[Community] [WIP] LCM Interpolation Pipeline (#5767)

* wip: add interpolate pipeline for lcm

* update documentation

* update documentation

examples/community/README.md

@@ -47,6 +47,7 @@ sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint m
 prompt-to-prompt | change parts of a prompt and retain image structure (see [paper page](https://prompt-to-prompt.github.io/)) | [Prompt2Prompt Pipeline](#prompt2prompt-pipeline) | - | [Umer H. Adil](https://twitter.com/UmerHAdil) | 
 |   Latent Consistency Pipeline                                                                                                    | Implementation of [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378)                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Latent Consistency Pipeline](#latent-consistency-pipeline)      | - |              [Simian Luo](https://github.com/luosiallen) |
 |   Latent Consistency Img2img Pipeline                                                                                                    | Img2img pipeline for Latent Consistency Models                                                                                                                                                                                                                                                                                                                                                                                                                                    | [Latent Consistency Img2Img Pipeline](#latent-consistency-img2img-pipeline)      | - |              [Logan Zoellner](https://github.com/nagolinc) |
+|   Latent Consistency Interpolation Pipeline                                                                                                    | Interpolate the latent space of Latent Consistency Models with multiple prompts                                                                                                                                                                                                                                                                                                                                                                                                                                    | [Latent Consistency Interpolation Pipeline](#latent-consistency-interpolation-pipeline) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pK3NrLWJSiJsBynLns1K1-IDTW9zbPvl?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
 
 
 To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly.
@@ -2295,3 +2296,50 @@ num_inference_steps = 4
 
 images = pipe(prompt=prompt, image=input_image, strength=strength, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil").images
 ```
+
+
+
+### Latent Consistency Interpolation Pipeline
+
+This pipeline extends the Latent Consistency Pipeline to allow for interpolation of the latent space between multiple prompts. It is similar to the [Stable Diffusion Interpolate](https://github.com/huggingface/diffusers/blob/main/examples/community/interpolate_stable_diffusion.py) and [unCLIP Interpolate](https://github.com/huggingface/diffusers/blob/main/examples/community/unclip_text_interpolation.py) community pipelines.
+
+```py
+import torch
+import numpy as np
+
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_interpolate")
+
+# To save GPU memory, torch.float16 can be used, but it may compromise image quality.
+pipe.to(torch_device="cuda", torch_dtype=torch.float32)
+
+prompts = [
+    "Self-portrait oil painting, a beautiful cyborg with golden hair, Margot Robbie, 8k",
+    "Self-portrait oil painting, an extremely strong man, body builder, Huge Jackman, 8k",
+    "An astronaut floating in space, renaissance art, realistic, high quality, 8k",
+    "Oil painting of a cat, cute, dream-like",
+    "Hugging face emoji, cute, realistic"
+]
+num_inference_steps = 4
+num_interpolation_steps = 60
+seed = 1337
+
+torch.manual_seed(seed)
+np.random.seed(seed)
+
+images = pipe(
+    prompt=prompts,
+    height=512,
+    width=512,
+    num_inference_steps=num_inference_steps,
+    num_interpolation_steps=num_interpolation_steps,
+    guidance_scale=8.0,
+    embedding_interpolation_type="lerp",
+    latent_interpolation_type="slerp",
+    process_batch_size=4, # Make it higher or lower based on your GPU memory
+    generator=torch.Generator(seed),
+)
+
+assert len(images) == (len(prompts) - 1) * num_interpolation_steps
+```

examples/community/latent_consistency_interpolate.py

+import inspect
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
+from diffusers.schedulers import LCMScheduler
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> import numpy as np
+
+        >>> from diffusers import DiffusionPipeline
+
+        >>> pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_interpolate")
+        >>> # To save GPU memory, torch.float16 can be used, but it may compromise image quality.
