Delete pipeline_ltx_condition.py

pipeline_ltx_condition.py

@@ -1,1194 +0,0 @@
-# Copyright 2024 Lightricks and 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 Any, Callable, Dict, List, Optional, Tuple, Union
-
-import PIL.Image
-import torch
-from transformers import T5EncoderModel, T5TokenizerFast
-
-from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
-from diffusers.image_processor import PipelineImageInput
-from diffusers.loaders import FromSingleFileMixin, LTXVideoLoraLoaderMixin
-from diffusers.models.autoencoders import AutoencoderKLLTXVideo
-from diffusers.models.transformers import LTXVideoTransformer3DModel
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.video_processor import VideoProcessor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.ltx.pipeline_output import LTXPipelineOutput
-
-
-if is_torch_xla_available():
-    import torch_xla.core.xla_model as xm
-
-    XLA_AVAILABLE = True
-else:
-    XLA_AVAILABLE = False
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXConditionPipeline, LTXVideoCondition
-        >>> from diffusers.utils import export_to_video, load_video, load_image
-
-        >>> pipe = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.5", torch_dtype=torch.bfloat16)
-        >>> pipe.to("cuda")
-
-        >>> # Load input image and video
-        >>> video = load_video(
-        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input-vid.mp4"
-        ... )
-        >>> image = load_image(
-        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input.jpg"
-        ... )
-
-        >>> # Create conditioning objects
-        >>> condition1 = LTXVideoCondition(
-        ...     image=image,
-        ...     frame_index=0,
-        ... )
-        >>> condition2 = LTXVideoCondition(
-        ...     video=video,
-        ...     frame_index=80,
-        ... )
-
-        >>> prompt = "The video depicts a long, straight highway stretching into the distance, flanked by metal guardrails. The road is divided into multiple lanes, with a few vehicles visible in the far distance. The surrounding landscape features dry, grassy fields on one side and rolling hills on the other. The sky is mostly clear with a few scattered clouds, suggesting a bright, sunny day. And then the camera switch to a winding mountain road covered in snow, with a single vehicle traveling along it. The road is flanked by steep, rocky cliffs and sparse vegetation. The landscape is characterized by rugged terrain and a river visible in the distance. The scene captures the solitude and beauty of a winter drive through a mountainous region."
-        >>> negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
-
-        >>> # Generate video
-        >>> generator = torch.Generator("cuda").manual_seed(0)
-        >>> # Text-only conditioning is also supported without the need to pass `conditions`
-        >>> video = pipe(
-        ...     conditions=[condition1, condition2],
-        ...     prompt=prompt,
-        ...     negative_prompt=negative_prompt,
-        ...     width=768,
-        ...     height=512,
-        ...     num_frames=161,
-        ...     num_inference_steps=40,
-        ...     generator=generator,
-        ... ).frames[0]
-
-        >>> export_to_video(video, "output.mp4", fps=24)
-        ```
-"""
-
-
-@dataclass
-class LTXVideoCondition:
-    """
-    Defines a single frame-conditioning item for LTX Video - a single frame or a sequence of frames.
-
-    Attributes:
-        image (`PIL.Image.Image`):
-            The image to condition the video on.
-        video (`List[PIL.Image.Image]`):
-            The video to condition the video on.
-        frame_index (`int`):
-            The frame index at which the image or video will conditionally effect the video generation.
-        strength (`float`, defaults to `1.0`):
-            The strength of the conditioning effect. A value of `1.0` means the conditioning effect is fully applied.
