v0.11.4

- 优化appinfo - 自动保存
- 优化mergeLayer，可以输出合成的mask
- 添加一个实验性的节点 ImageColorTransfer
- random prompt ,增加上传关键词功能
- 修复LoadImageFromPath的bug

README.md

@@ -33,6 +33,8 @@ APP-JSON:
 
 > 输出节点：PreviewImage 、SaveImage、ShowTextForGPT、VHS_VideoCombine
 
+> seed统一输入控件，支持：SamplerCustom、KSampler
+
 
 ## 🏃🚗🚚🚀  Real-time Design
 > ScreenShareNode & FloatingVideoNode. Now comfyui supports capturing screen pixel streams from any software and can be used for LCM-Lora integration. Let's get started with implementation and design! 💻🌐
@@ -71,6 +73,12 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 ![randomPrompt](./assets/randomPrompt.png)
 
+> ClipInterrogator
+
+[add clip-interrogator](https://github.com/pharmapsychotic/clip-interrogator)
+
+> PromptImage & PromptSimplification,Assist in simplifying prompt words, comparing images and prompt word nodes.
+
 
 ### 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.
@@ -167,9 +175,7 @@ v0.8.0 🚀🚗🚚🏃‍ LaMaInpainting
 
 [Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama
 
-<!-- ### Workflow
-[Workflow](./workflow.md) -->
-
+[Download Salesforce\blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base
 
 ## Installation
 

__init__.py

@@ -351,33 +351,59 @@ async def new_request(self, method, url, *args, **kwargs):
 
 # 应用 Monkey Patch
 aiohttp.ClientSession._request = new_request
+import socket
+
+async def check_port_available(address, port):
+    #检查端口是否可用
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+        sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        try:
+            sock.bind((address, port))
+            return True
+        except socket.error:
+            return False
 
 # https
 async def new_start(self, address, port, verbose=True, call_on_start=None):
-        
-
+    try:
         runner = web.AppRunner(self.app, access_log=None)
         await runner.setup()
+
+        if not await check_port_available(address, port):
+            raise RuntimeError(f"Port {port} is already in use.")
+
         site = web.TCPSite(runner, address, port)
         await site.start()
 
         import ssl
-        crt,key=create_for_https()
+        crt, key = create_for_https()
         ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
-        ssl_context.load_cert_chain(crt,key)
-        site2 = web.TCPSite(runner, address, port+1,ssl_context=ssl_context)
-        await site2.start()
+        ssl_context.load_cert_chain(crt, key)
+
+        success = False
+        for i in range(10):  # 尝试最多10次
+            if await check_port_available(address, port + 1 + i):
+                https_port = port + 1 + i
+                site2 = web.TCPSite(runner, address, https_port, ssl_context=ssl_context)
+                await site2.start()
+                success = True
+                break
+
+        if not success:
+            raise RuntimeError(f"Ports {port + 1} to {port + 10} are all in use.")
 
         if address == '':
             address = '0.0.0.0'
         if verbose:
-            # print('\033[91mMixlab Nodes: \033[93mLoaded\033[0m')
             print("\033[93mStarting server\n")
             print("\033[93mTo see the GUI go to: http://{}:{}".format(address, port))
-            print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, port+1))
+            print("\033[93mTo see the GUI go to: https://{}:{}\033[0m".format(address, https_port))
         if call_on_start is not None:
             call_on_start(address, port)
 
+    except Exception as e:
+        print(f"Error starting the server: {e}")
+
         # import webbrowser
         # if os.name == 'nt' and address == '0.0.0.0':
         #     address = '127.0.0.1'
@@ -477,6 +503,7 @@ 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
+        self.user_manager.add_routes(self.routes)
         self.app.add_routes(routes)
         self.app.add_routes(self.routes)
         for name, dir in nodes.EXTENSION_WEB_DIRS.items():
@@ -503,22 +530,27 @@ PromptServer.add_routes=new_add_routes
 
 
 # 导入节点
-from .nodes.PromptNode import RandomPrompt,PromptSlide
-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.PromptNode import RandomPrompt,PromptSlide,PromptSimplification,PromptImage
+from .nodes.ImageNode import 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.Utils import TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,GetImageSize_,MultiplicationNode
 from .nodes.Lama import LaMaInpainting
+from .nodes.ClipInterrogator import ClipInterrogator
 
 # 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,
     "NoiseImage":NoiseImage,
     "GradientImage":GradientImage,
     "TransparentImage":TransparentImage,
@@ -529,6 +561,7 @@ NODE_CLASS_MAPPINGS = {
     "EnhanceImage":EnhanceImage,
     "SvgImage":SvgImage,
     "3DImage":Image3D, 
+    "ImageColorTransfer":ImageColorTransfer,
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
     "MergeLayers":MergeLayers,

