utils/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py

import inspect
from dataclasses import dataclass
from typing import Callable, List, Optional, Union

import numpy as np
import PIL.Image
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionModelWithProjection,
    GPT2Tokenizer,
)

from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers
from ...utils.outputs import BaseOutput
from ...utils.torch_utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel


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


# New BaseOutput child class for joint image-text output
@dataclass
class ImageTextPipelineOutput(BaseOutput):
    """
    Output class for joint image-text pipelines.

    Args:
        images (`List[PIL.Image.Image]` or `np.ndarray`)
            List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
            num_channels)`.
        text (`List[str]` or `List[List[str]]`)
            List of generated text strings of length `batch_size` or a list of list of strings whose outer list has
            length `batch_size`.
    """

    images: Optional[Union[List[PIL.Image.Image], np.ndarray]]
    text: Optional[Union[List[str], List[List[str]]]]


class UniDiffuserPipeline(DiffusionPipeline):
    r"""
    Pipeline for a bimodal image-text model which supports unconditional text and image generation, text-conditioned
    image generation, image-conditioned text generation, and joint image-text generation.

    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.).

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. This
            is part of the UniDiffuser image representation along with the CLIP vision encoding.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        image_encoder ([`CLIPVisionModel`]):
            A [`~transformers.CLIPVisionModel`] to encode images as part of its image representation along with the VAE
            latent representation.
        image_processor ([`CLIPImageProcessor`]):
            [`~transformers.CLIPImageProcessor`] to preprocess an image before CLIP encoding it with `image_encoder`.
        clip_tokenizer ([`CLIPTokenizer`]):
             A [`~transformers.CLIPTokenizer`] to tokenize the prompt before encoding it with `text_encoder`.
        text_decoder ([`UniDiffuserTextDecoder`]):
            Frozen text decoder. This is a GPT-style model which is used to generate text from the UniDiffuser
            embedding.
        text_tokenizer ([`GPT2Tokenizer`]):
            A [`~transformers.GPT2Tokenizer`] to decode text for text generation; used along with the `text_decoder`.
        unet ([`UniDiffuserModel`]):
            A [U-ViT](https://github.com/baofff/U-ViT) model with UNNet-style skip connections between transformer
            layers to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image and/or text latents. The
            original UniDiffuser paper uses the [`DPMSolverMultistepScheduler`] scheduler.
    """

    # TODO: support for moving submodules for components with enable_model_cpu_offload
    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae->text_decoder"

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        image_encoder: CLIPVisionModelWithProjection,
        clip_image_processor: CLIPImageProcessor,
        clip_tokenizer: CLIPTokenizer,
        text_decoder: UniDiffuserTextDecoder,
        text_tokenizer: GPT2Tokenizer,
        unet: UniDiffuserModel,
        scheduler: KarrasDiffusionSchedulers,
    ):
        super().__init__()

        if text_encoder.config.hidden_size != text_decoder.prefix_inner_dim:
            raise ValueError(
                f"The text encoder hidden size and text decoder prefix inner dim must be the same, but"
                f" `text_encoder.config.hidden_size`: {text_encoder.config.hidden_size} and `text_decoder.prefix_inner_dim`: {text_decoder.prefix_inner_dim}"
            )

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            image_encoder=image_encoder,
            clip_image_processor=clip_image_processor,
            clip_tokenizer=clip_tokenizer,
            text_decoder=text_decoder,
            text_tokenizer=text_tokenizer,
            unet=unet,
            scheduler=scheduler,
        )

        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.num_channels_latents = vae.config.latent_channels
        self.text_encoder_seq_len = text_encoder.config.max_position_embeddings
        self.text_encoder_hidden_size = text_encoder.config.hidden_size
        self.image_encoder_projection_dim = image_encoder.config.projection_dim
        self.unet_resolution = unet.config.sample_size

        self.text_intermediate_dim = self.text_encoder_hidden_size
        if self.text_decoder.prefix_hidden_dim is not None:
            self.text_intermediate_dim = self.text_decoder.prefix_hidden_dim

        self.mode = None

        # TODO: handle safety checking?
        self.safety_checker = None

    # 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.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 _infer_mode(self, prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents):
        r"""
        Infer the generation task ('mode') from the inputs to `__call__`. If the mode has been manually set, the set
        mode will be used.
        """
        prompt_available = (prompt is not None) or (prompt_embeds is not None)
        image_available = image is not None
        input_available = prompt_available or image_available

