import inspect
import json
import subprocess
from pathlib import Path
from typing import Callable, List, Optional, Union

import numpy as np
import torch
from PIL import Image

import cv2
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, logging
from huggingface_hub import hf_hub_download
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer


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

default_scheduler = PNDMScheduler(
    beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
ddim_scheduler = DDIMScheduler(
    beta_start=0.00085,
    beta_end=0.012,
    beta_schedule="scaled_linear",
    clip_sample=False,
    set_alpha_to_one=False,
)
klms_scheduler = LMSDiscreteScheduler(
    beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
)
SCHEDULERS = dict(default=default_scheduler, ddim=ddim_scheduler, klms=klms_scheduler)


def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
    """helper function to spherically interpolate two arrays v1 v2"""

    if not isinstance(v0, np.ndarray):
        inputs_are_torch = True
        input_device = v0.device
        v0 = v0.cpu().numpy()
        v1 = v1.cpu().numpy()

    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
    if np.abs(dot) > DOT_THRESHOLD:
        v2 = (1 - t) * v0 + t * v1
    else:
        theta_0 = np.arccos(dot)
        sin_theta_0 = np.sin(theta_0)
        theta_t = theta_0 * t
        sin_theta_t = np.sin(theta_t)
        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
        s1 = sin_theta_t / sin_theta_0
        v2 = s0 * v0 + s1 * v1

    if inputs_are_torch:
        v2 = torch.from_numpy(v2).to(input_device)

    return v2


class RealESRGANModel(torch.nn.Module):
    def __init__(self, model_path, tile=0, tile_pad=10, pre_pad=0, fp32=False):
        super().__init__()
        try:
            from basicsr.archs.rrdbnet_arch import RRDBNet
            from realesrgan import RealESRGANer
        except ImportError as e:
            raise ImportError(
                "You tried to import realesrgan without having it installed properly. To install Real-ESRGAN, run:\n\n"
                "pip install realesrgan"
            )

        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
        self.upsampler = RealESRGANer(
            scale=4,
            model_path=model_path,
            model=model,
            tile=tile,
            tile_pad=tile_pad,
            pre_pad=pre_pad,
            half=not fp32
        )

    def forward(self, image, outscale=4, convert_to_pil=True):
        """Upsample an image array or path.

        Args:
            image (Union[np.ndarray, str]): Either a np array or an image path. np array is assumed to be in RGB format,
                and we convert it to BGR.
            outscale (int, optional): Amount to upscale the image. Defaults to 4.
            convert_to_pil (bool, optional): If True, return PIL image. Otherwise, return numpy array (BGR). Defaults to True.

        Returns:
            Union[np.ndarray, PIL.Image.Image]: An upsampled version of the input image.
        """
        if isinstance(image, (str, Path)):
            img = cv2.imread(image, cv2.IMREAD_UNCHANGED)
        else:
            img = image
            img = (img * 255).round().astype("uint8")
            img = img[:, :, ::-1]

        image, _ = self.upsampler.enhance(img, outscale=outscale)

        if convert_to_pil:
            image = Image.fromarray(image[:, :, ::-1])

        return image

    @classmethod
    def from_pretrained(cls, model_name_or_path='nateraw/real-esrgan'):
        """Initialize a pretrained Real-ESRGAN upsampler.

        Example:
            ```python
            >>> from stable_diffusion_videos import PipelineRealESRGAN
            >>> pipe = PipelineRealESRGAN.from_pretrained('nateraw/real-esrgan')
            >>> im_out = pipe('input_img.jpg')
            ```

        Args:
            model_name_or_path (str, optional): The Hugging Face repo ID or path to local model. Defaults to 'nateraw/real-esrgan'.

        Returns:
            stable_diffusion_videos.PipelineRealESRGAN: An instance of `PipelineRealESRGAN` instantiated from pretrained model.
        """
        # reuploaded form official ones mentioned here:
        # https://github.com/xinntao/Real-ESRGAN
        if Path(model_name_or_path).exists():
            file = model_name_or_path
        else:
            file = hf_hub_download(model_name_or_path, 'RealESRGAN_x4plus.pth')
        return cls(file)


    def upsample_imagefolder(self, in_dir, out_dir, suffix='out', outfile_ext='.png'):
        in_dir, out_dir = Path(in_dir), Path(out_dir)
        if not in_dir.exists():
            raise FileNotFoundError(f"Provided input directory {in_dir} does not exist")

        out_dir.mkdir(exist_ok=True, parents=True)
         
        image_paths = [x for x in in_dir.glob('*') if x.suffix.lower() in ['.png', '.jpg', '.jpeg']]
        for image in image_paths:
            im = self(str(image))
            out_filepath = out_dir / (image.stem + suffix + outfile_ext)
            im.save(out_filepath)

class NoUpsamplingModel(torch.nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, images):
        return images


def make_video_ffmpeg(frame_dir, output_file_name='output.mp4', frame_filename="frame%06d.png", fps=30):
    frame_ref_path = str(frame_dir / frame_filename)
    video_path = str(frame_dir / output_file_name)
    subprocess.call(
        f"ffmpeg -r {fps} -i {frame_ref_path} -vcodec libx264 -crf 10 -pix_fmt yuv420p"
        f" {video_path}".split()
    )
    return video_path


class StableDiffusionWalkPipeline(DiffusionPipeline):
    r"""
    Pipeline for generating videos by interpolating  Stable Diffusion's latent space.
    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 ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
        feature_extractor ([`CLIPFeatureExtractor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor,
    ):
        super().__init__()

