code: def __call__(
      self,
      base_prompt: str,
      target_prompt: str,
      image: Image.Image,
      guidance_scale: float = 3.0,
      num_inference_steps: int = 50,
      strength: float = 0.8,
      negative_prompt: Optional[str] = None,
      generator: Optional[torch.Generator] = None,
      output_type: Optional[str] = "pil",
  ):
      do_classifier_free_guidance = guidance_scale > 1.0
  
      image = self.image_processor.preprocess(image)
  
      base_embeds = self._encode_prompt(
          base_prompt, negative_prompt, do_classifier_free_guidance
      )
      target_embeds = self._encode_prompt(
          target_prompt, negative_prompt, do_classifier_free_guidance
      )
  
      self.scheduler.set_timesteps(num_inference_steps, self.device)
  
      t_limit = num_inference_steps - int(
          num_inference_steps * strength
      )
      fwd_timesteps = self.scheduler.timesteps[t_limit:]
      bwd_timesteps = fwd_timesteps.flip(0)
  
      coupled_latents = self.prepare_latents(
          image, base_embeds, bwd_timesteps, guidance_scale, generator
      )
  
      for i, t in tqdm(
          enumerate(fwd_timesteps), total=len(fwd_timesteps)
      ):
          # j - model_input index, k - base index
          for k in range(2):
              j = k ^ 1
  
              if self.leapfrog_steps:
                  if i % 2 == 1:
                      k, j = j, k
  
              model_input = coupled_latents[j]
              base = coupled_latents[k]
  
              latent_model_input = (
                  torch.cat([model_input] * 2)
                  if do_classifier_free_guidance
                  else model_input
              )
  
              noise_pred = self.unet(
                  latent_model_input,
                  t,
                  encoder_hidden_states=target_embeds,
              ).sample
  
              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
                  )
  
              base, model_input = self.denoise_step(
                  base=base,
                  model_input=model_input,
                  model_output=noise_pred,
                  timestep=t,
              )
  
              coupled_latents[k] = model_input
  
          coupled_latents = self.denoise_mixing_layer(
              x=coupled_latents[0], y=coupled_latents[1]
          )
  
      # either one is fine
      final_latent = coupled_latents[0]
  
      if output_type not in ["latent", "pt", "np", "pil"]:
          deprecation_message = (
              f"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
              "`pil`, `np`, `pt`, `latent`"
          )
          deprecate(
              "Unsupported output_type",
              "1.0.0",
              deprecation_message,
              standard_warn=False,
          )
          output_type = "np"
  
      if output_type == "latent":
          image = final_latent
      else:
          image = self.decode_latents(final_latent)
          image = self.image_processor.postprocess(
              image, output_type=output_type
          )
  
      return image
  
file_path: llmga/llmga/diffusers/examples/community/edict_pipeline.py
imports: from typing import Optional
  import torch
  from PIL import Image
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNet2DConditionModel
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.utils import deprecate
label_desc: Use image node_id 10 for calling the EDICTPipeline obj's underlying member method code with example usage: obj.__call__(base_prompt, target_prompt, image, guidance_scale, num_inference_steps, strength, negative_prompt, generator, output_type) and returns: image