Create pipeline.py

pipeline.py

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+# Copyright 2023 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, Union, Tuple
+
+import numpy as np
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+
+# Updated to use absolute paths
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel, ControlNetModel
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.models.unet_motion_model import MotionAdapter
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    BaseOutput,
+    logging,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
+
+# Added imports based on the working paths
+from diffusers.models import ControlNetModel
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.utils import deprecate
+
+import torchvision
+import PIL
+import PIL.Image
+import math
+import time
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
+        >>> from diffusers.utils import export_to_gif
+        >>> adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
+        >>> pipe = AnimateDiffPipeline.from_pretrained("frankjoshua/toonyou_beta6", motion_adapter=adapter)
+        >>> pipe.scheduler = DDIMScheduler(beta_schedule="linear", steps_offset=1, clip_sample=False)
+        >>> output = pipe(prompt="A corgi walking in the park")
+        >>> frames = output.frames[0]
+        >>> export_to_gif(frames, "animation.gif")
+        ```
+"""
+# 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")
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used,
+            `timesteps` must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+                Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
+                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
+                must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        # if not accepts_timesteps:
+        #     raise ValueError(
+        #         f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+        #         f" timestep schedules. Please check whether you are using the correct scheduler."
+        #     )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+def tensor2vid(video: torch.Tensor, processor, output_type="np"):
+    # Based on:
+    # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
+
+    batch_size, channels, num_frames, height, width = video.shape
+    outputs = []
+    for batch_idx in range(batch_size):
+        batch_vid = video[batch_idx].permute(1, 0, 2, 3)
+        batch_output = processor.postprocess(batch_vid, output_type)
+
+        outputs.append(batch_output)
+
+    return outputs
+
+@dataclass
+class AnimateDiffPipelineOutput(BaseOutput):
+    frames: Union[torch.Tensor, np.ndarray]
+
+class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin):
+    r"""
+    Pipeline for text-to-video generation.
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+    The pipeline also inherits the following loading methods:
+        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+        - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        tokenizer (`CLIPTokenizer`):
+            A [`~transformers.CLIPTokenizer`] to tokenize text.
+        unet ([`UNet2DConditionModel`]):
+            A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
+        motion_adapter ([`MotionAdapter`]):
+            A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+    """
+
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
+    _optional_components = ["feature_extractor", "image_encoder","controlnet"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        motion_adapter: MotionAdapter,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+        controlnet: Optional[Union[ControlNetModel, MultiControlNetModel]]=None,
+        feature_extractor: Optional[CLIPImageProcessor] = None,
+        image_encoder: Optional[CLIPVisionModelWithProjection] = None,
+    ):
+        super().__init__()
+        unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            motion_adapter=motion_adapter,
+            controlnet=controlnet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.control_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
+        )
+
+    def load_motion_adapter(self,motion_adapter):
+        self.register_modules(motion_adapter=motion_adapter)
+    
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            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`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. 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.
+            lora_scale (`float`, *optional*):
+                A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        """
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if not USE_PEFT_BACKEND:
+                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+            else:
+                scale_lora_layers(self.text_encoder, lora_scale)
+
+        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 prompt_embeds is None:
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            if clip_skip is None:
+                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
+                prompt_embeds = prompt_embeds[0]
+            else:
+                prompt_embeds = self.text_encoder(
+                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
+                )
+                # Access the `hidden_states` first, that contains a tuple of
+                # all the hidden states from the encoder layers. Then index into
+                # the tuple to access the hidden states from the desired layer.
+                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
+                # We also need to apply the final LayerNorm here to not mess with the
+                # representations. The `last_hidden_states` that we typically use for
+                # obtaining the final prompt representations passes through the LayerNorm
+                # layer.
