code: def __init__(
      self,
      plan_dir,
      checkpoint,
      sampler_type="DDIM",
      t_range=[0.02, 0.98],
      device=torch.device("cuda"),
  ):
      super().__init__()
      logging.info(f"loading stable diffusion...")
  
      self.device = device
      self.sampler_type = sampler_type
      self.model = LatentDiffusionWrapper(plan_dir, checkpoint)
  
      self.text_encoder = self.model.clip_encode
  
      self.num_train_timesteps = self.model.num_timesteps
      self.min_step = int(self.num_train_timesteps * t_range[0])
      self.max_step = int(self.num_train_timesteps * t_range[1])
      self.alphas = self.model.alphas_cumprod.to(
          self.device
      )  # for convenience
  
      logging.info(f"loaded stable diffusion!")
  
imports: import logging
  import os
  import tempfile
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from omegaconf import OmegaConf
  from polygraphy import cuda
  from transformers import CLIPTokenizer
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import LatentDiffusion
  from nemo.collections.multimodal.modules.nerf.guidance.txt2img_guidance_base import Txt2ImgGuidanceBase
  from nemo.collections.multimodal.modules.nerf.utils.trt_engine import Engine, device_view
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import extract_into_tensor, make_beta_schedule
  from nemo.collections.multimodal.parts.stable_diffusion.utils import default
  from nemo.collections.multimodal.parts.utils import randn_like
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
label_desc: Use image node_id 1 to create a new StableDiffusion object from inherited base classes: nn with example: obj = StableDiffusion(plan_dir, checkpoint, sampler_type, t_range, device)