+        >>> pipe.to(torch_device="cuda", torch_dtype=torch.float32)
+
+        >>> prompts = ["A cat", "A dog", "A horse"]
+        >>> num_inference_steps = 4
+        >>> num_interpolation_steps = 24
+        >>> seed = 1337
+
+        >>> torch.manual_seed(seed)
+        >>> np.random.seed(seed)
+
+        >>> images = pipe(
+                prompt=prompts,
+                height=512,
+                width=512,
+                num_inference_steps=num_inference_steps,
+                num_interpolation_steps=num_interpolation_steps,
+                guidance_scale=8.0,
+                embedding_interpolation_type="lerp",
+                latent_interpolation_type="slerp",
+                process_batch_size=4, # Make it higher or lower based on your GPU memory
+                generator=torch.Generator(seed),
+            )
+
+        >>> # Save the images as a video
+        >>> import imageio
+        >>> from PIL import Image
+
+        >>> def pil_to_video(images: List[Image.Image], filename: str, fps: int = 60) -> None:
+                frames = [np.array(image) for image in images]
+                with imageio.get_writer(filename, fps=fps) as video_writer:
+                    for frame in frames:
+                        video_writer.append_data(frame)
+
+        >>> pil_to_video(images, "lcm_interpolate.mp4", fps=24)
+        ```
+"""
+
+
+def lerp(
+    v0: Union[torch.Tensor, np.ndarray],
+    v1: Union[torch.Tensor, np.ndarray],
+    t: Union[float, torch.Tensor, np.ndarray],
+) -> Union[torch.Tensor, np.ndarray]:
+    """
+    Linearly interpolate between two vectors/tensors.
+
+    Args:
+        v0 (`torch.Tensor` or `np.ndarray`): First vector/tensor.
+        v1 (`torch.Tensor` or `np.ndarray`): Second vector/tensor.
+        t: (`float`, `torch.Tensor`, or `np.ndarray`):
+            Interpolation factor. If float, must be between 0 and 1. If np.ndarray or
+            torch.Tensor, must be one dimensional with values between 0 and 1.
+
+    Returns:
+        Union[torch.Tensor, np.ndarray]
+            Interpolated vector/tensor between v0 and v1.
+    """
+    inputs_are_torch = False
+    t_is_float = False
+
+    if isinstance(v0, torch.Tensor):
+        inputs_are_torch = True
+        input_device = v0.device
+        v0 = v0.cpu().numpy()
+        v1 = v1.cpu().numpy()
+
+    if isinstance(t, torch.Tensor):
+        inputs_are_torch = True
+        input_device = t.device
+        t = t.cpu().numpy()
+    elif isinstance(t, float):
+        t_is_float = True
+        t = np.array([t])
+
+    t = t[..., None]
+    v0 = v0[None, ...]
+    v1 = v1[None, ...]
+    v2 = (1 - t) * v0 + t * v1
+
+    if t_is_float and v0.ndim > 1:
+        assert v2.shape[0] == 1
+        v2 = np.squeeze(v2, axis=0)
+    if inputs_are_torch:
+        v2 = torch.from_numpy(v2).to(input_device)
+
+    return v2
+
+
+def slerp(
+    v0: Union[torch.Tensor, np.ndarray],
+    v1: Union[torch.Tensor, np.ndarray],
+    t: Union[float, torch.Tensor, np.ndarray],
+    DOT_THRESHOLD=0.9995,
+) -> Union[torch.Tensor, np.ndarray]:
+    """
+    Spherical linear interpolation between two vectors/tensors.
+
+    Args:
+        v0 (`torch.Tensor` or `np.ndarray`): First vector/tensor.
+        v1 (`torch.Tensor` or `np.ndarray`): Second vector/tensor.
+        t: (`float`, `torch.Tensor`, or `np.ndarray`):
+            Interpolation factor. If float, must be between 0 and 1. If np.ndarray or
+            torch.Tensor, must be one dimensional with values between 0 and 1.
+        DOT_THRESHOLD (`float`, *optional*, default=0.9995):
+            Threshold for when to use linear interpolation instead of spherical interpolation.
+
+    Returns:
+        `torch.Tensor` or `np.ndarray`:
+            Interpolated vector/tensor between v0 and v1.