+                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = prompt_embeds.dtype
+
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif 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 isinstance(negative_prompt, str):
+                uncond_tokens = [negative_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`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(self.text_encoder, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
+    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        if output_hidden_states:
+            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_enc_hidden_states = self.image_encoder(
+                torch.zeros_like(image), output_hidden_states=True
+            ).hidden_states[-2]
+            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
+                num_images_per_prompt, dim=0
+            )
+            return image_enc_hidden_states, uncond_image_enc_hidden_states
+        else:
+            image_embeds = self.image_encoder(image).image_embeds
+            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_embeds = torch.zeros_like(image_embeds)
+
+            return image_embeds, uncond_image_embeds
+
+    # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
+    def decode_latents(self, latents):
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        batch_size, channels, num_frames, height, width = latents.shape
+        latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
+
+        image = self.vae.decode(latents).sample
+        video = (
+            image[None, :]
+            .reshape(
+                (
+                    batch_size,
+                    num_frames,
+                    -1,
+                )
+                + image.shape[2:]
+            )
+            .permute(0, 2, 1, 3, 4)
+        )
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        video = video.float()
+        return video
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
+    def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
+        r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
+        The suffixes after the scaling factors represent the stages where they are being applied.
+        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
+        that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+        Args:
+            s1 (`float`):
+                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+                mitigate "oversmoothing effect" in the enhanced denoising process.
+            s2 (`float`):
+                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+                mitigate "oversmoothing effect" in the enhanced denoising process.
+            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+        """
+        if not hasattr(self, "unet"):
+            raise ValueError("The pipeline must have `unet` for using FreeU.")
+        self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
+    def disable_freeu(self):
+        """Disables the FreeU mechanism if enabled."""
+        self.unet.disable_freeu()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+        if 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 negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and 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}."
+                )
+
+    # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents
+    def prepare_latents(self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_frames,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+    
+    def prepare_latents_same_start(self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None, context_size=16, overlap=4, strength=0.5):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_frames,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+            
+        # make every (context_size-overlap) frames have the same noise
+        loop_size = context_size - overlap
+        loop_count = num_frames // loop_size
+        for i in range(loop_count):
+            # repeat the first frames noise for i*loop_size frame
+            # lerp the first frames noise
+            latents[:, :, i*loop_size:(i*loop_size)+overlap, :, :] = torch.lerp(latents[:, :, i*loop_size:(i*loop_size)+overlap, :, :], latents[:, :, 0:overlap, :, :], strength)
+            
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_latents_consistent(self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None,smooth_weight=0.5,smooth_steps=3):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_frames,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
+            # blend each frame with the surrounding N frames making sure to wrap around at the end
+            for i in range(num_frames):
+                blended_latent = torch.zeros_like(latents[:, :, i])
+                for s in range(-smooth_steps, smooth_steps + 1):
+                    if s == 0:
+                        continue
+                    frame_index = (i + s) % num_frames
+                    weight = (smooth_steps - abs(s)) / smooth_steps
+                    blended_latent += latents[:, :, frame_index] * weight
+                latents[:, :, i] = blended_latent / (2 * smooth_steps)
+                
+            latents = torch.lerp(randn_tensor(shape, generator=generator, device=device, dtype=dtype),latents, smooth_weight)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_motion_latents(self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, 
+                        latents=None, x_velocity=0, y_velocity=0, scale_velocity=0):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_frames,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        
+        for frame in range(num_frames):
+            x_offset = int(frame * x_velocity)  # Convert to int
+            y_offset = int(frame * y_velocity)  # Convert to int
+            scale_factor = 1 + frame * scale_velocity
+
+            # Apply offsets
+            latents[:, :, frame] = torch.roll(latents[:, :, frame], shifts=(x_offset,), dims=3)  # x direction
