"""simple docstring"""





from typing import List, Optional, Union

import torch

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,
)


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

__A  =				'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	int						,   _lowerCamelCase:	Dict=8      ) ->      Optional[int]:



							'''simple docstring'''

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







class  _snake_case     (					a__		):






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			UNetaDConditionModel							,					UpperCAmelCase      :			DDPMScheduler							,					UpperCAmelCase      :			VQModel							,					):
													super().__init__()

													self.register_modules(
													    unet=__UpperCamelCase							,					scheduler=__UpperCamelCase							,					movq=__UpperCamelCase							,					)
													__lowerCamelCase					:		Optional[Any]		   =     2 ** (len(self.movq.config.block_out_channels    ) - 1)






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Dict    ):
													if latents is None:
																				__lowerCamelCase					:		Optional[Any]		   =     randn_tensor(__UpperCamelCase							,					generator=__UpperCamelCase							,					device=__UpperCamelCase							,					dtype=__UpperCamelCase    )
													else:
																				if latents.shape != shape:
																											raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""    )
																				__lowerCamelCase					:		Tuple		   =     latents.to(__UpperCamelCase    )

													__lowerCamelCase					:		int		   =     latents * scheduler.init_noise_sigma
													return latents






						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			int=0    ):
													if is_accelerate_available():
																				from accelerate import cpu_offload
													else:
																				raise ImportError("Please install accelerate via `pip install accelerate`"    )

													__lowerCamelCase					:		Optional[int]		   =     torch.device(F"""cuda:{gpu_id}"""    )

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






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Any=0    ):
													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."    )

													__lowerCamelCase					:		Tuple		   =     torch.device(F"""cuda:{gpu_id}"""    )

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

													__lowerCamelCase					:		Optional[int]		   =     None
													for cpu_offloaded_model in [self.unet, self.movq]:
																				__lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     cpu_offload_with_hook(__UpperCamelCase							,					__UpperCamelCase							,					prev_module_hook=__UpperCamelCase    )

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






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													if not hasattr(self.unet							,					"_hf_hook"    ):
																				return self.device
													for module in self.unet.modules():
																				if (
																				    hasattr(__UpperCamelCase							,					"_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(__UpperCamelCase    )
						def __call__(       self      :			str							,					UpperCAmelCase      :			Union[torch.FloatTensor, List[torch.FloatTensor]]							,					UpperCAmelCase      :			Union[torch.FloatTensor, List[torch.FloatTensor]]							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 100							,					UpperCAmelCase      :			float = 4.0							,					UpperCAmelCase      :			int = 1							,					UpperCAmelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					UpperCAmelCase      :			Optional[torch.FloatTensor] = None							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					):
													__lowerCamelCase					:		Any		   =     self._execution_device

													__lowerCamelCase					:		int		   =     guidance_scale > 1.0

													if isinstance(__UpperCamelCase							,					__UpperCamelCase    ):
																				__lowerCamelCase					:		Dict		   =     torch.cat(__UpperCamelCase							,					dim=0    )
													if isinstance(__UpperCamelCase							,					__UpperCamelCase    ):
																				__lowerCamelCase					:		Union[str, Any]		   =     torch.cat(__UpperCamelCase							,					dim=0    )
													if isinstance(__UpperCamelCase							,					__UpperCamelCase    ):
																				__lowerCamelCase					:		Optional[Any]		   =     torch.cat(__UpperCamelCase							,					dim=0    )

													__lowerCamelCase					:		Optional[Any]		   =     image_embeds.shape[0] * num_images_per_prompt

													if do_classifier_free_guidance:
																				__lowerCamelCase					:		List[Any]		   =     image_embeds.repeat_interleave(__UpperCamelCase							,					dim=0    )
																				__lowerCamelCase					:		Any		   =     negative_image_embeds.repeat_interleave(__UpperCamelCase							,					dim=0    )
																				__lowerCamelCase					:		int		   =     hint.repeat_interleave(__UpperCamelCase							,					dim=0    )

													__lowerCamelCase					:		List[Any]		   =     torch.cat([negative_image_embeds, image_embeds]							,					dim=0    ).to(dtype=self.unet.dtype							,					device=__UpperCamelCase    )
													__lowerCamelCase					:		Optional[int]		   =     torch.cat([hint, hint]							,					dim=0    ).to(dtype=self.unet.dtype							,					device=__UpperCamelCase    )
													self.scheduler.set_timesteps(__UpperCamelCase							,					device=__UpperCamelCase    )
													__lowerCamelCase					:		List[Any]		   =     self.scheduler.timesteps

													__lowerCamelCase					:		int		   =     self.movq.config.latent_channels

													__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     downscale_height_and_width(__UpperCamelCase							,					__UpperCamelCase							,					self.movq_scale_factor    )

