correct Chinese text to prompt syntax

> ChinesePrompt && PromptGenerate，中文prompt节点，直接用中文书写你的prompt

![](./assets/ChinesePrompt_workflow.svg)

> Web App增加图片编辑器

README.md

@@ -31,7 +31,7 @@ APP-JSON:
 
 > 暂时支持8种节点作为界面上的输入节点：Load Image、CLIPTextEncode、PromptSlide、TextInput_、Color、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader
 
-> 输出节点：PreviewImage 、SaveImage、ShowTextForGPT、VHS_VideoCombine
+> 输出节点：PreviewImage 、SaveImage、ShowTextForGPT、VHS_VideoCombine、PromptImage
 
 > seed统一输入控件，支持：SamplerCustom、KSampler
 
@@ -67,7 +67,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 > PromptSlide
 ![](./assets/prompt_weight.png)
 
-![](./workflow/promptslide-appinfo-workflow.svg)
+<!-- ![](./workflow/promptslide-appinfo-workflow.svg) -->
 
 > randomPrompt
 
@@ -79,6 +79,10 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 > PromptImage & PromptSimplification,Assist in simplifying prompt words, comparing images and prompt word nodes.
 
+> ChinesePrompt && PromptGenerate，中文prompt节点，直接用中文书写你的prompt
+
+![](./assets/ChinesePrompt_workflow.svg)
+
 
 ### Layers
 > A new layer class node has been added, allowing you to separate the image into layers. After merging the images, you can input the controlnet for further processing.
@@ -159,23 +163,16 @@ An improvement has been made to directly redirect to GitHub to search for missin
 ![node-not-found](./assets/node-not-found.png)
 
 
-### Update 
-v0.8.0 🚀🚗🚚🏃‍ LaMaInpainting
-- 新增 LaMaInpainting
-- 优化color节点的输出
-- 修复高清显示屏上定位节点不准的情况
-
-- Add LaMaInpainting
-- Optimize the output of the color node
-- Fix the issue of inaccurate positioning node on high-definition display screens
-
-
 ### Models
 [Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : models/clipseg
 
 [Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama
 
-[Download Salesforce\blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base
+[Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base
+
+[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:text_generator/text2image-prompt-generator
+
+[Download Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main),move to:prompt_generator/opus-mt-zh-en
 
 ## Installation
 
@@ -216,9 +213,6 @@ pip3 install -r requirements.txt
 #### discussions:
 [discussions](https://github.com/shadowcz007/comfyui-mixlab-nodes/discussions)
 
-### TODO:
-- 音频播放节点：带可视化、支持多音轨、可配置音轨音量
-- vector https://github.com/GeorgLegato/stable-diffusion-webui-vectorstudio
 
 
 <picture>

__init__.py

@@ -503,6 +503,11 @@ async def nodes_map_hander(request):
 # 把插件自定义的路由添加到comfyui server里
 def new_add_routes(self):
         import nodes
+        try:
+            self.user_manager.add_routes(self.routes)
+        except:
+            print('pls update')
+        
         self.app.add_routes(routes)
         self.app.add_routes(self.routes)
         for name, dir in nodes.EXTENSION_WEB_DIRS.items():
@@ -530,25 +535,26 @@ PromptServer.add_routes=new_add_routes
 
 # 导入节点
 from .nodes.PromptNode import RandomPrompt,PromptSlide,PromptSimplification,PromptImage
-from .nodes.ImageNode import NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.ImageNode import GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 from .nodes.Clipseg import CLIPSeg,CombineMasks
 from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,GetImageSize_,MultiplicationNode
-from .nodes.Lama import LaMaInpainting
-from .nodes.ClipInterrogator import ClipInterrogator
+from .nodes.Utils import TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+
+
 
 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
 NODE_CLASS_MAPPINGS = {
     "AppInfo":AppInfo,
+    "TESTNODE_":TESTNODE_,
     "RandomPrompt":RandomPrompt,
     "PromptSlide":PromptSlide,
     "PromptSimplification":PromptSimplification,
     "PromptImage":PromptImage,
-    "ClipInterrogator":ClipInterrogator,
+    "MirroredImage":MirroredImage,
     "NoiseImage":NoiseImage,
     "GradientImage":GradientImage,
     "TransparentImage":TransparentImage,
@@ -559,6 +565,7 @@ NODE_CLASS_MAPPINGS = {
     "EnhanceImage":EnhanceImage,
     "SvgImage":SvgImage,
     "3DImage":Image3D, 
+    "ImageColorTransfer":ImageColorTransfer,
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
     "MergeLayers":MergeLayers,
@@ -590,7 +597,7 @@ NODE_CLASS_MAPPINGS = {
     "GetImageSize_":GetImageSize_,
     "SwitchByIndex":SwitchByIndex,
     "LimitNumber":LimitNumber, 
-    "LaMaInpainting":LaMaInpainting
+    # "LaMaInpainting":LaMaInpainting
     # "GamePal":GamePal
 }
 
@@ -612,14 +619,44 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "3DImage":"3DImage ♾️Mixlab",
     "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
     "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
-    "PromptSlide":"PromptSlide ♾️Mixlab"
-
-    # "GamePal":"GamePal ♾️Mixlab"
+    "PromptSlide":"PromptSlide ♾️Mixlab",
+    "PromptGenerate_Mix":"PromptGenerate ♾️Mixlab",
+    "ChinesePrompt_Mix":"ChinesePrompt ♾️Mixlab",
+    "GamePal":"GamePal ♾️Mixlab"
 }
 
 # web ui的节点功能
 WEB_DIRECTORY = "./web"
 
 print('--------------')
-print('\033[91m ### Mixlab Nodes: \033[93mLoaded\033[0m')
-print('--------------')
+print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
+
+try:
+    from .nodes.Lama import LaMaInpainting
+    print('LaMaInpainting.available',LaMaInpainting.available)
+    if LaMaInpainting.available:
+        NODE_CLASS_MAPPINGS['LaMaInpainting']=LaMaInpainting
+except:
+    print('LaMaInpainting.available',False)
+
+try:
+    from .nodes.ClipInterrogator import ClipInterrogator
+    print('ClipInterrogator.available',ClipInterrogator.available)
+    if ClipInterrogator.available:
+        NODE_CLASS_MAPPINGS['ClipInterrogator']=ClipInterrogator
+except:
+    print('ClipInterrogator.available',False)
+
+try:
+    from .nodes.TextGenerateNode import PromptGenerate,ChinesePrompt
+    print('PromptGenerate.available',PromptGenerate.available)
+    if PromptGenerate.available:
+        NODE_CLASS_MAPPINGS['PromptGenerate_Mix']=PromptGenerate
+    print('ChinesePrompt.available',ChinesePrompt.available)
+    if ChinesePrompt.available:
+        NODE_CLASS_MAPPINGS['ChinesePrompt_Mix']=ChinesePrompt
+except:
+    print('TextGenerateNode.available',False)
+ 
+
+print('\033[93m -------------- \033[0m')

data/clothes.txt

@@ -0,0 +1,30 @@
+Elegant evening gown
+Casual jeans and t-shirt
+Formal black suit
+Stylish leather jacket
+Flowy bohemian dress
+Sporty tracksuit
+Chic little black dress
+Trendy ripped jeans
+Classic white button-down shirt
+Cozy oversized sweater
+Sophisticated tailored blazer
+Quirky patterned leggings
+Striped sailor top
+Polished knee-length skirt
+Vintage-inspired floral dress
+Edgy motorcycle jacket
+Preppy polo shirt
+Boho maxi skirt
+Professional pinstripe suit
+Relaxed denim shorts
+Glamorous sequined dress
+Athletic running shoes
+Formal bow tie
+Casual baseball cap
+Stylish fedora hat
+Warm woolen scarf
+Comfortable cotton socks
+Trendy ankle boots
+Cute summer sandals
+Cozy pajama set

data/expressions.txt

@@ -0,0 +1,30 @@
+Happy
+Sad
+Angry
+Surprised
+Excited
+Worried
+Confused
+Disgusted
+Amused
+Bored
+Curious
+Embarrassed
+Frustrated
+Nervous
+Pleased
+Relieved
+Shy
+Tired
+Serious
+Silly
+Proud
+Grumpy
+Smug
+Sarcastic
+Flirty
+Skeptical
+Shocked
+Blissful
+Envious
+Mischievous

data/extension-node-map.json

@@ -4761,12 +4761,15 @@
     ],
     "https://github.com/shadowcz007/comfyui-mixlab-nodes": [
         [
+            "PromptGenerate_Mix",
+            "ChinesePrompt_Mix",
             "3DImage",
             "AppInfo",
             "IntNumber",
             "FloatSlider",
             "ResizeImage",
             "NoiseImage",
+            "PromptImage",
             "AreaToMask",
             "CLIPSeg_",
             "CharacterInText",

data/profession.txt

@@ -0,0 +1,101 @@
+Doctor
+Teacher
+Engineer
+Lawyer
+Accountant
+Nurse
+Architect
+Chef
+Pilot
+Scientist
+Artist
+Writer
+Musician
+Actor
+Photographer
+Police officer
+Firefighter
+Dentist
+Pharmacist
+Veterinarian
+Electrician
+Plumber
+Carpenter
+Mechanic
+Farmer
+Astronaut
+Athlete
+Journalist
+Politician
+Economist
+Psychologist
+Social worker
+Librarian
+Translator
+Salesperson
+Entrepreneur
+Financial advisor
+Graphic designer
+Web developer
+Marketing manager
+Human resources manager
+Project manager
+Event planner
+Fashion designer
+Interior decorator
+Real estate agent
+Archaeologist
+Biologist
+Chemist
+Geologist
+Physicist
+Mathematician
+Historian
+Geographer
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+Economist
+Sociologist
+Anthropologist
+Archaeologist
+Linguist
+Philosopher
+Geographer
+Historian
+                     #MixCopilot

nodes/Audio.py

@@ -24,7 +24,7 @@ class SpeechRecognition:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/audio"
+    CATEGORY = "♾️Mixlab/Audio"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -48,7 +48,7 @@ class SpeechSynthesis:
     OUTPUT_NODE = True
     OUTPUT_IS_LIST = (True,)
 
-    CATEGORY = "♾️Mixlab/audio"
+    CATEGORY = "♾️Mixlab/Audio"
 
     def run(self, text):
         # print(session_history)
@@ -82,7 +82,7 @@ class GamePal:
     OUTPUT_NODE = True
     OUTPUT_IS_LIST = (False,)
 
-    CATEGORY = "♾️Mixlab/audio"
+    CATEGORY = "♾️Mixlab/Audio"
 
     def run(self, input_text,input_num,python_code):
         exec(python_code)

nodes/ClipInterrogator.py

@@ -1,15 +1,55 @@
-import os
+import os,sys
 import folder_paths
 
 from PIL import Image
+import importlib.util
+
 import comfy.utils
 import numpy as np
 import json
 import torch
+import random
 