        prompt_latents_available = prompt_latents is not None
        vae_latents_available = vae_latents is not None
        clip_latents_available = clip_latents is not None
        full_latents_available = latents is not None
        image_latents_available = vae_latents_available and clip_latents_available
        all_indv_latents_available = prompt_latents_available and image_latents_available

        if self.mode is not None:
            # Preferentially use the mode set by the user
            mode = self.mode
        elif prompt_available:
            mode = "text2img"
        elif image_available:
            mode = "img2text"
        else:
            # Neither prompt nor image supplied, infer based on availability of latents
            if full_latents_available or all_indv_latents_available:
                mode = "joint"
            elif prompt_latents_available:
                mode = "text"
            elif image_latents_available:
                mode = "img"
            else:
                # No inputs or latents available
                mode = "joint"

        # Give warnings for ambiguous cases
        if self.mode is None and prompt_available and image_available:
            logger.warning(
                f"You have supplied both a text prompt and image to the pipeline and mode has not been set manually,"
                f" defaulting to mode '{mode}'."
            )

        if self.mode is None and not input_available:
            if vae_latents_available != clip_latents_available:
                # Exactly one of vae_latents and clip_latents is supplied
                logger.warning(
                    f"You have supplied exactly one of `vae_latents` and `clip_latents`, whereas either both or none"
                    f" are expected to be supplied. Defaulting to mode '{mode}'."
                )
            elif not prompt_latents_available and not vae_latents_available and not clip_latents_available:
                # No inputs or latents supplied
                logger.warning(
                    f"No inputs or latents have been supplied, and mode has not been manually set,"
                    f" defaulting to mode '{mode}'."
                )

        return mode

    # Functions to manually set the mode
    def set_text_mode(self):
        r"""Manually set the generation mode to unconditional ("marginal") text generation."""
        self.mode = "text"

    def set_image_mode(self):
        r"""Manually set the generation mode to unconditional ("marginal") image generation."""
        self.mode = "img"

    def set_text_to_image_mode(self):
        r"""Manually set the generation mode to text-conditioned image generation."""
        self.mode = "text2img"

    def set_image_to_text_mode(self):
        r"""Manually set the generation mode to image-conditioned text generation."""
        self.mode = "img2text"

    def set_joint_mode(self):
        r"""Manually set the generation mode to unconditional joint image-text generation."""
        self.mode = "joint"

    def reset_mode(self):
        r"""Removes a manually set mode; after calling this, the pipeline will infer the mode from inputs."""
        self.mode = None

    def _infer_batch_size(
        self,
        mode,
        prompt,
        prompt_embeds,
        image,
        num_images_per_prompt,
        num_prompts_per_image,
        latents,
        prompt_latents,
        vae_latents,
        clip_latents,
    ):
        r"""Infers the batch size and multiplier depending on mode and supplied arguments to `__call__`."""
        if num_images_per_prompt is None:
            num_images_per_prompt = 1
        if num_prompts_per_image is None:
            num_prompts_per_image = 1

        assert num_images_per_prompt > 0, "num_images_per_prompt must be a positive integer"
        assert num_prompts_per_image > 0, "num_prompts_per_image must be a positive integer"

        if mode in ["text2img"]:
            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:
                # Either prompt or prompt_embeds must be present for text2img.
                batch_size = prompt_embeds.shape[0]
            multiplier = num_images_per_prompt
        elif mode in ["img2text"]:
            if isinstance(image, PIL.Image.Image):
                batch_size = 1
            else:
                # Image must be available and type either PIL.Image.Image or torch.FloatTensor.
                # Not currently supporting something like image_embeds.
                batch_size = image.shape[0]
            multiplier = num_prompts_per_image
        elif mode in ["img"]:
            if vae_latents is not None:
                batch_size = vae_latents.shape[0]
            elif clip_latents is not None:
                batch_size = clip_latents.shape[0]
            else:
                batch_size = 1
            multiplier = num_images_per_prompt
        elif mode in ["text"]:
            if prompt_latents is not None:
                batch_size = prompt_latents.shape[0]
            else:
                batch_size = 1
            multiplier = num_prompts_per_image
        elif mode in ["joint"]:
            if latents is not None:
                batch_size = latents.shape[0]
            elif prompt_latents is not None:
                batch_size = prompt_latents.shape[0]
            elif vae_latents is not None:
                batch_size = vae_latents.shape[0]
            elif clip_latents is not None:
                batch_size = clip_latents.shape[0]
            else:
                batch_size = 1

            if num_images_per_prompt == num_prompts_per_image:
                multiplier = num_images_per_prompt
            else:
                multiplier = min(num_images_per_prompt, num_prompts_per_image)
                logger.warning(
                    f"You are using mode `{mode}` and `num_images_per_prompt`: {num_images_per_prompt} and"
                    f" num_prompts_per_image: {num_prompts_per_image} are not equal. Using batch size equal to"
                    f" `min(num_images_per_prompt, num_prompts_per_image) = {batch_size}."
                )
        return batch_size, multiplier

    # 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,
        **kwargs,
    ):
        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
        deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)

        prompt_embeds_tuple = self.encode_prompt(
            prompt=prompt,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=lora_scale,
            **kwargs,
        )

        # concatenate for backwards comp
        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])

        return prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with self.tokenizer->self.clip_tokenizer
    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.clip_tokenizer)

            text_inputs = self.clip_tokenizer(
                prompt,
                padding="max_length",
                max_length=self.clip_tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.clip_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.clip_tokenizer.batch_decode(
                    untruncated_ids[:, self.clip_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.clip_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.clip_tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.clip_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

    # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_instruct_pix2pix.StableDiffusionInstructPix2PixPipeline.prepare_image_latents
    # Add num_prompts_per_image argument, sample from autoencoder moment distribution
    def encode_image_vae_latents(
        self,
        image,
        batch_size,
        num_prompts_per_image,
        dtype,
        device,
        do_classifier_free_guidance,
        generator=None,
    ):
        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
            raise ValueError(
                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
            )

        image = image.to(device=device, dtype=dtype)

        batch_size = batch_size * num_prompts_per_image
        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 isinstance(generator, list):
            image_latents = [
                self.vae.encode(image[i : i + 1]).latent_dist.sample(generator=generator[i])
                * self.vae.config.scaling_factor
                for i in range(batch_size)
            ]
            image_latents = torch.cat(image_latents, dim=0)
        else:
            image_latents = self.vae.encode(image).latent_dist.sample(generator=generator)
            # Scale image_latents by the VAE's scaling factor
            image_latents = image_latents * self.vae.config.scaling_factor

        if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
            # expand image_latents for batch_size
            deprecation_message = (
                f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
                " your script to pass as many initial images as text prompts to suppress this warning."
            )
            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
            additional_image_per_prompt = batch_size // image_latents.shape[0]
            image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
        elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
            )
        else:
            image_latents = torch.cat([image_latents], dim=0)

        if do_classifier_free_guidance:
            uncond_image_latents = torch.zeros_like(image_latents)
            image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)

        return image_latents

    def encode_image_clip_latents(
        self,
        image,
        batch_size,
        num_prompts_per_image,
        dtype,
        device,
        generator=None,
    ):
        # Map image to CLIP embedding.
        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
            raise ValueError(
                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
            )

        preprocessed_image = self.clip_image_processor.preprocess(
            image,
            return_tensors="pt",
        )
        preprocessed_image = preprocessed_image.to(device=device, dtype=dtype)

        batch_size = batch_size * num_prompts_per_image
        if isinstance(generator, list):
            image_latents = [
                self.image_encoder(**preprocessed_image[i : i + 1]).image_embeds for i in range(batch_size)
            ]
            image_latents = torch.cat(image_latents, dim=0)
        else:
            image_latents = self.image_encoder(**preprocessed_image).image_embeds

        if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
            # expand image_latents for batch_size
            deprecation_message = (
                f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
                " your script to pass as many initial images as text prompts to suppress this warning."
            )
            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
            additional_image_per_prompt = batch_size // image_latents.shape[0]
            image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
        elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
            )
        else:
            image_latents = torch.cat([image_latents], dim=0)

        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."
            )

        return image_latents

    def prepare_text_latents(
        self, batch_size, num_images_per_prompt, seq_len, hidden_size, dtype, device, generator, latents=None
    ):
        # Prepare latents for the CLIP embedded prompt.
        shape = (batch_size * num_images_per_prompt, seq_len, hidden_size)
        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 is assumed to have shace (B, L, D)
            latents = latents.repeat(num_images_per_prompt, 1, 1)
            latents = latents.to(device=device, dtype=dtype)

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

    # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    # Rename prepare_latents -> prepare_image_vae_latents and add num_prompts_per_image argument.
    def prepare_image_vae_latents(
        self,
        batch_size,
        num_prompts_per_image,
        num_channels_latents,
        height,
        width,
        dtype,
        device,
        generator,
        latents=None,
    ):
        shape = (
            batch_size * num_prompts_per_image,
            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 is assumed to have shape (B, C, H, W)
            latents = latents.repeat(num_prompts_per_image, 1, 1, 1)
            latents = latents.to(device=device, dtype=dtype)

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

    def prepare_image_clip_latents(
        self, batch_size, num_prompts_per_image, clip_img_dim, dtype, device, generator, latents=None
    ):
        # Prepare latents for the CLIP embedded image.
        shape = (batch_size * num_prompts_per_image, 1, clip_img_dim)
        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 is assumed to have shape (B, L, D)
            latents = latents.repeat(num_prompts_per_image, 1, 1)
            latents = latents.to(device=device, dtype=dtype)

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

    def decode_text_latents(self, text_latents, device):
        output_token_list, seq_lengths = self.text_decoder.generate_captions(
            text_latents, self.text_tokenizer.eos_token_id, device=device
        )
        output_list = output_token_list.cpu().numpy()
        generated_text = [
            self.text_tokenizer.decode(output[: int(length)], skip_special_tokens=True)
            for output, length in zip(output_list, seq_lengths)
        ]
        return generated_text

    def _split(self, x, height, width):
        r"""
        Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim) into two tensors of shape (B, C, H, W)
        and (B, 1, clip_img_dim)
        """
        batch_size = x.shape[0]
        latent_height = height // self.vae_scale_factor
        latent_width = width // self.vae_scale_factor
        img_vae_dim = self.num_channels_latents * latent_height * latent_width

        img_vae, img_clip = x.split([img_vae_dim, self.image_encoder_projection_dim], dim=1)

        img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width))
        img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim))
        return img_vae, img_clip

    def _combine(self, img_vae, img_clip):
        r"""
        Combines a latent iamge img_vae of shape (B, C, H, W) and a CLIP-embedded image img_clip of shape (B, 1,
        clip_img_dim) into a single tensor of shape (B, C * H * W + clip_img_dim).
        """
        img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1))
        img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1))
        return torch.concat([img_vae, img_clip], dim=-1)

    def _split_joint(self, x, height, width):
        r"""
        Splits a flattened embedding x of shape (B, C * H * W + clip_img_dim + text_seq_len * text_dim] into (img_vae,
        img_clip, text) where img_vae is of shape (B, C, H, W), img_clip is of shape (B, 1, clip_img_dim), and text is
        of shape (B, text_seq_len, text_dim).
        """
        batch_size = x.shape[0]
        latent_height = height // self.vae_scale_factor
        latent_width = width // self.vae_scale_factor
        img_vae_dim = self.num_channels_latents * latent_height * latent_width
        text_dim = self.text_encoder_seq_len * self.text_intermediate_dim

        img_vae, img_clip, text = x.split([img_vae_dim, self.image_encoder_projection_dim, text_dim], dim=1)

        img_vae = torch.reshape(img_vae, (batch_size, self.num_channels_latents, latent_height, latent_width))
        img_clip = torch.reshape(img_clip, (batch_size, 1, self.image_encoder_projection_dim))
        text = torch.reshape(text, (batch_size, self.text_encoder_seq_len, self.text_intermediate_dim))
        return img_vae, img_clip, text

    def _combine_joint(self, img_vae, img_clip, text):
        r"""
        Combines a latent image img_vae of shape (B, C, H, W), a CLIP-embedded image img_clip of shape (B, L_img,
        clip_img_dim), and a text embedding text of shape (B, L_text, text_dim) into a single embedding x of shape (B,
        C * H * W + L_img * clip_img_dim + L_text * text_dim).
        """
        img_vae = torch.reshape(img_vae, (img_vae.shape[0], -1))
        img_clip = torch.reshape(img_clip, (img_clip.shape[0], -1))
        text = torch.reshape(text, (text.shape[0], -1))
        return torch.concat([img_vae, img_clip, text], dim=-1)

