initial CogVideoX-Fun 1.1 support

every possible model/workflow combination not tested yet

cogvideox_fun/fun_pab_transformer_3d.py

@@ -453,6 +453,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
         spatial_interpolation_scale: float = 1.875,
         temporal_interpolation_scale: float = 1.0,
         use_rotary_positional_embeddings: bool = False,
+        add_noise_in_inpaint_model: bool = False,
     ):
         super().__init__()
         inner_dim = num_attention_heads * attention_head_dim
@@ -568,6 +569,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
         timestep: Union[int, float, torch.LongTensor],
         timestep_cond: Optional[torch.Tensor] = None,
         inpaint_latents: Optional[torch.Tensor] = None,
+        control_latents: Optional[torch.Tensor] = None,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         return_dict: bool = True,
     ):
@@ -586,6 +588,8 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
         # 2. Patch embedding
         if inpaint_latents is not None:
             hidden_states = torch.concat([hidden_states, inpaint_latents], 2)
+        if control_latents is not None:
+            hidden_states = torch.concat([hidden_states, control_latents], 2)
         hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
 
         # 3. Position embedding

cogvideox_fun/pipeline_cogvideox_control.py

@@ -0,0 +1,707 @@
+# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
+# All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import math
+from dataclasses import dataclass
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel
+from diffusers.models.embeddings import get_3d_rotary_pos_embed
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
+from diffusers.utils import BaseOutput, logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from diffusers.image_processor import VaeImageProcessor
+from einops import rearrange
+
+from ..videosys.core.pipeline import VideoSysPipeline
+from ..videosys.cogvideox_transformer_3d import CogVideoXTransformer3DModel as CogVideoXTransformer3DModelPAB
+from ..videosys.core.pab_mgr import set_pab_manager
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers import CogVideoX_Fun_Pipeline
+        >>> from diffusers.utils import export_to_video
+
+        >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b"
+        >>> pipe = CogVideoX_Fun_Pipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda")
+        >>> prompt = (
+        ...     "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
+        ...     "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
+        ...     "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
+        ...     "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
+        ...     "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
+        ...     "atmosphere of this unique musical performance."
+        ... )
+        >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
+        >>> export_to_video(video, "output.mp4", fps=8)
+        ```
+"""
+
+
+# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid
+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
+
+
+@dataclass
+class CogVideoX_Fun_PipelineOutput(BaseOutput):
+    r"""
+    Output class for CogVideo pipelines.
+
+    Args:
+        video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
+            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
+            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
+            `(batch_size, num_frames, channels, height, width)`.
+    """
+
+    videos: torch.Tensor
+
+
+class CogVideoX_Fun_Pipeline_Control(VideoSysPipeline):
+    r"""
+    Pipeline for text-to-video 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.
+        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.
+    """
+
+    _optional_components = []
+    model_cpu_offload_seq = "vae->transformer->vae"
+
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKLCogVideoX,
+        transformer: CogVideoXTransformer3DModel,
+        scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
+        pab_config = None
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae, transformer=transformer, 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.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+        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.mask_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
+        )
+
+        if pab_config is not None:
+            set_pab_manager(pab_config)
+
+    def prepare_latents(
+        self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
+    ):
+        shape = (
+            batch_size,
+            (num_frames - 1) // self.vae_scale_factor_temporal + 1,
+            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."
