commit

.gitattributes

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+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.bmp filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

@@ -0,0 +1,22 @@
+# Sci-Fi
+Official PyTorch implementation of "Sci-Fi: Symmetric Constraint for Frame Inbetweening"
+
+## Quick Start
+### 1. Setup repository and environment
+```
+git clone https://github.com/LiuhanChen-github/Sci-Fi.git
+cd Sci-Fi
+conda create -n Sci-Fi python==3.12
+pip install -r requirements.txt
+```
+### 2. Download checkpoint
+Download the CogVideoX-I2V-5B model (due to fine-tuning, the weights of the transformer denoiser are different from the original) and EF-Net. [checkpoint](https://drive.google.com/drive/folders/1H7vgiNVbxSeeleyJOqhoyRbJ97kGWGOK?usp=sharing)
+
+### 3. Launch the inference script!
+The example input keyframe pairs are in `examples/` folder, and 
+the corresponding generated videos (720x480, 49 frames) are placed in `outputs/` folder.
+</br>
+To interpolate, run:
+```
+bash Sci_Fi_frame_inbetweening.sh
+```

cogvideo_EF_Net.py

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+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from einops import rearrange
+import torch.nn.functional as F
+from diffusers.models.transformers.cogvideox_transformer_3d import Transformer2DModelOutput, CogVideoXBlock
+from diffusers.utils import is_torch_version
+from diffusers.loaders import  PeftAdapterMixin
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.embeddings import CogVideoXPatchEmbed, TimestepEmbedding, Timesteps, get_3d_sincos_pos_embed
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.attention import Attention, FeedForward
+from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor2_0
+from diffusers.models.normalization import AdaLayerNorm, CogVideoXLayerNormZero, AdaLayerNormZeroSingle
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+
+
+class CogVideoX_EF_Net(ModelMixin, ConfigMixin, PeftAdapterMixin):
+    _supports_gradient_checkpointing = True
+    
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 30,
+        attention_head_dim: int = 64,
+        vae_channels: int = 16,
+        in_channels: int = 3,
+        downscale_coef: int = 8,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        time_embed_dim: int = 512,
+        num_layers: int = 8,
+        dropout: float = 0.0,
+        attention_bias: bool = True,
+        sample_width: int = 90,
+        sample_height: int = 60,
+        sample_frames: int = 1,
+        patch_size: int = 2,
+        temporal_compression_ratio: int = 4,
+        max_text_seq_length: int = 226,
+        activation_fn: str = "gelu-approximate",
+        timestep_activation_fn: str = "silu",
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-5,
+        spatial_interpolation_scale: float = 1.875,
+        temporal_interpolation_scale: float = 1.0,
+        use_rotary_positional_embeddings: bool = False,
+        use_learned_positional_embeddings: bool = False,
+        out_proj_dim = None,
+    ):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+        out_proj_dim = inner_dim
+
+        if not use_rotary_positional_embeddings and use_learned_positional_embeddings:
+            raise ValueError(
+                "There are no CogVideoX checkpoints available with disable rotary embeddings and learned positional "
+                "embeddings. If you're using a custom model and/or believe this should be supported, please open an "
+                "issue at https://github.com/huggingface/diffusers/issues."
+            )
+            
+        # 1. Patch embedding
+        self.patch_embed = CogVideoXPatchEmbed(
+            patch_size=patch_size,
+            in_channels=vae_channels,
+            embed_dim=inner_dim,
+            bias=True,
+            sample_width=sample_width,
+            sample_height=sample_height,
+            sample_frames=49,
+            temporal_compression_ratio=temporal_compression_ratio,
+            spatial_interpolation_scale=spatial_interpolation_scale,
+            temporal_interpolation_scale=temporal_interpolation_scale,
+            use_positional_embeddings=not use_rotary_positional_embeddings,
+            use_learned_positional_embeddings=use_learned_positional_embeddings,
+        )
+        
+        self.patch_embed_first = CogVideoXPatchEmbed(
+            patch_size=patch_size,
+            in_channels=vae_channels,
+            embed_dim=inner_dim,
+            bias=True,
+            sample_width=sample_width,
