Update README and refactor node categories to Craftopia
- Revised README to clarify node functionalities and typical use cases. - Changed category for SmartResize, SmartBatchResize, and SmartSizeMultiplier nodes to "Craftopia".
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rubz
@@ -1,6 +1,8 @@
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# ComfyUI-SmartResize
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Two focused resize nodes for ComfyUI — both with **Lanczos** interpolation and **multiple_of** snapping.
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Smart image resize nodes for ComfyUI — batch processing, LoRA prep & SeedVR support.
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All nodes appear under the **Craftopia** category in the node menu.
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---
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@@ -9,9 +11,13 @@ Two focused resize nodes for ComfyUI — both with **Lanczos** interpolation and
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### 📐 Smart Resize
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Resize any image (or batch) so the **longest side** equals `max_pixels`, with aspect ratio preserved.
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Use this as a **pipeline node** — IMAGE in, IMAGE out. Perfect for downscaling SeedVR upscales before LoRA training, or any workflow step where you need a clean downscale without saving to disk.
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Use this as a **pipeline node** — IMAGE in, IMAGE out. No files are saved to disk.
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**Typical usecases:**
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- Downscale a SeedVR (or other upscaler) result back to your target training size — the upscaler adds fine detail, Smart Resize brings it to the right dimensions for LoRA training
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- Any workflow step that needs a clean resize before the next node
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- Normalize mixed-resolution batches to a consistent size
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**Inputs:**
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| Input | Type | Default | Description |
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|---|---|---|---|
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| image | IMAGE | — | Single image or batch |
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@@ -20,55 +26,71 @@ Use this as a **pipeline node** — IMAGE in, IMAGE out. Perfect for downscaling
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| interpolation | ENUM | lanczos | lanczos / bicubic / bilinear / nearest |
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| upscale_if_smaller | BOOLEAN | false | Also upscale images already smaller than max_pixels |
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**Outputs:** `IMAGE`, `width`, `height`
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**Outputs:** `image`, `width`, `height`
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---
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### 📁 Batch Resize & Save
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Load **all images from a folder**, resize each one (longest side), and save them with the original filename + a suffix into a subfolder.
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### 📁 Smart Batch Resize
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Load **all images from a folder**, resize each one by longest side, and save with the original filename + a suffix into a subfolder. Original filenames are always preserved.
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Use this for **bulk preprocessing** — e.g. preparing a LoRA dataset from a folder of high-res images.
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**Inputs:**
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| Input | Type | Default | Description |
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|---|---|---|---|
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| input_folder | STRING | D:/images/mijnmap | Source folder path |
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| input_folder | STRING | D:/images/input | Source folder path |
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| max_pixels | INT | 1536 | Target size for longest side |
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| filename_suffix | STRING | _Small | Appended to each output filename |
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| filename_suffix | STRING | _Small | Appended to each output filename before the extension |
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| output_subfolder | STRING | resized | Subfolder created inside input_folder |
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| multiple_of | INT | 8 | Snap dimensions to this multiple |
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| interpolation | ENUM | lanczos | lanczos / nearest / bilinear / bicubic |
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| interpolation | ENUM | lanczos | lanczos / bicubic / bilinear / nearest |
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| skip_if_exists | BOOLEAN | false | Skip files that already exist in output |
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**Outputs:** `images` (list), `filenames` (list), `output_paths` (list), `count`
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---
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### 📏 Smart Size Multiplier
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Multiply image dimensions by a factor and output the results as integers.
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SeedVR takes a single `resolution` INT (the longest side) — not a width and height separately. Standard math nodes give you a FLOAT or require multiple steps to get there. This node does it cleanly in one step: give it your image and a multiplier, and it outputs `width`, `height`, and `resolution` (longest side) ready to connect directly to SeedVR's `max_resolution` input.
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Also useful for LoRA dataset prep — using mixed resolutions in your training set generally produces better results than training on a single fixed size. Smart Size Multiplier makes it easy to dynamically calculate the right target size per image rather than hardcoding a value.
