






'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=0.999						,		_UpperCamelCase="cosine"						,		):


   """simple docstring"""




   if alpha_transform_type == "cosine":

      def alpha_bar_fn(_UpperCamelCase							):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(_UpperCamelCase							):
         return math.exp(t * -12.0							)

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""							)

   lowercase_	:    Union[str, Any]           =     []
   for i in range(_UpperCamelCase							):
      lowercase_	:    Any           =     i / num_diffusion_timesteps
      lowercase_	:    int           =     (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(_UpperCamelCase							) / alpha_bar_fn(_UpperCamelCase							)						,		_UpperCamelCase							)							)
   return torch.tensor(_UpperCamelCase						,		dtype=torch.floataa							)





class        _UpperCAmelCase						(		snake_case				,					snake_case			):
    __lowerCamelCase:   Dict		       =    [e.name for e in KarrasDiffusionSchedulers]
    __lowerCamelCase:   Optional[Any]		       =    2




    @register_to_config
    def __init__(			self  :       Dict  ,     a  :       int = 1_0_0_0  ,     a  :       float = 0.0_0085  ,     a  :       float = 0.012  ,     a  :       str = "linear"  ,     a  :       Optional[Union[np.ndarray, List[float]]] = None  ,     a  :       str = "epsilon"  ,     a  :       str = "linspace"  ,     a  :       int = 0  ,     ):






       '''simple docstring'''






       if trained_betas is not None:
          lowercase_	:    Tuple           =     torch.tensor(a  ,     dtype=torch.floataa			)
       elif beta_schedule == "linear":
          lowercase_	:    Optional[int]           =     torch.linspace(a  ,     a  ,     a  ,     dtype=torch.floataa			)
       elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase_	:    Tuple           =     (
              torch.linspace(beta_start**0.5  ,     beta_end**0.5  ,     a  ,     dtype=torch.floataa			) ** 2
          )
       elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase_	:    str           =     betas_for_alpha_bar(a			)
       else:
          raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""			)

       lowercase_	:    int           =     1.0 - self.betas
       lowercase_	:    Tuple           =     torch.cumprod(self.alphas  ,     dim=0			)

       #  set all values
       self.set_timesteps(a  ,     a  ,     a			)




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       List[Any]  ,     a  :       Optional[Any]=None			):






       '''simple docstring'''






       if schedule_timesteps is None:
          lowercase_	:    Union[str, Any]           =     self.timesteps

       lowercase_	:    Tuple           =     (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter			) == 0:
          lowercase_	:    str           =     1 if len(a			) > 1 else 0
       else:
          lowercase_	:    Tuple           =     timestep.cpu().item() if torch.is_tensor(a			) else timestep
          lowercase_	:    Any           =     self._index_counter[timestep_int]

       return indices[pos].item()




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       torch.FloatTensor  ,     a  :       Union[float, torch.FloatTensor]  ,     ):






       '''simple docstring'''






       lowercase_	:    Any           =     self.index_for_timestep(a			)

       if self.state_in_first_order:
          lowercase_	:    List[Any]           =     self.sigmas[step_index]
       else:
          lowercase_	:    Union[str, Any]           =     self.sigmas_interpol[step_index]

       lowercase_	:    List[Any]           =     sample / ((sigma**2 + 1) ** 0.5)
       return sample




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       int  ,     a  :       Union[str, torch.device] = None  ,     a  :       Optional[int] = None  ,     ):






       '''simple docstring'''






       lowercase_	:    str           =     num_inference_steps

       lowercase_	:    Dict           =     num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
          lowercase_	:    List[str]           =     np.linspace(0  ,     num_train_timesteps - 1  ,     a  ,     dtype=a			)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
          lowercase_	:    List[str]           =     num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowercase_	:    List[Any]           =     (np.arange(0  ,     a			) * step_ratio).round()[::-1].copy().astype(a			)
          timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
          lowercase_	:    Union[str, Any]           =     num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowercase_	:    Optional[Any]           =     (np.arange(a  ,     0  ,     -step_ratio			)).round().copy().astype(a			)
          timesteps -= 1
       else:
          raise ValueError(
              f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""			)

       lowercase_	:    Tuple           =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
       lowercase_	:    List[str]           =     torch.from_numpy(np.log(a			)			).to(a			)

       lowercase_	:    Union[str, Any]           =     np.interp(a  ,     np.arange(0  ,     len(a			)			)  ,     a			)
       lowercase_	:    Any           =     np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
       lowercase_	:    List[Any]           =     torch.from_numpy(a			).to(device=a			)