+            sample_height=sample_height,
+            sample_frames=sample_frames,
+            temporal_compression_ratio=temporal_compression_ratio,
+            spatial_interpolation_scale=spatial_interpolation_scale,
+            temporal_interpolation_scale=temporal_interpolation_scale,
+            use_positional_embeddings=not use_rotary_positional_embeddings,
+            use_learned_positional_embeddings=use_learned_positional_embeddings,
+        )
+        
+        self.embedding_dropout = nn.Dropout(dropout)
+        self.weights = nn.ModuleList([nn.Linear(inner_dim, 13) for _ in range(num_layers)])
+        self.first_weights = nn.ModuleList([nn.Linear(2*inner_dim, inner_dim) for _ in range(num_layers)])
+
+        # 2. Time embeddings
+        self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift)
+        self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn)
+
+        # 3. Define spatio-temporal transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                CogVideoXBlock(
+                    dim=inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    time_embed_dim=time_embed_dim,
+                    dropout=dropout,
+                    activation_fn=activation_fn,
+                    attention_bias=attention_bias,
+                    norm_elementwise_affine=norm_elementwise_affine,
+                    norm_eps=norm_eps,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        self.out_projectors = None
+        self.relu = nn.LeakyReLU(negative_slope=0.01)
+        
+        if out_proj_dim is not None:
+            self.out_projectors = nn.ModuleList(
+                [nn.Linear(inner_dim, out_proj_dim) for _ in range(num_layers)]
+            )
+        
+        self.gradient_checkpointing = False
+        
+    def _set_gradient_checkpointing(self, enable=False, gradient_checkpointing_func=None):
+        self.gradient_checkpointing = enable
+
+        
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        EF_Net_states: torch.Tensor,
+        timestep: Union[int, float, torch.LongTensor],
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ):
+        batch_size, num_frames, channels, height, width = EF_Net_states.shape
+        o_hidden_states = hidden_states
+        hidden_states = EF_Net_states
+        encoder_hidden_states_ = encoder_hidden_states
+        
+        # 1. Time embedding
+        timesteps = timestep
+        t_emb = self.time_proj(timesteps)
+        
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=hidden_states.dtype)
+        emb = self.time_embedding(t_emb, timestep_cond)
+        
+        hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
+        hidden_states = self.embedding_dropout(hidden_states)
+
+        text_seq_length = encoder_hidden_states.shape[1]
+        encoder_hidden_states = hidden_states[:, :text_seq_length]
+        hidden_states = hidden_states[:, text_seq_length:]
+        
+        o_hidden_states = self.patch_embed_first(encoder_hidden_states_, o_hidden_states)
+        o_hidden_states = self.embedding_dropout(o_hidden_states)
+
+        text_seq_length = encoder_hidden_states_.shape[1]
+        o_hidden_states = o_hidden_states[:, text_seq_length:]
+        
+        EF_Net_hidden_states = ()
+        # 2. Transformer blocks
+        for i, block in enumerate(self.transformer_blocks):
+            #if self.training and self.gradient_checkpointing:
+            if self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    emb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=emb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+            
+            
+            if self.out_projectors is not None:
+                coff = self.weights[i](hidden_states)
+                temp_list = []
+                for j in range(coff.shape[2]):
+                    temp_list.append(hidden_states*coff[:,:,j:(j+1)])
+                out = torch.concat(temp_list, dim=1)
+                out = torch.concat([out, o_hidden_states], dim=2)
+                out = self.first_weights[i](out)
+                out = self.relu(out)
+                out = self.out_projectors[i](out)
+                EF_Net_hidden_states += (out,)
+            else:
+                out = torch.concat([weight*hidden_states for weight in self.weights], dim=1)
+                EF_Net_hidden_states += (out,)
+              
+        if not return_dict:
+            return (EF_Net_hidden_states,)
+        return Transformer2DModelOutput(sample=EF_Net_hidden_states)
+
+

