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	# Copyright 2023 The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import inspect
	import math
	import warnings
	from typing import Any, Callable, Dict, List, Optional, Union

	import PIL
	import torch
	import torchvision.transforms.functional as TF
	from diffusers.configuration_utils import ConfigMixin, FrozenDict, register_to_config
	from diffusers.image_processor import VaeImageProcessor
	from diffusers.models import AutoencoderKL, UNet2DConditionModel
	from diffusers.models.modeling_utils import ModelMixin
	from diffusers.pipelines.pipeline_utils import DiffusionPipeline
	from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
	from diffusers.pipelines.stable_diffusion.safety_checker import (
	StableDiffusionSafetyChecker,
	)
	from diffusers.schedulers import KarrasDiffusionSchedulers
	from diffusers.utils import deprecate, is_accelerate_available, logging
	from diffusers.utils.torch_utils import randn_tensor
	from packaging import version
	from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

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


	class CLIPCameraProjection(ModelMixin, ConfigMixin):
	"""
	A Projection layer for CLIP embedding and camera embedding.

	Parameters:
	embedding_dim (`int`, optional, defaults to 768): The dimension of the model input `clip_embed`
	additional_embeddings (`int`, optional, defaults to 4): The number of additional tokens appended to the
	projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings +
	additional_embeddings`.
	"""

	@register_to_config
	def __init__(self, embedding_dim: int = 768, additional_embeddings: int = 4):
	super().__init__()
	self.embedding_dim = embedding_dim
	self.additional_embeddings = additional_embeddings

	self.input_dim = self.embedding_dim + self.additional_embeddings
	self.output_dim = self.embedding_dim

	self.proj = torch.nn.Linear(self.input_dim, self.output_dim)

	def forward(
	self,
	embedding: torch.FloatTensor,
	):
	"""
	The [`PriorTransformer`] forward method.

	Args:
	hidden_states (`torch.FloatTensor` of shape `(batch_size, input_dim)`):
	The currently input embeddings.

	Returns:
	The output embedding projection (`torch.FloatTensor` of shape `(batch_size, output_dim)`).
	"""
	proj_embedding = self.proj(embedding)
	return proj_embedding


	class Zero123Pipeline(DiffusionPipeline):
	r"""
	Pipeline to generate variations from an input image using Stable Diffusion.

	This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
	library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

	Args:
	vae ([`AutoencoderKL`]):
	Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
	image_encoder ([`CLIPVisionModelWithProjection`]):
	Frozen CLIP image-encoder. Stable Diffusion Image Variation uses the vision portion of
	[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection),
	specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
	unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
	scheduler ([`SchedulerMixin`]):
	A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
	[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
	safety_checker ([`StableDiffusionSafetyChecker`]):
	Classification module that estimates whether generated images could be considered offensive or harmful.
	Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
	feature_extractor ([`CLIPImageProcessor`]):
	Model that extracts features from generated images to be used as inputs for the `safety_checker`.
	"""
	# TODO: feature_extractor is required to encode images (if they are in PIL format),
	# we should give a descriptive message if the pipeline doesn't have one.
	_optional_components = ["safety_checker"]

	def __init__(
	self,
	vae: AutoencoderKL,
	image_encoder: CLIPVisionModelWithProjection,
	unet: UNet2DConditionModel,
	scheduler: KarrasDiffusionSchedulers,
	safety_checker: StableDiffusionSafetyChecker,
	feature_extractor: CLIPImageProcessor,
	clip_camera_projection: CLIPCameraProjection,
	requires_safety_checker: bool = True,
	):
	super().__init__()

	if safety_checker is None and requires_safety_checker:
	logger.warn(
	f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
	" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
	" results in services or applications open to the public. Both the diffusers team and Hugging Face"
	" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
	" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
	" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
	)

	if safety_checker is not None and feature_extractor is None:
	raise ValueError(
	"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
	" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
	)

	is_unet_version_less_0_9_0 = hasattr(
	unet.config, "_diffusers_version"
	) and version.parse(
	version.parse(unet.config._diffusers_version).base_version
	) < version.parse(
	"0.9.0.dev0"
	)
	is_unet_sample_size_less_64 = (
	hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
	)
	if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
	deprecation_message = (
	"The configuration file of the unet has set the default `sample_size` to smaller than"
	" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
	" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
	" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
	" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
	" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
	" in the config might lead to incorrect results in future versions. If you have downloaded this"
	" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
	" the `unet/config.json` file"
	)
	deprecate(
	"sample_size<64", "1.0.0", deprecation_message, standard_warn=False
	)
	new_config = dict(unet.config)
	new_config["sample_size"] = 64
	unet._internal_dict = FrozenDict(new_config)