+            latents[:, :, frame] = torch.roll(latents[:, :, frame], shifts=(y_offset,), dims=2)  # y direction
+
+            # Apply scaling - This is a simple approach and might not be ideal for all applications
+            if scale_factor != 1:
+                scaled_size = (
+                    int(latents.shape[3] * scale_factor),
+                    int(latents.shape[4] * scale_factor)
+                )
+                latents[:, :, frame] = torch.nn.functional.interpolate(
+                    latents[:, :, frame].unsqueeze(0), size=scaled_size, mode='bilinear', align_corners=False
+                ).squeeze(0)
+
+        return latents
+
+    def generate_correlated_noise(self, latents, init_noise_correlation):
+        cloned_latents = latents.clone()
+        p = init_noise_correlation
+        flattened_latents = torch.flatten(cloned_latents)
+        noise = torch.randn_like(flattened_latents)
+        correlated_noise = flattened_latents * p + math.sqrt(1 - p**2) * noise
+
+        return correlated_noise.reshape(cloned_latents.shape)
+
+    def generate_correlated_latents(self, latents, init_noise_correlation):
+        cloned_latents = latents.clone()
+        for i in range(1, cloned_latents.shape[2]):
+            p = init_noise_correlation
+            flattened_latents = torch.flatten(cloned_latents[:, :, i])
+            prev_flattened_latents = torch.flatten(cloned_latents[:, :, i - 1])
+            correlated_latents = (prev_flattened_latents * p/math.sqrt((1+p**2))+flattened_latents * math.sqrt(1/(1 + p**2)))
+            cloned_latents[:, :, i] = correlated_latents.reshape(cloned_latents[:, :, i].shape)
+
+        return cloned_latents
+
+    def generate_correlated_latents_legacy(self, latents, init_noise_correlation):
+        cloned_latents = latents.clone()
+        for i in range(1, cloned_latents.shape[2]):
+            p = init_noise_correlation
+            flattened_latents = torch.flatten(cloned_latents[:, :, i])
+            prev_flattened_latents = torch.flatten(cloned_latents[:, :, i - 1])
+            correlated_latents = (
+                prev_flattened_latents * p
+                + 
+                flattened_latents * math.sqrt(1 - p**2)
+            )
+            cloned_latents[:, :, i] = correlated_latents.reshape(
+                cloned_latents[:, :, i].shape
+            )
+
+        return cloned_latents
+
+    def generate_mixed_noise(self, noise, init_noise_correlation):
+        shared_noise = torch.randn_like(noise[0, :, 0])
+        for b in range(noise.shape[0]):
+            for f in range(noise.shape[2]):
+                p = init_noise_correlation
+                flattened_latents = torch.flatten(noise[b, :, f])
+                shared_latents = torch.flatten(shared_noise)
+                correlated_latents = (
+                    shared_latents * math.sqrt(p**2/(1+p**2)) + 
+                    flattened_latents * math.sqrt(1/(1+p**2))
+                )
+                noise[b, :, f] = correlated_latents.reshape(noise[b, :, f].shape)
+
+        return noise
+
+    def prepare_correlated_latents(
+        self,
+        init_image,
+        init_image_strength,
+        init_noise_correlation,
+        batch_size,
+        num_channels_latents,
+        video_length,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            video_length,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+
+        if init_image is not None:
+            start_image = ((torchvision.transforms.functional.pil_to_tensor(init_image))/ 255 )[:3, :, :].to("cuda").to(dtype).unsqueeze(0)
+            start_image = (
+                self.vae.encode(start_image.mul(2).sub(1))
+                .latent_dist.sample()
+                .view(1, 4, height // 8, width // 8)
+                * 0.18215
+            )
+            init_latents = start_image.unsqueeze(2).repeat(1, 1, video_length, 1, 1)
+        else:
+            init_latents = None
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        if latents is None:
+            rand_device = "cpu" if device.type == "mps" else device
+            if isinstance(generator, list):
+                shape = shape
+                # shape = (1,) + shape[1:]
+                # ignore init latents for batch model
+                latents = [torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)for i in range(batch_size)]
+                latents = torch.cat(latents, dim=0).to(device)
+            else:
+                if init_latents is not None:
+                    offset = int(
+                        init_image_strength * (len(self.scheduler.timesteps) - 1)
+                    )
+                    noise = torch.randn_like(init_latents)
+                    noise = self.generate_correlated_latents(noise, init_noise_correlation)
+
+                    # Eric - some black magic here
+                    # We should be only adding the noise at timestep[offset], but I noticed that
+                    # we get more motion and cooler motion if we add the noise at timestep[offset - 1]
+                    # or offset - 2. However, this breaks the fewer timesteps there are, so let's interpolate
+                    timesteps = self.scheduler.timesteps
+                    average_timestep = None
+                    if offset == 0:
+                        average_timestep = timesteps[0]
+                    elif offset == 1:
+                        average_timestep = (
+                            timesteps[offset - 1] * (1 - init_image_strength)
+                            + timesteps[offset] * init_image_strength
+                        )
+                    else:
+                        average_timestep = timesteps[offset - 1]
+
+                    latents = self.scheduler.add_noise(
+                        init_latents, noise, average_timestep.long()
+                    )
+
+                    latents = self.scheduler.add_noise(
+                        latents, torch.randn_like(init_latents), timesteps[-2]