cogvideo_Sci_Fi_inbetweening_pipeline.py

@@ -0,0 +1,824 @@
+import inspect
+import math
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import numpy as np
+import PIL
+from PIL import Image
+from torchvision import transforms
+from einops import rearrange, repeat
+from transformers import T5EncoderModel, T5Tokenizer
+from diffusers.video_processor import VideoProcessor
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.models.embeddings import get_3d_rotary_pos_embed
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
+from diffusers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler, CogVideoXImageToVideoPipeline
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.pipelines.cogvideo.pipeline_cogvideox import CogVideoXPipelineOutput, CogVideoXLoraLoaderMixin
+
+from cogvideo_EF_Net import CogVideoX_EF_Net
+import torch
+
+def resize_for_crop(image, crop_h, crop_w):
+    img_h, img_w = image.shape[-2:]
+    if img_h >= crop_h and img_w >= crop_w:
+        coef = max(crop_h / img_h, crop_w / img_w)
+    elif img_h <= crop_h and img_w <= crop_w:
+        coef = max(crop_h / img_h, crop_w / img_w)
+    else:
+        coef = crop_h / img_h if crop_h > img_h else crop_w / img_w 
+    out_h, out_w = int(img_h * coef), int(img_w * coef)
+    resized_image = transforms.functional.resize(image, (out_h, out_w), antialias=True)
+    return resized_image
+
+
+def prepare_frames(input_images, video_size, do_resize=True, do_crop=True):
+    images_tensor = input_images
+    if do_resize:
+        images_tensor = [resize_for_crop(x, crop_h=video_size[0], crop_w=video_size[1]) for x in images_tensor]
+    if do_crop:
+        images_tensor = [transforms.functional.center_crop(x, video_size) for x in images_tensor]
+    if isinstance(images_tensor, list):
+        images_tensor = torch.stack(images_tensor)
+    print(images_tensor.shape)
+    return images_tensor.unsqueeze(0) 
+
+
+def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
+    tw = tgt_width
+    th = tgt_height
+    h, w = src
+    r = h / w
+    if r > (th / tw):
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+# 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,
+):
+    """
+    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
+    
+
+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 CogVideoXEFNetInbetweeningPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
+    r"""
+    Pipeline for frame inbetweening generation using CogVideoX.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder. CogVideoX uses
+            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the
+            [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
+        tokenizer (`T5Tokenizer`):
+            Tokenizer of class
+            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+        transformer ([`CogVideoXTransformer3DModel`]):
+            A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
+        EF-Net ([CogVideoX_EF_Net]):
+            Our proposed EF-Net.
+    """
+
+    _optional_components = []
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+    ]
+
+    def __init__(
+        self,
+        tokenizer: T5Tokenizer,
+        text_encoder: T5EncoderModel,
+        vae: AutoencoderKLCogVideoX,
+        transformer: CogVideoXTransformer3DModel,
+        EF_Net: CogVideoX_EF_Net,
+        scheduler: CogVideoXDDIMScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            vae=vae,
+            transformer=transformer,
+            EF_Net=EF_Net,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor_spatial = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
+        self.vae_scale_factor_temporal = (
+            self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4
+        )
+        self.vae_scaling_factor_image = (
+            self.vae.config.scaling_factor if hasattr(self, "vae") and self.vae is not None else 0.7
+        )
+
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 226,
+        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
+        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)
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 226,
+        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 = 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 = 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, negative_prompt_embeds
+
+    def prepare_latents(
+        self,
+        first_image: torch.Tensor,
+        last_image: torch.Tensor,
+        batch_size: int = 1,
+        num_channels_latents: int = 16,
+        num_frames: int = 13,
+        height: int = 60,
+        width: int = 90,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.Tensor] = None,
+    ):
+        num_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+        shape = (
+            batch_size,
+            num_frames,
+            num_channels_latents,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+        )
+
+        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."
+            )
+
+        first_image = first_image.unsqueeze(2)  # [B, C, F, H, W]
+        last_image = last_image.unsqueeze(2)  # [B, C, F, H, W] 
+
+        if isinstance(generator, list):
+            first_image_latents = [
+                retrieve_latents(self.vae.encode(first_image[i].unsqueeze(0)), generator[i]) for i in range(batch_size)
+            ]
+        else:
+            first_image_latents = [retrieve_latents(self.vae.encode(first_img.unsqueeze(0)), generator) for first_img in first_image]
+        
+        if isinstance(generator, list):
+            last_image_latents = [
+                retrieve_latents(self.vae.encode(last_image[i].unsqueeze(0)), generator[i]) for i in range(batch_size)
+            ]
+        else:
+            last_image_latents = [retrieve_latents(self.vae.encode(last_img.unsqueeze(0)), generator) for last_img in last_image]
