From 0178d77666c6681c27bee93cdfe49d33a8cd2be2 Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Wed, 18 Dec 2024 14:12:05 +0530
Subject: [PATCH 1/3] start ltx video2video.

---
 .../pipelines/ltx/pipeline_ltx_video2video.py | 859 ++++++++++++++++++
 1 file changed, 859 insertions(+)
 create mode 100644 src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py

diff --git a/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py b/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py
new file mode 100644
index 000000000000..56a7ac84fcd4
--- /dev/null
+++ b/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py
@@ -0,0 +1,859 @@
+# 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 typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import T5EncoderModel, T5TokenizerFast
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...loaders import FromSingleFileMixin, LTXVideoLoraLoaderMixin
+from ...models.autoencoders import AutoencoderKLLTXVideo
+from ...models.transformers import LTXVideoTransformer3DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from ...video_processor import VideoProcessor
+from ..pipeline_utils import DiffusionPipeline
+from .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
+        TODO
+        ```
+"""
+
+
+# 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 = 1.16,
+):
+    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 LTXVideoToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin):
+    r"""
+    Pipeline for 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 hasattr(self, "vae") else 32
+        self.vae_temporal_compression_ratio = self.vae.temporal_compression_ratio if hasattr(self, "vae") else 8
+        self.transformer_spatial_patch_size = self.transformer.config.patch_size if hasattr(self, "transformer") else 1
+        self.transformer_temporal_patch_size = (
+            self.transformer.config.patch_size_t if hasattr(self, "transformer") else 1
+        )
+
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
+        self.tokenizer_max_length = (
+            self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer 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 = 128,
+        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))[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 with 256->128
+    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 = 128,
+        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
+
+    # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        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}."
+                )
+
+    @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 prepare_latents(
+        self,
+        video: Optional[torch.Tensor] = None,
+        batch_size: int = 1,
+        num_channels_latents: int = 128,
+        height: int = 512,
+        width: int = 704,
+        num_frames: int = 161,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.Tensor] = None,
+        sigma: torch.Tensor = 1.0,
+    ) -> torch.Tensor:
+        # TODO: do we need the `conditioning_mask` here? I think `conditioning_mask` should be all ones.
+        height = height // self.vae_spatial_compression_ratio
+        width = width // self.vae_spatial_compression_ratio
+
+        # TODO: should video_processor take care of it? Because for Cog, we get a 5D tensor here.
+        if video.ndim == 4:
+            video = video.unsqueeze(0)
+
+        num_frames = (
+            (num_frames - 1) // self.vae_temporal_compression_ratio + 1 if latents is None else latents.size(2)
+        )
+        shape = (batch_size, num_channels_latents, num_frames, height, width)
+        mask_shape = (batch_size, 1, num_frames, height, width)
+
+        if latents is not None:
+            conditioning_mask = latents.new_ones(shape)
+            conditioning_mask = self._pack_latents(
+                conditioning_mask, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
+            )
+            return latents.to(device=device, dtype=dtype), conditioning_mask
+
+        if isinstance(generator, list):
+            if 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."
+                )
+
+            init_latents = [
+                retrieve_latents(self.vae.encode(video[i].unsqueeze(0).permute(0, 2, 1, 3, 4)), generator[i])
+                for i in range(batch_size)
+            ]
+        else:
+            init_latents = [
+                retrieve_latents(self.vae.encode(vid.unsqueeze(0).permute(0, 2, 1, 3, 4)), generator) for vid in video
+            ]
+
+        init_latents = torch.cat(init_latents, dim=0).to(dtype)
+        init_latents = self._normalize_latents(init_latents, self.vae.latents_mean, self.vae.latents_std)
+        # `ones()` as we want to condition on all?
+        conditioning_mask = torch.ones(mask_shape, device=device, dtype=dtype)
+
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        # TODO: consider adding the noise w.r.t the flow equation? CogVideoX vid2vid
+        # adds this noise with `add_noise()`.