+                    )
+                else:
+                    latents = torch.randn(
+                        shape, generator=generator, device=rand_device, dtype=dtype
+                    ).to(device)
+                    latents = self.generate_correlated_latents(
+                        latents, init_noise_correlation
+                    )
+        else:
+            if latents.shape != shape:
+                raise ValueError(
+                    f"Unexpected latents shape, got {latents.shape}, expected {shape}"
+                )
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        if init_latents is None:
+            latents = latents * self.scheduler.init_noise_sigma
+        # elif self.unet.trained_initial_frames and init_latents is not None:
+        #     # we only want to use this as the first frame
+        #     init_latents[:, :, 1:] = torch.zeros_like(init_latents[:, :, 1:])
+            
+        latents = latents.to(device)
+        return latents, init_latents        
+        
+    def prepare_video_latents(
+        self,
+        video,
+        height,
+        width,
+        num_channels_latents,
+        batch_size,
+        timestep,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        # video must be a list of list of images
+        # the outer list denotes having multiple videos as input, whereas inner list means the frames of the video
+        # as a list of images
+        if not isinstance(video[0], list):
+            video = [video]
+        if latents is None:
+            video = torch.cat(
+                [self.image_processor.preprocess(vid, height=height, width=width).unsqueeze(0) for vid in video], dim=0
+            )
+            video = video.to(device=device, dtype=dtype)
+            num_frames = video.shape[1]
+        else:
+            num_frames = latents.shape[2]
+
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_frames,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            if self.vae.config.force_upcast:
+                video = video.float()
+                self.vae.to(dtype=torch.float32)
+
+            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]), generator=generator[i]).unsqueeze(0)
+                    for i in range(batch_size)
+                ]
+            else:
+                init_latents = [
+                    retrieve_latents(self.vae.encode(vid), generator=generator).unsqueeze(0) for vid in video
+                ]
+
+            init_latents = torch.cat(init_latents, dim=0)
+
+            # restore vae to original dtype
+            if self.vae.config.force_upcast:
+                self.vae.to(dtype)
+
+            init_latents = init_latents.to(dtype)
+            init_latents = self.vae.config.scaling_factor * init_latents
+
+            if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+                # expand init_latents for batch_size
+                error_message = (
+                    f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
+                    " images (`image`). Please make sure to update your script to pass as many initial images as text prompts"
+                )
+                raise ValueError(error_message)
+            elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+                raise ValueError(
+                    f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+                )
+            else:
+                init_latents = torch.cat([init_latents], dim=0)
+
+            noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)
+            latents = self.scheduler.add_noise(init_latents, noise, timestep).permute(0, 2, 1, 3, 4)
+        else:
+            if shape != latents.shape:
+                # [B, C, F, H, W]
+                raise ValueError(f"`latents` expected to have {shape=}, but found {latents.shape=}")
+            latents = latents.to(device, dtype=dtype)
+
+        return latents 
+        
+        
+    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
+    def prepare_control_frames(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        guess_mode=False,
+    ):
+        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
+        # image_batch_size = image.shape[0]
+        image_batch_size = len(image)
+
+        # if image_batch_size == 1:
+        #     repeat_by = batch_size
+        # else:
+        #     # image batch size is the same as prompt batch size
+        #     repeat_by = num_images_per_prompt
+
+        # image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        # if do_classifier_free_guidance and not guess_mode:
+        #     image = torch.cat([image] * 2)
+
+        return image
+        
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_frames: Optional[int] = 16,
+        context_size=16,
+        overlap=2,
+        step=1,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_videos_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        ip_adapter_image: Optional[Union[PipelineImageInput, List[PipelineImageInput]]] = None,
+        output_type: Optional[str] = "pil",
+        output_path: Optional[str] = None,
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        clip_skip: Optional[int] = None,
+        x_velocity: Optional[float] = 0,
+        y_velocity: Optional[float] = 0,
+        scale_velocity: Optional[float] = 0,
+        init_image: Optional[PipelineImageInput] = None,
+        init_image_strength: Optional[float] = 1.0,
+        init_noise_correlation: Optional[float] = 0.0,
+        latent_mode: Optional[str] = "normal",
+        smooth_weight: Optional[float] = 0.5,
+        smooth_steps: Optional[int] = 3,
+        initial_context_scale: Optional[float] = 1.0,
+        conditioning_frames: Optional[List[PipelineImageInput]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
+        guess_mode: bool = False,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated video.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated video.
