class_docstr: Pipeline for text-to-image generation using stable unCLIP.
  
  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.).
  
  Args:
      prior_tokenizer ([`CLIPTokenizer`]):
          A [`CLIPTokenizer`].
      prior_text_encoder ([`CLIPTextModelWithProjection`]):
          Frozen [`CLIPTextModelWithProjection`] text-encoder.
      prior ([`PriorTransformer`]):
          The canonincal unCLIP prior to approximate the image embedding from the text embedding.
      prior_scheduler ([`KarrasDiffusionSchedulers`]):
          Scheduler used in the prior denoising process.
      image_normalizer ([`StableUnCLIPImageNormalizer`]):
          Used to normalize the predicted image embeddings before the noise is applied and un-normalize the image
          embeddings after the noise has been applied.
      image_noising_scheduler ([`KarrasDiffusionSchedulers`]):
          Noise schedule for adding noise to the predicted image embeddings. The amount of noise to add is determined
          by the `noise_level`.
      tokenizer ([`CLIPTokenizer`]):
          A [`CLIPTokenizer`].
      text_encoder ([`CLIPTextModel`]):
          Frozen [`CLIPTextModel`] text-encoder.
      unet ([`UNet2DConditionModel`]):
          A [`UNet2DConditionModel`] to denoise the encoded image latents.
      scheduler ([`KarrasDiffusionSchedulers`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents.
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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`.
      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 20):
          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 10.0):
          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`.
      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` or `List[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.ImagePipelineOutput`] 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.
      cross_attention_kwargs (`dict`, *optional*):
          A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
          [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
      noise_level (`int`, *optional*, defaults to `0`):
          The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in
          the final un-noised images. See [`StableUnCLIPPipeline.noise_image_embeddings`] for more details.
      prior_num_inference_steps (`int`, *optional*, defaults to 25):
          The number of denoising steps in the prior denoising process. More denoising steps usually lead to a
          higher quality image at the expense of slower inference.
      prior_guidance_scale (`float`, *optional*, defaults to 4.0):
          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`.
      prior_latents (`torch.FloatTensor`, *optional*):
          Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
          embedding generation in the prior denoising process. 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`.
      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.
  Examples:
  
  Returns:
      [`~pipelines.ImagePipelineOutput`] or `tuple`:
          [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning
          a tuple, the first element is a list with the generated images.
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code: def __call__(
      self,
      # regular denoising process args
      prompt: Optional[Union[str, List[str]]] = None,
      height: Optional[int] = None,
      width: Optional[int] = None,
      num_inference_steps: int = 20,
      guidance_scale: float = 10.0,
      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,
      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,
      cross_attention_kwargs: Optional[Dict[str, Any]] = None,
      noise_level: int = 0,
      # prior args
      prior_num_inference_steps: int = 25,
      prior_guidance_scale: float = 4.0,
      prior_latents: Optional[torch.FloatTensor] = None,
      clip_skip: Optional[int] = None,
  ):
      """
      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`.
          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 20):
              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 10.0):
              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`.
          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` or `List[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.ImagePipelineOutput`] 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.
          cross_attention_kwargs (`dict`, *optional*):
              A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
              [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
          noise_level (`int`, *optional*, defaults to `0`):
              The amount of noise to add to the image embeddings. A higher `noise_level` increases the variance in
              the final un-noised images. See [`StableUnCLIPPipeline.noise_image_embeddings`] for more details.
          prior_num_inference_steps (`int`, *optional*, defaults to 25):
              The number of denoising steps in the prior denoising process. More denoising steps usually lead to a
              higher quality image at the expense of slower inference.
          prior_guidance_scale (`float`, *optional*, defaults to 4.0):
              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`.
          prior_latents (`torch.FloatTensor`, *optional*):
              Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
              embedding generation in the prior denoising process. 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`.
          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.
      Examples:
  
      Returns:
          [`~pipelines.ImagePipelineOutput`] or `tuple`:
              [`~ pipeline_utils.ImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning
              a tuple, the first element is a list with the generated images.
      """
      # 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=prompt,
          height=height,
          width=width,
          callback_steps=callback_steps,
          noise_level=noise_level,
          negative_prompt=negative_prompt,
          prompt_embeds=prompt_embeds,
          negative_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]
  
      batch_size = batch_size * num_images_per_prompt
  
      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.
      prior_do_classifier_free_guidance = prior_guidance_scale > 1.0
  
      # 3. Encode input prompt
      (
          prior_prompt_embeds,
          prior_text_encoder_hidden_states,
          prior_text_mask,
      ) = self._encode_prior_prompt(
          prompt=prompt,
          device=device,
          num_images_per_prompt=num_images_per_prompt,
          do_classifier_free_guidance=prior_do_classifier_free_guidance,
      )
  