-from transformers import AutoProcessor, BlipForConditionalGeneration
 
-from clip_interrogator import Config, Interrogator
+# from clip_interrogator import Config, Interrogator
+
+
+global _available
+_available=False
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+try:
+    if is_installed('clip_interrogator')==False:
+        import subprocess
+
+        # 安装
+        print('#pip install clip-interrogator==0.6.0')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'clip-interrogator==0.6.0'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0:
+            print("#install success")
+            from transformers import AutoProcessor, BlipForConditionalGeneration
+            from clip_interrogator import Config, Interrogator
+            _available=True
+        else:
+            print("#install error")
+        
+    else:
+        from transformers import AutoProcessor, BlipForConditionalGeneration
+        from clip_interrogator import Config, Interrogator
+        _available=True
+
+except:
+    _available=False
+
 
 def load_caption_model(model_path,config,t='blip-base'):
     dtype=torch.float16 if config.device == 'cuda' else torch.float32
@@ -28,11 +68,11 @@ def load_caption_model(model_path,config,t='blip-base'):
 caption_model_path=os.path.join(folder_paths.models_dir, "clip_interrogator/Salesforce/blip-image-captioning-base")
 if not os.path.exists(caption_model_path):
     print(f"## clip_interrogator_model not found: {caption_model_path}, pls download from https://huggingface.co/Salesforce/blip-image-captioning-base")
+    caption_model_path='Salesforce/blip-image-captioning-base'
 
 cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
 
 
-
 # Tensor to PIL
 def tensor2pil(image):
     return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
@@ -58,7 +98,53 @@ def image_analysis_fn(ci,image):
     trending_ranks = {trending: sim for trending, sim in zip(top_trendings, ci.similarities(image_features, top_trendings))}
     flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))}
     
-    return medium_ranks, artist_ranks, movement_ranks, trending_ranks, flavor_ranks
+    return {
+        "medium_ranks":medium_ranks, 
+        "artist_ranks":artist_ranks, 
+        "movement_ranks":movement_ranks, 
+        "trending_ranks":trending_ranks, 
+        "flavor_ranks":flavor_ranks
+        }
+
+
+def generate_sentences(data):
+    sentences = []
+
+    # Get the length of data
+    data_length = len(data)
+
+    # Use a recursive function to handle variable-length data
+    def generate_recursive(index, current_sentence, current_score):
+        # Check if recursion is complete
+        if index == data_length:
+            sentences.append({"sentence": current_sentence, "score": current_score})
+            return
+
+        # Get the current level data
+        current_data = data[index]
+
+        # Iterate through the current level data
+        for phrase in current_data:
+            sentence = current_sentence + ("," if current_sentence.strip() else "") + phrase
+            score = current_score + current_data[phrase]
+            generate_recursive(index + 1, sentence, score)
+
+    # Start recursive generation of sentences
+    generate_recursive(0, "", 0)
+
+    # Sort the generated sentences by score in descending order
+    sentences.sort(key=lambda x: x["score"], reverse=True)
+
+    def get_random_elements(elements, num):
+        return random.sample(elements, num)
+
+    ps = get_random_elements(sentences, 5)
+    ps = [s["sentence"] for s in sorted(ps, key=lambda x: x["score"], reverse=True)]
+
+    return ps
+
+
+
 
 def image_to_prompt(ci,image, mode):
     ci.config.chunk_size = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
@@ -79,6 +165,10 @@ def image_to_prompt(ci,image, mode):
 
 
 class ClipInterrogator:
+
+    global _available
+    available=_available
+    
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
@@ -86,17 +176,22 @@ class ClipInterrogator:
             "prompt_mode": (['fast','classic','best','negative'],),
             "image_analysis": (["off","on"],), 
                              },
+
+                # "optional":{
+                #     "output":("CLIPINTERROGATOR", {"multiline": True,"default": "", "dynamicPrompts": False})
+                # },
+
                 }
     
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("prompt",)
+    RETURN_TYPES = ("STRING","STRING",)
+    RETURN_NAMES = ("prompt","random_samples",)
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/prompt"
+    CATEGORY = "♾️Mixlab/Prompt"
     OUTPUT_NODE = True
     INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,)
+    OUTPUT_IS_LIST = (True,True,)
     global ci
     ci = None
     def run(self,image,prompt_mode,image_analysis):
@@ -156,5 +251,21 @@ class ClipInterrogator:
             ci.caption_offloaded = True
 
         # analysis_result=[]
+        # items = app.graph.getNodeById(31).widgets[2].value["items"]
+        
+        random_samples=[]
 
-        return {"ui":{"prompt": prompt_result,"analysis":analysis_result},"result": (prompt_result,)}
+        for r in analysis_result:
+            random_sample = generate_sentences([r['medium_ranks'], r['artist_ranks'],r['movement_ranks'],r['trending_ranks'],r['flavor_ranks']])
+            for s in random_sample:
+                random_samples.append(s)
+        # print(len(random_samples))
+        # print('-----')
+        # print( random_samples)
+        return {
+            "ui":{
+                    "prompt": prompt_result,
+                    "analysis":analysis_result,
+                    "random_samples":random_samples
+                },
+            "result": (prompt_result,random_samples,)}

nodes/Clipseg.py

@@ -106,13 +106,13 @@ class CLIPSeg:
                      },
                 "optional":
                     {
-                        "blur": ("FLOAT", {"min": 0, "max": 15, "step": 0.1, "default": 7}),
-                        "threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.4}),
+                        "blur": ("FLOAT", {"min": 0, "max": 15, "step": 0.1, "default": 3}),
+                        "threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.3}),
                         "dilation_factor": ("INT", {"min": 0, "max": 10, "step": 1, "default": 4}),
                     }
                 }
 
-    CATEGORY = "♾️Mixlab/mask"
+    CATEGORY = "♾️Mixlab/Mask"
     RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
     RETURN_NAMES = ("Mask","Heatmap Mask", "BW Mask")
 
@@ -218,7 +218,7 @@ class CombineMasks:
                     },
                 }
         
-    CATEGORY = "♾️Mixlab/mask"
+    CATEGORY = "♾️Mixlab/Mask"
     RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
     RETURN_NAMES = ("Combined Mask","Heatmap Mask", "BW Mask")
 

nodes/ImageNode.py

@@ -10,7 +10,7 @@ import folder_paths
 import json,io
 from comfy.cli_args import args
 import cv2 
-
+import math
 from .Watcher import FolderWatcher
 
 
@@ -27,6 +27,52 @@ def pil2tensor(image):
     return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
 
 
+# 颜色迁移
+# Color-Transfer-between-Images https://github.com/chia56028/Color-Transfer-between-Images/blob/master/color_transfer.py
+
+def get_mean_and_std(x):
+	x_mean, x_std = cv2.meanStdDev(x)
+	x_mean = np.hstack(np.around(x_mean,2))
+	x_std = np.hstack(np.around(x_std,2))
+	return x_mean, x_std
+
+def color_transfer(source,target):
+	# sources = ['s1','s2','s3','s4','s5','s6']
+	# targets = ['t1','t2','t3','t4','t5','t6']
+
+     # 将PIL的Image类型转换为OpenCV的numpy数组
+    source = cv2.cvtColor(np.array(source), cv2.COLOR_RGB2LAB)
+    target = cv2.cvtColor(np.array(target), cv2.COLOR_RGB2LAB)
+
+    s_mean, s_std = get_mean_and_std(source)
+    t_mean, t_std = get_mean_and_std(target)
+
+    height, width, channel = source.shape
+	
+    for i in range(0,height):
+        for j in range(0,width):
+            for k in range(0,channel):
+                x = source[i,j,k]
+                x = ((x-s_mean[k])*(t_std[k]/s_std[k]))+t_mean[k]
+				# round or +0.5
+                x = round(x)
+				# boundary check
+                x = 0 if x<0 else x
+                x = 255 if x>255 else x
+                source[i,j,k] = x
+    
+    source = cv2.cvtColor(source,cv2.COLOR_LAB2RGB)
+ 
+    # 创建PIL图像对象
+    image_pil = Image.fromarray(source)
+    
+    return image_pil
+
+
+
+
+
+
 def naive_cutout(img, mask,invert=True):
     """
     Perform a simple cutout operation on an image using a mask.
@@ -203,6 +249,11 @@ def generate_gradient_image(width, height, start_color_hex, end_color_hex):
 # gradient_image = generate_gradient_image(width, height, start_color_hex, end_color_hex)
 # gradient_image.save('gradient_image.png')
 
+def rgb_to_hex(rgb):
+    r, g, b = rgb
+    hex_color = "#{:02x}{:02x}{:02x}".format(r, g, b)
+    return hex_color
+
 
 # 读取不了分层
 def load_psd(image):
@@ -344,7 +395,8 @@ def get_average_color_image(image):
 
     im = Image.new("RGB", (image.width, image.height), (average_red, average_green, average_blue))
 
-    return im
+    hex=rgb_to_hex((average_red, average_green, average_blue))
+    return (im,hex)
 
 
 
@@ -516,7 +568,7 @@ def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option)
     return bg_image
 
 
-
+# TODO 几个像素点的底
 def resize_image(layer_image, scale_option, width, height,color="white"):
     layer_image = layer_image.convert("RGB")
     original_width, original_height = layer_image.size
@@ -540,11 +592,11 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
     elif scale_option == "center":
         # Scale image to minimum of width and height, center it, and fill extra area with black
         scale = min(width / original_width, height / original_height)
-        new_width = int(original_width * scale)
-        new_height = int(original_height * scale)
+        new_width = math.ceil(original_width * scale)
+        new_height = math.ceil(original_height * scale)
         resized_image = Image.new("RGB", (width, height), color=color)
         resized_image.paste(layer_image.resize((new_width, new_height)), ((width - new_width) // 2, (height - new_height) // 2))
-        resized_image=resized_image.convert("RGB")
+        resized_image = resized_image.convert("RGB")
         return resized_image
     
     return layer_image
@@ -552,6 +604,8 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
 
 
 
+
+
 # def generate_text_image(text_list, font_path, font_size, text_color, vertical=True, spacing=0):
 #     # Load Chinese font
 #     font = ImageFont.truetype(font_path, font_size)
@@ -724,7 +778,7 @@ class SmoothMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/mask"
+    CATEGORY = "♾️Mixlab/Mask"
 
     INPUT_IS_LIST = False
 
@@ -772,7 +826,7 @@ class FeatheredMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/mask"
+    CATEGORY = "♾️Mixlab/Mask"
 
     OUTPUT_IS_LIST = (False,)
   
@@ -839,7 +893,7 @@ class SplitLongMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/mask"
+    CATEGORY = "♾️Mixlab/Mask"
 
     OUTPUT_IS_LIST = (True,)
   
@@ -883,7 +937,7 @@ class TransparentImage:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     # INPUT_IS_LIST = True， 一个batch传进来
     OUTPUT_IS_LIST = (True,True,True,)
@@ -955,7 +1009,7 @@ class EnhanceImage:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     INPUT_IS_LIST = True
 
@@ -1021,7 +1075,7 @@ class LoadImagesFromPath:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     # INPUT_IS_LIST = True
     OUTPUT_IS_LIST = (True,True,False,)
@@ -1047,7 +1101,7 @@ class LoadImagesFromPath:
             if watcher_folder!=None:
                 watcher_folder.stop()
 
-
+        #TODO 修bug： ps6477. tmp 
         images=get_images_filepath(file_path,white_bg=='enable')
 
         # 当开启了监听，则取最新的，第一个文件
@@ -1075,7 +1129,7 @@ class LoadImagesFromPath:
             print("发生了一个未知的错误：", str(e))
 
         # print('#prompt::::',prompt)
-        return (imgs,masks,prompt,)
+        return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,)}
 
