🔧 Transition Mask¶
Documentation¶
- Class name:
TransitionMask+ - Category:
essentials/mask - Output node:
False
The TransitionMask+ node specializes in generating dynamic transition effects within masks, offering a range of transition styles and timing functions to create complex visual transitions between frames or states. This node simplifies the creation of animated or static transition effects, making it easier for users to apply sophisticated mask transitions in their projects.
Input types¶
Required¶
width- Defines the width of the mask to be generated, setting the horizontal dimension of the transition effect.
- Comfy dtype:
INT - Python dtype:
int
height- Sets the height of the mask, determining the vertical dimension of the transition effect.
- Comfy dtype:
INT - Python dtype:
int
frames- Specifies the total number of frames in the transition animation, controlling the length of the transition effect.
- Comfy dtype:
INT - Python dtype:
int
start_frame- Indicates the starting frame number for the transition effect, allowing for control over the animation's beginning.
- Comfy dtype:
INT - Python dtype:
int
end_frame- Determines the ending frame number for the transition, enabling customization of the animation's duration.
- Comfy dtype:
INT - Python dtype:
int
transition_type- Selects the type of transition effect to be applied, such as slides, bars, boxes, or fades, offering a variety of visual styles.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
timing_function- Chooses the timing function for the transition effect, such as linear or ease-in-out, affecting the pacing of the transition.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
Output types¶
mask- Comfy dtype:
MASK - Produces a mask that represents the transition effect, which can be used to apply or visualize the transition within an image or a series of images.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class TransitionMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"width": ("INT", { "default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1, }),
"height": ("INT", { "default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1, }),
"frames": ("INT", { "default": 16, "min": 1, "max": 9999, "step": 1, }),
"start_frame": ("INT", { "default": 0, "min": 0, "step": 1, }),
"end_frame": ("INT", { "default": 9999, "min": 0, "step": 1, }),
"transition_type": (["horizontal slide", "vertical slide", "horizontal bar", "vertical bar", "center box", "horizontal door", "vertical door", "circle", "fade"],),
"timing_function": (["linear", "in", "out", "in-out"],)
}
}
RETURN_TYPES = ("MASK",)
FUNCTION = "execute"
CATEGORY = "essentials/mask"
def linear(self, i, t):
return i/t
def ease_in(self, i, t):
return pow(i/t, 2)
def ease_out(self, i, t):
return 1 - pow(1 - i/t, 2)
def ease_in_out(self, i, t):
if i < t/2:
return pow(i/(t/2), 2) / 2
else:
return 1 - pow(1 - (i - t/2)/(t/2), 2) / 2
def execute(self, width, height, frames, start_frame, end_frame, transition_type, timing_function):
if timing_function == 'in':
timing_function = self.ease_in
elif timing_function == 'out':
timing_function = self.ease_out
elif timing_function == 'in-out':
timing_function = self.ease_in_out
else:
timing_function = self.linear
out = []
end_frame = min(frames, end_frame)
transition = end_frame - start_frame
if start_frame > 0:
out = out + [torch.full((height, width), 0.0, dtype=torch.float32, device="cpu")] * start_frame
for i in range(transition):
frame = torch.full((height, width), 0.0, dtype=torch.float32, device="cpu")
progress = timing_function(i, transition-1)
if "horizontal slide" in transition_type:
pos = round(width*progress)
frame[:, :pos] = 1.0
elif "vertical slide" in transition_type:
pos = round(height*progress)
frame[:pos, :] = 1.0
elif "box" in transition_type:
box_w = round(width*progress)
box_h = round(height*progress)
x1 = (width - box_w) // 2
y1 = (height - box_h) // 2
x2 = x1 + box_w
y2 = y1 + box_h
frame[y1:y2, x1:x2] = 1.0
elif "circle" in transition_type:
radius = math.ceil(math.sqrt(pow(width,2)+pow(height,2))*progress/2)
c_x = width // 2
c_y = height // 2
# is this real life? Am I hallucinating?
x = torch.arange(0, width, dtype=torch.float32, device="cpu")
y = torch.arange(0, height, dtype=torch.float32, device="cpu")
y, x = torch.meshgrid((y, x), indexing="ij")
circle = ((x - c_x) ** 2 + (y - c_y) ** 2) <= (radius ** 2)
frame[circle] = 1.0
elif "horizontal bar" in transition_type:
bar = round(height*progress)
y1 = (height - bar) // 2
y2 = y1 + bar
frame[y1:y2, :] = 1.0
elif "vertical bar" in transition_type:
bar = round(width*progress)
x1 = (width - bar) // 2
x2 = x1 + bar
frame[:, x1:x2] = 1.0
elif "horizontal door" in transition_type:
bar = math.ceil(height*progress/2)
if bar > 0:
frame[:bar, :] = 1.0
frame[-bar:, :] = 1.0
elif "vertical door" in transition_type:
bar = math.ceil(width*progress/2)
if bar > 0:
frame[:, :bar] = 1.0
frame[:, -bar:] = 1.0
elif "fade" in transition_type:
frame[:,:] = progress
out.append(frame)
if end_frame < frames:
out = out + [torch.full((height, width), 1.0, dtype=torch.float32, device="cpu")] * (frames - end_frame)
out = torch.stack(out, dim=0)
return (out, )