+
+
+        first_image_latents = torch.cat(first_image_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4)  # [B, F, C, H, W]
+        first_image_latents = self.vae.config.scaling_factor * first_image_latents 
+        last_image_latents = torch.cat(last_image_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4)  # [B, F, C, H, W]
+        last_image_latents = self.vae.config.scaling_factor * last_image_latents 
+
+
+        padding_shape = (
+            batch_size,
+            num_frames - 2,
+            num_channels_latents,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+        )
+        latent_padding = torch.zeros(padding_shape, device=device, dtype=dtype)
+        image_latents = torch.cat([first_image_latents, latent_padding, last_image_latents], dim=1)
+
+        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, image_latents
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.decode_latents
+    def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:
+        latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
+        latents = 1 / self.vae_scaling_factor_image * latents
+
+        frames = self.vae.decode(latents).sample
+        return frames
+
+    # 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
+
+    # 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
+
+    def check_inputs(
+        self,
+        image,
+        prompt,
+        height,
+        width,
+        negative_prompt,
+        callback_on_step_end_tensor_inputs,
+        latents=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if (
+            not isinstance(image, torch.Tensor)
+            and not isinstance(image, PIL.Image.Image)
+            and not isinstance(image, list)
+        ):
+            raise ValueError(
+                "`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
+                f" {type(image)}"
+            )
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        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 is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        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.cogvideo.pipeline_cogvideox.CogVideoXPipeline.fuse_qkv_projections
+    def fuse_qkv_projections(self) -> None:
+        r"""Enables fused QKV projections."""
+        self.fusing_transformer = True
+        self.transformer.fuse_qkv_projections()
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.unfuse_qkv_projections
+    def unfuse_qkv_projections(self) -> None:
+        r"""Disable QKV projection fusion if enabled."""
+        if not self.fusing_transformer:
+            logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.")
+        else:
+            self.transformer.unfuse_qkv_projections()
+            self.fusing_transformer = False
+
+    # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._prepare_rotary_positional_embeddings
+    def _prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        p = self.transformer.config.patch_size
+        p_t = self.transformer.config.patch_size_t
+
+        base_size_width = self.transformer.config.sample_width // p
+        base_size_height = self.transformer.config.sample_height // p
+
+        if p_t is None:
+            # CogVideoX 1.0
+            grid_crops_coords = get_resize_crop_region_for_grid(
+                (grid_height, grid_width), base_size_width, base_size_height
+            )
+            freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+                embed_dim=self.transformer.config.attention_head_dim,
+                crops_coords=grid_crops_coords,
+                grid_size=(grid_height, grid_width),
+                temporal_size=num_frames,
+            )
+        else:
+            # CogVideoX 1.5
+            base_num_frames = (num_frames + p_t - 1) // p_t
+
+            freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
+                embed_dim=self.transformer.config.attention_head_dim,
+                crops_coords=None,
+                grid_size=(grid_height, grid_width),
+                temporal_size=base_num_frames,
+                grid_type="slice",
+                max_size=(base_size_height, base_size_width),
+            )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+    def prepare_EF_Net_frames(self, EF_Net_frames, height, width, do_classifier_free_guidance):
+        prepared_frames = prepare_frames(EF_Net_frames, (height, width))
+        EF_Net_encoded_frames = prepared_frames.to(dtype=self.vae.dtype, device='cuda')
+        EF_Net_encoded_frames = EF_Net_encoded_frames.permute(0, 2, 1, 3, 4)  # [B, C, F, H, W]
+        EF_Net_encoded_frames = self.vae.encode(EF_Net_encoded_frames).latent_dist.sample() * self.vae.config.scaling_factor
+        
+        EF_Net_encoded_frames = EF_Net_encoded_frames.permute(0, 2, 1, 3, 4).to(memory_format=torch.contiguous_format)
+        EF_Net_encoded_frames = torch.cat([EF_Net_encoded_frames] * 2) if do_classifier_free_guidance else EF_Net_encoded_frames
+        
+        return EF_Net_encoded_frames.contiguous()
+        
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @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()
+    def __call__(
+        self,
+        first_image,
+        last_image,
+        EF_Net_frames = None,
+        prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_frames: int = 49,
+        num_inference_steps: int = 50,
+        timesteps: Optional[List[int]] = None,
+        guidance_scale: float = 6,
+        use_dynamic_cfg: bool = False,
+        num_videos_per_prompt: 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,
+        EF_Net_latents: Optional[torch.FloatTensor] = None,
+        output_type: str = "pil",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 226,
+        EF_Net_weights: Optional[Union[float, list, np.ndarray, torch.FloatTensor]] = 1.0,
