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-# Copyright 2024 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import inspect
-from dataclasses import dataclass
-from typing import Callable, Dict, List, Optional, Union
-import numpy as np
-import PIL.Image
-import torch
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, CLIPTextModel, CLIPTokenizer
-import diffusers
-from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
-from diffusers.models import AutoencoderKLTemporalDecoder
-from diffusers.schedulers import EulerDiscreteScheduler
-from diffusers.utils import BaseOutput, logging, replace_example_docstring
-from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
-from diffusers import DiffusionPipeline
-from .unet_diffusion_vas import UNetSpatioTemporalConditionModel
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import StableVideoDiffusionPipeline
-        >>> from diffusers.utils import load_image, export_to_video
-        >>> pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
-        >>> pipe.to("cuda")
-        >>> image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd-docstring-example.jpeg")
-        >>> image = image.resize((1024, 576))
-        >>> frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]
-        >>> export_to_video(frames, "generated.mp4", fps=7)
-        ```
-"""
-def _append_dims(x, target_dims):
-    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
-    dims_to_append = target_dims - x.ndim
-    if dims_to_append < 0:
-        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
-    return x[(...,) + (None,) * dims_to_append]
-# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid
-def tensor2vid(video: torch.Tensor, processor: VaeImageProcessor, output_type: str = "np"):
-    batch_size, channels, num_frames, height, width = video.shape
-    outputs = []
-    for batch_idx in range(batch_size):
-        batch_vid = video[batch_idx].permute(1, 0, 2, 3)
-        batch_output = processor.postprocess(batch_vid, output_type)
-        outputs.append(batch_output)
-    if output_type == "np":
-        outputs = np.stack(outputs)
-    elif output_type == "pt":
-        outputs = torch.stack(outputs)
-    elif not output_type == "pil":
-        raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']")
-    return outputs
-@dataclass
-class StableVideoDiffusionPipelineOutput(BaseOutput):
-    r"""
-    Output class for Stable Video Diffusion pipeline.
-    Args:
-        frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.FloatTensor`]):
-            List of denoised PIL images of length `batch_size` or numpy array or torch tensor
-            of shape `(batch_size, num_frames, height, width, num_channels)`.
-    """
-    frames: Union[List[List[PIL.Image.Image]], np.ndarray, torch.FloatTensor]
-class DiffusionVASPipeline(DiffusionPipeline):
-    r"""
-    Pipeline to generate video from an input image using Stable Video Diffusion.
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-    Args:
-        vae ([`AutoencoderKLTemporalDecoder`]):
-            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-        image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
-            Frozen CLIP image-encoder ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)).
-        unet ([`UNetSpatioTemporalConditionModel`]):
-            A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents.
-        scheduler ([`EulerDiscreteScheduler`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents.
-        feature_extractor ([`~transformers.CLIPImageProcessor`]):
-            A `CLIPImageProcessor` to extract features from generated images.
-    """
-    model_cpu_offload_seq = "image_encoder->unet->vae"
-    _callback_tensor_inputs = ["latents"]
-    def __init__(
-        self,
-        vae: AutoencoderKLTemporalDecoder,
-        image_encoder: CLIPVisionModelWithProjection,
-        unet: UNetSpatioTemporalConditionModel,
-        scheduler: EulerDiscreteScheduler,
-        feature_extractor: CLIPImageProcessor,
-    ):
-        super().__init__()
-        self.register_modules(
-            vae=vae,
-            image_encoder=image_encoder,
-            unet=unet,
-            scheduler=scheduler,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-    # def _encode_prompt(
-    #     self,
-    #     prompt,
-    #     device,
-    #     do_classifier_free_guidance
-    # ):
-    #
-    #     dtype = next(self.image_encoder.parameters()).dtype
-    #     prompt = [prompt] if isinstance(prompt, str) else prompt
-    #     text_inputs = self.tokenizer(
-    #         prompt, max_length=self.tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
-    #     ).input_ids
-    #
-    #     text_inputs = text_inputs.to(self.text_encoder.device)
-    #
-    #     text_embeddings = self.text_encoder(text_inputs, return_dict=False)[0].to(device=device,dtype=dtype)
-    #     if do_classifier_free_guidance:
-    #         negative_text_embeddings = torch.zeros_like(text_embeddings)
-    #         text_embeddings = torch.cat([negative_text_embeddings, text_embeddings])
-    #
-    #     return text_embeddings
-    def _encode_image(
-        self,
-        image: PipelineImageInput,
-        device: Union[str, torch.device],
-        num_videos_per_prompt: int,
-        do_classifier_free_guidance: bool,
-    ) -> torch.FloatTensor:
-        dtype = next(self.image_encoder.parameters()).dtype
-        if not isinstance(image, torch.Tensor):
-            image = self.image_processor.pil_to_numpy(image)
-            image = self.image_processor.numpy_to_pt(image)
-            # We normalize the image before resizing to match with the original implementation.
