
import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)






def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     int			)			->  Optional[Any]:
			_snake_case          =      checkpoint

			_snake_case          =      {}

			_snake_case          =      vae_state_dict['encoder.conv_in.weight']
			_snake_case          =      vae_state_dict['encoder.conv_in.bias']
			_snake_case          =      vae_state_dict['encoder.conv_out.weight']
			_snake_case          =      vae_state_dict['encoder.conv_out.bias']
			_snake_case          =      vae_state_dict['encoder.norm_out.weight']
			_snake_case          =      vae_state_dict['encoder.norm_out.bias']

			_snake_case          =      vae_state_dict['decoder.conv_in.weight']
			_snake_case          =      vae_state_dict['decoder.conv_in.bias']
			_snake_case          =      vae_state_dict['decoder.conv_out.weight']
			_snake_case          =      vae_state_dict['decoder.conv_out.bias']
			_snake_case          =      vae_state_dict['decoder.norm_out.weight']
			_snake_case          =      vae_state_dict['decoder.norm_out.bias']

			_snake_case          =      vae_state_dict['quant_conv.weight']
			_snake_case          =      vae_state_dict['quant_conv.bias']
			_snake_case          =      vae_state_dict['post_quant_conv.weight']
			_snake_case          =      vae_state_dict['post_quant_conv.bias']

			# Retrieves the keys for the encoder down blocks only
			_snake_case          =      len({'.'.join(layer.split('.'			)[:3]			) for layer in vae_state_dict if 'encoder.down' in layer}			)
			_snake_case          =      {
			    layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(__lowercase			)
			}

			# Retrieves the keys for the decoder up blocks only
			_snake_case          =      len({'.'.join(layer.split('.'			)[:3]			) for layer in vae_state_dict if 'decoder.up' in layer}			)
			_snake_case          =      {
			    layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(__lowercase			)
			}

			for i in range(__lowercase			):
						_snake_case          =      [key for key in down_blocks[i] if f'''down.{i}''' in key and f'''down.{i}.downsample''' not in key]

						if f'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
									_snake_case          =      vae_state_dict.pop(
									    f'''encoder.down.{i}.downsample.conv.weight'''			)
									_snake_case          =      vae_state_dict.pop(
									    f'''encoder.down.{i}.downsample.conv.bias'''			)

						_snake_case          =      renew_vae_resnet_paths(__lowercase			)
						_snake_case          =      {'old': f'''down.{i}.block''', 'new': f'''down_blocks.{i}.resnets'''}
						assign_to_checkpoint(__lowercase	, __lowercase	, __lowercase	, additional_replacements=[meta_path]	, config=__lowercase			)

			_snake_case          =      [key for key in vae_state_dict if 'encoder.mid.block' in key]
			_snake_case          =      2
			for i in range(1	, num_mid_res_blocks + 1			):
						_snake_case          =      [key for key in mid_resnets if f'''encoder.mid.block_{i}''' in key]

						_snake_case          =      renew_vae_resnet_paths(__lowercase			)
						_snake_case          =      {'old': f'''mid.block_{i}''', 'new': f'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(__lowercase	, __lowercase	, __lowercase	, additional_replacements=[meta_path]	, config=__lowercase			)

			_snake_case          =      [key for key in vae_state_dict if 'encoder.mid.attn' in key]
			_snake_case          =      renew_vae_attention_paths(__lowercase			)
			_snake_case          =      {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
			assign_to_checkpoint(__lowercase	, __lowercase	, __lowercase	, additional_replacements=[meta_path]	, config=__lowercase			)
			conv_attn_to_linear(__lowercase			)

			for i in range(__lowercase			):
						_snake_case          =      num_up_blocks - 1 - i
						_snake_case          =      [
						    key for key in up_blocks[block_id] if f'''up.{block_id}''' in key and f'''up.{block_id}.upsample''' not in key
						]

						if f'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
									_snake_case          =      vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.weight'''
									]
									_snake_case          =      vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.bias'''
									]