       # interpolate sigmas
       lowercase_	:    str           =     sigmas.log().lerp(sigmas.roll(1			).log()  ,     0.5			).exp()

       lowercase_	:    Any           =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
       lowercase_	:    Tuple           =     torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2			), sigmas_interpol[-1:]]			)

       if str(a			).startswith("mps"			):
          # mps does not support float64
          lowercase_	:    List[Any]           =     torch.from_numpy(a			).to(a  ,     dtype=torch.floataa			)
       else:
          lowercase_	:    Any           =     torch.from_numpy(a			).to(a			)

       # interpolate timesteps
       lowercase_	:    List[str]           =     self.sigma_to_t(a			).to(a  ,     dtype=timesteps.dtype			)
       lowercase_	:    Tuple           =     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,     dim=-1			).flatten()

       lowercase_	:    Optional[Any]           =     torch.cat([timesteps[:1], interleaved_timesteps]			)

       lowercase_	:    int           =     None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       lowercase_	:    Optional[int]           =     defaultdict(a			)




    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int			):






       '''simple docstring'''






       lowercase_	:    Any           =     sigma.log()

       # get distribution
       lowercase_	:    Dict           =     log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       lowercase_	:    Union[str, Any]           =     dists.ge(0			).cumsum(dim=0			).argmax(dim=0			).clamp(max=self.log_sigmas.shape[0] - 2			)
       lowercase_	:    int           =     low_idx + 1

       lowercase_	:    Optional[int]           =     self.log_sigmas[low_idx]
       lowercase_	:    List[str]           =     self.log_sigmas[high_idx]

       # interpolate sigmas
       lowercase_	:    Union[str, Any]           =     (low - log_sigma) / (low - high)
       lowercase_	:    int           =     w.clamp(0  ,     1			)

       # transform interpolation to time range
       lowercase_	:    Union[str, Any]           =     (1 - w) * low_idx + w * high_idx
       lowercase_	:    int           =     t.view(sigma.shape			)
       return t




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return self.sample is None




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       Union[torch.FloatTensor, np.ndarray]  ,     a  :       Union[float, torch.FloatTensor]  ,     a  :       Union[torch.FloatTensor, np.ndarray]  ,     a  :       bool = True  ,     ):






       '''simple docstring'''






       lowercase_	:    str           =     self.index_for_timestep(a			)

       # advance index counter by 1
       lowercase_	:    List[str]           =     timestep.cpu().item() if torch.is_tensor(a			) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
          lowercase_	:    List[str]           =     self.sigmas[step_index]
          lowercase_	:    int           =     self.sigmas_interpol[step_index + 1]
          lowercase_	:    Optional[int]           =     self.sigmas[step_index + 1]
       else:
          # 2nd order / KDPM2's method
          lowercase_	:    List[Any]           =     self.sigmas[step_index - 1]
          lowercase_	:    Tuple           =     self.sigmas_interpol[step_index]
          lowercase_	:    Dict           =     self.sigmas[step_index]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       lowercase_	:    Optional[int]           =     0
       lowercase_	:    Any           =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
          lowercase_	:    Optional[int]           =     sigma_hat if self.state_in_first_order else sigma_interpol
          lowercase_	:    str           =     sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
          lowercase_	:    Optional[int]           =     sigma_hat if self.state_in_first_order else sigma_interpol
          lowercase_	:    Union[str, Any]           =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
              sample / (sigma_input**2 + 1)
          )
       elif self.config.prediction_type == "sample":
          raise NotImplementedError("prediction_type not implemented yet: sample"			)
       else:
          raise ValueError(
              f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""			)

       if self.state_in_first_order:
          # 2. Convert to an ODE derivative for 1st order
          lowercase_	:    Tuple           =     (sample - pred_original_sample) / sigma_hat
          # 3. delta timestep
          lowercase_	:    str           =     sigma_interpol - sigma_hat