+            )
+
+        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_control_latents(
+        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
+    ):
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+
+        if mask is not None:
+            mask = mask.to(device=device, dtype=self.vae.dtype)
+            bs = 1
+            new_mask = []
+            for i in range(0, mask.shape[0], bs):
+                mask_bs = mask[i : i + bs]
+                mask_bs = self.vae.encode(mask_bs)[0]
+                mask_bs = mask_bs.mode()
+                new_mask.append(mask_bs)
+            mask = torch.cat(new_mask, dim = 0)
+            mask = mask * self.vae.config.scaling_factor
+
+        if masked_image is not None:
+            masked_image = masked_image.to(device=device, dtype=self.vae.dtype)
+            bs = 1
+            new_mask_pixel_values = []
+            for i in range(0, masked_image.shape[0], bs):
+                mask_pixel_values_bs = masked_image[i : i + bs]
+                mask_pixel_values_bs = self.vae.encode(mask_pixel_values_bs)[0]
+                mask_pixel_values_bs = mask_pixel_values_bs.mode()
+                new_mask_pixel_values.append(mask_pixel_values_bs)
+            masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0)
+            masked_image_latents = masked_image_latents * self.vae.config.scaling_factor
+        else:
+            masked_image_latents = None
+
+        return mask, masked_image_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.config.scaling_factor * latents
+
+        frames = self.vae.decode(latents).sample
+        frames = (frames / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        frames = frames.cpu().float().numpy()
+        return frames
+
+    # 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.latte.pipeline_latte.LattePipeline.check_inputs
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt,
+        callback_on_step_end_tensor_inputs,
+        prompt_embeds=None,
+        negative_prompt_embeds=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_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}."
+                )
+
+    def fuse_qkv_projections(self) -> None:
+        r"""Enables fused QKV projections."""
+        self.fusing_transformer = True
+        self.transformer.fuse_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
+
+    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)
+        base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+        base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
+
+        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,
+            use_real=True,
+        )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, 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 = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+        return timesteps, num_inference_steps - t_start
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        height: int = 480,
+        width: int = 720,
+        video: Union[torch.FloatTensor] = None,
+        control_video: Union[torch.FloatTensor] = 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,
+        output_type: str = "numpy",
+        return_dict: bool = False,
+        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,
+        comfyui_progressbar: bool = False,
+    ) -> Union[CogVideoX_Fun_PipelineOutput, Tuple]:
+        """
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            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`).
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_frames (`int`, defaults to `48`):
+                Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
+                contain 1 extra frame because CogVideoX_Fun is conditioned with (num_seconds * fps + 1) frames where
+                num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
+                needs to be satisfied is that of divisibility mentioned above.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of videos to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.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.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, defaults to `226`):
+                Maximum sequence length in encoded prompt. Must be consistent with
+                `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] or `tuple`:
+            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        if num_frames > 49:
+            raise ValueError(
+                "The number of frames must be less than 49 for now due to static positional embeddings. This will be updated in the future to remove this limitation."
+            )
+
+        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_size * self.vae_scale_factor_spatial
+        width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial
+        num_videos_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt,