-            # Then we unnormalize it after resizing.
-            image = image * 2.0 - 1.0
-            image = _resize_with_antialiasing(image, (224, 224))
-            image = (image + 1.0) / 2.0
-        else:
-            image = _resize_with_antialiasing(image, (224, 224))
-            image = (image + 1.0) / 2.0
-        # Normalize the image with for CLIP input
-        image = self.feature_extractor(
-            images=image,
-            do_normalize=True,
-            do_center_crop=False,
-            do_resize=False,
-            do_rescale=False,
-            return_tensors="pt",
-        ).pixel_values
-        image = image.to(device=device, dtype=dtype)
-        image_embeddings = self.image_encoder(image).image_embeds
-        image_embeddings = image_embeddings.unsqueeze(1)
-        # duplicate image embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = image_embeddings.shape
-        image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1)
-        image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
-        if do_classifier_free_guidance:
-            negative_image_embeddings = torch.zeros_like(image_embeddings)
-            # 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
-            image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])
-        return image_embeddings
-    def _encode_vae_image(
-        self,
-        image: torch.Tensor,
-        device: Union[str, torch.device],
-        num_videos_per_prompt: int,
-        do_classifier_free_guidance: bool,
-    ):
-        image = image.to(device=device)
-        image_latents = self.vae.encode(image).latent_dist.mode()
-        if do_classifier_free_guidance:
-            negative_image_latents = torch.zeros_like(image_latents)
-            # 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
-            image_latents = torch.cat([negative_image_latents, image_latents])
-        # duplicate image_latents for each generation per prompt, using mps friendly method
-        image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1)
-        return image_latents
-    def _get_add_time_ids(
-        self,
-        fps: int,
-        motion_bucket_id: int,
-        noise_aug_strength: float,
-        dtype: torch.dtype,
-        batch_size: int,
-        num_videos_per_prompt: int,
-        do_classifier_free_guidance: bool,
-    ):
-        add_time_ids = [fps, motion_bucket_id, noise_aug_strength]
-        passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids)
-        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
-        if expected_add_embed_dim != passed_add_embed_dim:
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
-            )
-        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
-        add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)
-        if do_classifier_free_guidance:
-            add_time_ids = torch.cat([add_time_ids, add_time_ids])
-        return add_time_ids
-    def decode_latents(self, latents: torch.FloatTensor, num_frames: int, decode_chunk_size: int = 14):
-        # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]
-        latents = latents.flatten(0, 1)
-        latents = 1 / self.vae.config.scaling_factor * latents
-        forward_vae_fn = self.vae._orig_mod.forward if is_compiled_module(self.vae) else self.vae.forward
-        accepts_num_frames = "num_frames" in set(inspect.signature(forward_vae_fn).parameters.keys())
-        # decode decode_chunk_size frames at a time to avoid OOM
-        frames = []
-        for i in range(0, latents.shape[0], decode_chunk_size):
-            num_frames_in = latents[i : i + decode_chunk_size].shape[0]
-            decode_kwargs = {}
-            if accepts_num_frames:
-                # we only pass num_frames_in if it's expected
-                decode_kwargs["num_frames"] = num_frames_in
-            frame = self.vae.decode(latents[i : i + decode_chunk_size], **decode_kwargs).sample
-            frames.append(frame)
-        frames = torch.cat(frames, dim=0)
-        # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width]
-        frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        frames = frames.float()
-        return frames
-    def check_inputs(self, image, height, width):
-        if (
-            not isinstance(image, torch.Tensor)
-            and not isinstance(image, PIL.Image.Image)
-            and not isinstance(image, list)
-        ):
-            raise ValueError(
-                "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
-                f" {type(image)}"
-            )
-        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}.")
-    def prepare_latents(
-        self,
-        batch_size: int,
-        num_frames: int,
-        num_channels_latents: int,
-        height: int,
-        width: int,
-        dtype: torch.dtype,
-        device: Union[str, torch.device],
-        generator: torch.Generator,
-        latents: Optional[torch.FloatTensor] = None,
-    ):
-        shape = (
-            batch_size,
-            num_frames,
-            num_channels_latents // 2,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-    # 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.
