IFRNet VFI

Documentation

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

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

Input types

Required

• ckpt_name
  • Specifies the checkpoint name for the model to be loaded, determining the specific pre-trained weights to use for frame interpolation.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• frames
  • A tensor containing the sequence of frames to be interpolated. This is the primary input on which the interpolation process is applied.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• clear_cache_after_n_frames
  • Controls the cache clearing mechanism to manage memory usage during the interpolation process, by specifying after how many frames the cache should be cleared.
  • Comfy dtype: INT
  • Python dtype: int
• multiplier
  • Determines the number of intermediate frames to be generated between each pair of original frames, effectively controlling the output video's frame rate.
  • Comfy dtype: INT
  • Python dtype: int
• scale_factor
  • A scaling factor for adjusting the resolution of the interpolated frames relative to the original frames.
  • Comfy dtype: COMBO[FLOAT]
  • Python dtype: float

Optional

• optional_interpolation_states
  • Optional states that can be used to influence the interpolation process, allowing for customization and optimization based on specific requirements.
  • Comfy dtype: INTERPOLATION_STATES
  • Python dtype: InterpolationStateList

Output types

• image
  • Comfy dtype: IMAGE
  • The output tensor containing the interpolated frames, enhancing the smoothness and frame rate of the input video sequence.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class IFRNet_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}),
                "scale_factor": ([0.25, 0.5, 1.0, 2.0, 4.0], {"default": 1.0}),
            },
            "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,
        scale_factor: typing.SupportsFloat = 1.0,
        optional_interpolation_states: InterpolationStateList = None,
        **kwargs
    ):
        from .IFRNet_S_arch import IRFNet_S
        from .IFRNet_L_arch import IRFNet_L
        model_path = load_file_from_github_release(MODEL_TYPE, ckpt_name)
        interpolation_model = IRFNet_S() if 'S' in ckpt_name else IRFNet_L()
        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, timestep, model, scale_factor):
            return model(frame_0, frame_1, timestep, scale_factor)

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