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malkuthro
2025-09-28 23:22:31 +02:00
parent 7a13bc1d03
commit 789ebd35b1
1 changed files with 8 additions and 6 deletions
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__init__.py
+8 -6
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@@ -121,9 +121,7 @@ class ImageAspect:
"aspect_ratio": ("STRING", {"default": "16:9"}),
"upscale_method": (["nearest-exact", "bilinear", "area", "bicubic", "lanczos"], {"default": "nearest-exact"}),
"keep_proportion": (["stretch", "letterbox", "pillarbox"], {"default": "stretch"}),
"pad_red": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"pad_green": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"pad_blue": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"pad_color": ("STRING", {"default": "0, 0, 0"}),
"crop_position": (["top", "bottom", "left", "right", "center"], {"default": "center"}),
"divisible_by": ("INT", {"default": 32, "min": 1, "max": 128, "step": 1}),
"device": (["cpu", "cuda"], {"default": "cpu"}),
@@ -144,7 +142,7 @@ are divisible by the specified value. Supports stretch or pad/crop modes for pro
Outputs the resized image, optional mask, and final width/height for VFX pipeline integration.
"""
def adjust_to_aspect(self, image, base_on, base_size, aspect_ratio, upscale_method, keep_proportion, pad_red, pad_green, pad_blue, crop_position, divisible_by, device, mask=None):
def adjust_to_aspect(self, image, base_on, base_size, aspect_ratio, upscale_method, keep_proportion, pad_color, crop_position, divisible_by, device, mask=None):
def round_to_nearest_multiple(number, multiple):
return multiple * round(number / multiple)
@@ -225,10 +223,14 @@ Outputs the resized image, optional mask, and final width/height for VFX pipelin
pad_l = total_pad_w // 2
pad_r = total_pad_w - pad_l
# Parse pad_color
pad_color = [float(x.strip()) for x in pad_color.split(',')]
if len(pad_color) != 3:
raise ValueError("Pad color must be three comma-separated floats, e.g., '0, 0, 0'.")
# Create background with pad color (assume C=3 for RGB)
pad_color = [pad_red, pad_green, pad_blue]
pad_color_tensor = torch.tensor(pad_color, dtype=image.dtype, device=device).view(1, 3, 1, 1)
out_image = pad_color_tensor.expand(B, 3, target_height, target_width)
out_image = pad_color_tensor.expand(B, 3, target_height, target_width).clone()
out_image[:, :, pad_t:pad_t + new_h, pad_l:pad_l + new_w] = resized_image
if mask is not None: