xora/pipelines/pipeline_video_pixart_alpha.py

# # Adapted from: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py
import html
import inspect
import math
import re
import urllib.parse as ul
from typing import Callable, Dict, List, Optional, Tuple, Union
from abc import ABC, abstractmethod


import torch
import torch.nn.functional as F
from torch import Tensor
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DPMSolverMultistepScheduler
from diffusers.utils import (
    BACKENDS_MAPPING,
    deprecate,
    is_bs4_available,
    is_ftfy_available,
    logging,
    replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor
from einops import rearrange
from transformers import T5EncoderModel, T5Tokenizer

from xora.models.transformers.transformer3d import Transformer3DModel
from xora.models.transformers.symmetric_patchifier import Patchifier
from xora.models.autoencoders.vae_encode import get_vae_size_scale_factor, vae_decode, vae_encode
from xora.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
from xora.schedulers.rf import TimestepShifter
from xora.utils.conditioning_method import ConditioningMethod

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

if is_bs4_available():
    from bs4 import BeautifulSoup

if is_ftfy_available():
    import ftfy

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import PixArtAlphaPipeline

        >>> # You can replace the checkpoint id with "PixArt-alpha/PixArt-XL-2-512x512" too.
        >>> pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)
        >>> # Enable memory optimizations.
        >>> pipe.enable_model_cpu_offload()

        >>> prompt = "A small cactus with a happy face in the Sahara desert."
        >>> image = pipe(prompt).images[0]
        ```
"""

ASPECT_RATIO_1024_BIN = {
    "0.25": [512.0, 2048.0],
    "0.28": [512.0, 1856.0],
    "0.32": [576.0, 1792.0],
    "0.33": [576.0, 1728.0],
    "0.35": [576.0, 1664.0],
    "0.4": [640.0, 1600.0],
    "0.42": [640.0, 1536.0],
    "0.48": [704.0, 1472.0],
    "0.5": [704.0, 1408.0],
    "0.52": [704.0, 1344.0],
    "0.57": [768.0, 1344.0],
    "0.6": [768.0, 1280.0],
    "0.68": [832.0, 1216.0],
    "0.72": [832.0, 1152.0],
    "0.78": [896.0, 1152.0],
    "0.82": [896.0, 1088.0],
    "0.88": [960.0, 1088.0],
    "0.94": [960.0, 1024.0],
    "1.0": [1024.0, 1024.0],
    "1.07": [1024.0, 960.0],
    "1.13": [1088.0, 960.0],
    "1.21": [1088.0, 896.0],
    "1.29": [1152.0, 896.0],
    "1.38": [1152.0, 832.0],
    "1.46": [1216.0, 832.0],
    "1.67": [1280.0, 768.0],
    "1.75": [1344.0, 768.0],
    "2.0": [1408.0, 704.0],
    "2.09": [1472.0, 704.0],
    "2.4": [1536.0, 640.0],
    "2.5": [1600.0, 640.0],
    "3.0": [1728.0, 576.0],
    "4.0": [2048.0, 512.0],
}

ASPECT_RATIO_512_BIN = {
    "0.25": [256.0, 1024.0],
    "0.28": [256.0, 928.0],
    "0.32": [288.0, 896.0],
    "0.33": [288.0, 864.0],
    "0.35": [288.0, 832.0],
    "0.4": [320.0, 800.0],
    "0.42": [320.0, 768.0],
    "0.48": [352.0, 736.0],
    "0.5": [352.0, 704.0],
    "0.52": [352.0, 672.0],
    "0.57": [384.0, 672.0],
    "0.6": [384.0, 640.0],
    "0.68": [416.0, 608.0],
    "0.72": [416.0, 576.0],
    "0.78": [448.0, 576.0],
    "0.82": [448.0, 544.0],
    "0.88": [480.0, 544.0],
    "0.94": [480.0, 512.0],
    "1.0": [512.0, 512.0],
    "1.07": [512.0, 480.0],
    "1.13": [544.0, 480.0],
    "1.21": [544.0, 448.0],
    "1.29": [576.0, 448.0],
    "1.38": [576.0, 416.0],
    "1.46": [608.0, 416.0],
    "1.67": [640.0, 384.0],
    "1.75": [672.0, 384.0],
    "2.0": [704.0, 352.0],
    "2.09": [736.0, 352.0],
    "2.4": [768.0, 320.0],
    "2.5": [800.0, 320.0],
    "3.0": [864.0, 288.0],
    "4.0": [1024.0, 256.0],
}


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
    scheduler,
    num_inference_steps: Optional[int] = None,
    device: Optional[Union[str, torch.device]] = None,
    timesteps: Optional[List[int]] = None,
    **kwargs,
):
    """
    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.

