commit GetMainColors and ColorName node, Duplicate Brightness & Contrast node as BrightnessContrastV2, and Color of Shadow & Highlight node as ColorofShadowHighlightV2
This commit is contained in:
chflame163
@@ -116,7 +116,8 @@ When this error has occurred, please check the network environment.
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## Update
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<font size="4">**If the dependency package error after updating, please double clicking ```repair_dependency.bat``` (for Official ComfyUI Protable) or ```repair_dependency_aki.bat``` (for ComfyUI-aki-v1.x) in the plugin folder to reinstall the dependency packages. </font><br />
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* Commit [GetMainColors](#GetMainColors) node, it can obtained 5 main colors of image. Commit [ColorName](#ColorName) node, it can obtain the color name of input color value.
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* Duplicate the [Brightness & Contrast](#Brightness) node as [BrightnessContrastV2](#BrightnessContrastV2), and the [Color of Shadow & Highlight](#Highlight) node as [ColorofShadowHighlight](#HighlightV2) to avoid errors in ComfyUI workflow parsing caused by the "&" character in the node name.
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* Commit [VQAPrompt](#VQAPrompt) and [LoadVQAModel](#LoadVQAModel) nodes.
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Download the model from [BaiduNetdisk](https://pan.baidu.com/s/1ILREVgM0eFJlkWaYlKsR0g?pwd=yw75) or [huggingface.co/Salesforce/blip-vqa-capfilt-large](https://huggingface.co/Salesforce/blip-vqa-capfilt-large/tree/main) and [huggingface.co/Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base/tree/main) and copy to ```ComfyUI\models\VQA``` folder.
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* [Florence2Ultra](#Florence2Ultra), [Florence2Image2Prompt](#Florence2Image2Prompt) 和 [LoadFlorence2Model](#LoadFlorence2Model) nodes support the MiaoshouAI/Florence-2-large-PromptGen-v1.5 and MiaoshouAI/Florence-2-base-PromptGen-v1.5 model.
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@@ -503,6 +504,8 @@ Node options:
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Node option:
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* exposure: Exposure value. Higher values indicate brighter image.
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### Color of Shadow <a id="table1">HighlightV2</a>
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A replica of the ```Color of Shadow & Highlight``` node, with the "&" character removed from the node name to avoid ComfyUI workflow parsing errors.
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### <a id="table1">ColorTemperature</a>
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@@ -554,6 +557,9 @@ Node options:
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* contrast: Value of contrast.
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* saturation: Value of saturation.
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### <a id="table1">BrightnessContrastV2</a>
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A replica of the ```Brightness & Contrast``` node, with the "&" character removed from the node name to avoid ComfyUI workflow parsing errors.
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### <a id="table1">RGB</a>
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Adjust the RGB channels of the image.
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@@ -971,6 +977,32 @@ Output:
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* image: Solid color picture output, the size is the same as the input picture.
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* mask: Mask output.
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### <a id="table1">GetMainColors</a>
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Obtain the main color of the image. You can obtain 5 colors.
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Node Options:
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* image: The image input.
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* k_means_algorithm:K-Means algorithm options. "lloyd" is the standard K-Means algorithm, while "elkan" is the triangle inequality algorithm, suitable for larger images.
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Outputs:
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* preview_image: 5 main color preview images.
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* color_1~color_5: Color value output. Output an RGB string in HEX format.
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### <a id="table1">ColorName</a>
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Output the most similar color name in the color palette based on the color value.
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Node Options:
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* color: Color value input, in HEX format RGB string format.
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* palette: Color palette. ```xkcd``` includes 949 colors, ```css3``` includes 147 colors, and ```html4``` includes 16 colors.
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Output:
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* color_name: Color name in string.
