rubz
e7caae4a3e
- add IS_CHANGED so new/removed files in input_folder are picked up without a widget change - fix EXIF orientation ignored on load (phone photos were saved rotated) - when all files are skipped, load existing resized output from disk instead of returning an empty list — fixes IndexError downstream (e.g. Preview Image) and removes the misleading "No images processed" error - fix stale docstring in Smart Resolution Multiplier (SeedVR2's resolution input, not max_resolution)
227 lines
8.2 KiB
Python
227 lines
8.2 KiB
Python
import os
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from pathlib import Path
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from PIL import Image as PILImage, ImageOps
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import numpy as np
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import torch
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INTERP_MAP = {
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"lanczos": PILImage.LANCZOS,
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"bicubic": PILImage.BICUBIC,
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"bilinear": PILImage.BILINEAR,
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"nearest": PILImage.NEAREST,
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}
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SUPPORTED_EXT = {".png", ".jpg", ".jpeg", ".webp", ".bmp", ".tiff", ".tif"}
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def _calc_new_size(w, h, longest_side, multiple_of):
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longest = max(w, h)
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if longest <= longest_side:
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new_w, new_h = w, h
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else:
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scale = longest_side / longest
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new_w = int(w * scale)
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new_h = int(h * scale)
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if multiple_of > 1:
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new_w = max(multiple_of, round(new_w / multiple_of) * multiple_of)
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new_h = max(multiple_of, round(new_h / multiple_of) * multiple_of)
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return new_w, new_h
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def _build_stem(original_stem, prefix, use_original_name, use_counter, counter_index, counter_start, suffix_resolution, longest_side, delimiter):
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parts = []
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if prefix:
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parts.append(prefix)
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if use_original_name:
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parts.append(original_stem)
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if use_counter:
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number = counter_start + counter_index
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parts.append(f"{number:03d}")
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if suffix_resolution:
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parts.append(str(longest_side))
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if not parts:
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parts.append(original_stem)
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return delimiter.join(parts)
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class SmartBatchResize:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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# INPUT
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"input_folder": ("STRING", {
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"default": "",
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"multiline": False,
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"tooltip": "Folder containing images to resize."
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}),
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# RESIZE SETTINGS
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"longest_side": ("INT", {
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"default": 1024, "min": 64, "max": 8192, "step": 64,
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"tooltip": "Longest side target in pixels. Aspect ratio is always preserved."
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}),
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"multiple_of": ("INT", {
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"default": 8, "min": 1, "max": 128, "step": 1,
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"tooltip": "Snap both dimensions to a multiple of this value. Use 8 for SD/Flux."
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}),
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"interpolation": (["lanczos", "bicubic", "bilinear", "nearest"], {
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"default": "lanczos",
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"tooltip": "Resampling method. Lanczos is sharpest for downscaling."
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}),
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# OUTPUT NAMING
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"prefix": ("STRING", {
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"default": "",
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"multiline": False,
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"tooltip": "Label prepended to filename. E.g. 'headshot' → headshot_photo_001_1024.jpg"
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}),
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"use_original_name": ("BOOLEAN", {
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"default": True,
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"tooltip": "Include the original filename in the output name."
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}),
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"use_counter": ("BOOLEAN", {
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"default": False,
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"tooltip": "Add a sequential 3-digit counter to each filename (001, 002, ...)."
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}),
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"counter_start": ("INT", {
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"default": 1, "min": 0, "max": 99999, "step": 1,
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"tooltip": "Starting number for the counter. Useful when processing multiple batches."
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}),
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"suffix_resolution": ("BOOLEAN", {
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"default": True,
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"tooltip": "Append resolution to filename. E.g. photo_1024.png"
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}),
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# OUTPUT LOCATION
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"folder_resolution": ("BOOLEAN", {
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"default": False,
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"tooltip": "Append resolution to subfolder name. E.g. resized_1024"
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}),
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"folder_custom": ("STRING", {
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"default": "resized",
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"multiline": False,
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"tooltip": "Subfolder name. Resolution is appended if 'Create resolution subfolder' is on."
