xora/pipelines/pipeline_xora_video.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


import torch
import torch.nn.functional as F
from contextlib import nullcontext
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,
)
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

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 XoraVideoPipeline(DiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using Xora.

    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. This 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)

    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
                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.
            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()
    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,
        mixed_precision: bool = False,
        **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. 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)
                # Choose the appropriate context manager based on `mixed_precision`
                if mixed_precision:
                    if 'xla' in device.type:
                        raise NotImplementedError("Mixed precision is not supported yet on XLA devices.")
                        
                    context_manager = torch.autocast(device, dtype=torch.bfloat16)
                else:
                    context_manager = nullcontext()  # Dummy context manager

                # predict noise model_output
                with context_manager:
                    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