+            callback_on_step_end_tensor_inputs,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+        self._guidance_scale = guidance_scale
+        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
+
+        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)
+        if comfyui_progressbar:
+            from comfy.utils import ProgressBar
+            pbar = ProgressBar(num_inference_steps + 2)
+
+        # 5. Prepare latents.
+        latent_channels = self.vae.config.latent_channels
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            latent_channels,
+            num_frames,
+            height,
+            width,
+            self.vae.dtype,
+            device,
+            generator,
+            latents,
+        )
+        if comfyui_progressbar:
+            pbar.update(1)
+
+        if control_video is not None:
+            video_length = control_video.shape[2]
+            control_video = self.image_processor.preprocess(rearrange(control_video, "b c f h w -> (b f) c h w"), height=height, width=width) 
+            control_video = control_video.to(dtype=torch.float32)
+            control_video = rearrange(control_video, "(b f) c h w -> b c f h w", f=video_length)
+        else:
+            control_video = None
+        control_video_latents = self.prepare_control_latents(
+            None,
+            control_video,
+            batch_size,
+            height,
+            width,
+            self.vae.dtype,
+            device,
+            generator,
+            do_classifier_free_guidance
+        )[1]
+        control_video_latents_input = (
+            torch.cat([control_video_latents] * 2) if do_classifier_free_guidance else control_video_latents
+        )
+        control_latents = rearrange(control_video_latents_input, "b c f h w -> b f c h w")
+
+        if comfyui_progressbar:
+            pbar.update(1)
+
+        # 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. 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)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                # predict noise model_output
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep=timestep,
+                    image_rotary_emb=image_rotary_emb,
+                    return_dict=False,
+                    control_latents=control_latents,
+                )[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 comfyui_progressbar:
+                    pbar.update(1)
+
+        # if output_type == "numpy":
+        #     video = self.decode_latents(latents)
+        # elif not output_type == "latent":
+        #     video = self.decode_latents(latents)
+        #     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:
+        #     video = torch.from_numpy(video)
+
+        return latents

cogvideox_fun/pipeline_cogvideox_inpaint.py

@@ -179,6 +179,17 @@ def resize_mask(mask, latent, process_first_frame_only=True):
         )
     return resized_mask
 
+def add_noise_to_reference_video(image, ratio=None):
+    if ratio is None:
+        sigma = torch.normal(mean=-3.0, std=0.5, size=(image.shape[0],)).to(image.device)
+        sigma = torch.exp(sigma).to(image.dtype)
+    else:
+        sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio
+    
+    image_noise = torch.randn_like(image) * sigma[:, None, None, None, None]
+    image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise)
+    image = image + image_noise
+    return image
 
 @dataclass
 class CogVideoX_Fun_PipelineOutput(BaseOutput):
@@ -212,7 +223,7 @@ class CogVideoX_Fun_Pipeline_Inpaint(VideoSysPipeline):
     """
 
     _optional_components = []
-    model_cpu_offload_seq = ">vae->transformer->vae"
+    model_cpu_offload_seq = "vae->transformer->vae"
 
     _callback_tensor_inputs = [
         "latents",
@@ -319,7 +330,7 @@ class CogVideoX_Fun_Pipeline_Inpaint(VideoSysPipeline):
         return outputs
 
     def prepare_mask_latents(
-        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
+        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance, noise_aug_strength
     ):
         # resize the mask to latents shape as we concatenate the mask to the latents
         # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
@@ -338,6 +349,8 @@ class CogVideoX_Fun_Pipeline_Inpaint(VideoSysPipeline):
             mask = mask * self.vae.config.scaling_factor
 
         if masked_image is not None:
+            if self.transformer.config.add_noise_in_inpaint_model:
+                masked_image = add_noise_to_reference_video(masked_image, ratio=noise_aug_strength)
             masked_image = masked_image.to(device=device, dtype=self.vae.dtype)
             bs = 1
             new_mask_pixel_values = []
@@ -525,6 +538,7 @@ class CogVideoX_Fun_Pipeline_Inpaint(VideoSysPipeline):
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 226,
