diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py
index 90af386f316c..0e3d6ac2fab6 100644
--- a/src/diffusers/__init__.py
+++ b/src/diffusers/__init__.py
@@ -123,6 +123,7 @@
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
         VQDiffusionPipeline,
+        RDMPipeline,
     )
 
 try:
diff --git a/src/diffusers/models/retriever.py b/src/diffusers/models/retriever.py
new file mode 100644
index 000000000000..bfcce402fb13
--- /dev/null
+++ b/src/diffusers/models/retriever.py
@@ -0,0 +1,234 @@
+"""
+Idea for structure
+ Retriever aggregates an Index class and a RetrieverConfig class
+ The Index class aggregates a Dataset and RetrieverConfig class
+ from_pretrained in the retriever's class, it takes in a huggingface path to a dataset, optional path to an index file+config file in huggingface if there is one
+ If an index file is provided, add that index to the dataset.
+ If the dataset doesn't have the column embedding or a corresponding index file, in the Index class, the index is computed based on the clip model defined in the config. Then add that to the index of the dataset. This is done in the Index class
+ In retrieve we just call the retrieve method in the Index class that gets knn based on the faiss embedding.
+ In the save_pretrained method, save index using save_faiss_index. Save this dataset along with config.
+ The call method will just call retrieve.
+ I'll also have a way to pass the clip model and its components via default arguments.
+ Test save_pretrained and from_pretrained methods on new dataset.
+"""
+
+from transformers import CLIPModel, CLIPFeatureExtractor, CLIPTokenizer, PretrainedConfig
+from datasets import load_dataset, Image, load_dataset_builder, load_from_disk, Dataset
+import torch
+from typing import Callable, List, Optional, Union
+import numpy as np
+from ..utils import deprecate, logging
+from transformers.models.rag.retrieval_rag import LegacyIndex, CustomHFIndex, CanonicalHFIndex
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+from diffusers.pipelines.rdm.pipeline_rdm import preprocess_images, normalize_images
+import os
+from torch.nn import Module
+
+
+class IndexConfig(PretrainedConfig):
+    def __init__(
+        self,
+        clip_name_or_path="openai/clip-vit-large-patch14",
+        dataset_name="Isamu136/oxford_pets_with_l14_emb",
+        image_column="image",
+        index_name="embeddings",
+        index_path=None,
+        dataset_save_path=None,
+        dataset_set="train",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.clip_name_or_path = clip_name_or_path
+        self.dataset_name = dataset_name
+        self.image_column = image_column
+        self.index_name = index_name
+        self.index_path = index_path
+        self.dataset_save_path = dataset_save_path
+        self.dataset_set = dataset_set
+
+
+class Index:
+    """
+    Each index for a retrieval model is specific to the clip model used and the dataset used.
