commit Collage node
This commit is contained in:
chflame163
@@ -145,6 +145,7 @@ Please try downgrading the ```protobuf``` dependency package to 3.20.3, or set e
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**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.
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* Commit [Collage](#Collage) node to collage images into one.
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* Commit [DeepSeekAPI](DeepSeekAPI) node, Use DeepSeek API for text inference.
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* Commit [SegmentAnythingUltraV3](#SegmentAnythingUltraV3) and [LoadSegmentAnythingModels](#LoadSegmentAnythingModels) nodes, Avoid duplicating model loading when using multiple SAM nodes.
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* Commit [ZhipuGLM4](#ZhipuGLM4) and [ZhipuGLM4V](#ZhipuGLM4V) nodes, Use the Zhipu API for textual and visual inference. Among the current Zhipu models, GLM-4-Flash and glm-4v-flash models are free.
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@@ -162,6 +163,27 @@ download Florence-2-Flux-Large and Florence-2-Flux folder from [BaiduNetdisk](ht
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## Description
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### <a id="table1">Collage</a>
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Randomly collage the input images into one large image.
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Node Options:
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* images: The input images.
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* florence2_model: Optional input for object recognition and cropping.
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* canvas_width: Output the width of the image.
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* canvas_height: Output the height of the image.
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* border_width: The border width.
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* rounded_rect_radius: The border fillet radius.
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* uniformity: The randomness of image stitching size. The value range is 0-1, and the larger the value, the greater the randomness of the size.
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* background_color: The background color.
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* seed: The seed of random number.
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* control_after_generate: Seed change options. If this option is fixed, the generated random number will always be the same.
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* object_prompt: When connecting to florence2_model, fill in the prompt words for object recognition here.
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### <a id="table1">QWenImage2Prompt</a>
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Inference the prompts based on the image. this node is repackage of the [ComfyUI_VLM_nodes](https://github.com/gokayfem/ComfyUI_VLM_nodes)'s ```UForm-Gen2 Qwen Node```, thanks to the original author.
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@@ -121,6 +121,7 @@ If this call came from a _pb2.py file, your generated code is out of date and mu
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## 更新说明
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**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 [Collage](#Collage) 节点，将多张图片拼合为一张大图。
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* 添加 [DeepSeekAPI](DeepSeekAPI) 节点，使用DeepSeek API进行文本推理。
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* 添加 [SegmentAnythingUltraV3](#SegmentAnythingUltraV3) 和 [LoadSegmentAnythingModels](#LoadSegmentAnythingModels)节点， 在使用多个SAM节点时避免重复加载模型。
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* 添加 [ZhipuGLM4](#ZhipuGLM4) 和 [ZhipuGLM4V](#ZhipuGLM4V)节点，使用智谱API进行文本和视觉推理。目前的智谱模型中，GLM-4-Flash和glm-4v-flash模型是免费的。
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@@ -138,6 +139,28 @@ If this call came from a _pb2.py file, your generated code is out of date and mu
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## 节点说明
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### <a id="table1">Collage</a>
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将输入的批量图片随机拼合为一张大图。
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节点选项说明:
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* images: 图片输入。
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* florence2_model: 可选输入，用于物体识别裁切。
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* canvas_width: 输出图片的宽度。
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* canvas_height: 输出图片的高度。
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* border_width: 边框宽度。
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* rounded_rect_radius: 边框圆角半径。
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* uniformity: 图片拼合大小的随机性。取值范围为0-1，值越大，大小随机性越大。
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* background_color: 背景色。
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* seed: 随机种子。
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* control_after_generate: 设置每次执行时种子值的变化。
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* object_prompt: 当接入florence2_model时，此处填写物体识别的提示词。
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### <a id="table1">QWenImage2Prompt</a>
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根据图片反推提示词。这个节点是[ComfyUI_VLM_nodes](https://github.com/gokayfem/ComfyUI_VLM_nodes)中的```UForm-Gen2 Qwen Node```节点的重新封装，感谢原作者。
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从[huggingface](https://huggingface.co/unum-cloud/uform-gen2-qwen-500m)或者[百度网盘](https://pan.baidu.com/s/1oRkUoOKWaxGod_XTJ8NiTA?pwd=d5d2)下载模型到```ComfyUI/models/LLavacheckpoints/files_for_uform_gen2_qwen```文件夹。
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+251
@@ -0,0 +1,251 @@
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import torch
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import random
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import cv2
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import numpy as np
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from tqdm import tqdm
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from PIL import Image, ImageDraw, ImageFilter
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import copy
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from .imagefunc import log, tensor2pil, pil2tensor, image2mask
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from .imagefunc import fit_resize_image, extract_numbers, gaussian_blur, mask_area, draw_rounded_rectangle