+        latents = (1 - sigma) * init_latents + sigma * noise
+        # latents = init_latents * conditioning_mask + noise * (1 - conditioning_mask)
+
+        conditioning_mask = self._pack_latents(
+            conditioning_mask, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
+        ).squeeze(-1)
+        latents = self._pack_latents(
+            latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
+        )
+
+        return latents, conditioning_mask
+
+    @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 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,
+        video: List[Image.Image] = None,
+        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,
+        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,
+        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 = 128,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            video: TODO
+            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.
+            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
+
+        # 1. Check inputs. Raise error if not correct
+        # TODO: check for the `video`
+        self.check_inputs(
+            prompt=prompt,
+            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
+
+        # 3. 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]
+
+        device = self._execution_device
+
+        # 4. 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)
+
+        # 2. Prepare video inputs.
+        if latents is None:
+            video = self.video_processor.preprocess(video, height=height, width=width)
+            video = video.to(device=device, dtype=prompt_embeds.dtype)
+
+        if video is not None:
+            if video.ndim == 4:
+                actual_num_frames = video.size(0)
+            elif video.ndim == 5:
+                actual_num_frames = video.size(1)
+        elif latents is not None:
+            actual_num_frames = num_frames
+        num_frames = min(actual_num_frames, num_frames)
+
+        # 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
+        video_sequence_length = latent_num_frames * latent_height * latent_width
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        mu = calculate_shift(
+            video_sequence_length,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas=sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        latent_sigma = torch.tensor(
+            sigmas[:1].repeat(batch_size * num_videos_per_prompt), dtype=prompt_embeds.dtype, device=device
+        )
+        self._num_timesteps = len(timesteps)
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        latents, conditioning_mask = self.prepare_latents(
+            video,
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            num_frames,
+            torch.float32,
+            device,
+            generator,
+            latents,
+            sigma=latent_sigma,
+        )
+        if self.do_classifier_free_guidance:
+            conditioning_mask = torch.cat([conditioning_mask, conditioning_mask])
+
+        # 7. Prepare micro-conditions
+        latent_frame_rate = frame_rate / self.vae_temporal_compression_ratio
+        rope_interpolation_scale = (
+            1 / latent_frame_rate,
+            self.vae_spatial_compression_ratio,
+            self.vae_spatial_compression_ratio,
+        )
+
+        # 8. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                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])
+                # timestep = timestep.unsqueeze(-1) * (1 - conditioning_mask)
+                timestep = timestep.unsqueeze(-1)
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep=timestep,
+                    encoder_attention_mask=prompt_attention_mask,
+                    num_frames=latent_num_frames,
+                    height=latent_height,
+                    width=latent_width,
+                    rope_interpolation_scale=rope_interpolation_scale,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+                noise_pred = noise_pred.float()
+
+                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)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                noise_pred = self._unpack_latents(
+                    noise_pred,
+                    latent_num_frames,
+                    latent_height,
+                    latent_width,
+                    self.transformer_spatial_patch_size,
+                    self.transformer_temporal_patch_size,
+                )
+                latents = self._unpack_latents(
+                    latents,
+                    latent_num_frames,
+                    latent_height,
+                    latent_width,
+                    self.transformer_spatial_patch_size,
+                    self.transformer_temporal_patch_size,
+                )
+
+                noise_pred = noise_pred[:, :, 1:]
+                noise_latents = latents[:, :, 1:]
+                pred_latents = self.scheduler.step(noise_pred, t, noise_latents, return_dict=False)[0]
+
+                latents = torch.cat([latents[:, :, :1], pred_latents], dim=2)
+                latents = self._pack_latents(
+                    latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
+                )
+
+                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 output_type == "latent":
+            video = latents
+        else:
+            latents = self._unpack_latents(
+                latents,
+                latent_num_frames,
+                latent_height,
+                latent_width,
+                self.transformer_spatial_patch_size,
+                self.transformer_temporal_patch_size,
+            )
+            latents = self._denormalize_latents(
+                latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+            )
+            latents = latents.to(prompt_embeds.dtype)
+            video = self.vae.decode(latents, 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)

From 790735939334c5dc49c773ddb3c08e18e31f4019 Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Wed, 18 Dec 2024 14:30:14 +0530
Subject: [PATCH 2/3] comments

---
 src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py b/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py
index 56a7ac84fcd4..51174c81e5ea 100644
--- a/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py
+++ b/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py
@@ -463,6 +463,7 @@ def prepare_latents(
         width = width // self.vae_spatial_compression_ratio
 
