diff --git a/README.MD b/README.MD
index 8454687..7637075 100644
--- a/README.MD
+++ b/README.MD
@@ -17,6 +17,7 @@ Nodes are divided into 5 groups according to their functions: LayerStyle, LayerC
## Update
**If the dependency package error after updating, please reinstall the relevant dependency packages. for details, please refer to [here](https://github.com/chflame163/ComfyUI_LayerStyle/issues/5).
+* Commit [ImageScaleByAspectRatio](#ImageScaleByAspectRatio) node, it can be scaled image or mask according to frame ratio.
* Fix the bug of color gradation in [LUT Apply](#LUT) node rendering, and this node now support for log color space. *Please load the dedicated log lut file for the log color space image.
* Commit [CreateGradientMask](#CreateGradientMask) node. Commit [LayerImageTransform](#LayerImageTransform) and [LayerMaskTransform](#LayerMaskTransform) nodes.
* Commit [MaskEdgeUltraDetail](#MaskEdgeUltraDetail) node, it process rough masks to ultra fine edges.Commit [Exposure](#Exposure) node.
@@ -394,6 +395,25 @@ Outputs:
* mask: If there is a mask input, the scaled mask will be output.
* original_size: The original size data of the image is used for subsequent node recovery.
+### ImageScaleByAspectRatio
+Scale the image or mask by aspect ratio. the scaled size can be rounded to a multiple of 8 or 16, and can be scaled to the long side size.
+
+
+Node options:
+
+* aspect_ratio: Here are several common frame ratios provided. alternatively, you can customize the scale using "custom".
+* proportional_width: Proportional width. if the aspect ratio option is not "custom", this setting will be ignored.
+* proportional_height: Proportional height. if the aspect ratio option is not "custom", this setting will be ignored.
+* fit: Scale aspect ratio mode. when the width to height ratio of the original image does not match the scaled size, there are three modes to choose from,
+The _letterbox_ mode retains the complete frame and fills in the blank spaces with black;
+The _crop_ mode retains the complete short edge, and any excess of the long edge will be cut off;
+The _fill_ mode does not maintain frame ratio and fills the screen with width and height.
+* method: Scaling sampling methods, including lanczos, bicubic, hamming, bilinear, box, and nearest.
+* round_to_multiple: Round multiples. for example, setting it to 8 will force the width and height to be multiples of 8.
+* scale_by_longest_side: Allow scaling by long edge size.
+* longest_side: When the scale_by_longest_side is set to True, this will be used this value to the long edge of the image. when the original_size have input, this setting will be ignored.
+
+
### ImageShift
Shift the image. this node supports the output of displacement seam masks, making it convenient to create continuous textures.
diff --git a/README_CN.MD b/README_CN.MD
index 011dff8..bcb47fa 100644
--- a/README_CN.MD
+++ b/README_CN.MD
@@ -13,6 +13,7 @@
## 更新说明
**如果本插件更新后出现依赖包错误，请重新安装相关依赖包。详情见[这里](https://github.com/chflame163/ComfyUI_LayerStyle/issues/5)。
+* 添加 [ImageScaleByAspectRatio](#ImageScaleByAspectRatio) 节点， 可按画幅比例缩放图像。
* 改正 [LUT Apply](#LUT) 节点渲染出现色阶的bug, 并增加log色彩空间支持。*log色彩空间图片请加载专门的log lut。
* 添加[CreateGradientMask](#CreateGradientMask) 节点。添加 [LayerImageTransform](#LayerImageTransform) 和 [LayerMaskTransform](#LayerMaskTransform) 节点。
* 添加[MaskEdgeUltraDetail](#MaskEdgeUltraDetail) 节点，给粗糙的遮罩进行处理获得超精细的边缘。添加 [Exposure](#Exposure) 节点，调整图像曝光。
@@ -375,6 +376,28 @@
*仅限输入image和mask, 如果强制接入其他类型输入，将导致节点错误。
+输出:
+* image: 如果有image输入，将输出缩放后的图像。
+* mask: 如果有mask输入，将输出缩放后的遮罩。
+* original_size: 图像的原始大小数据，用于后续节点进行恢复。
+
+
+### ImageScaleByAspectRatio
+将图像或遮罩按宽高比缩放。可设置将缩放后的尺寸按8或者16的倍数取整，可按长边尺寸缩放。
+
+
+节点选项说明:
+
+* aspect_ratio: 宽高比。此处提供了几个常见画幅比例。也可选"custom"自定义比例。
+* proportional_width: 比例宽。如果aspect_ratio选项不是"custom"，此处设置将被忽略。
+* proportional_height: 比例高。如果aspect_ratio选项不是"custom"，此处设置将被忽略。
+* fit: 缩放画幅宽高比模式。有3种模式可以选择, letterbox模式保留完整的画幅，空白处用黑色补足；crop模式保留完整的短边，长边超出部分将被切除；fill模式不保持画幅比例，宽高各自填满画面。
+* method: 缩放的采样方法，包括lanczos、bicubic、hamming、bilinear、box和nearest。
+* round_to_multiple: 倍数取整。例如设置为8，宽和高将强制设置为8的倍数。
+* scale_by_longest_side: 允许按长边尺寸缩放。
+* longest_side: scale_by_longest_side被设置为True时，此项将作为是图像长边的长度。
+
+
输出:
* image: 如果有image输入，将输出缩放后的图像。
* mask: 如果有mask输入，将输出缩放后的遮罩。
diff --git a/image/image_scale_by_aspect_ratio_example.png b/image/image_scale_by_aspect_ratio_example.png
new file mode 100644
index 0000000..9b0a137
Binary files /dev/null and b/image/image_scale_by_aspect_ratio_example.png differ
diff --git a/image/image_scale_by_aspect_ratio_node.png b/image/image_scale_by_aspect_ratio_node.png
new file mode 100644
index 0000000..8d9f77f
Binary files /dev/null and b/image/image_scale_by_aspect_ratio_node.png differ
diff --git a/py/image_scale_by_aspect_ratio.py b/py/image_scale_by_aspect_ratio.py
new file mode 100644
index 0000000..8401c18
--- /dev/null
+++ b/py/image_scale_by_aspect_ratio.py
@@ -0,0 +1,157 @@
+import torch
+
+from .imagefunc import *
+
+NODE_NAME = 'ImageScaleByAspectRatio'
+
+class ImageScaleByAspectRatio:
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(self):
+ ratio_list = ['custom', '1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16']
+ fit_mode = ['letterbox', 'crop', 'fill']
+ method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
+ multiple_list = ['8', '16', 'None']
+
+ return {
+ "required": {
+ "aspect_ratio": (ratio_list,),
+ "proportional_width": ("INT", {"default": 2, "min": 1, "max": 999, "step": 1}),
+ "proportional_height": ("INT", {"default": 1, "min": 1, "max": 999, "step": 1}),
+ "fit": (fit_mode,),
+ "method": (method_mode,),
+ "round_to_multiple": (multiple_list,),
+ "scale_to_longest_side": ("BOOLEAN", {"default": False}), # 鏄惁鎸夐暱杈圭缉鏀
+ "longest_side": ("INT", {"default": 1024, "min": 4, "max": 999999, "step": 1}),
+ },
+ "optional": {
+ "image": ("IMAGE",), #
+ "mask": ("MASK",), #
+ }
+ }
+
+ RETURN_TYPES = ("IMAGE", "MASK", "BOX",)
+ RETURN_NAMES = ("image", "mask", "original_size")
+ FUNCTION = 'image_scale_by_aspect_ratio'
+ CATEGORY = '馃樅dzNodes/LayerUtility'
+ OUTPUT_NODE = True
+
+ def image_scale_by_aspect_ratio(self, aspect_ratio, proportional_width, proportional_height,
+ fit, method, round_to_multiple, scale_to_longest_side, longest_side,
+ image=None, mask = None,
+ ):
+ orig_images = []
+ orig_masks = []
+ orig_width = 0
+ orig_height = 0
+ target_width = 0
+ target_height = 0
+ ratio = 1.0
+ ret_images = []
+ ret_masks = []
+ if image is not None:
+ for i in image:
+ i = torch.unsqueeze(i, 0)
+ orig_images.append(i)
+ orig_width, orig_height = tensor2pil(orig_images[0]).size
+ if mask is not None:
+ if mask.dim() == 2:
+ mask = torch.unsqueeze(mask, 0)
+ for m in mask:
+ m = torch.unsqueeze(m, 0)
+ orig_masks.append(m)
+ _width, _height = tensor2pil(orig_masks[0]).size
+ if (orig_width > 0 and orig_width != _width) or (orig_height > 0 and orig_height != _height):
+ log(f"Error: {NODE_NAME} skipped, because the mask is does'nt match image.", message_type='error')
+ return (None, None,)
+ elif orig_width + orig_height == 0:
+ orig_width = _width
+ orig_height = _height
+
+ if orig_width + orig_height == 0:
+ log(f"Error: {NODE_NAME} skipped, because the image or mask at least one must be input.", message_type='error')
+ return (None, None,)
+
+ if aspect_ratio == 'custom':
+ ratio = proportional_width / proportional_height
+ else:
+ s = aspect_ratio.split(":")
+ ratio = int(s[0]) / int(s[1])
+
+ # calculate target width and height
+ if orig_width > orig_height:
+ if scale_to_longest_side:
+ target_width = longest_side
+ else:
+ target_width = orig_width
+ target_height = int(target_width / ratio)
+ else:
+ if scale_to_longest_side:
+ target_height = longest_side
+ else:
+ target_height = orig_height
+ target_width = int(target_height * ratio)
+
+ if ratio < 1:
+ if scale_to_longest_side:
+ _r = longest_side / target_height
+ target_height = longest_side
+ else:
+ _r = orig_height / target_height
+ target_height = orig_height
+ target_width = int(target_width * _r)
+
+
+ if round_to_multiple != 'None':
+ multiple = int(round_to_multiple)
+ target_width = num_round_to_multiple(target_width, multiple)
+ target_height = num_round_to_multiple(target_height, multiple)
+
+ _mask = Image.new('L', size=(target_width, target_height), color='black')
+ _image = Image.new('RGB', size=(target_width, target_height), color='black')
+
+ resize_sampler = Image.LANCZOS
+ if method == "bicubic":
+ resize_sampler = Image.BICUBIC
+ elif method == "hamming":
+ resize_sampler = Image.HAMMING
+ elif method == "bilinear":
+ resize_sampler = Image.BILINEAR
+ elif method == "box":
+ resize_sampler = Image.BOX
+ elif method == "nearest":
+ resize_sampler = Image.NEAREST
+
+ if len(orig_images) > 0:
+ for i in orig_images:
+ _image = tensor2pil(i).convert('RGB')
+ _image = fit_resize_image(_image, target_width, target_height, fit, resize_sampler)
+ ret_images.append(pil2tensor(_image))
+ if len(orig_masks) > 0:
+ for m in orig_masks:
+ _mask = tensor2pil(m).convert('L')
+ _mask = fit_resize_image(_mask, target_width, target_height, fit, resize_sampler).convert('L')
+ ret_masks.append(image2mask(_mask))
+ if len(ret_images) > 0 and len(ret_masks) >0:
+ log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+ return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
+ elif len(ret_images) > 0 and len(ret_masks) == 0:
+ log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+ return (torch.cat(ret_images, dim=0), None,)
+ elif len(ret_images) == 0 and len(ret_masks) > 0:
+ log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
+ return (None, torch.cat(ret_masks, dim=0),)
+ else:
+ log(f"Error: {NODE_NAME} skipped, because the available image or mask is not found.", message_type='error')
+ return (None, None,)
+
+NODE_CLASS_MAPPINGS = {
+ "LayerUtility: ImageScaleByAspectRatio": ImageScaleByAspectRatio
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "LayerUtility: ImageScaleByAspectRatio": "LayerUtility: ImageScaleByAspectRatio"
+}
\ No newline at end of file
diff --git a/py/imagefunc.py b/py/imagefunc.py
index 2338e1b..2ea9b80 100644
--- a/py/imagefunc.py
+++ b/py/imagefunc.py
@@ -925,6 +925,16 @@ def random_numbers(total:int, random_range:int, seed:int=0, sum_of_numbers:int=0
ret_list.append((sum_of_numbers - sum(ret_list)) // 2)
return ret_list
+def num_round_to_multiple(number:int, multiple:int) -> int:
+ remainder = number % multiple
+ if remainder == 0 :
+ return number
+ else:
+ factor = int(number / multiple)
+ if number - factor * multiple > multiple / 2:
+ factor += 1
+ return factor * multiple
+
'''CLASS'''
class AnyType(str):