docstr: Function invoked when calling the pipeline for generation.
  
  Args:
      prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
          instead.
      image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
              `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
          The initial image will be used as the starting point for the image generation process. Can also accept
          image latents as `image`, if passing latents directly, it will not be encoded again.
      control_image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
              `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
          The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
          the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
          also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
          height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
          specified in init, images must be passed as a list such that each element of the list can be correctly
          batched for input to a single controlnet.
      height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
          The height in pixels of the generated image.
      width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
          The width in pixels of the generated image.
      num_inference_steps (`int`, *optional*, defaults to 50):
          The number of denoising steps. More denoising steps usually lead to a higher quality image at the
          expense of slower inference.
      guidance_scale (`float`, *optional*, defaults to 7.5):
          Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
          `guidance_scale` is defined as `w` of equation 2. of [Imagen
          Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
          1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
          usually at the expense of lower image quality.
      negative_prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts not to guide the image generation. If not defined, one has to pass
          `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
          less than `1`).
      num_images_per_prompt (`int`, *optional*, defaults to 1):
          The number of images to generate per prompt.
      eta (`float`, *optional*, defaults to 0.0):
          Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
          [`schedulers.DDIMScheduler`], will be ignored for others.
      generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
          One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
          to make generation deterministic.
      latents (`torch.FloatTensor`, *optional*):
          Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
          generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
          tensor will ge generated by sampling using the supplied random `generator`.
      prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
          provided, text embeddings will be generated from `prompt` input argument.
      negative_prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
          weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
          argument.
      output_type (`str`, *optional*, defaults to `"pil"`):
          The output format of the generate image. Choose between
          [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
          plain tuple.
      callback (`Callable`, *optional*):
          A function that will be called every `callback_steps` steps during inference. The function will be
          called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
      callback_steps (`int`, *optional*, defaults to 1):
          The frequency at which the `callback` function will be called. If not specified, the callback will be
          called at every step.
      cross_attention_kwargs (`dict`, *optional*):
          A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
          `self.processor` in
          [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
      controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
          The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
          to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
          corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
          than for [`~StableDiffusionControlNetPipeline.__call__`].
      guess_mode (`bool`, *optional*, defaults to `False`):
          In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
          you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
      control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
          The percentage of total steps at which the controlnet starts applying.
      control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
          The percentage of total steps at which the controlnet stops applying.
  
  Examples:
  
  Returns:
      [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
      [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
      When returning a tuple, the first element is a list with the generated images, and the second element is a
      list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
      (nsfw) content, according to the `safety_checker`.
code: def __call__(
      self,
      num_controlnet: int,
      fp16: bool = True,
      prompt: Union[str, List[str]] = None,
      image: Union[
          torch.FloatTensor,
          PIL.Image.Image,
          np.ndarray,
          List[torch.FloatTensor],
          List[PIL.Image.Image],
          List[np.ndarray],
      ] = None,
      control_image: Union[
          torch.FloatTensor,
          PIL.Image.Image,
          np.ndarray,
          List[torch.FloatTensor],
          List[PIL.Image.Image],
          List[np.ndarray],
      ] = None,
      height: Optional[int] = None,
      width: Optional[int] = None,
      strength: float = 0.8,
      num_inference_steps: int = 50,
      guidance_scale: float = 7.5,
      negative_prompt: Optional[Union[str, List[str]]] = None,
      num_images_per_prompt: Optional[int] = 1,
      eta: float = 0.0,
      generator: Optional[
          Union[torch.Generator, List[torch.Generator]]
      ] = None,
      latents: Optional[torch.FloatTensor] = None,
      prompt_embeds: Optional[torch.FloatTensor] = None,
      negative_prompt_embeds: Optional[torch.FloatTensor] = None,
      output_type: Optional[str] = "pil",
      return_dict: bool = True,
      callback: Optional[
          Callable[[int, int, torch.FloatTensor], None]
      ] = None,
      callback_steps: int = 1,
      cross_attention_kwargs: Optional[Dict[str, Any]] = None,
      controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
      guess_mode: bool = False,
      control_guidance_start: Union[float, List[float]] = 0.0,
      control_guidance_end: Union[float, List[float]] = 1.0,
  ):
      r"""
      Function invoked when calling the pipeline for generation.
  
