class_docstr: Pipeline for imagic image editing.
  See paper here: https://arxiv.org/pdf/2210.09276.pdf
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
  library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
      text_encoder ([`CLIPTextModel`]):
          Frozen text-encoder. Stable Diffusion uses the text portion of
          [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
          the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
      tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
      scheduler ([`SchedulerMixin`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
          [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
      safety_checker ([`StableDiffusionSafetyChecker`]):
          Classification module that estimates whether generated images could be considered offsensive or harmful.
          Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
      feature_extractor ([`CLIPImageProcessor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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.
      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 `nd.array`.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
          plain tuple.
  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`.
docstr_tok: ["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",".","eta","(","`","float","`",",","*","optional","*",",","defaults","to","0.0",")",":","Corresponds","to","parameter","eta","(","\u03b7",")","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","`","nd.array","`",".","return_dict","(","`","bool","`",",","*","optional","*",",","defaults","to","`","True","`",")",":","Whether","or","not","to","return","a","[","`","~pipelines.stable_diffusion.StableDiffusionPipelineOutput","`","]","instead","of","a","plain","tuple",".","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,
      alpha: float = 1.2,
      height: Optional[int] = 512,
      width: Optional[int] = 512,
      num_inference_steps: Optional[int] = 50,
      generator: Optional[torch.Generator] = None,
      output_type: Optional[str] = "pil",
      return_dict: bool = True,
      guidance_scale: float = 7.5,
      eta: float = 0.0,
  ):
      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.
          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 `nd.array`.
          return_dict (`bool`, *optional*, defaults to `True`):
              Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
              plain tuple.
      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 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 self.text_embeddings is None:
          raise ValueError(
              "Please run the pipe.train() before trying to generate an image."
          )
      if self.text_embeddings_orig is None:
          raise ValueError(
              "Please run the pipe.train() before trying to generate an image."
          )
  
      text_embeddings = (
          alpha * self.text_embeddings_orig
          + (1 - alpha) * self.text_embeddings
      )
  
      # 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 = [""]
          max_length = self.tokenizer.model_max_length
          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.view(1, 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 = (
          1,
          self.unet.config.in_channels,
          height // 8,
          width // 8,
      )
      latents_dtype = text_embeddings.dtype
      if self.device.type == "mps":
          # randn does not exist 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,
          )
  
      # 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
  
      for i, t in enumerate(self.progress_bar(timesteps_tensor)):
          # 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
          noise_pred = self.unet(
              latent_model_input,
              t,
              encoder_hidden_states=text_embeddings,
          ).sample
  
          # 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
  
      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 bfloat16
      image = image.cpu().permute(0, 2, 3, 1).float().numpy()
  
      if self.safety_checker is not None:
          safety_checker_input = self.feature_extractor(
              self.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 = self.numpy_to_pil(image)
  
      if not return_dict:
          return (image, has_nsfw_concept)
  
      return StableDiffusionPipelineOutput(
          images=image, nsfw_content_detected=has_nsfw_concept
      )
  
imports: import inspect
  import warnings
  from typing import List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from accelerate import Accelerator
  from packaging import version
  from tqdm.auto import tqdm
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import logging
label_desc: Use image node_id 5 for calling the ImagicStableDiffusionPipeline obj's underlying member method code with example usage: obj.__call__(alpha, height, width, num_inference_steps, generator, output_type, return_dict, guidance_scale, eta) and returns: StableDiffusionPipelineOutput, image,  has_nsfw_concept