						_snake_case          =      renew_vae_resnet_paths(__lowercase			)
						_snake_case          =      {'old': f'''up.{block_id}.block''', 'new': f'''up_blocks.{i}.resnets'''}
						assign_to_checkpoint(__lowercase	, __lowercase	, __lowercase	, additional_replacements=[meta_path]	, config=__lowercase			)

			_snake_case          =      [key for key in vae_state_dict if 'decoder.mid.block' in key]
			_snake_case          =      2
			for i in range(1	, num_mid_res_blocks + 1			):
						_snake_case          =      [key for key in mid_resnets if f'''decoder.mid.block_{i}''' in key]

						_snake_case          =      renew_vae_resnet_paths(__lowercase			)
						_snake_case          =      {'old': f'''mid.block_{i}''', 'new': f'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(__lowercase	, __lowercase	, __lowercase	, additional_replacements=[meta_path]	, config=__lowercase			)

			_snake_case          =      [key for key in vae_state_dict if 'decoder.mid.attn' in key]
			_snake_case          =      renew_vae_attention_paths(__lowercase			)
			_snake_case          =      {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
			assign_to_checkpoint(__lowercase	, __lowercase	, __lowercase	, additional_replacements=[meta_path]	, config=__lowercase			)
			conv_attn_to_linear(__lowercase			)
			return new_checkpoint






def 	a_		(	__lowercase       :     str	, __lowercase       :     str	, )			->  List[str]:
			# Only support V1
			_snake_case          =      requests.get(
			    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'			)
			_snake_case          =      io.BytesIO(r.content			)

			_snake_case          =      OmegaConf.load(__lowercase			)
			_snake_case          =      512
			_snake_case          =      'cuda' if torch.cuda.is_available() else 'cpu'
			if checkpoint_path.endswith('safetensors'			):
						from safetensors import safe_open

						_snake_case          =      {}
						with safe_open(__lowercase	, framework='pt'	, device='cpu'			) as f:
									for key in f.keys():
												_snake_case          =      f.get_tensor(__lowercase			)
			else:
						_snake_case          =      torch.load(__lowercase	, map_location=__lowercase			)['state_dict']

			# Convert the VAE model.
			_snake_case          =      create_vae_diffusers_config(__lowercase	, image_size=__lowercase			)
			_snake_case          =      custom_convert_ldm_vae_checkpoint(__lowercase	, __lowercase			)

			_snake_case          =      AutoencoderKL(**__lowercase			)
			vae.load_state_dict(__lowercase			)
			vae.save_pretrained(__lowercase			)


if __name__ == "__main__":
			_lowerCamelCase	:    Any          =      argparse.ArgumentParser()

			parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')

			_lowerCamelCase	:    str          =      parser.parse_args()

			vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)

import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)






def 	a_		(	__lowercase       :     str	, __lowercase       :     List[str]	, __lowercase       :     Dict			)			->  List[str]:
			if isinstance(__lowercase	, torch.Tensor			):
						return image
			elif isinstance(__lowercase	, PIL.Image.Image			):
						_snake_case          =      [image]

			if isinstance(image[0]	, PIL.Image.Image			):
						_snake_case          =      [np.array(i.resize((w, h)	, resample=PIL_INTERPOLATION['lanczos']			)			)[None, :] for i in image]
						_snake_case          =      np.concatenate(__lowercase	, axis=0			)
						_snake_case          =      np.array(__lowercase			).astype(np.floataa			) / 2_5_5.0
						_snake_case          =      image.transpose(0	, 3	, 1	, 2			)
						_snake_case          =      2.0 * image - 1.0
						_snake_case          =      torch.from_numpy(__lowercase			)
			elif isinstance(image[0]	, torch.Tensor			):
						_snake_case          =      torch.cat(__lowercase	, dim=0			)
			return image






def 	a_		(	__lowercase       :     int	, __lowercase       :     Any	, __lowercase       :     List[Any]	, __lowercase       :     Tuple=0.9_9_9_5			)			->  List[str]:
			if not isinstance(__lowercase	, np.ndarray			):
						_snake_case          =      True
						_snake_case          =      va.device
						_snake_case          =      va.cpu().numpy()
						_snake_case          =      va.cpu().numpy()

			_snake_case          =      np.sum(va * va / (np.linalg.norm(__lowercase			) * np.linalg.norm(__lowercase			))			)
			if np.abs(__lowercase			) > DOT_THRESHOLD:
						_snake_case          =      (1 - t) * va + t * va
			else:
						_snake_case          =      np.arccos(__lowercase			)
						_snake_case          =      np.sin(__lowercase			)
						_snake_case          =      theta_a * t
						_snake_case          =      np.sin(__lowercase			)
						_snake_case          =      np.sin(theta_a - theta_t			) / sin_theta_a
						_snake_case          =      sin_theta_t / sin_theta_a
						_snake_case          =      sa * va + sa * va

			if inputs_are_torch:
						_snake_case          =      torch.from_numpy(__lowercase			).to(__lowercase			)

			return va






def 	a_		(	__lowercase       :     int	, __lowercase       :     Optional[int]			)			->  List[Any]:
			_snake_case          =      F.normalize(__lowercase	, dim=-1			)
			_snake_case          =      F.normalize(__lowercase	, dim=-1			)
			return (x - y).norm(dim=-1			).div(2			).arcsin().pow(2			).mul(2			)






def 	a_		(	__lowercase       :     int	, __lowercase       :     Any			)			->  Optional[Any]:
			for param in model.parameters():
						_snake_case          =      value




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   List[Any]      ,  lowercase     :   AutoencoderKL      ,  lowercase     :   CLIPTextModel      ,  lowercase     :   CLIPModel      ,  lowercase     :   CLIPTokenizer      ,  lowercase     :   UNetaDConditionModel      ,  lowercase     :   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]      ,  lowercase     :   CLIPFeatureExtractor      ,  lowercase     :   Any=None      ,  lowercase     :   List[str]=None      ,  lowercase     :   List[str]=None      ,  ):


				'''simple docstring'''


				super().__init__()
				self.register_modules(
				    vae=lowercase      ,  text_encoder=lowercase      ,  clip_model=lowercase      ,  tokenizer=lowercase      ,  unet=lowercase      ,  scheduler=lowercase      ,  feature_extractor=lowercase      ,  coca_model=lowercase      ,  coca_tokenizer=lowercase      ,  coca_transform=lowercase      ,  )
				_snake_case          =      (
				    feature_extractor.size
				    if isinstance(feature_extractor.size      ,  lowercase     )
				    else feature_extractor.size['shortest_edge']
				)
				_snake_case          =      transforms.Normalize(mean=feature_extractor.image_mean      ,  std=feature_extractor.image_std     )
				set_requires_grad(self.text_encoder      ,  lowercase     )
				set_requires_grad(self.clip_model      ,  lowercase     )



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Optional[Union[str, int]] = "auto"     ):


				'''simple docstring'''


				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							_snake_case          =      self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				self.enable_attention_slicing(lowercase     )



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				set_requires_grad(self.vae      ,  lowercase     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				set_requires_grad(self.vae      ,  lowercase     )



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				set_requires_grad(self.unet      ,  lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				set_requires_grad(self.unet      ,  lowercase     )



	def 			A							(  self     :   str      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      min(int(num_inference_steps * strength     )      ,  lowercase     )

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

				return timesteps, num_inference_steps - t_start



	def 			A							(  self     :   Optional[Any]      ,  lowercase     :   List[str]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   str      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   List[str]      ,  lowercase     :   List[Any]=None     ):


				'''simple docstring'''


				if not isinstance(lowercase      ,  torch.Tensor     ):
							raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(lowercase     )}'''     )

				_snake_case          =      image.to(device=lowercase      ,  dtype=lowercase     )

				if isinstance(lowercase      ,  lowercase     ):
							_snake_case          =      [
							    self.vae.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowercase     )
							]
							_snake_case          =      torch.cat(lowercase      ,  dim=0     )
				else:
							_snake_case          =      self.vae.encode(lowercase     ).latent_dist.sample(lowercase     )

				# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				_snake_case          =      0.18215 * init_latents
				_snake_case          =      init_latents.repeat_interleave(lowercase      ,  dim=0     )

				_snake_case          =      randn_tensor(init_latents.shape      ,  generator=lowercase      ,  device=lowercase      ,  dtype=lowercase     )

				# get latents
				_snake_case          =      self.scheduler.add_noise(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      init_latents

				return latents



	def 			A							(  self     :   int      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      self.coca_transform(lowercase     ).unsqueeze(0     )
				with torch.no_grad(), torch.cuda.amp.autocast():
							_snake_case          =      self.coca_model.generate(transformed_image.to(device=self.device      ,  dtype=self.coca_model.dtype     )     )
				_snake_case          =      self.coca_tokenizer.decode(generated[0].cpu().numpy()     )
				return generated.split('<end_of_text>'     )[0].replace('<start_of_text>'      ,  ''     ).rstrip(' .,'     )



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Dict      ,  lowercase     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.feature_extractor.preprocess(lowercase     )
				_snake_case          =      torch.from_numpy(clip_image_input['pixel_values'][0]     ).unsqueeze(0     ).to(self.device     ).half()
				_snake_case          =      self.clip_model.get_image_features(lowercase     )
				_snake_case          =      image_embeddings_clip / image_embeddings_clip.norm(p=2      ,  dim=-1      ,  keepdim=lowercase     )
				_snake_case          =      image_embeddings_clip.repeat_interleave(lowercase      ,  dim=0     )
				return image_embeddings_clip



	@torch.enable_grad()
	def 			A							(  self     :   int      ,  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]      ,  lowercase     :   Optional[int]      ,  lowercase     :   int      ,  lowercase     :   Any      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Optional[int]      ,  ):


				'''simple docstring'''


				_snake_case          =      latents.detach().requires_grad_()

				_snake_case          =      self.scheduler.scale_model_input(lowercase      ,  lowercase     )

				# predict the noise residual
				_snake_case          =      self.unet(lowercase      ,  lowercase      ,  encoder_hidden_states=lowercase     ).sample

				if isinstance(self.scheduler      ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)     ):
							_snake_case          =      self.scheduler.alphas_cumprod[timestep]
							_snake_case          =      1 - alpha_prod_t
							# compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case          =      (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

							_snake_case          =      torch.sqrt(lowercase     )
							_snake_case          =      pred_original_sample * (fac) + latents * (1 - fac)
				elif isinstance(self.scheduler      ,  lowercase     ):
							_snake_case          =      self.scheduler.sigmas[index]
							_snake_case          =      latents - sigma * noise_pred
				else:
							raise ValueError(f'''scheduler type {type(self.scheduler     )} not supported'''     )

				# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				_snake_case          =      1 / 0.18215 * sample
				_snake_case          =      self.vae.decode(lowercase     ).sample
				_snake_case          =      (image / 2 + 0.5).clamp(0      ,  1     )

				_snake_case          =      transforms.Resize(self.feature_extractor_size     )(lowercase     )
				_snake_case          =      self.normalize(lowercase     ).to(latents.dtype     )

				_snake_case          =      self.clip_model.get_image_features(lowercase     )
				_snake_case          =      image_embeddings_clip / image_embeddings_clip.norm(p=2      ,  dim=-1      ,  keepdim=lowercase     )

				_snake_case          =      spherical_dist_loss(lowercase      ,  lowercase     ).mean() * clip_guidance_scale

				_snake_case          =      -torch.autograd.grad(lowercase      ,  lowercase     )[0]

				if isinstance(self.scheduler      ,  lowercase     ):
							_snake_case          =      latents.detach() + grads * (sigma**2)
							_snake_case          =      noise_pred_original
				else:
							_snake_case          =      noise_pred_original - torch.sqrt(lowercase     ) * grads
				return noise_pred, latents





	@torch.no_grad()
	def __call__(  self     :   int      ,  lowercase     :   Union[torch.FloatTensor, PIL.Image.Image]      ,  lowercase     :   Union[torch.FloatTensor, PIL.Image.Image]      ,  lowercase     :   Optional[str] = None      ,  lowercase     :   Optional[str] = None      ,  lowercase     :   Optional[int] = 512      ,  lowercase     :   Optional[int] = 512      ,  lowercase     :   float = 0.6      ,  lowercase     :   Optional[int] = 50      ,  lowercase     :   Optional[float] = 7.5      ,  lowercase     :   Optional[int] = 1      ,  lowercase     :   float = 0.0      ,  lowercase     :   Optional[float] = 100      ,  lowercase     :   Optional[torch.Generator] = None      ,  lowercase     :   Optional[str] = "pil"      ,  lowercase     :   bool = True      ,  lowercase     :   float = 0.8      ,  lowercase     :   float = 0.1      ,  lowercase     :   float = 0.1      ,  ):


				'''simple docstring'''


				if isinstance(lowercase      ,  lowercase     ) and len(lowercase     ) != batch_size:
							raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(lowercase     )} generators.'''     )

				if height % 8 != 0 or width % 8 != 0:
							raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''     )

				if isinstance(lowercase      ,  torch.Generator     ) and batch_size > 1:
							_snake_case          =      [generator] + [None] * (batch_size - 1)

				_snake_case          =      [
				    ('model', self.coca_model is None),
				    ('tokenizer', self.coca_tokenizer is None),
				    ('transform', self.coca_transform is None),
				]
				_snake_case          =      [x[0] for x in coca_is_none if x[1]]
				_snake_case          =      ', '.join(lowercase     )
				# generate prompts with coca model if prompt is None
				if content_prompt is None:
							if len(lowercase     ):
										raise ValueError(
										    f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
										    f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
							_snake_case          =      self.get_image_description(lowercase     )
				if style_prompt is None:
							if len(lowercase     ):
										raise ValueError(
										    f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
										    f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
							_snake_case          =      self.get_image_description(lowercase     )

				# get prompt text embeddings for content and style
				_snake_case          =      self.tokenizer(
				    lowercase      ,  padding='max_length'      ,  max_length=self.tokenizer.model_max_length      ,  truncation=lowercase      ,  return_tensors='pt'      ,  )
				_snake_case          =      self.text_encoder(content_text_input.input_ids.to(self.device     )     )[0]

				_snake_case          =      self.tokenizer(
				    lowercase      ,  padding='max_length'      ,  max_length=self.tokenizer.model_max_length      ,  truncation=lowercase      ,  return_tensors='pt'      ,  )
				_snake_case          =      self.text_encoder(style_text_input.input_ids.to(self.device     )     )[0]

				_snake_case          =      slerp(lowercase      ,  lowercase      ,  lowercase     )

				# duplicate text embeddings for each generation per prompt
				_snake_case          =      text_embeddings.repeat_interleave(lowercase      ,  dim=0     )

				# set timesteps
				_snake_case          =      'offset' in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
				_snake_case          =      {}
				if accepts_offset:
							_snake_case          =      1

				self.scheduler.set_timesteps(lowercase      ,  **lowercase     )
				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				self.scheduler.timesteps.to(self.device     )

				_snake_case     , _snake_case          =      self.get_timesteps(lowercase      ,  lowercase      ,  self.device     )
				_snake_case          =      timesteps[:1].repeat(lowercase     )

