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更新版本到 1.4.8

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Cyber Dick Lang
2025-11-01 01:50:01 +08:00
parent 9f2257e81d
commit ca49258023
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README.md
+23 -5
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@@ -1,6 +1,6 @@
# ComfyUI-UniversalToolkit
[![Version](https://img.shields.io/badge/version-1.3.2-blue.svg)](https://github.com/whmc76/ComfyUI-UniversalToolkit)
[![Version](https://img.shields.io/badge/version-1.4.8-blue.svg)](https://github.com/whmc76/ComfyUI-UniversalToolkit)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![ComfyUI](https://img.shields.io/badge/ComfyUI-v3+-orange.svg)](https://github.com/comfyanonymous/ComfyUI)
@@ -52,9 +52,10 @@ tqdm
- **ImageConcatenate_UTK**：水平或垂直拼接两张图像
- **ImageConcatenateMulti_UTK**：智能拼接多张图像，支持2-4图自动布局
#### 图像变换与调整
- **ImageScaleByAspectRatio_UTK**：按指定宽高比缩放图像
- **ImageMaskScaleAs_UTK**：按参考图像尺寸缩放图像
- #### 图像变换与调整
- **ResizeImageVerKJ_UTK**：KJ v2 风格的高兼容缩放，支持 stretch/resize/pad/pad_edge/pad_edge_pixel/crop/pillarbox_blur/total_pixels 与 `crop_position`
- **ImageScaleByAspectRatio_UTK**：按指定宽高比缩放图像（已支持与 KJ v2 一致的 fit 模式与 `crop_position`，背景色为预设清单）
- **ImageMaskScaleAs_UTK**：按参考图像尺寸缩放图像（已支持与 KJ v2 一致的 fit 模式与 `crop_position`，pad_color 为预设清单）
- **ImageScaleRestore_UTK**：将图像恢复到原始尺寸
- **ImageRemoveAlpha_UTK**：移除图像的Alpha通道
- **ImageCombineAlpha_UTK**：合并Alpha通道到图像
@@ -81,6 +82,7 @@ tqdm
- **MaskAnd_UTK**：掩码与运算
- **MaskSub_UTK**：掩码减法运算
- **MaskAdd_UTK**：掩码加法运算
- **BlockifyMask_UTK**：将掩码按 block_size 马赛克化（支持 cpu/cuda；可选二值化）
### 🛠️ 工具节点
@@ -91,6 +93,7 @@ tqdm
#### 数学与逻辑
- **MathExpression_UTK**：数学表达式计算，支持复杂公式和函数
- **BestContextWindow_UTK**：最佳滑动窗口帧数计算（满足 4n+1，最小化补帧；输出 best_window/padding/padded_total/segments）
#### 系统工具
- **PurgeVRAM_UTK**：显存清理，支持选择性清理缓存和模型
@@ -176,7 +179,22 @@ AudioCropProcess_UTK
## 📋 版本历史
### v1.3.2 (最新)
### v1.4.7 (最新)
- 修复 resize 与 pad 方法表现相同的问题：
- ResizeImageVerKJ (UTK)：resize 模式只等比缩放不填充，pad 模式填充到目标尺寸
- ImageMaskScaleAs (UTK)：resize 返回实际缩放尺寸，pad 填充到目标尺寸并正确输出尺寸
- ImageScaleByAspectRatio (UTK)：resize 返回实际缩放尺寸，pad 填充到目标尺寸并正确输出尺寸
- resize：等比缩放，输出尺寸 = 缩放后尺寸（可能小于目标尺寸）
- pad：等比缩放 + 背景填充，输出尺寸 = 目标尺寸（固定尺寸）
### v1.4.6
- 新增 `Resize Image ver KJ (UTK)`，完整对齐 KJ v2 调整模式，支持 `crop_position` 与 mask 同步缩放；pad_edge/pad_edge_pixel 行为与 KJ 对齐
- 升级 `Image Mask Scale As (UTK)` 与 `Image Scale By Aspect Ratio (UTK)`：支持同样的 fit 模式、`crop_position`，并将背景色改为预设清单
- 新增 `Blockify Mask (UTK)`：掩码块化，支持二值化
- 新增 `Best Context Window (UTK)`：计算满足 4n+1 的最佳窗口，最小化补帧
- 统一分类命名：`UniversalToolkit/Tools`
### v1.3.2
- 新增电商应用类，重新组织预设分类结构
- 创建专门的电商应用类，包含6个专业电商功能：
- Ecommerce-Professional Product Photography (专业产品图)
__init__.py
+63 -1
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@@ -8,13 +8,48 @@ A comprehensive toolkit for ComfyUI that provides various utility nodes for imag
:license: MIT, see LICENSE for more details.
"""
__version__ = "1.4.4"
__version__ = "1.4.8"
__author__ = "CyberDickLang"
__email__ = "286878701@qq.com"
__url__ = "https://github.com/whmc76"
# 更新日志
CHANGELOG = {
"1.4.8": [
"版本更新和代码优化：",
"- 更新插件版本号为 1.4.8",
"- 代码优化和稳定性改进",
],
"1.4.7": [
"修复 resize 与 pad 方法表现相同的问题：",
"- ResizeImageVerKJ (UTK)：resize 模式只等比缩放不填充，pad 模式填充到目标尺寸",
"- ImageMaskScaleAs (UTK)：resize 返回实际缩放尺寸，pad 填充到目标尺寸并正确输出尺寸",
"- ImageScaleByAspectRatio (UTK)：resize 返回实际缩放尺寸，pad 填充到目标尺寸并正确输出尺寸",
"- resize：等比缩放，输出尺寸 = 缩放后尺寸（可能小于目标尺寸）",
"- pad：等比缩放 + 背景填充，输出尺寸 = 目标尺寸（固定尺寸）",
],
"1.4.6": [
"新增 Resize Image ver KJ (UTK)：",
"- 复刻 KJ v2 的调整模式：stretch/resize/pad/pad_edge/pad_edge_pixel/crop/pillarbox_blur/total_pixels",
"- 支持 mask 同步缩放与对齐，pad_edge/pad_edge_pixel 行为与 KJ 对齐",
"升级 Image Mask Scale As (UTK)：",
"- fit 与 KJ v2 对齐，新增 crop_position，支持预设 pad_color",
"升级 Image Scale By Aspect Ratio (UTK)：",
"- fit 与 KJ v2 对齐，新增 crop_position，background_color 改为预设清单",
"修正 pad_edge 与 pad_edge_pixel 的边缘与角点处理逻辑，匹配 KJ 视觉表现",
],
"1.4.5": [
"新增Best Context Window (UTK)节点：",
"- 计算满足4n+1且位于[min,max]区间的最佳窗口，以最小化补帧",
"- 输出best_window、padding、padded_total、segments",
"- 分类：UniversalToolkit/Tools",
"新增Blockify Mask (UTK)节点：",
"- 将掩码按block_size块化，支持cpu/cuda",
"- 可选二值化binarize与threshold",
"- 分类：UniversalToolkit/Mask",
"统一分类命名：将UniversalToolkit/tools合并为UniversalToolkit/Tools",
"修复：Get Image or Mask Range From Batch (UTK) 分类名不一致问题",
],
"1.4.4": [
"新增Get Image or Mask Range From Batch (UTK)节点：",
"- 支持从图像批次或遮罩批次中提取指定范围的元素",
@@ -742,9 +777,27 @@ try:
NODE_CLASS_MAPPINGS as GET_IMAGE_RANGE_MAPPINGS
from .nodes.tools.get_image_range_from_batch import \
NODE_DISPLAY_NAME_MAPPINGS as GET_IMAGE_RANGE_DISPLAY
from .nodes.tools.optimal_context_window_node import \
NODE_CLASS_MAPPINGS as BEST_CONTEXT_WINDOW_MAPPINGS
from .nodes.tools.optimal_context_window_node import \
NODE_DISPLAY_NAME_MAPPINGS as BEST_CONTEXT_WINDOW_DISPLAY
from .nodes.mask.blockify_mask import \
NODE_CLASS_MAPPINGS as BLOCKIFY_MASK_MAPPINGS
from .nodes.mask.blockify_mask import \
NODE_DISPLAY_NAME_MAPPINGS as BLOCKIFY_MASK_DISPLAY
from .nodes.image.resize_image_ver_kj import \
NODE_CLASS_MAPPINGS as RESIZE_VER_KJ_MAPPINGS
from .nodes.image.resize_image_ver_kj import \
NODE_DISPLAY_NAME_MAPPINGS as RESIZE_VER_KJ_DISPLAY
except ImportError:
GET_IMAGE_RANGE_MAPPINGS = {}
GET_IMAGE_RANGE_DISPLAY = {}
BEST_CONTEXT_WINDOW_MAPPINGS = {}
BEST_CONTEXT_WINDOW_DISPLAY = {}
BLOCKIFY_MASK_MAPPINGS = {}
BLOCKIFY_MASK_DISPLAY = {}
RESIZE_VER_KJ_MAPPINGS = {}
RESIZE_VER_KJ_DISPLAY = {}
# 合并所有节点映射
@@ -786,6 +839,9 @@ NODE_CLASS_MAPPINGS.update(COLOR_TO_MASK_MAPPINGS)
NODE_CLASS_MAPPINGS.update(LAZY_SWITCH_MAPPINGS)
NODE_CLASS_MAPPINGS.update(TEXT_TRANSLATOR_API_MAPPINGS)
NODE_CLASS_MAPPINGS.update(GET_IMAGE_RANGE_MAPPINGS)
NODE_CLASS_MAPPINGS.update(BEST_CONTEXT_WINDOW_MAPPINGS)
NODE_CLASS_MAPPINGS.update(BLOCKIFY_MASK_MAPPINGS)
NODE_CLASS_MAPPINGS.update(RESIZE_VER_KJ_MAPPINGS)
# 合并显示名称映射
NODE_DISPLAY_NAME_MAPPINGS = {}
@@ -826,6 +882,9 @@ NODE_DISPLAY_NAME_MAPPINGS.update(COLOR_TO_MASK_DISPLAY)
NODE_DISPLAY_NAME_MAPPINGS.update(LAZY_SWITCH_DISPLAY)
NODE_DISPLAY_NAME_MAPPINGS.update(TEXT_TRANSLATOR_API_DISPLAY)
NODE_DISPLAY_NAME_MAPPINGS.update(GET_IMAGE_RANGE_DISPLAY)
NODE_DISPLAY_NAME_MAPPINGS.update(BEST_CONTEXT_WINDOW_DISPLAY)
NODE_DISPLAY_NAME_MAPPINGS.update(BLOCKIFY_MASK_DISPLAY)
NODE_DISPLAY_NAME_MAPPINGS.update(RESIZE_VER_KJ_DISPLAY)
NODE_CATEGORIES = {
"UniversalToolkit": [
@@ -867,6 +926,9 @@ NODE_CATEGORIES = {
"APIImageGenerator_UTK",
"TextTranslatorAPI_UTK",
"GetImageRangeFromBatch_UTK",
"BestContextWindow_UTK",
"BlockifyMask_UTK",
"ResizeImageVerKJ_UTK",
]
}
ize 与 pad 方法表现相同的问题
+338
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@@ -0,0 +1,338 @@
diff --git a/README.md b/README.md
index e0ec9b9..2a0873a 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
# ComfyUI-UniversalToolkit