-    """
-
-    image: Optional[PIL.Image.Image] = None
-    video: Optional[List[PIL.Image.Image]] = None
-    frame_index: int = 0
-    strength: float = 1.0
-
-
-# from LTX-Video/ltx_video/schedulers/rf.py
-def linear_quadratic_schedule(num_steps, threshold_noise=0.025, linear_steps=None):
-    if linear_steps is None:
-        linear_steps = num_steps // 2
-    if num_steps < 2:
-        return torch.tensor([1.0])
-    linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)]
-    threshold_noise_step_diff = linear_steps - threshold_noise * num_steps
-    quadratic_steps = num_steps - linear_steps
-    quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps**2)
-    linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps**2)
-    const = quadratic_coef * (linear_steps**2)
-    quadratic_sigma_schedule = [
-        quadratic_coef * (i**2) + linear_coef * i + const for i in range(linear_steps, num_steps)
-    ]
-    sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0]
-    sigma_schedule = [1.0 - x for x in sigma_schedule]
-    return torch.tensor(sigma_schedule[:-1])
-
-
-# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
-def calculate_shift(
-    image_seq_len,
-    base_seq_len: int = 256,
-    max_seq_len: int = 4096,
-    base_shift: float = 0.5,
-    max_shift: float = 1.15,
-):
-    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
-    b = base_shift - m * base_seq_len
-    mu = image_seq_len * m + b
-    return mu
-
-
-# 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,
-    sigmas: Optional[List[float]] = None,
-    **kwargs,
-):
-    r"""
-    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 override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
-            `num_inference_steps` and `sigmas` must be `None`.
-        sigmas (`List[float]`, *optional*):
-            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
-            `num_inference_steps` and `timesteps` 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 and sigmas is not None:
-        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
-    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)
-    elif sigmas is not None:
-        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accept_sigmas:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" sigmas schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(sigmas=sigmas, 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
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
-def retrieve_latents(
-    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
-):
-    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
-        return encoder_output.latent_dist.sample(generator)
-    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
-        return encoder_output.latent_dist.mode()
-    elif hasattr(encoder_output, "latents"):
-        return encoder_output.latents
-    else:
-        raise AttributeError("Could not access latents of provided encoder_output")
-
-
-class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin):
-    r"""
-    Pipeline for text/image/video-to-video generation.
-
-    Reference: https://github.com/Lightricks/LTX-Video
-
-    Args:
-        transformer ([`LTXVideoTransformer3DModel`]):
-            Conditional Transformer architecture to denoise the encoded video latents.
-        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKLLTXVideo`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`T5EncoderModel`]):
-            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
-            the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
-        tokenizer (`T5TokenizerFast`):
-            Second Tokenizer of class
-            [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
-    """
-
-    model_cpu_offload_seq = "text_encoder->transformer->vae"
-    _optional_components = []
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
-
-    def __init__(
-        self,
-        scheduler: FlowMatchEulerDiscreteScheduler,
-        vae: AutoencoderKLLTXVideo,
-        text_encoder: T5EncoderModel,
-        tokenizer: T5TokenizerFast,
-        transformer: LTXVideoTransformer3DModel,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            transformer=transformer,
-            scheduler=scheduler,
-        )
-
-        self.vae_spatial_compression_ratio = (
-            self.vae.spatial_compression_ratio if getattr(self, "vae", None) is not None else 32
-        )
-        self.vae_temporal_compression_ratio = (
-            self.vae.temporal_compression_ratio if getattr(self, "vae", None) is not None else 8
-        )
-        self.transformer_spatial_patch_size = (
-            self.transformer.config.patch_size if getattr(self, "transformer", None) is not None else 1
-        )
-        self.transformer_temporal_patch_size = (
-            self.transformer.config.patch_size_t if getattr(self, "transformer") is not None else 1
-        )
-
-        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
-        self.tokenizer_max_length = (
-            self.tokenizer.model_max_length if getattr(self, "tokenizer", None) is not None else 128
-        )
-
-        self.default_height = 512
-        self.default_width = 704
-        self.default_frames = 121
-
-    def _get_t5_prompt_embeds(
-        self,
-        prompt: Union[str, List[str]] = None,
-        num_videos_per_prompt: int = 1,
-        max_sequence_length: int = 256,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        device = device or self._execution_device
-        dtype = dtype or self.text_encoder.dtype
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        batch_size = len(prompt)
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=max_sequence_length,
-            truncation=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        prompt_attention_mask = text_inputs.attention_mask
-        prompt_attention_mask = prompt_attention_mask.bool().to(device)
-
-        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_sequence_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because `max_sequence_length` is set to "
-                f" {max_sequence_length} tokens: {removed_text}"
-            )
-
-        prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)[0]
-        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        _, seq_len, _ = prompt_embeds.shape
-        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
-
-        prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
-        prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
-
-        return prompt_embeds, prompt_attention_mask
-
-    # Copied from diffusers.pipelines.mochi.pipeline_mochi.MochiPipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        do_classifier_free_guidance: bool = True,
-        num_videos_per_prompt: int = 1,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        prompt_attention_mask: Optional[torch.Tensor] = None,
-        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
-        max_sequence_length: int = 256,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        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 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`).