 
 # TODO 扩大选区的功能,重新输出mask
@@ -1086,29 +1140,81 @@ class ImageCropByAlpha:
                              "RGBA": ("RGBA",),  },
                 }
     
-    RETURN_TYPES = ("IMAGE",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_TYPES = ("IMAGE","MASK","MASK","INT","INT","INT","INT",)
+    RETURN_NAMES = ("IMAGE","MASK","AREA_MASK","x","y","width","height",)
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,True,True,True,True,True,)
 
     def run(self,image,RGBA):
+        # print(image.shape,RGBA.shape)
+
+        image=image[0]
+        RGBA=RGBA[0]
+
+        bf_im = tensor2pil(image)
+
         # print(RGBA)
         im=tensor2pil(RGBA)
         im=naive_cutout(im,im)
         x, y, w, h=get_not_transparent_area(im)
-        print('#ForImageCrop:',w, h,x, y,)
+        # print('#ForImageCrop:',w, h,x, y,)
 
         x = min(x, image.shape[2] - 1)
         y = min(y, image.shape[1] - 1)
         to_x = w + x
         to_y = h + y
+
+        x_1=x
+        y_1=y
+        width_1=w
+        height_1=h
+
         img = image[:,y:to_y, x:to_x, :]
-        return (img,)
+
+
+        # 原图的mask
+        ori=RGBA[:,y:to_y, x:to_x, :]
+        ori=tensor2pil(ori)
+
+        # 创建一个新的图像对象，大小和模式与原始图像相同
+        new_image = Image.new("RGBA", ori.size)
+
+        # 获取原始图像的像素数据
+        pixel_data = ori.load()
+
+        # 获取新图像的像素数据
+        new_pixel_data = new_image.load()
+
+        # 遍历图像的每个像素
+        for y in range(ori.size[1]):
+            for x in range(ori.size[0]):
+                # 获取当前像素的RGBA值
+                r, g, b, a = pixel_data[x, y]
+
+                # 如果a通道不为0（不透明），将当前像素设置为白色
+                if a != 0:
+                    new_pixel_data[x, y] = (255, 255, 255, 255)
+                else:
+                    new_pixel_data[x, y] = (r, g, b, a)
+
+        # 保存修改后的图像
+        # new_image.save("output.png")
+                    
+        ori=new_image.convert('L')
+        # threshold = 128
+        # ori = ori.point(lambda x: 0 if x < threshold else 255, '1')
+        ori=pil2tensor(ori)
+
+        # 矩形区域，mask
+        b_image =AreaToMask_run(RGBA)
+        # img=None
+        # b_image=None
+        return ([img],[ori],[b_image],[x_1],[y_1],[width_1],[height_1],)
 
 
 
@@ -1146,7 +1252,7 @@ class TextImage:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,)
@@ -1175,7 +1281,7 @@ class LoadImagesFromURL:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (True,True,)
@@ -1232,7 +1338,7 @@ class SvgImage:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,True,)
@@ -1266,7 +1372,7 @@ class Image3D:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,False,False,)
@@ -1306,6 +1412,24 @@ class Image3D:
 
 
 
+def AreaToMask_run(RGBA):
+    # print(RGBA)
+    im=tensor2pil(RGBA)
+    im=naive_cutout(im,im)
+    x, y, w, h=get_not_transparent_area(im)
+        
+    im=im.convert("RGBA")
+        # print('#AreaToMask:',im)
+    img=areaToMask(x,y,w,h,im)
+    img=img.convert("RGBA")
+    mask=pil2tensor(img)
+
+    channels = ["red", "green", "blue", "alpha"]
+    # print(mask,mask.shape)
+    mask = mask[:, :, :, channels.index("green")]
+
+    return mask
+
 
 class AreaToMask:
     @classmethod
@@ -1318,26 +1442,14 @@ class AreaToMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/mask"
+    CATEGORY = "♾️Mixlab/Mask"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
 
     def run(self,RGBA):
-        # print(RGBA)
-        im=tensor2pil(RGBA)
-        im=naive_cutout(im,im)
-        x, y, w, h=get_not_transparent_area(im)
-        
-        im=im.convert("RGBA")
-        # print('#AreaToMask:',im)
-        img=areaToMask(x,y,w,h,im)
-        img=img.convert("RGBA")
-        mask=pil2tensor(img)
 
-        channels = ["red", "green", "blue", "alpha"]
-        # print(mask,mask.shape)
-        mask = mask[:, :, :, channels.index("green")]
+        mask =AreaToMask_run(RGBA)
 
         return (mask,)
 
@@ -1353,7 +1465,7 @@ class FaceToMask:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/mask"
+    CATEGORY = "♾️Mixlab/Mask"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -1398,7 +1510,7 @@ class EmptyLayer:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/layer"
+    CATEGORY = "♾️Mixlab/Layer"
 
     OUTPUT_IS_LIST = (True,)
 
@@ -1479,7 +1591,7 @@ class NewLayer:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/layer"
+    CATEGORY = "♾️Mixlab/Layer"
 
     INPUT_IS_LIST = True
     OUTPUT_IS_LIST = (True,)
@@ -1567,7 +1679,7 @@ class ShowLayer:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/layer"
+    CATEGORY = "♾️Mixlab/Layer"
 
     INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (True,)
@@ -1606,12 +1718,12 @@ class MergeLayers:
 
                 }
     
-    RETURN_TYPES = ("IMAGE",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_TYPES = ("IMAGE","MASK",)
+    RETURN_NAMES = ("IMAGE","MASK",)
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/layer"
+    CATEGORY = "♾️Mixlab/Layer"
 
     INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (False,)
@@ -1632,7 +1744,10 @@ class MergeLayers:
                 bg_image=tensor2pil(bg_image)
                 # 按z-index排序
                 layers_new = sorted(layers, key=lambda x: x["z_index"])
-            
+                
+                width, height = bg_image.size
+                final_mask= Image.new('L', (width, height), 0)
+
                 for layer in layers_new:
                     image=layer['image']
                     mask=layer['mask']
@@ -1658,25 +1773,32 @@ class MergeLayers:
                                         layer['scale_option']
                                         )
                     
-                mask=bg_image.convert('RGBA')
-                mask=pil2tensor(mask)
+                    final_mask=merge_images(final_mask,
+                                        layer_mask.convert('RGB'),
+                                        layer_mask,
+                                        layer['x'],
+                                        layer['y'],
+                                        layer['width'],
+                                        layer['height'],
+                                        layer['scale_option']
+                                        )
+                    
+                    final_mask=final_mask.convert('L')
+                    
+                # mask=bg_image.convert('RGBA')
+                final_mask=pil2tensor(final_mask)
                 
                 bg_image=bg_image.convert('RGB')
                 bg_image=pil2tensor(bg_image)
 
-                channels = ["red", "green", "blue", "alpha"]
-                # print(mask,mask.shape)
-                mask = mask[:, :, :, channels.index("green")]
-
                 bg_images.append(bg_image)
-                masks.append(mask)
+                masks.append(final_mask)
         
         bg_images=torch.cat(bg_images, dim=0)
         masks=torch.cat(masks, dim=0)
         return (bg_images,masks,)
     
 
-
 class GradientImage:
     @classmethod
     def INPUT_TYPES(s):
@@ -1707,7 +1829,7 @@ class GradientImage:
     FUNCTION = "run"
 
     # 右键菜单目录
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     # 输入是否为列表
     INPUT_IS_LIST = False
@@ -1787,7 +1909,7 @@ class NoiseImage:
     FUNCTION = "run"
 
     # 右键菜单目录
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     # 输入是否为列表
     INPUT_IS_LIST = False
@@ -1857,15 +1979,15 @@ class ResizeImage:
             }
                 }
     
-    RETURN_TYPES = ("IMAGE","IMAGE")
-    RETURN_NAMES = ("image","average_image",)
+    RETURN_TYPES = ("IMAGE","IMAGE","STRING",)
+    RETURN_NAMES = ("image","average_image","average_hex",)
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,)
+    OUTPUT_IS_LIST = (True,True,True,)
 
     def run(self,width,height,scale_option,image=None,average_color=['on'],fill_color=["#FFFFFF"]):
         
@@ -1877,28 +1999,133 @@ class ResizeImage:
 
         imgs=[]
         average_images=[]
+        hexs=[]
 
         if image==None:
             im=create_noisy_image(w,h,"RGB")
-            a_im=get_average_color_image(im)
+            a_im,hex=get_average_color_image(im)
             
             im=pil2tensor(im)
             imgs.append(im)
 
             a_im=pil2tensor(a_im)
             average_images.append(a_im)
+            hexs.append(hex)
         else:
-            for im in image:
-                im=tensor2pil(im)
-                im=resize_image(im,scale_option,w,h,fill_color)
-                im=im.convert('RGB')
+            for ims in image:
+                for im in ims:
+                    im=tensor2pil(im)
+                    im=resize_image(im,scale_option,w,h,fill_color)
+                    im=im.convert('RGB')
 
-                a_im=get_average_color_image(im)
+                    a_im,hex=get_average_color_image(im)
 
-                im=pil2tensor(im)
-                imgs.append(im)
+                    im=pil2tensor(im)
+                    imgs.append(im)
 
-                a_im=pil2tensor(a_im)
-                average_images.append(a_im)
+                    a_im=pil2tensor(a_im)
+                    average_images.append(a_im)
+                    hexs.append(hex)
         
-        return (imgs,average_images,)
+        return (imgs,average_images,hexs,)
+
+
+class MirroredImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                "image": ("IMAGE",),
+                },
+                }
+    
+    # 输出的数据类型
+    RETURN_TYPES = ("IMAGE",)
+
+    # 运行时方法名称
+    FUNCTION = "run"
+
+    # 右键菜单目录
+    CATEGORY = "♾️Mixlab/Image"
+
+    # 输入是否为列表
+    INPUT_IS_LIST = True
+
+    # 输出是否为列表
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self,image):
+        res=[]
+        for ims in image:
+            for im in ims:
+                img=tensor2pil(im)
+                mirrored_image = img.transpose(Image.FLIP_LEFT_RIGHT)
+                img=pil2tensor(mirrored_image)
+                res.append(img)
+        return (res,)
+
+
+
+class GetImageSize_:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("INT", "INT")
+    RETURN_NAMES = ("width", "height")
+
+    FUNCTION = "get_size"
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    def get_size(self, image):
+        _, height, width, _ = image.shape
+        return (width, height)
+
+
+
+class ImageColorTransfer:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                "source": ("IMAGE",),
+                "target": ("IMAGE",),
+                },
+                }
+    
+    # 输出的数据类型
+    RETURN_TYPES = ("IMAGE",)
+
+    # 运行时方法名称
+    FUNCTION = "run"
+
+    # 右键菜单目录
+    CATEGORY = "♾️Mixlab/Image"
+
+    # 输入是否为列表
+    INPUT_IS_LIST = True
+
+    # 输出是否为列表
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self,source,target):
+
+        res=[]
+
+        target=target[0][0]
+        print(target.shape)
+        target=tensor2pil(target)
+
+        for ims in source:
+            for im in ims:
+                image=tensor2pil(im)
+                image=color_transfer(image,target)
+                image=pil2tensor(image)
+                res.append(image)
+
+        return (res,)
+
+

nodes/Lama.py

@@ -1,12 +1,46 @@
-import os
+import os,sys
 import folder_paths
-from simple_lama_inpainting import SimpleLama
-from PIL import Image
 