													# create initial latent
													__lowerCamelCase					:		Optional[Any]		   =     self.prepare_latents(
													    (batch_size, num_channels_latents, height, width)							,					image_embeds.dtype							,					__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase							,					self.scheduler							,					)

													for i, t in enumerate(self.progress_bar(__UpperCamelCase    )    ):
																				# expand the latents if we are doing classifier free guidance
																				__lowerCamelCase					:		List[Any]		   =     torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

																				__lowerCamelCase					:		List[Any]		   =     {"image_embeds": image_embeds, "hint": hint}
																				__lowerCamelCase					:		Tuple		   =     self.unet(
																				    sample=__UpperCamelCase							,					timestep=__UpperCamelCase							,					encoder_hidden_states=__UpperCamelCase							,					added_cond_kwargs=__UpperCamelCase							,					return_dict=__UpperCamelCase							,					)[0]

																				if do_classifier_free_guidance:
																											__lowerCamelCase   ,  __lowerCamelCase					:		Tuple		   =     noise_pred.split(latents.shape[1]							,					dim=1    )
																											__lowerCamelCase   ,  __lowerCamelCase					:		Any		   =     noise_pred.chunk(2    )
																											__lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     variance_pred.chunk(2    )
																											__lowerCamelCase					:		Dict		   =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																											__lowerCamelCase					:		Union[str, 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"]
																				):
																											__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     noise_pred.split(latents.shape[1]							,					dim=1    )

																				# compute the previous noisy sample x_t -> x_t-1
																				__lowerCamelCase					:		str		   =     self.scheduler.step(
																				    __UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase							,					generator=__UpperCamelCase							,					)[0]
													# post-processing
													__lowerCamelCase					:		str		   =     self.movq.decode(__UpperCamelCase							,					force_not_quantize=__UpperCamelCase    )["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"]:
																				__lowerCamelCase					:		Union[str, Any]		   =     image * 0.5 + 0.5
																				__lowerCamelCase					:		Union[str, Any]		   =     image.clamp(0							,					1    )
																				__lowerCamelCase					:		List[str]		   =     image.cpu().permute(0							,					2							,					3							,					1    ).float().numpy()

													if output_type == "pil":
																				__lowerCamelCase					:		List[str]		   =     self.numpy_to_pil(__UpperCamelCase    )

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=__UpperCamelCase    )
"""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


__A  =				'''CompVis/stable-diffusion-v1-1'''
__A  =				'''CompVis/stable-diffusion-v1-2'''
__A  =				'''CompVis/stable-diffusion-v1-3'''
__A  =				'''CompVis/stable-diffusion-v1-4'''
class  _snake_case     (					a__		):






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

													__lowerCamelCase					:		Optional[int]		   =     StableDiffusionPipeline.from_pretrained(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     StableDiffusionPipeline.from_pretrained(UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     StableDiffusionPipeline.from_pretrained(UpperCAmelCase    )
													__lowerCamelCase					:		List[str]		   =     StableDiffusionPipeline(
													    vae=UpperCAmelCase							,					text_encoder=UpperCAmelCase							,					tokenizer=UpperCAmelCase							,					unet=UpperCAmelCase							,					scheduler=UpperCAmelCase							,					safety_checker=UpperCAmelCase							,					feature_extractor=UpperCAmelCase							,					requires_safety_checker=UpperCAmelCase							,					)

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






						@property
						def       lowerCamelCase__     (       self      :			Dict    ):
													return {k: getattr(self							,					UpperCAmelCase    ) for k in self.config.keys() if not k.startswith("_"    )}






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






						def       lowerCamelCase__     (       self      :			Tuple    ):
													self.enable_attention_slicing(UpperCAmelCase    )






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






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






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






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







						@torch.no_grad()
						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Union[str, List[str]]							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			float = 7.5							,					UpperCAmelCase      :			Optional[Union[str, List[str]]] = None							,					UpperCAmelCase      :			Optional[int] = 1							,					UpperCAmelCase      :			float = 0.0							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			Optional[torch.FloatTensor] = None							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,					UpperCAmelCase      :			int = 1							,					**UpperCAmelCase      :			Tuple							,					):
													__lowerCamelCase					:		Optional[int]		   =     "cuda" if torch.cuda.is_available() else "cpu"
													self.to(UpperCAmelCase    )

													# 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					:		str		   =     self.textaimg_sda_a(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)

													# Get first result from Stable Diffusion Checkpoint v1.2
													__lowerCamelCase					:		Optional[int]		   =     self.textaimg_sda_a(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)

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

													# Get first result from Stable Diffusion Checkpoint v1.4
													__lowerCamelCase					:		Optional[Any]		   =     self.textaimg_sda_a(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)

													# 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]]    )