	self.register_modules(
	vae=vae,
	image_encoder=image_encoder,
	unet=unet,
	scheduler=scheduler,
	safety_checker=safety_checker,
	feature_extractor=feature_extractor,
	clip_camera_projection=clip_camera_projection,
	)
	self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
	self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
	self.register_to_config(requires_safety_checker=requires_safety_checker)

	def enable_sequential_cpu_offload(self, gpu_id=0):
	r"""
	Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
	text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
	`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
	"""
	if is_accelerate_available():
	from accelerate import cpu_offload
	else:
	raise ImportError("Please install accelerate via `pip install accelerate`")

	device = torch.device(f"cuda:{gpu_id}")

	for cpu_offloaded_model in [
	self.unet,
	self.image_encoder,
	self.vae,
	self.safety_checker,
	]:
	if cpu_offloaded_model is not None:
	cpu_offload(cpu_offloaded_model, device)

	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def _execution_device(self):
	r"""
	Returns the device on which the pipeline's models will be executed. After calling
	`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
	hooks.
	"""
	if not hasattr(self.unet, "_hf_hook"):
	return self.device
	for module in self.unet.modules():
	if (
	hasattr(module, "_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

	def _encode_image(
	self,
	image,
	elevation,
	azimuth,
	distance,
	device,
	num_images_per_prompt,
	do_classifier_free_guidance,
	clip_image_embeddings=None,
	image_camera_embeddings=None,
	):
	dtype = next(self.image_encoder.parameters()).dtype

	if image_camera_embeddings is None:
	if image is None:
	assert clip_image_embeddings is not None
	image_embeddings = clip_image_embeddings.to(device=device, dtype=dtype)
	else:
	if not isinstance(image, torch.Tensor):
	image = self.feature_extractor(
	images=image, return_tensors="pt"
	).pixel_values

	image = image.to(device=device, dtype=dtype)
	image_embeddings = self.image_encoder(image).image_embeds
	image_embeddings = image_embeddings.unsqueeze(1)

	bs_embed, seq_len, _ = image_embeddings.shape

	if isinstance(elevation, float):
	elevation = torch.as_tensor(
	[elevation] * bs_embed, dtype=dtype, device=device
	)
	if isinstance(azimuth, float):
	azimuth = torch.as_tensor(
	[azimuth] * bs_embed, dtype=dtype, device=device
	)
	if isinstance(distance, float):
	distance = torch.as_tensor(
	[distance] * bs_embed, dtype=dtype, device=device
	)

	camera_embeddings = torch.stack(
	[
	torch.deg2rad(elevation),
	torch.sin(torch.deg2rad(azimuth)),
	torch.cos(torch.deg2rad(azimuth)),
	distance,
	],
	dim=-1,
	)[:, None, :]

	image_embeddings = torch.cat([image_embeddings, camera_embeddings], dim=-1)

	# project (image, camera) embeddings to the same dimension as clip embeddings
	image_embeddings = self.clip_camera_projection(image_embeddings)
	else:
	image_embeddings = image_camera_embeddings.to(device=device, dtype=dtype)
	bs_embed, seq_len, _ = image_embeddings.shape

	# duplicate image embeddings for each generation per prompt, using mps friendly method
	image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
	image_embeddings = image_embeddings.view(
	bs_embed * num_images_per_prompt, seq_len, -1
	)

	if do_classifier_free_guidance:
	negative_prompt_embeds = torch.zeros_like(image_embeddings)

	# 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
	image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])

	return image_embeddings

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
	def run_safety_checker(self, image, device, dtype):
	if self.safety_checker is None:
	has_nsfw_concept = None
	else:
	if torch.is_tensor(image):
	feature_extractor_input = self.image_processor.postprocess(
	image, output_type="pil"
	)
	else:
	feature_extractor_input = self.image_processor.numpy_to_pil(image)
	safety_checker_input = self.feature_extractor(
	feature_extractor_input, return_tensors="pt"
	).to(device)
	image, has_nsfw_concept = self.safety_checker(
	images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
	)
	return image, has_nsfw_concept

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
	def decode_latents(self, latents):
	warnings.warn(
	"The decode_latents method is deprecated and will be removed in a future version. Please"
	" use VaeImageProcessor instead",
	FutureWarning,
	)
	latents = 1 / self.vae.config.scaling_factor * latents
	image = self.vae.decode(latents, return_dict=False)[0]
	image = (image / 2 + 0.5).clamp(0, 1)
	# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
	image = image.cpu().permute(0, 2, 3, 1).float().numpy()
	return image