-[![Version](https://img.shields.io/badge/version-1.3.2-blue.svg)](https://github.com/whmc76/ComfyUI-UniversalToolkit)
+[![Version](https://img.shields.io/badge/version-1.4.7-blue.svg)](https://github.com/whmc76/ComfyUI-UniversalToolkit)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![ComfyUI](https://img.shields.io/badge/ComfyUI-v3+-orange.svg)](https://github.com/comfyanonymous/ComfyUI)

@@ -52,9 +52,10 @@ tqdm
- **ImageConcatenate_UTK**：水平或垂直拼接两张图像
- **ImageConcatenateMulti_UTK**：智能拼接多张图像，支持2-4图自动布局

-#### 图像变换与调整
-- **ImageScaleByAspectRatio_UTK**：按指定宽高比缩放图像
-- **ImageMaskScaleAs_UTK**：按参考图像尺寸缩放图像
+- #### 图像变换与调整
+- **ResizeImageVerKJ_UTK**：KJ v2 风格的高兼容缩放，支持 stretch/resize/pad/pad_edge/pad_edge_pixel/crop/pillarbox_blur/total_pixels 与 `crop_position`
+- **ImageScaleByAspectRatio_UTK**：按指定宽高比缩放图像（已支持与 KJ v2 一致的 fit 模式与 `crop_position`，背景色为预设清单）
+- **ImageMaskScaleAs_UTK**：按参考图像尺寸缩放图像（已支持与 KJ v2 一致的 fit 模式与 `crop_position`，pad_color 为预设清单）
- **ImageScaleRestore_UTK**：将图像恢复到原始尺寸
- **ImageRemoveAlpha_UTK**：移除图像的Alpha通道
- **ImageCombineAlpha_UTK**：合并Alpha通道到图像
@@ -81,6 +82,7 @@ tqdm
- **MaskAnd_UTK**：掩码与运算
- **MaskSub_UTK**：掩码减法运算
- **MaskAdd_UTK**：掩码加法运算
+- **BlockifyMask_UTK**：将掩码按 block_size 马赛克化（支持 cpu/cuda；可选二值化）

### 🛠️ 工具节点

@@ -91,6 +93,7 @@ tqdm

#### 数学与逻辑
- **MathExpression_UTK**：数学表达式计算，支持复杂公式和函数
+- **BestContextWindow_UTK**：最佳滑动窗口帧数计算（满足 4n+1，最小化补帧；输出 best_window/padding/padded_total/segments）

#### 系统工具
- **PurgeVRAM_UTK**：显存清理，支持选择性清理缓存和模型
@@ -176,7 +179,22 @@ AudioCropProcess_UTK

## 📋 版本历史

-### v1.3.2 (最新)
+### v1.4.7 (最新)
+- 修复 resize 与 pad 方法表现相同的问题：
+ - ResizeImageVerKJ (UTK)：resize 模式只等比缩放不填充，pad 模式填充到目标尺寸
+ - ImageMaskScaleAs (UTK)：resize 返回实际缩放尺寸，pad 填充到目标尺寸并正确输出尺寸
+ - ImageScaleByAspectRatio (UTK)：resize 返回实际缩放尺寸，pad 填充到目标尺寸并正确输出尺寸
+ - resize：等比缩放，输出尺寸 = 缩放后尺寸（可能小于目标尺寸）
+ - pad：等比缩放 + 背景填充，输出尺寸 = 目标尺寸（固定尺寸）
+
+### v1.4.6
+- 新增 `Resize Image ver KJ (UTK)`，完整对齐 KJ v2 调整模式，支持 `crop_position` 与 mask 同步缩放；pad_edge/pad_edge_pixel 行为与 KJ 对齐
+- 升级 `Image Mask Scale As (UTK)` 与 `Image Scale By Aspect Ratio (UTK)`：支持同样的 fit 模式、`crop_position`，并将背景色改为预设清单
+- 新增 `Blockify Mask (UTK)`：掩码块化，支持二值化
+- 新增 `Best Context Window (UTK)`：计算满足 4n+1 的最佳窗口，最小化补帧
+- 统一分类命名：`UniversalToolkit/Tools`
+
+### v1.3.2
- 新增电商应用类，重新组织预设分类结构
- 创建专门的电商应用类，包含6个专业电商功能：
- Ecommerce-Professional Product Photography (专业产品图)
diff --git a/__init__.py b/__init__.py
index a72fc01..2b6dda9 100644
--- a/__init__.py
+++ b/__init__.py
@@ -8,13 +8,43 @@ A comprehensive toolkit for ComfyUI that provides various utility nodes for imag
:license: MIT, see LICENSE for more details.
"""

-__version__ = "1.4.4"
+__version__ = "1.4.7"
__author__ = "CyberDickLang"
__email__ = "286878701@qq.com"
__url__ = "https://github.com/whmc76"