+            num_frames (`int`, *optional*, defaults to 16):
+                The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
+                amounts to 2 seconds of video.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`. Latents should be of shape
+                `(batch_size, num_channel, num_frames, height, width)`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or
+                `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead
+                of a plain tuple.
+            callback (`Callable`, *optional*):
+                A function that calls every `callback_steps` steps during inference. The function is called with the
+                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function is called. If not specified, the callback is called at
+                every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        Examples:
+        Returns:
+            [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is
+                returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
+        """
+        
+        if self.controlnet != None:
+            controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
+
+            # align format for control guidance
+            control_end = control_guidance_end
+            if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+                control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+            elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+                control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+            elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+                mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
+                control_guidance_start, control_guidance_end = (
+                    mult * [control_guidance_start],
+                    mult * [control_guidance_end],
+                )
+            
+            
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        num_videos_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
+        )
+
+        # 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 = 1
+            
+
+        device = self._execution_device
+        
+        if self.controlnet != None:
+            if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
+                controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
+
+            global_pool_conditions = (
+                controlnet.config.global_pool_conditions
+                if isinstance(controlnet, ControlNetModel)
+                else controlnet.nets[0].config.global_pool_conditions
+            )
+            guess_mode = guess_mode or global_pool_conditions
+            
+            
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        
+        # if promptEmbeds size
+        num_prompts = prompt_embeds.size(0) if prompt_embeds is not None else 0
+        # foreach prompt embed
+        
+        prompt_embeds_list = []
+        for p in range(num_prompts):
+            single_prompt_embeds, single_negative_prompt_embeds = self.encode_prompt(
+                prompt,
+                device,
+                num_videos_per_prompt,
+                do_classifier_free_guidance,
+                negative_prompt,
+                prompt_embeds=prompt_embeds[p].unsqueeze(0),
+                negative_prompt_embeds=negative_prompt_embeds[p].unsqueeze(0),
+                lora_scale=text_encoder_lora_scale,
+                clip_skip=clip_skip,
+            )
+        
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            if do_classifier_free_guidance:
+                # concatenate negative prompt embeddings with prompt embeddings on a new dimension after the first batch dimension
+                single_prompt_embeds = torch.cat([single_negative_prompt_embeds, single_prompt_embeds])
+    
+            prompt_embeds_list.append(single_prompt_embeds)
+            
+        if ip_adapter_image is not None:
+            output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
+            # foreach ip_adapter_image in ip_adapter_image
+            image_embed_list = []
+            # if ip_adapter_image is not list, convert to list
+            ip_adapter_image = [ip_adapter_image] if not isinstance(ip_adapter_image, list) else ip_adapter_image
+            for image in ip_adapter_image:
+                image_embeds, negative_image_embeds = self.encode_image(image, device, num_videos_per_prompt, output_hidden_state)
+                if do_classifier_free_guidance:image_embeds = torch.cat([negative_image_embeds, image_embeds])
+                image_embed_list.append(image_embeds)
+            image_embeds = image_embed_list
+        if self.controlnet != None:
+            if isinstance(controlnet, ControlNetModel):
+                # conditioning_frames = self.prepare_image(
+                #     image=conditioning_frames,
+                #     width=width,
+                #     height=height,
+                #     batch_size=batch_size * num_videos_per_prompt * num_frames,
+                #     num_images_per_prompt=num_videos_per_prompt,
+                #     device=device,
+                #     dtype=controlnet.dtype,
+                #     do_classifier_free_guidance=self.do_classifier_free_guidance,
+                #     guess_mode=guess_mode,
+                # )
+                conditioning_frames = self.prepare_control_frames(
+                    image=conditioning_frames,
+                    width=width,
+                    height=height,
+                    batch_size=batch_size * num_videos_per_prompt * num_frames,
+                    num_images_per_prompt=num_videos_per_prompt,
+                    device=device,
+                    dtype=controlnet.dtype,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    guess_mode=guess_mode,
+                )
+                                    
+            elif isinstance(controlnet, MultiControlNetModel):
+                cond_prepared_frames = []
+                for frame_ in conditioning_frames:
+                    # prepared_frame = self.prepare_image(
+                    #     image=frame_,
+                    #     width=width,
+                    #     height=height,
+                    #     batch_size=batch_size * num_videos_per_prompt * num_frames,
+                    #     num_images_per_prompt=num_videos_per_prompt,
+                    #     device=device,
+                    #     dtype=controlnet.dtype,
+                    #     do_classifier_free_guidance=self.do_classifier_free_guidance,
+                    #     guess_mode=guess_mode,
+                    # )
+        
+                    prepared_frame = self.prepare_control_frames(
+                        image=frame_,
+                        width=width,
+                        height=height,
+                        batch_size=batch_size * num_videos_per_prompt * num_frames,
+                        num_images_per_prompt=num_videos_per_prompt,
+                        device=device,
+                        dtype=controlnet.dtype,
+                        do_classifier_free_guidance=do_classifier_free_guidance,
+                        guess_mode=guess_mode,
+                    )
+                    
+                    cond_prepared_frames.append(prepared_frame)
+
+                conditioning_frames = cond_prepared_frames
+            else:
+                assert False
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+        
+
+        # round num frames to the nearest multiple of context size - overlap
+        num_frames = (num_frames // (context_size - overlap)) * (context_size - overlap)
+        
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        if(latent_mode == "normal"):
+            latents = self.prepare_latents(
+                batch_size * num_videos_per_prompt,
+                num_channels_latents,
+                num_frames,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                latents,
+            )
+        if(latent_mode == "same_start"):
+            latents = self.prepare_latents_same_start(
+                batch_size * num_videos_per_prompt,
+                num_channels_latents,
+                num_frames,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                latents,
+                context_size=context_size,
+                overlap=overlap,
+                strength=init_image_strength,
+            )
+        elif(latent_mode == "motion"):
+            latents = self.prepare_motion_latents(
+                batch_size * num_videos_per_prompt,
+                num_channels_latents,
+                num_frames,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                latents,
+                x_velocity=x_velocity,
+                y_velocity=y_velocity,
+                scale_velocity=scale_velocity,
+            )
+        elif(latent_mode == "correlated"):        
+            latents, init_latents = self.prepare_correlated_latents(
+                init_image,
+                init_image_strength,
+                init_noise_correlation,
+                batch_size,
+                num_channels_latents,
+                num_frames,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+            )
+        elif(latent_mode == "consistent"):
+            latents = self.prepare_latents_consistent(
+                batch_size * num_videos_per_prompt,
+                num_channels_latents,
+                num_frames,
+                height,
+                width,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                latents,
+                smooth_weight,
+                smooth_steps,
+            )
+        elif(latent_mode == "video"):
+            # 4. Prepare timesteps
+            # timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+            # timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, init_image_strength, device)
+            latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
+            self._num_timesteps = len(timesteps)
+            num_channels_latents = self.unet.config.in_channels
+            latents = self.prepare_video_latents(
+                video=init_image,
+                height=height,
+                width=width,
+                num_channels_latents=num_channels_latents,
+                batch_size=batch_size * num_videos_per_prompt,
+                timestep=latent_timestep,
+                dtype=prompt_embeds.dtype,
