M2M VFI

Documentation

• Class name: M2M VFI
• Category: ComfyUI-Frame-Interpolation/VFI
• Output node: False

The M2M VFI node specializes in video frame interpolation, leveraging deep learning models to predict and generate intermediate frames between existing frames in a video sequence. This process enhances video fluidity and can be used to increase the frame rate of videos.

Input types

Required

• ckpt_name
  • The checkpoint name for the model, determining which pretrained model weights to load for frame interpolation.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• frames
  • The input video frames to be interpolated. These frames provide the visual context for generating intermediate frames.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• clear_cache_after_n_frames
  • Specifies after how many frames the cache should be cleared to prevent memory overflow, affecting performance and resource management.
  • Comfy dtype: INT
  • Python dtype: int
• multiplier
  • A factor that determines how many intermediate frames are to be generated between each pair of input frames, directly influencing the output video's frame rate.
  • Comfy dtype: INT
  • Python dtype: int

Optional

• optional_interpolation_states
  • Optional states that can influence frame skipping and interpolation behavior, allowing for more control over the interpolation process.
  • Comfy dtype: INTERPOLATION_STATES
  • Python dtype: InterpolationStateList

Output types

• image
  • Comfy dtype: IMAGE
  • The output interpolated frames, enhancing the video's smoothness and frame rate.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class M2M_VFI:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "ckpt_name": (CKPT_NAMES, ),
                "frames": ("IMAGE", ),
                "clear_cache_after_n_frames": ("INT", {"default": 10, "min": 1, "max": 1000}),
                "multiplier": ("INT", {"default": 2, "min": 2, "max": 1000}),
            },
            "optional": {
                "optional_interpolation_states": ("INTERPOLATION_STATES", )
            }
        }

    RETURN_TYPES = ("IMAGE", )
    FUNCTION = "vfi"
    CATEGORY = "ComfyUI-Frame-Interpolation/VFI"

    def vfi(
        self,
        ckpt_name: typing.AnyStr, 
        frames: torch.Tensor, 
        clear_cache_after_n_frames: typing.SupportsInt = 1,
        multiplier: typing.SupportsInt = 2,
        optional_interpolation_states: InterpolationStateList = None,
        **kwargs
    ):
        from .M2M_arch import M2M_PWC
        model_path = load_file_from_github_release(MODEL_TYPE, ckpt_name)
        interpolation_model = M2M_PWC()
        interpolation_model.load_state_dict(torch.load(model_path))
        interpolation_model.eval().to(get_torch_device())
        frames = preprocess_frames(frames)

        def return_middle_frame(frame_0, frame_1, int_timestep, model):
            tenSteps = [
                torch.FloatTensor([int_timestep] * len(frame_0)).view(len(frame_0), 1, 1, 1).to(get_torch_device())
            ]
            return model(frame_0, frame_1, tenSteps)[0]

        args = [interpolation_model]
        out = postprocess_frames(
            generic_frame_loop(frames, clear_cache_after_n_frames, multiplier, return_middle_frame, *args, 
                               interpolation_states=optional_interpolation_states, dtype=torch.float32)
        )
        return (out,)