      Args:
          prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
              instead.
          image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
                  `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
              The initial image will be used as the starting point for the image generation process. Can also accept
              image latents as `image`, if passing latents directly, it will not be encoded again.
          control_image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
                  `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
              The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
              the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
              also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
              height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
              specified in init, images must be passed as a list such that each element of the list can be correctly
              batched for input to a single controlnet.
          height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
              The height in pixels of the generated image.
          width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
              The width in pixels of the generated image.
          num_inference_steps (`int`, *optional*, defaults to 50):
              The number of denoising steps. More denoising steps usually lead to a higher quality image at the
              expense of slower inference.
          guidance_scale (`float`, *optional*, defaults to 7.5):
              Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
              `guidance_scale` is defined as `w` of equation 2. of [Imagen
              Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
              1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
              usually at the expense of lower image quality.
          negative_prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts not to guide the image generation. If not defined, one has to pass
              `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
              less than `1`).
          num_images_per_prompt (`int`, *optional*, defaults to 1):
              The number of images to generate per prompt.
          eta (`float`, *optional*, defaults to 0.0):
              Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
              [`schedulers.DDIMScheduler`], will be ignored for others.
          generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
              One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
              to make generation deterministic.
          latents (`torch.FloatTensor`, *optional*):
              Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
              generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
              tensor will ge generated by sampling using the supplied random `generator`.
          prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
              provided, text embeddings will be generated from `prompt` input argument.
          negative_prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
              weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
              argument.
          output_type (`str`, *optional*, defaults to `"pil"`):
              The output format of the generate image. Choose between
              [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
          return_dict (`bool`, *optional*, defaults to `True`):
              Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
              plain tuple.
          callback (`Callable`, *optional*):
              A function that will be called every `callback_steps` steps during inference. The function will be
              called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
          callback_steps (`int`, *optional*, defaults to 1):
              The frequency at which the `callback` function will be called. If not specified, the callback will be
              called at every step.
          cross_attention_kwargs (`dict`, *optional*):
              A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
              `self.processor` in
              [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
          controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
              The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
              to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
              corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
              than for [`~StableDiffusionControlNetPipeline.__call__`].
          guess_mode (`bool`, *optional*, defaults to `False`):
              In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
              you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
          control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
              The percentage of total steps at which the controlnet starts applying.
          control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
              The percentage of total steps at which the controlnet stops applying.
  
      Examples:
  
      Returns:
          [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
          [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
          When returning a tuple, the first element is a list with the generated images, and the second element is a
          list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
          (nsfw) content, according to the `safety_checker`.
      """
      if fp16:
          torch_dtype = torch.float16
          np_dtype = np.float16
      else:
          torch_dtype = torch.float32
          np_dtype = np.float32
  
      # align format for control guidance
      if not isinstance(control_guidance_start, list) and isinstance(
          control_guidance_end, list
      ):
          control_guidance_start = len(control_guidance_end) * [
              control_guidance_start
          ]
      elif not isinstance(control_guidance_end, list) and isinstance(
          control_guidance_start, list
      ):
          control_guidance_end = len(control_guidance_start) * [
              control_guidance_end
          ]
      elif not isinstance(
          control_guidance_start, list
      ) and not isinstance(control_guidance_end, list):
          mult = num_controlnet
          control_guidance_start, control_guidance_end = (
              mult * [control_guidance_start],
              mult * [control_guidance_end],
          )
  
      # 1. Check inputs. Raise error if not correct
      self.check_inputs(
          num_controlnet,
          prompt,
          control_image,
          callback_steps,
          negative_prompt,
          prompt_embeds,
          negative_prompt_embeds,
          controlnet_conditioning_scale,
          control_guidance_start,
          control_guidance_end,
      )
  
