⚙ Image Sequence Tweening

Documentation

• Class name: Image Sequence Tweening [Dream]
• Category: ✨ Dream/🎥 animation/⚙ postprocessing
• Output node: False

The node is designed to enhance animation sequences by interpolating additional frames between existing ones, effectively increasing the smoothness and fluidity of the animation. It utilizes a multiplier to determine the number of frames to be added between each original frame, allowing for customizable levels of tweening.

Input types

Required

• sequence
  • The animation sequence to be processed. It is the primary input that defines the frames to be tweened.
  • Comfy dtype: ANIMATION_SEQUENCE
  • Python dtype: AnimationSequence
• multiplier
  • Determines the number of interpolated frames to be added between each original frame in the sequence, allowing for control over the smoothness of the resulting animation.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• sequence
  • Comfy dtype: ANIMATION_SEQUENCE
  • The enhanced animation sequence with additional tweened frames inserted between the original frames.
  • Python dtype: AnimationSequence

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class DreamSequenceTweening:
    NODE_NAME = "Image Sequence Tweening"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": SharedTypes.sequence | {
                "multiplier": ("INT", {"default": 2, "min": 2, "max": 10}),
            },
        }

    CATEGORY = NodeCategories.ANIMATION_POSTPROCESSING
    RETURN_TYPES = (AnimationSequence.ID,)
    RETURN_NAMES = ("sequence",)
    OUTPUT_NODE = False
    FUNCTION = "process"

    @classmethod
    def IS_CHANGED(cls, sequence: AnimationSequence, **kwargs):
        return sequence.is_defined

    def process(self, sequence: AnimationSequence, multiplier):
        if not sequence.is_defined:
            return (sequence,)

        def _generate_extra_frames(input_index, last_index, images):
            results = {}
            if input_index == last_index:
                # special case
                for i in range(multiplier):
                    results[input_index * multiplier + i] = images[0]
                return results

            # normal case
            current_frame = images[0]
            next_frame = images[1]
            for i in range(multiplier):
                alpha = float(i + 1) / multiplier
                results[multiplier * input_index + i] = current_frame.blend(next_frame, 1.0 - alpha, alpha)
            return results

        proc = AnimationSeqProcessor(sequence)
        return (proc.process([0, 1], _generate_extra_frames),)