    def _get_noise_pred(
        self,
        mode,
        latents,
        t,
        prompt_embeds,
        img_vae,
        img_clip,
        max_timestep,
        data_type,
        guidance_scale,
        generator,
        device,
        height,
        width,
    ):
        r"""
        Gets the noise prediction using the `unet` and performs classifier-free guidance, if necessary.
        """
        if mode == "joint":
            # Joint text-image generation
            img_vae_latents, img_clip_latents, text_latents = self._split_joint(latents, height, width)

            img_vae_out, img_clip_out, text_out = self.unet(
                img_vae_latents, img_clip_latents, text_latents, timestep_img=t, timestep_text=t, data_type=data_type
            )

            x_out = self._combine_joint(img_vae_out, img_clip_out, text_out)

            if guidance_scale <= 1.0:
                return x_out

            # Classifier-free guidance
            img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype)
            img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype)
            text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)

            _, _, text_out_uncond = self.unet(
                img_vae_T, img_clip_T, text_latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type
            )

            img_vae_out_uncond, img_clip_out_uncond, _ = self.unet(
                img_vae_latents,
                img_clip_latents,
                text_T,
                timestep_img=t,
                timestep_text=max_timestep,
                data_type=data_type,
            )

            x_out_uncond = self._combine_joint(img_vae_out_uncond, img_clip_out_uncond, text_out_uncond)

            return guidance_scale * x_out + (1.0 - guidance_scale) * x_out_uncond
        elif mode == "text2img":
            # Text-conditioned image generation
            img_vae_latents, img_clip_latents = self._split(latents, height, width)

            img_vae_out, img_clip_out, text_out = self.unet(
                img_vae_latents, img_clip_latents, prompt_embeds, timestep_img=t, timestep_text=0, data_type=data_type
            )

            img_out = self._combine(img_vae_out, img_clip_out)

            if guidance_scale <= 1.0:
                return img_out

            # Classifier-free guidance
            text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)

            img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet(
                img_vae_latents,
                img_clip_latents,
                text_T,
                timestep_img=t,
                timestep_text=max_timestep,
                data_type=data_type,
            )

            img_out_uncond = self._combine(img_vae_out_uncond, img_clip_out_uncond)

            return guidance_scale * img_out + (1.0 - guidance_scale) * img_out_uncond
        elif mode == "img2text":
            # Image-conditioned text generation
            img_vae_out, img_clip_out, text_out = self.unet(
                img_vae, img_clip, latents, timestep_img=0, timestep_text=t, data_type=data_type
            )

            if guidance_scale <= 1.0:
                return text_out

            # Classifier-free guidance
            img_vae_T = randn_tensor(img_vae.shape, generator=generator, device=device, dtype=img_vae.dtype)
            img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype)

            img_vae_out_uncond, img_clip_out_uncond, text_out_uncond = self.unet(
                img_vae_T, img_clip_T, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type
            )

            return guidance_scale * text_out + (1.0 - guidance_scale) * text_out_uncond
        elif mode == "text":
            # Unconditional ("marginal") text generation (no CFG)
            img_vae_out, img_clip_out, text_out = self.unet(
                img_vae, img_clip, latents, timestep_img=max_timestep, timestep_text=t, data_type=data_type
            )

            return text_out
        elif mode == "img":
            # Unconditional ("marginal") image generation (no CFG)
            img_vae_latents, img_clip_latents = self._split(latents, height, width)

            img_vae_out, img_clip_out, text_out = self.unet(
                img_vae_latents,
                img_clip_latents,
                prompt_embeds,
                timestep_img=t,
                timestep_text=max_timestep,
                data_type=data_type,
            )