+        EF_Net_guidance_start: float = 0.0,
+        EF_Net_guidance_end: float = 1.0,
+    ) -> Union[CogVideoXPipelineOutput, Tuple]:
+        
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial
+        width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial
+        num_frames = num_frames or self.transformer.config.sample_frames
+
+        num_videos_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            image=first_image,
+            prompt=prompt,
+            height=height,
+            width=width,
+            negative_prompt=negative_prompt,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            latents=latents,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+        )
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._interrupt = False
+
+        # 2. Default 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
+
+        # 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
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+        
+        self._num_timesteps = len(timesteps)
+
+        # 5. Prepare latents
+        latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+    
+        first_image = self.video_processor.preprocess(first_image, height=height, width=width).to(
+            device, dtype=prompt_embeds.dtype
+        )
+        last_image = self.video_processor.preprocess(last_image, height=height, width=width).to(
+            device, dtype=prompt_embeds.dtype
+        )
+
+        latent_channels = self.transformer.config.in_channels // 2
+        latents, image_latents = self.prepare_latents(
+            first_image,
+            last_image,
+            batch_size * num_videos_per_prompt,
+            latent_channels,
+            num_frames,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            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. Create rotary embeds if required
+        image_rotary_emb = (
+            self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)
+            if self.transformer.config.use_rotary_positional_embeddings
+            else None
+        )
+
+        # 8. Create ofs embeds if required
+        ofs_emb = None if self.transformer.config.ofs_embed_dim is None else latents.new_full((1,), fill_value=2.0)
+
+        # 9. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            # for DPM-solver++
+            old_pred_original_sample = None
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+                
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                latent_image_input = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents
+                latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                current_sampling_percent = i / len(timesteps)
+                
+                EF_Net_states = []
+                if (EF_Net_guidance_start <= current_sampling_percent < EF_Net_guidance_end):
+                    # extract EF_Net hidden state
+                    EF_Net_states = self.EF_Net(
+                        hidden_states=latent_image_input[:,:,0:16,:,:],
+                        encoder_hidden_states=prompt_embeds,
+                        image_rotary_emb=None,
+                        EF_Net_states=latent_image_input[:,12::,:,:,:],
+                        timestep=timestep,
+                        return_dict=False,
+                    )[0]
+                    if isinstance(EF_Net_states, (tuple, list)):
+                        EF_Net_states = [x.to(dtype=self.transformer.dtype) for x in EF_Net_states]
+                    else:
+                        EF_Net_states = EF_Net_states.to(dtype=self.transformer.dtype)
+                        
+                # predict noise model_output
+                
+                noise_pred = self.transformer(
+                        hidden_states=latent_model_input,
+                        encoder_hidden_states=prompt_embeds,
+                        timestep=timestep,
+                        # ofs=ofs_emb,
+                        image_rotary_emb=image_rotary_emb,
+                        # attention_kwargs=attention_kwargs,
+                        EF_Net_states=EF_Net_states,
+                        EF_Net_weights=EF_Net_weights,
+                        return_dict=False,
+                )[0]
+                noise_pred = noise_pred.float()
+                
+                # perform guidance
+                if use_dynamic_cfg:
+                    self._guidance_scale = 1 + guidance_scale * (
+                        (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
+                    )
+                if 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)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                if not isinstance(self.scheduler, CogVideoXDPMScheduler):
+                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                    
+                else:
+                    latents, old_pred_original_sample = self.scheduler.step(
+                        noise_pred,
+                        old_pred_original_sample,
+                        t,
+                        timesteps[i - 1] if i > 0 else None,
+                        latents,
+                        **extra_step_kwargs,
+                        return_dict=False,
+                    )
+                latents = latents.to(prompt_embeds.dtype)
+
+                # call the callback, if provided
+                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)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if not output_type == "latent":
+            video = self.decode_latents(latents.to(torch.bfloat16))
+            video = self.video_processor.postprocess_video(video=video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return CogVideoXPipelineOutput(frames=video)