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
	def prepare_extra_step_kwargs(self, generator, eta):
	# 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]

	accepts_eta = "eta" in set(
	inspect.signature(self.scheduler.step).parameters.keys()
	)
	extra_step_kwargs = {}
	if accepts_eta:
	extra_step_kwargs["eta"] = eta

	# check if the scheduler accepts generator
	accepts_generator = "generator" in set(
	inspect.signature(self.scheduler.step).parameters.keys()
	)
	if accepts_generator:
	extra_step_kwargs["generator"] = generator
	return extra_step_kwargs

	def check_inputs(self, image, height, width, callback_steps):
	# TODO: check image size or adjust image size to (height, width)

	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 (callback_steps is None) or (
	callback_steps is not None
	and (not isinstance(callback_steps, int) or callback_steps <= 0)
	):
	raise ValueError(
	f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
	f" {type(callback_steps)}."
	)

	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
	def prepare_latents(
	self,
	batch_size,
	num_channels_latents,
	height,
	width,
	dtype,
	device,
	generator,
	latents=None,
	):
	shape = (
	batch_size,
	num_channels_latents,
	height // self.vae_scale_factor,
	width // self.vae_scale_factor,
	)
	if isinstance(generator, list) and len(generator) != batch_size:
	raise ValueError(
	f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
	f" size of {batch_size}. Make sure the batch size matches the length of the generators."
	)

	if latents is None:
	latents = randn_tensor(
	shape, generator=generator, device=device, dtype=dtype
	)
	else:
	latents = latents.to(device)

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

	def _get_latent_model_input(
	self,
	latents: torch.FloatTensor,
	image: Optional[
	Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor]
	],
	num_images_per_prompt: int,
	do_classifier_free_guidance: bool,
	image_latents: Optional[torch.FloatTensor] = None,
	):
	if isinstance(image, PIL.Image.Image):
	image_pt = TF.to_tensor(image).unsqueeze(0).to(latents)
	elif isinstance(image, list):
	image_pt = torch.stack([TF.to_tensor(img) for img in image], dim=0).to(
	latents
	)
	elif isinstance(image, torch.Tensor):
	image_pt = image
	else:
	image_pt = None

	if image_pt is None:
	assert image_latents is not None
	image_pt = image_latents.repeat_interleave(num_images_per_prompt, dim=0)
	else:
	image_pt = image_pt * 2.0 - 1.0 # scale to [-1, 1]
	# FIXME: encoded latents should be multiplied with self.vae.config.scaling_factor
	# but zero123 was not trained this way
	image_pt = self.vae.encode(image_pt).latent_dist.mode()
	image_pt = image_pt.repeat_interleave(num_images_per_prompt, dim=0)
	if do_classifier_free_guidance:
	latent_model_input = torch.cat(
	[
	torch.cat([latents, latents], dim=0),
	torch.cat([torch.zeros_like(image_pt), image_pt], dim=0),
	],
	dim=1,
	)
	else:
	latent_model_input = torch.cat([latents, image_pt], dim=1)

	return latent_model_input

	@torch.no_grad()
	def __call__(
	self,
	image: Optional[
	Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor]
	] = None,
	elevation: Optional[Union[float, torch.FloatTensor]] = None,
	azimuth: Optional[Union[float, torch.FloatTensor]] = None,
	distance: Optional[Union[float, torch.FloatTensor]] = None,
	height: Optional[int] = None,
	width: Optional[int] = None,
	num_inference_steps: int = 50,
	guidance_scale: float = 3.0,
	num_images_per_prompt: int = 1,
	eta: float = 0.0,
	generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
	latents: Optional[torch.FloatTensor] = None,
	clip_image_embeddings: Optional[torch.FloatTensor] = None,
	image_camera_embeddings: Optional[torch.FloatTensor] = None,
	image_latents: Optional[torch.FloatTensor] = None,
	output_type: Optional[str] = "pil",
	return_dict: bool = True,
	callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
	callback_steps: int = 1,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	):
	r"""
	Function invoked when calling the pipeline for generation.