				# Preprocess image
				_snake_case          =      preprocess(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      self.prepare_latents(
				    lowercase      ,  lowercase      ,  lowercase      ,  text_embeddings.dtype      ,  self.device      ,  lowercase     )

				_snake_case          =      preprocess(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      self.prepare_latents(
				    lowercase      ,  lowercase      ,  lowercase      ,  text_embeddings.dtype      ,  self.device      ,  lowercase     )

				_snake_case          =      slerp(lowercase      ,  lowercase      ,  lowercase     )

				if clip_guidance_scale > 0:
							_snake_case          =      self.get_clip_image_embeddings(lowercase      ,  lowercase     )
							_snake_case          =      self.get_clip_image_embeddings(lowercase      ,  lowercase     )
							_snake_case          =      slerp(
							    lowercase      ,  lowercase      ,  lowercase     )

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				_snake_case          =      guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
							_snake_case          =      content_text_input.input_ids.shape[-1]
							_snake_case          =      self.tokenizer(['']      ,  padding='max_length'      ,  max_length=lowercase      ,  return_tensors='pt'     )
							_snake_case          =      self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
							# duplicate unconditional embeddings for each generation per prompt
							_snake_case          =      uncond_embeddings.repeat_interleave(lowercase      ,  dim=0     )

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							_snake_case          =      torch.cat([uncond_embeddings, text_embeddings]     )

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				_snake_case          =      (batch_size, self.unet.config.in_channels, height // 8, width // 8)
				_snake_case          =      text_embeddings.dtype
				if latents is None:
							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										_snake_case          =      torch.randn(lowercase      ,  generator=lowercase      ,  device='cpu'      ,  dtype=lowercase     ).to(
										    self.device     )
							else:
										_snake_case          =      torch.randn(lowercase      ,  generator=lowercase      ,  device=self.device      ,  dtype=lowercase     )
				else:
							if latents.shape != latents_shape:
										raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''     )
							_snake_case          =      latents.to(self.device     )

				# scale the initial noise by the standard deviation required by the scheduler
				_snake_case          =      latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				_snake_case          =      'eta' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
				_snake_case          =      {}
				if accepts_eta:
							_snake_case          =      eta

				# check if the scheduler accepts generator
				_snake_case          =      'generator' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
				if accepts_generator:
							_snake_case          =      generator

				with self.progress_bar(total=lowercase     ):
							for i, t in enumerate(lowercase     ):
										# expand the latents if we are doing classifier free guidance
										_snake_case          =      torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents
										_snake_case          =      self.scheduler.scale_model_input(lowercase      ,  lowercase     )

										# predict the noise residual
										_snake_case          =      self.unet(lowercase      ,  lowercase      ,  encoder_hidden_states=lowercase     ).sample

										# perform classifier free guidance
										if do_classifier_free_guidance:
													_snake_case     , _snake_case          =      noise_pred.chunk(2     )
													_snake_case          =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

										# perform clip guidance
										if clip_guidance_scale > 0:
													_snake_case          =      (
													    text_embeddings.chunk(2     )[1] if do_classifier_free_guidance else text_embeddings
													)
													_snake_case     , _snake_case          =      self.cond_fn(
													    lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  lowercase      ,  )

										# compute the previous noisy sample x_t -> x_t-1
										_snake_case          =      self.scheduler.step(lowercase      ,  lowercase      ,  lowercase      ,  **lowercase     ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				_snake_case          =      1 / 0.18215 * latents
				_snake_case          =      self.vae.decode(lowercase     ).sample

				_snake_case          =      (image / 2 + 0.5).clamp(0      ,  1     )
				_snake_case          =      image.cpu().permute(0      ,  2      ,  3      ,  1     ).numpy()

				if output_type == "pil":
							_snake_case          =      self.numpy_to_pil(lowercase     )

				if not return_dict:
							return (image, None)

				return StableDiffusionPipelineOutput(images=lowercase      ,  nsfw_content_detected=lowercase     )