         # TODO: should video_processor take care of it? Because for Cog, we get a 5D tensor here.
+        # `video` memory layout is (num_frames, num_channels, height, width)
         if video.ndim == 4:
             video = video.unsqueeze(0)
 
@@ -490,7 +491,7 @@ def prepare_latents(
                 retrieve_latents(self.vae.encode(video[i].unsqueeze(0).permute(0, 2, 1, 3, 4)), generator[i])
                 for i in range(batch_size)
             ]
-        else:
+        else: # `premute()` because we want `batch_size, num_channels, num_frames, height, width`
             init_latents = [
                 retrieve_latents(self.vae.encode(vid.unsqueeze(0).permute(0, 2, 1, 3, 4)), generator) for vid in video
             ]

From 2bf5b72d9550bd78f6266725f5fa28312ac29725 Mon Sep 17 00:00:00 2001
From: sayakpaul <spsayakpaul@gmail.com>
Date: Thu, 19 Dec 2024 15:14:18 +0530
Subject: [PATCH 3/3] fixes

---
 .../pipelines/ltx/pipeline_ltx_video2video.py | 62 +++++++------------
 1 file changed, 24 insertions(+), 38 deletions(-)

diff --git a/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py b/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py
index 51174c81e5ea..35cadf028ff8 100644
--- a/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py
+++ b/src/diffusers/pipelines/ltx/pipeline_ltx_video2video.py
@@ -456,9 +456,8 @@ def prepare_latents(
         device: Optional[torch.device] = None,
         generator: Optional[torch.Generator] = None,
         latents: Optional[torch.Tensor] = None,
-        sigma: torch.Tensor = 1.0,
+        timestep: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        # TODO: do we need the `conditioning_mask` here? I think `conditioning_mask` should be all ones.
         height = height // self.vae_spatial_compression_ratio
         width = width // self.vae_spatial_compression_ratio
 
@@ -471,14 +470,9 @@ def prepare_latents(
             (num_frames - 1) // self.vae_temporal_compression_ratio + 1 if latents is None else latents.size(2)
         )
         shape = (batch_size, num_channels_latents, num_frames, height, width)
-        mask_shape = (batch_size, 1, num_frames, height, width)
 
         if latents is not None:
-            conditioning_mask = latents.new_ones(shape)
-            conditioning_mask = self._pack_latents(
-                conditioning_mask, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
-            )
-            return latents.to(device=device, dtype=dtype), conditioning_mask
+            return latents.to(device=device, dtype=dtype)
 
         if isinstance(generator, list):
             if len(generator) != batch_size:
@@ -491,30 +485,21 @@ def prepare_latents(
                 retrieve_latents(self.vae.encode(video[i].unsqueeze(0).permute(0, 2, 1, 3, 4)), generator[i])
                 for i in range(batch_size)
             ]
-        else: # `premute()` because we want `batch_size, num_channels, num_frames, height, width`
+        else:  # `premute()` because we want `batch_size, num_channels, num_frames, height, width`
             init_latents = [
                 retrieve_latents(self.vae.encode(vid.unsqueeze(0).permute(0, 2, 1, 3, 4)), generator) for vid in video
             ]
 
         init_latents = torch.cat(init_latents, dim=0).to(dtype)
         init_latents = self._normalize_latents(init_latents, self.vae.latents_mean, self.vae.latents_std)
-        # `ones()` as we want to condition on all?