-            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
-                Whether to use classifier free guidance or not.
-            num_videos_per_prompt (`int`, *optional*, defaults to 1):
-                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
-            prompt_embeds (`torch.Tensor`, *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.Tensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            device: (`torch.device`, *optional*):
-                torch device
-            dtype: (`torch.dtype`, *optional*):
-                torch dtype
-        """
-        device = device or self._execution_device
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        if prompt is not None:
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(
-                prompt=prompt,
-                num_videos_per_prompt=num_videos_per_prompt,
-                max_sequence_length=max_sequence_length,
-                device=device,
-                dtype=dtype,
-            )
-
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            negative_prompt = negative_prompt or ""
-            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-
-            if prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-
-            negative_prompt_embeds, negative_prompt_attention_mask = self._get_t5_prompt_embeds(
-                prompt=negative_prompt,
-                num_videos_per_prompt=num_videos_per_prompt,
-                max_sequence_length=max_sequence_length,
-                device=device,
-                dtype=dtype,
-            )
-
-        return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
-
-    def check_inputs(
-        self,
-        prompt,
-        conditions,
-        image,
-        video,
-        frame_index,
-        strength,
-        height,
-        width,
-        callback_on_step_end_tensor_inputs=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        prompt_attention_mask=None,
-        negative_prompt_attention_mask=None,
-    ):
-        if height % 32 != 0 or width % 32 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.")
-
-        if callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
-        ):
-            raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
-            )
-
-        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_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}."
-                )
-
-        if conditions is not None and (image is not None or video is not None):
-            raise ValueError("If `conditions` is provided, `image` and `video` must not be provided.")
-
-        if conditions is None:
-            if isinstance(image, list) and isinstance(frame_index, list) and len(image) != len(frame_index):
-                raise ValueError(
-                    "If `conditions` is not provided, `image` and `frame_index` must be of the same length."
-                )
-            elif isinstance(image, list) and isinstance(strength, list) and len(image) != len(strength):
-                raise ValueError("If `conditions` is not provided, `image` and `strength` must be of the same length.")
-            elif isinstance(video, list) and isinstance(frame_index, list) and len(video) != len(frame_index):
-                raise ValueError(
-                    "If `conditions` is not provided, `video` and `frame_index` must be of the same length."
-                )
-            elif isinstance(video, list) and isinstance(strength, list) and len(video) != len(strength):
-                raise ValueError("If `conditions` is not provided, `video` and `strength` must be of the same length.")
-
-    @staticmethod
-    def _prepare_video_ids(
-        batch_size: int,
-        num_frames: int,
-        height: int,
-        width: int,
-        patch_size: int = 1,
-        patch_size_t: int = 1,
-        device: torch.device = None,
-    ) -> torch.Tensor:
-        latent_sample_coords = torch.meshgrid(
-            torch.arange(0, num_frames, patch_size_t, device=device),
-            torch.arange(0, height, patch_size, device=device),
-            torch.arange(0, width, patch_size, device=device),
-            indexing="ij",
-        )
-        latent_sample_coords = torch.stack(latent_sample_coords, dim=0)
-        latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
-        latent_coords = latent_coords.reshape(batch_size, -1, num_frames * height * width)
-
-        return latent_coords
-
-    @staticmethod
-    def _scale_video_ids(
-        video_ids: torch.Tensor,
-        scale_factor: int = 32,
-        scale_factor_t: int = 8,
-        frame_index: int = 0,
-        device: torch.device = None,
-    ) -> torch.Tensor:
-        scaled_latent_coords = (
-            video_ids
-            * torch.tensor([scale_factor_t, scale_factor, scale_factor], device=video_ids.device)[None, :, None]
-        )
-        scaled_latent_coords[:, 0] = (scaled_latent_coords[:, 0] + 1 - scale_factor_t).clamp(min=0)
-        scaled_latent_coords[:, 0] += frame_index
-
-        return scaled_latent_coords
-
-    @staticmethod
-    # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._pack_latents
-    def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int = 1) -> torch.Tensor:
-        # Unpacked latents of shape are [B, C, F, H, W] are patched into tokens of shape [B, C, F // p_t, p_t, H // p, p, W // p, p].