            img_out = self._combine(img_vae_out, img_clip_out)
            return img_out

    def check_latents_shape(self, latents_name, latents, expected_shape):
        latents_shape = latents.shape
        expected_num_dims = len(expected_shape) + 1  # expected dimensions plus the batch dimension
        expected_shape_str = ", ".join(str(dim) for dim in expected_shape)
        if len(latents_shape) != expected_num_dims:
            raise ValueError(
                f"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape"
                f" {latents_shape} has {len(latents_shape)} dimensions."
            )
        for i in range(1, expected_num_dims):
            if latents_shape[i] != expected_shape[i - 1]:
                raise ValueError(
                    f"`{latents_name}` should have shape (batch_size, {expected_shape_str}), but the current shape"
                    f" {latents_shape} has {latents_shape[i]} != {expected_shape[i - 1]} at dimension {i}."
                )

    def check_inputs(
        self,
        mode,
        prompt,
        image,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        latents=None,
        prompt_latents=None,
        vae_latents=None,
        clip_latents=None,
    ):
        # Check inputs before running the generative process.
        if height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0:
            raise ValueError(
                f"`height` and `width` have to be divisible by {self.vae_scale_factor} 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)}."
            )

        if mode == "text2img":
            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)}")

            if negative_prompt is not None and negative_prompt_embeds is not None:
                raise ValueError(
                    f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                    f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
                )

            if prompt_embeds is not None and negative_prompt_embeds is not None:
                if prompt_embeds.shape != negative_prompt_embeds.shape:
                    raise ValueError(
                        "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                        f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                        f" {negative_prompt_embeds.shape}."
                    )

        if mode == "img2text":
            if image is None:
                raise ValueError("`img2text` mode requires an image to be provided.")

        # Check provided latents
        latent_height = height // self.vae_scale_factor
        latent_width = width // self.vae_scale_factor
        full_latents_available = latents is not None
        prompt_latents_available = prompt_latents is not None
        vae_latents_available = vae_latents is not None
        clip_latents_available = clip_latents is not None

        if full_latents_available:
            individual_latents_available = (
                prompt_latents is not None or vae_latents is not None or clip_latents is not None
            )
            if individual_latents_available:
                logger.warning(
                    "You have supplied both `latents` and at least one of `prompt_latents`, `vae_latents`, and"
                    " `clip_latents`. The value of `latents` will override the value of any individually supplied latents."
                )
            # Check shape of full latents
            img_vae_dim = self.num_channels_latents * latent_height * latent_width
            text_dim = self.text_encoder_seq_len * self.text_encoder_hidden_size
            latents_dim = img_vae_dim + self.image_encoder_projection_dim + text_dim
            latents_expected_shape = (latents_dim,)
            self.check_latents_shape("latents", latents, latents_expected_shape)

        # Check individual latent shapes, if present
        if prompt_latents_available:
            prompt_latents_expected_shape = (self.text_encoder_seq_len, self.text_encoder_hidden_size)
            self.check_latents_shape("prompt_latents", prompt_latents, prompt_latents_expected_shape)

        if vae_latents_available:
            vae_latents_expected_shape = (self.num_channels_latents, latent_height, latent_width)
            self.check_latents_shape("vae_latents", vae_latents, vae_latents_expected_shape)

        if clip_latents_available:
            clip_latents_expected_shape = (1, self.image_encoder_projection_dim)
            self.check_latents_shape("clip_latents", clip_latents, clip_latents_expected_shape)

        if mode in ["text2img", "img"] and vae_latents_available and clip_latents_available:
            if vae_latents.shape[0] != clip_latents.shape[0]:
                raise ValueError(
                    f"Both `vae_latents` and `clip_latents` are supplied, but their batch dimensions are not equal:"
                    f" {vae_latents.shape[0]} != {clip_latents.shape[0]}."
                )

        if mode == "joint" and prompt_latents_available and vae_latents_available and clip_latents_available:
            if prompt_latents.shape[0] != vae_latents.shape[0] or prompt_latents.shape[0] != clip_latents.shape[0]:
                raise ValueError(
                    f"All of `prompt_latents`, `vae_latents`, and `clip_latents` are supplied, but their batch"
                    f" dimensions are not equal: {prompt_latents.shape[0]} != {vae_latents.shape[0]}"
                    f" != {clip_latents.shape[0]}."
                )