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}),
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# OPTIONS
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"skip_if_exists": ("BOOLEAN", {
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"default": True,
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"tooltip": "Skip files that already exist in the output folder."
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}),
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"delimiter": ("STRING", {
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"default": "_",
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"multiline": False,
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"tooltip": "Separator between filename parts. E.g. _ or -"
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}),
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}
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}
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RETURN_TYPES = ("IMAGE", "INT")
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RETURN_NAMES = ("images", "count")
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OUTPUT_IS_LIST = (True, False)
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FUNCTION = "run"
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CATEGORY = "CraftKit"
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OUTPUT_NODE = True
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@classmethod
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def IS_CHANGED(cls, input_folder, **kwargs):
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# Folder contents aren't a widget input, so ComfyUI can't see when files
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# are added/removed. Force re-execution every run; skip_if_exists keeps
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# already-processed files cheap.
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return float("nan")
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def run(self, input_folder, longest_side, multiple_of, interpolation,
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prefix, use_original_name, use_counter, counter_start, suffix_resolution,
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folder_resolution, folder_custom,
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skip_if_exists, delimiter):
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# Resolve output subfolder
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subfolder = folder_custom.strip()
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if subfolder == "":
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subfolder = str(longest_side) if folder_resolution else "resized"
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elif folder_resolution:
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subfolder = f"{subfolder}{delimiter}{longest_side}"
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input_folder = input_folder.strip()
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if not input_folder:
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raise ValueError("[SmartBatchResize] No folder selected. Enter a path or use the Browse button.")
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if not os.path.isdir(input_folder):
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raise ValueError(f"[SmartBatchResize] Folder not found: {input_folder}")
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files = sorted([
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f for f in Path(input_folder).iterdir()
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if f.is_file() and f.suffix.lower() in SUPPORTED_EXT
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])
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if not files:
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raise ValueError(f"[SmartBatchResize] No images found in: {input_folder}")
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out_dir = Path(input_folder) / subfolder
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out_dir.mkdir(parents=True, exist_ok=True)
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interp = INTERP_MAP[interpolation]
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images_out = []
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counter_index = 0
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skipped_count = 0
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for f in files:
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stem = _build_stem(
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original_stem=f.stem,
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prefix=prefix.strip(),
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use_original_name=use_original_name,
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use_counter=use_counter,
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counter_index=counter_index,
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counter_start=counter_start,
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suffix_resolution=suffix_resolution,
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longest_side=longest_side,
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delimiter=delimiter,
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)
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out_name = f"{stem}{f.suffix.lower()}"
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out_path = out_dir / out_name
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if skip_if_exists and out_path.exists():
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print(f"[SmartBatchResize] Skipped (exists): {out_name}")
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skipped_count += 1
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counter_index += 1
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existing = PILImage.open(out_path).convert("RGB")
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arr = np.array(existing).astype("float32") / 255.0
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images_out.append(torch.from_numpy(arr).unsqueeze(0))
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continue
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img = PILImage.open(f)
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img = ImageOps.exif_transpose(img)
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img = img.convert("RGB")
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w, h = img.size
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new_w, new_h = _calc_new_size(w, h, longest_side, multiple_of)
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img_resized = img.resize((new_w, new_h), interp)
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save_kwargs = {"quality": 95} if f.suffix.lower() in (".jpg", ".jpeg") else {}
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img_resized.save(out_path, **save_kwargs)
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print(f"[SmartBatchResize] {f.name} → {out_name} ({w}x{h} → {new_w}x{new_h})")
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arr = np.array(img_resized).astype("float32") / 255.0
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images_out.append(torch.from_numpy(arr).unsqueeze(0))
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counter_index += 1
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processed_count = len(images_out) - skipped_count
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if skipped_count:
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summary = f"✓ {processed_count} new, {skipped_count} already existed → {subfolder}/"
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else:
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summary = f"✓ {processed_count} images saved → {subfolder}/"
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return {"ui": {"text": [summary]}, "result": (images_out, len(images_out))}
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NODE_CLASS_MAPPINGS = {"SmartBatchResize": SmartBatchResize}
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NODE_DISPLAY_NAME_MAPPINGS = {"SmartBatchResize": "Smart Batch Resize 📁"}
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