         strength: float = 1,
+        noise_aug_strength: float = 0.0563,
         comfyui_progressbar: bool = False,
     ) -> Union[CogVideoX_Fun_PipelineOutput, Tuple]:
         """
@@ -732,6 +746,7 @@ class CogVideoX_Fun_Pipeline_Inpaint(VideoSysPipeline):
                         device,
                         generator,
                         do_classifier_free_guidance,
+                        noise_aug_strength=noise_aug_strength,
                     )
                     mask_latents = resize_mask(1 - mask_condition, masked_video_latents)
                     mask_latents = mask_latents.to(masked_video_latents.device) * self.vae.config.scaling_factor

cogvideox_fun/transformer_3d.py

@@ -277,6 +277,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
         spatial_interpolation_scale: float = 1.875,
         temporal_interpolation_scale: float = 1.0,
         use_rotary_positional_embeddings: bool = False,
+        add_noise_in_inpaint_model: bool = False,
     ):
         super().__init__()
         inner_dim = num_attention_heads * attention_head_dim
@@ -452,6 +453,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
         timestep: Union[int, float, torch.LongTensor],
         timestep_cond: Optional[torch.Tensor] = None,
         inpaint_latents: Optional[torch.Tensor] = None,
+        control_latents: Optional[torch.Tensor] = None,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         return_dict: bool = True,
     ):
@@ -470,6 +472,8 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin):
         # 2. Patch embedding
         if inpaint_latents is not None:
             hidden_states = torch.concat([hidden_states, inpaint_latents], 2)
+        if control_latents is not None:
+            hidden_states = torch.concat([hidden_states, control_latents], 2)
         hidden_states = self.patch_embed(encoder_hidden_states, hidden_states)
 
         # 3. Position embedding

cogvideox_fun/utils.py

@@ -159,13 +159,21 @@ def get_image_to_video_latent(validation_image_start, validation_image_end, vide
 
     return  input_video, input_video_mask, clip_image
 
-def get_video_to_video_latent(input_video_path, video_length, sample_size):
+def get_video_to_video_latent(input_video_path, video_length, sample_size, validation_video_mask=None):
     input_video = input_video_path
 
     input_video = torch.from_numpy(np.array(input_video))[:video_length]
     input_video = input_video.permute([3, 0, 1, 2]).unsqueeze(0) / 255
 
-    input_video_mask = torch.zeros_like(input_video[:, :1])
-    input_video_mask[:, :, :] = 255
+    if validation_video_mask is not None:
+        validation_video_mask = Image.open(validation_video_mask).convert('L').resize((sample_size[1], sample_size[0]))
+        input_video_mask = np.where(np.array(validation_video_mask) < 240, 0, 255)
+        
+        input_video_mask = torch.from_numpy(np.array(input_video_mask)).unsqueeze(0).unsqueeze(-1).permute([3, 0, 1, 2]).unsqueeze(0)
+        input_video_mask = torch.tile(input_video_mask, [1, 1, input_video.size()[2], 1, 1])
+        input_video_mask = input_video_mask.to(input_video.device, input_video.dtype)
+    else:
+        input_video_mask = torch.zeros_like(input_video[:, :1])
+        input_video_mask[:, :, :] = 255
 
     return  input_video, input_video_mask, None

nodes.py

@@ -52,6 +52,7 @@ from .cogvideox_fun.fun_pab_transformer_3d import CogVideoXTransformer3DModel as
 from .cogvideox_fun.autoencoder_magvit import AutoencoderKLCogVideoX as AutoencoderKLCogVideoXFun
 from .cogvideox_fun.utils import get_image_to_video_latent, get_video_to_video_latent, ASPECT_RATIO_512, get_closest_ratio, to_pil
 from .cogvideox_fun.pipeline_cogvideox_inpaint import CogVideoX_Fun_Pipeline_Inpaint
+from .cogvideox_fun.pipeline_cogvideox_control import CogVideoX_Fun_Pipeline_Control
 from PIL import Image
 import numpy as np
 import json
@@ -216,6 +217,10 @@ class DownloadAndLoadCogVideoModel:
                         "bertjiazheng/KoolCogVideoX-5b",
                         "kijai/CogVideoX-Fun-2b",
                         "kijai/CogVideoX-Fun-5b",
+                        "alibaba-pai/CogVideoX-Fun-V1.1-2b-InP",
+                        "alibaba-pai/CogVideoX-Fun-V1.1-5b-InP",
+                        "alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose",
+                        "alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose",
                     ],
                 ),
 
@@ -246,31 +251,38 @@ class DownloadAndLoadCogVideoModel:
         mm.soft_empty_cache()
 
         dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
-
+        download_path = os.path.join(folder_paths.models_dir, "CogVideo")
         if "Fun" in model:
-            repo_id = "kijai/CogVideoX-Fun-pruned"
-            download_path = os.path.join(folder_paths.models_dir, "CogVideo")
-            if "2b" in model:
-                base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-2b-InP") # location of the official model
-                scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
+            if not "1.1" in model:
+                repo_id = "kijai/CogVideoX-Fun-pruned"
+                if "2b" in model:
+                    base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-2b-InP") # location of the official model
+                    if not os.path.exists(base_path):
+                        base_path = os.path.join(download_path, "CogVideoX-Fun-2b-InP")
+                elif "5b" in model:
+                    base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-5b-InP") # location of the official model
+                    if not os.path.exists(base_path):
+                        base_path = os.path.join(download_path, "CogVideoX-Fun-5b-InP")
+            elif "1.1" in model:
+                repo_id = model
+                base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", (model.split("/")[-1])) # location of the official model
                 if not os.path.exists(base_path):
-                    base_path = os.path.join(download_path, "CogVideoX-Fun-2b-InP")
-            elif "5b" in model:
-                base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-5b-InP") # location of the official model
-                scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
-                if not os.path.exists(base_path):
-                    base_path = os.path.join(download_path, "CogVideoX-Fun-5b-InP")
-            
+                    base_path = os.path.join(download_path, (model.split("/")[-1]))
+                download_path = base_path
+
         elif "2b" in model:
             base_path = os.path.join(folder_paths.models_dir, "CogVideo", "CogVideo2B")
-            scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
             download_path = base_path
             repo_id = model
         elif "5b" in model:
             base_path = os.path.join(folder_paths.models_dir, "CogVideo", (model.split("/")[-1]))
-            scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
             download_path = base_path
             repo_id = model
+
+        if "2b" in model:
+            scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_2b.json')
+        elif "5b" in model:
+            scheduler_path = os.path.join(script_directory, 'configs', 'scheduler_config_5b.json')
         
         if not os.path.exists(base_path):
             log.info(f"Downloading model to: {base_path}")
@@ -323,7 +335,10 @@ class DownloadAndLoadCogVideoModel:
         # VAE
         if "Fun" in model:
             vae = AutoencoderKLCogVideoXFun.from_pretrained(base_path, subfolder="vae").to(dtype).to(offload_device)
-            pipe = CogVideoX_Fun_Pipeline_Inpaint(vae, transformer, scheduler, pab_config=pab_config)
+            if "Pose" in model:
+                pipe = CogVideoX_Fun_Pipeline_Control(vae, transformer, scheduler, pab_config=pab_config)
+            else:
+                pipe = CogVideoX_Fun_Pipeline_Inpaint(vae, transformer, scheduler, pab_config=pab_config)
         else:
             vae = AutoencoderKLCogVideoX.from_pretrained(base_path, subfolder="vae").to(dtype).to(offload_device)
             pipe = CogVideoXPipeline(vae, transformer, scheduler, pab_config=pab_config)
@@ -369,7 +384,7 @@ class DownloadAndLoadCogVideoGGUFModel:
                         "CogVideoX_5b_GGUF_Q4_0.safetensors",
                         "CogVideoX_5b_I2V_GGUF_Q4_0.safetensors",
                         "CogVideoX_5b_fun_GGUF_Q4_0.safetensors",
-                        #"CogVideoX_2b_fun_GGUF_Q4_0.safetensors"
+                        "CogVideoX_5b_fun_1_1_GGUF_Q4_0.safetensors"
                     ],
                 ),
             "vae_precision": (["fp16", "fp32", "bf16"], {"default": "bf16", "tooltip": "VAE dtype"}),
@@ -967,6 +982,7 @@ class CogVideoXFunSampler:
                 "start_img": ("IMAGE",),
                 "end_img": ("IMAGE",),
                 "opt_empty_latent": ("LATENT",),
+                "noise_aug_strength": ("FLOAT", {"default": 0.0563, "min": 0.0, "max": 1.0, "step": 0.001}),
             },
         }
     
@@ -976,7 +992,7 @@ class CogVideoXFunSampler:
     CATEGORY = "CogVideoWrapper"
 
     def process(self, pipeline,  positive, negative, video_length, base_resolution, seed, steps, cfg, scheduler, 
-                start_img=None, end_img=None, opt_empty_latent=None):
+                start_img=None, end_img=None, opt_empty_latent=None, noise_aug_strength=0.0563):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         pipe = pipeline["pipe"]
@@ -1034,6 +1050,7 @@ class CogVideoXFunSampler:
                 video        = input_video,
                 mask_video   = input_video_mask,
                 comfyui_progressbar = True,
+                noise_aug_strength = noise_aug_strength,