    Args:
        scheduler (`SchedulerMixin`):
            The scheduler to get timesteps from.
        num_inference_steps (`int`):
            The number of diffusion steps used when generating samples with a pre-trained model. If used,
            `timesteps` must be `None`.
        device (`str` or `torch.device`, *optional*):
            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
        timesteps (`List[int]`, *optional*):
                Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
                must be `None`.

    Returns:
        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
        second element is the number of inference steps.
    """
    if timesteps is not None:
        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accepts_timesteps:
            raise ValueError(
                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                f" timestep schedules. Please check whether you are using the correct scheduler."
            )
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps


class VideoPixArtAlphaPipeline(DiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using PixArt-Alpha.

    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.
        text_encoder ([`T5EncoderModel`]):
            Frozen text-encoder. PixArt-Alpha uses
            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
        tokenizer (`T5Tokenizer`):
            Tokenizer of class
            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
        transformer ([`Transformer2DModel`]):
            A text conditioned `Transformer2DModel` to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
    """

    bad_punct_regex = re.compile(
        r"["
        + "#®•©™&@·º½¾¿¡§~"
        + r"\)"
        + r"\("
        + r"\]"
        + r"\["
        + r"\}"
        + r"\{"
        + r"\|"
        + "\\"
        + r"\/"
        + r"\*"
        + r"]{1,}"
    )  # noqa

    _optional_components = ["tokenizer", "text_encoder"]
    model_cpu_offload_seq = "text_encoder->transformer->vae"

    def __init__(
        self,
        tokenizer: T5Tokenizer,
        text_encoder: T5EncoderModel,
        vae: AutoencoderKL,
        transformer: Transformer3DModel,
        scheduler: DPMSolverMultistepScheduler,
        patchifier: Patchifier,
    ):
        super().__init__()

        self.register_modules(
            tokenizer=tokenizer,
            text_encoder=text_encoder,
            vae=vae,
            transformer=transformer,
            scheduler=scheduler,
            patchifier=patchifier,
        )

        self.video_scale_factor, self.vae_scale_factor, _ = get_vae_size_scale_factor(self.vae)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)

    # Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/utils.py
    def mask_text_embeddings(self, emb, mask):
        if emb.shape[0] == 1:
            keep_index = mask.sum().item()
            return emb[:, :, :keep_index, :], keep_index
        else:
            masked_feature = emb * mask[:, None, :, None]
            return masked_feature, emb.shape[2]

    # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
    def encode_prompt(
        self,
        prompt: Union[str, List[str]],
        do_classifier_free_guidance: bool = True,
        negative_prompt: str = "",
        num_images_per_prompt: int = 1,
        device: Optional[torch.device] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        prompt_attention_mask: Optional[torch.FloatTensor] = None,
        negative_prompt_attention_mask: Optional[torch.FloatTensor] = None,
        clean_caption: bool = False,
        **kwargs,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt 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`). For
                PixArt-Alpha, this should be "".
            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
                whether to use classifier free guidance or not
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                number of images that should be generated per prompt
            device: (`torch.device`, *optional*):
                torch device to place the resulting embeddings on
            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. For PixArt-Alpha, it's should be the embeddings of the ""
                string.
            clean_caption (bool, defaults to `False`):
                If `True`, the function will preprocess and clean the provided caption before encoding.
        """

        if "mask_feature" in kwargs:
            deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version."
            deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False)

        if device is None:
            device = self._execution_device

        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]

