class_docstr: Pipeline for pixel-level image editing by following text instructions (based on Stable Diffusion).
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
  implemented for all pipelines (downloading, saving, running on a particular device, etc.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
      - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
      - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      text_encoder ([`~transformers.CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` 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 offensive or harmful.
          Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
          about a model's potential harms.
      feature_extractor ([`~transformers.CLIPImageProcessor`]):
          A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
class_docstr_tok: ["Pipeline","for","pixel-level","image","editing","by","following","text","instructions","(","based","on","Stable","Diffusion",")",".","This","model","inherits","from","[","`","DiffusionPipeline","`","]",".","Check","the","superclass","documentation","for","the","generic","methods","implemented","for","all","pipelines","(","downloading",",","saving",",","running","on","a","particular","device",",","etc.",")",".","The","pipeline","also","inherits","the","following","loading","methods",":","-","[","`","~loaders.TextualInversionLoaderMixin.load_textual_inversion","`","]","for","loading","textual","inversion","embeddings","-","[","`","~loaders.LoraLoaderMixin.load_lora_weights","`","]","for","loading","LoRA","weights","-","[","`","~loaders.LoraLoaderMixin.save_lora_weights","`","]","for","saving","LoRA","weights","Args",":","vae","(","[","`","AutoencoderKL","`","]",")",":","Variational","Auto-Encoder","(","VAE",")","model","to","encode","and","decode","images","to","and","from","latent","representations",".","text_encoder","(","[","`","~transformers.CLIPTextModel","`","]",")",":","Frozen","text-encoder","(","[","clip-vit-large-patch14","]","(","https",":","\/\/huggingface.co\/openai\/clip-vit-large-patch14",")",")",".","tokenizer","(","[","`","~transformers.CLIPTokenizer","`","]",")",":","A","`","CLIPTokenizer","`","to","tokenize","text",".","unet","(","[","`","UNet2DConditionModel","`","]",")",":","A","`","UNet2DConditionModel","`","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","offensive","or","harmful",".","Please","refer","to","the","[","model","card","]","(","https",":","\/\/huggingface.co\/runwayml\/stable-diffusion-v1-5",")","for","more","details","about","a","model","'s","potential","harms",".","feature_extractor","(","[","`","~transformers.CLIPImageProcessor","`","]",")",":","A","`","CLIPImageProcessor","`","to","extract","features","from","generated","images",";","used","as","inputs","to","the","`","safety_checker","`","."]
docstr: The call function to the pipeline for generation.
  
  Args:
      prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
      image (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
          `Image` or tensor representing an image batch to be repainted according to `prompt`. Can also accept
          image latents as `image`, but if passing latents directly it is not encoded again.
      num_inference_steps (`int`, *optional*, defaults to 100):
          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):
          A higher guidance scale value encourages the model to generate images closely linked to the text
          `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
      image_guidance_scale (`float`, *optional*, defaults to 1.5):
          Push the generated image towards the inital `image`. Image guidance scale is enabled by setting
          `image_guidance_scale > 1`. Higher image guidance scale encourages generated images that are closely
          linked to the source `image`, usually at the expense of lower image quality. This pipeline requires a
          value of at least `1`.
      negative_prompt (`str` or `List[str]`, *optional*):
          The prompt or prompts to guide what to not include in image generation. If not defined, you need to
          pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
          to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
      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 is 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 (prompt weighting). If not
          provided, text embeddings are generated from the `prompt` input argument.
      negative_prompt_embeds (`torch.FloatTensor`, *optional*):
          Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
          not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
      output_type (`str`, *optional*, defaults to `"pil"`):
          The output format of the generated image. Choose between `PIL.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 calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
          every step.
  
  Examples:
  
  ```py
  >>> import PIL
  >>> import requests
  >>> import torch
  >>> from io import BytesIO
  
  >>> from diffusers import StableDiffusionInstructPix2PixPipeline
  
  
  >>> def download_image(url):
  ...     response = requests.get(url)
  ...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
  
  
  >>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
  
  >>> image = download_image(img_url).resize((512, 512))
  
  >>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
  ...     "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
  ... )
  >>> pipe = pipe.to("cuda")
  
  >>> prompt = "make the mountains snowy"
  >>> image = pipe(prompt=prompt, image=image).images[0]
  ```
  
  Returns:
      [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
          If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
          otherwise a `tuple` is returned where the first element is a list with the generated images and the
          second element is a list of `bool`s indicating whether the corresponding generated image contains
          "not-safe-for-work" (nsfw) content.
docstr_tok: 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code: def __call__(
      self,
      prompt: Union[str, List[str]] = None,
      image: PipelineImageInput = None,
      num_inference_steps: int = 100,
      guidance_scale: float = 7.5,
      image_guidance_scale: float = 1.5,
      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,
  ):
      r"""
      The call function to the pipeline for generation.
  
      Args:
          prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
          image (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
              `Image` or tensor representing an image batch to be repainted according to `prompt`. Can also accept
              image latents as `image`, but if passing latents directly it is not encoded again.
          num_inference_steps (`int`, *optional*, defaults to 100):
              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):
              A higher guidance scale value encourages the model to generate images closely linked to the text
              `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
          image_guidance_scale (`float`, *optional*, defaults to 1.5):
              Push the generated image towards the inital `image`. Image guidance scale is enabled by setting
              `image_guidance_scale > 1`. Higher image guidance scale encourages generated images that are closely
              linked to the source `image`, usually at the expense of lower image quality. This pipeline requires a
              value of at least `1`.
          negative_prompt (`str` or `List[str]`, *optional*):
              The prompt or prompts to guide what to not include in image generation. If not defined, you need to
              pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
              to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
          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 is 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 (prompt weighting). If not
              provided, text embeddings are generated from the `prompt` input argument.
          negative_prompt_embeds (`torch.FloatTensor`, *optional*):
              Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
              not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
          output_type (`str`, *optional*, defaults to `"pil"`):
              The output format of the generated image. Choose between `PIL.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 calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
              every step.
  
      Examples:
  
      ```py
      >>> import PIL
      >>> import requests
      >>> import torch
      >>> from io import BytesIO
  
      >>> from diffusers import StableDiffusionInstructPix2PixPipeline
  
  
      >>> def download_image(url):
      ...     response = requests.get(url)
      ...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")
  
  
      >>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
  
      >>> image = download_image(img_url).resize((512, 512))
  
      >>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
      ...     "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
      ... )
      >>> pipe = pipe.to("cuda")
  
      >>> prompt = "make the mountains snowy"
      >>> image = pipe(prompt=prompt, image=image).images[0]
      ```
  
      Returns:
          [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
              If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
              otherwise a `tuple` is returned where the first element is a list with the generated images and the
              second element is a list of `bool`s indicating whether the corresponding generated image contains
              "not-safe-for-work" (nsfw) content.
      """
      # 0. Check inputs
      self.check_inputs(
          prompt,
          callback_steps,
          negative_prompt,
          prompt_embeds,
          negative_prompt_embeds,
      )
  
      if image is None:
          raise ValueError("`image` input cannot be undefined.")
  
      # 1. 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 and image_guidance_scale >= 1.0
      )
      # check if scheduler is in sigmas space
      scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")
  
      # 2. Encode input prompt
      prompt_embeds = self._encode_prompt(
          prompt,
          device,
          num_images_per_prompt,
          do_classifier_free_guidance,
          negative_prompt,
          prompt_embeds=prompt_embeds,
          negative_prompt_embeds=negative_prompt_embeds,
      )
  
      # 3. Preprocess image
      image = self.image_processor.preprocess(image)
  
      # 4. set timesteps
      self.scheduler.set_timesteps(num_inference_steps, device=device)
      timesteps = self.scheduler.timesteps
  
      # 5. Prepare Image latents
      image_latents = self.prepare_image_latents(
          image,
          batch_size,
          num_images_per_prompt,
          prompt_embeds.dtype,
          device,
          do_classifier_free_guidance,
          generator,
      )
  
      height, width = image_latents.shape[-2:]
      height = height * self.vae_scale_factor
      width = width * self.vae_scale_factor
  
