code: def _test_stable_diffusion_compile(in_queue, out_queue, timeout):
      error = None
      try:
          _ = in_queue.get(timeout=timeout)
  
          controlnet = ControlNetModel.from_pretrained(
              "lllyasviel/sd-controlnet-canny"
          )
  
          pipe = StableDiffusionControlNetPipeline.from_pretrained(
              "runwayml/stable-diffusion-v1-5",
              safety_checker=None,
              controlnet=controlnet,
          )
          pipe.to("cuda")
          pipe.set_progress_bar_config(disable=None)
  
          pipe.unet.to(memory_format=torch.channels_last)
          pipe.unet = torch.compile(
              pipe.unet, mode="reduce-overhead", fullgraph=True
          )
  
          pipe.controlnet.to(memory_format=torch.channels_last)
          pipe.controlnet = torch.compile(
              pipe.controlnet, mode="reduce-overhead", fullgraph=True
          )
  
          generator = torch.Generator(device="cpu").manual_seed(0)
          prompt = "bird"
          image = load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
          ).resize((512, 512))
  
          output = pipe(
              prompt,
              image,
              num_inference_steps=10,
              generator=generator,
              output_type="np",
          )
          image = output.images[0]
  
          assert image.shape == (512, 512, 3)
  
          expected_image = load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out_full.npy"
          )
          expected_image = np.resize(expected_image, (512, 512, 3))
  
          assert np.abs(expected_image - image).max() < 1.0
  
      except Exception:
          error = f"{traceback.format_exc()}"
  
      results = {"error": error}
      out_queue.put(results, timeout=timeout)
      out_queue.join()
  
imports: import gc
  import tempfile
  import traceback
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, ControlNetModel, DDIMScheduler, EulerDiscreteScheduler, StableDiffusionControlNetPipeline, UNet2DConditionModel
  from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, load_image, load_numpy, require_python39_or_higher, require_torch_2, require_torch_gpu, run_test_in_subprocess, slow, torch_device
  from diffusers.utils.torch_utils import randn_tensor
  from ..pipeline_params import IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 1 for calling a global function with example usage: _test_stable_diffusion_compile(in_queue, out_queue, timeout) without return types