data/Product-designer.txt

@@ -0,0 +1,30 @@
+Jony Ive
+Dieter Rams
+Philippe Starck
+Karim Rashid
+Yves Béhar
+Marc Newson
+Naoto Fukasawa
+Jonathan Adler
+Patricia Urquiola
+Ross Lovegrove
+Tom Dixon
+Jasper Morrison
+Charles Eames
+Ray Eames
+Achille Castiglioni
+Ron Arad
+Konstantin Grcic
+Marcel Wanders
+Maarten Baas
+Stefan Sagmeister
+Ingo Maurer
+Hella Jongerius
+Sam Hecht
+Kim Colin
+Jaime Hayon
+Michael Anastassiades
+Nendo
+Oki Sato
+Matali Crasset
+Tokujin Yoshioka

data/extension-node-map.json

@@ -4786,6 +4786,8 @@
             "NewLayer",
             "RandomPrompt",
             "PromptSlide",
+            "PromptSimplification",
+            "ClipInterrogator",
             "ScreenShare",
             "ShowLayer",
             "ShowTextForGPT",

nodes/ChatGPT.py

@@ -180,8 +180,9 @@ class ShowTextForGPT:
     CATEGORY = "♾️Mixlab/GPT"
 
     def run(self, text):
-        # print(session_history)
+        # print(text)
         return {"ui": {"text": text}, "result": (text,)}
+        
 
 
 class CharacterInText:

nodes/ClipInterrogator.py

@@ -0,0 +1,228 @@
+import os
+import folder_paths
+
+from PIL import Image
+import comfy.utils
+import numpy as np
+import json
+import torch
+import random
+
+from transformers import AutoProcessor, BlipForConditionalGeneration
+
+from clip_interrogator import Config, Interrogator
+
+def load_caption_model(model_path,config,t='blip-base'):
+    dtype=torch.float16 if config.device == 'cuda' else torch.float32
+    caption_model = BlipForConditionalGeneration.from_pretrained(model_path, torch_dtype=dtype)
+    
+    caption_processor = AutoProcessor.from_pretrained(model_path)
+
+    caption_model.eval()
+    if not config.caption_offload:
+        caption_model = caption_model.to(config.device)
+    
+    return (caption_model,caption_processor)
+    
+
+
+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")
+
+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))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+def image_analysis_fn(ci,image):
+    image = image.convert('RGB')
+    image_features = ci.image_to_features(image)
+
+    top_mediums = ci.mediums.rank(image_features, 5)
+    top_artists = ci.artists.rank(image_features, 5)
+    top_movements = ci.movements.rank(image_features, 5)
+    top_trendings = ci.trendings.rank(image_features, 5)
+    top_flavors = ci.flavors.rank(image_features, 5)
+
+    medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))}
+    artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))}
+    movement_ranks = {movement: sim for movement, sim in zip(top_movements, ci.similarities(image_features, top_movements))}
+    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":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
+    ci.config.flavor_intermediate_count = 2048 if ci.config.clip_model_name == "ViT-L-14/openai" else 1024
+    image = image.convert('RGB')
+    if mode == 'best':
+        return ci.interrogate(image)
+    elif mode == 'classic':
+        return ci.interrogate_classic(image)
+    elif mode == 'fast':
+        return ci.interrogate_fast(image)
+    elif mode == 'negative':
+        return ci.interrogate_negative(image)
+
+# image = Image.open(image_path).convert('RGB')
+# ci = Interrogator(Config(clip_model_name="ViT-L-14/openai"))
+# print(ci.interrogate(image))
+
+
+class ClipInterrogator:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "prompt_mode": (['fast','classic','best','negative'],),
+            "image_analysis": (["off","on"],), 
+                             },
+
+                # "optional":{
+                #     "output":("CLIPINTERROGATOR", {"multiline": True,"default": "", "dynamicPrompts": False})
+                # },
+
+                }
+    
+    RETURN_TYPES = ("STRING","STRING",)
+    RETURN_NAMES = ("prompt","random_samples",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/prompt"
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,)
+    global ci
+    ci = None
+    def run(self,image,prompt_mode,image_analysis):
+        global ci
+
+        prompt_mode=prompt_mode[0]
+        analysis=image_analysis[0]
+
+        prompt_result=[]
+        analysis_result=[]
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(image)*(2 if analysis=='on' else 1))
+        
+        if ci==None:
+            config=Config(
+                clip_model_name="ViT-L-14/openai",
+                device="cuda" if torch.cuda.is_available() else "cpu",
+                download_cache=True,
+                clip_model_path=cache_path,
+                cache_path=cache_path
+                )
+            config.apply_low_vram_defaults()
+
+            caption_model,caption_processor=load_caption_model(caption_model_path,config)
+
+            config.caption_model= caption_model
+            config.caption_processor= caption_processor
+
+            ci = Interrogator(config)
+        # else:
+        #     simple_lama.model.to("cuda" if torch.cuda.is_available() else "cpu")
+
+        for i in range(len(image)):
+            im=image[i]
+
+            im=tensor2pil(im)
+            im=im.convert('RGB')
+
+            if analysis=='on':
+                analysis_res=image_analysis_fn(ci,im)
+                analysis_result.append( analysis_res )
+                pbar.update(1)
+
+            prompt=image_to_prompt(ci,im,prompt_mode)
+            pbar.update(1)
+            prompt_result.append(prompt)
+
+
+        # result.save("inpainted.png")
+        if ci.config.clip_offload and not ci.clip_offloaded:
+            ci.clip_model = ci.clip_model.to('cpu')
+            ci.clip_offloaded = True
+
+        if ci.config.caption_offload and not ci.caption_offloaded:
+            ci.caption_model = ci.caption_model.to('cpu')
+            ci.caption_offloaded = True
+
+        # analysis_result=[]
+        # items = app.graph.getNodeById(31).widgets[2].value["items"]
+        
+        random_samples=[]
+
+        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/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.
@@ -516,26 +562,44 @@ def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option)
     return bg_image
 
