xinyun/ComfyUI-KJNodes
1
0
Fork 0
mirror of https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git synced 2026-05-21 10:07:22 +08:00
Code Issues Packages Projects Releases Wiki Activity

optimize draw mask on image

Browse Source
This commit is contained in:
kijai 2025-10-03 19:06:09 +03:00
parent b9abf5df31
commit d1f6cf77b0
1 changed files with 13 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
nodes/mask_nodes.py
View File

@ -1529,8 +1529,11 @@ class DrawMaskOnImage:
"image": ("IMAGE", ), "image": ("IMAGE", ),
"mask": ("MASK", ), "mask": ("MASK", ),
"color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255 or 0.0-1.0, separated by commas."}), "color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255 or 0.0-1.0, separated by commas."}),
} },
"optional": {
"device": (["cpu", "gpu"], {"default": "cpu", "tooltip": "Device to use for processing"}),
} }
}
RETURN_TYPES = ("IMAGE", ) RETURN_TYPES = ("IMAGE", )
RETURN_NAMES = ("images",) RETURN_NAMES = ("images",)
@ -1538,11 +1541,14 @@ class DrawMaskOnImage:
CATEGORY = "KJNodes/masking" CATEGORY = "KJNodes/masking"
DESCRIPTION = "Applies the provided masks to the input images." DESCRIPTION = "Applies the provided masks to the input images."
def apply(self, image, mask, color): def apply(self, image, mask, color, device="cpu"):
B, H, W, C = image.shape B, H, W, C = image.shape
BM, HM, WM = mask.shape BM, HM, WM = mask.shape
in_masks = mask.clone() processing_device = main_device if device == "gpu" else torch.device("cpu")
in_masks = mask.clone().to(processing_device)
in_images = image.clone().to(processing_device)
if HM != H or WM != W: if HM != H or WM != W:
in_masks = F.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1) in_masks = F.interpolate(mask.unsqueeze(1), size=(H, W), mode='nearest-exact').squeeze(1)
@ -1562,12 +1568,12 @@ class DrawMaskOnImage:
else: else:
bg_values.append(int(val_str) / 255.0) bg_values.append(int(val_str) / 255.0)
background_color = torch.tensor(bg_values, dtype=torch.float32, device=image.device) background_color = torch.tensor(bg_values, dtype=torch.float32, device=in_images.device)
for i in range(B): for i in tqdm(range(B), desc="DrawMaskOnImage batch"):
curr_mask = in_masks[i] curr_mask = in_masks[i]
img_idx = min(i, B - 1) img_idx = min(i, B - 1)
curr_image = image[img_idx] curr_image = in_images[img_idx]
mask_expanded = curr_mask.unsqueeze(-1).expand(-1, -1, 3) mask_expanded = curr_mask.unsqueeze(-1).expand(-1, -1, 3)
masked_image = curr_image * (1 - mask_expanded) + background_color * (mask_expanded) masked_image = curr_image * (1 - mask_expanded) + background_color * (mask_expanded)
output_images.append(masked_image) output_images.append(masked_image)
@ -1576,7 +1582,7 @@ class DrawMaskOnImage:
if not output_images: if not output_images:
return (torch.zeros((0, H, W, 3), dtype=image.dtype),) return (torch.zeros((0, H, W, 3), dtype=image.dtype),)
out_rgb = torch.stack(output_images, dim=0) out_rgb = torch.stack(output_images, dim=0).cpu()
return (out_rgb, ) return (out_rgb, )