


"""simple docstring"""







lowerCamelCase_   :   List[Any]									=  """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
       if not is_onnx_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_onnx_objects import *  # noqa F403
else:
       from .pipelines import OnnxRuntimeModel

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_pt_objects import *  # noqa F403
else:
       from .models import (
           AutoencoderKL,
           ControlNetModel,
           ModelMixin,
           PriorTransformer,
           TaFilmDecoder,
           TransformeraDModel,
           UNetaDModel,
           UNetaDConditionModel,
           UNetaDModel,
           UNetaDConditionModel,
           VQModel,
       )
       from .optimization import (
           get_constant_schedule,
           get_constant_schedule_with_warmup,
           get_cosine_schedule_with_warmup,
           get_cosine_with_hard_restarts_schedule_with_warmup,
           get_linear_schedule_with_warmup,
           get_polynomial_decay_schedule_with_warmup,
           get_scheduler,
       )
       from .pipelines import (
           AudioPipelineOutput,
           ConsistencyModelPipeline,
           DanceDiffusionPipeline,
           DDIMPipeline,
           DDPMPipeline,
           DiffusionPipeline,
           DiTPipeline,
           ImagePipelineOutput,
           KarrasVePipeline,
           LDMPipeline,
           LDMSuperResolutionPipeline,
           PNDMPipeline,
           RePaintPipeline,
           ScoreSdeVePipeline,
       )
       from .schedulers import (
           CMStochasticIterativeScheduler,
           DDIMInverseScheduler,
           DDIMParallelScheduler,
           DDIMScheduler,
           DDPMParallelScheduler,
           DDPMScheduler,
           DEISMultistepScheduler,
           DPMSolverMultistepInverseScheduler,
           DPMSolverMultistepScheduler,
           DPMSolverSinglestepScheduler,
           EulerAncestralDiscreteScheduler,
           EulerDiscreteScheduler,
           HeunDiscreteScheduler,
           IPNDMScheduler,
           KarrasVeScheduler,
           KDPMaAncestralDiscreteScheduler,
           KDPMaDiscreteScheduler,
           PNDMScheduler,
           RePaintScheduler,
           SchedulerMixin,
           ScoreSdeVeScheduler,
           UnCLIPScheduler,
           UniPCMultistepScheduler,
           VQDiffusionScheduler,
       )
       from .training_utils import EMAModel

try:
       if not (is_torch_available() and is_scipy_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
       from .schedulers import LMSDiscreteScheduler

try:
       if not (is_torch_available() and is_torchsde_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
       from .schedulers import DPMSolverSDEScheduler

try:
       if not (is_torch_available() and is_transformers_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
       from .pipelines import (
           AltDiffusionImgaImgPipeline,
           AltDiffusionPipeline,
           AudioLDMPipeline,
           CycleDiffusionPipeline,
           IFImgaImgPipeline,
           IFImgaImgSuperResolutionPipeline,
           IFInpaintingPipeline,
           IFInpaintingSuperResolutionPipeline,
           IFPipeline,
           IFSuperResolutionPipeline,
           ImageTextPipelineOutput,
           KandinskyImgaImgPipeline,
           KandinskyInpaintPipeline,
           KandinskyPipeline,
           KandinskyPriorPipeline,
           KandinskyVaaControlnetImgaImgPipeline,
           KandinskyVaaControlnetPipeline,
           KandinskyVaaImgaImgPipeline,
           KandinskyVaaInpaintPipeline,
           KandinskyVaaPipeline,
           KandinskyVaaPriorEmbaEmbPipeline,
           KandinskyVaaPriorPipeline,
           LDMTextToImagePipeline,
           PaintByExamplePipeline,
           SemanticStableDiffusionPipeline,
           ShapEImgaImgPipeline,
           ShapEPipeline,
           StableDiffusionAttendAndExcitePipeline,
           StableDiffusionControlNetImgaImgPipeline,
           StableDiffusionControlNetInpaintPipeline,
           StableDiffusionControlNetPipeline,
           StableDiffusionDepthaImgPipeline,
           StableDiffusionDiffEditPipeline,
           StableDiffusionImageVariationPipeline,
           StableDiffusionImgaImgPipeline,
           StableDiffusionInpaintPipeline,
           StableDiffusionInpaintPipelineLegacy,
           StableDiffusionInstructPixaPixPipeline,
           StableDiffusionLatentUpscalePipeline,
           StableDiffusionLDMaDPipeline,
           StableDiffusionModelEditingPipeline,
           StableDiffusionPanoramaPipeline,
           StableDiffusionParadigmsPipeline,
           StableDiffusionPipeline,
           StableDiffusionPipelineSafe,
           StableDiffusionPixaPixZeroPipeline,
           StableDiffusionSAGPipeline,
           StableDiffusionUpscalePipeline,
           StableUnCLIPImgaImgPipeline,
           StableUnCLIPPipeline,
           TextToVideoSDPipeline,
           TextToVideoZeroPipeline,
           UnCLIPImageVariationPipeline,
           UnCLIPPipeline,
           UniDiffuserModel,
           UniDiffuserPipeline,
           UniDiffuserTextDecoder,
           VersatileDiffusionDualGuidedPipeline,
           VersatileDiffusionImageVariationPipeline,
           VersatileDiffusionPipeline,
           VersatileDiffusionTextToImagePipeline,
           VideoToVideoSDPipeline,
           VQDiffusionPipeline,
       )

