code: def __init__(self, args):
      pipe = self.pipeline_class.from_pretrained(
          args.ckpt, torch_dtype=torch.float16
      )
      pipe = pipe.to("cuda")
  
      if args.run_compile:
          if not isinstance(pipe, WuerstchenCombinedPipeline):
              pipe.unet.to(memory_format=torch.channels_last)
              print("Run torch compile")
              pipe.unet = torch.compile(
                  pipe.unet, mode="reduce-overhead", fullgraph=True
              )
  
              if (
                  hasattr(pipe, "movq")
                  and getattr(pipe, "movq", None) is not None
              ):
                  pipe.movq.to(memory_format=torch.channels_last)
                  pipe.movq = torch.compile(
                      pipe.movq, mode="reduce-overhead", fullgraph=True
                  )
          else:
              print("Run torch compile")
              pipe.decoder = torch.compile(
                  pipe.decoder, mode="reduce-overhead", fullgraph=True
              )
              pipe.vqgan = torch.compile(
                  pipe.vqgan, mode="reduce-overhead", fullgraph=True
              )
  
      pipe.set_progress_bar_config(disable=True)
      self.pipe = pipe
  
imports: import os
  import sys
  import torch
  from diffusers import AutoPipelineForImage2Image, AutoPipelineForInpainting, AutoPipelineForText2Image, ControlNetModel, LCMScheduler, StableDiffusionAdapterPipeline, StableDiffusionControlNetPipeline, StableDiffusionXLAdapterPipeline, StableDiffusionXLControlNetPipeline, T2IAdapter, WuerstchenCombinedPipeline
  from diffusers.utils import load_image
  from utils import BASE_PATH, PROMPT, BenchmarkInfo, benchmark_fn, bytes_to_giga_bytes, flush, generate_csv_dict, write_to_csv
label_desc: Use image node_id 1 to create a new TextToImageBenchmark object from inherited base classes: BaseBenchmak with example: obj = TextToImageBenchmark(args)