      # 2. Define call parameters
      if prompt is not None and isinstance(prompt, str):
          batch_size = 1
      elif prompt is not None and isinstance(prompt, list):
          batch_size = len(prompt)
      else:
          batch_size = prompt_embeds.shape[0]
  
      device = self._execution_device
      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      do_classifier_free_guidance = guidance_scale > 1.0
  
      if num_controlnet > 1 and isinstance(
          controlnet_conditioning_scale, float
      ):
          controlnet_conditioning_scale = [
              controlnet_conditioning_scale
          ] * num_controlnet
  
      # 3. Encode input prompt
      prompt_embeds = self._encode_prompt(
          prompt,
          num_images_per_prompt,
          do_classifier_free_guidance,
          negative_prompt,
          prompt_embeds=prompt_embeds,
          negative_prompt_embeds=negative_prompt_embeds,
      )
      # 4. Prepare image
      image = self.image_processor.preprocess(image).to(
          dtype=torch.float32
      )
  
      # 5. Prepare controlnet_conditioning_image
      if num_controlnet == 1:
          control_image = self.prepare_control_image(
              image=control_image,
              width=width,
              height=height,
              batch_size=batch_size * num_images_per_prompt,
              num_images_per_prompt=num_images_per_prompt,
              device=device,
              dtype=torch_dtype,
              do_classifier_free_guidance=do_classifier_free_guidance,
              guess_mode=guess_mode,
          )
      elif num_controlnet > 1:
          control_images = []
  
          for control_image_ in control_image:
              control_image_ = self.prepare_control_image(
                  image=control_image_,
                  width=width,
                  height=height,
                  batch_size=batch_size * num_images_per_prompt,
                  num_images_per_prompt=num_images_per_prompt,
                  device=device,
                  dtype=torch_dtype,
                  do_classifier_free_guidance=do_classifier_free_guidance,
                  guess_mode=guess_mode,
              )
  
              control_images.append(control_image_)
  
          control_image = control_images
      else:
          assert False
  
      # 5. Prepare timesteps
      self.scheduler.set_timesteps(num_inference_steps, device=device)
      timesteps, num_inference_steps = self.get_timesteps(
          num_inference_steps, strength, device
      )
      latent_timestep = timesteps[:1].repeat(
          batch_size * num_images_per_prompt
      )
  
      # 6. Prepare latent variables
      latents = self.prepare_latents(
          image,
          latent_timestep,
          batch_size,
          num_images_per_prompt,
          torch_dtype,
          device,
          generator,
      )
  
      # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
  
      # 7.1 Create tensor stating which controlnets to keep
      controlnet_keep = []
      for i in range(len(timesteps)):
          keeps = [
              1.0
              - float(
                  i / len(timesteps) < s or (i + 1) / len(timesteps) > e
              )
              for s, e in zip(
                  control_guidance_start, control_guidance_end
              )
          ]
          controlnet_keep.append(
              keeps[0] if num_controlnet == 1 else keeps
          )
  
      # 8. Denoising loop
      num_warmup_steps = (
          len(timesteps) - num_inference_steps * self.scheduler.order
      )
      with self.progress_bar(total=num_inference_steps) as progress_bar:
          for i, t in enumerate(timesteps):
              # expand the latents if we are doing classifier free guidance
              latent_model_input = (
                  torch.cat([latents] * 2)
                  if do_classifier_free_guidance
                  else latents
              )
              latent_model_input = self.scheduler.scale_model_input(
                  latent_model_input, t
              )
  
              if isinstance(controlnet_keep[i], list):
                  cond_scale = [
                      c * s
                      for c, s in zip(
                          controlnet_conditioning_scale,
                          controlnet_keep[i],
                      )
                  ]
              else:
                  controlnet_cond_scale = controlnet_conditioning_scale
                  if isinstance(controlnet_cond_scale, list):
                      controlnet_cond_scale = controlnet_cond_scale[0]
                  cond_scale = (
                      controlnet_cond_scale * controlnet_keep[i]
                  )
  
