class_docstr: Pipeline to generate image variations from an input image using 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.).
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
          Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      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","to","generate","image","variations","from","an","input","image","using","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.",")",".","Args",":","vae","(","[","`","AutoencoderKL","`","]",")",":","Variational","Auto-Encoder","(","VAE",")","model","to","encode","and","decode","images","to","and","from","latent","representations",".","image_encoder","(","[","`","~transformers.CLIPVisionModelWithProjection","`","]",")",":","Frozen","CLIP","image-encoder","(","[","clip-vit-large-patch14","]","(","https",":","\/\/huggingface.co\/openai\/clip-vit-large-patch14",")",")",".","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:
      image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
          Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
          [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
      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. This parameter is modulated by `strength`.
      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`.
      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`.
      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.
  
  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.
  
  Examples:
  
  ```py
  from diffusers import StableDiffusionImageVariationPipeline
  from PIL import Image
  from io import BytesIO
  import requests
  
  pipe = StableDiffusionImageVariationPipeline.from_pretrained(
      "lambdalabs/sd-image-variations-diffusers", revision="v2.0"
  )
  pipe = pipe.to("cuda")
  
  url = "https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200"
  
  response = requests.get(url)
  image = Image.open(BytesIO(response.content)).convert("RGB")
  
  out = pipe(image, num_images_per_prompt=3, guidance_scale=15)
  out["images"][0].save("result.jpg")
  ```
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code: def __call__(
      self,
      image: Union[
          PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor
      ],
      height: Optional[int] = None,
      width: Optional[int] = None,
      num_inference_steps: int = 50,
      guidance_scale: float = 7.5,
      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,
      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:
          image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
              Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
              [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
          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. This parameter is modulated by `strength`.
          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`.
          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`.
          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.
  
      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.
  
      Examples:
  
      ```py
      from diffusers import StableDiffusionImageVariationPipeline
      from PIL import Image
      from io import BytesIO
      import requests
  
      pipe = StableDiffusionImageVariationPipeline.from_pretrained(
          "lambdalabs/sd-image-variations-diffusers", revision="v2.0"
      )
      pipe = pipe.to("cuda")
  
      url = "https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200"
  
      response = requests.get(url)
      image = Image.open(BytesIO(response.content)).convert("RGB")
  
      out = pipe(image, num_images_per_prompt=3, guidance_scale=15)
      out["images"][0].save("result.jpg")
      ```
      """
      # 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(image, height, width, callback_steps)
  
      # 2. Define call parameters
      if isinstance(image, PIL.Image.Image):
          batch_size = 1
      elif isinstance(image, list):
          batch_size = len(image)
      else:
          batch_size = image.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
  
      # 3. Encode input image
      image_embeddings = self._encode_image(
          image,
          device,
          num_images_per_prompt,
          do_classifier_free_guidance,
      )
  
      # 4. Prepare timesteps
      self.scheduler.set_timesteps(num_inference_steps, device=device)
      timesteps = self.scheduler.timesteps
  
      # 5. 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,
          image_embeddings.dtype,
          device,
          generator,
          latents,
      )
  
      # 6. 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)
  
      # 7. 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
              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=image_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 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)
  
      self.maybe_free_model_hooks()
  
      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, image_embeddings.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
      )
  
      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 PIL.Image
  import torch
  from packaging import version
  from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
  from ...configuration_utils import FrozenDict
  from ...image_processor import VaeImageProcessor
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import 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 10 for calling the StableDiffusionImageVariationPipeline obj's underlying member method code with example usage: obj.__call__(image, height, width, num_inference_steps, guidance_scale, num_images_per_prompt, eta, generator, latents, output_type, return_dict, callback, callback_steps) and returns: StableDiffusionPipelineOutput, image,  has_nsfw_concept