mirrors/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-07-27 16:26:39 +00:00
Code Issues Packages Projects Releases Wiki Activity

Allow padding in ImageStitch node to be white. (#8631)

Browse Source
This commit is contained in:
comfyanonymous 2025-06-21 21:19:41 -07:00 committed by GitHub
parent 1883e70b43
commit 78f79266a9
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 17 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
comfy_extras/nodes_images.py
View File

@ -304,10 +304,23 @@ Optional spacing can be added between images.
image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled"
).movedim(1, -1)
color_map = {
"white": 1.0,
"black": 0.0,
"red": (1.0, 0.0, 0.0),
"green": (0.0, 1.0, 0.0),
"blue": (0.0, 0.0, 1.0),
}
color_val = color_map[spacing_color]
# When not matching sizes, pad to align non-concat dimensions
if not match_image_size:
h1, w1 = image1.shape[1:3]
h2, w2 = image2.shape[1:3]
pad_value = 0.0
if not isinstance(color_val, tuple):
pad_value = color_val
if direction in ["left", "right"]:
# For horizontal concat, pad heights to match
@ -316,11 +329,11 @@ Optional spacing can be added between images.
if h1 < target_h:
pad_h = target_h - h1
pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value)
if h2 < target_h:
pad_h = target_h - h2
pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value)
else: # up, down
# For vertical concat, pad widths to match
if w1 != w2:
@ -328,11 +341,11 @@ Optional spacing can be added between images.
if w1 < target_w:
pad_w = target_w - w1
pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=pad_value)
if w2 < target_w:
pad_w = target_w - w2
pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=pad_value)
# Ensure same number of channels
if image1.shape[-1] != image2.shape[-1]:
@ -366,15 +379,6 @@ Optional spacing can be added between images.
if spacing_width > 0:
spacing_width = spacing_width + (spacing_width % 2) # Ensure even
color_map = {
"white": 1.0,
"black": 0.0,
"red": (1.0, 0.0, 0.0),
"green": (0.0, 1.0, 0.0),
"blue": (0.0, 0.0, 1.0),
}
color_val = color_map[spacing_color]
if direction in ["left", "right"]:
spacing_shape = (
image1.shape[0],