      # 4. Prepare prior timesteps
      self.prior_scheduler.set_timesteps(
          prior_num_inference_steps, device=device
      )
      prior_timesteps_tensor = self.prior_scheduler.timesteps
  
      # 5. Prepare prior latent variables
      embedding_dim = self.prior.config.embedding_dim
      prior_latents = self.prepare_latents(
          (batch_size, embedding_dim),
          prior_prompt_embeds.dtype,
          device,
          generator,
          prior_latents,
          self.prior_scheduler,
      )
  
      # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      prior_extra_step_kwargs = self.prepare_prior_extra_step_kwargs(
          generator, eta
      )
  
      # 7. Prior denoising loop
      for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
          # expand the latents if we are doing classifier free guidance
          latent_model_input = (
              torch.cat([prior_latents] * 2)
              if prior_do_classifier_free_guidance
              else prior_latents
          )
          latent_model_input = self.prior_scheduler.scale_model_input(
              latent_model_input, t
          )
  
          predicted_image_embedding = self.prior(
              latent_model_input,
              timestep=t,
              proj_embedding=prior_prompt_embeds,
              encoder_hidden_states=prior_text_encoder_hidden_states,
              attention_mask=prior_text_mask,
          ).predicted_image_embedding
  
          if prior_do_classifier_free_guidance:
              (
                  predicted_image_embedding_uncond,
                  predicted_image_embedding_text,
              ) = predicted_image_embedding.chunk(2)
              predicted_image_embedding = (
                  predicted_image_embedding_uncond
                  + prior_guidance_scale
                  * (
                      predicted_image_embedding_text
                      - predicted_image_embedding_uncond
                  )
              )
  
          prior_latents = self.prior_scheduler.step(
              predicted_image_embedding,
              timestep=t,
              sample=prior_latents,
              **prior_extra_step_kwargs,
              return_dict=False,
          )[0]
  
          if callback is not None and i % callback_steps == 0:
              callback(i, t, prior_latents)
  
      prior_latents = self.prior.post_process_latents(prior_latents)
  
      image_embeds = prior_latents
  
      # done prior
  
      # 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
  
      # 8. Encode input prompt
      text_encoder_lora_scale = (
          cross_attention_kwargs.get("scale", None)
          if cross_attention_kwargs is not None
          else None
      )
      prompt_embeds, negative_prompt_embeds = self.encode_prompt(
          prompt=prompt,
          device=device,
          num_images_per_prompt=num_images_per_prompt,
          do_classifier_free_guidance=do_classifier_free_guidance,
          negative_prompt=negative_prompt,
          prompt_embeds=prompt_embeds,
          negative_prompt_embeds=negative_prompt_embeds,
          lora_scale=text_encoder_lora_scale,
          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]
          )
  
      # 9. Prepare image embeddings
      image_embeds = self.noise_image_embeddings(
          image_embeds=image_embeds,
          noise_level=noise_level,
          generator=generator,
      )
  
      if do_classifier_free_guidance:
          negative_prompt_embeds = torch.zeros_like(image_embeds)
  
          # 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
          image_embeds = torch.cat(
              [negative_prompt_embeds, image_embeds]
          )
  
      # 10. Prepare timesteps
      self.scheduler.set_timesteps(num_inference_steps, device=device)
      timesteps = self.scheduler.timesteps
  
      # 11. Prepare latent variables
      num_channels_latents = self.unet.config.in_channels
      shape = (
          batch_size,
          num_channels_latents,
          height // self.vae_scale_factor,
          width // self.vae_scale_factor,
      )
      latents = self.prepare_latents(
          shape=shape,
          dtype=prompt_embeds.dtype,
          device=device,
          generator=generator,
          latents=latents,
          scheduler=self.scheduler,
      )
  
      # 12. 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)
  
      # 13. Denoising loop
      for i, t in enumerate(self.progress_bar(timesteps)):
          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=prompt_embeds,
              class_labels=image_embeds,
              cross_attention_kwargs=cross_attention_kwargs,
              return_dict=False,
          )[0]
  
          # 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,
              return_dict=False,
          )[0]
  
          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]
      else:
          image = latents
  
      image = self.image_processor.postprocess(
          image, output_type=output_type
      )
  
      # Offload last model to CPU
      if (
          hasattr(self, "final_offload_hook")
          and self.final_offload_hook is not None
      ):
          self.final_offload_hook.offload()
  
      if not return_dict:
          return (image,)
  
      return ImagePipelineOutput(images=image)
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from transformers.models.clip.modeling_clip import CLIPTextModelOutput
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, PriorTransformer, UNet2DConditionModel
  from ...models.embeddings import get_timestep_embedding
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
label_desc: Use image node_id 13 for calling the StableUnCLIPPipeline 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, cross_attention_kwargs, noise_level, prior_num_inference_steps, prior_guidance_scale, prior_latents, clip_skip) and returns: ImagePipelineOutput, image