	Args:
	image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
	The image or images to guide the image generation. If you provide a tensor, it needs to comply with the
	configuration of
	[this](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json)
	`CLIPImageProcessor`
	height (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor):
	The height in pixels of the generated image.
	width (`int`, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor):
	The width in pixels of the generated image.
	num_inference_steps (`int`, optional, defaults to 50):
	The number of denoising steps. More denoising steps usually lead to a higher quality image at the
	expense of slower inference.
	guidance_scale (`float`, optional, defaults to 7.5):
	Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
	`guidance_scale` is defined as `w` of equation 2. of [Imagen
	Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
	1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
	usually at the expense of lower image quality.
	num_images_per_prompt (`int`, optional, defaults to 1):
	The number of images to generate per prompt.
	eta (`float`, optional, defaults to 0.0):
	Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
	[`schedulers.DDIMScheduler`], will be ignored for others.
	generator (`torch.Generator`, optional):
	One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
	to make generation deterministic.
	latents (`torch.FloatTensor`, optional):
	Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
	generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
	tensor will ge generated by sampling using the supplied random `generator`.
	output_type (`str`, optional, defaults to `"pil"`):
	The output format of the generate image. Choose between
	[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
	plain tuple.
	callback (`Callable`, optional):
	A function that will be called every `callback_steps` steps during inference. The function will be
	called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
	callback_steps (`int`, optional, defaults to 1):
	The frequency at which the `callback` function will be called. If not specified, the callback will be
	called at every step.

	Returns:
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
	[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
	When returning a tuple, the first element is a list with the generated images, and the second element is a
	list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
	(nsfw) content, according to the `safety_checker`.
	"""
	# 0. Default height and width to unet
	height = height or self.unet.config.sample_size * self.vae_scale_factor
	width = width or self.unet.config.sample_size * self.vae_scale_factor

	# 1. Check inputs. Raise error if not correct
	# TODO: check input elevation, azimuth, and distance
	# TODO: check image, clip_image_embeddings, image_latents
	self.check_inputs(image, height, width, callback_steps)

	# 2. Define call parameters
	if isinstance(image, PIL.Image.Image):
	batch_size = 1
	elif isinstance(image, list):
	batch_size = len(image)
	elif isinstance(image, torch.Tensor):
	batch_size = image.shape[0]
	else:
	assert image_latents is not None
	assert (
	clip_image_embeddings is not None or image_camera_embeddings is not None
	)
	batch_size = image_latents.shape[0]

	device = self._execution_device
	# 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.
	do_classifier_free_guidance = guidance_scale > 1.0

	# 3. Encode input image
	if isinstance(image, PIL.Image.Image) or isinstance(image, list):
	pil_image = image
	elif isinstance(image, torch.Tensor):
	pil_image = [TF.to_pil_image(image[i]) for i in range(image.shape[0])]
	else:
	pil_image = None
	image_embeddings = self._encode_image(
	pil_image,
	elevation,
	azimuth,
	distance,
	device,
	num_images_per_prompt,
	do_classifier_free_guidance,
	clip_image_embeddings,
	image_camera_embeddings,
	)

	# 4. Prepare timesteps
	self.scheduler.set_timesteps(num_inference_steps, device=device)
	timesteps = self.scheduler.timesteps

	# 5. Prepare latent variables
	# num_channels_latents = self.unet.config.in_channels
	num_channels_latents = 4 # FIXME: hard-coded
	latents = self.prepare_latents(
	batch_size * num_images_per_prompt,
	num_channels_latents,
	height,
	width,
	image_embeddings.dtype,
	device,
	generator,
	latents,
	)

	# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
	extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

	# 7. Denoising loop
	num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
	with self.progress_bar(total=num_inference_steps) as progress_bar:
	for i, t in enumerate(timesteps):
	# expand the latents if we are doing classifier free guidance
	latent_model_input = self._get_latent_model_input(
	latents,
	image,
	num_images_per_prompt,
	do_classifier_free_guidance,
	image_latents,
	)
	latent_model_input = self.scheduler.scale_model_input(
	latent_model_input, t
	)

	# predict the noise residual
	noise_pred = self.unet(
	latent_model_input,
	t,
	encoder_hidden_states=image_embeddings,
	cross_attention_kwargs=cross_attention_kwargs,
	).sample

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

	# compute the previous noisy sample x_t -> x_t-1
	latents = self.scheduler.step(
	noise_pred, t, latents, **extra_step_kwargs
	).prev_sample

	# call the callback, if provided
	if i == len(timesteps) - 1 or (
	(i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
	):
	progress_bar.update()
	if callback is not None and i % callback_steps == 0:
	callback(i, t, latents)

	if not output_type == "latent":
	image = self.vae.decode(
	latents / self.vae.config.scaling_factor, return_dict=False
	)[0]
	image, has_nsfw_concept = self.run_safety_checker(
	image, device, image_embeddings.dtype
	)
	else:
	image = latents
	has_nsfw_concept = None

	if has_nsfw_concept is None:
	do_denormalize = [True] * image.shape[0]
	else:
	do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

	image = self.image_processor.postprocess(
	image, output_type=output_type, do_denormalize=do_denormalize
	)

	if not return_dict:
	return (image, has_nsfw_concept)

	return StableDiffusionPipelineOutput(
	images=image, nsfw_content_detected=has_nsfw_concept
	)