              # predict the noise residual
              _latent_model_input = (
                  latent_model_input.cpu().detach().numpy()
              )
              _prompt_embeds = np.array(prompt_embeds, dtype=np_dtype)
              _t = np.array([t.cpu().detach().numpy()], dtype=np_dtype)
  
              if num_controlnet == 1:
                  control_images = np.array(
                      [control_image], dtype=np_dtype
                  )
              else:
                  control_images = []
                  for _control_img in control_image:
                      _control_img = _control_img.cpu().detach().numpy()
                      control_images.append(_control_img)
                  control_images = np.array(
                      control_images, dtype=np_dtype
                  )
  
              control_scales = np.array(cond_scale, dtype=np_dtype)
              control_scales = np.resize(
                  control_scales, (num_controlnet, 1)
              )
  
              noise_pred = self.unet(
                  sample=_latent_model_input,
                  timestep=_t,
                  encoder_hidden_states=_prompt_embeds,
                  controlnet_conds=control_images,
                  conditioning_scales=control_scales,
              )["noise_pred"]
              noise_pred = torch.from_numpy(noise_pred).to(device)
  
              # perform guidance
              if do_classifier_free_guidance:
                  noise_pred_uncond, noise_pred_text = noise_pred.chunk(
                      2
                  )
                  noise_pred = noise_pred_uncond + guidance_scale * (
                      noise_pred_text - noise_pred_uncond
                  )
  
              # compute the previous noisy sample x_t -> x_t-1
              latents = self.scheduler.step(
                  noise_pred,
                  t,
                  latents,
                  **extra_step_kwargs,
                  return_dict=False,
              )[0]
  
              # call the callback, if provided
              if i == len(timesteps) - 1 or (
                  (i + 1) > num_warmup_steps
                  and (i + 1) % self.scheduler.order == 0
              ):
                  progress_bar.update()
                  if callback is not None and i % callback_steps == 0:
                      step_idx = i // getattr(
                          self.scheduler, "order", 1
                      )
                      callback(step_idx, t, latents)
  
      if not output_type == "latent":
          _latents = latents.cpu().detach().numpy() / 0.18215
          _latents = np.array(_latents, dtype=np_dtype)
          image = self.vae_decoder(latent_sample=_latents)[0]
          image = torch.from_numpy(image).to(
              device, dtype=torch.float32
          )
          has_nsfw_concept = None
      else:
          image = latents
          has_nsfw_concept = None
  
      if has_nsfw_concept is None:
          do_denormalize = [True] * image.shape[0]
      else:
          do_denormalize = [
              not has_nsfw for has_nsfw in has_nsfw_concept
          ]
  
      image = self.image_processor.postprocess(
          image, output_type=output_type, do_denormalize=do_denormalize
      )
  
      if not return_dict:
          return (image, has_nsfw_concept)
  
      return StableDiffusionPipelineOutput(
          images=image, nsfw_content_detected=has_nsfw_concept
      )
  
imports: import argparse
  import atexit
  import inspect
  import os
  import time
  import warnings
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import pycuda.driver
  import tensorrt
  import torch
  from PIL import Image
  from pycuda.tools import make_default_context
  from transformers import CLIPTokenizer
  from diffusers import OnnxRuntimeModel, StableDiffusionImg2ImgPipeline, UniPCMultistepScheduler
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import deprecate, logging, replace_example_docstring
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 10 for calling the TensorRTStableDiffusionControlNetImg2ImgPipeline obj's underlying member method code with example usage: obj.__call__(num_controlnet, fp16, prompt, image, control_image, height, width, strength, num_inference_steps, guidance_scale, negative_prompt, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, output_type, return_dict, callback, callback_steps, cross_attention_kwargs, controlnet_conditioning_scale, guess_mode, control_guidance_start, control_guidance_end) and returns: StableDiffusionPipelineOutput, image,  has_nsfw_concept