    @torch.no_grad()
    def __call__(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        image: Optional[Union[torch.FloatTensor, PIL.Image.Image]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        data_type: Optional[int] = 1,
        num_inference_steps: int = 50,
        guidance_scale: float = 8.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        num_prompts_per_image: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_latents: Optional[torch.FloatTensor] = None,
        vae_latents: Optional[torch.FloatTensor] = None,
        clip_latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: 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,
    ):
        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`.
                Required for text-conditioned image generation (`text2img`) mode.
            image (`torch.FloatTensor` or `PIL.Image.Image`, *optional*):
                `Image` or tensor representing an image batch. Required for image-conditioned text generation
                (`img2text`) mode.
            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.
            data_type (`int`, *optional*, defaults to 1):
                The data type (either 0 or 1). Only used if you are loading a checkpoint which supports a data type
                embedding; this is added for compatibility with the
                [UniDiffuser-v1](https://huggingface.co/thu-ml/unidiffuser-v1) checkpoint.
            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 8.0):
                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`.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). Used in
                text-conditioned image generation (`text2img`) mode.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt. Used in `text2img` (text-conditioned image generation) and
                `img` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are
                supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples are generated.
            num_prompts_per_image (`int`, *optional*, defaults to 1):
                The number of prompts to generate per image. Used in `img2text` (image-conditioned text generation) and
                `text` mode. If the mode is joint and both `num_images_per_prompt` and `num_prompts_per_image` are
                supplied, `min(num_images_per_prompt, num_prompts_per_image)` samples are generated.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            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 joint
                image-text 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`. This assumes
                a full set of VAE, CLIP, and text latents, if supplied, overrides the value of `prompt_latents`,
                `vae_latents`, and `clip_latents`.
            prompt_latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for text
                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`.
            vae_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`.
            clip_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. Used in text-conditioned
                image generation (`text2img`) mode.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
                not provided, `negative_prompt_embeds` are be generated from the `negative_prompt` input argument. Used
                in text-conditioned image generation (`text2img`) mode.
            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.ImageTextPipelineOutput`] instead of a plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is 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 is called. If not specified, the callback is called at
                every step.

        Returns:
            [`~pipelines.unidiffuser.ImageTextPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.unidiffuser.ImageTextPipelineOutput`] 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 generated texts.
        """

        # 0. Default height and width to unet
        height = height or self.unet_resolution * self.vae_scale_factor
        width = width or self.unet_resolution * self.vae_scale_factor

        # 1. Check inputs
        # Recalculate mode for each call to the pipeline.
        mode = self._infer_mode(prompt, prompt_embeds, image, latents, prompt_latents, vae_latents, clip_latents)
        self.check_inputs(
            mode,
            prompt,
            image,
            height,
            width,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            latents,
            prompt_latents,
            vae_latents,
            clip_latents,
        )

        # 2. Define call parameters
        batch_size, multiplier = self._infer_batch_size(
            mode,
            prompt,
            prompt_embeds,
            image,
            num_images_per_prompt,
            num_prompts_per_image,
            latents,
            prompt_latents,
            vae_latents,
            clip_latents,
        )
        device = self._execution_device
        reduce_text_emb_dim = self.text_intermediate_dim < self.text_encoder_hidden_size or self.mode != "text2img"

        # 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.
        # Note that this differs from the formulation in the unidiffusers paper!
        do_classifier_free_guidance = guidance_scale > 1.0

        # check if scheduler is in sigmas space
        # scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")

        # 3. Encode input prompt, if available; otherwise prepare text latents
        if latents is not None:
            # Overwrite individual latents
            vae_latents, clip_latents, prompt_latents = self._split_joint(latents, height, width)

        if mode in ["text2img"]:
            # 3.1. Encode input prompt, if available
            assert prompt is not None or prompt_embeds is not None
            prompt_embeds, negative_prompt_embeds = self.encode_prompt(
                prompt=prompt,
                device=device,
                num_images_per_prompt=multiplier,
                do_classifier_free_guidance=do_classifier_free_guidance,
                negative_prompt=negative_prompt,
                prompt_embeds=prompt_embeds,
                negative_prompt_embeds=negative_prompt_embeds,
            )

            # if do_classifier_free_guidance:
            #     prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
        else:
            # 3.2. Prepare text latent variables, if input not available
            prompt_embeds = self.prepare_text_latents(
                batch_size=batch_size,
                num_images_per_prompt=multiplier,
                seq_len=self.text_encoder_seq_len,
                hidden_size=self.text_encoder_hidden_size,
                dtype=self.text_encoder.dtype,  # Should work with both full precision and mixed precision
                device=device,
                generator=generator,
                latents=prompt_latents,
            )

        if reduce_text_emb_dim:
            prompt_embeds = self.text_decoder.encode(prompt_embeds)