-        # The patch dimensions are then permuted and collapsed into the channel dimension of shape:
-        # [B, F // p_t * H // p * W // p, C * p_t * p * p] (an ndim=3 tensor).
-        # dim=0 is the batch size, dim=1 is the effective video sequence length, dim=2 is the effective number of input features
-        batch_size, num_channels, num_frames, height, width = latents.shape
-        post_patch_num_frames = num_frames // patch_size_t
-        post_patch_height = height // patch_size
-        post_patch_width = width // patch_size
-        latents = latents.reshape(
-            batch_size,
-            -1,
-            post_patch_num_frames,
-            patch_size_t,
-            post_patch_height,
-            patch_size,
-            post_patch_width,
-            patch_size,
-        )
-        latents = latents.permute(0, 2, 4, 6, 1, 3, 5, 7).flatten(4, 7).flatten(1, 3)
-        return latents
-
-    @staticmethod
-    # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._unpack_latents
-    def _unpack_latents(
-        latents: torch.Tensor, num_frames: int, height: int, width: int, patch_size: int = 1, patch_size_t: int = 1
-    ) -> torch.Tensor:
-        # Packed latents of shape [B, S, D] (S is the effective video sequence length, D is the effective feature dimensions)
-        # are unpacked and reshaped into a video tensor of shape [B, C, F, H, W]. This is the inverse operation of
-        # what happens in the `_pack_latents` method.
-        batch_size = latents.size(0)
-        latents = latents.reshape(batch_size, num_frames, height, width, -1, patch_size_t, patch_size, patch_size)
-        latents = latents.permute(0, 4, 1, 5, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(2, 3)
-        return latents
-
-    @staticmethod
-    # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._normalize_latents
-    def _normalize_latents(
-        latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
-    ) -> torch.Tensor:
-        # Normalize latents across the channel dimension [B, C, F, H, W]
-        latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
-        latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
-        latents = (latents - latents_mean) * scaling_factor / latents_std
-        return latents
-
-    @staticmethod
-    # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._denormalize_latents
-    def _denormalize_latents(
-        latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
-    ) -> torch.Tensor:
-        # Denormalize latents across the channel dimension [B, C, F, H, W]
-        latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
-        latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
-        latents = latents * latents_std / scaling_factor + latents_mean
-        return latents
-
-    def trim_conditioning_sequence(self, start_frame: int, sequence_num_frames: int, target_num_frames: int):
-        """
-        Trim a conditioning sequence to the allowed number of frames.
-
-        Args:
-            start_frame (int): The target frame number of the first frame in the sequence.
-            sequence_num_frames (int): The number of frames in the sequence.
-            target_num_frames (int): The target number of frames in the generated video.
-        Returns:
-            int: updated sequence length
-        """
-        scale_factor = self.vae_temporal_compression_ratio
-        num_frames = min(sequence_num_frames, target_num_frames - start_frame)
-        # Trim down to a multiple of temporal_scale_factor frames plus 1
-        num_frames = (num_frames - 1) // scale_factor * scale_factor + 1
-        return num_frames
-
-    @staticmethod
-    def add_noise_to_image_conditioning_latents(
-        t: float,
-        init_latents: torch.Tensor,
-        latents: torch.Tensor,
-        noise_scale: float,
-        conditioning_mask: torch.Tensor,
-        generator,
-        eps=1e-6,
-    ):
-        """
-        Add timestep-dependent noise to the hard-conditioning latents. This helps with motion continuity, especially
-        when conditioned on a single frame.
