code: 
  
  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, UNetaDConditionModel
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.utils import (
      deprecate,
  )
  
  
  class    SCREAMING_SNAKE_CASE__      (SCREAMING_SNAKE_CASE__  ):
  
  
  
  
  
      def __init__(   self		,     a		,     a		,     a		,     a		,     a		,     a = 0.93		,     a = True		,     ):
           lowercase__					:     Optional[Any]   = mixing_coeff
           lowercase__					:     int   = leapfrog_steps
  
           super().__init__()
           self.register_modules(
               vae=a		,     text_encoder=a		,     tokenizer=a		,     unet=a		,     scheduler=a		,     )
  
           lowercase__					:     Any   = 2 ** (len(self.vae.config.block_out_channels) - 1)
           lowercase__					:     int   = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a = None		,     a = False):
           lowercase__					:     Dict   = self.tokenizer(
               a		,     padding='max_length'		,     max_length=self.tokenizer.model_max_length		,     truncation=a		,     return_tensors='pt'		,     )
  
           lowercase__					:     Optional[Any]   = self.text_encoder(text_inputs.input_ids.to(self.device)).last_hidden_state
  
           lowercase__					:     Union[str, Any]   = prompt_embeds.to(dtype=self.text_encoder.dtype		,     device=self.device)
  
           if do_classifier_free_guidance:
                lowercase__					:     Any   = '' if negative_prompt is None else negative_prompt
  
                lowercase__					:     str   = self.tokenizer(
                    a		,     padding='max_length'		,     max_length=self.tokenizer.model_max_length		,     truncation=a		,     return_tensors='pt'		,     )
  
                lowercase__					:     Optional[Any]   = self.text_encoder(uncond_input.input_ids.to(self.device)).last_hidden_state
  
                lowercase__					:     Dict   = torch.cat([negative_prompt_embeds, prompt_embeds])
  
           return prompt_embeds
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a):
           lowercase__					:     Optional[int]   = self.mixing_coeff * x + (1 - self.mixing_coeff) * y
           lowercase__					:     Optional[int]   = self.mixing_coeff * y + (1 - self.mixing_coeff) * x
  
           return [x, y]
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a):
           lowercase__					:     Union[str, Any]   = (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
           lowercase__					:     List[str]   = (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff
  
           return [x, y]
  
  
  
  
  
      def    snake_case_       (   self		,     a):
           lowercase__					:     Any   = int(a)
  
           lowercase__					:     Union[str, Any]   = self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod
  
           return alpha_prod, 1 - alpha_prod
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     ):
           lowercase__					:     Tuple   = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
           lowercase__   ,				lowercase__					:     int   = self._get_alpha_and_beta(a)
           lowercase__   ,				lowercase__					:     Any   = self._get_alpha_and_beta(a)
  
           lowercase__					:     Optional[int]   = (alpha_prod_t_prev / alpha_prod_t) ** 0.5
           lowercase__					:     str   = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
  
           lowercase__					:     Optional[int]   = (base - b_t * model_output) / a_t
  
           return model_input, next_model_input.to(base.dtype)
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     ):
           lowercase__					:     Any   = timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
           lowercase__   ,				lowercase__					:     List[str]   = self._get_alpha_and_beta(a)
           lowercase__   ,				lowercase__					:     Optional[Any]   = self._get_alpha_and_beta(a)
  
           lowercase__					:     str   = (alpha_prod_t_prev / alpha_prod_t) ** 0.5
           lowercase__					:     Optional[int]   = -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
           lowercase__					:     Tuple   = a_t * base + b_t * model_output
  
           return model_input, next_model_input.to(base.dtype)
  
  
  
  
  
      @torch.no_grad()
      def    snake_case_       (   self		,     a):
           lowercase__					:     Union[str, Any]   = 1 / self.vae.config.scaling_factor * latents
           lowercase__					:     Tuple   = self.vae.decode(a).sample
           lowercase__					:     List[Any]   = (image / 2 + 0.5).clamp(0		,     1)
           return image
  
  
  
  
  