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class LS_CollageGenerator:
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"""
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随机分割生成指定数量的不规则小矩形。
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"""
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def __init__(self, width, height, num, border_width, r, uniformity, seed ):
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self.width = width
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self.height = height
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self.num = num
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self.border_width = int((self.width + self.height) * border_width / 200)
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self.r = r
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self.seed = seed
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self.split_num = int(1e18)
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self.uniformity = uniformity
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self.rectangles = self.adjust_bboxes_with_gaps(self.split_rec())
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def split_rec(self):
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random.seed(self.seed)
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if self.num <= 0 or self.width <= 0 or self.height <= 0:
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raise ValueError("Value mast be positive integer")
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current_rectangles = [(0, 0, self.width, self.height, 0)]
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while len(current_rectangles) < self.num:
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split_counts = [rect[4] for rect in current_rectangles]
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min_splits = min(split_counts)
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max_splits = max(split_counts)
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probabilities = []
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for rect in current_rectangles:
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split_count = rect[4]
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normalized_splits = (split_count - min_splits) / (
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max_splits - min_splits if max_splits > min_splits else 1)
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probability = 1 - (normalized_splits * (1 - self.uniformity))
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probabilities.append(probability)
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if sum(probabilities) > 0:
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probabilities = [p / sum(probabilities) for p in probabilities]
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else:
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probabilities = [1.0 / len(probabilities)] * len(probabilities)
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rect_index = random.choices(range(len(current_rectangles)),
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weights=probabilities, k=1)[0]
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x, y, w, h, split_count = current_rectangles.pop(rect_index)
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if w > h or (w == h and random.choice([True, False])):
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split = random.uniform(0.3, 0.7) * w
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rect1 = (x, y, split, h, split_count + 1)
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rect2 = (x + split, y, w - split, h, split_count + 1)
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else:
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split = random.uniform(0.3, 0.7) * h
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rect1 = (x, y, w, split, split_count + 1)
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rect2 = (x, y + split, w, h - split, split_count + 1)
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current_rectangles.extend([rect1, rect2])
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rectangles = [(int(x), int(y), int(w), int(h))
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for x, y, w, h, _ in current_rectangles]
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return rectangles
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def adjust_bboxes_with_gaps(self, rectangles):
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MIN_SIZE = 1
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adjusted_bboxes = []
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for x, y, w, h in rectangles:
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new_x = min(x + self.border_width, self.width - MIN_SIZE)
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new_y = min(y + self.border_width, self.height - MIN_SIZE)
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new_w = max(MIN_SIZE, w - 2 * self.border_width)
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new_h = max(MIN_SIZE, h - 2 * self.border_width)
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if new_x + new_w > self.width:
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new_x = max(0, self.width - new_w)
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if new_y + new_h > self.height:
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new_y = max(0, self.height - new_h)
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adjusted_bboxes.append((new_x, new_y, new_w, new_h))
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return adjusted_bboxes
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def draw_mask(self):
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bboxes = []
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for bbox in self.rectangles:
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bboxes.append((bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]))
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scale_factor = 2
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img = Image.new('RGB', (self.width, self.height), color='white')
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img = draw_rounded_rectangle(img, self.r, bboxes, scale_factor, color='black')
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return img
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class LS_Collage:
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def __init__(self):
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self.NODE_NAME = 'Collage'
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": ("IMAGE",),
