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: Load concepts from one or more Determined checkpoint uuids and returns a list of all
  downloaded checkpoint paths. Must be logged into the Determined cluster to use this method.
  If not logged-in, call determined.experimental.client.login first.
docstr_tok: ["Load","concepts","from","one","or","more","Determined","checkpoint","uuids","and","returns","a","list","of","all","downloaded","checkpoint","paths",".","Must","be","logged","into","the","Determined","cluster","to","use","this","method",".","If","not","logged-in",",","call","determined.experimental.client.login","first","."]
code: def load_from_uuids(
      self,
      uuids: Union[str, Sequence[str]],
  ) -> List[pathlib.Path]:
      """Load concepts from one or more Determined checkpoint uuids and returns a list of all
      downloaded checkpoint paths. Must be logged into the Determined cluster to use this method.
      If not logged-in, call determined.experimental.client.login first.
      """
      if isinstance(uuids, str):
          uuids = [uuids]
      checkpoint_paths = []
      for u in uuids:
          checkpoint = client.get_checkpoint(u)
          checkpoint_paths.append(pathlib.Path(checkpoint.download()))
      for path in checkpoint_paths:
          self.load_from_checkpoint_dir(path)
      return checkpoint_paths
  
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 4 for calling the DetSDTextualInversionPipeline obj's underlying member method code with example usage: obj.load_from_uuids(uuids) and returns: checkpoint_paths