xinyun/ComfyUI-KJNodes
1
0
Fork 0
mirror of https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git synced 2025-12-09 21:04:41 +08:00
Code Issues Packages Projects Releases Wiki Activity

Update nodes.py

Browse Source
This commit is contained in:
kijai 2023-11-12 23:53:53 +02:00
parent c4381dfe36
commit 9fa712f0a2
1 changed files with 27 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

47
nodes.py
View File

@ -243,10 +243,11 @@ class CreateFadeMask:
"start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}), "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}), "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}), "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): def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame=None):
def ease_in(t): def ease_in(t):
return t * t return t * t
@ -259,32 +260,38 @@ class CreateFadeMask:
batch_size = frames batch_size = frames
out = [] out = []
image_batch = np.zeros((batch_size, height, width), dtype=np.float32) image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
if midpoint_frame is None:
midpoint_frame = batch_size // 2
for i in range(batch_size): for i in range(batch_size):
t = i / (batch_size - 1) if i <= midpoint_frame:
t = i / midpoint_frame
if interpolation == "ease_in": if interpolation == "ease_in":
t = ease_in(t) t = ease_in(t)
elif interpolation == "ease_out": elif interpolation == "ease_out":
t = ease_out(t) t = ease_out(t)
elif interpolation == "ease_in_out": elif interpolation == "ease_in_out":
t = ease_in_out(t) t = ease_in_out(t)
color = start_level - t * (start_level - midpoint_level)
if midpoint_level is not None:
if t < 0.5:
color = start_level - t * (start_level - midpoint_level) * 2
else:
color = midpoint_level - (t - 0.5) * (midpoint_level - end_level) * 2
else: else:
color = start_level - t * (start_level - end_level) 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 = np.full((height, width), color, dtype=np.float32)
image_batch[i] = image image_batch[i] = image
output = torch.from_numpy(image_batch) output = torch.from_numpy(image_batch)
mask = output mask = output
out.append(mask) out.append(mask)
if invert: if invert:
return (1.0 - torch.cat(out, dim=0),) return (1.0 - torch.cat(out, dim=0),)
return (torch.cat(out, dim=0),) return (torch.cat(out, dim=0),)