class_docstr: Pipeline for imagic image editing.
  See paper here: https://arxiv.org/pdf/2210.09276.pdf
  
  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 offsensive 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.
      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 `nd.array`.
      return_dict (`bool`, *optional*, defaults to `True`):
          Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
          plain tuple.
  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 train(
      self,
      prompt: Union[str, List[str]],
      image: Union[torch.FloatTensor, PIL.Image.Image],
      height: Optional[int] = 512,
      width: Optional[int] = 512,
      generator: Optional[torch.Generator] = None,
      embedding_learning_rate: float = 0.001,
      diffusion_model_learning_rate: float = 2e-6,
      text_embedding_optimization_steps: int = 500,
      model_fine_tuning_optimization_steps: int = 1000,
      **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.
          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 `nd.array`.
          return_dict (`bool`, *optional*, defaults to `True`):
              Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
              plain tuple.
      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`.
      """
      accelerator = Accelerator(
          gradient_accumulation_steps=1,
          mixed_precision="fp16",
      )
  
      if "torch_device" in kwargs:
          device = kwargs.pop("torch_device")
          warnings.warn(
              "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
              " Consider using `pipe.to(torch_device)` instead."
          )
  
          if device is None:
              device = "cuda" if torch.cuda.is_available() else "cpu"
          self.to(device)
  
      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}."
          )
  
      # Freeze vae and unet
      self.vae.requires_grad_(False)
      self.unet.requires_grad_(False)
      self.text_encoder.requires_grad_(False)
      self.unet.eval()
      self.vae.eval()
      self.text_encoder.eval()
  
      if accelerator.is_main_process:
          accelerator.init_trackers(
              "imagic",
              config={
                  "embedding_learning_rate": embedding_learning_rate,
                  "text_embedding_optimization_steps": text_embedding_optimization_steps,
              },
          )
  
      # get text embeddings for prompt
      text_input = self.tokenizer(
          prompt,
          padding="max_length",
          max_length=self.tokenizer.model_max_length,
          truncation=True,
          return_tensors="pt",
      )
      text_embeddings = torch.nn.Parameter(
          self.text_encoder(text_input.input_ids.to(self.device))[0],
          requires_grad=True,
      )
      text_embeddings = text_embeddings.detach()
      text_embeddings.requires_grad_()
      text_embeddings_orig = text_embeddings.clone()
  
      # Initialize the optimizer
      optimizer = torch.optim.Adam(
          [text_embeddings],  # only optimize the embeddings
          lr=embedding_learning_rate,
      )
  
      if isinstance(image, PIL.Image.Image):
          image = preprocess(image)
  
      latents_dtype = text_embeddings.dtype
      image = image.to(device=self.device, dtype=latents_dtype)
      init_latent_image_dist = self.vae.encode(image).latent_dist
      image_latents = init_latent_image_dist.sample(generator=generator)
      image_latents = 0.18215 * image_latents
  
      progress_bar = tqdm(
          range(text_embedding_optimization_steps),
          disable=not accelerator.is_local_main_process,
      )
      progress_bar.set_description("Steps")
  
      global_step = 0
  
      logger.info(
          "First optimizing the text embedding to better reconstruct the init image"
      )
      for _ in range(text_embedding_optimization_steps):
          with accelerator.accumulate(text_embeddings):
              # Sample noise that we'll add to the latents
              noise = torch.randn(image_latents.shape).to(
                  image_latents.device
              )
              timesteps = torch.randint(
                  1000, (1,), device=image_latents.device
              )
  
              # Add noise to the latents according to the noise magnitude at each timestep
              # (this is the forward diffusion process)
              noisy_latents = self.scheduler.add_noise(
                  image_latents, noise, timesteps
              )
  
              # Predict the noise residual
              noise_pred = self.unet(
                  noisy_latents, timesteps, text_embeddings
              ).sample
  
              loss = (
                  F.mse_loss(noise_pred, noise, reduction="none")
                  .mean([1, 2, 3])
                  .mean()
              )
              accelerator.backward(loss)
  
              optimizer.step()
              optimizer.zero_grad()
  
          # Checks if the accelerator has performed an optimization step behind the scenes
          if accelerator.sync_gradients:
              progress_bar.update(1)
              global_step += 1
  
          logs = {
              "loss": loss.detach().item()
          }  # , "lr": lr_scheduler.get_last_lr()[0]}
          progress_bar.set_postfix(**logs)
          accelerator.log(logs, step=global_step)
  
      accelerator.wait_for_everyone()
  
      text_embeddings.requires_grad_(False)
  
      # Now we fine tune the unet to better reconstruct the image
      self.unet.requires_grad_(True)
      self.unet.train()
      optimizer = torch.optim.Adam(
          self.unet.parameters(),  # only optimize unet
          lr=diffusion_model_learning_rate,
      )
      progress_bar = tqdm(
          range(model_fine_tuning_optimization_steps),
          disable=not accelerator.is_local_main_process,
      )
  
      logger.info(
          "Next fine tuning the entire model to better reconstruct the init image"
      )
      for _ in range(model_fine_tuning_optimization_steps):
          with accelerator.accumulate(self.unet.parameters()):
              # Sample noise that we'll add to the latents
              noise = torch.randn(image_latents.shape).to(
                  image_latents.device
              )
              timesteps = torch.randint(
                  1000, (1,), device=image_latents.device
              )
  
              # Add noise to the latents according to the noise magnitude at each timestep
              # (this is the forward diffusion process)
              noisy_latents = self.scheduler.add_noise(
                  image_latents, noise, timesteps
              )
  
              # Predict the noise residual
              noise_pred = self.unet(
                  noisy_latents, timesteps, text_embeddings
              ).sample
  
              loss = (
                  F.mse_loss(noise_pred, noise, reduction="none")
                  .mean([1, 2, 3])
                  .mean()
              )
              accelerator.backward(loss)
  
              optimizer.step()
              optimizer.zero_grad()
  
          # Checks if the accelerator has performed an optimization step behind the scenes
          if accelerator.sync_gradients:
              progress_bar.update(1)
              global_step += 1
  
          logs = {
              "loss": loss.detach().item()
          }  # , "lr": lr_scheduler.get_last_lr()[0]}
          progress_bar.set_postfix(**logs)
          accelerator.log(logs, step=global_step)
  
      accelerator.wait_for_everyone()
      self.text_embeddings_orig = text_embeddings_orig
      self.text_embeddings = text_embeddings
  
imports: import inspect
  import warnings
  from typing import List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from accelerate import Accelerator
  from packaging import version
  from tqdm.auto import tqdm
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers import DiffusionPipeline
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils import logging
label_desc: Use image node_id 4 for calling the ImagicStableDiffusionPipeline obj's underlying member method code with example usage: obj.train(prompt, image, height, width, generator, embedding_learning_rate, diffusion_model_learning_rate, text_embedding_optimization_steps, model_fine_tuning_optimization_steps) without return types