docstr: Function invoked when calling the pipeline for generation.
  
  Args:
      prompt (`str` or `List[str]`):
          The prompt or prompts to guide the image generation.
      height (`int`, *optional*, defaults to 512):
          The height in pixels of the generated image.
      width (`int`, *optional*, defaults to 512):
          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. 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`, *optional*):
          A [torch generator](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`.
      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.
  
  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`.
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code: def generate(
      self,
      prompt: Union[str, List[str]],
      height: int = 512,
      width: int = 512,
      num_inference_steps: int = 50,
      guidance_scale: float = 7.5,
      guidance_threshold: int = 51,
      negative_prompt: Optional[Union[str, List[str]]] = None,
      num_images_per_prompt: Optional[int] = 1,
      eta: float = 0.0,
      generator: Optional[torch.Generator] = None,
      latents: Optional[torch.FloatTensor] = None,
      output_type: Optional[str] = "pil",
      return_dict: bool = True,
      callback: Optional[
          Callable[[int, int, torch.FloatTensor], None]
      ] = None,
      callback_steps: Optional[int] = 1,
      return_samples: bool = False,
      **kwargs,
  ):
      r"""
      Function invoked when calling the pipeline for generation.
  
      Args:
          prompt (`str` or `List[str]`):
              The prompt or prompts to guide the image generation.
          height (`int`, *optional*, defaults to 512):
              The height in pixels of the generated image.
          width (`int`, *optional*, defaults to 512):
              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. 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`, *optional*):
              A [torch generator](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`.
          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.
  
      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 isinstance(prompt, str):
          batch_size = 1
      elif isinstance(prompt, list):
          batch_size = len(prompt)
      else:
          raise ValueError(
              f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
          )
  
      if height % 8 != 0 or width % 8 != 0:
          raise ValueError(
              f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
          )
  
      if (callback_steps is None) or (
          callback_steps is not None
          and (
              not isinstance(callback_steps, int) or callback_steps <= 0
          )
      ):
          raise ValueError(
              f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
              f" {type(callback_steps)}."
          )
  
      # get prompt text embeddings
      text_inputs = self.tokenizer(
          prompt,
          padding="max_length",
          max_length=self.tokenizer.model_max_length,
          return_tensors="pt",
      )
      text_input_ids = text_inputs.input_ids
  
      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          removed_text = self.tokenizer.batch_decode(
              text_input_ids[:, self.tokenizer.model_max_length :]
          )
          logger.warning(
              "The following part of your input was truncated because CLIP can only handle sequences up to"
              f" {self.tokenizer.model_max_length} tokens: {removed_text}"
          )
          text_input_ids = text_input_ids[
              :, : self.tokenizer.model_max_length
          ]
      text_embeddings = self.text_encoder(
          text_input_ids.to(self.device)
      )[0]
  
      # duplicate text embeddings for each generation per prompt, using mps friendly method
      bs_embed, seq_len, _ = text_embeddings.shape
      text_embeddings = text_embeddings.repeat(
          1, num_images_per_prompt, 1
      )
      text_embeddings = text_embeddings.view(
          bs_embed * num_images_per_prompt, seq_len, -1
      )
  
      # 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
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
          uncond_tokens: List[str]
          if negative_prompt is None:
              uncond_tokens = [""]
          elif type(prompt) is not type(negative_prompt):
              raise TypeError(
                  f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                  f" {type(prompt)}."
              )
          elif isinstance(negative_prompt, str):
              uncond_tokens = [negative_prompt]
          elif batch_size != len(negative_prompt):
              raise ValueError(
                  f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                  f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                  " the batch size of `prompt`."
              )
          else:
              uncond_tokens = negative_prompt
  
          max_length = text_input_ids.shape[-1]
          uncond_input = self.tokenizer(
              uncond_tokens,
              padding="max_length",
              max_length=max_length,
              truncation=True,
              return_tensors="pt",
          )
          uncond_embeddings = self.text_encoder(
              uncond_input.input_ids.to(self.device)
          )[0]
  
          # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
          seq_len = uncond_embeddings.shape[1]
          uncond_embeddings = uncond_embeddings.repeat(
              batch_size, num_images_per_prompt, 1
          )
          uncond_embeddings = uncond_embeddings.view(
              batch_size * num_images_per_prompt, seq_len, -1
          )
  
          # For classifier free guidance, we need to do two forward passes.
          # Here we concatenate the unconditional and text embeddings into a single batch
          # to avoid doing two forward passes
          text_embeddings = torch.cat(
              [uncond_embeddings, text_embeddings]
          )
  