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| Input | Type | Default | Description |
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|---|---|---|---|
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| image | IMAGE | — | Source image |
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| multiplier | FLOAT | 2.0 | Multiply width and height by this factor |
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| multiple_of | INT | 8 | Snap dimensions to this multiple |
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**Outputs:** `width`, `height`, `resolution` (longest side as INT → directly into SeedVR)
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---
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## Why these nodes?
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Existing nodes like `JWImageResizeByLongerSide` lack **Lanczos** interpolation (the best choice for downscaling). Other batch nodes don't preserve original filenames or offer `multiple_of` snapping.
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These nodes fill that gap with a minimal, focused feature set.
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- ComfyUI's built-in `Resize Images by Longer Edge` [BETA] has no Lanczos and no `multiple_of` snapping
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- `JWImageResizeByLongerSide` (comfyui-various) has no Lanczos in the official release
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- No existing node combines batch folder loading + longest-side resize + original filename preservation
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- No existing node outputs a ready-to-use `resolution` INT for SeedVR
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---
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## Installation
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**Option A — ComfyUI Manager:**
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Search for `ComfyUI-SmartResize` and install.
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**ComfyUI Manager:** search for `ComfyUI-SmartResize` and install.
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**Option B — Manual:**
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**Manual:**
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```bash
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cd ComfyUI/custom_nodes
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git clone https://github.com/YOUR_USERNAME/ComfyUI-SmartResize
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git clone https://github.com/CraftopiaStudio/ComfyUI-SmartResize
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```
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Restart ComfyUI. Nodes appear under:
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- `image/transform` → **Smart Resize 📐**
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- `image/batch` → **Batch Resize & Save 📁**
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Restart ComfyUI. All nodes appear under **Craftopia** in the node menu:
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- `Craftopia` → **Smart Resize 📐**
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- `Craftopia` → **Smart Batch Resize 📁**
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- `Craftopia` → **Smart Size Multiplier 📏**
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---
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## Requirements
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- ComfyUI
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- Pillow (included with ComfyUI)
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- PyTorch (included with ComfyUI)
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Pillow, NumPy, PyTorch — all included with ComfyUI. No extra dependencies.
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@@ -74,7 +74,7 @@ class SmartBatchResize:
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RETURN_NAMES = ("images", "filenames", "output_paths", "count")
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OUTPUT_IS_LIST = (True, True, True, False)
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FUNCTION = "run"
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CATEGORY = "SmartResize"
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CATEGORY = "Craftopia"
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OUTPUT_NODE = True
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def run(self, input_folder, max_pixels, filename_suffix,
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+49
-73
@@ -1,48 +1,53 @@
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import math
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from PIL import Image as PILImage
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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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def _calc_new_size(w, h, max_pixels, multiple_of, upscale_if_smaller):
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longest = max(w, h)
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if longest <= max_pixels and not upscale_if_smaller:
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new_w, new_h = w, h
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else:
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scale = max_pixels / 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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class SmartResize:
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"""
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Resize images so the longest side equals max_pixels.
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Supports lanczos interpolation and multiple_of snapping.
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Works with single images and batches.
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Resize IMAGE by longest side. Lanczos included. Multiple_of snapping.
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Works with single images and batches. IMAGE in → IMAGE out.
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"""
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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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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"max_pixels": ("INT", {
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"default": 1536,
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"min": 64,
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"max": 8192,
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"step": 8,
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"tooltip": "Longest side will be resized to this value (aspect ratio preserved)"
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"default": 1536, "min": 64, "max": 8192, "step": 8,
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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,
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"min": 1,
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"max": 64,
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"step": 1,
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"tooltip": "Round dimensions to a multiple of this value (8 = SD compatible)"
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}),
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"interpolation": (["lanczos", "bicubic", "bilinear", "nearest"], {
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"default": "lanczos"
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"default": 8, "min": 1, "max": 64, "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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"upscale_if_smaller": ("BOOLEAN", {
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"default": False,
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"tooltip": "Also upscale images that are already smaller than max_pixels"
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"tooltip": "Upscale images that are already smaller than max_pixels."
