CreateFadeMask (Deprecated)

Documentation

• Class name: CreateFadeMask
• Category: KJNodes/deprecated
• Output node: False

The CreateFadeMask node is designed to generate fade masks for images or sequences, allowing for smooth transitions between frames or visual elements. It utilizes parameters such as frame count, dimensions, and fade characteristics to craft customizable fade effects.

Input types

Required

• invert
  • A boolean flag that, when true, inverts the fade effect, offering an alternative visual transition.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• frames
  • Specifies the number of frames for which the fade mask will be generated, affecting the length of the transition.
  • Comfy dtype: INT
  • Python dtype: int
• width
  • Determines the width of the fade mask, directly influencing the size of the generated mask.
  • Comfy dtype: INT
  • Python dtype: int
• height
  • Sets the height of the fade mask, impacting the vertical dimension of the generated mask.
  • Comfy dtype: INT
  • Python dtype: int
• interpolation
  • Defines the method of interpolation used for the fade effect, affecting the smoothness and style of the transition.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• start_level
  • The initial opacity level of the fade, setting the starting point of the fade effect.
  • Comfy dtype: FLOAT
  • Python dtype: float
• midpoint_level
  • The opacity level at the midpoint of the fade, allowing for control over the fade's progression.
  • Comfy dtype: FLOAT
  • Python dtype: float
• end_level
  • The final opacity level of the fade, determining the end point of the fade effect.
  • Comfy dtype: FLOAT
  • Python dtype: float
• midpoint_frame
  • Specifies the frame at which the midpoint opacity level is reached, influencing the timing of the fade's progression.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• mask
  • Comfy dtype: MASK
  • The generated fade mask, which can be used to create smooth transitions in images or sequences.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class CreateFadeMask:

    RETURN_TYPES = ("MASK",)
    FUNCTION = "createfademask"
    CATEGORY = "KJNodes/deprecated"

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                 "invert": ("BOOLEAN", {"default": False}),
                 "frames": ("INT", {"default": 2,"min": 2, "max": 255, "step": 1}),
                 "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
                 "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
                 "start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
                 "midpoint_level": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}),
                 "end_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
                 "midpoint_frame": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
        },
    } 

    def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame):
        def ease_in(t):
            return t * t

        def ease_out(t):
            return 1 - (1 - t) * (1 - t)

        def ease_in_out(t):
            return 3 * t * t - 2 * t * t * t

        batch_size = frames
        out = []
        image_batch = np.zeros((batch_size, height, width), dtype=np.float32)

        if midpoint_frame == 0:
            midpoint_frame = batch_size // 2

        for i in range(batch_size):
            if i <= midpoint_frame:
                t = i / midpoint_frame
                if interpolation == "ease_in":
                    t = ease_in(t)
                elif interpolation == "ease_out":
                    t = ease_out(t)
                elif interpolation == "ease_in_out":
                    t = ease_in_out(t)
                color = start_level - t * (start_level - midpoint_level)
            else:
                t = (i - midpoint_frame) / (batch_size - midpoint_frame)
                if interpolation == "ease_in":
                    t = ease_in(t)
                elif interpolation == "ease_out":
                    t = ease_out(t)
                elif interpolation == "ease_in_out":
                    t = ease_in_out(t)
                color = midpoint_level - t * (midpoint_level - end_level)

            color = np.clip(color, 0, 255)
            image = np.full((height, width), color, dtype=np.float32)
            image_batch[i] = image

        output = torch.from_numpy(image_batch)
        mask = output
        out.append(mask)

        if invert:
            return (1.0 - torch.cat(out, dim=0),)
        return (torch.cat(out, dim=0),)