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"canvas_width": ("INT", {"default": 2048, "min": 512, "max": 8192, "step": 16}),
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"canvas_height": ("INT", {"default": 2048, "min": 512, "max": 8192, "step": 16}),
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"border_width": ("FLOAT", {"default": 2, "min": 0, "max": 20, "step": 0.1}),
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"rounded_rect_radius": ("INT", {"default": 8, "min": 0, "max": 100, "step": 1}),
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"uniformity": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": 0.1}), # 分割均匀权重 0均匀分割，1不均匀分割
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"background_color": ("STRING", {"default": "#FFFFFF"}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 1e18, "step": 1}),
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},
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"optional": {
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"florence2_model": ("FLORENCE2",),
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"object_prompt": ("STRING", {"default": "face"}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image", "mask",)
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FUNCTION = "collage"
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CATEGORY = '😺dzNodes/LayerUtility'
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def collage(self, images, canvas_width, canvas_height, border_width, rounded_rect_radius,
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uniformity, background_color, seed, florence2_model=None, object_prompt="face"):
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batch_size = images.shape[0]
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rects = LS_CollageGenerator(width=canvas_width,
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height=canvas_height,
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num=batch_size,
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border_width=border_width,
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r=rounded_rect_radius,
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uniformity=uniformity,
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seed=seed)
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rects_border_image = rects.draw_mask()
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canvas = Image.new("RGB", (canvas_width, canvas_height), color=background_color)
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color_image = copy.deepcopy(canvas)
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from .object_detector import LS_OBJECT_DETECTOR_FL2
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for i in tqdm(range(batch_size)):
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img = tensor2pil(images[i]).convert("RGB")
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img_x = rects.rectangles[i][0]
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img_y = rects.rectangles[i][1]
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img_target_width = rects.rectangles[i][2]
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img_target_height = rects.rectangles[i][3]
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od = LS_OBJECT_DETECTOR_FL2()
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if florence2_model is not None:
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bboxes = od.object_detector_fl2(image=[images[i]], prompt=object_prompt, florence2_model=florence2_model,
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sort_method="confidence", bbox_select="first", select_index="0")[0]
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bbox_mask = self.draw_bbox_mask(img, bboxes, 0, 0, 0, 0)
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resized_img = self.image_auto_crop_v3(img, img_target_width, img_target_height, bbox_mask)
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else:
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resized_img = fit_resize_image(img, img_target_width, img_target_height, fit="crop", resize_sampler=Image.LANCZOS)
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canvas.paste(resized_img, box=(img_x, img_y))
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canvas.paste(color_image, box=(0, 0), mask=rects_border_image.convert("L"))
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return (pil2tensor(canvas), 1 - image2mask(rects_border_image),)
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def draw_bbox_mask(self, image, bboxes, grow_top, grow_bottom, grow_left, grow_right
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):
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mask = Image.new("L", image.size, color='black')
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for bbox in bboxes:
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try:
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if len(bbox) == 0:
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continue
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else:
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x1, y1, x2, y2 = bbox
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except ValueError:
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if len(bbox) == 0:
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continue
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else:
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x1, y1, x2, y2 = bbox[0]
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w = x2 - x1
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h = y2 - y1
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if grow_top:
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y1 = int(y1 - h * grow_top)
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if grow_bottom:
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y2 = int(y2 + h * grow_bottom)
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if grow_left:
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x1 = int(x1 - w * grow_left)
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if grow_right:
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x2 = int(x2 + w * grow_right)
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if y1 > y2 or x1 > x2:
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continue
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draw = ImageDraw.Draw(mask)
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draw.rectangle([x1, y1, x2, y2], fill='white', outline='white', width=0)
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return mask