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}),
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}
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}
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@@ -50,60 +55,31 @@ class SmartResize:
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RETURN_TYPES = ("IMAGE", "INT", "INT")
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RETURN_NAMES = ("image", "width", "height")
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FUNCTION = "run"
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CATEGORY = "image/transform"
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def _resize_one(self, img_tensor, max_pixels, multiple_of, interp, upscale_if_smaller):
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"""Resize a single [H, W, C] tensor."""
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h, w = img_tensor.shape[0], img_tensor.shape[1]
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longest = max(w, h)
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# Skip if already small enough and upscale disabled
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if longest <= max_pixels and not upscale_if_smaller:
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# Still snap to multiple_of
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new_w = max(multiple_of, round(w / multiple_of) * multiple_of) if multiple_of > 1 else w
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new_h = max(multiple_of, round(h / multiple_of) * multiple_of) if multiple_of > 1 else h
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if new_w == w and new_h == h:
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return img_tensor, w, h
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else:
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scale = max_pixels / longest
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new_w = int(w * scale)
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new_h = int(h * scale)
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# Snap to multiple_of
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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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# Convert tensor [H, W, C] float32 → PIL → resize → tensor
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arr = (img_tensor.numpy() * 255).astype(np.uint8)
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pil = PILImage.fromarray(arr, mode="RGB")
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pil_resized = pil.resize((new_w, new_h), interp)
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arr_resized = np.array(pil_resized).astype(np.float32) / 255.0
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return torch.from_numpy(arr_resized), new_w, new_h
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CATEGORY = "Craftopia"
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def run(self, image, max_pixels, multiple_of, interpolation, upscale_if_smaller):
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# image shape: [B, H, W, C]
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interp = self.INTERP_MAP[interpolation]
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interp = INTERP_MAP[interpolation]
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results = []
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last_w, last_h = 0, 0
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for i in range(image.shape[0]):
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resized, w, h = self._resize_one(
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image[i], max_pixels, multiple_of, interp, upscale_if_smaller
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)
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results.append(resized)
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last_w, last_h = w, h
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frame = image[i] # [H, W, C]
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h, w = frame.shape[0], frame.shape[1]
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new_w, new_h = _calc_new_size(w, h, max_pixels, multiple_of, upscale_if_smaller)
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# Stack back to [B, H, W, C] — all frames must be same size
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# (they will be if all input frames are same size, which is the ComfyUI norm)
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output = torch.stack(results, dim=0)
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return (output, last_w, last_h)
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if new_w == w and new_h == h:
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results.append(frame)
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else:
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arr = (frame.numpy() * 255).astype("uint8")
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pil = PILImage.fromarray(arr, mode="RGB")
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pil = pil.resize((new_w, new_h), interp)
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arr = np.array(pil).astype("float32") / 255.0
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results.append(torch.from_numpy(arr))
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last_w, last_h = new_w, new_h
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return (torch.stack(results, dim=0), last_w, last_h)
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NODE_CLASS_MAPPINGS = {
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"SmartResize": SmartResize,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"SmartResize": "Smart Resize 📐",
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}
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NODE_CLASS_MAPPINGS = {"SmartResize": SmartResize}
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NODE_DISPLAY_NAME_MAPPINGS = {"SmartResize": "Smart Resize 📐"}
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@@ -31,7 +31,7 @@ class SmartSizeMultiplier:
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RETURN_TYPES = ("INT", "INT", "INT")
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RETURN_NAMES = ("width", "height", "resolution")
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FUNCTION = "run"
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CATEGORY = "SmartResize"
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CATEGORY = "Craftopia"
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def run(self, image, multiplier, multiple_of):
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h, w = image.shape[1], image.shape[2]
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