# 更新日志
CHANGELOG = {
+ "1.4.7": [
+ "修复 resize 与 pad 方法表现相同的问题：",
+ "- ResizeImageVerKJ (UTK)：resize 模式只等比缩放不填充，pad 模式填充到目标尺寸",
+ "- ImageMaskScaleAs (UTK)：resize 返回实际缩放尺寸，pad 填充到目标尺寸并正确输出尺寸",
+ "- ImageScaleByAspectRatio (UTK)：resize 返回实际缩放尺寸，pad 填充到目标尺寸并正确输出尺寸",
+ "- resize：等比缩放，输出尺寸 = 缩放后尺寸（可能小于目标尺寸）",
+ "- pad：等比缩放 + 背景填充，输出尺寸 = 目标尺寸（固定尺寸）",
+ ],
+ "1.4.6": [
+ "新增 Resize Image ver KJ (UTK)：",
+ "- 复刻 KJ v2 的调整模式：stretch/resize/pad/pad_edge/pad_edge_pixel/crop/pillarbox_blur/total_pixels",
+ "- 支持 mask 同步缩放与对齐，pad_edge/pad_edge_pixel 行为与 KJ 对齐",
+ "升级 Image Mask Scale As (UTK)：",
+ "- fit 与 KJ v2 对齐，新增 crop_position，支持预设 pad_color",
+ "升级 Image Scale By Aspect Ratio (UTK)：",
+ "- fit 与 KJ v2 对齐，新增 crop_position，background_color 改为预设清单",
+ "修正 pad_edge 与 pad_edge_pixel 的边缘与角点处理逻辑，匹配 KJ 视觉表现",
+ ],
+ "1.4.5": [
+ "新增Best Context Window (UTK)节点：",
+ "- 计算满足4n+1且位于[min,max]区间的最佳窗口，以最小化补帧",
+ "- 输出best_window、padding、padded_total、segments",
+ "- 分类：UniversalToolkit/Tools",
+ "新增Blockify Mask (UTK)节点：",
+ "- 将掩码按block_size块化，支持cpu/cuda",
+ "- 可选二值化binarize与threshold",
+ "- 分类：UniversalToolkit/Mask",
+ "统一分类命名：将UniversalToolkit/tools合并为UniversalToolkit/Tools",
+ "修复：Get Image or Mask Range From Batch (UTK) 分类名不一致问题",
+ ],
"1.4.4": [
"新增Get Image or Mask Range From Batch (UTK)节点：",
"- 支持从图像批次或遮罩批次中提取指定范围的元素",
@@ -742,9 +772,27 @@ try:
NODE_CLASS_MAPPINGS as GET_IMAGE_RANGE_MAPPINGS
from .nodes.tools.get_image_range_from_batch import \
NODE_DISPLAY_NAME_MAPPINGS as GET_IMAGE_RANGE_DISPLAY
+ from .nodes.tools.optimal_context_window_node import \
+ NODE_CLASS_MAPPINGS as BEST_CONTEXT_WINDOW_MAPPINGS
+ from .nodes.tools.optimal_context_window_node import \
+ NODE_DISPLAY_NAME_MAPPINGS as BEST_CONTEXT_WINDOW_DISPLAY
+ from .nodes.mask.blockify_mask import \
+ NODE_CLASS_MAPPINGS as BLOCKIFY_MASK_MAPPINGS
+ from .nodes.mask.blockify_mask import \
+ NODE_DISPLAY_NAME_MAPPINGS as BLOCKIFY_MASK_DISPLAY
+ from .nodes.image.resize_image_ver_kj import \
+ NODE_CLASS_MAPPINGS as RESIZE_VER_KJ_MAPPINGS
+ from .nodes.image.resize_image_ver_kj import \
+ NODE_DISPLAY_NAME_MAPPINGS as RESIZE_VER_KJ_DISPLAY
except ImportError:
GET_IMAGE_RANGE_MAPPINGS = {}
GET_IMAGE_RANGE_DISPLAY = {}
+ BEST_CONTEXT_WINDOW_MAPPINGS = {}
+ BEST_CONTEXT_WINDOW_DISPLAY = {}
+ BLOCKIFY_MASK_MAPPINGS = {}
+ BLOCKIFY_MASK_DISPLAY = {}
+ RESIZE_VER_KJ_MAPPINGS = {}
+ RESIZE_VER_KJ_DISPLAY = {}


# 合并所有节点映射
@@ -786,6 +834,9 @@ NODE_CLASS_MAPPINGS.update(COLOR_TO_MASK_MAPPINGS)
NODE_CLASS_MAPPINGS.update(LAZY_SWITCH_MAPPINGS)
NODE_CLASS_MAPPINGS.update(TEXT_TRANSLATOR_API_MAPPINGS)
NODE_CLASS_MAPPINGS.update(GET_IMAGE_RANGE_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(BEST_CONTEXT_WINDOW_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(BLOCKIFY_MASK_MAPPINGS)
+NODE_CLASS_MAPPINGS.update(RESIZE_VER_KJ_MAPPINGS)

# 合并显示名称映射
NODE_DISPLAY_NAME_MAPPINGS = {}
@@ -826,6 +877,9 @@ NODE_DISPLAY_NAME_MAPPINGS.update(COLOR_TO_MASK_DISPLAY)
NODE_DISPLAY_NAME_MAPPINGS.update(LAZY_SWITCH_DISPLAY)
NODE_DISPLAY_NAME_MAPPINGS.update(TEXT_TRANSLATOR_API_DISPLAY)
NODE_DISPLAY_NAME_MAPPINGS.update(GET_IMAGE_RANGE_DISPLAY)
+NODE_DISPLAY_NAME_MAPPINGS.update(BEST_CONTEXT_WINDOW_DISPLAY)
+NODE_DISPLAY_NAME_MAPPINGS.update(BLOCKIFY_MASK_DISPLAY)
+NODE_DISPLAY_NAME_MAPPINGS.update(RESIZE_VER_KJ_DISPLAY)

NODE_CATEGORIES = {
"UniversalToolkit": [
@@ -867,6 +921,9 @@ NODE_CATEGORIES = {
"APIImageGenerator_UTK",
"TextTranslatorAPI_UTK",
"GetImageRangeFromBatch_UTK",
+ "BestContextWindow_UTK",
+ "BlockifyMask_UTK",
+ "ResizeImageVerKJ_UTK",
]
}

diff --git a/nodes/image/image_mask_scale_as.py b/nodes/image/image_mask_scale_as.py
index 7debdaa..46af644 100644
--- a/nodes/image/image_mask_scale_as.py
+++ b/nodes/image/image_mask_scale_as.py
@@ -9,7 +9,7 @@ Scales images and masks to match the dimensions of a reference image.
"""

import torch
-from PIL import Image
+from PIL import Image, ImageFilter

from ..image_utils import image2mask, pil2tensor, tensor2pil

@@ -32,10 +32,18 @@ def fit_resize_image(
target_height,
fit_mode,
resize_sampler,
- background_color="#000000",
+ background_color="black",
+ crop_position="center",
):
"""Resize image according to fit mode"""
- if fit_mode == "letterbox":
+ if fit_mode == "resize":
+ # resize: 只等比缩放，不填充，直接返回缩放后的图像
+ scale = min(target_width / image.width, target_height / image.height)
+ new_width = int(image.width * scale)
+ new_height = int(image.height * scale)
+ return image.resize((new_width, new_height), resize_sampler)
+ 
+ if fit_mode in ["letterbox", "pad", "pad_edge", "pad_edge_pixel", "pillarbox_blur"]:
# Calculate scaling factor to fit within target dimensions
scale = min(target_width / image.width, target_height / image.height)
new_width = int(image.width * scale)
@@ -44,13 +52,164 @@ def fit_resize_image(
# Resize image
resized = image.resize((new_width, new_height), resize_sampler)