+                device=device,
+                generator=generator,
+                latents=latents,
+            )
+
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+        
+        
+        # 7.1 Create tensor stating which controlnets to keep
+        if self.controlnet != None:
+            controlnet_keep = []
+            for i in range(len(timesteps)):
+                keeps = [
+                    1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                    for s, e in zip(control_guidance_start, control_guidance_end)
+                ]
+                controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
+
+        # divide the initial latents into context groups
+        
+        def context_scheduler(context_size, overlap, offset, total_frames, total_timesteps):
+            # Calculate the number of context groups based on frame count and context size
+            number_of_context_groups = (total_frames // (context_size - overlap))
+            # Initialize a list to store context indexes for all timesteps
+            all_context_indexes = []
+            # Iterate over each timestep
+            for timestep in range(total_timesteps):
+                # Initialize a list to store groups of context indexes for this timestep
+                timestep_context_groups = []
+                # Iterate over each context group
+                for group_index in range(number_of_context_groups):
+                    # Initialize a list to store context indexes for this group
+                    context_group_indexes = []
+                    # Iterate over each index in the context group
+                    local_context_size = context_size
+                    if timestep <= 1:
+                        local_context_size = int(context_size * initial_context_scale)
+                    for index in range(local_context_size):
+                        # if its the first timestep, spread the indexes out evenly over the full frame range, offset by the group index
+                        frame_index = (group_index * (local_context_size - overlap)) + (offset * timestep) + index
+                        # If frame index exceeds total frames, wrap around
+                        if frame_index >= total_frames:
+                            frame_index %= total_frames
+                        # Add the frame index to the group's list
+                        context_group_indexes.append(frame_index)
+                    # Add the group's indexes to the timestep's list
+                    timestep_context_groups.append(context_group_indexes)
+                # Add the timestep's context groups to the overall list
+                all_context_indexes.append(timestep_context_groups)
+            return all_context_indexes
+                
+        context_indexes = context_scheduler(context_size, overlap, step, num_frames, len(timesteps))
+        
+        # Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=len(timesteps)) as progress_bar:
+            for i, t in enumerate(timesteps):
+                noise_pred_uncond_sum = torch.zeros_like(latents).to(device).to(dtype=torch.float16)
+                noise_pred_text_sum = torch.zeros_like(latents).to(device).to(dtype=torch.float16)
+                latent_counter = torch.zeros(num_frames).to(device).to(dtype=torch.float16)
+        
+                # foreach context group seperately denoise the current timestep
+                for context_group in range(len(context_indexes[i])):
+                    # calculate to current indexes, considering overlapa
+                    current_context_indexes = context_indexes[i][context_group]
+
+                    # select the relevent context from the latents
+                    current_context_latents = latents[:, :, current_context_indexes, :, :]
+                    
+                    # expand the latents if we are doing classifier free guidance
+                    latent_model_input = torch.cat([current_context_latents] * 2) if do_classifier_free_guidance else current_context_latents
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                    
+                    # get the current prompt index based on the current context group (for blending between multiple prompts)
+                    current_prompt_index =  int((context_group / len(context_indexes[i])) * len(prompt_embeds_list))
+                                        
+                    # 7 Add image embeds for IP-Adapter
+                    added_cond_kwargs = {"image_embeds": image_embeds[min(current_prompt_index, len(image_embeds) - 1)]} if ip_adapter_image is not None else None
+                    
+                    if self.controlnet != None and i < int(control_end*num_inference_steps):
+                
+                        current_context_conditioning_frames = conditioning_frames[current_context_indexes, :, :, :] 
+                        current_context_conditioning_frames = torch.cat([current_context_conditioning_frames] * 2) if do_classifier_free_guidance else current_context_conditioning_frames
+
+                    
+                        if guess_mode and self.do_classifier_free_guidance:
+                            # Infer ControlNet only for the conditional batch.