try:
       if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
       from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
       if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
       from .pipelines import StableDiffusionKDiffusionPipeline

try:
       if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
       from .pipelines import (
           OnnxStableDiffusionImgaImgPipeline,
           OnnxStableDiffusionInpaintPipeline,
           OnnxStableDiffusionInpaintPipelineLegacy,
           OnnxStableDiffusionPipeline,
           OnnxStableDiffusionUpscalePipeline,
           StableDiffusionOnnxPipeline,
       )

try:
       if not (is_torch_available() and is_librosa_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
       from .pipelines import AudioDiffusionPipeline, Mel

try:
       if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
       from .pipelines import SpectrogramDiffusionPipeline

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_flax_objects import *  # noqa F403
else:
       from .models.controlnet_flax import FlaxControlNetModel
       from .models.modeling_flax_utils import FlaxModelMixin
       from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
       from .models.vae_flax import FlaxAutoencoderKL
       from .pipelines import FlaxDiffusionPipeline
       from .schedulers import (
           FlaxDDIMScheduler,
           FlaxDDPMScheduler,
           FlaxDPMSolverMultistepScheduler,
           FlaxKarrasVeScheduler,
           FlaxLMSDiscreteScheduler,
           FlaxPNDMScheduler,
           FlaxSchedulerMixin,
           FlaxScoreSdeVeScheduler,
       )


try:
       if not (is_flax_available() and is_transformers_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
       from .pipelines import (
           FlaxStableDiffusionControlNetPipeline,
           FlaxStableDiffusionImgaImgPipeline,
           FlaxStableDiffusionInpaintPipeline,
           FlaxStableDiffusionPipeline,
       )

try:
       if not (is_note_seq_available()):
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from .utils.dummy_note_seq_objects import *  # noqa F403
else:
       from .pipelines import MidiProcessor







"""simple docstring"""





from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						'''CompVis/stable-diffusion-v1-1'''
_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						'''CompVis/stable-diffusion-v1-2'''
_SCREAMING_SNAKE_CASE					:				int					    =						'''CompVis/stable-diffusion-v1-3'''
_SCREAMING_SNAKE_CASE					:				str					    =						'''CompVis/stable-diffusion-v1-4'''






class      a						(					__snake_case		):






    def __init__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							AutoencoderKL       ,						__SCREAMING_SNAKE_CASE     :							CLIPTextModel       ,						__SCREAMING_SNAKE_CASE     :							CLIPTokenizer       ,						__SCREAMING_SNAKE_CASE     :							UNetaDConditionModel       ,						__SCREAMING_SNAKE_CASE     :							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,						__SCREAMING_SNAKE_CASE     :							StableDiffusionSafetyChecker       ,						__SCREAMING_SNAKE_CASE     :							CLIPImageProcessor       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		List[str]:
      super()._init_()

      lowerCamelCase_         =						StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						StableDiffusionPipeline(
          vae=__SCREAMING_SNAKE_CASE       ,						text_encoder=__SCREAMING_SNAKE_CASE       ,						tokenizer=__SCREAMING_SNAKE_CASE       ,						unet=__SCREAMING_SNAKE_CASE       ,						scheduler=__SCREAMING_SNAKE_CASE       ,						safety_checker=__SCREAMING_SNAKE_CASE       ,						feature_extractor=__SCREAMING_SNAKE_CASE       ,						requires_safety_checker=__SCREAMING_SNAKE_CASE       ,						)

      self.register_modules(pipelinea=self.pipea       ,						pipelinea=self.pipea       ,						pipelinea=self.pipea       ,						pipelinea=self.pipea					)