             )
         #if not pipeline["cpu_offloading"]:
         #     pipe.transformer.to(offload_device)
@@ -1084,8 +1101,11 @@ class CogVideoXFunVid2VidSampler:
                     }
                 ),
                 "denoise_strength": ("FLOAT", {"default": 0.70, "min": 0.05, "max": 1.00, "step": 0.01}),
+            },
+             "optional":{
                 "validation_video": ("IMAGE",),
-            }
+                "control_video": ("IMAGE",),
+            },
         }
     
     RETURN_TYPES = ("COGVIDEOPIPE", "LATENT",)
@@ -1093,7 +1113,7 @@ class CogVideoXFunVid2VidSampler:
     FUNCTION = "process"
     CATEGORY = "CogVideoWrapper"
 
-    def process(self, pipeline, positive, negative, video_length, base_resolution, seed, steps, cfg, denoise_strength, scheduler, validation_video):
+    def process(self, pipeline, positive, negative, video_length, base_resolution, seed, steps, cfg, denoise_strength, scheduler, validation_video=None, control_video=None):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         pipe = pipeline["pipe"]
@@ -1109,8 +1129,14 @@ class CogVideoXFunVid2VidSampler:
 
         # Count most suitable height and width
         aspect_ratio_sample_size    = {key : [x / 512 * base_resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
-        validation_video = np.array(validation_video.cpu().numpy() * 255, np.uint8)
-        original_width, original_height = Image.fromarray(validation_video[0]).size
+
+        if validation_video is not None:
+            validation_video = np.array(validation_video.cpu().numpy() * 255, np.uint8)
+            original_width, original_height = Image.fromarray(validation_video[0]).size
+        elif control_video is not None:
+            control_video = np.array(control_video.cpu().numpy() * 255, np.uint8)
+            original_width, original_height = Image.fromarray(control_video[0]).size
+
         closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
         height, width = [int(x / 16) * 16 for x in closest_size]
 
@@ -1128,26 +1154,38 @@ class CogVideoXFunVid2VidSampler:
         autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
         with autocast_context:
             video_length = int((video_length - 1) // pipe.vae.config.temporal_compression_ratio * pipe.vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
-            input_video, input_video_mask, clip_image = get_video_to_video_latent(validation_video, video_length=video_length, sample_size=(height, width))
+            if validation_video is not None:
+                input_video, input_video_mask, clip_image = get_video_to_video_latent(validation_video, video_length=video_length, sample_size=(height, width))
+            elif control_video is not None:
+                input_video, input_video_mask, clip_image = get_video_to_video_latent(control_video, video_length=video_length, sample_size=(height, width))
 
             # for _lora_path, _lora_weight in zip(cogvideoxfun_model.get("loras", []), cogvideoxfun_model.get("strength_model", [])):
             #     pipeline = merge_lora(pipeline, _lora_path, _lora_weight)
 
-            latents = pipe(
-                prompt_embeds=positive.to(dtype).to(device),
-                negative_prompt_embeds=negative.to(dtype).to(device),
-                num_frames = video_length,
-                height      = height,
-                width       = width,
-                generator   = generator,
-                guidance_scale = cfg,
-                num_inference_steps = steps,
+            common_params = {
+                "prompt_embeds": positive.to(dtype).to(device),
+                "negative_prompt_embeds": negative.to(dtype).to(device),
+                "num_frames": video_length,
+                "height": height,
+                "width": width,
+                "generator": generator,
+                "guidance_scale": cfg,
+                "num_inference_steps": steps,
+                "comfyui_progressbar": True,
+            }
 
-                video        = input_video,
-                mask_video   = input_video_mask,
-                strength = float(denoise_strength),
-                comfyui_progressbar = True,
-            )
+            if control_video is not None:
+                latents = pipe(
+                    **common_params,
+                    control_video=input_video
+                )
+            else:
+                latents = pipe(
+                    **common_params,
+                    video=input_video,
+                    mask_video=input_video_mask,
+                    strength=float(denoise_strength)
+                )
 
             # for _lora_path, _lora_weight in zip(cogvideoxfun_model.get("loras", []), cogvideoxfun_model.get("strength_model", [])):
             #     pipeline = unmerge_lora(pipeline, _lora_path, _lora_weight)