-        conditioning_mask = torch.ones(mask_shape, device=device, dtype=dtype)
-
         noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        # TODO: consider adding the noise w.r.t the flow equation? CogVideoX vid2vid
-        # adds this noise with `add_noise()`.
-        latents = (1 - sigma) * init_latents + sigma * noise
-        # latents = init_latents * conditioning_mask + noise * (1 - conditioning_mask)
-
-        conditioning_mask = self._pack_latents(
-            conditioning_mask, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
-        ).squeeze(-1)
+
+        latents = self.scheduler.scale_noise(sample=init_latents, timestep=timestep, noise=noise)
         latents = self._pack_latents(
             latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
         )
 
-        return latents, conditioning_mask
+        return latents
 
     @property
     def guidance_scale(self):
@@ -536,6 +521,16 @@ def attention_kwargs(self):
     def interrupt(self):
         return self._interrupt
 
+    # Copied from diffusers.pipelines.animatediff.pipeline_animatediff_video2video.AnimateDiffVideoToVideoPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, timesteps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = timesteps[t_start * self.scheduler.order :]
+
+        return timesteps, num_inference_steps - t_start
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -549,6 +544,7 @@ def __call__(
         frame_rate: int = 25,
         num_inference_steps: int = 50,
         timesteps: List[int] = None,
+        strength: float = 0.8,
         guidance_scale: float = 3,
         num_videos_per_prompt: Optional[int] = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
@@ -585,6 +581,7 @@ def __call__(
                 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.
+            strength: TODO
             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
@@ -643,7 +640,7 @@ def __call__(
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
         # 1. Check inputs. Raise error if not correct
-        # TODO: check for the `video`
+        # TODO: check for the `video`, `strength`
         self.check_inputs(
             prompt=prompt,
             height=height,
@@ -726,15 +723,14 @@ def __call__(
             sigmas=sigmas,
             mu=mu,
         )
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        latent_sigma = torch.tensor(
-            sigmas[:1].repeat(batch_size * num_videos_per_prompt), dtype=prompt_embeds.dtype, device=device
-        )
         self._num_timesteps = len(timesteps)
 
         # 6. Prepare latent variables
         num_channels_latents = self.transformer.config.in_channels
-        latents, conditioning_mask = self.prepare_latents(
+        latents = self.prepare_latents(
             video,
             batch_size * num_videos_per_prompt,
             num_channels_latents,
@@ -745,10 +741,8 @@ def __call__(
             device,
             generator,
             latents,
-            sigma=latent_sigma,
+            timestep=latent_timestep,
         )
-        if self.do_classifier_free_guidance:
-            conditioning_mask = torch.cat([conditioning_mask, conditioning_mask])
 
         # 7. Prepare micro-conditions
         latent_frame_rate = frame_rate / self.vae_temporal_compression_ratio
@@ -769,8 +763,6 @@ def __call__(
 
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 timestep = t.expand(latent_model_input.shape[0])
-                # timestep = timestep.unsqueeze(-1) * (1 - conditioning_mask)
-                timestep = timestep.unsqueeze(-1)
                 noise_pred = self.transformer(
                     hidden_states=latent_model_input,
                     encoder_hidden_states=prompt_embeds,
@@ -788,7 +780,6 @@ def __call__(
                 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)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 noise_pred = self._unpack_latents(
@@ -807,12 +798,7 @@ def __call__(
                     self.transformer_spatial_patch_size,
                     self.transformer_temporal_patch_size,
                 )
-
-                noise_pred = noise_pred[:, :, 1:]
-                noise_latents = latents[:, :, 1:]
-                pred_latents = self.scheduler.step(noise_pred, t, noise_latents, return_dict=False)[0]
-
-                latents = torch.cat([latents[:, :, :1], pred_latents], dim=2)
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
                 latents = self._pack_latents(
                     latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
                 )