    @property
    def    UpperCamelCase       (       self     :							List[str]					)     ->		Dict[str, Any]:
      return {k: getattr(self       ,						__SCREAMING_SNAKE_CASE					) for k in self.config.keys() if not k.startswith('_'					)}






    def    UpperCamelCase       (       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, int]] = "auto"					)     ->		Any:
      if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        lowerCamelCase_         =						self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Any					)     ->		List[Any]:
      self.enable_attention_slicing(__SCREAMING_SNAKE_CASE					)






    @torch.no_grad()
    def    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 50       ,						__SCREAMING_SNAKE_CASE     :							float = 7.5       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, List[str]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = 1       ,						__SCREAMING_SNAKE_CASE     :							float = 0.0       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.Generator] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.FloatTensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = "pil"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						**__SCREAMING_SNAKE_CASE     :							int       ,						)     ->		Tuple:
      return self.pipea(
          prompt=__SCREAMING_SNAKE_CASE       ,						height=__SCREAMING_SNAKE_CASE       ,						width=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=__SCREAMING_SNAKE_CASE       ,						guidance_scale=__SCREAMING_SNAKE_CASE       ,						negative_prompt=__SCREAMING_SNAKE_CASE       ,						num_images_per_prompt=__SCREAMING_SNAKE_CASE       ,						eta=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						latents=__SCREAMING_SNAKE_CASE       ,						output_type=__SCREAMING_SNAKE_CASE       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						callback=__SCREAMING_SNAKE_CASE       ,						callback_steps=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)






    @torch.no_grad()
    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 50       ,						__SCREAMING_SNAKE_CASE     :							float = 7.5       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, List[str]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = 1       ,						__SCREAMING_SNAKE_CASE     :							float = 0.0       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.Generator] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.FloatTensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = "pil"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						**__SCREAMING_SNAKE_CASE     :							List[str]       ,						)     ->		Optional[int]:
      return self.pipea(
          prompt=__SCREAMING_SNAKE_CASE       ,						height=__SCREAMING_SNAKE_CASE       ,						width=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=__SCREAMING_SNAKE_CASE       ,						guidance_scale=__SCREAMING_SNAKE_CASE       ,						negative_prompt=__SCREAMING_SNAKE_CASE       ,						num_images_per_prompt=__SCREAMING_SNAKE_CASE       ,						eta=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						latents=__SCREAMING_SNAKE_CASE       ,						output_type=__SCREAMING_SNAKE_CASE       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						callback=__SCREAMING_SNAKE_CASE       ,						callback_steps=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)






    @torch.no_grad()
    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 50       ,						__SCREAMING_SNAKE_CASE     :							float = 7.5       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, List[str]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = 1       ,						__SCREAMING_SNAKE_CASE     :							float = 0.0       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.Generator] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.FloatTensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = "pil"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						**__SCREAMING_SNAKE_CASE     :							Optional[int]       ,						)     ->		Tuple:
      return self.pipea(
          prompt=__SCREAMING_SNAKE_CASE       ,						height=__SCREAMING_SNAKE_CASE       ,						width=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=__SCREAMING_SNAKE_CASE       ,						guidance_scale=__SCREAMING_SNAKE_CASE       ,						negative_prompt=__SCREAMING_SNAKE_CASE       ,						num_images_per_prompt=__SCREAMING_SNAKE_CASE       ,						eta=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						latents=__SCREAMING_SNAKE_CASE       ,						output_type=__SCREAMING_SNAKE_CASE       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						callback=__SCREAMING_SNAKE_CASE       ,						callback_steps=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)