+    """
+
+    def __init__(self, config: IndexConfig, dataset: Dataset):
+        self.config = config
+        self.dataset = dataset
+        self.index_initialized = False
+        self.index_name = config.index_name
+        self.index_path = config.index_path
+        self.init_index()
+
+    def set_index_name(self, index_name: str):
+        self.index_name = index_name
+
+    def init_index(self):
+        if not self.index_initialized:
+            if self.index_path and self.index_name:
+                try:
+                    self.dataset.load_faiss_index(self.index_name, self.index_path)
+                    self.index_initialized = True
+                except:
+                    logger.info("Index not initialized")
+            if self.index_name in self.dataset.features:
+                self.dataset.add_faiss_index(column=self.index_name)
+                self.index_initialized = True
+
+    def build_index(
+        self,
+        model=None,
+        feature_extractor: CLIPFeatureExtractor = None,
+        torch_dtype=torch.float32,
+    ):
+        if not self.index_initialized:
+            model = model or CLIPModel.from_pretrained(self.config.clip_name_or_path).to(dtype=torch_dtype)
+            feature_extractor = feature_extractor or CLIPFeatureExtractor.from_pretrained(
+                self.config.clip_name_or_path
+            )
+            self.dataset = get_dataset_with_emb_from_model(
+                self.dataset,
+                model,
+                feature_extractor,
+                device=model.device,
+                image_column=self.config.image_column,
+                index_name=self.config.index_name,
+            )
+            self.init_index()
+
+    def retrieve_imgs(self, vec, k: int = 20):
+        vec = np.array(vec).astype(np.float32)
+        return self.dataset.get_nearest_examples(self.index_name, vec, k=k)
+
+    def retrieve_indices(self, vec, k: int = 20):
+        vec = np.array(vec).astype(np.float32)
+        return self.dataset.search(self.index_name, vec, k=k)
+
+
+class Retriever:
+    def __init__(
+        self,
+        config: IndexConfig,
+        index: Index = None,
+        dataset: Dataset = None,
+        model=None,
+        feature_extractor: CLIPFeatureExtractor = None,
+    ):
+        self.config = config
+        self.index = index or self._build_index(config, dataset, model=model, feature_extractor=feature_extractor)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        retriever_name_or_path: str,
+        index: Index = None,
+        dataset: Dataset = None,
+        model=None,
+        feature_extractor: CLIPFeatureExtractor = None,
+        **kwargs,
+    ):
+        config = kwargs.pop("config", None) or IndexConfig.from_pretrained(retriever_name_or_path, **kwargs)
+        return cls(config, index=index, dataset=dataset, model=model, feature_extractor=feature_extractor)
+
+    @staticmethod
+    def _build_index(
+        config: IndexConfig, dataset: Dataset = None, model=None, feature_extractor: CLIPFeatureExtractor = None
+    ):
+        dataset = dataset or load_dataset(config.dataset_name)
+        dataset = dataset[config.dataset_set]
+        index = Index(config, dataset)
+        index.build_index(model=model, feature_extractor=feature_extractor)
+        return index
+
+    def save_pretrained(self, save_directory):
+        os.makedirs(save_directory, exist_ok=True)
+        if self.config.index_path is None:
+            index_path = os.path.join(save_directory, "hf_dataset_index.faiss")
+            self.index.dataset.get_index(self.config.index_name).save(index_path)
+            self.config.index_path = index_path
+        if self.config.dataset_save_path is None:
+            dataset_save_path = os.path.join(save_directory, "hf_dataset")
+            # datasets don't support save_to_disk with indexes right now
+            faiss_index = self.index.dataset._indexes.pop(self.config.index_name)
+            self.index.dataset.save_to_disk(dataset_save_path)
+            self.index.dataset._indexes[self.config.index_name] = faiss_index
+            self.config.dataset_save_path = dataset_save_path
+        self.config.save_pretrained(save_directory)
+
+    def init_retrieval(self):
+        logger.info("initializing retrieval")
+        self.index.init_index()
+
+    def retrieve_imgs(self, embeddings: np.ndarray, k: int):
+        return self.index.retrieve_imgs(embeddings, k)
+
+    def retrieve_indices(self, embeddings: np.ndarray, k: int):
+        return self.index.retrieve_indices(embeddings, k)
+
+    def __call__(
+        self,
+        embeddings,
+        k: int = 20,
+    ):
+        return self.index.retrieve_imgs(embeddings, k)
+
+
+def map_txt_to_clip_feature(clip_model, tokenizer, prompt):
+    text_inputs = tokenizer(
+        prompt,
+        padding="max_length",
+        max_length=tokenizer.model_max_length,
+        return_tensors="pt",
+    )
+    text_input_ids = text_inputs.input_ids
+
+    if text_input_ids.shape[-1] > tokenizer.model_max_length:
+        removed_text = tokenizer.batch_decode(text_input_ids[:, tokenizer.model_max_length :])
+        logger.warning(
+            "The following part of your input was truncated because CLIP can only handle sequences up to"
+            f" {tokenizer.model_max_length} tokens: {removed_text}"
+        )
+        text_input_ids = text_input_ids[:, : tokenizer.model_max_length]
+    text_embeddings = clip_model.get_text_features(text_input_ids.to(clip_model.device))
+    text_embeddings = text_embeddings / torch.linalg.norm(text_embeddings, dim=-1, keepdim=True)
+    text_embeddings = text_embeddings[:, None, :]
+    return text_embeddings[0][0].cpu().detach().numpy()
+
+
+def map_img_to_model_feature(model, feature_extractor, imgs):
+    for i, image in enumerate(imgs):
+        if not image.mode == "RGB":
+            imgs[i] = image.convert("RGB")
+    imgs = normalize_images(imgs)
+    retrieved_images = preprocess_images(imgs, feature_extractor).to(model.device)
+    image_embeddings = model(retrieved_images)
+    image_embeddings = image_embeddings / torch.linalg.norm(image_embeddings, dim=-1, keepdim=True)
+    image_embeddings = image_embeddings[None, ...]