- # Create new image with target dimensions and paste resized image
- if image.mode == "RGB":
- result = Image.new("RGB", (target_width, target_height), background_color)
+ # Create background
+ if fit_mode == "pillarbox_blur":
+ # create scaled background then blur and dim
+ scale_fill = max(target_width / max(1, image.width), target_height / max(1, image.height))
+ bg_w = max(1, int(round(image.width * scale_fill)))
+ bg_h = max(1, int(round(image.height * scale_fill)))
+ bg = image.resize((bg_w, bg_h), Image.BILINEAR)
+ # center crop to canvas
+ x0 = max(0, (bg_w - target_width) // 2)
+ y0 = max(0, (bg_h - target_height) // 2)
+ bg = bg.crop((x0, y0, x0 + target_width, y0 + target_height))
+ sigma = max(1.0, 0.006 * float(min(target_width, target_height)))
+ bg = bg.filter(ImageFilter.GaussianBlur(radius=sigma))
+ # desaturate slightly if RGB
+ if bg.mode == "RGB":
+ r, g, b = bg.split()
+ # simple luminance
+ l = r.point(lambda v: int(0.2126 * v))
+ l = Image.merge("RGB", (l, l, l))
+ def mix(a, b, t=0.2):
+ return Image.blend(a, b, t)
+ bg = mix(bg, l)
+ # dim
+ bg = bg.point(lambda v: int(v * 0.35))
+ result = bg
+ elif fit_mode in ["pad_edge", "pad_edge_pixel"]:
+ # start with empty canvas
+ result = Image.new("RGB" if image.mode == "RGB" else "L", (target_width, target_height))
else:
- result = Image.new("L", (target_width, target_height), 0)
- paste_x = (target_width - new_width) // 2
- paste_y = (target_height - new_height) // 2
+ if image.mode == "RGB":
+ # preset color names
+ preset_colors = {
+ "black": "#000000",
+ "white": "#FFFFFF",
+ "gray": "#808080",
+ "red": "#FF0000",
+ "green": "#00FF00",
+ "blue": "#0000FF",
+ "yellow": "#FFFF00",
+ "cyan": "#00FFFF",
+ "magenta": "#FF00FF",
+ }
+ fill_color = preset_colors.get(str(background_color).lower(), background_color)
+ result = Image.new("RGB", (target_width, target_height), fill_color)
+ else:
+ result = Image.new("L", (target_width, target_height), 0)
+
+ # paste location
+ if crop_position == "center":
+ paste_x = (target_width - new_width) // 2
+ paste_y = (target_height - new_height) // 2
+ elif crop_position == "top":
+ paste_x = (target_width - new_width) // 2
+ paste_y = 0
+ elif crop_position == "bottom":
+ paste_x = (target_width - new_width) // 2
+ paste_y = target_height - new_height
+ elif crop_position == "left":
+ paste_x = 0
+ paste_y = (target_height - new_height) // 2
+ elif crop_position == "right":
+ paste_x = target_width - new_width
+ paste_y = (target_height - new_height) // 2
+ else:
+ paste_x = (target_width - new_width) // 2
+ paste_y = (target_height - new_height) // 2
+ # specialized edge padding behaviors
+ if fit_mode == "pad_edge" or fit_mode == "pad_edge_pixel":
+ left_pad = paste_x
+ right_pad = target_width - (paste_x + new_width)
+ top_pad = paste_y
+ bottom_pad = target_height - (paste_y + new_height)
+
+ # left/right stripes from image columns
+ if left_pad > 0:
+ col = resized.crop((0, 0, 1, new_height))
+ if fit_mode == "pad_edge_pixel":
+ col = col.resize((left_pad, new_height), Image.NEAREST)
+ result.paste(col, (0, paste_y))
+ else:
+ # mean color of left edge
+ if col.mode == "RGB":
+ pixels = list(col.getdata())
+ r = sum(p[0] for p in pixels) // len(pixels)
+ g = sum(p[1] for p in pixels) // len(pixels)
+ b = sum(p[2] for p in pixels) // len(pixels)
+ fill = (r, g, b)
+ else:
+ v = sum(col.getdata()) // len(col.getdata())
+ fill = v
+ Image.Image.paste(result, Image.new(result.mode, (left_pad, new_height), fill), (0, paste_y))
+
+ if right_pad > 0:
+ col = resized.crop((new_width - 1, 0, new_width, new_height))
+ if fit_mode == "pad_edge_pixel":
+ col = col.resize((right_pad, new_height), Image.NEAREST)
+ result.paste(col, (paste_x + new_width, paste_y))
+ else:
+ if col.mode == "RGB":
+ pixels = list(col.getdata())
+ r = sum(p[0] for p in pixels) // len(pixels)
+ g = sum(p[1] for p in pixels) // len(pixels)
+ b = sum(p[2] for p in pixels) // len(pixels)
+ fill = (r, g, b)
+ else:
+ v = sum(col.getdata()) // len(col.getdata())
+ fill = v
+ Image.Image.paste(result, Image.new(result.mode, (right_pad, new_height), fill), (paste_x + new_width, paste_y))
+
+ # top/bottom stripes from image rows
+ if top_pad > 0:
+ row = resized.crop((0, 0, new_width, 1))
+ if fit_mode == "pad_edge_pixel":
+ row = row.resize((new_width, top_pad), Image.NEAREST)
+ result.paste(row, (paste_x, 0))
+ # corners by corner pixels
+ if left_pad > 0:
+ c = resized.getpixel((0, 0))
+ Image.Image.paste(result, Image.new(result.mode, (left_pad, top_pad), c), (0, 0))
+ if right_pad > 0:
+ c = resized.getpixel((new_width - 1, 0
nodes/image/image_mask_scale_as.py
+222 -15
View File
@@ -9,7 +9,7 @@ Scales images and masks to match the dimensions of a reference image.
"""
import torch
from PIL import Image
from PIL import Image, ImageFilter
from ..image_utils import image2mask, pil2tensor, tensor2pil
@@ -32,10 +32,18 @@ def fit_resize_image(
target_height,
fit_mode,
resize_sampler,
background_color="#000000",
background_color="black",
crop_position="center",
):
"""Resize image according to fit mode"""
if fit_mode == "letterbox":
if fit_mode == "resize":
# resize: 只等比缩放，不填充，直接返回缩放后的图像
scale = min(target_width / image.width, target_height / image.height)
new_width = int(image.width * scale)
new_height = int(image.height * scale)
return image.resize((new_width, new_height), resize_sampler)
if fit_mode in ["letterbox", "pad", "pad_edge", "pad_edge_pixel", "pillarbox_blur"]:
# Calculate scaling factor to fit within target dimensions
scale = min(target_width / image.width, target_height / image.height)
new_width = int(image.width * scale)
@@ -44,13 +52,164 @@ def fit_resize_image(
# Resize image
resized = image.resize((new_width, new_height), resize_sampler)
# Create new image with target dimensions and paste resized image
# Create background
if fit_mode == "pillarbox_blur":
# create scaled background then blur and dim
scale_fill = max(target_width / max(1, image.width), target_height / max(1, image.height))
bg_w = max(1, int(round(image.width * scale_fill)))
bg_h = max(1, int(round(image.height * scale_fill)))
bg = image.resize((bg_w, bg_h), Image.BILINEAR)
# center crop to canvas
x0 = max(0, (bg_w - target_width) // 2)
y0 = max(0, (bg_h - target_height) // 2)
bg = bg.crop((x0, y0, x0 + target_width, y0 + target_height))
sigma = max(1.0, 0.006 * float(min(target_width, target_height)))
bg = bg.filter(ImageFilter.GaussianBlur(radius=sigma))
# desaturate slightly if RGB
if bg.mode == "RGB":
r, g, b = bg.split()
# simple luminance
l = r.point(lambda v: int(0.2126 * v))
l = Image.merge("RGB", (l, l, l))
def mix(a, b, t=0.2):
return Image.blend(a, b, t)
bg = mix(bg, l)
# dim
bg = bg.point(lambda v: int(v * 0.35))
result = bg
elif fit_mode in ["pad_edge", "pad_edge_pixel"]:
# start with empty canvas
result = Image.new("RGB" if image.mode == "RGB" else "L", (target_width, target_height))
else:
if image.mode == "RGB":
result = Image.new("RGB", (target_width, target_height), background_color)
# preset color names
preset_colors = {
"black": "#000000",
"white": "#FFFFFF",
"gray": "#808080",
"red": "#FF0000",
"green": "#00FF00",
"blue": "#0000FF",
"yellow": "#FFFF00",
"cyan": "#00FFFF",
"magenta": "#FF00FF",
}
fill_color = preset_colors.get(str(background_color).lower(), background_color)
result = Image.new("RGB", (target_width, target_height), fill_color)
else:
result = Image.new("L", (target_width, target_height), 0)
# paste location
if crop_position == "center":
paste_x = (target_width - new_width) // 2
paste_y = (target_height - new_height) // 2
