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Pad node update

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kijai 2025-04-05 19:35:16 +03:00
parent 26aea1889d
commit 5f16aeec88
1 changed files with 20 additions and 10 deletions
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30
nodes/image_nodes.py
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@ -3149,9 +3149,11 @@ class ImagePadKJ:
"extra_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"pad_mode": (["edge", "color"],),
"color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255, separated by commas."}),
}
, "optional": {
},
"optional": {
"mask": ("MASK", ),
"target_width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
"target_height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
}
}
@ -3161,7 +3163,7 @@ class ImagePadKJ:
CATEGORY = "KJNodes/image"
DESCRIPTION = "Pad the input image and optionally mask with the specified padding."
def pad(self, image, left, right, top, bottom, extra_padding, color, pad_mode, mask=None):
def pad(self, image, left, right, top, bottom, extra_padding, color, pad_mode, mask=None, target_width=None, target_height=None):
B, H, W, C = image.shape
# Resize masks to image dimensions if necessary
@ -3175,15 +3177,23 @@ class ImagePadKJ:
if len(bg_color) == 1:
bg_color = bg_color * 3 # Grayscale to RGB
bg_color = torch.tensor(bg_color, dtype=image.dtype, device=image.device)
# Calculate padding sizes with extra padding
pad_left = left + extra_padding
pad_right = right + extra_padding
pad_top = top + extra_padding
pad_bottom = bottom + extra_padding
if target_width is not None and target_height is not None:
padded_width = target_width
padded_height = target_height
pad_left = (padded_width - W) // 2
pad_right = padded_width - W - pad_left
pad_top = (padded_height - H) // 2
pad_bottom = padded_height - H - pad_top
else:
pad_left = left + extra_padding
pad_right = right + extra_padding
pad_top = top + extra_padding
pad_bottom = bottom + extra_padding
padded_width = W + pad_left + pad_right
padded_height = H + pad_top + pad_bottom
padded_width = W + pad_left + pad_right
padded_height = H + pad_top + pad_bottom
out_image = torch.zeros((B, padded_height, padded_width, C), dtype=image.dtype, device=image.device)
# Fill padded areas