      # 6. Prepare latent variables
      num_channels_latents = self.vae.config.latent_channels
      latents = self.prepare_latents(
          batch_size * num_images_per_prompt,
          num_channels_latents,
          height,
          width,
          prompt_embeds.dtype,
          device,
          generator,
          latents,
      )
  
      # 7. Check that shapes of latents and image match the UNet channels
      num_channels_image = image_latents.shape[1]
      if (
          num_channels_latents + num_channels_image
          != self.unet.config.in_channels
      ):
          raise ValueError(
              f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
              f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
              f" `num_channels_image`: {num_channels_image} "
              f" = {num_channels_latents+num_channels_image}. Please verify the config of"
              " `pipeline.unet` or your `image` input."
          )
  
      # 8. 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)
  
      # 9. 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.
              # The latents are expanded 3 times because for pix2pix the guidance\
              # is applied for both the text and the input image.
              latent_model_input = (
                  torch.cat([latents] * 3)
                  if do_classifier_free_guidance
                  else latents
              )
  
              # concat latents, image_latents in the channel dimension
              scaled_latent_model_input = (
                  self.scheduler.scale_model_input(
                      latent_model_input, t
                  )
              )
              scaled_latent_model_input = torch.cat(
                  [scaled_latent_model_input, image_latents], dim=1
              )
  
              # predict the noise residual
              noise_pred = self.unet(
                  scaled_latent_model_input,
                  t,
                  encoder_hidden_states=prompt_embeds,
                  return_dict=False,
              )[0]
  
              # Hack:
              # For karras style schedulers the model does classifer free guidance using the
              # predicted_original_sample instead of the noise_pred. So we need to compute the
              # predicted_original_sample here if we are using a karras style scheduler.
              if scheduler_is_in_sigma_space:
                  step_index = (
                      (self.scheduler.timesteps == t)
                      .nonzero()[0]
                      .item()
                  )
                  sigma = self.scheduler.sigmas[step_index]
                  noise_pred = latent_model_input - sigma * noise_pred
  
              # perform guidance
              if do_classifier_free_guidance:
                  (
                      noise_pred_text,
                      noise_pred_image,
                      noise_pred_uncond,
                  ) = noise_pred.chunk(3)
                  noise_pred = (
                      noise_pred_uncond
                      + guidance_scale
                      * (noise_pred_text - noise_pred_image)
                      + image_guidance_scale
                      * (noise_pred_image - noise_pred_uncond)
                  )
  
              # Hack:
              # For karras style schedulers the model does classifer free guidance using the
              # predicted_original_sample instead of the noise_pred. But the scheduler.step function
              # expects the noise_pred and computes the predicted_original_sample internally. So we
              # need to overwrite the noise_pred here such that the value of the computed
              # predicted_original_sample is correct.
              if scheduler_is_in_sigma_space:
                  noise_pred = (noise_pred - latents) / (-sigma)
  
              # 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":
          image = self.vae.decode(
              latents / self.vae.config.scaling_factor,
              return_dict=False,
          )[0]
          image, has_nsfw_concept = self.run_safety_checker(
              image, device, prompt_embeds.dtype
          )
      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
      )
  
      # Offload all models
      self.maybe_free_model_hooks()
  
      if not return_dict:
          return (image, has_nsfw_concept)
  
      return StableDiffusionPipelineOutput(
          images=image, nsfw_content_detected=has_nsfw_concept
      )
  
imports: import inspect
  from typing import Callable, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import PIL_INTERPOLATION, deprecate, logging
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from .None import StableDiffusionPipelineOutput
  from .safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 2 for calling the StableDiffusionInstructPix2PixPipeline obj's underlying member method code with example usage: obj.__call__(prompt, image, num_inference_steps, guidance_scale, image_guidance_scale, negative_prompt, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, output_type, return_dict, callback, callback_steps) and returns: StableDiffusionPipelineOutput, image,  has_nsfw_concept