-        """
-        noise = randn_tensor(
-            latents.shape,
-            generator=generator,
-            device=latents.device,
-            dtype=latents.dtype,
-        )
-        # Add noise only to hard-conditioning latents (conditioning_mask = 1.0)
-        need_to_noise = (conditioning_mask > 1.0 - eps).unsqueeze(-1)
-        noised_latents = init_latents + noise_scale * noise * (t**2)
-        latents = torch.where(need_to_noise, noised_latents, latents)
-        return latents
-
-    def prepare_latents(
-        self,
-        conditions: Optional[List[torch.Tensor]] = None,
-        condition_strength: Optional[List[float]] = None,
-        condition_frame_index: Optional[List[int]] = None,
-        batch_size: int = 1,
-        num_channels_latents: int = 128,
-        height: int = 512,
-        width: int = 704,
-        num_frames: int = 161,
-        num_prefix_latent_frames: int = 2,
-        generator: Optional[torch.Generator] = None,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int]:
-        num_latent_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
-        latent_height = height // self.vae_spatial_compression_ratio
-        latent_width = width // self.vae_spatial_compression_ratio
-
-        shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width)
-        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        if len(conditions) > 0:
-            condition_latent_frames_mask = torch.zeros(
-                (batch_size, num_latent_frames), device=device, dtype=torch.float32
-            )
-
-            extra_conditioning_latents = []
-            extra_conditioning_video_ids = []
-            extra_conditioning_mask = []
-            extra_conditioning_num_latents = 0
-            for data, strength, frame_index in zip(conditions, condition_strength, condition_frame_index):
-                condition_latents = retrieve_latents(self.vae.encode(data), generator=generator)
-                condition_latents = self._normalize_latents(
-                    condition_latents, self.vae.latents_mean, self.vae.latents_std
-                ).to(device, dtype=dtype)
-
-                num_data_frames = data.size(2)
-                num_cond_frames = condition_latents.size(2)
-
-                if frame_index == 0:
-                    latents[:, :, :num_cond_frames] = torch.lerp(
-                        latents[:, :, :num_cond_frames], condition_latents, strength
-                    )
-                    condition_latent_frames_mask[:, :num_cond_frames] = strength
-
-                else:
-                    if num_data_frames > 1:
-                        if num_cond_frames < num_prefix_latent_frames:
-                            raise ValueError(
-                                f"Number of latent frames must be at least {num_prefix_latent_frames} but got {num_data_frames}."
-                            )
-
-                        if num_cond_frames > num_prefix_latent_frames:
-                            start_frame = frame_index // self.vae_temporal_compression_ratio + num_prefix_latent_frames
-                            end_frame = start_frame + num_cond_frames - num_prefix_latent_frames
-                            latents[:, :, start_frame:end_frame] = torch.lerp(
-                                latents[:, :, start_frame:end_frame],
-                                condition_latents[:, :, num_prefix_latent_frames:],
-                                strength,
-                            )
-                            condition_latent_frames_mask[:, start_frame:end_frame] = strength
-                            condition_latents = condition_latents[:, :, :num_prefix_latent_frames]
-
-                    noise = randn_tensor(condition_latents.shape, generator=generator, device=device, dtype=dtype)
-                    condition_latents = torch.lerp(noise, condition_latents, strength)
-
-                    condition_video_ids = self._prepare_video_ids(
-                        batch_size,
-                        condition_latents.size(2),
-                        latent_height,
-                        latent_width,
-                        patch_size=self.transformer_spatial_patch_size,
-                        patch_size_t=self.transformer_temporal_patch_size,
-                        device=device,
-                    )
-                    condition_video_ids = self._scale_video_ids(
-                        condition_video_ids,
-                        scale_factor=self.vae_spatial_compression_ratio,
-                        scale_factor_t=self.vae_temporal_compression_ratio,
-                        frame_index=frame_index,
-                        device=device,
-                    )
-                    condition_latents = self._pack_latents(
-                        condition_latents,
-                        self.transformer_spatial_patch_size,
-                        self.transformer_temporal_patch_size,
-                    )
-                    condition_conditioning_mask = torch.full(
-                        condition_latents.shape[:2], strength, device=device, dtype=dtype
-                    )
-
-                    extra_conditioning_latents.append(condition_latents)
-                    extra_conditioning_video_ids.append(condition_video_ids)
-                    extra_conditioning_mask.append(condition_conditioning_mask)
-                    extra_conditioning_num_latents += condition_latents.size(1)
-
-        video_ids = self._prepare_video_ids(
-            batch_size,
-            num_latent_frames,
-            latent_height,
-            latent_width,
-            patch_size_t=self.transformer_temporal_patch_size,