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### <a id="table1">ExtendCanvas</a>
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Extend the canvas
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@@ -116,6 +116,8 @@ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
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## 更新说明
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<font size="4">**如果本插件更新后出现依赖包错误，请双击运行插件目录下的```install_requirements.bat```(官方便携包)，或 ```install_requirements_aki.bat```(秋叶整合包) 重新安装依赖包。
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* 添加 [GetMainColors](#GetMainColors) 节点，可获得图片的5个主要颜色。 添加 [ColorName](#ColorName) 节点，可获得颜色名称。
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* 复制 [Brightness & Contrast](#Brightness) 节点为 [BrightnessContrastV2](#BrightnessContrastV2), [Color of Shadow & Highlight](#Highlight) 节点为 [ColorofShadowHighlight](#HighlightV2), 避免节点名称中的"&"字符造成ComfyUI工作流解析错误。
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* 添加 [VQAPrompt](#VQAPrompt) 和 [LoadVQAModel](#LoadVQAModel) 节点。
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请从[百度网盘](https://pan.baidu.com/s/1ILREVgM0eFJlkWaYlKsR0g?pwd=yw75) 或者 [huggingface.co/Salesforce/blip-vqa-capfilt-large](https://huggingface.co/Salesforce/blip-vqa-capfilt-large/tree/main) 和 [huggingface.co/Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base/tree/main) 下载全部模型文件并放到 ```ComfyUI\models\VQA```文件夹。
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* [Florence2Ultra](#Florence2Ultra), [Florence2Image2Prompt](#Florence2Image2Prompt) 和 [LoadFlorence2Model](#LoadFlorence2Model) 节点支持MiaoshouAI/Florence-2-large-PromptGen-v1.5 和 MiaoshouAI/Florence-2-base-PromptGen-v1.5 模型。
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@@ -497,6 +499,9 @@ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
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* highlight_level_offset: 亮部取值的偏移量，更小的数值使更多靠近阴暗的区域纳入亮部。
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* highlight_range: 亮部的过渡范围。
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### Color of Shadow <a id="table1">HighlightV2</a>
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Color of Shadow & Highlight 节点的复制品，去掉了节点名称中的"&"字符以避免ComfyUI工作流解析错误。
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### <a id="table1">ColorTemperature</a>
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改变图像的色温。
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@@ -547,6 +552,8 @@ os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
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* contrast: 图像的对比度。
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* saturation: 图像的色彩饱和度。
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### <a id="table1">BrightnessContrastV2</a>
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```Brightness & Contrast```节点的复制品，去掉了节点名称中的"&"字符以避免ComfyUI工作流解析错误。
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### <a id="table1">RGB</a>
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对图像的RGB各通道进行调整。
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@@ -956,6 +963,32 @@ GetColorTone的V2升级版。可以指定获取主体或背景的主色或平均
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* image: 纯色图片输出, 尺寸与输入的图片相同。
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* mask: 遮罩输出。
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### <a id="table1">GetMainColors</a>
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获得图片的主色。可获得5个颜色。
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节点选项:
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* image: 图片输入。
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* k_means_algorithm: K-Means 算法选项。 "lloyd" 为标准K-Means算法， "elkan" 为三角不等式算法，适合更大的图片。
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输出:
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* preview_image: 5个主色预览图片。
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* color_1~color_5: 色值输出。输出格式为HEX格式的RGB字符串。
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### <a id="table1">ColorName</a>
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根据色值输出调色盘里最近似的颜色名称。
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节点选项:
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* color: 颜色色值输入，格式为HEX格式的RGB字符串。
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* palette: 调色板。 ```xkcd```包括了949种颜色， ```css3```包括了147种颜色, ```html4```包括了16种颜色。
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输出:
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* color_name: 颜色名称，格式为字符串。
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### <a id="table1">ExtendCanvas</a>
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扩展画布。
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@@ -1,11 +1,11 @@
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from .imagefunc import *
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NODE_NAME = 'Brightness & Contrast'
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class ColorCorrectBrightnessAndContrast:
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def __init__(self):
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pass
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self.NODE_NAME = 'Brightness & Contrast'
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@classmethod
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def INPUT_TYPES(self):
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@@ -48,13 +48,64 @@ class ColorCorrectBrightnessAndContrast:
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ret_image = RGB2RGBA(ret_image, __image.split()[-1])
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ret_images.append(pil2tensor(ret_image))
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log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0),)
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# 节点名称去掉“&”
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class LS_ColorCorrect_Brightness_And_Contrast_V2:
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def __init__(self):
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self.NODE_NAME = 'Brightness Contrast V2'
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE", ), #
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"brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
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"contrast": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