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def image_auto_crop_v3(self, image, proportional_width, proportional_height, mask,
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):
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scale_to_length = proportional_width
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_image = image
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ratio = proportional_width / proportional_height
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resize_sampler = Image.LANCZOS
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# calculate target width and height
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if ratio > 1:
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target_width = scale_to_length
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target_height = int(target_width / ratio)
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else:
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target_width = scale_to_length
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target_height = int(target_width / ratio)
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_mask = mask
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bluredmask = gaussian_blur(_mask, 20).convert('L')
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(mask_x, mask_y, mask_w, mask_h) = mask_area(bluredmask)
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orig_ratio = _image.width / _image.height
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target_ratio = target_width / target_height
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# crop image to target ratio
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if orig_ratio > target_ratio: # crop LiftRight side
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crop_w = int(_image.height * target_ratio)
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crop_h = _image.height
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else: # crop TopBottom side
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crop_w = _image.width
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crop_h = int(_image.width / target_ratio)
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crop_x = mask_w // 2 + mask_x - crop_w // 2
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if crop_x < 0:
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crop_x = 0
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if crop_x + crop_w > _image.width:
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crop_x = _image.width - crop_w
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crop_y = mask_h // 2 + mask_y - crop_h // 2
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if crop_y < 0:
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crop_y = 0
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if crop_y + crop_h > _image.height:
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crop_y = _image.height - crop_h
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crop_image = _image.crop((crop_x, crop_y, crop_x + crop_w, crop_y + crop_h))
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ret_image = crop_image.resize((target_width, target_height), resize_sampler)
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return ret_image
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: Collage": LS_Collage,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: Collage": "LayerUtility: Collage(Advance)",
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}
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+23
-17
@@ -124,10 +124,11 @@ class LS_OBJECT_DETECTOR_FL2:
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preview = draw_bounding_boxes(img, bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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ret_bboxes.append(standardize_bbox(bboxes))
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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# if len(bboxes) == 0:
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# log(f"{self.NODE_NAME} no object found", message_type='warning')
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# else:
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# log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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return (ret_bboxes, torch.cat(ret_previews, dim=0))
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@@ -175,7 +176,9 @@ class LS_OBJECT_DETECTOR_FL2:
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x2_c = max(x2_c, int(max(polygon[0::2])))
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y1_c = min(y1_c, int(min(polygon[1::2])))
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y2_c = max(y2_c, int(max(polygon[1::2])))
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ret_bboxes.append(x1_c, y1_c, x2_c, y2_c)
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ret_bboxes.append([x1_c, y1_c, x2_c, y2_c])
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if len(ret_bboxes) == 0:
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ret_bboxes.append([x1_c, y1_c, x2_c, y2_c])
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return ret_bboxes
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class LS_OBJECT_DETECTOR_MASK:
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@@ -226,10 +229,11 @@ class LS_OBJECT_DETECTOR_MASK:
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preview = draw_bounding_boxes(tensor2pil(msk).convert("RGB"), bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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# if len(bboxes) == 0:
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# log(f"{self.NODE_NAME} no object found", message_type='warning')
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# else:
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# log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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ret_bboxes.append(standardize_bbox(bboxes))
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return (ret_bboxes, torch.cat(ret_previews, dim=0))
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@@ -290,10 +294,11 @@ class LS_OBJECT_DETECTOR_YOLO8:
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preview = draw_bounding_boxes(_image.convert("RGB"), bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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if len(bboxes) == 0:
|
||||
log(f"{self.NODE_NAME} no object found", message_type='warning')
|
||||
else:
|
||||
log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
|
||||
# if len(bboxes) == 0:
|
||||