      # get the initial random noise unless the user supplied it
  
      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      latents_shape = (
          batch_size * num_images_per_prompt,
          self.unet.in_channels,
          height // 8,
          width // 8,
      )
      latents_dtype = text_embeddings.dtype
      if latents is None:
          if self.device.type == "mps":
              # randn does not work reproducibly on mps
              latents = torch.randn(
                  latents_shape,
                  generator=generator,
                  device="cpu",
                  dtype=latents_dtype,
              ).to(self.device)
          else:
              latents = torch.randn(
                  latents_shape,
                  generator=generator,
                  device=self.device,
                  dtype=latents_dtype,
              )
      else:
          if latents.shape != latents_shape:
              raise ValueError(
                  f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
              )
          latents = latents.to(self.device)
  
      # set timesteps
      self.scheduler.set_timesteps(num_inference_steps)
  
      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      timesteps_tensor = self.scheduler.timesteps.to(self.device)
  
      # scale the initial noise by the standard deviation required by the scheduler
      latents = latents * self.scheduler.init_noise_sigma
  
      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      accepts_eta = "eta" in set(
          inspect.signature(self.scheduler.step).parameters.keys()
      )
      extra_step_kwargs = {}
      if accepts_eta:
          extra_step_kwargs["eta"] = eta
  
      # check if the scheduler accepts generator
      accepts_generator = "generator" in set(
          inspect.signature(self.scheduler.step).parameters.keys()
      )
      if accepts_generator:
          extra_step_kwargs["generator"] = generator
  
      input_samples = []
  
      for i, t in enumerate(tqdm(timesteps_tensor)):
          if do_classifier_free_guidance and i >= guidance_threshold:
              do_classifier_free_guidance_ = False
              text_embeddings_ = text_embeddings_[:1]
          else:
              text_embeddings_ = text_embeddings
              do_classifier_free_guidance_ = do_classifier_free_guidance
  
          # 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
          )
  
          # predict the noise residual
          t = t[None]
          # predict the noise residual
  
          noise_pred = self.unet(
              latent_model_input, t, text_embeddings_
          )
          if hasattr(noise_pred, "sample"):
              noise_pred = noise_pred.sample
          if isinstance(noise_pred, tuple):
              noise_pred = noise_pred[0]
          elif isinstance(noise_pred, dict):
              noise_pred = noise_pred["sample"]
  
          if return_samples:
              input_samples.append(
                  [(latent_model_input, t, text_embeddings), noise_pred]
              )
  
          # 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
          ).prev_sample
  
          # call the callback, if provided
          if callback is not None and i % callback_steps == 0:
              callback(i, t, latents)
  
      latents = 1 / 0.18215 * latents
      image = self.vae.decode(latents).sample
  
      image = (image / 2 + 0.5).clamp(0, 1)
  
      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
      image = image.cpu().permute(0, 2, 3, 1).float().numpy()
  
      if self.safety_checker is not None:
          safety_checker_input = self.feature_extractor(
              DiffusionPipeline.numpy_to_pil(image), return_tensors="pt"
          ).to(self.device)
          image, has_nsfw_concept = self.safety_checker(
              images=image,
              clip_input=safety_checker_input.pixel_values.to(
                  text_embeddings.dtype
              ),
          )
      else:
          has_nsfw_concept = None
  
      if output_type == "pil":
          image = DiffusionPipeline.numpy_to_pil(image)
  
      if not return_dict:
          return (image, has_nsfw_concept)
  
      if return_samples:
          return (
              StableDiffusionPipelineOutput(
                  images=image, nsfw_content_detected=has_nsfw_concept
              ),
              input_samples,
          )
      else:
          return StableDiffusionPipelineOutput(
              images=image, nsfw_content_detected=has_nsfw_concept
          )
  
imports: import os
  import torch
  import inspect
  import numpy
  from tqdm.auto import tqdm
  import multiprocessing
  from bigdl.nano.pytorch import InferenceOptimizer
  from typing import Callable, List, Optional, Union
  from diffusers import AutoencoderKL
  from transformers import CLIPTokenizer, CLIPTextModel
  from diffusers.schedulers import *
  from diffusers.utils import logging
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipeline_utils import DiffusionPipeline
  from diffusers.models import UNet2DConditionModel
label_desc: Use image node_id 5 for calling the NanoStableDiffusionPipeline obj's underlying member method code with example usage: obj.generate(prompt, height, width, num_inference_steps, guidance_scale, guidance_threshold, negative_prompt, num_images_per_prompt, eta, generator, latents, output_type, return_dict, callback, callback_steps, return_samples) and returns: image,  has_nsfw_concept, StableDiffusionPipelineOutput,  input_samples, StableDiffusionPipelineOutput