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"saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = 'color_correct_brightness_contrast_v2'
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CATEGORY = '😺dzNodes/LayerColor'
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def color_correct_brightness_contrast_v2(self, image, brightness, contrast, saturation):
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ret_images = []
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for i in image:
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i = torch.unsqueeze(i,0)
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__image = tensor2pil(i)
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ret_image = __image.convert('RGB')
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if brightness != 1:
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brightness_image = ImageEnhance.Brightness(ret_image)
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ret_image = brightness_image.enhance(factor=brightness)
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if contrast != 1:
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contrast_image = ImageEnhance.Contrast(ret_image)
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ret_image = contrast_image.enhance(factor=contrast)
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if saturation != 1:
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color_image = ImageEnhance.Color(ret_image)
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ret_image = color_image.enhance(factor=saturation)
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if __image.mode == 'RGBA':
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ret_image = RGB2RGBA(ret_image, __image.split()[-1])
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ret_images.append(pil2tensor(ret_image))
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log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerColor: Brightness & Contrast": ColorCorrectBrightnessAndContrast
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"LayerColor: Brightness & Contrast": ColorCorrectBrightnessAndContrast,
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"LayerColor: BrightnessContrastV2": LS_ColorCorrect_Brightness_And_Contrast_V2
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerColor: Brightness & Contrast": "LayerColor: Brightness & Contrast"
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"LayerColor: BrightnessContrastV2": "LayerColor: Brightness Contrast V2"
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}
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@@ -1,6 +1,6 @@
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from .imagefunc import *
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NODE_NAME = 'Color of Shadow & Highlight'
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def norm_value(value):
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if value < 0.01:
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@@ -8,10 +8,11 @@ def norm_value(value):
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if value > 0.99:
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value = 0.99
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return value
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class ColorCorrectShadowAndHighlight:
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def __init__(self):
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pass
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self.NODE_NAME = 'Color of Shadow & Highlight'
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@classmethod
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def INPUT_TYPES(self):
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@@ -118,10 +119,125 @@ class ColorCorrectShadowAndHighlight:
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log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0),)
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# 名称去掉“&”
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class LS_ColorCorrectShadow_And_Highlight_V2:
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def __init__(self):
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self.NODE_NAME = 'Color of Shadow & Highlight V2'
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE", ),
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"shadow_brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
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"shadow_saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
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"shadow_hue": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
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"shadow_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
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"shadow_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
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"highlight_brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
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"highlight_saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
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"highlight_hue": ("INT", {"default": 0, "min": -255, "max": 255, "step": 1}),
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"highlight_level_offset": ("INT", {"default": 0, "min": -99, "max": 99, "step": 1}),
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"highlight_range": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.99, "step": 0.01}),
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},
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"optional": {
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"mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = 'color_shadow_and_highlight_v2'
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CATEGORY = '😺dzNodes/LayerColor'
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def color_shadow_and_highlight_v2(self, image,
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shadow_brightness, shadow_saturation,
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shadow_level_offset, shadow_range, shadow_hue,