cogvideo_transformer.py

@@ -0,0 +1,113 @@
+from typing import Any, Dict, Optional, Tuple, Union
+
+import torch
+import numpy as np
+from diffusers.utils import is_torch_version
+from diffusers.models.transformers.cogvideox_transformer_3d import CogVideoXTransformer3DModel, Transformer2DModelOutput
+
+
+class CustomCogVideoXTransformer3DModel(CogVideoXTransformer3DModel):        
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: Union[int, float, torch.LongTensor],
+        start_frame = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        EF_Net_states: torch.Tensor = None,
+        EF_Net_weights: Optional[Union[float, int, list, np.ndarray, torch.FloatTensor]] = 1.0,
+        return_dict: bool = True,
+    ):
+        batch_size, num_frames, channels, height, width = hidden_states.shape
+
+        if start_frame is not None:
+            hidden_states = torch.cat([start_frame, hidden_states], dim=2)
+        # 1. Time embedding
+        timesteps = timestep
+        
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=hidden_states.dtype)
+        emb = self.time_embedding(t_emb, timestep_cond)
+        
+        
+        # 2. Patch embedding
+        hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
+        hidden_states = self.embedding_dropout(hidden_states)
+
+        text_seq_length = encoder_hidden_states.shape[1]
+        encoder_hidden_states = hidden_states[:, :text_seq_length]
+        hidden_states = hidden_states[:, text_seq_length:]
+
+        # 3. Transformer blocks
+        for i, block in enumerate(self.transformer_blocks):
+            #if self.training and self.gradient_checkpointing:
+            if self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    emb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=emb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+            
+            if (EF_Net_states is not None) and (i < len(EF_Net_states)):
+                EF_Net_states_block = EF_Net_states[i]
+                EF_Net_block_weight = 1.0
+                
+                if isinstance(EF_Net_weights, (float, int)):
+                    EF_Net_block_weight = EF_Net_weights
+                else:
+                    EF_Net_block_weight = EF_Net_weights[i]
+                
+                
+                hidden_states = hidden_states + EF_Net_states_block * EF_Net_block_weight
+            
+            
+        if not self.config.use_rotary_positional_embeddings:
+            hidden_states = self.norm_final(hidden_states)
+        else:
+            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+            hidden_states = self.norm_final(hidden_states)
+            hidden_states = hidden_states[:, text_seq_length:]
+
+        # 4. Final block
+        hidden_states = self.norm_out(hidden_states, temb=emb)
+        hidden_states = self.proj_out(hidden_states)
+
+        # 5. Unpatchify
+        p = self.config.patch_size
+        p_t = self.config.patch_size_t
+
+        if p_t is None:
+            output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
+            output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
+        else:
+            output = hidden_states.reshape(
+                batch_size, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
+            )
+            output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
+
+        if not return_dict:
+            return (output,)
+        return Transformer2DModelOutput(sample=output)

requirements.txt

@@ -0,0 +1,21 @@
+spaces>=0.29.3
+safetensors>=0.4.5
+spandrel>=0.4.0
+tqdm>=4.66.5
+scikit-video>=1.1.11
+diffusers==0.32.0
+transformers>=4.44.0
+accelerate>=0.34.2
+opencv-python>=4.10.0.84
+sentencepiece>=0.2.0
+numpy==1.26.0
+torch>=2.4.0
+torchvision>=0.19.0
+gradio>=4.44.0
+imageio>=2.34.2
+imageio-ffmpeg>=0.5.1
+openai>=1.45.0
+moviepy>=1.0.3
+pillow>=9.5.0
+denku==0.0.51
+decord==0.6.0