class_docstr: Pipeline for text-to-image generation 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.)
  
  <Tip warning={true}>
  
      This is an experimental pipeline and is likely to change in the future.
  
  </Tip>
  
  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/runwayml/stable-diffusion-v1-5) for details.
      feature_extractor ([`CLIPImageProcessor`]):
          Model that extracts features from generated images to be used as inputs for the `safety_checker`.
class_docstr_tok: ["Pipeline","for","text-to-image","generation","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",".",")","<","Tip","warning=","{","true","}",">","This","is","an","experimental","pipeline","and","is","likely","to","change","in","the","future",".","<","\/Tip",">","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\/runwayml\/stable-diffusion-v1-5",")","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]`, *optional*):
          The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
          instead.
      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`, *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.
      use_karras_sigmas (`bool`, *optional*, defaults to `False`):
          Use karras sigmas. For example, specifying `sample_dpmpp_2m` to `set_scheduler` will be equivalent to
          `DPM++2M` in stable-diffusion-webui. On top of that, setting this option to True will make it `DPM++2M
          Karras`.
      noise_sampler_seed (`int`, *optional*, defaults to `None`):
          The random seed to use for the noise sampler. If `None`, a random seed will be generated.
      clip_skip (`int`, *optional*):
          Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
          the output of the pre-final layer will be used for computing the prompt embeddings.
  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]] = None,
      height: Optional[int] = None,
      width: Optional[int] = None,
      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,
      use_karras_sigmas: Optional[bool] = False,
      noise_sampler_seed: Optional[int] = None,
      clip_skip: int = None,
  ):
      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.
          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`, *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.
          use_karras_sigmas (`bool`, *optional*, defaults to `False`):
              Use karras sigmas. For example, specifying `sample_dpmpp_2m` to `set_scheduler` will be equivalent to
              `DPM++2M` in stable-diffusion-webui. On top of that, setting this option to True will make it `DPM++2M
              Karras`.
          noise_sampler_seed (`int`, *optional*, defaults to `None`):
              The random seed to use for the noise sampler. If `None`, a random seed will be generated.
          clip_skip (`int`, *optional*):
              Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
              the output of the pre-final layer will be used for computing the prompt embeddings.
      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`.
      """
      # 0. Default height and width to unet
      height = (
          height or self.unet.config.sample_size * self.vae_scale_factor
      )
      width = (
          width or self.unet.config.sample_size * self.vae_scale_factor
      )
  
      # 1. Check inputs. Raise error if not correct
      self.check_inputs(
          prompt,
          height,
          width,
          callback_steps,
          negative_prompt,
          prompt_embeds,
          negative_prompt_embeds,
      )
  
      # 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 = True
      if guidance_scale <= 1.0:
          raise ValueError("has to use guidance_scale")
  
      # 3. Encode input prompt
      prompt_embeds, negative_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,
          clip_skip=clip_skip,
      )
      # 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
      if do_classifier_free_guidance:
          prompt_embeds = torch.cat(
              [negative_prompt_embeds, prompt_embeds]
          )
  
      # 4. Prepare timesteps
      self.scheduler.set_timesteps(
          num_inference_steps, device=prompt_embeds.device
      )
  
      # 5. Prepare sigmas
      if use_karras_sigmas:
          sigma_min: float = self.k_diffusion_model.sigmas[0].item()
          sigma_max: float = self.k_diffusion_model.sigmas[-1].item()
          sigmas = get_sigmas_karras(
              n=num_inference_steps,
              sigma_min=sigma_min,
              sigma_max=sigma_max,
          )
          sigmas = sigmas.to(device)
      else:
          sigmas = self.scheduler.sigmas
      sigmas = sigmas.to(prompt_embeds.dtype)
  
      # 6. Prepare latent variables
      num_channels_latents = self.unet.config.in_channels
      latents = self.prepare_latents(
          batch_size * num_images_per_prompt,
          num_channels_latents,
          height,
          width,
          prompt_embeds.dtype,
          device,
          generator,
          latents,
      )
      latents = latents * sigmas[0]
      self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(
          latents.device
      )
      self.k_diffusion_model.log_sigmas = (
          self.k_diffusion_model.log_sigmas.to(latents.device)
      )
  
      # 7. Define model function
      def model_fn(x, t):
          latent_model_input = torch.cat([x] * 2)
          t = torch.cat([t] * 2)
  
          noise_pred = self.k_diffusion_model(
              latent_model_input, t, cond=prompt_embeds
          )
  
          noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
          noise_pred = noise_pred_uncond + guidance_scale * (
              noise_pred_text - noise_pred_uncond
          )
          return noise_pred
  
      # 8. Run k-diffusion solver
      sampler_kwargs = {}
  
      if "noise_sampler" in inspect.signature(self.sampler).parameters:
          min_sigma, max_sigma = sigmas[sigmas > 0].min(), sigmas.max()
          noise_sampler = BrownianTreeNoiseSampler(
              latents, min_sigma, max_sigma, noise_sampler_seed
          )
          sampler_kwargs["noise_sampler"] = noise_sampler
  
      if "generator" in inspect.signature(self.sampler).parameters:
          sampler_kwargs["generator"] = generator
  
      latents = self.sampler(
          model_fn, latents, sigmas, **sampler_kwargs
      )
  
      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 importlib
  import inspect
  from typing import Callable, List, Optional, Union
  import torch
  from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
  from k_diffusion.sampling import BrownianTreeNoiseSampler, get_sigmas_karras
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import LMSDiscreteScheduler
  from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from .None import StableDiffusionPipelineOutput
label_desc: Use image node_id 9 for calling the StableDiffusionKDiffusionPipeline 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, prompt_embeds, negative_prompt_embeds, output_type, return_dict, callback, callback_steps, use_karras_sigmas, noise_sampler_seed, clip_skip) and returns: StableDiffusionPipelineOutput, noise_pred, image,  has_nsfw_concept