+    """
+    inputs_are_torch = False
+    t_is_float = False
+
+    if isinstance(v0, torch.Tensor):
+        inputs_are_torch = True
+        input_device = v0.device
+        v0 = v0.cpu().numpy()
+        v1 = v1.cpu().numpy()
+
+    if isinstance(t, torch.Tensor):
+        inputs_are_torch = True
+        input_device = t.device
+        t = t.cpu().numpy()
+    elif isinstance(t, float):
+        t_is_float = True
+        t = np.array([t], dtype=v0.dtype)
+
+    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+    if np.abs(dot) > DOT_THRESHOLD:
+        # v1 and v2 are close to parallel
+        # Use linear interpolation instead
+        v2 = lerp(v0, v1, t)
+    else:
+        theta_0 = np.arccos(dot)
+        sin_theta_0 = np.sin(theta_0)
+        theta_t = theta_0 * t
+        sin_theta_t = np.sin(theta_t)
+        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+        s1 = sin_theta_t / sin_theta_0
+        s0 = s0[..., None]
+        s1 = s1[..., None]
+        v0 = v0[None, ...]
+        v1 = v1[None, ...]
+        v2 = s0 * v0 + s1 * v1
+
+    if t_is_float and v0.ndim > 1:
+        assert v2.shape[0] == 1
+        v2 = np.squeeze(v2, axis=0)
+    if inputs_are_torch:
+        v2 = torch.from_numpy(v2).to(input_device)
+
+    return v2
+
+
+class LatentConsistencyModelWalkPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
+    r"""
+    Pipeline for text-to-image generation using a latent consistency model.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    The pipeline also inherits the following loading methods:
+        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
+        text_encoder ([`~transformers.CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        tokenizer ([`~transformers.CLIPTokenizer`]):
+            A `CLIPTokenizer` to tokenize text.
+        unet ([`UNet2DConditionModel`]):
+            A `UNet2DConditionModel` to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only
+            supports [`LCMScheduler`].
+        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 more details
+            about a model's potential harms.
+        feature_extractor ([`~transformers.CLIPImageProcessor`]):
+            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
+        requires_safety_checker (`bool`, *optional*, defaults to `True`):
+            Whether the pipeline requires a safety checker component.
+    """
+    model_cpu_offload_seq = "text_encoder->unet->vae"
+    _optional_components = ["safety_checker", "feature_extractor"]
+    _exclude_from_cpu_offload = ["safety_checker"]
+    _callback_tensor_inputs = ["latents", "denoised", "prompt_embeds", "w_embedding"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: LCMScheduler,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                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."
+            )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        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)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
+    def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
+        r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
+
+        The suffixes after the scaling factors represent the stages where they are being applied.
+
+        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
+        that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+        Args:
+            s1 (`float`):
+                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+                mitigate "oversmoothing effect" in the enhanced denoising process.
+            s2 (`float`):
+                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+                mitigate "oversmoothing effect" in the enhanced denoising process.
+            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+        """
+        if not hasattr(self, "unet"):
+            raise ValueError("The pipeline must have `unet` for using FreeU.")
+        self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
+    def disable_freeu(self):
+        """Disables the FreeU mechanism if enabled."""
+        self.unet.disable_freeu()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            lora_scale (`float`, *optional*):
+                A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        """
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if not USE_PEFT_BACKEND:
+                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+            else:
+                scale_lora_layers(self.text_encoder, lora_scale)
+
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            if clip_skip is None:
+                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
+                prompt_embeds = prompt_embeds[0]
+            else:
+                prompt_embeds = self.text_encoder(
+                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
+                )
+                # Access the `hidden_states` first, that contains a tuple of
+                # all the hidden states from the encoder layers. Then index into
+                # the tuple to access the hidden states from the desired layer.
+                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
+                # We also need to apply the final LayerNorm here to not mess with the
+                # representations. The `last_hidden_states` that we typically use for
+                # obtaining the final prompt representations passes through the LayerNorm
+                # layer.