+from PIL import Image
+import importlib.util
 import numpy as np
 
 import torch
 
+global _available
+_available=False
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+if is_installed('simple_lama_inpainting')==False:
+    import subprocess
+    from packaging import version
+    
+    if version.parse(torch.__version__)>=version.parse('2.1'):
+        # 安装
+        print('#pip install simple_lama_inpainting')
+
+        result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'simple_lama_inpainting'], capture_output=True, text=True)
+
+        #检查命令执行结果
+        if result.returncode == 0:
+            print("#install success")
+            from simple_lama_inpainting import SimpleLama
+            _available=True
+        else:
+            print("#install error")
+    else:
+        print('#pls check your torch version >= 2.1')
+
+else:
+    from simple_lama_inpainting import SimpleLama
+    _available=True
+
 
  
 llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
@@ -38,6 +72,8 @@ def pil2tensor(image):
 
 
 class LaMaInpainting:
+    global _available
+    available=_available
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
@@ -52,7 +88,7 @@ class LaMaInpainting:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     INPUT_IS_LIST = True
     OUTPUT_IS_LIST = (True,)

nodes/PromptNode.py

@@ -114,7 +114,7 @@ class PromptImage:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/prompt"
+    CATEGORY = "♾️Mixlab/Prompt"
 
     # 运行的函数
     def run(self,prompts,images,save_to_image):
@@ -193,7 +193,7 @@ class PromptSimplification:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/prompt"
+    CATEGORY = "♾️Mixlab/Prompt"
 
     INPUT_IS_LIST = True
     OUTPUT_IS_LIST = (True,)
@@ -254,7 +254,7 @@ class PromptSlide:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/prompt"
+    CATEGORY = "♾️Mixlab/Prompt"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -306,7 +306,7 @@ class RandomPrompt:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/prompt"
+    CATEGORY = "♾️Mixlab/Prompt"
 
     OUTPUT_IS_LIST = (True,)
     OUTPUT_NODE = True

nodes/ScreenShareNode.py

@@ -93,7 +93,7 @@ class ScreenShareNode:
     RETURN_NAMES = ("IMAGE","PROMPT","FLOAT","INT")
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     # INPUT_IS_LIST = True
     OUTPUT_IS_LIST = (False,False,False,False)
@@ -118,7 +118,7 @@ class FloatingVideo:
     OUTPUT_NODE = True
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/image"
+    CATEGORY = "♾️Mixlab/Image"
 
     # INPUT_IS_LIST = True
     # OUTPUT_IS_LIST = (False,False,)

nodes/TextGenerateNode.py

@@ -0,0 +1,271 @@
+from transformers import pipeline, set_seed,AutoTokenizer, AutoModelForSeq2SeqLM
+import random
+import re
+
+import os,sys
+import folder_paths
+
+# from PIL import Image
+# import importlib.util
+
+import comfy.utils
+# import numpy as np
+import torch
+import random
+
+global _available
+_available=True
+
+
+text_generator_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/text2image-prompt-generator")
+if not os.path.exists(text_generator_model_path):
+    print(f"## text_generator_model not found: {text_generator_model_path}, pls download from https://huggingface.co/succinctly/text2image-prompt-generator/tree/main")
+    text_generator_model_path='succinctly/text2image-prompt-generator'
+
+zh_en_model_path=os.path.join(folder_paths.models_dir, "prompt_generator/opus-mt-zh-en")
+if not os.path.exists(zh_en_model_path):
+    print(f"## zh_en_model not found: {zh_en_model_path}, pls download from https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main")
+    zh_en_model_path='Helsinki-NLP/opus-mt-zh-en'
+
+
+
+def translate(zh_en_tokenizer,zh_en_model,texts):
+    with torch.no_grad():
+        encoded = zh_en_tokenizer(texts, return_tensors="pt")
+        encoded.to(zh_en_model.device)
+        sequences = zh_en_model.generate(**encoded)
+        return zh_en_tokenizer.batch_decode(sequences, skip_special_tokens=True)
+
+input = "青春不能回头，所以青春没有终点。 ——《火影忍者》"
+# print(input, translate(input))
+
+
+def text_generate(text_pipe,input,seed=None):
+    
+    if seed==None:
+        seed = random.randint(100, 1000000)
+    
+    set_seed(seed)
+
+    for count in range(6):    
+        sequences = text_pipe(input, max_length=random.randint(60, 90), num_return_sequences=8)
+        list = []
+        for sequence in sequences:
+            line = sequence['generated_text'].strip()
+            if line != input and len(line) > (len(input) + 4) and line.endswith((":", "-", "—")) is False:
+                list.append(line)
+
+        result = "\n".join(list)
+        result = re.sub('[^ ]+\.[^ ]+','', result)
+        result = result.replace("<", "").replace(">", "")
+        if result != "":
+            return result
+        if count == 5:
+            return result
+
+# input = "Youth can't turn back, so there's no end to youth."
+# print(input, text_generate(input))
+
+    
+import re
+
+def correct_prompt_syntax(prompt):
+
+    print("input prompt",prompt)
+    corrected_elements = []
+    # 处理成统一的英文标点
+    prompt = prompt.replace('（', '(').replace('）', ')').replace('，', ',').replace(';', ',').replace('。', '.').replace('：',':')
+    # 删除多余的空格
+    prompt = re.sub(r'\s+', ' ', prompt).strip()
+
+    # 分词
+    prompt_elements = prompt.split(',')
+
+    for element in prompt_elements:
+        element = element.strip()
+
+        # 处理空元素
+        if not element:
+            continue
+
+        # 检查并处理圆括号、方括号、尖括号
+        if element[0] in '([':
+            corrected_element = balance_brackets(element, '(', ')') if element[0] == '(' else balance_brackets(element, '[', ']')
+        elif element[0] == '<':
+            corrected_element = balance_brackets(element, '<', '>')
+        else:
+            # 删除开头的右括号或右方括号
+            corrected_element = element.lstrip(')]')
+
+        corrected_elements.append(corrected_element)
+
+    # 重组修正后的prompt
+    corrected_prompt = ', '.join(corrected_elements)
+    print("output prompt",corrected_prompt)
+    return corrected_prompt
+
+def balance_brackets(element, open_bracket, close_bracket):
+    open_brackets_count = element.count(open_bracket)
+    close_brackets_count = element.count(close_bracket)
+    return element + close_bracket * (open_brackets_count - close_brackets_count)
+
+# # 示例使用
+# test_prompt = "((middle-century castles)), [forsaken: 0.8], (mystery dragons: 1.3, mist forests, sunsets, quiet; (((dummy)), [fisting city: 0.5] background, radiant, soft and flavoured,] promising mountains, ((starry: 1.6), [[crowds], [middle-century castle: urban landscapes of the future: 0.5], [yellow: bright sun: 0.7], overlooking"
+# corrected_prompt = correct_prompt_syntax(test_prompt)
+# print(corrected_prompt)
+
+        
+        
+class ChinesePrompt:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+               "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
+               "generation": (["on","off"],{"default": "off"}), 
+                             },
+               
+                "optional":{
+                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
+                    
+                },
+
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    global text_pipe,zh_en_model,zh_en_tokenizer
+
+    text_pipe= None
+    zh_en_model=None
+    zh_en_tokenizer=None 
+
+    def run(self,text,seed,generation):
+        global text_pipe,zh_en_model,zh_en_tokenizer
+
+        seed=seed[0]
+        generation=generation[0]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(text)+1)
+
+        text = correct_prompt_syntax(text[0])
+
+        if zh_en_model==None:
+            zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval()
+            zh_en_tokenizer = AutoTokenizer.from_pretrained(zh_en_model_path)
+        
+        zh_en_model.to("cuda" if torch.cuda.is_available() else "cpu")
+            # zh_en_tokenizer.to("cuda" if torch.cuda.is_available() else "cpu")
+        
+        text_pipe=pipeline('text-generation', model=text_generator_model_path,device="cuda" if torch.cuda.is_available() else "cpu")
+        
+        # text_pipe.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        prompt_result=[]
+
+        # print('zh_en_model device',zh_en_model.device,text_pipe.model.device,torch.cuda.current_device() )
+        en_text=translate(zh_en_tokenizer,zh_en_model,text)
+        
+        zh_en_model.to('cpu')
+        print("test en_text",en_text)
+        # en_text.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        pbar.update(1)
+        for t in en_text:
+            if generation=='on':
+                prompt =text_generate(text_pipe,t,seed)
+                # 多条，还是单条
+                lines = prompt.split("\n")
+                longest_line = max(lines, key=len)
+                # print(longest_line)
+                prompt_result.append(longest_line)
+            else:
+                prompt_result.append(t)
+            pbar.update(1)
+
+        text_pipe.model.to('cpu')
+        prompt_result = [correct_prompt_syntax(prompt_result[0])]
+        
+        return {
+            "ui":{
+                    "prompt": prompt_result
+                },
+            "result": (prompt_result,)}
+
+
+class PromptGenerate:
+
+    global _available
+    available=_available
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "text": ("STRING",{"multiline": True,"default": "", "dynamicPrompts": False}),
+                             },
+
+                "optional":{
+                    "multiple": (["off","on"],), 
+                    "seed":("INT", {"default": 100, "min": 100, "max": 1000000}), 
+                },
+
+                }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompt",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Prompt"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    global text_pipe
+
+    text_pipe= None
+    # 
+
+    def run(self,text,multiple,seed):
+        global text_pipe
+
+        seed=seed[0]
+
+        multiple=multiple[0]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(text))
+        
+        text_pipe=pipeline('text-generation', model=text_generator_model_path,device="cuda" if torch.cuda.is_available() else "cpu")
+        
+        prompt_result=[]
+
+        for t in text:
+            prompt =text_generate(text_pipe,t,seed)
+            prompt = prompt.split("\n")
+            if multiple=='off':
+                prompt = [max(prompt, key=len)]
+            
+            for p in prompt:
+                prompt_result.append(p)
+            pbar.update(1)
+
+        text_pipe.model.to('cpu')
+
+        return {
+            "ui":{
+                    "prompt": prompt_result
+                },
+            "result": (prompt_result,)}

nodes/Utils.py

@@ -1,10 +1,56 @@
-import os
-import re,random
+import os,platform
+import re,random,json
 from PIL import Image
 import numpy as np
 # FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
 import folder_paths
 import matplotlib.font_manager as fm
+import torch
+
+def recursive_search(directory, excluded_dir_names=None):
+    if not os.path.isdir(directory):
+        return [], {}
+
+    if excluded_dir_names is None:
+        excluded_dir_names = []
+
+    result = []
+    dirs = {directory: os.path.getmtime(directory)}
+    for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
+        subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
+        for file_name in filenames:
+            relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
+            result.append(relative_path)
+        for d in subdirs:
+            path = os.path.join(dirpath, d)
+            dirs[path] = os.path.getmtime(path)
+    return result, dirs
+
+def filter_files_extensions(files, extensions):
+    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))
+
+
+def get_system_font_path():
+    ps=[]
+    system = platform.system()
+    if system == "Windows":
+        ps.append(os.path.join(os.environ["WINDIR"], "Fonts"))
+    elif system == "Darwin":
+        ps.append(os.path.join("/Library", "Fonts"))
+    elif system == "Linux":
+        ps.append(os.path.join("/usr", "share", "fonts"))
+        ps.append(os.path.join("/usr", "local", "share", "fonts"))
+    ps=[p for p in ps if os.path.exists(p)]
+    file_paths=[]
+    for f in ps:
+        result, dirs=recursive_search(f)
+        for r in result:
+            file_paths.append(r)
+    file_paths=filter_files_extensions(file_paths,[".otf", ".ttf"])
+
+    return file_paths
+
+
 
