
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		lowercase_							(							snake_case_				,						snake_case_				,						snake_case_				,						unittest.TestCase					):
 A__    :					Tuple    =     StableDiffusionImageVariationPipeline
 A__    :					Tuple    =     IMAGE_VARIATION_PARAMS
 A__    :					Tuple    =     IMAGE_VARIATION_BATCH_PARAMS
 A__    :					Union[str, Any]    =     frozenset([]					)
 # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 A__    :					int    =     frozenset([]					)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        torch.manual_seed(0  )
        UpperCamelCase_											=							UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)  ,						layers_per_block=2  ,						sample_size=3_2  ,						in_channels=4  ,						out_channels=4  ,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,						cross_attention_dim=3_2  ,						)
        UpperCamelCase_											=							PNDMScheduler(skip_prk_steps=lowercase_  )
        torch.manual_seed(0  )
        UpperCamelCase_											=							AutoencoderKL(
            block_out_channels=[3_2, 6_4]  ,						in_channels=3  ,						out_channels=3  ,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,						latent_channels=4  ,						)
        torch.manual_seed(0  )
        UpperCamelCase_											=							CLIPVisionConfig(
            hidden_size=3_2  ,						projection_dim=3_2  ,						intermediate_size=3_7  ,						layer_norm_eps=1e-05  ,						num_attention_heads=4  ,						num_hidden_layers=5  ,						image_size=3_2  ,						patch_size=4  ,						)
        UpperCamelCase_											=							CLIPVisionModelWithProjection(lowercase_  )
        UpperCamelCase_											=							CLIPImageProcessor(crop_size=3_2  ,						size=3_2  )

        UpperCamelCase_											=							{
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """image_encoder""": image_encoder,
            """feature_extractor""": feature_extractor,
            """safety_checker""": None,
        }
        return components

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase=0  ):




        """simple docstring"""


        UpperCamelCase_											=							floats_tensor((1, 3, 3_2, 3_2)  ,						rng=random.Random(lowercase_  )  )
        UpperCamelCase_											=							image.cpu().permute(0  ,						2  ,						3  ,						1  )[0]
        UpperCamelCase_											=							Image.fromarray(np.uinta(lowercase_  )  ).convert("""RGB"""  ).resize((3_2, 3_2)  )
        if str(lowercase_  ).startswith("""mps"""  ):
               UpperCamelCase_											=							torch.manual_seed(lowercase_  )
        else:
               UpperCamelCase_											=							torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
        UpperCamelCase_											=							{
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
        }
        return inputs

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        UpperCamelCase_											=							self.get_dummy_components()
        UpperCamelCase_											=							StableDiffusionImageVariationPipeline(**lowercase_  )
        UpperCamelCase_											=							sd_pipe.to(lowercase_  )
        sd_pipe.set_progress_bar_config(disable=lowercase_  )

        UpperCamelCase_											=							self.get_dummy_inputs(lowercase_  )
        UpperCamelCase_											=							sd_pipe(**lowercase_  ).images
        UpperCamelCase_											=							image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)
        UpperCamelCase_											=							np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        UpperCamelCase_											=							self.get_dummy_components()
        UpperCamelCase_											=							StableDiffusionImageVariationPipeline(**lowercase_  )
        UpperCamelCase_											=							sd_pipe.to(lowercase_  )
        sd_pipe.set_progress_bar_config(disable=lowercase_  )

        UpperCamelCase_											=							self.get_dummy_inputs(lowercase_  )
        UpperCamelCase_											=							2 * [inputs["""image"""]]
        UpperCamelCase_											=							sd_pipe(**lowercase_  )

        UpperCamelCase_											=							output.images

        UpperCamelCase_											=							image[-1, -3:, -3:, -1]

        assert image.shape == (2, 6_4, 6_4, 3)
        UpperCamelCase_											=							np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class 		lowercase_							(							unittest.TestCase					):

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase="cpu"  ,						__UpperCamelCase=torch.floataa  ,						__UpperCamelCase=0  ):




        """simple docstring"""


        UpperCamelCase_											=							torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
        UpperCamelCase_											=							load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
            """/stable_diffusion_imgvar/input_image_vermeer.png"""  )
        UpperCamelCase_											=							np.random.RandomState(lowercase_  ).standard_normal((1, 4, 6_4, 6_4)  )
        UpperCamelCase_											=							torch.from_numpy(lowercase_  ).to(device=lowercase_  ,						dtype=lowercase_  )
        UpperCamelCase_											=							{
            """image""": init_image,
            """latents""": latents,
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							StableDiffusionImageVariationPipeline.from_pretrained(
            """lambdalabs/sd-image-variations-diffusers"""  ,						safety_checker=lowercase_  )
        UpperCamelCase_											=							sd_pipe.to(lowercase_  )
        sd_pipe.set_progress_bar_config(disable=lowercase_  )

        UpperCamelCase_											=							self.get_inputs(lowercase_  )
        UpperCamelCase_											=							sd_pipe(**lowercase_  ).images
        UpperCamelCase_											=							image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        UpperCamelCase_											=							np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379]  )
        assert np.abs(image_slice - expected_slice  ).max() < 6e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							0

        def callback_fn(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ) -> None:
               UpperCamelCase_											=							True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                      UpperCamelCase_											=							latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      UpperCamelCase_											=							latents[0, -3:, -3:, -1]
                      UpperCamelCase_											=							np.array(
                          [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309]  )

                      assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5e-2
               elif step == 2:
                      UpperCamelCase_											=							latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      UpperCamelCase_											=							latents[0, -3:, -3:, -1]
                      UpperCamelCase_											=							np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273]  )

                      assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5e-2

        UpperCamelCase_											=							False

        UpperCamelCase_											=							StableDiffusionImageVariationPipeline.from_pretrained(
            """fusing/sd-image-variations-diffusers"""  ,						safety_checker=lowercase_  ,						torch_dtype=torch.floataa  ,						)
        pipe.to(lowercase_  )
        pipe.set_progress_bar_config(disable=lowercase_  )
        pipe.enable_attention_slicing()

        UpperCamelCase_											=							self.get_inputs(lowercase_  ,						dtype=torch.floataa  )
        pipe(**lowercase_  ,						callback=lowercase_  ,						callback_steps=1  )
        assert callback_fn.has_been_called
        assert number_of_steps == inputs["num_inference_steps"]

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        UpperCamelCase_											=							"""fusing/sd-image-variations-diffusers"""
        UpperCamelCase_											=							StableDiffusionImageVariationPipeline.from_pretrained(
            lowercase_  ,						safety_checker=lowercase_  ,						torch_dtype=torch.floataa  )
        UpperCamelCase_											=							pipe.to(lowercase_  )
        pipe.set_progress_bar_config(disable=lowercase_  )
        pipe.enable_attention_slicing(1  )
        pipe.enable_sequential_cpu_offload()

        UpperCamelCase_											=							self.get_inputs(lowercase_  ,						dtype=torch.floataa  )
        UpperCamelCase_											=							pipe(**lowercase_  )

        UpperCamelCase_											=							torch.cuda.max_memory_allocated()
        # make sure that less than 2.6 GB is allocated
        assert mem_bytes < 2.6 * 1_0**9



@nightly
@require_torch_gpu
class 		lowercase_							(							unittest.TestCase					):

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase="cpu"  ,						__UpperCamelCase=torch.floataa  ,						__UpperCamelCase=0  ):




        """simple docstring"""


        UpperCamelCase_											=							torch.Generator(device=lowercase_  ).manual_seed(lowercase_  )
        UpperCamelCase_											=							load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
            """/stable_diffusion_imgvar/input_image_vermeer.png"""  )
        UpperCamelCase_											=							np.random.RandomState(lowercase_  ).standard_normal((1, 4, 6_4, 6_4)  )
        UpperCamelCase_											=							torch.from_numpy(lowercase_  ).to(device=lowercase_  ,						dtype=lowercase_  )
        UpperCamelCase_											=							{
            """image""": init_image,
            """latents""": latents,
            """generator""": generator,
            """num_inference_steps""": 5_0,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""  )
        sd_pipe.to(lowercase_  )
        sd_pipe.set_progress_bar_config(disable=lowercase_  )

        UpperCamelCase_											=							self.get_inputs(lowercase_  )
        UpperCamelCase_											=							sd_pipe(**lowercase_  ).images[0]

        UpperCamelCase_											=							load_numpy(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
            """/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"""  )
        UpperCamelCase_											=							np.abs(expected_image - image  ).max()
        assert max_diff < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""  )
        UpperCamelCase_											=							DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config  )
        sd_pipe.to(lowercase_  )
        sd_pipe.set_progress_bar_config(disable=lowercase_  )

        UpperCamelCase_											=							self.get_inputs(lowercase_  )
        UpperCamelCase_											=							2_5
        UpperCamelCase_											=							sd_pipe(**lowercase_  ).images[0]

        UpperCamelCase_											=							load_numpy(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
            """/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"""  )
        UpperCamelCase_											=							np.abs(expected_image - image  ).max()
        assert max_diff < 1e-3








import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

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






class 	_a			(      nn.Module  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_=1.0 )	->					Optional[int]:
									super().__init__()

									lowerCAmelCase	:   Tuple					     =					cut_size
									lowerCAmelCase	:   str					     =					cut_power






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					str:
									lowerCAmelCase     ,	lowerCAmelCase	:   Any					     =					pixel_values.shape[2:4]
									lowerCAmelCase	:   str					     =					min(lowercase_    ,  lowercase_ )
									lowerCAmelCase	:   Union[str, Any]					     =					min(lowercase_    ,  lowercase_    ,  self.cut_size )
									lowerCAmelCase	:   Union[str, Any]					     =					[]
									for _ in range(lowercase_ ):
															lowerCAmelCase	:   List[Any]					     =					int(torch.rand([] ) ** self.cut_power * (max_size - min_size) + min_size )
															lowerCAmelCase	:   List[Any]					     =					torch.randint(0    ,  sideX - size + 1    ,  () )
															lowerCAmelCase	:   List[Any]					     =					torch.randint(0    ,  sideY - size + 1    ,  () )
															lowerCAmelCase	:   Any					     =					pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
															cutouts.append(F.adaptive_avg_poolad(lowercase_    ,  self.cut_size ) )
									return torch.cat(lowercase_ )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   str					     =					F.normalize(SCREAMING_SNAKE_CASE__			,dim=-1  )
						lowerCAmelCase	:   Tuple					     =					F.normalize(SCREAMING_SNAKE_CASE__			,dim=-1  )
						return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						for param in model.parameters():
												lowerCAmelCase	:   Optional[int]					     =					value






class 	_a			(      snake_case_  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  )	->					Optional[Any]:
									super().__init__()
									self.register_modules(
									    vae=lowercase_    ,  text_encoder=lowercase_    ,  clip_model=lowercase_    ,  tokenizer=lowercase_    ,  unet=lowercase_    ,  scheduler=lowercase_    ,  feature_extractor=lowercase_    ,  )

									lowerCAmelCase	:   Optional[Any]					     =					transforms.Normalize(mean=feature_extractor.image_mean    ,  std=feature_extractor.image_std )
									lowerCAmelCase	:   Dict					     =					(
									    feature_extractor.size
									    if isinstance(feature_extractor.size    ,  lowercase_ )
									    else feature_extractor.size["""shortest_edge"""]
									)
									lowerCAmelCase	:   str					     =					MakeCutouts(self.cut_out_size )

									set_requires_grad(self.text_encoder    ,  lowercase_ )
									set_requires_grad(self.clip_model    ,  lowercase_ )






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






			def 							_snake_case   (    self )	->					str:
									self.enable_attention_slicing(lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									set_requires_grad(self.vae    ,  lowercase_ )






			def 							_snake_case   (    self )	->					List[Any]:
									set_requires_grad(self.vae    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Union[str, Any]:
									set_requires_grad(self.unet    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Dict:
									set_requires_grad(self.unet    ,  lowercase_ )






			@torch.enable_grad()
			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_=True    ,  )	->					Dict:
									lowerCAmelCase	:   Optional[int]					     =					latents.detach().requires_grad_()

									lowerCAmelCase	:   Optional[int]					     =					self.scheduler.scale_model_input(lowercase_    ,  lowercase_ )

									# predict the noise residual
									lowerCAmelCase	:   Dict					     =					self.unet(lowercase_    ,  lowercase_    ,  encoder_hidden_states=lowercase_ ).sample

									if isinstance(self.scheduler    ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
															lowerCAmelCase	:   Any					     =					self.scheduler.alphas_cumprod[timestep]
															lowerCAmelCase	:   Union[str, Any]					     =					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
															lowerCAmelCase	:   Tuple					     =					(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

															lowerCAmelCase	:   Any					     =					torch.sqrt(lowercase_ )
															lowerCAmelCase	:   Tuple					     =					pred_original_sample * (fac) + latents * (1 - fac)
									elif isinstance(self.scheduler    ,  lowercase_ ):
															lowerCAmelCase	:   Optional[int]					     =					self.scheduler.sigmas[index]
															lowerCAmelCase	:   str					     =					latents - sigma * noise_pred
									else:
															raise ValueError(f"""scheduler type {type(self.scheduler )} not supported""" )

									lowerCAmelCase	:   Dict					     =					1 / self.vae.config.scaling_factor * sample
									lowerCAmelCase	:   List[str]					     =					self.vae.decode(lowercase_ ).sample
									lowerCAmelCase	:   Any					     =					(image / 2 + 0.5).clamp(0    ,  1 )

									if use_cutouts:
															lowerCAmelCase	:   Union[str, Any]					     =					self.make_cutouts(lowercase_    ,  lowercase_ )
									else:
															lowerCAmelCase	:   Union[str, Any]					     =					transforms.Resize(self.cut_out_size )(lowercase_ )
									lowerCAmelCase	:   Any					     =					self.normalize(lowercase_ ).to(latents.dtype )

									lowerCAmelCase	:   Union[str, Any]					     =					self.clip_model.get_image_features(lowercase_ )
									lowerCAmelCase	:   Dict					     =					image_embeddings_clip / image_embeddings_clip.norm(p=2    ,  dim=-1    ,  keepdim=lowercase_ )

									if use_cutouts:
															lowerCAmelCase	:   Optional[int]					     =					spherical_dist_loss(lowercase_    ,  lowercase_ )
															lowerCAmelCase	:   Optional[int]					     =					dists.view([num_cutouts, sample.shape[0], -1] )
															lowerCAmelCase	:   int					     =					dists.sum(2 ).mean(0 ).sum() * clip_guidance_scale
									else:
															lowerCAmelCase	:   List[Any]					     =					spherical_dist_loss(lowercase_    ,  lowercase_ ).mean() * clip_guidance_scale

									lowerCAmelCase	:   Optional[Any]					     =					-torch.autograd.grad(lowercase_    ,  lowercase_ )[0]

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






			@torch.no_grad()
			def __call__(    self    ,  lowercase_    ,  lowercase_ = 512    ,  lowercase_ = 512    ,  lowercase_ = 50    ,  lowercase_ = 7.5    ,  lowercase_ = 1    ,  lowercase_ = 0.0    ,  lowercase_ = 100    ,  lowercase_ = None    ,  lowercase_ = 4    ,  lowercase_ = True    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = "pil"    ,  lowercase_ = True    ,  )	->					str:
									if isinstance(lowercase_    ,  lowercase_ ):
															lowerCAmelCase	:   str					     =					1
									elif isinstance(lowercase_    ,  lowercase_ ):
															lowerCAmelCase	:   Optional[Any]					     =					len(lowercase_ )
									else:
															raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase_ )}""" )

									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}.""" )