          # store for 2nd order step
          lowercase_	:    str           =     sample
       else:
          # DPM-Solver-2
          # 2. Convert to an ODE derivative for 2nd order
          lowercase_	:    Optional[int]           =     (sample - pred_original_sample) / sigma_interpol

          # 3. delta timestep
          lowercase_	:    Union[str, Any]           =     sigma_next - sigma_hat

          lowercase_	:    int           =     self.sample
          lowercase_	:    Any           =     None

       lowercase_	:    Optional[Any]           =     sample + derivative * dt

       if not return_dict:
          return (prev_sample,)

       return SchedulerOutput(prev_sample=a			)




    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       torch.FloatTensor  ,     a  :       torch.FloatTensor  ,     a  :       torch.FloatTensor  ,     ):






       '''simple docstring'''






       lowercase_	:    str           =     self.sigmas.to(device=original_samples.device  ,     dtype=original_samples.dtype			)
       if original_samples.device.type == "mps" and torch.is_floating_point(a			):
          # mps does not support float64
          lowercase_	:    Optional[int]           =     self.timesteps.to(original_samples.device  ,     dtype=torch.floataa			)
          lowercase_	:    int           =     timesteps.to(original_samples.device  ,     dtype=torch.floataa			)
       else:
          lowercase_	:    Union[str, Any]           =     self.timesteps.to(original_samples.device			)
          lowercase_	:    Tuple           =     timesteps.to(original_samples.device			)

       lowercase_	:    Union[str, Any]           =     [self.index_for_timestep(a  ,     a			) for t in timesteps]

       lowercase_	:    List[Any]           =     sigmas[step_indices].flatten()
       while len(sigma.shape			) < len(original_samples.shape			):
          lowercase_	:    Tuple           =     sigma.unsqueeze(-1			)

       lowercase_	:    Any           =     original_samples + noise * sigma
       return noisy_samples





    def __len__(			self  :       List[str]			):






       '''simple docstring'''






       return self.config.num_train_timesteps












'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCamelCase__ =			logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase__ =			'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=8							):


   """simple docstring"""




   lowercase_	:    Union[str, Any]           =     height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   lowercase_	:    Union[str, Any]           =     width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=512						,		_UpperCamelCase=512							):


   """simple docstring"""




   lowercase_	:    Union[str, Any]           =     pil_image.resize((w, h)						,		resample=Image.BICUBIC						,		reducing_gap=1							)
   lowercase_	:    str           =     np.array(pil_image.convert("RGB"							)							)
   lowercase_	:    Optional[int]           =     arr.astype(np.floataa							) / 127.5 - 1
   lowercase_	:    int           =     np.transpose(_UpperCamelCase						,		[2, 0, 1]							)
   lowercase_	:    str           =     torch.from_numpy(_UpperCamelCase							).unsqueeze(0							)
   return image





class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       List[Any]  ,     a  :       UNetaDConditionModel  ,     a  :       DDPMScheduler  ,     a  :       VQModel  ,     ):






       '''simple docstring'''






       super().__init__()

       self.register_modules(
           unet=a  ,     scheduler=a  ,     movq=a  ,     )
       lowercase_	:    Dict           =     2 ** (len(self.movq.config.block_out_channels			) - 1)




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Tuple  ,     a  :       List[str]  ,     a  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Dict           =     min(int(num_inference_steps * strength			)  ,     a			)

       lowercase_	:    str           =     max(num_inference_steps - init_timestep  ,     0			)
       lowercase_	:    Tuple           =     self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       int  ,     a  :       List[Any]  ,     a  :       Tuple  ,     a  :       Union[str, Any]  ,     a  :       int  ,     a  :       Tuple  ,     a  :       Optional[Any]=None			):