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highlight_brightness, highlight_saturation, highlight_hue,
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highlight_level_offset, highlight_range,
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mask=None
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):
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ret_images = []
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input_images = []
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input_masks = []
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for i in image:
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input_images.append(torch.unsqueeze(i, 0))
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m = tensor2pil(i)
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if m.mode == 'RGBA':
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input_masks.append(m.split()[-1])
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else:
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input_masks.append(Image.new('L', size=m.size, color='white'))
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if mask is not None:
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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input_masks = []
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for m in mask:
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input_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
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max_batch = max(len(input_images), len(input_masks))
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for i in range(max_batch):
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_image = input_images[i] if i < len(input_images) else input_images[-1]
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_image = tensor2pil(_image).convert('RGB')
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_mask = input_masks[i] if i < len(input_masks) else input_masks[-1]
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avg_gray = get_gray_average(_image, _mask)
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shadow_level, highlight_level = calculate_shadow_highlight_level(avg_gray)
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_canvas = _image.copy()
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if shadow_saturation !=1 or shadow_brightness !=1 or shadow_hue:
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shadow_low_threshold = (shadow_level + shadow_level_offset) / 100 + shadow_range / 2
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shadow_low_threshold = norm_value(shadow_low_threshold)
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shadow_high_threshold = (shadow_level + shadow_level_offset) / 100 - shadow_range / 2
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shadow_high_threshold = norm_value(shadow_high_threshold)
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_shadow_mask = luminance_keyer(_image, shadow_low_threshold, shadow_high_threshold)
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_shadow = _image.copy()
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if shadow_brightness != 1:
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brightness_image = ImageEnhance.Brightness(_shadow)
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_shadow = brightness_image.enhance(factor=shadow_brightness)
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if shadow_saturation != 1:
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color_image = ImageEnhance.Color(_shadow)
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_shadow = color_image.enhance(factor=shadow_saturation)
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if shadow_hue:
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_h, _s, _v = _shadow.convert('HSV').split()
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_h = image_hue_offset(_h, shadow_hue)
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_shadow = image_channel_merge((_h, _s, _v), 'HSV')
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_canvas.paste(_shadow, mask=gaussian_blur(_shadow_mask,(_shadow_mask.width + _shadow_mask.height)//800))
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_canvas.paste(_image, mask=ImageChops.invert(_mask))
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if highlight_saturation != 1 or highlight_brightness != 1 or highlight_hue:
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highlight_low_threshold = (highlight_level + highlight_level_offset) / 100 - highlight_range / 2
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highlight_low_threshold = norm_value(highlight_low_threshold)
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highlight_high_threshold = (highlight_level + highlight_level_offset) / 100 + highlight_range / 2
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highlight_high_threshold = norm_value(highlight_high_threshold)
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_highlight_mask = luminance_keyer(_image, highlight_low_threshold, highlight_high_threshold)
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_highlight = _image.copy()
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if highlight_brightness != 1:
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brightness_image = ImageEnhance.Brightness(_highlight)
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_highlight = brightness_image.enhance(factor=highlight_brightness)
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if highlight_saturation != 1:
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color_image = ImageEnhance.Color(_highlight)
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_highlight = color_image.enhance(factor=highlight_saturation)
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if highlight_hue:
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_h, _s, _v = _highlight.convert('HSV').split()
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_h = image_hue_offset(_h, highlight_hue)
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_highlight = image_channel_merge((_h, _s, _v), 'HSV')
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_canvas.paste(_highlight, mask=gaussian_blur(_highlight_mask, (_highlight_mask.width + _highlight_mask.height)//800))
