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[ImagePad/Resize][Added] Control over padding transparency

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By default is transparent
This commit is contained in:
Salvador E. Tropea
2025-10-13 20:00:58 -03:00
parent aa8dc7b2ce
commit e159265648
1 changed files with 18 additions and 6 deletions
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src/nodes/nodes_img.py
+18 -6
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@@ -78,6 +78,14 @@ SIZE_OPT = ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1})
SIZE_OPT_FI = deepcopy(SIZE_OPT)
SIZE_OPT_FI[1]["forceInput"] = True
SIZE_OPT[1]["tooltip"] = "Used when no `get_image_size` is provided"
PAD_TRANS = ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 1.0,
"step": 0.1,
"display": "number",
"tooltip": ("The transparency for the padded area for all modes except `edge_pixel`."
"1.0 is fully transparent, 0.0 is fully opaque.")})
def tensor_to_pil(tensor: torch.Tensor) -> Image.Image:
@@ -736,6 +744,7 @@ class ImagePad:
"mask": ("MASK", ),
"target_width": SIZE_OPT_FI,
"target_height": SIZE_OPT_FI,
"pad_transparency": PAD_TRANS,
}
}
@@ -748,7 +757,7 @@ class ImagePad:
DISPLAY_NAME = "Pad Image (KJ/SET)"
def pad(self, image, left, right, top, bottom, extra_padding, color, pad_mode, mask=None, target_width=None,
target_height=None):
target_height=None, pad_transparency=1.0):
B, H, W, C = image.shape
# Resize masks to image dimensions if necessary
@@ -760,7 +769,7 @@ class ImagePad:
# Parse background color
color_tuple = color_to_rgb_float(logger, color)
if C == 4 and len(color_tuple) == 3:
color_tuple += (0.0,) # Use transparent color to pad RGBA images
color_tuple += (1.0 - pad_transparency,) # Use transparent color to pad RGBA images. 0 is transparent for RGBA
bg_color = torch.tensor(color_tuple, dtype=image.dtype, device=image.device)
# Calculate padding sizes with extra padding
@@ -881,15 +890,17 @@ class ImagePad:
out_image[b, :, :, :] = bg_color.unsqueeze(0).unsqueeze(0)
out_image[b, pad_top:pad_top+H, pad_left:pad_left+W, :] = image[b]
# Note: in the mask 1 is transparent and 0 opaque (reverse of RGBA)
if mask is not None:
out_masks = torch.nn.functional.pad(
mask,
(pad_left, pad_right, pad_top, pad_bottom),
mode='replicate' if pad_mode == "edge_pixel" else 'constant',
value=None if pad_mode == "edge_pixel" else 1.0,
value=None if pad_mode == "edge_pixel" else pad_transparency,
)
else:
out_masks = torch.ones((B, padded_height, padded_width), dtype=image.dtype, device=image.device)
out_masks = torch.full((B, padded_height, padded_width), pad_transparency, dtype=image.dtype,
device=image.device)
for m in range(B):
out_masks[m, pad_top:pad_top+H, pad_left:pad_left+W] = 0.0
@@ -933,6 +944,7 @@ class ImageResize:
"per_batch": ("INT", {
"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1,
"tooltip": "Process images in sub-batches to reduce memory usage. 0 disables sub-batching."}),
"pad_transparency": PAD_TRANS,
},
"hidden": {
"unique_id": "UNIQUE_ID",
@@ -951,7 +963,7 @@ class ImageResize:
DISPLAY_NAME = "Resize Image (KJ/SET)"
def resize(self, image, width, height, keep_proportion, upscale_method, divisible_by, pad_color, crop_position,
unique_id, device="cpu", mask=None, get_image_size=None, per_batch=0):
unique_id, device="cpu", mask=None, get_image_size=None, per_batch=0, pad_transparency=1.0):
B, H, W, C = image.shape
if device == "gpu":
@@ -1109,7 +1121,7 @@ class ImageResize:
"color"
)
out_image, out_mask = ImagePad.pad(self, out_image, pad_left, pad_right, pad_top, pad_bottom, 0, pad_color,
pad_mode, mask=out_mask)
pad_mode, mask=out_mask, pad_transparency=pad_transparency)
return out_image, out_mask