 # import json
 # import hashlib
@@ -60,14 +106,14 @@ def get_font_files(directory):
 
     # 尝试获取系统字体
     try:
-        font_paths = fm.findSystemFonts()
-        for path in font_paths:
+        font_paths = get_system_font_path()
+        for file in font_paths:
             try:
-                font_prop = fm.FontProperties(fname=path)
-                font_name = font_prop.get_name()
-                font_files[font_name] = path
+                font_name = os.path.splitext(file)[0]
+                font_path = file
+                font_files[font_name] = os.path.abspath(font_path)
             except Exception as e:
-                print(f"Error processing font {path}: {e}")
+                print(f"Error processing font {file}: {e}")
     except Exception as e:
         print(f"Error finding system fonts: {e}")
 
@@ -85,7 +131,12 @@ def flatten_list(nested_list):
         if isinstance(item, list):
             flat_list.extend(flatten_list(item))
         else:
-            flat_list.append(item)
+            if torch.is_tensor(item):
+                print('item.shape',item.shape)
+                for i in range(item.shape[0]):
+                    flat_list.append(item[i:i + 1, ...])
+            else:
+                flat_list.append(item)
     return flat_list
 
 
@@ -103,7 +154,7 @@ class ColorInput:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,False,False,False,)
@@ -132,7 +183,7 @@ class FontInput:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -162,7 +213,7 @@ class TextToNumber:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -220,7 +271,7 @@ class FloatSlider:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -273,7 +324,7 @@ class IntNumber:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -304,7 +355,7 @@ class MultiplicationNode:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,)
@@ -326,7 +377,7 @@ class TextInput:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -400,7 +451,7 @@ class DynamicDelayProcessor:
     RETURN_TYPES = (any_type,)
     RETURN_NAMES = ('output',)
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
     def run(self,any_input,delay_seconds,delay_by_text,words_per_seconds,replace_output,replace_value):
         # print(f"Delay text:",delay_by_text )
         # 获取开始时间戳
@@ -434,12 +485,12 @@ class AppInfo:
     def INPUT_TYPES(s):
         return {"required": { 
                     "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
-                    "image": ("IMAGE",),
                     "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
                     "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
                              },
 
                 "optional":{
+                    "IMAGE": ("IMAGE",),
                     "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                     "version":("INT", {
                         "default": 1, 
@@ -451,12 +502,13 @@ class AppInfo:
                     "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
                     "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
                     "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "auto_save": (["enable","disable"],),
                 }
 
                 }
     
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("IMAGE",)
+    RETURN_TYPES = ()
+    # RETURN_NAMES = ("IMAGE",)
 
     FUNCTION = "run"
 
@@ -464,11 +516,16 @@ class AppInfo:
 
     OUTPUT_NODE = True
     INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,)
+    # OUTPUT_IS_LIST = (True,)
 
-    def run(self,name,image,input_ids,output_ids,description,version,share_prefix,link,category):
+    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
         name=name[0]
-        im=image[0][0]
+        
+        im=None
+        if IMAGE:
+            im=IMAGE[0][0]
+            #TODO batch 的方式需要处理
+            im=create_temp_file(im)
         # image [img,] img[batch,w,h,a] 列表里面是batch，
 
         input_ids=input_ids[0]
@@ -478,38 +535,14 @@ class AppInfo:
         share_prefix=share_prefix[0]
         link=link[0]
         category=category[0]
-
-        #TODO batch 的方式需要处理
-        im=create_temp_file(im)
         
         # id=get_json_hash([name,im,input_ids,output_ids,description,version])
 
-        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": (image,)}
+        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": ()}
     
 
     
 
-class GetImageSize_:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-            }
-        }
-
-    RETURN_TYPES = ("INT", "INT")
-    RETURN_NAMES = ("width", "height")
-
-    FUNCTION = "get_size"
-
-    CATEGORY = "♾️Mixlab/utils"
-
-    def get_size(self, image):
-        _, height, width, _ = image.shape
-        return (width, height)
-
-
 
 class SwitchByIndex:
     @classmethod
@@ -534,7 +567,7 @@ class SwitchByIndex:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = True
     OUTPUT_IS_LIST = (True,)
@@ -545,7 +578,8 @@ class SwitchByIndex:
 
         C=[]
         index=index[0]
-        for a in A:
+
+        for a in A:    
             C.append(a)
         for b in B:
             C.append(b)
@@ -558,6 +592,7 @@ class SwitchByIndex:
                 C=[C[index]]
             except Exception as e:
                 C=[]
+        
         return (C,)
 
 
@@ -590,7 +625,7 @@ class LimitNumber:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/utils"
+    CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
@@ -612,3 +647,61 @@ class LimitNumber:
         
         return (nn,)
 
+
+
+class ListStatistics:
+    @staticmethod
+    def count_types(lst):
+        type_count = {}
+
+        for item in lst:
+            item_type = type(item).__name__
+            if item_type not in type_count:
+                type_count[item_type] = []
+
+            if item_type in ['dict', 'str', 'int', 'float']:
+                type_count[item_type].append(item)
+
+        return type_count
+
+# # 示例列表
+# my_list = [1, 'hello', {'name': 'John'}, 3.14, {'age': 25}, 'world', 10]
+
+# # 创建ListStatistics对象
+# list_stats = ListStatistics()
+
+# # 调用count_types方法进行统计
+# result = list_stats.count_types(my_list)
+
+# # 输出结果
+# for item_type, values in result.items():
+#     print(item_type + ':')
+#     for value in values:
+#         print(value)
+#     print('---')
+
+class TESTNODE_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "ANY":(any_type,), },
+                }
+    
+    RETURN_TYPES = (any_type,)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/__TEST"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self,ANY):
+        print(ANY)
+        # data=ANY
+        list_stats = ListStatistics()
+
+        # 调用count_types方法进行统计
+        result = list_stats.count_types(ANY)
+            
+        return {"ui": {"data": result,"type":[str(type(ANY[0]))]}, "result": (ANY,)}

nodes/Vae.py

@@ -145,7 +145,7 @@ class VAELoader:
     RETURN_TYPES = ("VAE",)
     FUNCTION = "load_vae"
 
-    CATEGORY = "♾️Mixlab/_test"
+    CATEGORY = "♾️Mixlab/__TEST"
 
     #TODO: scale factor?
     def load_vae(self, vae_name):
@@ -165,7 +165,7 @@ class VAEDecode:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
 
-    CATEGORY = "♾️Mixlab/_test"
+    CATEGORY = "♾️Mixlab/__TEST"
 
     def decode(self, vae, samples):
         image = vae.decode(samples["samples"].to("cuda:0"))

requirements.txt

@@ -4,5 +4,5 @@ watchdog
 opencv-python-headless
 matplotlib
 openai
-simple-lama-inpainting
-clip-interrogator==0.6.0
+# simple-lama-inpainting
+# clip-interrogator==0.6.0

web/index.html

@@ -303,10 +303,9 @@
         }
 
 
-
-        select,
-        button,
-        input {
+        .card select,
+        .card button,
+        .card input {
             height: 32px;
             cursor: pointer;
             background: #000000bf;
@@ -332,14 +331,53 @@
             font-size: 12px;
             width: 200px;
         }
+
+
+        /* 图像编辑器 */
+        #editor_container {
+            position: fixed;
+            top: 0;
+            z-index: 9;
+            background: #eee;
+            height: 100vh;
+            width: 100%;
+            display: none;
+        }
+
+
+        /* 图片的字幕 */
+        .pswp__custom-caption {
+            background: rgb(20 27 70);
+            font-size: 16px;
+            color: #fff;
+            width: calc(100% - 32px);
+            max-width: 400px;
+            padding: 2px 8px;
+            border-radius: 4px;
+            position: absolute;
+            left: 50%;
+            bottom: 16px;
+            transform: translateX(-50%);
+        }
+
+        .pswp__custom-caption a {
+            color: #fff;
+            text-decoration: underline;
+        }
+
+        .hidden-caption-content {
+            display: none;
+        }
     </style>
     <!-- <script src="../../../scripts/api.js" type="module"></script> -->
     <link href="/extensions/comfyui-mixlab-nodes/lib/photoswipe.min.css" rel="stylesheet">
     <link href="/extensions/comfyui-mixlab-nodes/lib/classic.min.css" rel="stylesheet">
     <script src="/extensions/comfyui-mixlab-nodes/lib/pickr.min.js"></script>
+    <script src="/extensions/comfyui-mixlab-nodes/lib/filerobot-image-editor.min.js"></script>
 </head>
 
 <body>
+    <div id="editor_container"></div>
     <div style="display: flex;
     align-items: center;
     justify-content: space-around;">
@@ -363,8 +401,8 @@
             </svg> Code
 
         </a>
-
     </div>
+
     <!-- <script type="module" src="https://cdn.bootcdn.net/ajax/libs/photoswipe/5.4.0/photoswipe-lightbox.esm.min.js"></script> -->
     <script type="module">
         import PhotoSwipeLightbox from '/extensions/comfyui-mixlab-nodes/lib/photoswipe-lightbox.esm.min.js'
@@ -406,8 +444,164 @@
 
             return { url: src, name }
 
+        };
+
+
+        function base64ToBlob(base64) {
+            // 去除base64编码中的前缀
+            const base64WithoutPrefix = base64.replace(/^data:image\/\w+;base64,/, '');
+
+            // 将base64编码转换为字节数组
+            const byteCharacters = atob(base64WithoutPrefix);
+
+            // 创建一个存储字节数组的数组
+            const byteArrays = [];
+
+            // 将字节数组放入数组中
+            for (let offset = 0; offset < byteCharacters.length; offset += 1024) {
+                const slice = byteCharacters.slice(offset, offset + 1024);
+
+                const byteNumbers = new Array(slice.length);
+                for (let i = 0; i < slice.length; i++) {
+                    byteNumbers[i] = slice.charCodeAt(i);
+                }
+
+                const byteArray = new Uint8Array(byteNumbers);
+                byteArrays.push(byteArray);
+            }
+
+            // 创建blob对象
+            const blob = new Blob(byteArrays, { type: 'image/png' }); // 根据实际情况设置MIME类型
+
+            return blob;
         }
 