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_canvas.paste(_image, mask=ImageChops.invert(_mask))
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ret_images.append(pil2tensor(_canvas))
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log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerColor: Color of Shadow & Highlight": ColorCorrectShadowAndHighlight
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"LayerColor: Color of Shadow & Highlight": ColorCorrectShadowAndHighlight,
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"LayerColor: ColorofShadowHighlightV2": LS_ColorCorrectShadow_And_Highlight_V2
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}
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|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LayerColor: Color of Shadow & Highlight": "LayerColor: Color of Shadow & Highlight"
|
||||
"LayerColor: Color of Shadow & Highlight": "LayerColor: Color of Shadow & Highlight",
|
||||
"LayerColor: ColorofShadowHighlightV2": "LayerColor: Colorof Shadow Highlight V2"
|
||||
}
|
||||
+1177
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,88 @@
|
||||
from .imagefunc import *
|
||||
|
||||
any = AnyType("*")
|
||||
|
||||
class LS_GetMainColors:
|
||||
|
||||
def __init__(self):
|
||||
self.NODE_NAME = 'Get Main Colors'
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(self):
|
||||
size_list = ['custom']
|
||||
size_list.extend(load_custom_size())
|
||||
k_means_algorithm_list = ["lloyd", "elkan"]
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE", ), #
|
||||
"k_means_algorithm": (k_means_algorithm_list,),
|
||||
},
|
||||
"optional": {
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "STRING", "STRING", "STRING", "STRING", "STRING",)
|
||||
RETURN_NAMES = ("preview_image", "color_1", "color_2", "color_3", "color_4", "color_5",)
|
||||
FUNCTION = 'get_main_colors'
|
||||
CATEGORY = '😺dzNodes/LayerUtility'
|
||||
|
||||
def get_main_colors(self, image, k_means_algorithm):
|
||||
|
||||
ret_images = []
|
||||
|
||||
grid_width = 512
|
||||
grid_height = 128
|
||||
line_width = 5
|
||||
|
||||
for i in range(len(image)):
|
||||
pil_img = tensor2pil(torch.unsqueeze(image[i], 0)).convert("RGB")
|
||||
blured_image = gaussian_blur(pil_img, (pil_img.width + pil_img.height) // 400)
|
||||
|
||||
accuracy = 60 # Adjusts accuracy by changing number of iterations of the K-means algorithm
|
||||
num_colors = 5
|
||||
num_iterations = int(512 * (accuracy / 100))
|
||||
original_colors = self.interrogate_colors(
|
||||
pil2tensor(blured_image), num_colors=num_colors, algorithm=k_means_algorithm, mix_iter=num_iterations, random_state=0)
|
||||
|
||||
main_colors = self.ndarrays_to_colorhex(original_colors)
|
||||
log(f"main_colors={main_colors}")
|
||||
# draw colors image
|
||||
ret_image = Image.new('RGB', size=(grid_width, grid_height * len(main_colors)), color="white")
|
||||
draw = ImageDraw.Draw(ret_image)
|
||||
|
||||
for j in range(len(main_colors)):
|
||||
x1 = 0
|
||||
y1 = grid_height * j
|
||||
draw.rectangle((x1, y1, x1 + grid_width, y1 + grid_height), fill=main_colors[j], outline=main_colors[j])
|
||||
ret_images.append(pil2tensor(ret_image))
|
||||
|
||||
log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
|
||||
return (torch.cat(ret_images, dim=0), main_colors[0], main_colors[1], main_colors[2], main_colors[3], main_colors[4],)
|
||||
|
||||
def ndarrays_to_colorhex(self, colors:list) -> list:
|
||||
return [RGB_to_Hex((int(color[0]), int(color[1]), int(color[2]))) for color in colors]
|
||||
|
||||
def interrogate_colors(self, image:torch.Tensor, num_colors:int, algorithm:str, mix_iter:int, random_state:int) -> list:
|
||||
from sklearn.cluster import KMeans
|
||||
pixels = image.view(-1, image.shape[-1]).numpy()
|
||||
colors = (
|
||||
KMeans(
|
||||
n_clusters=num_colors,
|
||||
algorithm=algorithm,
|
||||
max_iter=mix_iter,
|
||||
random_state=random_state,
|
||||
)
|
||||
.fit(pixels)
|
||||
.cluster_centers_
|
||||
* 255
|
||||
)
|
||||
return colors
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"LayerUtility: GetMainColors": LS_GetMainColors
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LayerUtility: GetMainColors": "LayerUtility: Get Main Colors"
|
||||
}
|
||||
+10
-3
@@ -1722,7 +1722,6 @@ def mask_white_area(mask:Image, white_point:int) -> float:
|
||||
|
||||
'''Color Functions'''
|
||||
|
||||
|
||||
def color_balance(image:Image, shadows:list, midtones:list, highlights:list,
|
||||
shadow_center:float=0.15, midtone_center:float=0.5, highlight_center:float=0.8,
|
||||
shadow_max:float=0.1, midtone_max:float=0.3, highlight_max:float=0.2,
|
||||
@@ -1765,7 +1764,6 @@ def color_balance(image:Image, shadows:list, midtones:list, highlights:list,
|
||||
|
||||
return tensor2pil(img_copy)
|
||||
|
||||
|
||||
def RGB_to_Hex(RGB:tuple) -> str:
|
||||
color = '#'
|
||||
for i in RGB:
|
||||
@@ -1798,7 +1796,6 @@ def Hex_to_HSV_255level(inhex:str) -> list:
|
||||
HSV = rgb_to_hsv(RGB[0] / 255.0, RGB[1] / 255.0, RGB[2] / 255.0)
|
||||
return [int(x * 255) for x in HSV]
|
||||
|
||||
|
||||
def HSV_255level_to_Hex(HSV: list) -> str:
|
||||
if len(HSV) != 3 or any((not isinstance(v, int) or v < 0 or v > 255) for v in HSV):
|
||||
raise ValueError('Invalid HSV values, each value should be an integer between 0 and 255')
|
||||
@@ -1813,6 +1810,16 @@ def HSV_255level_to_Hex(HSV: list) -> str:
|
||||
|
||||
return '#' + hex_r + hex_g + hex_b
|
||||
|
||||
# 返回补色色值
|
||||
def complementary_color(color: str) -> str:
|
||||
color = Hex_to_RGB(color)
|
||||
return RGB_to_Hex((255 - color[0], 255 - color[1], 255 - color[2]))
|
||||
|
||||
# 返回颜色对应灰度值
|
||||
def rgb2gray(color:str)->int:
|
||||
(r, g, b) = Hex_to_RGB(color)
|
||||
return int((r * 299 + g * 587 + b * 114) / 1000)
|
||||
|
||||
'''Value Functions'''
|
||||
def is_valid_mask(tensor:torch.Tensor) -> bool:
|
||||
return not bool(torch.all(tensor == 0).item())
|
||||
|
||||
+1
-1
@@ -20,7 +20,7 @@ class XYtoPercent:
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("FLOAT", "FLOAT",)
|
||||
RETURN_NAMES = ("x_percent", "x_percent",)
|
||||
RETURN_NAMES = ("x_percent", "y_percent",)
|
||||
FUNCTION = 'xy_to_percent'
|
||||
CATEGORY = '😺dzNodes/LayerUtility/Data'
|
||||
|
||||
|
||||
+2
-2
@@ -1,9 +1,9 @@
|
||||
[project]
|
||||
name = "comfyui_layerstyle"
|
||||
description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress."