elif crop_position == "top":
paste_x = (target_width - new_width) // 2
paste_y = 0
elif crop_position == "bottom":
paste_x = (target_width - new_width) // 2
paste_y = target_height - new_height
elif crop_position == "left":
paste_x = 0
paste_y = (target_height - new_height) // 2
elif crop_position == "right":
paste_x = target_width - new_width
paste_y = (target_height - new_height) // 2
else:
paste_x = (target_width - new_width) // 2
paste_y = (target_height - new_height) // 2
# specialized edge padding behaviors
if fit_mode == "pad_edge" or fit_mode == "pad_edge_pixel":
left_pad = paste_x
right_pad = target_width - (paste_x + new_width)
top_pad = paste_y
bottom_pad = target_height - (paste_y + new_height)
# left/right stripes from image columns
if left_pad > 0:
col = resized.crop((0, 0, 1, new_height))
if fit_mode == "pad_edge_pixel":
col = col.resize((left_pad, new_height), Image.NEAREST)
result.paste(col, (0, paste_y))
else:
# mean color of left edge
if col.mode == "RGB":
pixels = list(col.getdata())
r = sum(p[0] for p in pixels) // len(pixels)
g = sum(p[1] for p in pixels) // len(pixels)
b = sum(p[2] for p in pixels) // len(pixels)
fill = (r, g, b)
else:
v = sum(col.getdata()) // len(col.getdata())
fill = v
Image.Image.paste(result, Image.new(result.mode, (left_pad, new_height), fill), (0, paste_y))
if right_pad > 0:
col = resized.crop((new_width - 1, 0, new_width, new_height))
if fit_mode == "pad_edge_pixel":
col = col.resize((right_pad, new_height), Image.NEAREST)
result.paste(col, (paste_x + new_width, paste_y))
else:
if col.mode == "RGB":
pixels = list(col.getdata())
r = sum(p[0] for p in pixels) // len(pixels)
g = sum(p[1] for p in pixels) // len(pixels)
b = sum(p[2] for p in pixels) // len(pixels)
fill = (r, g, b)
else:
v = sum(col.getdata()) // len(col.getdata())
fill = v
Image.Image.paste(result, Image.new(result.mode, (right_pad, new_height), fill), (paste_x + new_width, paste_y))
# top/bottom stripes from image rows
if top_pad > 0:
row = resized.crop((0, 0, new_width, 1))
if fit_mode == "pad_edge_pixel":
row = row.resize((new_width, top_pad), Image.NEAREST)
result.paste(row, (paste_x, 0))
# corners by corner pixels
if left_pad > 0:
c = resized.getpixel((0, 0))
Image.Image.paste(result, Image.new(result.mode, (left_pad, top_pad), c), (0, 0))
if right_pad > 0:
c = resized.getpixel((new_width - 1, 0))
Image.Image.paste(result, Image.new(result.mode, (right_pad, top_pad), c), (paste_x + new_width, 0))
else:
if row.mode == "RGB":
pixels = list(row.getdata())
r = sum(p[0] for p in pixels) // len(pixels)
g = sum(p[1] for p in pixels) // len(pixels)
b = sum(p[2] for p in pixels) // len(pixels)
fill = (r, g, b)
else:
v = sum(row.getdata()) // len(row.getdata())
fill = v
Image.Image.paste(result, Image.new(result.mode, (target_width, top_pad), fill), (0, 0))
if bottom_pad > 0:
row = resized.crop((0, new_height - 1, new_width, new_height))
if fit_mode == "pad_edge_pixel":
row = row.resize((new_width, bottom_pad), Image.NEAREST)
result.paste(row, (paste_x, paste_y + new_height))
if left_pad > 0:
c = resized.getpixel((0, new_height - 1))
Image.Image.paste(result, Image.new(result.mode, (left_pad, bottom_pad), c), (0, paste_y + new_height))
if right_pad > 0:
c = resized.getpixel((new_width - 1, new_height - 1))
Image.Image.paste(result, Image.new(result.mode, (right_pad, bottom_pad), c), (paste_x + new_width, paste_y + new_height))
else:
if row.mode == "RGB":
pixels = list(row.getdata())
r = sum(p[0] for p in pixels) // len(pixels)
g = sum(p[1] for p in pixels) // len(pixels)
b = sum(p[2] for p in pixels) // len(pixels)
fill = (r, g, b)
else:
v = sum(row.getdata()) // len(row.getdata())
fill = v
Image.Image.paste(result, Image.new(result.mode, (target_width, bottom_pad), fill), (0, paste_y + new_height))
# finally paste the resized content
result.paste(resized, (paste_x, paste_y))
return result
@@ -64,13 +223,29 @@ def fit_resize_image(
resized = image.resize((new_width, new_height), resize_sampler)
# Crop to target dimensions
if crop_position == "center":
crop_x = (new_width - target_width) // 2
crop_y = (new_height - target_height) // 2
elif crop_position == "top":
crop_x = (new_width - target_width) // 2
crop_y = 0
elif crop_position == "bottom":
crop_x = (new_width - target_width) // 2
crop_y = new_height - target_height
elif crop_position == "left":
crop_x = 0
crop_y = (new_height - target_height) // 2
elif crop_position == "right":
crop_x = new_width - target_width
crop_y = (new_height - target_height) // 2
else:
crop_x = (new_width - target_width) // 2
crop_y = (new_height - target_height) // 2
return resized.crop(
(crop_x, crop_y, crop_x + target_width, crop_y + target_height)
)
else: # fill
else: # stretch/fill
# Simple resize to target dimensions
return image.resize((target_width, target_height), resize_sampler)
@@ -80,7 +255,15 @@ class ImageMaskScaleAs_UTK:
@classmethod
def INPUT_TYPES(cls):
fit_mode = ["letterbox", "crop", "fill"]
fit_mode = [
"stretch",
"resize",
"pad",
"pad_edge",
"pad_edge_pixel",
"crop",
"pillarbox_blur",
]
method_mode = ["lanczos", "bicubic", "hamming", "bilinear", "box", "nearest"]
return {
@@ -92,6 +275,18 @@ class ImageMaskScaleAs_UTK:
"optional": {
"image": ("IMAGE",), #
"mask": ("MASK",), #
"pad_color": ([
"black",
"white",
"gray",
"red",
"green",
"blue",
"yellow",
"cyan",
"magenta",
], {"default": "black"}),
"crop_position": (["center", "top", "bottom", "left", "right"], {"default": "center"}),
},
}
@@ -112,6 +307,8 @@ class ImageMaskScaleAs_UTK:
method,
image=None,
mask=None,
pad_color="black",
crop_position="center",
):
if scale_as.shape[0] > 0:
_asimage = tensor2pil(scale_as[0])
@@ -137,14 +334,20 @@ class ImageMaskScaleAs_UTK:
ret_images = []
ret_masks = []
output_width = target_width
output_height = target_height
if image is not None:
for i in image:
i = torch.unsqueeze(i, 0)
_image = tensor2pil(i).convert("RGB")
orig_width, orig_height = _image.size
_image = fit_resize_image(
_image, target_width, target_height, fit, resize_sampler
_image, target_width, target_height, fit, resize_sampler, pad_color, crop_position
)
# For resize mode, use actual image size instead of target size
if fit == "resize":
output_width, output_height = _image.size
ret_images.append(pil2tensor(_image))
if mask is not None:
if mask.dim() == 2:
@@ -153,9 +356,13 @@ class ImageMaskScaleAs_UTK:
m = torch.unsqueeze(m, 0)
_mask = tensor2pil(m).convert("L")
orig_width, orig_height = _mask.size
# Mask padding背景始终为黑
_mask = fit_resize_image(
_mask, target_width, target_height, fit, resize_sampler
_mask, target_width, target_height, fit, resize_sampler, "#000000", crop_position
).convert("L")
# For resize mode, use actual mask size instead of target size
if fit == "resize":
output_width, output_height = _mask.size
ret_masks.append(image2mask(_mask))
if len(ret_images) > 0 and len(ret_masks) > 0:
log(
@@ -166,8 +373,8 @@ class ImageMaskScaleAs_UTK:
torch.cat(ret_images, dim=0),
torch.cat(ret_masks, dim=0),
[orig_width, orig_height],
target_width,
target_height,
output_width,
output_height,
)
elif len(ret_images) > 0 and len(ret_masks) == 0:
log(
@@ -178,8 +385,8 @@ class ImageMaskScaleAs_UTK:
torch.cat(ret_images, dim=0),
None,
[orig_width, orig_height],
target_width,
target_height,
output_width,
output_height,
)
elif len(ret_images) == 0 and len(ret_masks) > 0:
log(
@@ -190,8 +397,8 @@ class ImageMaskScaleAs_UTK:
None,
torch.cat(ret_masks, dim=0),
[orig_width, orig_height],
target_width,
target_height,
output_width,
output_height,
)
else:
log(
nodes/image/image_scale_by_aspect_ratio.py
+190 -13
View File
@@ -11,7 +11,7 @@ Scales images to specific aspect ratios with various fitting modes.
import math
import torch
from PIL import Image
from PIL import Image, ImageFilter
from ..image_utils import (fit_resize_image, image2mask, is_valid_mask, log,
num_round_up_to_multiple, pil2tensor, tensor2pil)
@@ -35,10 +35,23 @@ def num_round_up_to_multiple(num, multiple):
def fit_resize_image(
image, target_width, target_height, fit_mode, resize_sampler, background_color
image,
target_width,
target_height,
fit_mode,
resize_sampler,
background_color,
crop_position="center",
):
"""Resize image according to fit mode"""
if fit_mode == "letterbox":
if fit_mode == "resize":
# resize: 只等比缩放，不填充，直接返回缩放后的图像
scale = min(target_width / image.width, target_height / image.height)
new_width = int(image.width * scale)
new_height = int(image.height * scale)
return image.resize((new_width, new_height), resize_sampler)
if fit_mode in ["letterbox", "pad", "pad_edge", "pad_edge_pixel", "pillarbox_blur"]:
# Calculate scaling factor to fit within target dimensions
scale = min(target_width / image.width, target_height / image.height)
new_width = int(image.width * scale)
@@ -47,10 +60,127 @@ def fit_resize_image(
# Resize image
resized = image.resize((new_width, new_height), resize_sampler)
# Create new image with target dimensions and paste resized image
# Create background
if fit_mode == "pillarbox_blur":
scale_fill = max(target_width / max(1, image.width), target_height / max(1, image.height))
bg_w = max(1, int(round(image.width * scale_fill)))
bg_h = max(1, int(round(image.height * scale_fill)))
bg = image.resize((bg_w, bg_h), Image.BILINEAR)
x0 = max(0, (bg_w - target_width) // 2)
y0 = max(0, (bg_h - target_height) // 2)
bg = bg.crop((x0, y0, x0 + target_width, y0 + target_height))
sigma = max(1.0, 0.006 * float(min(target_width, target_height)))
bg = bg.filter(ImageFilter.GaussianBlur(radius=sigma))
if bg.mode == "RGB":
r, g, b = bg.split()
l = r.point(lambda v: int(0.2126 * v))
l = Image.merge("RGB", (l, l, l))
bg = Image.blend(bg, l, 0.2)
bg = bg.point(lambda v: int(v * 0.35))
result = bg
else:
result = Image.new(image.mode, (target_width, target_height), background_color)
# paste position
if crop_position == "center":
paste_x = (target_width - new_width) // 2
paste_y = (target_height - new_height) // 2
elif crop_position == "top":
paste_x = (target_width - new_width) // 2
paste_y = 0
elif crop_position == "bottom":
paste_x = (target_width - new_width) // 2
paste_y = target_height - new_height
elif crop_position == "left":
paste_x = 0
paste_y = (target_height - new_height) // 2
elif crop_position == "right":
paste_x = target_width - new_width
paste_y = (target_height - new_height) // 2
else:
paste_x = (target_width - new_width) // 2
paste_y = (target_height - new_height) // 2
# Apply pad_edge / pad_edge_pixel stripes
if fit_mode in ["pad_edge", "pad_edge_pixel"]:
left_pad = paste_x
right_pad = target_width - (paste_x + new_width)
top_pad = paste_y
bottom_pad = target_height - (paste_y + new_height)
if left_pad > 0:
col = resized.crop((0, 0, 1, new_height))
if fit_mode == "pad_edge_pixel":
result.paste(col.resize((left_pad, new_height), Image.NEAREST), (0, paste_y))
else:
if col.mode == "RGB":
pixels = list(col.getdata())
r = sum(p[0] for p in pixels) // len(pixels)
g = sum(p[1] for p in pixels) // len(pixels)
b = sum(p[2] for p in pixels) // len(pixels)
fill = (r, g, b)
else:
v = sum(col.getdata()) // len(col.getdata())
fill = v
Image.Image.paste(result, Image.new(result.mode, (left_pad, new_height), fill), (0, paste_y))
if right_pad > 0:
col = resized.crop((new_width - 1, 0, new_width, new_height))
if fit_mode == "pad_edge_pixel":
result.paste(col.resize((right_pad, new_height), Image.NEAREST), (paste_x + new_width, paste_y))
else:
if col.mode == "RGB":
pixels = list(col.getdata())
r = sum(p[0] for p in pixels) // len(pixels)
g = sum(p[1] for p in pixels) // len(pixels)
b = sum(p[2] for p in pixels) // len(pixels)
fill = (r, g, b)
else:
v = sum(col.getdata()) // len(col.getdata())
fill = v
Image.Image.paste(result, Image.new(result.mode, (right_pad, new_height), fill), (paste_x + new_width, paste_y))
if top_pad > 0:
row = resized.crop((0, 0, new_width, 1))
if fit_mode == "pad_edge_pixel":
result.paste(row.resize((new_width, top_pad), Image.NEAREST), (paste_x, 0))
if left_pad > 0:
c = resized.getpixel((0, 0))
Image.Image.paste(result, Image.new(result.mode, (left_pad, top_pad), c), (0, 0))
if right_pad > 0:
c = resized.getpixel((new_width - 1, 0))
Image.Image.paste(result, Image.new(result.mode, (right_pad, top_pad), c), (paste_x + new_width, 0))
else:
if row.mode == "RGB":
pixels = list(row.getdata())
r = sum(p[0] for p in pixels) // len(pixels)
g = sum(p[1] for p in pixels) // len(pixels)
b = sum(p[2] for p in pixels) // len(pixels)
fill = (r, g, b)
else:
v = sum(row.getdata()) // len(row.getdata())
fill = v
Image.Image.paste(result, Image.new(result.mode, (target_width, top_pad), fill), (0, 0))
if bottom_pad > 0:
row = resized.crop((0, new_height - 1, new_width, new_height))
if fit_mode == "pad_edge_pixel":
result.paste(row.resize((new_width, bottom_pad), Image.NEAREST), (paste_x, paste_y + new_height))
if left_pad > 0:
c = resized.getpixel((0, new_height - 1))
Image.Image.paste(result, Image.new(result.mode, (left_pad, bottom_pad), c), (0, paste_y + new_height))
if right_pad > 0:
c = resized.getpixel((new_width - 1, new_height - 1))
Image.Image.paste(result, Image.new(result.mode, (right_pad, bottom_pad), c), (paste_x + new_width, paste_y + new_height))
else:
if row.mode == "RGB":
pixels = list(row.getdata())
r = sum(p[0] for p in pixels) // len(pixels)
g = sum(p[1] for p in pixels) // len(pixels)
b = sum(p[2] for p in pixels) // len(pixels)
fill = (r, g, b)
else:
v = sum(row.getdata()) // len(row.getdata())
fill = v
Image.Image.paste(result, Image.new(result.mode, (target_width, bottom_pad), fill), (0, paste_y + new_height))
result.paste(resized, (paste_x, paste_y))
return result
@@ -64,6 +194,22 @@ def fit_resize_image(
resized = image.resize((new_width, new_height), resize_sampler)
# Crop to target dimensions
if crop_position == "center":
crop_x = (new_width - target_width) // 2
crop_y = (new_height - target_height) // 2
elif crop_position == "top":
crop_x = (new_width - target_width) // 2
crop_y = 0
elif crop_position == "bottom":
crop_x = (new_width - target_width) // 2
crop_y = new_height - target_height
elif crop_position == "left":
crop_x = 0
crop_y = (new_height - target_height) // 2
elif crop_position == "right":
crop_x = new_width - target_width
crop_y = (new_height - target_height) // 2
else:
crop_x = (new_width - target_width) // 2
crop_y = (new_height - target_height) // 2
return resized.crop(
@@ -91,7 +237,15 @@ class ImageScaleByAspectRatio_UTK:
"3:4",
"9:16",
]
fit_mode = ["letterbox", "crop", "fill"]
fit_mode = [
"stretch",
"resize",
"pad",
"pad_edge",
"pad_edge_pixel",
"crop",
"pillarbox_blur",
]
method_mode = ["lanczos", "bicubic", "hamming", "bilinear", "box", "nearest"]
multiple_list = ["8", "16", "32", "64", "128", "256", "512", "None"]
scale_to_list = [
@@ -121,7 +275,18 @@ class ImageScaleByAspectRatio_UTK:
"INT",
{"default": 1024, "min": 4, "max": 1e8, "step": 1},
),
"background_color": ("STRING", {"default": "#000000"}),
"background_color": ([
"black",
"white",
"gray",
"red",
"green",
"blue",
"yellow",
"cyan",
"magenta",
], {"default": "black"}),
"crop_position": (["center", "top", "bottom", "left", "right"], {"default": "center"}),
},
"optional": {
"image": ("IMAGE",),