    @torch.no_grad()
    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 50       ,						__SCREAMING_SNAKE_CASE     :							float = 7.5       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, List[str]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = 1       ,						__SCREAMING_SNAKE_CASE     :							float = 0.0       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.Generator] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.FloatTensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = "pil"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						**__SCREAMING_SNAKE_CASE     :							Tuple       ,						)     ->		Tuple:
      return self.pipea(
          prompt=__SCREAMING_SNAKE_CASE       ,						height=__SCREAMING_SNAKE_CASE       ,						width=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=__SCREAMING_SNAKE_CASE       ,						guidance_scale=__SCREAMING_SNAKE_CASE       ,						negative_prompt=__SCREAMING_SNAKE_CASE       ,						num_images_per_prompt=__SCREAMING_SNAKE_CASE       ,						eta=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						latents=__SCREAMING_SNAKE_CASE       ,						output_type=__SCREAMING_SNAKE_CASE       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						callback=__SCREAMING_SNAKE_CASE       ,						callback_steps=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)






    @torch.no_grad()
    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Union[str, List[str]]       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 50       ,						__SCREAMING_SNAKE_CASE     :							float = 7.5       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[str, List[str]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = 1       ,						__SCREAMING_SNAKE_CASE     :							float = 0.0       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.Generator] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.FloatTensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = "pil"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						**__SCREAMING_SNAKE_CASE     :							int       ,						)     ->		str:
      lowerCamelCase_         =						'cuda' if torch.cuda.is_available() else 'cpu'
      self.to(__SCREAMING_SNAKE_CASE					)

      # Checks if the height and width are divisible by 8 or not
      if height % 8 != 0 or width % 8 != 0:
        raise ValueError(F'''`height` and `width` must be divisible by 8 but are {height} and {width}.'''					)

      # Get first result from Stable Diffusion Checkpoint v1.1
      lowerCamelCase_         =						self.textaimg_sda_a(
          prompt=__SCREAMING_SNAKE_CASE       ,						height=__SCREAMING_SNAKE_CASE       ,						width=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=__SCREAMING_SNAKE_CASE       ,						guidance_scale=__SCREAMING_SNAKE_CASE       ,						negative_prompt=__SCREAMING_SNAKE_CASE       ,						num_images_per_prompt=__SCREAMING_SNAKE_CASE       ,						eta=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						latents=__SCREAMING_SNAKE_CASE       ,						output_type=__SCREAMING_SNAKE_CASE       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						callback=__SCREAMING_SNAKE_CASE       ,						callback_steps=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      # Get first result from Stable Diffusion Checkpoint v1.2
      lowerCamelCase_         =						self.textaimg_sda_a(
          prompt=__SCREAMING_SNAKE_CASE       ,						height=__SCREAMING_SNAKE_CASE       ,						width=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=__SCREAMING_SNAKE_CASE       ,						guidance_scale=__SCREAMING_SNAKE_CASE       ,						negative_prompt=__SCREAMING_SNAKE_CASE       ,						num_images_per_prompt=__SCREAMING_SNAKE_CASE       ,						eta=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						latents=__SCREAMING_SNAKE_CASE       ,						output_type=__SCREAMING_SNAKE_CASE       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						callback=__SCREAMING_SNAKE_CASE       ,						callback_steps=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      # Get first result from Stable Diffusion Checkpoint v1.3
      lowerCamelCase_         =						self.textaimg_sda_a(
          prompt=__SCREAMING_SNAKE_CASE       ,						height=__SCREAMING_SNAKE_CASE       ,						width=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=__SCREAMING_SNAKE_CASE       ,						guidance_scale=__SCREAMING_SNAKE_CASE       ,						negative_prompt=__SCREAMING_SNAKE_CASE       ,						num_images_per_prompt=__SCREAMING_SNAKE_CASE       ,						eta=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						latents=__SCREAMING_SNAKE_CASE       ,						output_type=__SCREAMING_SNAKE_CASE       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						callback=__SCREAMING_SNAKE_CASE       ,						callback_steps=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      # Get first result from Stable Diffusion Checkpoint v1.4
      lowerCamelCase_         =						self.textaimg_sda_a(
          prompt=__SCREAMING_SNAKE_CASE       ,						height=__SCREAMING_SNAKE_CASE       ,						width=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=__SCREAMING_SNAKE_CASE       ,						guidance_scale=__SCREAMING_SNAKE_CASE       ,						negative_prompt=__SCREAMING_SNAKE_CASE       ,						num_images_per_prompt=__SCREAMING_SNAKE_CASE       ,						eta=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						latents=__SCREAMING_SNAKE_CASE       ,						output_type=__SCREAMING_SNAKE_CASE       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						callback=__SCREAMING_SNAKE_CASE       ,						callback_steps=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
      return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]					)