|
||||
version = "1.0.55"
|
||||
version = "1.0.56"
|
||||
license = "MIT"
|
||||
dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "transparent-background", "huggingface_hub", "accelerate", "bitsandbytes", "torchscale", "wandb", "hydra-core", "psd-tools", "inference-cli[yolo-world]", "inference-gpu[yolo-world]", "onnxruntime"]
|
||||
dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "transparent-background", "huggingface_hub", "accelerate", "bitsandbytes", "torchscale", "wandb", "hydra-core", "psd-tools", "inference-cli[yolo-world]", "inference-gpu[yolo-world]", "onnxruntime"]
|
||||
|
||||
[project.urls]
|
||||
Repository = "https://github.com/chflame163/ComfyUI_LayerStyle"
|
||||
|
||||
@@ -4,6 +4,7 @@ torch
|
||||
matplotlib
|
||||
Scipy
|
||||
scikit_image
|
||||
scikit_learn
|
||||
opencv-contrib-python
|
||||
pymatting
|
||||
segment_anything
|
||||
|
||||
@@ -0,0 +1,488 @@
|
||||
{
|
||||
"last_node_id": 41,
|
||||
"last_link_id": 61,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 17,
|
||||
"type": "LayerUtility: GetMainColors",
|
||||
"pos": {
|
||||
"0": 660,
|
||||
"1": 320
|
||||
},
|
||||
"size": {
|
||||
"0": 311.9536437988281,
|
||||
"1": 158
|
||||
},
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 28
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "preview_image",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
46
|
||||
],
|
||||
"slot_index": 0,
|
||||
"shape": 3
|
||||
},
|
||||
{
|
||||
"name": "color_1",
|
||||
"type": "STRING",
|
||||
"links": [],
|
||||
"slot_index": 1,
|
||||
"shape": 3
|
||||
},
|
||||
{
|
||||
"name": "color_2",
|
||||
"type": "STRING",
|
||||
"links": [
|
||||
48,
|
||||
55
|
||||
],
|
||||
"slot_index": 2,
|
||||
"shape": 3
|
||||
},
|
||||
{
|
||||
"name": "color_3",
|
||||
"type": "STRING",
|
||||
"links": [],
|
||||
"slot_index": 3,
|
||||
"shape": 3
|
||||
},
|
||||
{
|
||||
"name": "color_4",
|
||||
"type": "STRING",
|
||||
"links": [],
|
||||
"slot_index": 4,
|
||||
"shape": 3
|
||||
},
|
||||
{
|
||||
"name": "color_5",
|
||||
"type": "STRING",
|
||||
"links": [],
|
||||
"slot_index": 5,
|
||||
"shape": 3
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LayerUtility: GetMainColors"
|
||||
},
|
||||
"widgets_values": [
|
||||
"lloyd"
|
||||
],
|
||||
"color": "rgba(38, 73, 116, 0.7)"
|
||||
},
|
||||
{
|
||||
"id": 38,
|
||||
"type": "LayerUtility: SimpleTextImage",
|
||||
"pos": {
|
||||
"0": 1400,
|
||||
"1": 490
|
||||
},
|
||||
"size": [
|
||||
333.2885159321711,
|
||||
364.1828329417617
|
||||
],
|
||||
"flags": {},
|
||||
"order": 5,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "size_as",
|
||||
"type": "*",
|
||||
"link": 53
|
||||
},
|
||||
{
|
||||
"name": "text",
|
||||
"type": "STRING",
|
||||
"link": 54,
|
||||
"widget": {
|
||||
"name": "text"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "text_color",
|
||||
"type": "STRING",
|
||||
"link": 55,
|
||||
"widget": {
|
||||
"name": "text_color"
|
||||
}
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
60
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "mask",
|
||||
"type": "MASK",
|
||||
"links": null,
|
||||
"shape": 3
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LayerUtility: SimpleTextImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