									# get prompt text embeddings
									lowerCAmelCase	:   Tuple					     =					self.tokenizer(
									    lowercase_    ,  padding="""max_length"""    ,  max_length=self.tokenizer.model_max_length    ,  truncation=lowercase_    ,  return_tensors="""pt"""    ,  )
									lowerCAmelCase	:   str					     =					self.text_encoder(text_input.input_ids.to(self.device ) )[0]
									# duplicate text embeddings for each generation per prompt
									lowerCAmelCase	:   Dict					     =					text_embeddings.repeat_interleave(lowercase_    ,  dim=0 )

									if clip_guidance_scale > 0:
															if clip_prompt is not None:
																					lowerCAmelCase	:   int					     =					self.tokenizer(
																					    lowercase_    ,  padding="""max_length"""    ,  max_length=self.tokenizer.model_max_length    ,  truncation=lowercase_    ,  return_tensors="""pt"""    ,  ).input_ids.to(self.device )
															else:
																					lowerCAmelCase	:   Tuple					     =					text_input.input_ids.to(self.device )
															lowerCAmelCase	:   Dict					     =					self.clip_model.get_text_features(lowercase_ )
															lowerCAmelCase	:   Tuple					     =					text_embeddings_clip / text_embeddings_clip.norm(p=2    ,  dim=-1    ,  keepdim=lowercase_ )
															# duplicate text embeddings clip for each generation per prompt
															lowerCAmelCase	:   Any					     =					text_embeddings_clip.repeat_interleave(lowercase_    ,  dim=0 )

									# 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.
									lowerCAmelCase	:   Optional[Any]					     =					guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
															lowerCAmelCase	:   Optional[Any]					     =					text_input.input_ids.shape[-1]
															lowerCAmelCase	:   Dict					     =					self.tokenizer([""""""]    ,  padding="""max_length"""    ,  max_length=lowercase_    ,  return_tensors="""pt""" )
															lowerCAmelCase	:   str					     =					self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
															# duplicate unconditional embeddings for each generation per prompt
															lowerCAmelCase	:   Optional[int]					     =					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
															lowerCAmelCase	:   str					     =					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`.
									lowerCAmelCase	:   Optional[int]					     =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									lowerCAmelCase	:   int					     =					text_embeddings.dtype
									if latents is None:
															if self.device.type == "mps":
																					# randn does not work reproducibly on mps
																					lowerCAmelCase	:   str					     =					torch.randn(lowercase_    ,  generator=lowercase_    ,  device="""cpu"""    ,  dtype=lowercase_ ).to(
																					    self.device )
															else:
																					lowerCAmelCase	:   Optional[int]					     =					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}""" )
															lowerCAmelCase	:   Any					     =					latents.to(self.device )

									# set timesteps
									lowerCAmelCase	:   List[str]					     =					"""offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
									lowerCAmelCase	:   int					     =					{}
									if accepts_offset:
															lowerCAmelCase	:   List[str]					     =					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
									lowerCAmelCase	:   Union[str, Any]					     =					self.scheduler.timesteps.to(self.device )

									# scale the initial noise by the standard deviation required by the scheduler
									lowerCAmelCase	:   List[Any]					     =					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]
									lowerCAmelCase	:   List[str]					     =					"""eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
									lowerCAmelCase	:   int					     =					{}
									if accepts_eta:
															lowerCAmelCase	:   Any					     =					eta

									# check if the scheduler accepts generator
									lowerCAmelCase	:   Union[str, Any]					     =					"""generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
									if accepts_generator:
															lowerCAmelCase	:   Dict					     =					generator

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

															# predict the noise residual
															lowerCAmelCase	:   Optional[Any]					     =					self.unet(lowercase_    ,  lowercase_    ,  encoder_hidden_states=lowercase_ ).sample

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

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

															# compute the previous noisy sample x_t -> x_t-1
															lowerCAmelCase	:   List[Any]					     =					self.scheduler.step(lowercase_    ,  lowercase_    ,  lowercase_    ,  **lowercase_ ).prev_sample

									# scale and decode the image latents with vae
									lowerCAmelCase	:   Dict					     =					1 / self.vae.config.scaling_factor * latents
									lowerCAmelCase	:   Optional[int]					     =					self.vae.decode(lowercase_ ).sample

									lowerCAmelCase	:   Any					     =					(image / 2 + 0.5).clamp(0    ,  1 )
									lowerCAmelCase	:   str					     =					image.cpu().permute(0    ,  2    ,  3    ,  1 ).numpy()

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

									if not return_dict:
															return (image, None)

									return StableDiffusionPipelineOutput(images=lowercase_    ,  nsfw_content_detected=lowercase_ )