        # See Section 3.1. of the paper.
        # FIXME: to be configured in config not hardecoded. Fix in separate PR with rest of config
        max_length = 128  # TPU supports only lengths multiple of 128

        if prompt_embeds is None:
            prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                add_special_tokens=True,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids
            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                text_input_ids, untruncated_ids
            ):
                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {max_length} tokens: {removed_text}"
                )

            prompt_attention_mask = text_inputs.attention_mask
            prompt_attention_mask = prompt_attention_mask.to(device)

            prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)
            prompt_embeds = prompt_embeds[0]

        if self.text_encoder is not None:
            dtype = self.text_encoder.dtype
        elif self.transformer is not None:
            dtype = self.transformer.dtype
        else:
            dtype = None

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

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings and attention mask 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)
        prompt_attention_mask = prompt_attention_mask.repeat(1, num_images_per_prompt)
        prompt_attention_mask = prompt_attention_mask.view(bs_embed * num_images_per_prompt, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens = [negative_prompt] * batch_size
            uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_attention_mask=True,
                add_special_tokens=True,
                return_tensors="pt",
            )
            negative_prompt_attention_mask = uncond_input.attention_mask
            negative_prompt_attention_mask = negative_prompt_attention_mask.to(device)

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device), attention_mask=negative_prompt_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=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)

            negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(1, num_images_per_prompt)
            negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed * num_images_per_prompt, -1)
        else:
            negative_prompt_embeds = None
            negative_prompt_attention_mask = None

        return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

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

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

    def check_inputs(
        self,
        prompt,
        height,
        width,
        negative_prompt,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        prompt_attention_mask=None,
        negative_prompt_attention_mask=None,
    ):
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

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

        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 prompt_attention_mask is None:
            raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")

        if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
            raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")

        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 prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
                raise ValueError(
                    "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
                    f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
                    f" {negative_prompt_attention_mask.shape}."
                )

    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
    def _text_preprocessing(self, text, clean_caption=False):
        if clean_caption and not is_bs4_available():
            logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
            logger.warn("Setting `clean_caption` to False...")
            clean_caption = False

        if clean_caption and not is_ftfy_available():
            logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
            logger.warn("Setting `clean_caption` to False...")
            clean_caption = False

        if not isinstance(text, (tuple, list)):
            text = [text]

        def process(text: str):
            if clean_caption:
                text = self._clean_caption(text)
                text = self._clean_caption(text)
            else:
                text = text.lower().strip()
            return text

        return [process(t) for t in text]

    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
    def _clean_caption(self, caption):
        caption = str(caption)
        caption = ul.unquote_plus(caption)
        caption = caption.strip().lower()
        caption = re.sub("<person>", "person", caption)
        # urls:
        caption = re.sub(
            r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
            "",
            caption,
        )  # regex for urls
        caption = re.sub(
            r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
            "",
            caption,
        )  # regex for urls
        # html:
        caption = BeautifulSoup(caption, features="html.parser").text

        # @<nickname>
        caption = re.sub(r"@[\w\d]+\b", "", caption)

        # 31C0—31EF CJK Strokes
        # 31F0—31FF Katakana Phonetic Extensions
        # 3200—32FF Enclosed CJK Letters and Months
        # 3300—33FF CJK Compatibility
        # 3400—4DBF CJK Unified Ideographs Extension A
        # 4DC0—4DFF Yijing Hexagram Symbols
        # 4E00—9FFF CJK Unified Ideographs
        caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
        caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
        caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
        caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
        caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
        caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
        caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
        #######################################################

        # все виды тире / all types of dash --> "-"
        caption = re.sub(
            r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
            "-",
            caption,
        )

        # кавычки к одному стандарту
        caption = re.sub(r"[`´«»“”¨]", '"', caption)
        caption = re.sub(r"[‘’]", "'", caption)

        # &quot;
        caption = re.sub(r"&quot;?", "", caption)
        # &amp
        caption = re.sub(r"&amp", "", caption)

        # ip adresses:
        caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)

        # article ids:
        caption = re.sub(r"\d:\d\d\s+$", "", caption)

        # \n
        caption = re.sub(r"\\n", " ", caption)

        # "#123"
        caption = re.sub(r"#\d{1,3}\b", "", caption)
        # "#12345.."
        caption = re.sub(r"#\d{5,}\b", "", caption)
        # "123456.."
        caption = re.sub(r"\b\d{6,}\b", "", caption)
        # filenames:
        caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)