+                            control_model_input = latents
+                            control_model_input = self.scheduler.scale_model_input(control_model_input, t)
+                            controlnet_prompt_embeds = prompt_embeds_list[current_prompt_index].chunk(2)[1]
+                        else:
+                            control_model_input = latent_model_input
+                            controlnet_prompt_embeds = prompt_embeds_list[current_prompt_index]
+                        controlnet_prompt_embeds = controlnet_prompt_embeds.repeat_interleave(len(current_context_indexes), dim=0)
+
+                        if isinstance(controlnet_keep[i], list):
+                            cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
+                        else:
+                            controlnet_cond_scale = controlnet_conditioning_scale
+                            if isinstance(controlnet_cond_scale, list):
+                                controlnet_cond_scale = controlnet_cond_scale[0]
+                            cond_scale = controlnet_cond_scale * controlnet_keep[i]
+                            
+                                           
+                        control_model_input = torch.transpose(control_model_input, 1, 2)
+                        control_model_input = control_model_input.reshape(
+                            (-1, control_model_input.shape[2], control_model_input.shape[3], control_model_input.shape[4])
+                        )
+                        
+                        down_block_res_samples, mid_block_res_sample = self.controlnet(
+                            control_model_input,
+                            t,
+                            encoder_hidden_states=controlnet_prompt_embeds,
+                            controlnet_cond=current_context_conditioning_frames,
+                            conditioning_scale=cond_scale,
+                            guess_mode=guess_mode,
+                            return_dict=False,
+                        )
+                        
+                        # predict the noise residual with the added controlnet residuals
+                        noise_pred = self.unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=prompt_embeds_list[current_prompt_index],
+                            cross_attention_kwargs=cross_attention_kwargs,
+                            added_cond_kwargs=added_cond_kwargs,
+                            down_block_additional_residuals=down_block_res_samples,
+                            mid_block_additional_residual=mid_block_res_sample,
+                        ).sample
+                    
+                    else:
+                        noise_pred = self.unet(
+                            latent_model_input,
+                            t,
+                            encoder_hidden_states=prompt_embeds_list[current_prompt_index],
+                            cross_attention_kwargs=cross_attention_kwargs,
+                            added_cond_kwargs=added_cond_kwargs,
+                        ).sample
+
+                    if do_classifier_free_guidance:
+
+                        noise_pred_uncond, noise_pred_text = torch.chunk(noise_pred, 2, dim=0)
+                        
+                        # Perform batch addition
+                        noise_pred_uncond_sum[..., current_context_indexes, :, :] += noise_pred_uncond
+                        noise_pred_text_sum[..., current_context_indexes, :, :] += noise_pred_text
+                        latent_counter[current_context_indexes] += 1
+
+                    # set the step index to the current batch
+                    self.scheduler._step_index = i
+                
+                # perform guidance
+                if do_classifier_free_guidance:
+                    latent_counter = latent_counter.reshape(1, 1, num_frames, 1, 1)
+                    noise_pred_uncond = noise_pred_uncond_sum / latent_counter
+                    noise_pred_text = noise_pred_text_sum / latent_counter
+                    
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+                
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, None)
+        
+        if output_type == "latent":
+            return AnimateDiffPipelineOutput(frames=latents)
+
+        # save frames
+        if output_path is not None:
+            output_batch_size = 2 # prevents out of memory errors with large videos
+            num_digits = output_path.count('#')  # count the number of '#' characters
+            frame_format = output_path.replace('#' * num_digits, '{:0' + str(num_digits) + 'd}')
+            
+            # if we had more then one prompt, we need to offset the video frames back by number of inference steps
+            if len(prompt_embeds_list) > 1:
+                
+                # wrap the first n number of frames to the end of the video to fix the offseting from the context scheduler
+                offset_frames = num_inference_steps
+                print("Offsetting video frames by ", offset_frames)
+                latents = torch.cat((latents[:, :, offset_frames:, :, :], latents[:, :, :offset_frames, :, :]), dim=2)
+            
+            for batch in range((num_frames + output_batch_size - 1) // output_batch_size):
+                start_id = batch * output_batch_size
+                end_id = min((batch + 1) * output_batch_size, num_frames)
+                video_tensor = self.decode_latents(latents[:, :, start_id:end_id, :, :])
+
+                video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
+                for f_id, frame in enumerate(video[0]):
+                    frame.save(frame_format.format(start_id + f_id))
+            return output_path
+        
+            
+        # Post-processing
+        video_tensor = self.decode_latents(latents)
+
+        if output_type == "pt":
+            video = video_tensor
+        else:
+            video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return AnimateDiffPipelineOutput(frames=video)