-            patch_size=self.transformer_spatial_patch_size,
-            device=device,
-        )
-        if len(conditions) > 0:
-            conditioning_mask = condition_latent_frames_mask.gather(1, video_ids[:, 0])
-        else:
-            conditioning_mask, extra_conditioning_num_latents = None, 0
-        video_ids = self._scale_video_ids(
-            video_ids,
-            scale_factor=self.vae_spatial_compression_ratio,
-            scale_factor_t=self.vae_temporal_compression_ratio,
-            frame_index=0,
-            device=device,
-        )
-        latents = self._pack_latents(
-            latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
-        )
-
-        if len(conditions) > 0 and len(extra_conditioning_latents) > 0:
-            latents = torch.cat([*extra_conditioning_latents, latents], dim=1)
-            video_ids = torch.cat([*extra_conditioning_video_ids, video_ids], dim=2)
-            conditioning_mask = torch.cat([*extra_conditioning_mask, conditioning_mask], dim=1)
-
-        return latents, conditioning_mask, video_ids, extra_conditioning_num_latents
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1.0
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def current_timestep(self):
-        return self._current_timestep
-
-    @property
-    def attention_kwargs(self):
-        return self._attention_kwargs
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        conditions: Union[LTXVideoCondition, List[LTXVideoCondition]] = None,
-        image: Union[PipelineImageInput, List[PipelineImageInput]] = None,
-        video: List[PipelineImageInput] = None,
-        frame_index: Union[int, List[int]] = 0,
-        strength: Union[float, List[float]] = 1.0,
-        prompt: Union[str, List[str]] = None,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        height: int = 512,
-        width: int = 704,
-        num_frames: int = 161,
-        frame_rate: int = 25,
-        num_inference_steps: int = 50,
-        timesteps: List[int] = None,
-        guidance_scale: float = 3,
-        image_cond_noise_scale: float = 0.15,
-        num_videos_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.Tensor] = None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        prompt_attention_mask: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
-        decode_timestep: Union[float, List[float]] = 0.0,
-        decode_noise_scale: Optional[Union[float, List[float]]] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        max_sequence_length: int = 256,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            conditions (`List[LTXVideoCondition], *optional*`):
-                The list of frame-conditioning items for the video generation.If not provided, conditions will be
-                created using `image`, `video`, `frame_index` and `strength`.
-            image (`PipelineImageInput` or `List[PipelineImageInput]`, *optional*):
-                The image or images to condition the video generation. If not provided, one has to pass `video` or
-                `conditions`.
-            video (`List[PipelineImageInput]`, *optional*):
-                The video to condition the video generation. If not provided, one has to pass `image` or `conditions`.
-            frame_index (`int` or `List[int]`, *optional*):
-                The frame index or frame indices at which the image or video will conditionally effect the video
-                generation. If not provided, one has to pass `conditions`.
-            strength (`float` or `List[float]`, *optional*):
-                The strength or strengths of the conditioning effect. If not provided, one has to pass `conditions`.
-            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.
-            height (`int`, defaults to `512`):
-                The height in pixels of the generated image. This is set to 480 by default for the best results.
-            width (`int`, defaults to `704`):
-                The width in pixels of the generated image. This is set to 848 by default for the best results.
-            num_frames (`int`, defaults to `161`):
-                The number of video frames to generate
-            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.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
-                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
-                passed will be used. Must be in descending order.
-            guidance_scale (`float`, defaults to `3 `):
-                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_videos_per_prompt (`int`, *optional*, defaults to 1):
-                The number of videos to generate per prompt.
-            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.Tensor`, *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.Tensor`, *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.Tensor`, *optional*):
-                Pre-generated attention mask for text embeddings.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. For PixArt-Sigma 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.
-            decode_timestep (`float`, defaults to `0.0`):
-                The timestep at which generated video is decoded.
-            decode_noise_scale (`float`, defaults to `None`):
-                The interpolation factor between random noise and denoised latents at the decode timestep.
-            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.ltx.LTXPipelineOutput`] instead of a plain tuple.
-            attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            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`.
