class_docstr: Example Usage:
      pipe = WildcardStableDiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4",
  
          torch_dtype=torch.float16,
      )
      prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
      out = pipe(
          prompt,
          wildcard_option_dict={
              "clothing":["hat", "shirt", "scarf", "beret"]
          },
          wildcard_files=["object.txt", "animal.txt"],
          num_prompt_samples=1
      )
  
  
  Pipeline for text-to-image generation with wild cards using Stable Diffusion.
  
  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 offensive 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.
      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.
      wildcard_option_dict (Dict[str, List[str]]):
          dict with key as `wildcard` and values as a list of possible replacements. For example if a prompt, "A __animal__ sitting on a chair". A wildcard_option_dict can provide possible values for "animal" like this: {"animal":["dog", "cat", "fox"]}
      wildcard_files: (List[str])
         List of filenames of txt files for wildcard replacements. For example if a prompt, "A __animal__ sitting on a chair". A file can be provided ["animal.txt"]
      num_prompt_samples: int
          Number of times to sample wildcards for each prompt provided
  
  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 __call__(
      self,
      prompt: Union[str, List[str]],
      height: int = 512,
      width: int = 512,
      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[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: int = 1,
      wildcard_option_dict: Dict[str, List[str]] = {},
      wildcard_files: List[str] = [],
      num_prompt_samples: Optional[int] = 1,
      **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.
          wildcard_option_dict (Dict[str, List[str]]):
              dict with key as `wildcard` and values as a list of possible replacements. For example if a prompt, "A __animal__ sitting on a chair". A wildcard_option_dict can provide possible values for "animal" like this: {"animal":["dog", "cat", "fox"]}
          wildcard_files: (List[str])
             List of filenames of txt files for wildcard replacements. For example if a prompt, "A __animal__ sitting on a chair". A file can be provided ["animal.txt"]
          num_prompt_samples: int
              Number of times to sample wildcards for each prompt provided
  
      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):
          prompt = [
              replace_prompt_with_wildcards(
                  prompt, wildcard_option_dict, wildcard_files
              )
              for i in range(num_prompt_samples)
          ]
          batch_size = len(prompt)
      elif isinstance(prompt, list):
          prompt_list = []
          for p in prompt:
              for i in range(num_prompt_samples):
                  prompt_list.append(
                      replace_prompt_with_wildcards(
                          p, wildcard_option_dict, wildcard_files
                      )
                  )
          prompt = 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 = [""] * batch_size
          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(
              1, 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.config.in_channels,
          height // 8,
          width // 8,
      )
      latents_dtype = text_embeddings.dtype
      if latents is None:
          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,
              )
      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
  
      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
  
          # call the callback, if provided
          if callback is not None and i % callback_steps == 0:
              step_idx = i // getattr(self.scheduler, "order", 1)
              callback(step_idx, 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 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 WildcardStableDiffusionOutput(
          images=image,
          nsfw_content_detected=has_nsfw_concept,
          prompts=prompt,
      )
  
imports: import inspect
  import os
  import random
  import re
  from dataclasses import dataclass
  from typing import Callable, Dict, List, Optional, Union
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline
  from diffusers.configuration_utils import FrozenDict
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import deprecate, logging
label_desc: Use image node_id 2 for calling the WildcardStableDiffusionPipeline obj's underlying member method code with example usage: obj.__call__(prompt, height, width, num_inference_steps, guidance_scale, negative_prompt, num_images_per_prompt, eta, generator, latents, output_type, return_dict, callback, callback_steps, wildcard_option_dict, wildcard_files, num_prompt_samples) and returns: WildcardStableDiffusionOutput, image,  has_nsfw_concept