"text",
|
||||
"Alibaba-PuHuiTi-Heavy.ttf",
|
||||
"center",
|
||||
80,
|
||||
8,
|
||||
400,
|
||||
"#FFFFFF",
|
||||
0,
|
||||
"#FF8000",
|
||||
0,
|
||||
0,
|
||||
512,
|
||||
512
|
||||
],
|
||||
"color": "rgba(38, 73, 116, 0.7)"
|
||||
},
|
||||
{
|
||||
"id": 40,
|
||||
"type": "PreviewImage",
|
||||
"pos": {
|
||||
"0": 2160,
|
||||
"1": 410
|
||||
},
|
||||
"size": [
|
||||
697.0121532340336,
|
||||
405.5093656051548
|
||||
],
|
||||
"flags": {},
|
||||
"order": 7,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 61
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"properties": {
|
||||
"Node name for S&R": "PreviewImage"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 12,
|
||||
"type": "LoadImage",
|
||||
"pos": {
|
||||
"0": 240,
|
||||
"1": 420
|
||||
},
|
||||
"size": [
|
||||
369.854603490381,
|
||||
334.3001040695102
|
||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"inputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
28,
|
||||
53,
|
||||
59
|
||||
],
|
||||
"slot_index": 0,
|
||||
"shape": 3
|
||||
},
|
||||
{
|
||||
"name": "MASK",
|
||||
"type": "MASK",
|
||||
"links": null,
|
||||
"shape": 3
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LoadImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
"3840x2160car.jpg",
|
||||
"image"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 37,
|
||||
"type": "PreviewImage",
|
||||
"pos": {
|
||||
"0": 660,
|
||||
"1": 530
|
||||
},
|
||||
"size": [
|
||||
313.2302664167988,
|
||||
331.1310906195131
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 46
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"properties": {
|
||||
"Node name for S&R": "PreviewImage"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 41,
|
||||
"type": "LayerStyle: DropShadow V2",
|
||||
"pos": {
|
||||
"0": 1780,
|
||||
"1": 490
|
||||
},
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 266
|
||||
},
|
||||
"flags": {},
|
||||
"order": 6,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "background_image",
|
||||
"type": "IMAGE",
|
||||
"link": 59
|
||||
},
|
||||
{
|
||||
"name": "layer_image",
|
||||
"type": "IMAGE",
|
||||
"link": 60
|
||||
},
|
||||
{
|
||||
"name": "layer_mask",
|
||||
"type": "MASK",
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
61
|
||||
],
|
||||
"shape": 3,
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LayerStyle: DropShadow V2"
|
||||
},
|
||||
"widgets_values": [
|
||||
true,
|
||||
"normal",
|
||||
50,
|
||||
25,
|
||||
25,
|
||||
6,
|
||||
18,
|
||||
"#000000"
|
||||
],
|
||||
"color": "rgba(20, 95, 121, 0.7)"
|
||||
},
|
||||
{
|
||||
"id": 30,
|
||||
"type": "ShowText|pysssss",
|
||||
"pos": {
|
||||
"0": 1410,
|
||||
"1": 350
|
||||
},
|
||||
"size": [
|
||||
320.67863272622117,
|
||||
76
|
||||
],
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "text",
|
||||
"type": "STRING",
|
||||
"link": 35,
|
||||
"widget": {
|
||||
"name": "text"
|
||||
}
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "STRING",
|
||||
"type": "STRING",
|
||||
"links": null,
|
||||
"shape": 6
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
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Reference in New Issue
Block a user