# log(f"{self.NODE_NAME} no object found", message_type='warning')
|
||||
# else:
|
||||
# log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
|
||||
|
||||
ret_bboxes.append(standardize_bbox(bboxes))
|
||||
|
||||
return (ret_bboxes, torch.cat(ret_previews, dim=0),)
|
||||
@@ -373,10 +378,11 @@ class LS_OBJECT_DETECTOR_YOLOWORLD:
|
||||
preview = draw_bounding_boxes(tensor2pil(i.unsqueeze(0)).convert('RGB'), bboxes, color="random", line_width=-1)
|
||||
ret_previews.append(pil2tensor(preview))
|
||||
|
||||
if len(bboxes) == 0:
|
||||
log(f"{self.NODE_NAME} no object found", message_type='warning')
|
||||
else:
|
||||
log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
|
||||
# if len(bboxes) == 0:
|
||||
# log(f"{self.NODE_NAME} no object found", message_type='warning')
|
||||
# else:
|
||||
# log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
|
||||
|
||||
ret_bboxes.append(standardize_bbox(bboxes))
|
||||
|
||||
return (ret_bboxes, torch.cat(ret_previews, dim=0))
|
||||
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
[project]
|
||||
name = "comfyui_layerstyle_advance"
|
||||
description = "The nodes detached from ComfyUI Layer Style are mainly those with complex requirements for dependency packages."
|
||||
version = "2.0.12"
|
||||
version = "2.0.13"
|
||||
license = "MIT"
|
||||
dependencies = ["numpy", "matplotlib", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "timm", "blend_modes", "transformers", "diffusers", "loguru", "colour-science", "huggingface_hub", "segment_anything", "addict", "omegaconf", "yapf", "wget", "iopath", "mediapipe", "typer_config", "fastapi", "rich", "google-generativeai", "ultralytics", "transparent-background", "accelerate", "onnxruntime", "bitsandbytes", "peft", "protobuf", "hydra-core", "blind-watermark", "qrcode", "pyzbar", "psd-tools", "wandb", "zhipuai", "openai"]
|
||||
|
||||
|
||||
@@ -0,0 +1,275 @@
|
||||
{
|
||||
"last_node_id": 14,
|
||||
"last_link_id": 14,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 6,
|
||||
"type": "LayerMask: LoadFlorence2Model",
|
||||
"pos": [
|
||||
702.1195068359375,
|
||||
804.4620971679688
|
||||
],
|
||||
"size": [
|
||||
378.0740966796875,
|
||||
62.4444580078125
|
||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"inputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "florence2_model",
|
||||
"type": "FLORENCE2",
|
||||
"links": [
|
||||
14
|
||||
],
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LayerMask: LoadFlorence2Model"
|
||||
},
|
||||
"widgets_values": [
|
||||
"base"
|
||||
],
|
||||
"color": "rgba(27, 80, 119, 0.7)"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "LayerUtility: Collage",
|
||||
"pos": [
|
||||
699.5286254882812,
|
||||
926.8685913085938
|
||||
],
|
||||
"size": [
|
||||
378,
|
||||
270
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 12
|
||||
},
|
||||
{
|
||||
"name": "florence2_model",
|
||||
"type": "FLORENCE2",
|
||||
"link": 14,
|
||||
"shape": 7
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
7
|
||||
],
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "mask",
|
||||
"type": "MASK",
|
||||
"links": [
|
||||
8
|
||||
],
|
||||
"slot_index": 1
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LayerUtility: Collage"
|
||||
},
|
||||
"widgets_values": [
|
||||
2048,
|
||||
2048,
|
||||
1,
|
||||
20,
|
||||
1,
|
||||
"#FFFFFF",
|
||||
1078302422640976,
|
||||
"randomize",
|
||||
"subject"
|
||||
],
|
||||
"color": "rgba(38, 73, 116, 0.7)"
|
||||
},
|
||||
{
|
||||
"id": 9,
|
||||
"type": "PreviewImage",
|
||||
"pos": [
|
||||
1151.0084228515625,
|
||||
604.7581787109375
|
||||
],
|
||||
"size": [
|
||||
598.259765625,
|
||||
317.0512390136719
|
||||
],
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 7
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"properties": {
|
||||
"Node name for S&R": "PreviewImage"
|
||||
},
|
||||
"widgets_values": []
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"type": "LayerMask: MaskPreview",
|
||||
"pos": [
|
||||
1152.8602294921875,
|
||||
972.933349609375
|
||||
],
|
||||
"size": [
|
||||
593.96630859375,
|
||||
310.0398864746094
|
||||
],
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "mask",
|
||||
"type": "MASK",
|
||||
"link": 8
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"properties": {
|
||||
"Node name for S&R": "LayerMask: MaskPreview"
|
||||
},
|
||||
"widgets_values": [],
|
||||
"color": "rgba(27, 80, 119, 0.7)"
|
||||
},
|
||||
{
|
||||
"id": 13,
|
||||
"type": "VHS_LoadImagesPath",
|
||||
"pos": [
|
||||
397.5517883300781,
|
||||
821.9283447265625
|
||||
],
|
||||
"size": [
|
||||
242.24609375,
|
||||
194
|
||||
],
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "meta_batch",
|
||||
"type": "VHS_BatchManager",
|
||||
"link": null,
|
||||
"shape": 7
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
12
|
||||
],
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "MASK",
|
||||
"type": "MASK",
|
||||
"links": null
|
||||
},
|
||||
{
|
||||
"name": "frame_count",
|
||||
"type": "INT",
|
||||
"links": null
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "VHS_LoadImagesPath"
|
||||
},
|
||||
"widgets_values": {
|
||||
"directory": "c:\\images",
|
||||
"image_load_cap": 0,
|
||||
"skip_first_images": 0,
|
||||
"select_every_nth": 1,
|
||||
"choose folder to upload": "image",
|
||||
"videopreview": {
|
||||
"hidden": false,
|
||||
"paused": false,
|
||||
"params": {
|
||||
"frame_load_cap": 0,
|
||||
"skip_first_frames": 0,
|
||||
"select_every_nth": 1,
|
||||
"filename": "c:\\images",
|
||||
"type": "path",
|
||||
"format": "folder"
|
||||
},
|
||||
"muted": false
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"links": [
|
||||
[
|
||||
7,
|
||||
1,
|
||||
0,
|
||||
9,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
8,
|
||||
1,
|
||||
1,
|
||||
10,
|
||||
0,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
12,
|
||||
13,
|
||||
0,
|
||||
1,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
14,
|
||||
6,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
"FLORENCE2"
|
||||
]
|
||||
],
|
||||
"groups": [],
|
||||
"config": {},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"scale": 1.2100000000000002,
|
||||
"offset": [
|
||||
-33.25166101546846,
|
||||
-396.5863340766634
|
||||
]
|
||||
},
|
||||
"node_versions": {
|
||||
"ComfyUI_LayerStyle_Advance": "7fdcbce0727a541efcd3ff393a099b3f0fa52d33",
|
||||
"comfy-core": "0.3.12",
|
||||
"ComfyUI_LayerStyle": "3bf7244fa652322c3307609b928f7aed8c3b3707",
|
||||
"ComfyUI-VideoHelperSuite": "c47b10ca1798b4925ff5a5f07d80c51ca80a837d"
|
||||
},
|
||||
"VHS_latentpreview": false,
|
||||
"VHS_latentpreviewrate": 0
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
Reference in New Issue
Block a user