-    @property
-    def do_classifier_free_guidance(self):
-        if isinstance(self.guidance_scale, (int, float)):
-            return self.guidance_scale > 1
-        return self.guidance_scale.max() > 1
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        images,
-        rgb_images,
-        height: int = 576,
-        width: int = 1024,
-        num_frames: Optional[int] = None,
-        num_inference_steps: int = 25,
-        min_guidance_scale: float = 1.5,
-        max_guidance_scale: float = 1.5,
-        fps: int = 7,
-        motion_bucket_id: int = 127,
-        noise_aug_strength: float = 0.02,
-        decode_chunk_size: Optional[int] = None,
-        num_videos_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        return_dict: bool = True,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-        Args:
-            image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
-                Image(s) to guide image generation. If you provide a tensor, the expected value range is between `[0, 1]`.
-            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            num_frames (`int`, *optional*):
-                The number of video frames to generate. Defaults to `self.unet.config.num_frames`
-                (14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt`).
-            num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps. More denoising steps usually lead to a higher quality video at the
-                expense of slower inference. This parameter is modulated by `strength`.
-            min_guidance_scale (`float`, *optional*, defaults to 1.0):
-                The minimum guidance scale. Used for the classifier free guidance with first frame.
-            max_guidance_scale (`float`, *optional*, defaults to 3.0):
-                The maximum guidance scale. Used for the classifier free guidance with last frame.
-            fps (`int`, *optional*, defaults to 7):
-                Frames per second. The rate at which the generated images shall be exported to a video after generation.
-                Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training.
-            motion_bucket_id (`int`, *optional*, defaults to 127):
-                Used for conditioning the amount of motion for the generation. The higher the number the more motion
-                will be in the video.
-            noise_aug_strength (`float`, *optional*, defaults to 0.02):
-                The amount of noise added to the init image, the higher it is the less the video will look like the init image. Increase it for more motion.
-            decode_chunk_size (`int`, *optional*):
-                The number of frames to decode at a time. Higher chunk size leads to better temporal consistency at the expense of more memory usage. By default, the decoder decodes all frames at once for maximal
-                quality. For lower memory usage, reduce `decode_chunk_size`.
-            num_videos_per_prompt (`int`, *optional*, defaults to 1):
-                The number of videos to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `pil`, `np` or `pt`.
-            callback_on_step_end (`Callable`, *optional*):
-                A function that is called at the end of each denoising step during 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`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeline class.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-        Examples:
-        Returns:
-            [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` of (`List[List[PIL.Image.Image]]` or `np.ndarray` or `torch.FloatTensor`) is returned.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-        num_frames = num_frames if num_frames is not None else self.unet.config.num_frames
-        decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(images[0], height, width)
-        # 2. Define call parameters
-        batch_size = 1
-        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.
-        self._guidance_scale = max_guidance_scale
-        # 3. Encode input image
-        image_embeddings = [self._encode_image(images[:,i,:,:,:], device, num_videos_per_prompt, self.do_classifier_free_guidance) for i in range(images.shape[1])]
-        image_embeddings = torch.cat(image_embeddings, dim=0)
-        # NOTE: Stable Video Diffusion was conditioned on fps - 1, which is why it is reduced here.
-        # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188
-        fps = fps - 1
-        # 4. Encode input image using VAE
-        images = torch.stack([self.image_processor.preprocess(images[:,i,:,:,:], height=height, width=width).to(device) for i in range(images.shape[1])])
-        noise = randn_tensor(images.shape, generator=generator, device=device, dtype=images.dtype)
-        images = images + noise_aug_strength * noise
-        rgb_images = torch.stack([self.image_processor.preprocess(rgb_images[:,i,:,:,:], height=height, width=width).to(device) for i in range(rgb_images.shape[1])])
-        noise = randn_tensor(rgb_images.shape, generator=generator, device=device, dtype=rgb_images.dtype)
-        rgb_images = rgb_images + noise_aug_strength * noise
-        needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
-        if needs_upcasting:
-            self.vae.to(dtype=torch.float32)
-        image_latents = torch.stack([self._encode_vae_image(
-                image,
-                device=device,
-                num_videos_per_prompt=num_videos_per_prompt,
-                do_classifier_free_guidance=self.do_classifier_free_guidance,
-            ).to(image_embeddings.dtype) for image in images], dim=1)
-        rgb_image_latents = torch.stack([self._encode_vae_image(
-                rgb_image,
-                device=device,
-                num_videos_per_prompt=num_videos_per_prompt,
-                do_classifier_free_guidance=self.do_classifier_free_guidance,