+
+        function editImage(img, data) {
+
+
+            const update = async () => {
+                const { imageData } = filerobotImageEditor.getCurrentImgData()
+                let base64 = imageData.imageBase64;
+
+                let fileBlob = base64ToBlob(base64)
+                // // 获取读取的文件内容，即 Blob 对象
+                let hashId = await calculateImageHash(fileBlob)
+
+                if (hashId == window._appData.data[data.id].hashId) return
+
+                let { url, name } = await uploadImage(fileBlob);
+                // 在这里可以对 Blob 对象进行进一步处理
+                // imageElement.src = url;
+                window._appData.data[data.id].inputs.image = name;
+                window._appData.data[data.id].hashId = hashId;
+
+                console.log("上传的文件：", url, data.id, name);
+
+                img.src = base64;
+            }
+
+            const { TABS, TOOLS } = FilerobotImageEditor;
+            const config = {
+                source: img.src,
+                // loadableDesignState:{ //默认值
+                //     annotations:{
+                //         watermark:{
+                //             image:'https://127.0.0.1:8189/view?filename=1703554480406.png&type=input&subfolder=&rand=0.044164708320141965',
+                //             width:100,
+                //             height:200,
+                //             x:10,
+                //             y:50,
+                //             name: "Image",
+                //             id:'watermark'
+                //         }
+                //     }
+                // },
+                // annotationsCommon: {
+                //     fill: '#ff0000',
+                // },
+                // Text: { text: 'Filerobot...' },
+                Rotate: { angle: 90, componentType: 'slider' },
+
+                Crop: {
+                    presetsItems: [
+                        {
+                            titleKey: 'classicTv',
+                            descriptionKey: '4:3',
+                            ratio: 4 / 3,
+                            // icon: CropClassicTv,  
+                        },
+                        {
+                            titleKey: 'cinemascope',
+                            descriptionKey: '21:9',
+                            ratio: 21 / 9,
+                            // icon: CropCinemaScope, 
+                        },
+                    ],
+                    presetsFolders: [
+                        {
+                            titleKey: 'socialMedia', // will be translated into Social Media as backend contains this translation key
+                            // icon: Social, // optional,  
+                            groups: [
+                                {
+                                    titleKey: 'facebook',
+                                    items: [
+                                        {
+                                            titleKey: 'profile',
+                                            width: 180,
+                                            height: 180,
+                                            descriptionKey: 'fbProfileSize',
+                                        },
+                                        {
+                                            titleKey: 'coverPhoto',
+                                            width: 820,
+                                            height: 312,
+                                            descriptionKey: 'fbCoverPhotoSize',
+                                        },
+                                    ],
+                                },
+                            ],
+                        },
+                    ],
+                },
+                tabsIds: [...Object.values(TABS)], // or ['Adjust', 'Annotate', 'Watermark']
+                defaultTabId: TABS.WATERMARK, // or 'Annotate'
+                defaultToolId: TOOLS.WATERMARK, // or 'Text'
+                closeAfterSave: true
+            };
+
+            let editor = document.querySelector('#editor_container')
+            // Assuming we have a div with id="editor_container"
+            editor.style.display = 'block';
+            // console.log(img,editor,data)
+            document.body.style.overflow = 'hidden'
+
+            const filerobotImageEditor = new FilerobotImageEditor(
+                editor,
+                config,
+            );
+
+            filerobotImageEditor.render({
+                onSave: (editedImageObject, designState) => {
+                    console.log('saved', designState)
+                    //adjustments
+                    update()
+                },
+                onClose: (closingReason) => {
+                    console.log('Closing reason', closingReason);
+
+                    filerobotImageEditor.terminate();
+                    editor.style.display = 'none'
+                    document.body.style.overflow = 'auto'
+
+                },
+            });
+
+        }
+
+
+
+
         async function getQueue(clientId) {
             try {
 
@@ -892,6 +1086,12 @@
                     btnFromClipboard.innerText = 'paste from clipboard'
                     actionDiv.appendChild(btnFromClipboard);
 
+                    const btnForImageEdit = document.createElement("button");
+                    btnForImageEdit.style = ` width: 32px; background: none;margin-left: 18px;`
+                    btnForImageEdit.innerHTML = '<?xml version="1.0" ?><svg version="1.1" style="width: 24px;" viewBox="0 0 50 50" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g id="Layer_1_1_"><path d="M18.293,31.707h6.414l24-24l-6.414-6.414l-24,24V31.707z M45.879,7.707l-3.586,3.586l-3.586-3.586l3.586-3.586   L45.879,7.707z M20.293,26.121l17-17l3.586,3.586l-17,17h-3.586V26.121z"/><polygon points="43.293,19.707 41.293,19.707 41.293,46.707 3.293,46.707 3.293,8.707 31.293,8.707 31.293,6.707 1.293,6.707    1.293,48.707 43.293,48.707  "/></g></svg>'
+                    actionDiv.appendChild(btnForImageEdit);
+
+
                     uploadContainer.appendChild(actionDiv)
 
                     // Create an image element to display the uploaded image
@@ -902,6 +1102,8 @@
 
                     btnFromClipboard.addEventListener('click', (event) => handleClipboardImage(imageElement, data));
 
+                    btnForImageEdit.addEventListener('click', e => editImage(imageElement, data))
+
 
                     uploadImageInput.addEventListener('click', (event) => {
                         uploadImageInputHide.click()
@@ -1624,6 +1826,7 @@
                                     p.className = 'prompt_image'
                                     p.innerText = prompt;
                                     a.appendChild(p)
+                                    img.alt=prompt
                                 }
 
                                 // imgDiv.parentElement.appendChild(a);
@@ -1931,6 +2134,36 @@
                 children: 'a',
                 pswpModule: () => import('/extensions/comfyui-mixlab-nodes/lib/photoswipe.esm.min.js')
             });
+            lightbox.on('uiRegister', function () {
+                lightbox.pswp.ui.registerElement({
+                    name: 'custom-caption',
+                    order: 9,
+                    isButton: false,
+                    appendTo: 'root',
+                    html: 'Caption text',
+                    onInit: (el, pswp) => {
+                        lightbox.pswp.on('change', () => {
+                            const currSlideElement = lightbox.pswp.currSlide.data.element
+                            let captionHTML = ''
+                            if (currSlideElement) {
+                                const hiddenCaption = currSlideElement.querySelector(
+                                    '.hidden-caption-content'
+                                )
+                                if (hiddenCaption) {
+                                    // get caption from element with class hidden-caption-content
+                                    captionHTML = hiddenCaption.innerHTML
+                                } else {
+                                    // get caption from alt attribute
+                                    captionHTML = currSlideElement
+                                        .querySelector('img')
+                                        .getAttribute('alt')
+                                }
+                            }
+                            el.innerHTML = captionHTML || ''
+                        })
+                    }
+                })
+            })
             lightbox.init();
             // window._lightbox = lightbox
 
@@ -2006,13 +2239,14 @@
 
             createApp(window._appData);
 
-
         };
 
-        init_app()
-    </script>
+        init_app();
 
 
+
+    </script>
+
 </body>
 
 </html>

web/javascript/app_mixlab.js

@@ -100,16 +100,17 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
           // min max step
           options = node.widgets.filter(w => w.type === 'slider')[0].options
           // 备选的keywords清单
-          let ks = getLocalData(`_mixlab_PromptSlide`)
-          let keywords = ks[id]
-          // console.log('keywords',keywords)
-          if (keywords && keywords[0]) {
+          try {
+            let keywords = node.widgets.filter(w => w.name === 'upload')[0]
+              .value
+            keywords = JSON.parse(keywords)
             options.keywords = keywords
+          } catch (error) {
+            console.log(error)
           }
         }
 
-        if(node.type=='Color'){
-          
+        if (node.type == 'Color') {
         }
 
         input[inputIds.indexOf(id)] = {
@@ -126,11 +127,11 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
         output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
       }
 
-      if (node.type === 'KSampler'||node.type=='SamplerCustom') {
+      if (node.type === 'KSampler' || node.type == 'SamplerCustom') {
         // seed 的类型收集
         try {
           seed[id] = node.widgets.filter(
-            w => (w.name === 'seed'||w.name=='noise_seed')
+            w => w.name === 'seed' || w.name == 'noise_seed'
           )[0].linkedWidgets[0].value
         } catch (error) {}
       }
@@ -188,7 +189,8 @@ function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
   }, 0)
 }
 
-async function save (json, download = false) {
+async function save (json, download = false, showInfo = true) {
+  console.log('####SAVE', json[0])
   const name = json[0],
     version = json[5],
     share_prefix = json[6], //用于分享的功能扩展
@@ -237,22 +239,53 @@ async function save (json, download = false) {
     if (download) {
       await downloadJsonFile(data, data.app.filename)
     }
-    let open = window.confirm(
-      `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app?filename=${encodeURIComponent(
-        data.app.filename
-      )}&category=${encodeURIComponent(data.app.category)}`
-    )
-    if (open)
-      window.open(
-        `${getUrl()}/mixlab/app?filename=${encodeURIComponent(
+
+    if (showInfo) {
+      let open = window.confirm(
+        `You can now access the standalone application on a new page!\n${getUrl()}/mixlab/app?filename=${encodeURIComponent(
           data.app.filename
         )}&category=${encodeURIComponent(data.app.category)}`
       )
+      if (open)
+        window.open(
+          `${getUrl()}/mixlab/app?filename=${encodeURIComponent(
+            data.app.filename
+          )}&category=${encodeURIComponent(data.app.category)}`
+        )
+    }
   } catch (error) {
     console.log('###error', error)
   }
 }
 
+function getInputsAndOutputs () {
+  const inputs =
+      `LoadImage CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+        ' '
+      ),
+    outputs = `PreviewImage SaveImage ShowTextForGPT VHS_VideoCombine`.split(
+      ' '
+    )
+
+  let inputsId = [],
+    outputsId = []
+
+  for (let node of app.graph._nodes) {
+    if (inputs.includes(node.type)) {
+      inputsId.push(node.id)
+    }
+
+    if (outputs.includes(node.type)) {
+      outputsId.push(node.id)
+    }
+  }
+
+  return {
+    input: inputsId,
+    output: outputsId
+  }
+}
+
 app.registerExtension({
   name: 'Mixlab.utils.AppInfo',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
@@ -260,7 +293,16 @@ app.registerExtension({
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
-        console.log('#orig_nodeCreated', this)
+        // console.log('#orig_nodeCreated', this)
+
+        // 自动计算workflow里哪些节点支持
+        let input_ids = this.widgets.filter(w => w.name == 'input_ids')[0],
+          output_ids = this.widgets.filter(w => w.name == 'output_ids')[0]
+
+        const { input, output } = getInputsAndOutputs()
+        input_ids.value = input.join('\n')
+        output_ids.value = output.join('\n')
+
         const widget = {
           type: 'div',
           name: 'AppInfoRun',
@@ -331,6 +373,8 @@ app.registerExtension({
         }
 
         this.serialize_widgets = true //需要保存参数
+
+        window._mixlab_app_json = null
       }
 
       const onExecuted = nodeType.prototype.onExecuted
@@ -339,6 +383,12 @@ app.registerExtension({
         console.log(message.json)
         window._mixlab_app_json = message.json
         try {
+          let a = this.widgets.filter(w => w.name === 'AppInfoRun')[0]
+          if (a) {
+            if (!a.value) a.value = 0
+            a.value += 1
+          }
+
           const div = this.widgets.filter(w => w.div)[0].div
           Array.from(
             div.querySelectorAll('button'),
@@ -347,5 +397,39 @@ app.registerExtension({
         } catch (error) {}
       }
     }
+  },
+  async loadedGraphNode (node, app) {
+    console.log('#loadedGraphNode1111')
+    window._mixlab_app_json = null //切换workflow需要清空
+    if (node.type === 'AppInfo') {
+      let auto_save = node.widgets.filter(w => w.name == 'auto_save')[0]
+      if (auto_save) {
+        if (!['enable', 'disable'].includes(auto_save.value)) {
+          auto_save.value = 'enable'
+        }
+      }
+    }
+  }
+})
+
+api.addEventListener('execution_start', async ({ detail }) => {
+  console.log('#execution_start', detail)
+  window._mixlab_app_json = null
+})
+
+api.addEventListener('executed', async ({ detail }) => {
+  console.log('#executed', detail)
+  // window._mixlab_app_json=null;
+  const { output } = getInputsAndOutputs()
+  if (output.includes(parseInt(detail.node))) {
+    let appinfo = app.graph.findNodesByType('AppInfo')[0]
+    if (appinfo) {
+      let auto_save = appinfo.widgets.filter(w => w.name == 'auto_save')[0]
+      if (auto_save?.value === 'enable') {
+        // 自动保存
+        console.log('auto_save')
+        if (window._mixlab_app_json) save(window._mixlab_app_json, false, false)
+      }
+    }
   }
 })

web/javascript/checkVersion_mixlab.js

@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
 const repoOwner = 'shadowcz007' // 替换为仓库的所有者
 const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称
 
