Timestep Keyframe 🛂🅐🅒🅝

Documentation

• Class name: TimestepKeyframe
• Category: Adv-ControlNet 🛂🅐🅒🅝/keyframes
• Output node: False

The TimestepKeyframe node is designed to encapsulate the concept of a keyframe within a timeline, specifying the start percentage, strength, and optional control weights, latent keyframes, and mask hints. It serves as a foundational element for defining and manipulating keyframes in advanced control networks, enabling precise control over the animation or transformation process.

Input types

Required

• start_percent
  • Specifies the starting point of the keyframe as a percentage of the total timeline, allowing for precise positioning within the sequence.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

• prev_timestep_kf
  • References a previous timestep keyframe, enabling the chaining or sequencing of keyframes over time.
  • Comfy dtype: TIMESTEP_KEYFRAME
  • Python dtype: TimestepKeyframeGroup
• strength
  • Determines the influence or intensity of the keyframe, enabling the modulation of effects or transformations applied at this point in the timeline.
  • Comfy dtype: FLOAT
  • Python dtype: float
• cn_weights
  • Optional control weights that can be applied to the keyframe, providing additional control over the effects or transformations.
  • Comfy dtype: CONTROL_NET_WEIGHTS
  • Python dtype: ControlWeights
• latent_keyframe
  • Optional latent keyframes that can be associated with this keyframe, allowing for complex transformations or animations.
  • Comfy dtype: LATENT_KEYFRAME
  • Python dtype: LatentKeyframeGroup
• null_latent_kf_strength
  • Specifies the strength of a null or default latent keyframe, used when no specific latent keyframe is provided.
  • Comfy dtype: FLOAT
  • Python dtype: float
• inherit_missing
  • A boolean flag indicating whether missing attributes should be inherited from previous keyframes, ensuring continuity in the animation or transformation process.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• guarantee_usage
  • A boolean flag that guarantees the usage of this keyframe, ensuring it is considered in the animation or transformation process.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• mask_optional
  • Optional mask hints that can be associated with the keyframe, providing additional control over the areas affected by the transformations.
  • Comfy dtype: MASK
  • Python dtype: Tensor

Output types

• TIMESTEP_KF
  • Comfy dtype: TIMESTEP_KEYFRAME
  • Returns a modified or new timestep keyframe, incorporating the specified parameters and potentially adding it to a sequence of keyframes.
  • Python dtype: TimestepKeyframeGroup

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class TimestepKeyframeNode:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
            },
            "optional": {
                "prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "cn_weights": ("CONTROL_NET_WEIGHTS", ),
                "latent_keyframe": ("LATENT_KEYFRAME", ),
                "null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "inherit_missing": ("BOOLEAN", {"default": True}, ),
                "guarantee_usage": ("BOOLEAN", {"default": True}, ),
                "mask_optional": ("MASK", ),
                #"interpolation": ([SI.LINEAR, SI.EASE_IN, SI.EASE_OUT, SI.EASE_IN_OUT, SI.NONE], {"default": SI.NONE}, ),
            }
        }

    RETURN_NAMES = ("TIMESTEP_KF", )
    RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
    FUNCTION = "load_keyframe"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"

    def load_keyframe(self,
                      start_percent: float,
                      strength: float=1.0,
                      cn_weights: ControlWeights=None, control_net_weights: ControlWeights=None, # old name
                      latent_keyframe: LatentKeyframeGroup=None,
                      prev_timestep_kf: TimestepKeyframeGroup=None, prev_timestep_keyframe: TimestepKeyframeGroup=None, # old name
                      null_latent_kf_strength: float=0.0,
                      inherit_missing=True,
                      guarantee_usage=True,
                      mask_optional=None,
                      interpolation: str=SI.NONE,):
        control_net_weights = control_net_weights if control_net_weights else cn_weights
        prev_timestep_keyframe = prev_timestep_keyframe if prev_timestep_keyframe else prev_timestep_kf
        if not prev_timestep_keyframe:
            prev_timestep_keyframe = TimestepKeyframeGroup()
        else:
            prev_timestep_keyframe = prev_timestep_keyframe.clone()
        keyframe = TimestepKeyframe(start_percent=start_percent, strength=strength, interpolation=interpolation, null_latent_kf_strength=null_latent_kf_strength,
                                    control_weights=control_net_weights, latent_keyframes=latent_keyframe, inherit_missing=inherit_missing, guarantee_usage=guarantee_usage,
                                    mask_hint_orig=mask_optional)
        prev_timestep_keyframe.add(keyframe)
        return (prev_timestep_keyframe,)