+                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = prompt_embeds.dtype
+
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(self.text_encoder, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds
+
+    # 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.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_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+        """
+        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+        Args:
+            timesteps (`torch.Tensor`):
+                generate embedding vectors at these timesteps
+            embedding_dim (`int`, *optional*, defaults to 512):
+                dimension of the embeddings to generate
+            dtype:
+                data type of the generated embeddings
+
+        Returns:
+            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+        """
+        assert len(w.shape) == 1
+        w = w * 1000.0
+
+        half_dim = embedding_dim // 2
+        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+        emb = w.to(dtype)[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if embedding_dim % 2 == 1:  # zero pad
+            emb = torch.nn.functional.pad(emb, (0, 1))
+        assert emb.shape == (w.shape[0], embedding_dim)
+        return emb
+
+    # 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
+
+    # Currently StableDiffusionPipeline.check_inputs with negative prompt stuff removed
+    def check_inputs(
+        self,
+        prompt: Union[str, List[str]],
+        height: int,
+        width: int,
+        callback_steps: int,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        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 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)}."
+            )
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+    @torch.no_grad()
+    def interpolate_embedding(
+        self,
+        start_embedding: torch.FloatTensor,
+        end_embedding: torch.FloatTensor,
+        num_interpolation_steps: Union[int, List[int]],
+        interpolation_type: str,
+    ) -> torch.FloatTensor:
+        if interpolation_type == "lerp":
+            interpolation_fn = lerp
+        elif interpolation_type == "slerp":
+            interpolation_fn = slerp
+        else:
+            raise ValueError(
+                f"embedding_interpolation_type must be one of ['lerp', 'slerp'], got {interpolation_type}."
+            )
+
+        embedding = torch.cat([start_embedding, end_embedding])
+        steps = torch.linspace(0, 1, num_interpolation_steps, dtype=embedding.dtype).cpu().numpy()
+        steps = np.expand_dims(steps, axis=tuple(range(1, embedding.ndim)))
+        interpolations = []
+
+        # Interpolate between text embeddings
+        # TODO(aryan): Think of a better way of doing this
+        # See if it can be done parallelly instead
+        for i in range(embedding.shape[0] - 1):
+            interpolations.append(interpolation_fn(embedding[i], embedding[i + 1], steps).squeeze(dim=1))
+
+        interpolations = torch.cat(interpolations)
+        return interpolations
+
+    @torch.no_grad()
+    def interpolate_latent(
+        self,
+        start_latent: torch.FloatTensor,
+        end_latent: torch.FloatTensor,
+        num_interpolation_steps: Union[int, List[int]],
+        interpolation_type: str,
+    ) -> torch.FloatTensor:
+        if interpolation_type == "lerp":
+            interpolation_fn = lerp
+        elif interpolation_type == "slerp":
+            interpolation_fn = slerp
+
+        latent = torch.cat([start_latent, end_latent])
+        steps = torch.linspace(0, 1, num_interpolation_steps, dtype=latent.dtype).cpu().numpy()
+        steps = np.expand_dims(steps, axis=tuple(range(1, latent.ndim)))
+        interpolations = []
+
+        # Interpolate between latents
+        # TODO: Think of a better way of doing this
+        # See if it can be done parallelly instead
+        for i in range(latent.shape[0] - 1):
+            interpolations.append(interpolation_fn(latent[i], latent[i + 1], steps).squeeze(dim=1))
+
+        return torch.cat(interpolations)
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    def clip_skip(self):
+        return self._clip_skip
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 4,
+        num_interpolation_steps: int = 8,
+        original_inference_steps: int = None,
+        guidance_scale: float = 8.5,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        embedding_interpolation_type: str = "lerp",
+        latent_interpolation_type: str = "slerp",
+        process_batch_size: int = 4,
+        **kwargs,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            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.
+            original_inference_steps (`int`, *optional*):
+                The original number of inference steps use to generate a linearly-spaced timestep schedule, from which
+                we will draw `num_inference_steps` evenly spaced timesteps from as our final timestep schedule,
+                following the Skipping-Step method in the paper (see Section 4.3). If not set this will default to the
+                scheduler's `original_inference_steps` attribute.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+                Note that the original latent consistency models paper uses a different CFG formulation where the
+                guidance scales are decreased by 1 (so in the paper formulation CFG is enabled when `guidance_scale >
+                0`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](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 is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.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.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeine class.
+            embedding_interpolation_type (`str`, *optional*, defaults to `"lerp"`):
+                The type of interpolation to use for interpolating between text embeddings. Choose between `"lerp"` and `"slerp"`.
+            latent_interpolation_type (`str`, *optional*, defaults to `"slerp"`):
+                The type of interpolation to use for interpolating between latents. Choose between `"lerp"` and `"slerp"`.
+            process_batch_size (`int`, *optional*, defaults to 4):
+                The batch size to use for processing the images. This is useful when generating a large number of images
+                and you want to avoid running out of memory.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is a list with the generated images and the
+                second element is a list of `bool`s indicating whether the corresponding generated image contains
+                "not-safe-for-work" (nsfw) content.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+
+        # 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
+        self.check_inputs(prompt, height, width, callback_steps, prompt_embeds, callback_on_step_end_tensor_inputs)
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+        if batch_size < 2:
+            raise ValueError(f"`prompt` must have length of atleast 2 but found {batch_size}")
+        if num_images_per_prompt != 1:
+            raise ValueError("`num_images_per_prompt` must be `1` as no other value is supported yet")
+        if prompt_embeds is not None:
+            raise ValueError("`prompt_embeds` must be None since it is not supported yet")
+        if latents is not None:
+            raise ValueError("`latents` must be None since it is not supported yet")
+
+        device = self._execution_device
+        # do_classifier_free_guidance = guidance_scale > 1.0
+
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+
+        self.scheduler.set_timesteps(num_inference_steps, device, original_inference_steps=original_inference_steps)
+        timesteps = self.scheduler.timesteps
+        num_channels_latents = self.unet.config.in_channels
+        # bs = batch_size * num_images_per_prompt
+
+        # 3. Encode initial input prompt
+        prompt_embeds_1, _ = self.encode_prompt(
+            prompt[:1],
+            device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=False,
+            negative_prompt=None,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=None,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # 4. Prepare initial latent variables
+        latents_1 = self.prepare_latents(
+            1,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds_1.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, None)
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+        images = []
+
+        # 5. Iterate over prompts and perform latent walk. Note that we do this two prompts at a time
+        #    otherwise the memory usage ends up being too high.
+        with self.progress_bar(total=batch_size - 1) as prompt_progress_bar:
+            for i in range(1, batch_size):
+                # 6. Encode current prompt
+                prompt_embeds_2, _ = self.encode_prompt(
+                    prompt[i : i + 1],
+                    device,
+                    num_images_per_prompt=num_images_per_prompt,
+                    do_classifier_free_guidance=False,
+                    negative_prompt=None,
+                    prompt_embeds=prompt_embeds,
+                    negative_prompt_embeds=None,
+                    lora_scale=lora_scale,
+                    clip_skip=self.clip_skip,
+                )
+
+                # 7. Prepare current latent variables
+                latents_2 = self.prepare_latents(
+                    1,
+                    num_channels_latents,
+                    height,
+                    width,
+                    prompt_embeds_2.dtype,
+                    device,
+                    generator,
+                    latents,
+                )
+
+                # 8. Interpolate between previous and current prompt embeddings and latents
+                inference_embeddings = self.interpolate_embedding(
+                    start_embedding=prompt_embeds_1,
+                    end_embedding=prompt_embeds_2,
+                    num_interpolation_steps=num_interpolation_steps,
+                    interpolation_type=embedding_interpolation_type,
+                )
+                inference_latents = self.interpolate_latent(
+                    start_latent=latents_1,
+                    end_latent=latents_2,
+                    num_interpolation_steps=num_interpolation_steps,
+                    interpolation_type=latent_interpolation_type,
+                )
+                next_prompt_embeds = inference_embeddings[-1:].detach().clone()
+                next_latents = inference_latents[-1:].detach().clone()
+                bs = num_interpolation_steps
+
+                # 9. Perform inference in batches. Note the use of `process_batch_size` to control the batch size
+                #    of the inference. This is useful for reducing memory usage and can be configured based on the
+                #    available GPU memory.
+                with self.progress_bar(
+                    total=(bs + process_batch_size - 1) // process_batch_size
+                ) as batch_progress_bar:
+                    for batch_index in range(0, bs, process_batch_size):
+                        batch_inference_latents = inference_latents[batch_index : batch_index + process_batch_size]
+                        batch_inference_embedddings = inference_embeddings[
+                            batch_index : batch_index + process_batch_size
+                        ]
+
+                        self.scheduler.set_timesteps(
+                            num_inference_steps, device, original_inference_steps=original_inference_steps
+                        )
+                        timesteps = self.scheduler.timesteps
+
+                        current_bs = batch_inference_embedddings.shape[0]
+                        w = torch.tensor(self.guidance_scale - 1).repeat(current_bs)
+                        w_embedding = self.get_guidance_scale_embedding(
+                            w, embedding_dim=self.unet.config.time_cond_proj_dim
+                        ).to(device=device, dtype=latents_1.dtype)
+
+                        # 10. Perform inference for current batch
+                        with self.progress_bar(total=num_inference_steps) as progress_bar:
+                            for index, t in enumerate(timesteps):
+                                batch_inference_latents = batch_inference_latents.to(batch_inference_embedddings.dtype)
+
+                                # model prediction (v-prediction, eps, x)
+                                model_pred = self.unet(
+                                    batch_inference_latents,
+                                    t,
+                                    timestep_cond=w_embedding,
+                                    encoder_hidden_states=batch_inference_embedddings,
+                                    cross_attention_kwargs=self.cross_attention_kwargs,
+                                    return_dict=False,
+                                )[0]
+
+                                # compute the previous noisy sample x_t -> x_t-1
+                                batch_inference_latents, denoised = self.scheduler.step(
+                                    model_pred, t, batch_inference_latents, **extra_step_kwargs, return_dict=False
+                                )
+                                if callback_on_step_end is not None:
+                                    callback_kwargs = {}
+                                    for k in callback_on_step_end_tensor_inputs:
+                                        callback_kwargs[k] = locals()[k]
+                                    callback_outputs = callback_on_step_end(self, index, t, callback_kwargs)
+
+                                    batch_inference_latents = callback_outputs.pop("latents", batch_inference_latents)
+                                    batch_inference_embedddings = callback_outputs.pop(
+                                        "prompt_embeds", batch_inference_embedddings
+                                    )
+                                    w_embedding = callback_outputs.pop("w_embedding", w_embedding)
+                                    denoised = callback_outputs.pop("denoised", denoised)
+
+                                # call the callback, if provided
+                                if index == len(timesteps) - 1 or (
+                                    (index + 1) > num_warmup_steps and (index + 1) % self.scheduler.order == 0
+                                ):
+                                    progress_bar.update()
+                                    if callback is not None and index % callback_steps == 0:
+                                        step_idx = index // getattr(self.scheduler, "order", 1)
+                                        callback(step_idx, t, batch_inference_latents)
+
+                        denoised = denoised.to(batch_inference_embedddings.dtype)
+
+                        # Note: This is not supported because you would get black images in your latent walk if
+                        #       NSFW concept is detected
+                        # if not output_type == "latent":
+                        #     image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]
+                        #     image, has_nsfw_concept = self.run_safety_checker(image, device, inference_embeddings.dtype)
+                        # else:
+                        #     image = denoised
+                        #     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.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]
+                        do_denormalize = [True] * image.shape[0]
+                        has_nsfw_concept = None
+
+                        image = self.image_processor.postprocess(
+                            image, output_type=output_type, do_denormalize=do_denormalize
+                        )
+                        images.append(image)
+
+                        batch_progress_bar.update()
+
+                prompt_embeds_1 = next_prompt_embeds
+                latents_1 = next_latents
+
+                prompt_progress_bar.update()
+
+        # 11. Determine what should be returned
+        if output_type == "pil":
+            images = [image for image_list in images for image in image_list]
+        elif output_type == "np":
+            images = np.concatenate(images)
+        elif output_type == "pt":
+            images = torch.cat(images)
+        else:
+            raise ValueError("`output_type` must be one of 'pil', 'np' or 'pt'.")
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (images, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)