-            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.
-            max_sequence_length (`int` defaults to `128 `):
-                Maximum sequence length to use with the `prompt`.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ltx.LTXPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ltx.LTXPipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images.
-        """
-
-        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
-            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
-        if latents is not None:
-            raise ValueError("Passing latents is not yet supported.")
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt=prompt,
-            conditions=conditions,
-            image=image,
-            video=video,
-            frame_index=frame_index,
-            strength=strength,
-            height=height,
-            width=width,
-            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            prompt_attention_mask=prompt_attention_mask,
-            negative_prompt_attention_mask=negative_prompt_attention_mask,
-        )
-
-        self._guidance_scale = guidance_scale
-        self._attention_kwargs = attention_kwargs
-        self._interrupt = False
-        self._current_timestep = None
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if conditions is not None:
-            if not isinstance(conditions, list):
-                conditions = [conditions]
-
-            strength = [condition.strength for condition in conditions]
-            frame_index = [condition.frame_index for condition in conditions]
-            image = [condition.image for condition in conditions]
-            video = [condition.video for condition in conditions]
-        elif image is not None or video is not None:
-            if not isinstance(image, list):
-                image = [image]
-                num_conditions = 1
-            elif isinstance(image, list):
-                num_conditions = len(image)
-            if not isinstance(video, list):
-                video = [video]
-                num_conditions = 1
-            elif isinstance(video, list):
-                num_conditions = len(video)
-
-            if not isinstance(frame_index, list):
-                frame_index = [frame_index] * num_conditions
-            if not isinstance(strength, list):
-                strength = [strength] * num_conditions
-
-        device = self._execution_device
-
-        # 3. Prepare text embeddings
-        (
-            prompt_embeds,
-            prompt_attention_mask,
-            negative_prompt_embeds,
-            negative_prompt_attention_mask,
-        ) = self.encode_prompt(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            do_classifier_free_guidance=self.do_classifier_free_guidance,
-            num_videos_per_prompt=num_videos_per_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            prompt_attention_mask=prompt_attention_mask,
-            negative_prompt_attention_mask=negative_prompt_attention_mask,
-            max_sequence_length=max_sequence_length,
-            device=device,
-        )
-        if self.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)
-
-        vae_dtype = self.vae.dtype
-
-        conditioning_tensors = []
-        is_conditioning_image_or_video = image is not None or video is not None
-        if is_conditioning_image_or_video:
-            for condition_image, condition_video, condition_frame_index, condition_strength in zip(
-                image, video, frame_index, strength
-            ):
-                if condition_image is not None:
-                    condition_tensor = (
-                        self.video_processor.preprocess(condition_image, height, width)
-                        .unsqueeze(2)
-                        .to(device, dtype=vae_dtype)
-                    )
-                elif condition_video is not None:
-                    condition_tensor = self.video_processor.preprocess_video(condition_video, height, width)
-                    num_frames_input = condition_tensor.size(2)
-                    num_frames_output = self.trim_conditioning_sequence(
-                        condition_frame_index, num_frames_input, num_frames
-                    )
-                    condition_tensor = condition_tensor[:, :, :num_frames_output]
-                    condition_tensor = condition_tensor.to(device, dtype=vae_dtype)
-                else:
-                    raise ValueError("Either `image` or `video` must be provided for conditioning.")
-
-                if condition_tensor.size(2) % self.vae_temporal_compression_ratio != 1:
-                    raise ValueError(
-                        f"Number of frames in the video must be of the form (k * {self.vae_temporal_compression_ratio} + 1) "
-                        f"but got {condition_tensor.size(2)} frames."