+    return image_embeddings
+
+
+def get_dataset_with_emb_from_model(dataset, model, feature_extractor, image_column="image", index_name="embeddings"):
+    return dataset.map(
+        lambda example: {
+            index_name: map_img_to_model_feature(model, feature_extractor, [example[image_column]], model.device)
+            .cpu()
+            .detach()
+            .numpy()[0][0]
+        }
+    )
+
+
+def get_dataset_with_emb_from_clip_model(
+    dataset, clip_model, feature_extractor, image_column="image", index_name="embeddings"
+):
+    return dataset.map(
+        lambda example: {
+            index_name: map_img_to_model_feature(
+                clip_model.get_image_features, feature_extractor, [example[image_column]], clip_model.device
+            )
+            .cpu()
+            .detach()
+            .numpy()[0][0]
+        }
+    )
diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py
index 5b461ba879c5..086e37c5fdb6 100644
--- a/src/diffusers/pipelines/__init__.py
+++ b/src/diffusers/pipelines/__init__.py
@@ -62,6 +62,7 @@
         VersatileDiffusionTextToImagePipeline,
     )
     from .vq_diffusion import VQDiffusionPipeline
+    from .rdm import RDMPipeline
 
 try:
     if not is_onnx_available():
diff --git a/src/diffusers/pipelines/rdm/__init__.py b/src/diffusers/pipelines/rdm/__init__.py
new file mode 100644
index 000000000000..084ab0794d18
--- /dev/null
+++ b/src/diffusers/pipelines/rdm/__init__.py
@@ -0,0 +1,5 @@
+from ...utils import is_torch_available, is_transformers_available
+
+
+if is_transformers_available() and is_torch_available():
+    from .pipeline_rdm import RDMPipeline
diff --git a/src/diffusers/pipelines/rdm/pipeline_rdm.py b/src/diffusers/pipelines/rdm/pipeline_rdm.py
new file mode 100644
index 000000000000..49ed0688c847
--- /dev/null
+++ b/src/diffusers/pipelines/rdm/pipeline_rdm.py
@@ -0,0 +1,412 @@
+import inspect
+from typing import Callable, List, Optional, Union
+
+import torch
+import numpy as np
+from PIL import Image
+
+from diffusers.utils import is_accelerate_available
+from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTokenizer
+
+from ...configuration_utils import FrozenDict
+from ...models import AutoencoderKL, UNet2DConditionModel
+from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from ...schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from ...utils import deprecate, logging
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def normalize_images(images: List[Image.Image]):
+    images = [np.array(image) for image in images]
+    images = [image / 127.5 - 1 for image in images]
+    return images
+
+
+def preprocess_images(images: List[np.array], feature_extractor: CLIPFeatureExtractor) -> torch.FloatTensor:
+    """
+    Preprocesses a list of images into a batch of tensors.
+
+    Args:
+        images (:obj:`List[Image.Image]`):
+            A list of images to preprocess.
