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feat: add progress bar for batches of 100+ images in resize node

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2026-01-20 12:17:55 +07:00
parent 00b25f82a6
commit d7f7da69d0
1 changed files with 77 additions and 23 deletions
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resize_image.py
+77 -23
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@@ -123,8 +123,15 @@ Combines PixelForge resolution selection with KJNodes Resize v2 features.
def resize(self, image, aspect_ratio, orientation, divisible_by, max_megapixels, resolution,
upscale_method, keep_proportion, pad_color, crop_position, mask=None):
from comfy.utils import ProgressBar
B, H, W, C = image.shape
# Initialize progress bar for large batches
pbar = None
if B >= 100:
pbar = ProgressBar(B)
# Parse target resolution from PixelForge parameters
target_width, target_height = map(int, resolution.split("×"))
@@ -227,34 +234,81 @@ Combines PixelForge resolution selection with KJNodes Resize v2 features.
width = target_width
height = target_height
# Resize image
resized_image = common_upscale(
image.movedim(-1, 1),
width,
height,
upscale_method,
crop="disabled"
).movedim(1, -1)
# Resize mask if present
resized_mask = None
if mask is not None:
if upscale_method == "lanczos":
resized_mask = common_upscale(
mask.unsqueeze(1).repeat(1, 3, 1, 1),
# Process images with progress tracking for large batches
if pbar is not None:
# Process images one by one for progress tracking
resized_images = []
resized_masks = []
for i in range(B):
single_image = image[i:i+1]
single_mask = mask[i:i+1] if mask is not None else None
# Resize single image
resized_single = common_upscale(
single_image.movedim(-1, 1),
width,
height,
upscale_method,
crop="disabled"
).movedim(1, -1)[:, :, :, 0]
else:
resized_mask = common_upscale(
mask.unsqueeze(1),
width,
height,
upscale_method,
crop="disabled"
).squeeze(1)
).movedim(1, -1)
resized_images.append(resized_single)
# Resize mask if present
if single_mask is not None:
if upscale_method == "lanczos":
resized_single_mask = common_upscale(
single_mask.unsqueeze(1).repeat(1, 3, 1, 1),
width,
height,
upscale_method,
crop="disabled"
).movedim(1, -1)[:, :, :, 0]
else:
resized_single_mask = common_upscale(
single_mask.unsqueeze(1),
width,
height,
upscale_method,
crop="disabled"
).squeeze(1)
resized_masks.append(resized_single_mask)
pbar.update(1)
resized_image = torch.cat(resized_images, dim=0)
resized_mask = torch.cat(resized_masks, dim=0) if resized_masks else None
else:
# Process entire batch at once for small batches
resized_image = common_upscale(
image.movedim(-1, 1),
width,
height,
upscale_method,
crop="disabled"
).movedim(1, -1)
# Resize mask if present
resized_mask = None
if mask is not None:
if upscale_method == "lanczos":
resized_mask = common_upscale(
mask.unsqueeze(1).repeat(1, 3, 1, 1),
width,
height,
upscale_method,
crop="disabled"
).movedim(1, -1)[:, :, :, 0]
else:
resized_mask = common_upscale(
mask.unsqueeze(1),
width,
height,
upscale_method,
crop="disabled"
).squeeze(1)
# Apply padding if needed
if (keep_proportion in ["pad", "pad_edge"]) and (pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0):