       '''simple docstring'''






       if not isinstance(a  ,     (torch.Tensor, PIL.Image.Image, list)			):
          raise ValueError(
              f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a			)}"""			)

       lowercase_	:    str           =     image.to(device=a  ,     dtype=a			)

       lowercase_	:    Any           =     batch_size * num_images_per_prompt

       if image.shape[1] == 4:
          lowercase_	:    str           =     image

       else:
          if isinstance(a  ,     a			) and len(a			) != batch_size:
             raise ValueError(
                 f"""You have passed a list of generators of length {len(a			)}, but requested an effective batch"""
                 f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""			)

          elif isinstance(a  ,     a			):
             lowercase_	:    str           =     [
                 self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(a			)
             ]
             lowercase_	:    List[Any]           =     torch.cat(a  ,     dim=0			)
          else:
             lowercase_	:    Tuple           =     self.movq.encode(a			).latent_dist.sample(a			)

          lowercase_	:    Union[str, Any]           =     self.movq.config.scaling_factor * init_latents

       lowercase_	:    Tuple           =     torch.cat([init_latents]  ,     dim=0			)

       lowercase_	:    List[Any]           =     init_latents.shape
       lowercase_	:    Union[str, Any]           =     randn_tensor(a  ,     generator=a  ,     device=a  ,     dtype=a			)

       # get latents
       lowercase_	:    Dict           =     self.scheduler.add_noise(a  ,     a  ,     a			)

       lowercase_	:    Tuple           =     init_latents

       return latents




    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       str=0			):






       '''simple docstring'''






       if is_accelerate_available():
          from accelerate import cpu_offload
       else:
          raise ImportError("Please install accelerate via `pip install accelerate`"			)

       lowercase_	:    Optional[Any]           =     torch.device(f"""cuda:{gpu_id}"""			)

       lowercase_	:    Optional[Any]           =     [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
          if cpu_offloaded_model is not None:
             cpu_offload(a  ,     a			)




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       Union[str, Any]=0			):






       '''simple docstring'''






       if is_accelerate_available() and is_accelerate_version(">="  ,     "0.17.0.dev0"			):
          from accelerate import cpu_offload_with_hook
       else:
          raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."			)

       lowercase_	:    Any           =     torch.device(f"""cuda:{gpu_id}"""			)

       if self.device.type != "cpu":
          self.to("cpu"  ,     silence_dtype_warnings=a			)
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       lowercase_	:    Optional[int]           =     None
       for cpu_offloaded_model in [self.unet, self.movq]:
          lowercase_    ,		lowercase_	:    Union[str, Any]           =     cpu_offload_with_hook(a  ,     a  ,     prev_module_hook=a			)

       # We'll offload the last model manually.
       lowercase_	:    List[Any]           =     hook




    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       if not hasattr(self.unet  ,     "_hf_hook"			):
          return self.device
       for module in self.unet.modules():
          if (
              hasattr(a  ,     "_hf_hook"			)
              and hasattr(module._hf_hook  ,     "execution_device"			)
              and module._hf_hook.execution_device is not None
          ):
             return torch.device(module._hf_hook.execution_device			)
       return self.device





    @torch.no_grad()
    @replace_example_docstring(a			)
    def __call__(			self  :       Optional[int]  ,     a  :       Union[torch.FloatTensor, List[torch.FloatTensor]]  ,     a  :       Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]  ,     a  :       Union[torch.FloatTensor, List[torch.FloatTensor]]  ,     a  :       int = 5_1_2  ,     a  :       int = 5_1_2  ,     a  :       int = 1_0_0  ,     a  :       float = 4.0  ,     a  :       float = 0.3  ,     a  :       int = 1  ,     a  :       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     a  :       Optional[str] = "pil"  ,     a  :       bool = True  ,     ):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self._execution_device

       lowercase_	:    Dict           =     guidance_scale > 1.0

       if isinstance(a  ,     a			):
          lowercase_	:    Dict           =     torch.cat(a  ,     dim=0			)
       lowercase_	:    Dict           =     image_embeds.shape[0]
       if isinstance(a  ,     a			):
          lowercase_	:    str           =     torch.cat(a  ,     dim=0			)

       if do_classifier_free_guidance:
          lowercase_	:    Optional[Any]           =     image_embeds.repeat_interleave(a  ,     dim=0			)
          lowercase_	:    int           =     negative_image_embeds.repeat_interleave(a  ,     dim=0			)

       lowercase_	:    int           =     torch.cat([negative_image_embeds, image_embeds]  ,     dim=0			).to(dtype=self.unet.dtype  ,     device=a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    List[Any]           =     [image]
       if not all(isinstance(a  ,     (PIL.Image.Image, torch.Tensor)			) for i in image			):
          raise ValueError(
              f"""Input is in incorrect format: {[type(a			) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""			)

       lowercase_	:    List[Any]           =     torch.cat([prepare_image(a  ,     a  ,     a			) for i in image]  ,     dim=0			)
       lowercase_	:    List[Any]           =     image.to(dtype=image_embeds.dtype  ,     device=a			)

       lowercase_	:    Optional[int]           =     self.movq.encode(a			)["latents"]
       lowercase_	:    Dict           =     latents.repeat_interleave(a  ,     dim=0			)
       self.scheduler.set_timesteps(a  ,     device=a			)
       lowercase_    ,		lowercase_	:    List[Any]           =     self.get_timesteps(a  ,     a  ,     a			)
       lowercase_	:    Tuple           =     timesteps[:1].repeat(batch_size * num_images_per_prompt			)
       lowercase_    ,		lowercase_	:    Optional[Any]           =     downscale_height_and_width(a  ,     a  ,     self.movq_scale_factor			)
       lowercase_	:    Tuple           =     self.prepare_latents(
           a  ,     a  ,     a  ,     a  ,     image_embeds.dtype  ,     a  ,     a			)
       for i, t in enumerate(self.progress_bar(a			)			):
          # expand the latents if we are doing classifier free guidance
          lowercase_	:    Tuple           =     torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

          lowercase_	:    int           =     {"image_embeds": image_embeds}
          lowercase_	:    Optional[int]           =     self.unet(
              sample=a  ,     timestep=a  ,     encoder_hidden_states=a  ,     added_cond_kwargs=a  ,     return_dict=a  ,     )[0]

          if do_classifier_free_guidance:
             lowercase_    ,		lowercase_	:    List[Any]           =     noise_pred.split(latents.shape[1]  ,     dim=1			)
             lowercase_    ,		lowercase_	:    int           =     noise_pred.chunk(2			)
             lowercase_    ,		lowercase_	:    Any           =     variance_pred.chunk(2			)
             lowercase_	:    Dict           =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             lowercase_	:    Optional[Any]           =     torch.cat([noise_pred, variance_pred_text]  ,     dim=1			)

          if not (
              hasattr(self.scheduler.config  ,     "variance_type"			)
              and self.scheduler.config.variance_type in ["learned", "learned_range"]
          ):
             lowercase_    ,		lowercase_	:    List[Any]           =     noise_pred.split(latents.shape[1]  ,     dim=1			)

          # compute the previous noisy sample x_t -> x_t-1
          lowercase_	:    Dict           =     self.scheduler.step(
              a  ,     a  ,     a  ,     generator=a  ,     )[0]

       # post-processing
       lowercase_	:    Optional[Any]           =     self.movq.decode(a  ,     force_not_quantize=a			)["sample"]

       if output_type not in ["pt", "np", "pil"]:
          raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""			)

       if output_type in ["np", "pil"]:
          lowercase_	:    Tuple           =     image * 0.5 + 0.5
          lowercase_	:    Any           =     image.clamp(0  ,     1			)
          lowercase_	:    Any           =     image.cpu().permute(0  ,     2  ,     3  ,     1			).float().numpy()

       if output_type == "pil":
          lowercase_	:    Tuple           =     self.numpy_to_pil(a			)

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=a			)




