(
              kwargs["prompt"]
          )
      else:
          kwargs["prompt"] = [
              self._replace_concepts_with_dummies(p)
              for p in kwargs["prompt"]
          ]
      output = self.hf_pipeline(**kwargs)
      return output
  
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 7 for calling the DetSDTextualInversionPipeline obj's underlying member method code with example usage: obj.__call__() and returns: output