zero123.py

# 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
        )