        #
        caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
        caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""

        caption = re.sub(self.bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
        caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "

        # this-is-my-cute-cat / this_is_my_cute_cat
        regex2 = re.compile(r"(?:\-|\_)")
        if len(re.findall(regex2, caption)) > 3:
            caption = re.sub(regex2, " ", caption)

        caption = ftfy.fix_text(caption)
        caption = html.unescape(html.unescape(caption))

        caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
        caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
        caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231

        caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
        caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
        caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
        caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
        caption = re.sub(r"\bpage\s+\d+\b", "", caption)

        caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...

        caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)

        caption = re.sub(r"\b\s+\:\s+", r": ", caption)
        caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
        caption = re.sub(r"\s+", " ", caption)

        caption.strip()

        caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
        caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
        caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
        caption = re.sub(r"^\.\S+$", "", caption)

        return caption.strip()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(
        self, batch_size, num_latent_channels, num_patches, dtype, device, generator, latents=None, latents_mask=None
    ):
        shape = (
            batch_size,
            num_patches // math.prod(self.patchifier.patch_size),
            num_latent_channels,
        )

        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)
        elif latents_mask is not None:
            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
            latents = latents * latents_mask[..., None] + noise * (1 - latents_mask[..., None])
        else:
            latents = latents.to(device)

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

    @staticmethod
    def classify_height_width_bin(height: int, width: int, ratios: dict) -> Tuple[int, int]:
        """Returns binned height and width."""
        ar = float(height / width)
        closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))
        default_hw = ratios[closest_ratio]
        return int(default_hw[0]), int(default_hw[1])

    @staticmethod
    def resize_and_crop_tensor(samples: torch.Tensor, new_width: int, new_height: int) -> torch.Tensor:
        n_frames, orig_height, orig_width = samples.shape[-3:]

        # Check if resizing is needed
        if orig_height != new_height or orig_width != new_width:
            ratio = max(new_height / orig_height, new_width / orig_width)
            resized_width = int(orig_width * ratio)
            resized_height = int(orig_height * ratio)

            # Resize
            samples = rearrange(samples, "b c n h w -> (b n) c h w")
            samples = F.interpolate(samples, size=(resized_height, resized_width), mode="bilinear", align_corners=False)
            samples = rearrange(samples, "(b n) c h w -> b c n h w", n=n_frames)

            # Center Crop
            start_x = (resized_width - new_width) // 2
            end_x = start_x + new_width
            start_y = (resized_height - new_height) // 2
            end_y = start_y + new_height
            samples = samples[..., start_y:end_y, start_x:end_x]

        return samples

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        height: int,
        width: int,
        num_frames: int,
        frame_rate: float,
        prompt: Union[str, List[str]] = None,
        negative_prompt: str = "",
        num_inference_steps: int = 20,
        timesteps: List[int] = None,
        guidance_scale: float = 4.5,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        prompt_attention_mask: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_attention_mask: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        clean_caption: bool = True,
        media_items: Optional[torch.FloatTensor] = None,
        **kwargs,
    ) -> Union[ImagePipelineOutput, Tuple]:
        """
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            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`).
            num_inference_steps (`int`, *optional*, defaults to 100):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            timesteps (`List[int]`, *optional*):
                Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
                timesteps are used. Must be in descending order.
            guidance_scale (`float`, *optional*, defaults to 4.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            height (`int`, *optional*, defaults to self.unet.config.sample_size):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size):
                The width in pixels of the generated image.
            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` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            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.
            prompt_attention_mask (`torch.FloatTensor`, *optional*): Pre-generated attention mask for text embeddings.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. For PixArt-Alpha this negative prompt should be "". If not
                provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
            negative_prompt_attention_mask (`torch.FloatTensor`, *optional*):
                Pre-generated attention mask for negative text embeddings.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            clean_caption (`bool`, *optional*, defaults to `True`):
                Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
                be installed. If the dependencies are not installed, the embeddings will be created from the raw
                prompt.
            use_resolution_binning (`bool` defaults to `True`):
                If set to `True`, the requested height and width are first mapped to the closest resolutions using
                `ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to
                the requested resolution. Useful for generating non-square images.