@@ -158,6 +323,7 @@ class ImageScaleByAspectRatio_UTK:
scale_to_side,
scale_to_length,
background_color,
crop_position,
image=None,
mask=None,
):
@@ -294,6 +460,9 @@ class ImageScaleByAspectRatio_UTK:
elif method == "nearest":
resize_sampler = Image.NEAREST
output_width = target_width
output_height = target_height
if len(orig_images) > 0:
for i in orig_images:
_image = tensor2pil(i).convert("RGB")
@@ -304,7 +473,11 @@ class ImageScaleByAspectRatio_UTK:
fit,
resize_sampler,
background_color,
crop_position,
)
# For resize mode, use actual image size instead of target size
if fit == "resize":
output_width, output_height = _image.size
ret_images.append(pil2tensor(_image))
if len(orig_masks) > 0:
for m in orig_masks:
@@ -315,8 +488,12 @@ class ImageScaleByAspectRatio_UTK:
target_height,
fit,
resize_sampler,
background_color,
"black",
crop_position,
).convert("L")
# For resize mode, use actual mask size instead of target size
if fit == "resize":
output_width, output_height = _mask.size
ret_masks.append(image2mask(_mask))
if len(ret_images) > 0 and len(ret_masks) > 0:
log(
@@ -327,8 +504,8 @@ class ImageScaleByAspectRatio_UTK:
torch.cat(ret_images, dim=0),
torch.cat(ret_masks, dim=0),
[orig_width, orig_height],
target_width,
target_height,
output_width,
output_height,
len(ret_images),
)
elif len(ret_images) > 0 and len(ret_masks) == 0:
@@ -340,8 +517,8 @@ class ImageScaleByAspectRatio_UTK:
torch.cat(ret_images, dim=0),
None,
[orig_width, orig_height],
target_width,
target_height,
output_width,
output_height,
len(ret_images),
)
elif len(ret_images) == 0 and len(ret_masks) > 0:
@@ -353,8 +530,8 @@ class ImageScaleByAspectRatio_UTK:
None,
torch.cat(ret_masks, dim=0),
[orig_width, orig_height],
target_width,
target_height,
output_width,
output_height,
len(ret_masks),
)
else:
nodes/image/resize_image_ver_kj.py
+264
View File
@@ -0,0 +1,264 @@
import math
import torch
import torch.nn.functional as F
from comfy import model_management
from comfy.utils import common_upscale
class ResizeImageVerKJ_UTK:
upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"width": ("INT", {"default": 512, "min": 0, "max": 16384, "step": 1}),
"height": ("INT", {"default": 512, "min": 0, "max": 16384, "step": 1}),
"upscale_method": (cls.upscale_methods,),
"keep_proportion": (
[
"stretch",
"resize",
"pad",
"pad_edge",
"pad_edge_pixel",
"crop",
"pillarbox_blur",
"total_pixels",
],
{"default": "resize"},
),
"pad_color": ("STRING", {"default": "0, 0, 0"}),
"crop_position": ("STRING", {"default": "center"}),
"divisible_by": ("INT", {"default": 2, "min": 0, "max": 512, "step": 1}),
},
"optional": {
"mask": ("MASK",),
"device": (["cpu", "gpu"], {"default": "cpu"}),
},
}
RETURN_TYPES = ("IMAGE", "INT", "INT", "MASK")
RETURN_NAMES = ("IMAGE", "width", "height", "mask")
FUNCTION = "resize"
CATEGORY = "UniversalToolkit/Image"
def _parse_color(self, s: str, dtype, device):
try:
vals = [int(x.strip()) for x in s.split(",")]
except Exception:
vals = [0, 0, 0]
if len(vals) == 1:
vals = vals * 3
vals = [max(0, min(255, v)) / 255.0 for v in vals[:3]]
return torch.tensor(vals, dtype=dtype, device=device)
def _compute_crop_rect(self, old_w, old_h, target_w, target_h, position: str):
old_aspect = old_w / old_h
new_aspect = target_w / target_h
if old_aspect > new_aspect:
crop_w = round(old_h * new_aspect)
crop_h = old_h
else:
crop_w = old_w
crop_h = round(old_w / new_aspect)
if position == "center":
x = (old_w - crop_w) // 2
y = (old_h - crop_h) // 2
elif position == "top":
x, y = (old_w - crop_w) // 2, 0
elif position == "bottom":
x, y = (old_w - crop_w) // 2, old_h - crop_h
elif position == "left":
x, y = 0, (old_h - crop_h) // 2
elif position == "right":
x, y = old_w - crop_w, (old_h - crop_h) // 2
else:
x, y = (old_w - crop_w) // 2, (old_h - crop_h) // 2
return x, y, crop_w, crop_h
def resize(self, image: torch.Tensor, width: int, height: int, keep_proportion: str, upscale_method: str,
divisible_by: int, pad_color: str, crop_position: str, device: str = "cpu", mask: torch.Tensor = None):
B, H, W, C = image.shape
if device == "gpu":
if upscale_method == "lanczos":
raise Exception("Lanczos is not supported on the GPU")
torch_device = model_management.get_torch_device()
else:
torch_device = torch.device("cpu")
pillarbox_blur = keep_proportion == "pillarbox_blur"
pad_left = pad_right = pad_top = pad_bottom = 0
if keep_proportion in ["resize", "total_pixels", "pad", "pad_edge", "pad_edge_pixel", "pillarbox_blur"]:
if keep_proportion == "total_pixels":
total_pixels = max(1, width * height)
aspect_ratio = W / H if H != 0 else 1.0
new_h = int(math.sqrt(total_pixels / aspect_ratio))
new_w = int(math.sqrt(total_pixels * aspect_ratio))
elif width == 0 and height == 0:
new_w, new_h = W, H
elif width == 0 and height != 0:
ratio = height / H if H != 0 else 1.0
new_w, new_h = round(W * ratio), height
elif height == 0 and width != 0:
ratio = width / W if W != 0 else 1.0
new_w, new_h = width, round(H * ratio)
else:
ratio = min(width / W if W != 0 else 1.0, height / H if H != 0 else 1.0)
new_w, new_h = max(1, round(W * ratio)), max(1, round(H * ratio))
if keep_proportion in ["pad", "pad_edge", "pad_edge_pixel", "pillarbox_blur"]:
if crop_position == "center":
pad_left = (width - new_w) // 2
pad_right = width - new_w - pad_left
pad_top = (height - new_h) // 2
pad_bottom = height - new_h - pad_top
elif crop_position == "top":
pad_left = (width - new_w) // 2
pad_right = width - new_w - pad_left
pad_top = 0
pad_bottom = height - new_h
elif crop_position == "bottom":
pad_left = (width - new_w) // 2
pad_right = width - new_w - pad_left
pad_top = height - new_h
pad_bottom = 0
elif crop_position == "left":
pad_left = 0
pad_right = width - new_w
pad_top = (height - new_h) // 2
pad_bottom = height - new_h - pad_top
elif crop_position == "right":
pad_left = width - new_w
pad_right = 0
pad_top = (height - new_h) // 2
pad_bottom = height - new_h - pad_top
width, height = new_w, new_h
else:
# stretch or crop path keeps requested width/height directly
if width == 0:
width = W
if height == 0:
height = H
if divisible_by > 1:
width = width - (width % divisible_by)
height = height - (height % divisible_by)
# Crop prior to resizing
x_in = image if image.device == torch_device else image.to(torch_device)
m_in = None if mask is None else (mask if mask.device == torch_device else mask.to(torch_device))
if keep_proportion == "crop":
x, y, cw, ch = self._compute_crop_rect(W, H, width, height, crop_position)
x_in = x_in.narrow(-2, x, cw).narrow(-3, y, ch)
if m_in is not None:
m_in = m_in.narrow(-1, x, cw).narrow(-2, y, ch)
# Resize image and optional mask
out_img = common_upscale(x_in.movedim(-1, 1), width, height, upscale_method, crop="disabled").movedim(1, -1)
out_m = None
if m_in is not None:
if upscale_method == "lanczos":
out_m = common_upscale(m_in.unsqueeze(1).repeat(1, 3, 1, 1), width, height, upscale_method, crop="disabled").movedim(1, -1)[:, :, :, 0]
else:
out_m = common_upscale(m_in.unsqueeze(1), width, height, upscale_method, crop="disabled").squeeze(1)
# resize mode: just return the resized image, no padding
if keep_proportion == "resize":
return (out_img.cpu(), out_img.shape[2], out_img.shape[1], out_m.cpu() if out_m is not None else torch.zeros(64, 64))
# Padding if requested
if (keep_proportion in ["pad", "pad_edge", "pad_edge_pixel", "pillarbox_blur"]) and (pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0):
padded_w = width + pad_left + pad_right
padded_h = height + pad_top + pad_bottom
if divisible_by > 1:
w_rem = padded_w % divisible_by
h_rem = padded_h % divisible_by
if w_rem > 0:
pad_right += divisible_by - w_rem
if h_rem > 0:
pad_bottom += divisible_by - h_rem
padded_w = width + pad_left + pad_right
padded_h = height + pad_top + pad_bottom
if keep_proportion == "pad_edge" or keep_proportion == "pad_edge_pixel":
# Build canvas and apply edge/edge_pixel logic similar to KJ implementation
canvas = torch.zeros((B, padded_h, padded_w, C), dtype=out_img.dtype, device=out_img.device)
for b in range(B):
# content
canvas[b, pad_top:pad_top+height, pad_left:pad_left+width, :] = out_img[b]
if keep_proportion == "pad_edge":
# mean color along edges
top_edge = out_img[b, 0, :, :]
bottom_edge = out_img[b, height-1, :, :]
left_edge = out_img[b, :, 0, :]
right_edge = out_img[b, :, width-1, :]
if pad_top > 0:
canvas[b, :pad_top, :, :] = top_edge.mean(dim=0)
if pad_bottom > 0:
canvas[b, pad_top+height:, :, :] = bottom_edge.mean(dim=0)
if pad_left > 0:
canvas[b, :, :pad_left, :] = left_edge.mean(dim=0)
if pad_right > 0:
canvas[b, :, pad_left+width:, :] = right_edge.mean(dim=0)
else:
# edge_pixel: extend exact edge rows/columns
if pad_top > 0:
row = out_img[b, 0:1, :, :].expand(pad_top, width, C)
canvas[b, :pad_top, pad_left:pad_left+width, :] = row
# corners
if pad_left > 0:
tl = out_img[b, 0, 0, :]
canvas[b, :pad_top, :pad_left, :] = tl
if pad_right > 0:
tr = out_img[b, 0, width-1, :]
canvas[b, :pad_top, pad_left+width:, :] = tr
if pad_bottom > 0:
row = out_img[b, height-1:height, :, :].expand(pad_bottom, width, C)
canvas[b, pad_top+height:, pad_left:pad_left+width, :] = row
if pad_left > 0:
bl = out_img[b, height-1, 0, :]
canvas[b, pad_top+height:, :pad_left, :] = bl
if pad_right > 0:
br = out_img[b, height-1, width-1, :]
canvas[b, pad_top+height:, pad_left+width:, :] = br
if pad_left > 0:
col = out_img[b, :, 0:1, :].expand(height, pad_left, C)
canvas[b, pad_top:pad_top+height, :pad_left, :] = col
if pad_right > 0:
col = out_img[b, :, width-1:width, :].expand(height, pad_right, C)
canvas[b, pad_top:pad_top+height, pad_left+width:, :] = col
out_img = canvas
if out_m is not None:
# replicate for mask to keep crisp edges
out_m = F.pad(out_m.unsqueeze(1), (pad_left, pad_right, pad_top, pad_bottom), mode="replicate").squeeze(1)
else:
# color padding
bg = self._parse_color(pad_color, out_img.dtype, out_img.device)
canvas = torch.zeros((B, padded_h, padded_w, C), dtype=out_img.dtype, device=out_img.device)
canvas[:, :, :, 0] = bg[0]
if C > 1:
canvas[:, :, :, 1] = bg[1]
if C > 2:
canvas[:, :, :, 2] = bg[2]
canvas[:, pad_top:pad_top+height, pad_left:pad_left+width, :] = out_img
out_img = canvas
if out_m is not None:
mcanvas = torch.zeros((B, padded_h, padded_w), dtype=out_img.dtype, device=out_img.device)
mcanvas[:, pad_top:pad_top+height, pad_left:pad_left+width] = out_m
out_m = mcanvas
return (out_img.cpu(), out_img.shape[2], out_img.shape[1], out_m.cpu() if out_m is not None else torch.zeros(64, 64))
NODE_CLASS_MAPPINGS = {"ResizeImageVerKJ_UTK": ResizeImageVerKJ_UTK}
NODE_DISPLAY_NAME_MAPPINGS = {"ResizeImageVerKJ_UTK": "Resize Image ver KJ (UTK)"}
nodes/mask/blockify_mask.py
+60
View File
@@ -0,0 +1,60 @@
import torch
import torch.nn.functional as F
class BlockifyMask_UTK:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"masks": ("MASK",),
"block_size": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}),
"device": (["cpu", "cuda"], {"default": "cpu"}),
},
"optional": {
# 可选二值化
"binarize": ("BOOLEAN", {"default": True}),
"threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("mask",)
FUNCTION = "blockify"
CATEGORY = "UniversalToolkit/Mask"
DESCRIPTION = "将连续掩码按 block_size 进行像素块化（马赛克化），可选二值化。"
def blockify(self, masks: torch.Tensor, block_size: int, device: str, binarize: bool = True, threshold: float = 0.5):
mask_tensor = masks
if block_size <= 1:
out = torch.clamp(mask_tensor, 0.0, 1.0)
return (out,)
# 选择设备（多数情况下 CPU 足够；如选 cuda 则尝试放到 GPU）
use_cuda = device == "cuda" and torch.cuda.is_available()
x_in = mask_tensor
if use_cuda:
x_in = x_in.to("cuda")
# BxHxW -> Bx1xHxW for pooling
x = x_in.unsqueeze(1).contiguous()
# 平均池化到较小网格；ceil 对齐，边缘使用对称填充避免尺寸不整除
pooled = F.avg_pool2d(x, kernel_size=block_size, stride=block_size, ceil_mode=True)
# 还原到原尺寸，使用最近邻形成块状
out = F.interpolate(pooled, size=(mask_tensor.shape[1], mask_tensor.shape[2]), mode="nearest").squeeze(1)
if binarize:
out = (out >= threshold).float()
out = torch.clamp(out, 0.0, 1.0)
if use_cuda:
out = out.to("cpu")
return (out,)
NODE_CLASS_MAPPINGS = {"BlockifyMask_UTK": BlockifyMask_UTK}
NODE_DISPLAY_NAME_MAPPINGS = {"BlockifyMask_UTK": "Blockify Mask (UTK)"}
nodes/tools/get_image_range_from_batch.py
+1 -1
View File
@@ -11,7 +11,7 @@ class GetImageRangeFromBatch_UTK:
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("image", "mask")
FUNCTION = "get_range_from_batch"
CATEGORY = "UniversalToolkit/tools"
CATEGORY = "UniversalToolkit/Tools"
@classmethod
def INPUT_TYPES(cls):
nodes/tools/optimal_context_window_node.py
+84
View File
@@ -0,0 +1,84 @@
class BestContextWindow_UTK:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"total_frames": ("INT", {"default": 1, "min": 0}),
"min_window_frames": ("INT", {"default": 61, "min": 1}),
"max_window_frames": ("INT", {"default": 81, "min": 1}),
}
}
RETURN_TYPES = (
"INT", # best_window
"INT", # padding (冗余帧数)
"INT", # padded_total 实际处理帧数
"INT", # segments 段数k
)
RETURN_NAMES = (
"best_window",
"padding",
"padded_total",
"segments",
)
FUNCTION = "compute"
CATEGORY = "UniversalToolkit/Tools"
@staticmethod
def _best_window(total_frames: int, min_window: int, max_window: int) -> tuple[int, int, int, int]:
# sanitize inputs
if max_window is None or max_window < 1:
max_window = 1
if total_frames is None or total_frames < 0:
total_frames = 0
if min_window is None or min_window < 1:
min_window = 1
if max_window < min_window:
max_window = min_window
# generate candidates that satisfy 4n+1 and within [min_window, max_window]
candidates = []
# align start to first (4n+1) >= min_window
start = min_window if min_window % 4 == 1 else (min_window + (4 - ((min_window - 1) % 4) - 1))
for w in range(start, max_window + 1):
if w % 4 == 1:
candidates.append(w)
if not candidates:
# Fallback: choose closest valid 4n+1 not exceeding max_window
# compute nearest below max_window
w = max_window - ((max_window - 1) % 4)
if w < 1:
w = 1
candidates = [w]
# choose window minimizing padding to next multiple; tie -> larger window
def metrics(w: int):
k = (total_frames + w - 1) // w # ceil(total_frames / w)
padding = k * w - total_frames
padded_total = k * w
return padding, padded_total, k
best_w = candidates[0]
best_pad, best_padded_total, best_k = metrics(best_w)
for w in candidates[1:]:
pad, padded_total, k = metrics(w)
if pad < best_pad or (pad == best_pad and w > best_w):
best_w, best_pad, best_padded_total, best_k = w, pad, padded_total, k
return best_w, best_pad, best_padded_total, best_k
def compute(self, total_frames: int, min_window_frames: int, max_window_frames: int):
best_w, padding, padded_total, k = self._best_window(int(total_frames), int(min_window_frames), int(max_window_frames))
return (best_w, padding, padded_total, k)
NODE_CLASS_MAPPINGS = {
"BestContextWindow_UTK": BestContextWindow_UTK,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"BestContextWindow_UTK": "Best Context Window (UTK)",
}
pyproject.toml
+1 -1
View File
@@ -1,7 +1,7 @@
[project]
name = "universaltoolkit"
description = "A comprehensive toolkit based on ComfyUI, providing image, mask, audio, and tools nodes, fully modular and v3 compatible."
version = "1.4.4"
version = "1.4.8"
license = {file = "LICENSE"}
dependencies = [
"torch>=1.9.0",