        # 4. Encode image, if available; otherwise prepare image latents
        if mode in ["img2text"]:
            # 4.1. Encode images, if available
            assert image is not None, "`img2text` requires a conditioning image"
            # Encode image using VAE
            image_vae = self.image_processor.preprocess(image)
            height, width = image_vae.shape[-2:]
            image_vae_latents = self.encode_image_vae_latents(
                image=image_vae,
                batch_size=batch_size,
                num_prompts_per_image=multiplier,
                dtype=prompt_embeds.dtype,
                device=device,
                do_classifier_free_guidance=False,  # Copied from InstructPix2Pix, don't use their version of CFG
                generator=generator,
            )

            # Encode image using CLIP
            image_clip_latents = self.encode_image_clip_latents(
                image=image,
                batch_size=batch_size,
                num_prompts_per_image=multiplier,
                dtype=prompt_embeds.dtype,
                device=device,
                generator=generator,
            )
            # (batch_size, clip_hidden_size) => (batch_size, 1, clip_hidden_size)
            image_clip_latents = image_clip_latents.unsqueeze(1)
        else:
            # 4.2. Prepare image latent variables, if input not available
            # Prepare image VAE latents in latent space
            image_vae_latents = self.prepare_image_vae_latents(
                batch_size=batch_size,
                num_prompts_per_image=multiplier,
                num_channels_latents=self.num_channels_latents,
                height=height,
                width=width,
                dtype=prompt_embeds.dtype,
                device=device,
                generator=generator,
                latents=vae_latents,
            )

            # Prepare image CLIP latents
            image_clip_latents = self.prepare_image_clip_latents(
                batch_size=batch_size,
                num_prompts_per_image=multiplier,
                clip_img_dim=self.image_encoder_projection_dim,
                dtype=prompt_embeds.dtype,
                device=device,
                generator=generator,
                latents=clip_latents,
            )

        # 5. Set timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps
        # max_timestep = timesteps[0]
        max_timestep = self.scheduler.config.num_train_timesteps

        # 6. Prepare latent variables
        if mode == "joint":
            latents = self._combine_joint(image_vae_latents, image_clip_latents, prompt_embeds)
        elif mode in ["text2img", "img"]:
            latents = self._combine(image_vae_latents, image_clip_latents)
        elif mode in ["img2text", "text"]:
            latents = prompt_embeds

        # 7. 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)

        logger.debug(f"Scheduler extra step kwargs: {extra_step_kwargs}")

        # 8. 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):
                # predict the noise residual
                # Also applies classifier-free guidance as described in the UniDiffuser paper
                noise_pred = self._get_noise_pred(
                    mode,
                    latents,
                    t,
                    prompt_embeds,
                    image_vae_latents,
                    image_clip_latents,
                    max_timestep,
                    data_type,
                    guidance_scale,
                    generator,
                    device,
                    height,
                    width,
                )

                # 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:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

        # 9. Post-processing
        image = None
        text = None
        if mode == "joint":
            image_vae_latents, image_clip_latents, text_latents = self._split_joint(latents, height, width)

            if not output_type == "latent":
                # Map latent VAE image back to pixel space
                image = self.vae.decode(image_vae_latents / self.vae.config.scaling_factor, return_dict=False)[0]
            else:
                image = image_vae_latents

            text = self.decode_text_latents(text_latents, device)
        elif mode in ["text2img", "img"]:
            image_vae_latents, image_clip_latents = self._split(latents, height, width)

            if not output_type == "latent":
                # Map latent VAE image back to pixel space
                image = self.vae.decode(image_vae_latents / self.vae.config.scaling_factor, return_dict=False)[0]
            else:
                image = image_vae_latents
        elif mode in ["img2text", "text"]:
            text_latents = latents
            text = self.decode_text_latents(text_latents, device)

        self.maybe_free_model_hooks()

        # 10. Postprocess the image, if necessary
        if image is not None:
            do_denormalize = [True] * image.shape[0]
            image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

        # Offload last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, text)

        return ImageTextPipelineOutput(images=image, text=text)