-            ).to(image_embeddings.dtype) for rgb_image in rgb_images], dim=1)
-        # cast back to fp16 if needed
-        if needs_upcasting:
-            self.vae.to(dtype=torch.float16)
-        # 5. Get Added Time IDs
-        added_time_ids = self._get_add_time_ids(
-            fps,
-            motion_bucket_id,
-            noise_aug_strength,
-            image_embeddings.dtype,
-            batch_size,
-            num_videos_per_prompt,
-            self.do_classifier_free_guidance,
-        )
-        added_time_ids = added_time_ids.to(device)
-        # 6. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-        # 7. Prepare latent variables
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_videos_per_prompt,
-            num_frames,
-            num_channels_latents,
-            height,
-            width,
-            image_embeddings.dtype,
-            device,
-            generator,
-            latents,
-        )
-        # 8. Prepare guidance scale
-        guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)
-        guidance_scale = guidance_scale.to(device, latents.dtype)
-        guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)
-        guidance_scale = _append_dims(guidance_scale, latents.ndim)
-        self._guidance_scale = guidance_scale
-        # 9. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        self._num_timesteps = len(timesteps)
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-                # Concatenate image_latents over channels dimension
-                latent_model_input = torch.cat([latent_model_input, image_latents, rgb_image_latents], dim=2)
-                # predict the noise residual
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=image_embeddings,
-                    added_time_ids=added_time_ids,
-                    return_dict=False
-                )[0]
-                # perform guidance
-                if self.do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents).prev_sample
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-                    latents = callback_outputs.pop("latents", latents)
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-        if not output_type == "latent":
-            # cast back to fp16 if needed
-            if needs_upcasting:
-                self.vae.to(dtype=torch.float16)
-            frames = self.decode_latents(latents, num_frames, decode_chunk_size)
-            frames = tensor2vid(frames, self.image_processor, output_type=output_type)
-        else:
-            frames = latents
-        self.maybe_free_model_hooks()
-        if not return_dict:
-            return frames
-        return StableVideoDiffusionPipelineOutput(frames=frames)
-# resizing utils
-# TODO: clean up later
-def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True):
-    h, w = input.shape[-2:]
-    factors = (h / size[0], w / size[1])
-    # First, we have to determine sigma
-    # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171
-    sigmas = (
-        max((factors[0] - 1.0) / 2.0, 0.001),
-        max((factors[1] - 1.0) / 2.0, 0.001),
-    )
-    # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma
-    # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206
-    # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now
-    ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3))
-    # Make sure it is odd
-    if (ks[0] % 2) == 0:
-        ks = ks[0] + 1, ks[1]
-    if (ks[1] % 2) == 0:
-        ks = ks[0], ks[1] + 1
-    input = _gaussian_blur2d(input, ks, sigmas)
-    output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners)
-    return output
-def _compute_padding(kernel_size):
-    """Compute padding tuple."""
-    # 4 or 6 ints:  (padding_left, padding_right,padding_top,padding_bottom)
-    # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad
-    if len(kernel_size) < 2:
-        raise AssertionError(kernel_size)
-    computed = [k - 1 for k in kernel_size]
-    # for even kernels we need to do asymmetric padding :(
-    out_padding = 2 * len(kernel_size) * [0]
-    for i in range(len(kernel_size)):
-        computed_tmp = computed[-(i + 1)]
-        pad_front = computed_tmp // 2
-        pad_rear = computed_tmp - pad_front
-        out_padding[2 * i + 0] = pad_front
-        out_padding[2 * i + 1] = pad_rear
-    return out_padding
-def _filter2d(input, kernel):
-    # prepare kernel
-    b, c, h, w = input.shape
-    tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype)
-    tmp_kernel = tmp_kernel.expand(-1, c, -1, -1)
-    height, width = tmp_kernel.shape[-2:]
-    padding_shape: list[int] = _compute_padding([height, width])
-    input = torch.nn.functional.pad(input, padding_shape, mode="reflect")
-    # kernel and input tensor reshape to align element-wise or batch-wise params
-    tmp_kernel = tmp_kernel.reshape(-1, 1, height, width)
-    input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1))
-    # convolve the tensor with the kernel.
-    output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1)
-    out = output.view(b, c, h, w)
-    return out
-def _gaussian(window_size: int, sigma):
-    if isinstance(sigma, float):
-        sigma = torch.tensor([[sigma]])
-    batch_size = sigma.shape[0]
-    x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)
-    if window_size % 2 == 0:
-        x = x + 0.5
-    gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))
-    return gauss / gauss.sum(-1, keepdim=True)
-def _gaussian_blur2d(input, kernel_size, sigma):
-    if isinstance(sigma, tuple):
-        sigma = torch.tensor([sigma], dtype=input.dtype)
-    else:
-        sigma = sigma.to(dtype=input.dtype)
-    ky, kx = int(kernel_size[0]), int(kernel_size[1])
-    bs = sigma.shape[0]
-    kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1))
-    kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1))
-    out_x = _filter2d(input, kernel_x[..., None, :])
-    out = _filter2d(out_x, kernel_y[..., None])
-    return out