-const version = 'v0.11.0'
+const version = 'v0.12.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/clipInterrogator_mixlab.js

@@ -3,11 +3,57 @@ import { app } from '../../../scripts/app.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
 
+function getRandomElements (arr, num) {
+  var result = []
+  var len = arr.length
+
+  for (var i = 0; i < num; i++) {
+    var randomIndex = Math.floor(Math.random() * len)
+    result.push(arr[randomIndex])
+  }
+
+  return result
+}
+
+const createPrompt = (node, prompts, items, sample) => {
+  const w = ComfyWidgets['STRING'](
+    node,
+    'text',
+    ['STRING', { multiline: true }],
+    app
+  ).widget
+  w.inputEl.readOnly = true
+  w.inputEl.style.opacity = 0.6
+
+  w.value = typeof prompts === 'string' ? prompts : prompts.join('\n\n')
+
+  const w2 = ComfyWidgets['STRING'](
+    node,
+    'text',
+    ['STRING', { multiline: true }],
+    app
+  ).widget
+  w2.inputEl.readOnly = true
+  w2.inputEl.style.opacity = 0.6
+
+  w2.value = typeof items === 'string' ? items : JSON.stringify(items, null, 2)
+
+  const w3 = ComfyWidgets['STRING'](
+    node,
+    'text',
+    ['STRING', { multiline: true }],
+    app
+  ).widget
+  w3.inputEl.readOnly = true
+  w3.inputEl.style.opacity = 0.6
+  w3.value = typeof sample === 'string' ? sample : sample.join('\n\n')
+}
+
 app.registerExtension({
   name: 'Mixlab.prompt.ClipInterrogator',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeData.name === 'ClipInterrogator') {
-      function populate (prompts, items) {
+      function populate (prompts, items, random_samples) {
         if (this.widgets) {
           for (let i = 0; i < this.widgets.length; i++) {
             if (this.widgets[i].type !== 'combo') this.widgets[i].onRemove?.()
@@ -15,29 +61,9 @@ app.registerExtension({
           this.widgets.length = 2
         }
 
-        const w = ComfyWidgets['STRING'](
-          this,
-          'text',
-          ['STRING', { multiline: true }],
-          app
-        ).widget
-        w.inputEl.readOnly = true
-        w.inputEl.style.opacity = 0.6
+        createPrompt(this, prompts, items, random_samples)
 
-        w.value = prompts.join('\n\n')
-
-        const w2 = ComfyWidgets['STRING'](
-          this,
-          'text',
-          ['STRING', { multiline: true }],
-          app
-        ).widget
-        w2.inputEl.readOnly = true
-        w2.inputEl.style.opacity = 0.6
-
-        w2.value = JSON.stringify(items, null, 2)
-
-        console.log('ClipInterrogator',w,w2)
+        // console.log('ClipInterrogator', w, w2)
         requestAnimationFrame(() => {
           const sz = this.computeSize()
           if (sz[0] < this.size[0]) {
@@ -55,11 +81,39 @@ app.registerExtension({
       const onExecuted = nodeType.prototype.onExecuted
       nodeType.prototype.onExecuted = function (message) {
         onExecuted?.apply(this, arguments)
-        console.log('##', message)
-        populate.call(this, message.prompt, message.analysis)
+        // console.log('##', message)
+        populate.call(
+          this,
+          message.prompt,
+          message.analysis,
+          message.random_samples
+        )
       }
 
       this.serialize_widgets = true //需要保存参数
     }
+  },
+  async loadedGraphNode (node, app) {
+    // Fires every time a node is constructed
+    // You can modify widgets/add handlers/etc here
+
+    if (node.type === 'ClipInterrogator') {
+      try {
+        
+        let widgets_values = node.widgets_values
+        console.log(widgets_values )
+        try {
+          if (widgets_values[2] && widgets_values[3] && widgets_values[4])
+            createPrompt(
+              node,
+              widgets_values[2],
+              widgets_values[3],
+              widgets_values[4]
+            )
+        } catch (error) {
+          console.log(error)
+        }
+      } catch (error) {}
+    }
   }
 })

web/javascript/prompt_mixlab.js

@@ -3,6 +3,87 @@ import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
 
+import PhotoSwipeLightbox from '/extensions/comfyui-mixlab-nodes/lib/photoswipe-lightbox.esm.min.js'
+function loadCSS (url) {
+  var link = document.createElement('link')
+  link.rel = 'stylesheet'
+  link.type = 'text/css'
+  link.href = url
+  document.getElementsByTagName('head')[0].appendChild(link)
+
+  // Create a style element
+  const style = document.createElement('style')
+  // Define the CSS rule for scrollbar width
+  const cssRule = `.pswp__custom-caption {
+    background: rgb(20 27 70);
+    font-size: 16px;
+    color: #fff;
+    width: calc(100% - 32px);
+    max-width: 980px;
+    padding: 2px 8px;
+    border-radius: 4px;
+    position: absolute;
+    left: 50%;
+    bottom: 16px;
+    transform: translateX(-50%);
+  }
+  .pswp__custom-caption a {
+    color: #fff;
+    text-decoration: underline;
+  }
+  .hidden-caption-content {
+    display: none;
+  }`
+  // Add the CSS rule to the style element
+  style.appendChild(document.createTextNode(cssRule))
+
+  // Append the style element to the document head
+  document.head.appendChild(style)
+}
+loadCSS('/extensions/comfyui-mixlab-nodes/lib/photoswipe.min.css')
+
+function initLightBox () {
+  const lightbox = new PhotoSwipeLightbox({
+    gallery: '.prompt_image_output',
+    children: 'a',
+    pswpModule: () =>
+      import('/extensions/comfyui-mixlab-nodes/lib/photoswipe.esm.min.js')
+  })
+
+  lightbox.on('uiRegister', function () {
+    lightbox.pswp.ui.registerElement({
+      name: 'custom-caption',
+      order: 9,
+      isButton: false,
+      appendTo: 'root',
+      html: 'Caption text',
+      onInit: (el, pswp) => {
+        lightbox.pswp.on('change', () => {
+          const currSlideElement = lightbox.pswp.currSlide.data.element
+          let captionHTML = ''
+          if (currSlideElement) {
+            const hiddenCaption = currSlideElement.querySelector(
+              '.hidden-caption-content'
+            )
+            if (hiddenCaption) {
+              // get caption from element with class hidden-caption-content
+              captionHTML = hiddenCaption.innerHTML
+            } else {
+              // get caption from alt attribute
+              captionHTML = currSlideElement
+                .querySelector('img')
+                .getAttribute('alt')
+            }
+          }
+          el.innerHTML = captionHTML || ''
+        })
+      }
+    })
+  })
+
+  lightbox.init()
+}
+
 function get_position_style (ctx, widget_width, y, node_height) {
   const MARGIN = 4 // the margin around the html element
 
@@ -381,11 +462,19 @@ const _createResult = async (node, widget, message) => {
       let h = (image.naturalHeight * width) / image.naturalWidth
       if (index % 2 === 0) height_add += h
       div.style = `width: ${width}px;height:${h}px;position: relative;margin: 4px;`
-      div.innerHTML = `<img src="${url}" style='width: 100%'/>
-            <p style="position: absolute;
+
+      div.innerHTML = `<a href="${url}" 
+      data-pswp-width="${image.naturalWidth}" 
+      data-pswp-height="${image.naturalHeight}" 
+      target="_blank">
+      <img src="${url}" style='width: 100%' alt="${message.prompts[index]}"/>
+    </a>
+    <p style="position: absolute;
             bottom: 0;
-            left: 0; 
-            background:#444444c2;
+            left: 0;
+            opacity: 0.6;
+            background-color: var(--comfy-input-bg); 
+            color: var(--descrip-text);
             margin: 0;
             font-size: 12px;
             padding: 5px;
@@ -422,11 +511,14 @@ app.registerExtension({
         }
 
         widget.div = $el('div', {})
+        widget.div.className = 'prompt_image_output'
 
         document.body.appendChild(widget.div)
 
         this.addCustomWidget(widget)
 
+        initLightBox()
+
         const onRemoved = this.onRemoved
         this.onRemoved = () => {
           widget.div.remove()
@@ -472,6 +564,9 @@ app.registerExtension({
       // await sleep(0)
       let widget = node.widgets.filter(w => w.name === 'result')[0]
       console.log('widget.value', widget.value)
+
+      initLightBox()
+
       let cards = widget.div.querySelectorAll('.card')
       if (cards.length == 0) node.size = [280, 120]
 

web/javascript/utils_mixlab.js

@@ -269,7 +269,6 @@ app.registerExtension({
           const n = this.widgets.filter(w => w.name === 'number')[0]
           n.value = message.num[0]
         }
-
         console.log('TextToNumber', random_number.value)
       }
     }
@@ -307,11 +306,16 @@ const min_max = node => {
 app.registerExtension({
   name: 'Mixlab.utils.FloatSlider',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    const orig_nodeCreated = nodeType.prototype.onNodeCreated
-    nodeType.prototype.onNodeCreated = function () {
-      orig_nodeCreated?.apply(this, arguments)
-      min_max(this)
+
+    if (nodeType.comfyClass == 'FloatSlider') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated;
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        min_max(this)
+      }
     }
+
+   
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'FloatSlider') {
@@ -322,11 +326,15 @@ app.registerExtension({
 app.registerExtension({
   name: 'Mixlab.utils.IntNumber',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    const orig_nodeCreated = nodeType.prototype.onNodeCreated
-    nodeType.prototype.onNodeCreated = function () {
-      orig_nodeCreated?.apply(this, arguments)
-      min_max(this)
+    
+    if (nodeType.comfyClass == 'IntNumber') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        min_max(this)
+      }
     }
+   
   },
   async loadedGraphNode (node, app) {
     if (node.type === 'IntNumber') {
@@ -334,3 +342,23 @@ app.registerExtension({
     }
   }
 })
+
+
+app.registerExtension({
+  name: 'Mixlab.utils.TESTNODE_',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    
+    if (nodeType.comfyClass == 'TESTNODE_') {
+    
+      const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+        console.log('##',message)
+				 
+			};
+
+
+    }
+   
+  }, 
+})

web/javascript/watcher_mixlab.js

@@ -60,30 +60,7 @@ app.registerExtension({
           type: inputData[0], // the type, CHEESE
           name: inputName, // the name, slice
           size: [128, 24], // a default size
-          draw (ctx, node, width, y) {
-            // // 绘制文件图标的函数
-            // function drawFileIcon () {
-            //   // 清空画布
-            //   // ctx.clearRect(0, 0, canvas.width, canvas.height)
-
-            //   // 绘制文件外框
-            //   ctx.fillStyle = '#000'
-            //   ctx.fillRect(5, 5, 40, 40)
-
-            //   // 绘制文件夹图标
-            //   ctx.fillStyle = '#f00'
-            //   ctx.fillRect(10, 15, 30, 20)
-
-            //   // 绘制监听符号
-            //   ctx.beginPath()
-            //   ctx.arc(30, 35, 5, 0, 2 * Math.PI)
-            //   ctx.fillStyle = '#00f'
-            //   ctx.fill()
-            // }
-
-            // // 调用绘制函数
-            // drawFileIcon()
-          },
+          draw (ctx, node, width, y) {},
           computeSize (...args) {
             return [128, 24] // a method to compute the current size of the widget
           },
@@ -124,11 +101,19 @@ app.registerExtension({
       nodeType.prototype.onNodeCreated = function () {
         orig_nodeCreated?.apply(this, arguments)
 
