🔧 Transition Mask¶
Documentation¶
- Class name:
TransitionMask+ - Category:
essentials - Output node:
False
The TransitionMask node is designed to facilitate transitions between different mask states or values within a digital image processing or generation context. Its primary function is to apply transformations to masks, enabling dynamic changes in mask properties or the blending of multiple masks. This node likely plays a crucial role in operations where mask manipulation is essential for achieving desired visual effects or for preparing masks for further processing steps.
Input types¶
Required¶
width- The 'width' parameter specifies the width of the mask, affecting the spatial dimensions of the transition effect.
- Comfy dtype:
INT - Python dtype:
int
height- The 'height' parameter specifies the height of the mask, impacting the spatial dimensions of the transition effect.
- Comfy dtype:
INT - Python dtype:
int
frames- The 'frames' parameter determines the number of frames to be generated for the transition, defining the length of the transition animation.
- Comfy dtype:
INT - Python dtype:
int
start_frame- The 'start_frame' parameter indicates the starting frame of the transition, setting the initial state of the animation.
- Comfy dtype:
INT - Python dtype:
int
end_frame- The 'end_frame' parameter specifies the ending frame of the transition, determining the final state of the animation.
- Comfy dtype:
INT - Python dtype:
int
transition_type- The 'transition_type' parameter defines the type of transition effect to be applied, such as fading or morphing, influencing the visual style of the transition.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
timing_function- The 'timing_function' parameter controls the pacing of the transition, affecting how the transition progresses over time.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
Output types¶
mask- Comfy dtype:
MASK - The output is a transformed mask that has undergone the specified transition, reflecting changes in mask properties or values.
- 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"
def execute(self, width, height, frames, start_frame, end_frame, transition_type, timing_function):
if timing_function == 'in':
tf = [0.0, 0.0, 0.5, 1.0]
elif timing_function == 'out':
tf = [0.0, 0.5, 1.0, 1.0]
elif timing_function == 'in-out':
tf = [0, 1, 0, 1]
#elif timing_function == 'back':
# tf = [0, 1.334, 1.334, 0]
else:
tf = [0, 0, 1, 1]
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 = i/(transition-1)
if timing_function != 'linear':
progress = cubic_bezier(progress, tf)
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, )