xinyun/ComfyUI-KJNodes
1
0
Fork 0
mirror of https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git synced 2025-12-22 11:14:35 +08:00
Code Issues Packages Projects Releases Wiki Activity

Update nodes.py

Browse Source
This commit is contained in:
Kijai 2024-04-05 15:17:17 +03:00
parent 0a341a2811
commit f81567595e
1 changed files with 19 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
nodes.py
View File

@ -811,7 +811,7 @@ class GrowMaskWithBlur:
if fill_holes: if fill_holes:
binary_mask = output > 0 binary_mask = output > 0
output = scipy.ndimage.binary_fill_holes(binary_mask) output = scipy.ndimage.binary_fill_holes(binary_mask)
output = output.astype(np.uint8) * 255 output = output.astype(np.float32) * 255
output = torch.from_numpy(output) output = torch.from_numpy(output)
if alpha < 1.0 and previous_output is not None: if alpha < 1.0 and previous_output is not None:
# Interpolate between the previous and current frame # Interpolate between the previous and current frame
@ -1755,8 +1755,15 @@ class BatchUncrop:
draw.rectangle((width - border_width, 0, width, height), fill=border_color) draw.rectangle((width - border_width, 0, width, height), fill=border_color)
return image return image
if len(original_images) != len(cropped_images) or len(original_images) != len(bboxes): if len(original_images) != len(cropped_images):
raise ValueError("The number of images, crop_images, and bboxes should be the same") raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same")
# Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
if len(bboxes) > len(original_images):
print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}")
bboxes = bboxes[:len(original_images)]
elif len(bboxes) < len(original_images):
raise ValueError("There should be at least as many bboxes as there are original and cropped images")
input_images = tensor2pil(original_images) input_images = tensor2pil(original_images)
crop_imgs = tensor2pil(cropped_images) crop_imgs = tensor2pil(cropped_images)
@ -2140,8 +2147,15 @@ class BatchUncropAdvanced:
draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width) draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width)
return bordered_image return bordered_image
if len(original_images) != len(cropped_images) or len(original_images) != len(bboxes): if len(original_images) != len(cropped_images):
raise ValueError("The number of images, crop_images, and bboxes should be the same") raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same")
# Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
if len(bboxes) > len(original_images):
print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}")
bboxes = bboxes[:len(original_images)]
elif len(bboxes) < len(original_images):
raise ValueError("There should be at least as many bboxes as there are original and cropped images")
crop_imgs = tensor2pil(cropped_images) crop_imgs = tensor2pil(cropped_images)
input_images = tensor2pil(original_images) input_images = tensor2pil(original_images)