        Examples:

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
                returned where the first element is a list with the generated images
        """
        if "mask_feature" in kwargs:
            deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version."
            deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False)

        is_video = kwargs.get("is_video", False)
        self.check_inputs(
            prompt,
            height,
            width,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            prompt_attention_mask,
            negative_prompt_attention_mask,
        )

        # 2. Default height and width to transformer
        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]

        device = self._execution_device

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        (
            prompt_embeds,
            prompt_attention_mask,
            negative_prompt_embeds,
            negative_prompt_attention_mask,
        ) = self.encode_prompt(
            prompt,
            do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            device=device,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            prompt_attention_mask=prompt_attention_mask,
            negative_prompt_attention_mask=negative_prompt_attention_mask,
            clean_caption=clean_caption,
        )
        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
            prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)

        # 3b. Encode and prepare conditioning data
        self.video_scale_factor = self.video_scale_factor if is_video else 1
        conditioning_method = kwargs.get("conditioning_method", None)
        vae_per_channel_normalize = kwargs.get("vae_per_channel_normalize", False)
        init_latents, conditioning_mask = self.prepare_conditioning(
            media_items, num_frames, height, width, conditioning_method, vae_per_channel_normalize
        )

        # 4. Prepare latents.
        latent_height = height // self.vae_scale_factor
        latent_width = width // self.vae_scale_factor
        latent_num_frames = num_frames // self.video_scale_factor
        if isinstance(self.vae, CausalVideoAutoencoder) and is_video:
            latent_num_frames += 1
        latent_frame_rate = frame_rate / self.video_scale_factor
        num_latent_patches = latent_height * latent_width * latent_num_frames
        latents = self.prepare_latents(
            batch_size=batch_size * num_images_per_prompt,
            num_latent_channels=self.transformer.config.in_channels,
            num_patches=num_latent_patches,
            dtype=prompt_embeds.dtype,
            device=device,
            generator=generator,
            latents=init_latents,
            latents_mask=conditioning_mask,
        )
        if conditioning_mask is not None and is_video:
            assert num_images_per_prompt == 1
            conditioning_mask = torch.cat([conditioning_mask] * 2) if do_classifier_free_guidance else conditioning_mask

        # 5. Prepare timesteps
        retrieve_timesteps_kwargs = {}
        if isinstance(self.scheduler, TimestepShifter):
            retrieve_timesteps_kwargs["samples"] = latents
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler, num_inference_steps, device, timesteps, **retrieve_timesteps_kwargs
        )

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

        # 7. Denoising loop
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                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)

                latent_frame_rates = (
                    torch.ones(latent_model_input.shape[0], 1, device=latent_model_input.device) * latent_frame_rate
                )

                current_timestep = t
                if not torch.is_tensor(current_timestep):
                    # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                    # This would be a good case for the `match` statement (Python 3.10+)
                    is_mps = latent_model_input.device.type == "mps"
                    if isinstance(current_timestep, float):
                        dtype = torch.float32 if is_mps else torch.float64
                    else:
                        dtype = torch.int32 if is_mps else torch.int64
                    current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
                elif len(current_timestep.shape) == 0:
                    current_timestep = current_timestep[None].to(latent_model_input.device)
                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                current_timestep = current_timestep.expand(latent_model_input.shape[0]).unsqueeze(-1)
                scale_grid = (
                    (1 / latent_frame_rates, self.vae_scale_factor, self.vae_scale_factor)
                    if self.transformer.use_rope
                    else None
                )
                indices_grid = self.patchifier.get_grid(
                    orig_num_frames=latent_num_frames,
                    orig_height=latent_height,
                    orig_width=latent_width,
                    batch_size=latent_model_input.shape[0],
                    scale_grid=scale_grid,
                    device=latents.device,
                )

                if conditioning_mask is not None:
                    current_timestep = current_timestep * (1 - conditioning_mask)

                # predict noise model_output
                noise_pred = self.transformer(
                    latent_model_input.to(self.transformer.dtype),
                    indices_grid,
                    encoder_hidden_states=prompt_embeds.to(self.transformer.dtype),
                    encoder_attention_mask=prompt_attention_mask,
                    timestep=current_timestep,
                    return_dict=False,
                )[0]

                # 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)
                    current_timestep, _ = current_timestep.chunk(2)

                # learned sigma
                if self.transformer.config.out_channels // 2 == self.transformer.config.in_channels:
                    noise_pred = noise_pred.chunk(2, dim=1)[0]

                # compute previous image: x_t -> x_t-1
                latents = self.scheduler.step(
                    noise_pred,
                    t if current_timestep is None else current_timestep,
                    latents,
                    **extra_step_kwargs,
                    return_dict=False,
                )[0]

                # 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_on_step_end is not None:
                    callback_on_step_end(self, i, t, {})

        latents = self.patchifier.unpatchify(
            latents=latents,
            output_height=latent_height,
            output_width=latent_width,
            output_num_frames=latent_num_frames,
            out_channels=self.transformer.in_channels // math.prod(self.patchifier.patch_size),
        )
        if output_type != "latent":
            image = vae_decode(
                latents, self.vae, is_video, vae_per_channel_normalize=kwargs["vae_per_channel_normalize"]
            )
            image = self.image_processor.postprocess(image, output_type=output_type)

        else:
            image = latents

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)

    def prepare_conditioning(
        self,
        media_items: torch.Tensor,
        num_frames: int,
        height: int,
        width: int,
        method: ConditioningMethod = ConditioningMethod.UNCONDITIONAL,
        vae_per_channel_normalize: bool = False,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Prepare the conditioning data for the video generation. If an input media item is provided, encode it
        and set the conditioning_mask to indicate which tokens to condition on. Input media item should have
        the same height and width as the generated video.

        Args:
            media_items (torch.Tensor): media items to condition on (images or videos)
            num_frames (int): number of frames to generate
            height (int): height of the generated video
            width (int): width of the generated video
            method (ConditioningMethod, optional): conditioning method to use. Defaults to ConditioningMethod.UNCONDITIONAL.
            vae_per_channel_normalize (bool, optional): whether to normalize the input to the VAE per channel. Defaults to False.

        Returns:
            Tuple[torch.Tensor, torch.Tensor]: the conditioning latents and the conditioning mask
        """
        if media_items is None or method == ConditioningMethod.UNCONDITIONAL:
            return None, None

        assert media_items.ndim == 5
        assert height == media_items.shape[-2] and width == media_items.shape[-1]

        # Encode the input video and repeat to the required number of frame-tokens
        init_latents = vae_encode(
            media_items.to(dtype=self.vae.dtype, device=self.vae.device),
            self.vae,
            vae_per_channel_normalize=vae_per_channel_normalize,
        ).float()

        init_len, target_len = init_latents.shape[2], num_frames // self.video_scale_factor
        if isinstance(self.vae, CausalVideoAutoencoder):
            target_len += 1
        init_latents = init_latents[:, :, :target_len]
        if target_len > init_len:
            repeat_factor = (target_len + init_len - 1) // init_len  # Ceiling division
            init_latents = init_latents.repeat(1, 1, repeat_factor, 1, 1)[:, :, :target_len]

        # Prepare the conditioning mask (1.0 = condition on this token)
        b, n, f, h, w = init_latents.shape
        conditioning_mask = torch.zeros([b, 1, f, h, w], device=init_latents.device)
        if method in [ConditioningMethod.FIRST_FRAME, ConditioningMethod.FIRST_AND_LAST_FRAME]:
            conditioning_mask[:, :, 0] = 1.0
        if method in [ConditioningMethod.LAST_FRAME, ConditioningMethod.FIRST_AND_LAST_FRAME]:
            conditioning_mask[:, :, -1] = 1.0

        # Patchify the init latents and the mask
        conditioning_mask = self.patchifier.patchify(conditioning_mask).squeeze(-1)
        init_latents = self.patchifier.patchify(latents=init_latents)
        return init_latents, conditioning_mask