"""simple docstring"""





import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A  =				logging.get_logger(__name__)


def    lowercase_   (   _lowerCamelCase:	int      ) ->      Tuple:



							'''simple docstring'''

							__lowerCamelCase					:		int		   =     OrderedDict()
							for key, value in state_dict.items():
														if key.startswith("module.encoder"      ):
																					__lowerCamelCase					:		Any		   =     key.replace("module.encoder"						,   "glpn.encoder"      )
														if key.startswith("module.decoder"      ):
																					__lowerCamelCase					:		Tuple		   =     key.replace("module.decoder"						,   "decoder.stages"      )
														if "patch_embed" in key:
																					# replace for example patch_embed1 by patch_embeddings.0
																					__lowerCamelCase					:		Union[str, Any]		   =     key[key.find("patch_embed"      ) + len("patch_embed"      )]
																					__lowerCamelCase					:		str		   =     key.replace(F"""patch_embed{idx}"""						,   F"""patch_embeddings.{int(_lowerCamelCase      )-1}"""      )
														if "norm" in key:
																					__lowerCamelCase					:		Any		   =     key.replace("norm"						,   "layer_norm"      )
														if "glpn.encoder.layer_norm" in key:
																					# replace for example layer_norm1 by layer_norm.0
																					__lowerCamelCase					:		int		   =     key[key.find("glpn.encoder.layer_norm"      ) + len("glpn.encoder.layer_norm"      )]
																					__lowerCamelCase					:		Dict		   =     key.replace(F"""layer_norm{idx}"""						,   F"""layer_norm.{int(_lowerCamelCase      )-1}"""      )
														if "layer_norm1" in key:
																					__lowerCamelCase					:		Optional[int]		   =     key.replace("layer_norm1"						,   "layer_norm_1"      )
														if "layer_norm2" in key:
																					__lowerCamelCase					:		Tuple		   =     key.replace("layer_norm2"						,   "layer_norm_2"      )
														if "block" in key:
																					# replace for example block1 by block.0
																					__lowerCamelCase					:		Dict		   =     key[key.find("block"      ) + len("block"      )]
																					__lowerCamelCase					:		Tuple		   =     key.replace(F"""block{idx}"""						,   F"""block.{int(_lowerCamelCase      )-1}"""      )
														if "attn.q" in key:
																					__lowerCamelCase					:		List[str]		   =     key.replace("attn.q"						,   "attention.self.query"      )
														if "attn.proj" in key:
																					__lowerCamelCase					:		Optional[int]		   =     key.replace("attn.proj"						,   "attention.output.dense"      )
														if "attn" in key:
																					__lowerCamelCase					:		Dict		   =     key.replace("attn"						,   "attention.self"      )
														if "fc1" in key:
																					__lowerCamelCase					:		Optional[Any]		   =     key.replace("fc1"						,   "dense1"      )
														if "fc2" in key:
																					__lowerCamelCase					:		Union[str, Any]		   =     key.replace("fc2"						,   "dense2"      )
														if "linear_pred" in key:
																					__lowerCamelCase					:		str		   =     key.replace("linear_pred"						,   "classifier"      )
														if "linear_fuse" in key:
																					__lowerCamelCase					:		List[str]		   =     key.replace("linear_fuse.conv"						,   "linear_fuse"      )
																					__lowerCamelCase					:		List[str]		   =     key.replace("linear_fuse.bn"						,   "batch_norm"      )
														if "linear_c" in key:
																					# replace for example linear_c4 by linear_c.3
																					__lowerCamelCase					:		Union[str, Any]		   =     key[key.find("linear_c"      ) + len("linear_c"      )]
																					__lowerCamelCase					:		Optional[Any]		   =     key.replace(F"""linear_c{idx}"""						,   F"""linear_c.{int(_lowerCamelCase      )-1}"""      )
														if "bot_conv" in key:
																					__lowerCamelCase					:		List[Any]		   =     key.replace("bot_conv"						,   "0.convolution"      )
														if "skip_conv1" in key:
																					__lowerCamelCase					:		List[Any]		   =     key.replace("skip_conv1"						,   "1.convolution"      )
														if "skip_conv2" in key:
																					__lowerCamelCase					:		Optional[int]		   =     key.replace("skip_conv2"						,   "2.convolution"      )
														if "fusion1" in key:
																					__lowerCamelCase					:		Tuple		   =     key.replace("fusion1"						,   "1.fusion"      )
														if "fusion2" in key:
																					__lowerCamelCase					:		Any		   =     key.replace("fusion2"						,   "2.fusion"      )
														if "fusion3" in key:
																					__lowerCamelCase					:		Union[str, Any]		   =     key.replace("fusion3"						,   "3.fusion"      )
														if "fusion" in key and "conv" in key:
																					__lowerCamelCase					:		Dict		   =     key.replace("conv"						,   "convolutional_layer"      )
														if key.startswith("module.last_layer_depth"      ):
																					__lowerCamelCase					:		Dict		   =     key.replace("module.last_layer_depth"						,   "head.head"      )
														__lowerCamelCase					:		Optional[Any]		   =     value

							return new_state_dict


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	int      ) ->      List[Any]:



							'''simple docstring'''

							for i in range(config.num_encoder_blocks      ):
														for j in range(config.depths[i]      ):
																					# read in weights + bias of keys and values (which is a single matrix in the original implementation)
																					__lowerCamelCase					:		Any		   =     state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""      )
																					__lowerCamelCase					:		Optional[int]		   =     state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""      )
																					# next, add keys and values (in that order) to the state dict
																					__lowerCamelCase					:		Tuple		   =     kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					__lowerCamelCase					:		List[Any]		   =     kv_bias[: config.hidden_sizes[i]]
																					__lowerCamelCase					:		Optional[int]		   =     kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					__lowerCamelCase					:		Optional[int]		   =     kv_bias[config.hidden_sizes[i] :]


def    lowercase_   (   ) ->      Any:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[int]		   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
							__lowerCamelCase					:		str		   =     Image.open(requests.get(_lowerCamelCase						,   stream=_lowerCamelCase      ).raw      )

							return image


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	List[str]=False						,   _lowerCamelCase:	Optional[Any]=None      ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,   decoder_hidden_size=64						,   depths=[3, 8, 27, 3]      )

							# load image processor (only resize + rescale)
							__lowerCamelCase					:		int		   =     GLPNImageProcessor()

							# prepare image
							__lowerCamelCase					:		List[str]		   =     prepare_img()
							__lowerCamelCase					:		Any		   =     image_processor(images=_lowerCamelCase						,   return_tensors="pt"      ).pixel_values

							logger.info("Converting model..."      )

							# load original state dict
							__lowerCamelCase					:		Union[str, Any]		   =     torch.load(_lowerCamelCase						,   map_location=torch.device("cpu"      )      )

							# rename keys
							__lowerCamelCase					:		int		   =     rename_keys(_lowerCamelCase      )

							# key and value matrices need special treatment
							read_in_k_v(_lowerCamelCase						,   _lowerCamelCase      )

							# create HuggingFace model and load state dict
							__lowerCamelCase					:		Dict		   =     GLPNForDepthEstimation(_lowerCamelCase      )
							model.load_state_dict(_lowerCamelCase      )
							model.eval()

							# forward pass
							__lowerCamelCase					:		Union[str, Any]		   =     model(_lowerCamelCase      )
							__lowerCamelCase					:		Optional[int]		   =     outputs.predicted_depth

							# verify output
							if model_name is not None:
														if "nyu" in model_name:
																					__lowerCamelCase					:		List[str]		   =     torch.tensor(
																					    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]      )
														elif "kitti" in model_name:
																					__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor(
																					    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]      )
														else:
																					raise ValueError(F"""Unknown model name: {model_name}"""      )

														__lowerCamelCase					:		Union[str, Any]		   =     torch.Size([1, 480, 640]      )

														assert predicted_depth.shape == expected_shape
														assert torch.allclose(predicted_depth[0, :3, :3]						,   _lowerCamelCase						,   atol=1E-4      )
														print("Looks ok!"      )

							# finally, push to hub if required
							if push_to_hub:
														logger.info("Pushing model and image processor to the hub..."      )
														model.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,   _lowerCamelCase      )						,   organization="nielsr"						,   commit_message="Add model"						,   use_temp_dir=_lowerCamelCase						,   )
														image_processor.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase						,   _lowerCamelCase      )						,   organization="nielsr"						,   commit_message="Add image processor"						,   use_temp_dir=_lowerCamelCase						,   )


if __name__ == "__main__":
				__A  =				argparse.ArgumentParser()

				parser.add_argument(
				    '''--checkpoint_path''',
				    default=None,
				    type=str,
				    help='''Path to the original PyTorch checkpoint (.pth file).''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
				)
				parser.add_argument(
				    '''--model_name''',
				    default='''glpn-kitti''',
				    type=str,
				    help='''Name of the model in case you\'re pushing to the hub.''',
				)
				__A  =				parser.parse_args()
				convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
"""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]]    )