+
+    Returns:
+        :obj:`torch.FloatTensor`: A batch of tensors.
+    """
+    images = [np.array(image) for image in images]
+    images = [(image + 1.0) / 2.0 for image in images]
+    images = feature_extractor(images, return_tensors="pt").pixel_values
+    return images
+
+
+class RDMPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Retrieval Augmented 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.
+        clip ([`CLIPModel`]):
+            Frozen CLIP model. Retrieval Augmented Diffusion uses the CLIP model, specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        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`].
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        clip: CLIPModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+        if vae.config.latent_channels == 4:
+            self.scaling_factor = 0.18215
+        elif vae.config.latent_channels == 16:
+            self.scaling_factor = 0.22765929
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led 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 `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` 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 `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            clip=clip,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_xformers_memory_efficient_attention(self):
+        r"""
+        Enable memory efficient attention as implemented in xformers.
+
+        When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
+        time. Speed up at training time is not guaranteed.
+
+        Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
+        is used.
+        """
+        self.unet.set_use_memory_efficient_attention_xformers(True)
+
+    def disable_xformers_memory_efficient_attention(self):
+        r"""
+        Disable memory efficient attention as implemented in xformers.
+        """
+        self.unet.set_use_memory_efficient_attention_xformers(False)
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            if isinstance(self.unet.config.attention_head_dim, int):
+                slice_size = self.unet.config.attention_head_dim // 2
+            else:
+                slice_size = self.unet.config.attention_head_dim[0] // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    def enable_sequential_cpu_offload(self):
+        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("cuda")
+
+        for cpu_offloaded_model in [self.unet, self.clip, self.vae]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        retrieved_images: Optional[List[Image.Image]] = None,
+        height: int = 768,
+        width: int = 768,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[torch.Generator] = None,
+        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: Optional[int] = 1,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                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.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
+                if `guidance_scale` is less than `1`).
+            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*):
+                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 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 [`~pipeline_utils.ImagePipelineOutput`] 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:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
+        """
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        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)}."
+            )
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            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}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.clip.get_text_features(text_input_ids.to(self.device))
+        text_embeddings = text_embeddings / torch.linalg.norm(text_embeddings, dim=-1, keepdim=True)
+        text_embeddings = text_embeddings[:, None, :]
+
+        if retrieved_images is not None:
+            # preprocess retrieved images
+            retrieved_images = normalize_images(retrieved_images)
+            retrieved_images = preprocess_images(retrieved_images, self.feature_extractor).to(
+                self.clip.device, dtype=self.clip.dtype
+            )
+            image_embeddings = self.clip.get_image_features(retrieved_images)
+            image_embeddings = image_embeddings / torch.linalg.norm(image_embeddings, dim=-1, keepdim=True)
+            image_embeddings = image_embeddings[None, ...]
+
+            text_embeddings = torch.cat([text_embeddings, image_embeddings], dim=1)
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # 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
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_embeddings = torch.zeros_like(text_embeddings).to(text_embeddings.device)
+
+            # 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
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+        # get the initial random noise unless the user supplied it
+
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 16, width // 16)
+        latents_dtype = text_embeddings.dtype
+        if latents is None:
+            if self.device.type == "mps":
+                # randn does not work reproducibly on mps
+                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
+                    self.device
+                )
+            else:
+                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+            latents = latents.to(self.device)
+
+        # set timesteps
+        self.scheduler.set_timesteps(num_inference_steps)
+
+        # Some schedulers like PNDM have timesteps as arrays
+        # It's more optimized to move all timesteps to correct device beforehand
+        timesteps_tensor = self.scheduler.timesteps.to(self.device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        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
+
+        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else 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=text_embeddings).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 callback is not None and i % callback_steps == 0:
+                callback(i, t, latents)
+
+        latents = 1 / self.scaling_factor * latents
+        image = self.vae.decode(latents).sample
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)