-        console.log('watch widtget', this.widgets)
+        // 虚拟的widget，用于更新节点，让其每次都运行
+        const widget = {
+          type: 'div',
+          name: 'seed',
+          draw (ctx, node, widget_width, y, widget_height) {}
+        }
+
+        this.addCustomWidget(widget)
 
         const watcher = this.widgets.filter(w => w.name == 'watcher')[0]
 
         watcher.callback = () => {
+          console.log('watcher', watcher.value)
           if (watcher.value === 'enable') {
             if (window._mixlab_watcher_t)
               clearInterval(window._mixlab_watcher_t)
@@ -140,7 +125,7 @@ app.registerExtension({
                   window._mixlab_file_path_watcher = json.event_type
                   // widget.card.innerText = window._mixlab_file_path_watcher || ''
                   //运行
-                  document.querySelector('#queue-button').click()
+                  // document.querySelector('#queue-button').click()
                 }
               })
             }, 1000)
@@ -162,15 +147,59 @@ app.registerExtension({
           window._mixlab_file_path_watcher = json.event_type
         })
 
-        /*
-                Add the widget, make sure we clean up nicely, and we do not want to be serialized!
-                */
-        // this.addCustomWidget(widget)
         this.onRemoved = function () {
           // widget.card.remove()
         }
         this.serialize_widgets = true
       }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log(message)
+        try {
+          let seed = this.widgets.filter(w => w.name === 'seed')[0]
+          if (seed) {
+            if (!seed.value) seed.value = 0
+            seed.value += 1
+          }
+        } catch (error) {}
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'LoadImagesFromPath') {
+      const watcher = node.widgets.filter(w => w.name == 'watcher')[0]
+      if (watcher) {
+        if (watcher.value === 'enable') {
+          if (window._mixlab_watcher_t) clearInterval(window._mixlab_watcher_t)
+          window._mixlab_watcher_t = setInterval(() => {
+            // 上次路径填充
+            getConfig().then(json => {
+              console.log(json.event_type)
+              if (json.event_type != window._mixlab_file_path_watcher) {
+                window._mixlab_file_path_watcher = json.event_type
+                // widget.card.innerText = window._mixlab_file_path_watcher || ''
+                //运行
+                document.querySelector('#queue-button').click()
+              }
+            })
+          }, 1000)
+        } else {
+          if (window._mixlab_watcher_t) {
+            clearInterval(window._mixlab_watcher_t)
+          }
+          window._mixlab_watcher_t = null
+        }
+      }
+
+      try {
+        let seed = node.widgets.filter(w => w.name === 'seed')[0]
+        if (seed) {
+          if (!seed.value) seed.value = 0
+          seed.value += 1
+        }
+      } catch (error) {}
     }
   }
 })

workflow/2-screeshare.json

@@ -0,0 +1,736 @@
+{
+  "last_node_id": 29,
+  "last_link_id": 32,
+  "nodes": [
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        108,
+        316
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 16
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            6
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
+      "id": 18,
+      "type": "ControlNetApply",
+      "pos": [
+        485,
+        792
+      ],
+      "size": {
+        "0": 317.4000244140625,
+        "1": 98
+      },
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "conditioning",
+          "type": "CONDITIONING",
+          "link": 22,
+          "slot_index": 0
+        },
+        {
+          "name": "control_net",
+          "type": "CONTROL_NET",
+          "link": 18,
+          "slot_index": 1
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 26
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            21
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ControlNetApply"
+      },
+      "widgets_values": [
+        1
+      ]
+    },
+    {
+      "id": 6,
+      "type": "CLIPTextEncode",
+      "pos": [
+        117,
+        95
+      ],
+      "size": {
+        "0": 422.84503173828125,
+        "1": 164.31304931640625
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 15
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            22
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "a future city,buiding,future,magic,under water"
+      ]
+    },
+    {
+      "id": 15,
+      "type": "LoraLoader",
+      "pos": [
+        116,
+        -113
+      ],
+      "size": {
+        "0": 315,
+        "1": 126
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 13
+        },
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 14
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            12,
+            23
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            15,
+            16
+          ],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoraLoader"
+      },
+      "widgets_values": [
+        "lcm-lora-sdv1-5.safetensors",
+        1,
+        1
+      ]
+    },
+    {
+      "id": 4,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        -380,
+        185
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            13
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            14
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            8
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "deliberate_v2.safetensors"
+      ]
+    },
+    {
+      "id": 19,
+      "type": "DiffControlNetLoader",
+      "pos": [
+        40,
+        792
+      ],
+      "size": {
+        "0": 367.8165283203125,
+        "1": 58.083831787109375
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 23,
+          "slot_index": 0
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONTROL_NET",
+          "type": "CONTROL_NET",
+          "links": [
+            18
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "DiffControlNetLoader"
+      },
+      "widgets_values": [
+        "control_v11f1p_sd15_depth.pth"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "EmptyLatentImage",
+      "pos": [
+        65,
+        573
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            2
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 25,
+      "type": "LeReS-DepthMapPreprocessor",
+      "pos": [
+        47,
+        904
+      ],
+      "size": {
+        "0": 369.6000061035156,
+        "1": 130
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 31
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            26,
+            27
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LeReS-DepthMapPreprocessor"
+      },
+      "widgets_values": [
+        0.1,
+        0,
+        "disable",
+        512
+      ]
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        1100,
+        -97
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 12,
+          "slot_index": 0
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 21
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 6
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 2
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            7
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        170633013599955,
+        "fixed",
+        4,
+        1.6,
+        "lcm",
+        "simple",
+        1
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1100,
+        -241
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 7
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 8
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            28
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 20,
+      "type": "PreviewImage",
+      "pos": [
+        492,
+        955
+      ],
+      "size": {
+        "0": 526.9624633789062,
+        "1": 367.3833923339844
+      },
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 27
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 26,
+      "type": "FloatingVideo",
+      "pos": [
+        1565,
+        -181
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 28
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "FloatingVideo"
+      },
+      "widgets_values": [
+        null
+      ]
+    },
+    {
+      "id": 28,
+      "type": "PreviewImage",
+      "pos": [
+        -241,
+        953
+      ],
+      "size": {
+        "0": 210,
+        "1": 246
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 32
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 29,
+      "type": "ScreenShare",
+      "pos": [
+        -639,
+        366
+      ],
+      "size": [
+        315,
+        644
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            31,
+            32
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "PROMPT",
+          "type": "STRING",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "FLOAT",
+          "type": "FLOAT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "INT",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ScreenShare"
+      },
+      "widgets_values": [
+        null,
+        500,
+        null,
+        null,
+        null,
+        null
+      ]
+    }
+  ],
+  "links": [
+    [
+      2,
+      5,
+      0,
+      3,
+      3,
+      "LATENT"
+    ],
+    [
+      6,
+      7,
+      0,
+      3,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      7,
+      3,
+      0,
+      8,
+      0,
+      "LATENT"
+    ],
+    [
+      8,
+      4,
+      2,
+      8,
+      1,
+      "VAE"
+    ],
+    [
+      12,
+      15,
+      0,
+      3,
+      0,
+      "MODEL"
+    ],
+    [
+      13,
+      4,
+      0,
+      15,
+      0,
+      "MODEL"
+    ],
+    [
+      14,
+      4,
+      1,
+      15,
+      1,
+      "CLIP"
+    ],
+    [
+      15,
+      15,
+      1,
+      6,
+      0,
+      "CLIP"
+    ],
+    [
+      16,
+      15,
+      1,
+      7,
+      0,
+      "CLIP"
+    ],
+    [
+      18,
+      19,
+      0,
+      18,
+      1,
+      "CONTROL_NET"
+    ],
+    [
+      21,
+      18,
+      0,
+      3,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      22,
+      6,
+      0,
+      18,
+      0,
+      "CONDITIONING"
+    ],
+    [
+      23,
+      15,
+      0,
+      19,
+      0,
+      "MODEL"
+    ],
+    [
+      26,
+      25,
+      0,
+      18,
+      2,
+      "IMAGE"
+    ],
+    [
+      27,
+      25,
+      0,
+      20,
+      0,
+      "IMAGE"
+    ],
+    [
+      28,
+      8,
+      0,
+      26,
+      0,
+      "IMAGE"
+    ],
+    [
+      31,
+      29,
+      0,
+      25,
+      0,
+      "IMAGE"
+    ],
+    [
+      32,
+      29,
+      0,
+      28,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

workflow/3-FloatVideo-workflow.json

@@ -1,6 +1,6 @@
 {
-  "last_node_id": 23,
-  "last_link_id": 51,
+  "last_node_id": 22,
+  "last_link_id": 48,
   "nodes": [
     {
       "id": 7,
@@ -105,7 +105,7 @@
         {
           "name": "text",
           "type": "STRING",
-          "link": 51,
+          "link": 48,
           "widget": {
             "name": "text"
           }
@@ -295,7 +295,7 @@
         "Node name for S&R": "KSampler"
       },
       "widgets_values": [
-        482859286431021,
+        644769503212755,
         "randomize",
         4,
         1.6,
@@ -316,19 +316,47 @@
         "1": 246
       },
       "flags": {},
-      "order": 7,
+      "order": 6,
       "mode": 0,
       "inputs": [
         {
           "name": "images",
           "type": "IMAGE",
-          "link": 50
+          "link": 46
         }
       ],
       "properties": {
         "Node name for S&R": "PreviewImage"
       }
     },
+    {
+      "id": 20,
+      "type": "FloatingVideo",
+      "pos": [
+        2041,
+        277
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 41
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "FloatingVideo"
+      },
+      "widgets_values": [
+        null
+      ]
+    },
     {
       "id": 6,
       "type": "LoraLoader",
@@ -445,13 +473,13 @@
         "1": 58
       },
       "flags": {},
-      "order": 6,
+      "order": 7,
       "mode": 0,
       "inputs": [
         {
           "name": "image",
           "type": "IMAGE",
-          "link": 49
+          "link": 47
         }
       ],
       "outputs": [
@@ -473,43 +501,15 @@
       ]
     },
     {
-      "id": 20,
-      "type": "FloatingVideo",
+      "id": 22,
+      "type": "ScreenShare",
       "pos": [
-        1928,
-        295
+        -111,
+        427
       ],
       "size": [
         315,
-        58
-      ],
-      "flags": {},
-      "order": 12,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 41
-        }
-      ],
-      "properties": {
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workflow/AIPC大赛模板-图片参赛模板-workflow.json

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workflow/Prompt-weight-workflow.json

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workflow/Text-to-Image-app.json

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