        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file"
            )
            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )

    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
        r"""
        Enable sliced attention computation.
        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
        in several steps. This is useful to save some memory in exchange for a small speed decrease.
        Args:
            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
                `attention_head_dim` must be a multiple of `slice_size`.
        """
        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(slice_size)

    def disable_attention_slicing(self):
        r"""
        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
        back to computing attention in one step.
        """
        # set slice_size = `None` to disable `attention slicing`
        self.enable_attention_slicing(None)

    @torch.no_grad()
    def step(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        text_embeddings: Optional[torch.FloatTensor] = None,
        **kwargs,
    ):
        r"""
        Function invoked when calling the pipeline for generation.
        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            height (`int`, *optional*, defaults to 512):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 512):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator`, *optional*):
                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            text_embeddings(`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings.
        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if text_embeddings is None:
            if isinstance(prompt, str):
                batch_size = 1
            elif isinstance(prompt, list):
                batch_size = len(prompt)
            else:
                raise ValueError(
                    f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
                )

            # get prompt text embeddings
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )
                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
        else:
            batch_size = text_embeddings.shape[0]

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            # HACK - Not setting text_input_ids here when walking, so hard coding to max length of tokenizer
            # TODO - Determine if this is OK to do
            # max_length = text_input_ids.shape[-1]
            max_length = self.tokenizer.model_max_length
            uncond_input = self.tokenizer(
                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
            )
            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        latents_device = "cpu" if self.device.type == "mps" else self.device
        latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
        if latents is None:
            latents = torch.randn(
                latents_shape,
                generator=generator,
                device=latents_device,
                dtype=text_embeddings.dtype,
            )
        else:
            if latents.shape != latents_shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
            latents = latents.to(latents_device)

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimzed to move all timesteps to correct device beforehand
        if torch.is_tensor(self.scheduler.timesteps):
            timesteps_tensor = self.scheduler.timesteps.to(self.device)
        else:
            timesteps_tensor = torch.tensor(self.scheduler.timesteps.copy(), device=self.device)

        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
        if isinstance(self.scheduler, LMSDiscreteScheduler):
            latents = latents * self.scheduler.sigmas[0]

        # 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

        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            if isinstance(self.scheduler, LMSDiscreteScheduler):
                sigma = self.scheduler.sigmas[i]
                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)

            # predict the noise residual
            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            if isinstance(self.scheduler, LMSDiscreteScheduler):
                latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
            else:
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(i, t, latents)

        latents = 1 / 0.18215 * latents
        image = self.vae.decode(latents).sample

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()

        safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
        image, has_nsfw_concept = self.safety_checker(
            images=image, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype)
        )

        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

    def __call__(
        self,
        prompts: List[str] = ["blueberry spaghetti", "strawberry spaghetti"],
        seeds: List[int] = [42, 123],
        num_interpolation_steps: Union[int, List[int]] = 5,
        output_dir: str = "dreams",
        name: str = "berry_good_spaghetti",
        height: int = 512,
        width: int = 512,
        guidance_scale: float = 7.5,
        eta: float = 0.0,
        num_inference_steps: int = 50,
        do_loop: bool = False,
        make_video: bool = False,
        use_lerp_for_text: bool = True,
        scheduler: str = "klms",  # choices: default, ddim, klms
        disable_tqdm: bool = False,
        upsample: bool = False,
        fps: int = 30,
        resume: bool = False,
        batch_size: int = 1,
        frame_filename_ext: str = '.png',
    ):
        if upsample:
            if getattr(self, 'upsampler', None) is None:
                self.upsampler = RealESRGANModel.from_pretrained('nateraw/real-esrgan')
            self.upsampler.to(self.device)

        output_path = Path(output_dir) / name
        output_path.mkdir(exist_ok=True, parents=True)
        prompt_config_path = output_path / 'prompt_config.json'

        if not resume:
            # Write prompt info to file in output dir so we can keep track of what we did
            prompt_config_path.write_text(
                json.dumps(
                    dict(
                        prompts=prompts,
                        seeds=seeds,
                        num_interpolation_steps=num_interpolation_steps,
                        name=name,
                        guidance_scale=guidance_scale,
                        eta=eta,
                        num_inference_steps=num_inference_steps,
                        do_loop=do_loop,
                        make_video=make_video,
                        use_lerp_for_text=use_lerp_for_text,
                        scheduler=scheduler,
                        upsample=upsample,
                        fps=fps,
                        height=height,
                        width=width,
                    ),
                    indent=2,
                    sort_keys=False,
                )
            )
        else:
            # When resuming, we load all available info from existing prompt config, using kwargs passed in where necessary
            if not prompt_config_path.exists():
                raise FileNotFoundError(f"You specified resume=True, but no prompt config file was found at {prompt_config_path}")

            data = json.load(open(prompt_config_path))
            prompts = data['prompts']
            seeds = data['seeds']
            # NOTE - num_steps was renamed to num_interpolation_steps. Including it here for backwards compatibility.
            num_interpolation_steps = data.get('num_interpolation_steps') or data.get('num_steps')
            height = data['height'] if 'height' in data else height
            width = data['width'] if 'width' in data else width
            guidance_scale = data['guidance_scale']
            eta = data['eta']
            num_inference_steps = data['num_inference_steps']
            do_loop = data['do_loop']
            make_video = data['make_video']
            use_lerp_for_text = data['use_lerp_for_text']
            scheduler = data['scheduler']
            disable_tqdm=disable_tqdm
            upsample = data['upsample'] if 'upsample' in data else upsample
            fps = data['fps'] if 'fps' in data else fps

            resume_step = int(sorted(output_path.glob(f"frame*{frame_filename_ext}"))[-1].stem[5:])
            print(f"\nResuming {output_path} from step {resume_step}...")

        self.set_progress_bar_config(disable=disable_tqdm)
        self.scheduler = SCHEDULERS[scheduler]

        if isinstance(num_interpolation_steps, int):
            num_interpolation_steps = [num_interpolation_steps] * (len(prompts)-1)

        assert len(prompts) == len(seeds) == len(num_interpolation_steps) +1

        first_prompt, *prompts = prompts
        embeds_a = self.embed_text(first_prompt)

        first_seed, *seeds = seeds

        latents_a = torch.randn(
            (1, self.unet.in_channels, height // 8, width // 8),
            device=self.device,
            generator=torch.Generator(device=self.device).manual_seed(first_seed),
        )

        if do_loop:
            prompts.append(first_prompt)
            seeds.append(first_seed)
            num_interpolation_steps.append(num_interpolation_steps[0])


        frame_index = 0
        total_frame_count = sum(num_interpolation_steps)
        for prompt, seed, num_step in zip(prompts, seeds, num_interpolation_steps):
            # Text
            embeds_b = self.embed_text(prompt)

            # Latent Noise
            latents_b = torch.randn(
                (1, self.unet.in_channels, height // 8, width // 8),
                device=self.device,
                generator=torch.Generator(device=self.device).manual_seed(seed),
            )

            latents_batch, embeds_batch = None, None
            for i, t in enumerate(np.linspace(0, 1, num_step)):

                frame_filepath = output_path / (f"frame%06d{frame_filename_ext}" % frame_index)
                if resume and frame_filepath.is_file():
                    frame_index += 1
                    continue

                if use_lerp_for_text:
                    embeds = torch.lerp(embeds_a, embeds_b, float(t))
                else:
                    embeds = slerp(float(t), embeds_a, embeds_b)
                latents = slerp(float(t), latents_a, latents_b)

                embeds_batch = embeds if embeds_batch is None else torch.cat([embeds_batch, embeds])
                latents_batch = latents if latents_batch is None else torch.cat([latents_batch, latents])

                del embeds
                del latents
                torch.cuda.empty_cache()

                batch_is_ready = embeds_batch.shape[0] == batch_size or t == 1.0
                if not batch_is_ready:
                    continue

                do_print_progress = (i == 0) or ((frame_index) % 20 == 0)
                if do_print_progress:
                    print(f"COUNT: {frame_index}/{total_frame_count}")

                with torch.autocast("cuda"):
                    outputs = self.step(
                        latents=latents_batch,
                        text_embeddings=embeds_batch,
                        height=height,
                        width=width,
                        guidance_scale=guidance_scale,
                        eta=eta,
                        num_inference_steps=num_inference_steps,
                        output_type='pil' if not upsample else 'numpy'
                    )["sample"]

                    del embeds_batch
                    del latents_batch
                    torch.cuda.empty_cache()
                    latents_batch, embeds_batch = None, None

                    if upsample:
                        images = []
                        for output in outputs:
                            images.append(self.upsampler(output))
                    else:
                        images = outputs
                for image in images:
                    frame_filepath = output_path / (f"frame%06d{frame_filename_ext}" % frame_index)
                    image.save(frame_filepath)
                    frame_index += 1

            embeds_a = embeds_b
            latents_a = latents_b

        if make_video:
            return make_video_ffmpeg(output_path, f"{name}.mp4", fps=fps, frame_filename=f"frame%06d{frame_filename_ext}")

    def embed_text(self, text):
        """Helper to embed some text"""
        with torch.autocast("cuda"):
            text_input = self.tokenizer(
                text,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            with torch.no_grad():
                embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
        return embed