      @torch.no_grad()
      def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a = None		,     ):
           lowercase__					:     str   = guidance_scale > 1.0
  
           lowercase__					:     int   = image.to(device=self.device		,     dtype=text_embeds.dtype)
           lowercase__					:     Optional[int]   = self.vae.encode(a).latent_dist.sample(a)
  
           lowercase__					:     Any   = self.vae.config.scaling_factor * latent
  
           lowercase__					:     Optional[int]   = [latent.clone(), latent.clone()]
  
           for i, t in tqdm(enumerate(a)		,     total=len(a)):
                lowercase__					:     List[str]   = self.noise_mixing_layer(x=coupled_latents[0]		,     y=coupled_latents[1])
  
                # j - model_input index, k - base index
                for j in range(2):
                     lowercase__					:     Optional[int]   = j ^ 1
  
                     if self.leapfrog_steps:
                          if i % 2 == 0:
                               lowercase__   ,				lowercase__					:     List[str]   = j, k
  
                     lowercase__					:     List[str]   = coupled_latents[j]
                     lowercase__					:     Any   = coupled_latents[k]
  
                     lowercase__					:     Union[str, Any]   = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
  
                     lowercase__					:     Tuple   = self.unet(a		,     a		,     encoder_hidden_states=a).sample
  
                     if do_classifier_free_guidance:
                          lowercase__   ,				lowercase__					:     str   = noise_pred.chunk(2)
                          lowercase__					:     Union[str, Any]   = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                     lowercase__   ,				lowercase__					:     Any   = self.noise_step(
                         base=a		,     model_input=a		,     model_output=a		,     timestep=a		,     )
  
                     lowercase__					:     Any   = model_input
  
           return coupled_latents
  
  
  
  
  
      @torch.no_grad()
      def __call__(   self		,     a		,     a		,     a		,     a = 3.0		,     a = 50		,     a = 0.8		,     a = None		,     a = None		,     a = "pil"		,     ):
           lowercase__					:     Optional[int]   = guidance_scale > 1.0
  
           lowercase__					:     Optional[Any]   = self.image_processor.preprocess(a)
  
           lowercase__					:     int   = self._encode_prompt(a		,     a		,     a)
           lowercase__					:     List[str]   = self._encode_prompt(a		,     a		,     a)
  
           self.scheduler.set_timesteps(a		,     self.device)
  
           lowercase__					:     List[str]   = num_inference_steps - int(num_inference_steps * strength)
           lowercase__					:     Any   = self.scheduler.timesteps[t_limit:]
           lowercase__					:     str   = fwd_timesteps.flip(0)
  
           lowercase__					:     Optional[Any]   = self.prepare_latents(a		,     a		,     a		,     a		,     a)
  
           for i, t in tqdm(enumerate(a)		,     total=len(a)):
                # j - model_input index, k - base index
                for k in range(2):
                     lowercase__					:     Dict   = k ^ 1
  
                     if self.leapfrog_steps:
                          if i % 2 == 1:
                               lowercase__   ,				lowercase__					:     Union[str, Any]   = j, k
  
                     lowercase__					:     List[Any]   = coupled_latents[j]
                     lowercase__					:     Optional[Any]   = coupled_latents[k]
  
                     lowercase__					:     Union[str, Any]   = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
  
                     lowercase__					:     Optional[int]   = self.unet(a		,     a		,     encoder_hidden_states=a).sample
  
                     if do_classifier_free_guidance:
                          lowercase__   ,				lowercase__					:     Union[str, Any]   = noise_pred.chunk(2)
                          lowercase__					:     int   = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                     lowercase__   ,				lowercase__					:     List[Any]   = self.denoise_step(
                         base=a		,     model_input=a		,     model_output=a		,     timestep=a		,     )
  
                     lowercase__					:     int   = model_input
  
                lowercase__					:     int   = self.denoise_mixing_layer(x=coupled_latents[0]		,     y=coupled_latents[1])
  
           # either one is fine
           lowercase__					:     Optional[Any]   = coupled_latents[0]
  
           if output_type not in ["latent", "pt", "np", "pil"]:
                lowercase__					:     Union[str, Any]   = (
                    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'		,     a		,     standard_warn=a)
                lowercase__					:     str   = 'np'
  
           if output_type == "latent":
                lowercase__					:     Any   = final_latent
           else:
                lowercase__					:     Any   = self.decode_latents(a)
                lowercase__					:     Optional[int]   = self.image_processor.postprocess(a		,     output_type=a)
  
           return image
  
  
style_context: 
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  
  from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: Union[List[PIL.Image.Image], np.ndarray]
  
  
  if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
         from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
         from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
  
  