-                    )
-                conditioning_tensors.append(condition_tensor)
-
-        # 4. Prepare latent variables
-        num_channels_latents = self.transformer.config.in_channels
-        latents, conditioning_mask, video_coords, extra_conditioning_num_latents = self.prepare_latents(
-            conditioning_tensors,
-            strength,
-            frame_index,
-            batch_size=batch_size * num_videos_per_prompt,
-            num_channels_latents=num_channels_latents,
-            height=height,
-            width=width,
-            num_frames=num_frames,
-            generator=generator,
-            device=device,
-            dtype=torch.float32,
-        )
-
-        video_coords = video_coords.float()
-        video_coords[:, 0] = video_coords[:, 0] * (1.0 / frame_rate)
-
-        init_latents = latents.clone() if is_conditioning_image_or_video else None
-
-        if self.do_classifier_free_guidance:
-            video_coords = torch.cat([video_coords, video_coords], dim=0)
-
-        # 5. Prepare timesteps
-        latent_num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
-        latent_height = height // self.vae_spatial_compression_ratio
-        latent_width = width // self.vae_spatial_compression_ratio
-        sigmas = linear_quadratic_schedule(num_inference_steps)
-        timesteps = sigmas * 1000
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            timesteps=timesteps,
-        )
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
-
-        # 6. Denoising loop
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-
-                self._current_timestep = t
-
-                if image_cond_noise_scale > 0 and init_latents is not None:
-                    # Add timestep-dependent noise to the hard-conditioning latents
-                    # This helps with motion continuity, especially when conditioned on a single frame
-                    latents = self.add_noise_to_image_conditioning_latents(
-                        t / 1000.0,
-                        init_latents,
-                        latents,
-                        image_cond_noise_scale,
-                        conditioning_mask,
-                        generator,
-                    )
-
-                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
-                if is_conditioning_image_or_video:
-                    conditioning_mask_model_input = (
-                        torch.cat([conditioning_mask, conditioning_mask])
-                        if self.do_classifier_free_guidance
-                        else conditioning_mask
-                    )
-                latent_model_input = latent_model_input.to(prompt_embeds.dtype)
-
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latent_model_input.shape[0]).unsqueeze(-1).float()
-                if is_conditioning_image_or_video:
-                    timestep = torch.min(timestep, (1 - conditioning_mask_model_input) * 1000.0)
-
-                noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
-                    encoder_hidden_states=prompt_embeds,
-                    timestep=timestep,
-                    encoder_attention_mask=prompt_attention_mask,
-                    video_coords=video_coords,
-                    attention_kwargs=attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                if self.do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-                    timestep, _ = timestep.chunk(2)
-
-                denoised_latents = self.scheduler.step(
-                    -noise_pred, t, latents, per_token_timesteps=timestep, return_dict=False
-                )[0]
-                if is_conditioning_image_or_video:
-                    tokens_to_denoise_mask = (t / 1000 - 1e-6 < (1.0 - conditioning_mask)).unsqueeze(-1)
-                    latents = torch.where(tokens_to_denoise_mask, denoised_latents, latents)
-                else:
-                    latents = denoised_latents
-
-                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)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-
-                # 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 XLA_AVAILABLE:
-                    xm.mark_step()
-
-        if is_conditioning_image_or_video:
-            latents = latents[:, extra_conditioning_num_latents:]
-
-        latents = self._unpack_latents(
-            latents,
-            latent_num_frames,
-            latent_height,
-            latent_width,
-            self.transformer_spatial_patch_size,
-            self.transformer_temporal_patch_size,
-        )
-
-        if output_type == "latent":
-            video = latents
-        else:
-            latents = self._denormalize_latents(
-                latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
-            )
-            latents = latents.to(prompt_embeds.dtype)
-
-            if not self.vae.config.timestep_conditioning:
-                timestep = None
-            else:
-                noise = torch.randn(latents.shape, generator=generator, device=device, dtype=latents.dtype)
-                if not isinstance(decode_timestep, list):
-                    decode_timestep = [decode_timestep] * batch_size
-                if decode_noise_scale is None:
-                    decode_noise_scale = decode_timestep
-                elif not isinstance(decode_noise_scale, list):
-                    decode_noise_scale = [decode_noise_scale] * batch_size
-
-                timestep = torch.tensor(decode_timestep, device=device, dtype=latents.dtype)
-                decode_noise_scale = torch.tensor(decode_noise_scale, device=device, dtype=latents.dtype)[
-                    :, None, None, None, None
-                ]
-                latents = (1 - decode_noise_scale) * latents + decode_noise_scale * noise
-
-            video = self.vae.decode(latents, timestep, return_dict=False)[0]
-            video = self.video_processor.postprocess_video(video, output_type=output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (video,)
-
-        return LTXPipelineOutput(frames=video)