 
-def resize_image(layer_image,scale_option,width,height):
+# 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
+    
     if scale_option == "height":
-        # 按照高度比例缩放
-        original_width, original_height = layer_image.size
+        # Scale image based on height
         scale = height / original_height
         new_width = int(original_width * scale)
         layer_image = layer_image.resize((new_width, height))
+        
     elif scale_option == "width":
-        # 按照宽度比例缩放
-        original_width, original_height = layer_image.size
+        # Scale image based on width
         scale = width / original_width
         new_height = int(original_height * scale)
         layer_image = layer_image.resize((width, new_height))
+        
     elif scale_option == "overall":
-        # 整体缩放
+        # Scale image overall
         layer_image = layer_image.resize((width, height))
+        
+    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 = 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")
+        return resized_image
+    
     return layer_image
 
 
+
+
+
+
 # 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)
@@ -575,8 +639,7 @@ def resize_image(layer_image,scale_option,width,height):
 #     image=image.convert('RGB')
 
 #     return (image,alpha_image)
-
-def generate_text_image(text, font_path, font_size, text_color, vertical=True, spacing=0):
+def generate_text_image(text, font_path, font_size, text_color, vertical=True, stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0):
     # Split text into lines based on line breaks
     lines = text.split("\n")
 
@@ -592,19 +655,17 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
         x = 0
         y = 0
         for i in range(len(lines)):
-            line=lines[i]
+            line = lines[i]
             for char in line:
                 char_coordinates.append((x, y))
                 y += font_size + spacing
             x += font_size + spacing
             y = 0
-        # print(char_coordinates)
     else:
         x = 0
         y = 0
         for line in lines:
             for char in line:
-                #print('char',char)
                 char_coordinates.append((x, y))
                 x += font_size + spacing
             y += font_size + spacing
@@ -613,35 +674,42 @@ def generate_text_image(text, font_path, font_size, text_color, vertical=True, s
     # 3. Calculate image width and height
     if layout == "vertical":
         width = (len(lines) * (font_size + spacing)) - spacing
-        height = ((len(max(lines, key=len))+1) * (font_size + spacing)) + spacing
+        height = ((len(max(lines, key=len)) + 1) * (font_size + spacing)) + spacing
     else:
         width = (len(max(lines, key=len)) * (font_size + spacing)) - spacing
-        height = ((len(lines)-1) * (font_size + spacing)) + font_size
+        height = ((len(lines) - 1) * (font_size + spacing)) + font_size
 
     # 4. Draw each character on the image
-    image = Image.new('RGBA', (width, height), (255, 255, 255,0))
+    image = Image.new('RGBA', (width, height), (255, 255, 255, 0))
     draw = ImageDraw.Draw(image)
     font = ImageFont.truetype(font_path, font_size)
-    
-    index=0
+
+    index = 0
     for i, line in enumerate(lines):
         for j, char in enumerate(line):
             x, y = char_coordinates[index]
+            
+            if stroke:
+                draw.text((x-stroke_width, y), char, font=font, fill=stroke_color)
+                draw.text((x+stroke_width, y), char, font=font, fill=stroke_color)
+                draw.text((x, y-stroke_width), char, font=font, fill=stroke_color)
+                draw.text((x, y+stroke_width), char, font=font, fill=stroke_color)
+            
             draw.text((x, y), char, font=font, fill=text_color)
-            index+=1
+            index += 1
 
-    # image.save(output_image_path)
-
-    # 分离alpha通道
+    # Separate alpha channel
     alpha_channel = image.split()[3]
 
-    # 创建一个只有alpha通道的新图像
+    # Create a new image with only the alpha channel
     alpha_image = Image.new('L', image.size)
     alpha_image.putdata(alpha_channel.getdata())
 
-    image=image.convert('RGB')
+    image = image.convert('RGB')
+
+    return (image, alpha_image)
+
 
-    return (image,alpha_image)
 
 def base64_to_image(base64_string):
     # 去除前缀
@@ -937,20 +1005,27 @@ class EnhanceImage:
 
     CATEGORY = "♾️Mixlab/image"
 
-    INPUT_IS_LIST = False
+    INPUT_IS_LIST = True
 
-    OUTPUT_IS_LIST = (False,)
+    OUTPUT_IS_LIST = (True,)
   
     # 运行的函数
     def run(self,image,contrast):
-        # print('EnhanceImage',image.shape)
-        image=tensor2pil(image)
-     
-        image=enhance_depth_map(image,contrast)
+        # print('EnhanceImage',len(image),image[0].shape)
+        contrast=contrast[0]
+        res=[]
+        for ims in image:
+            for im in ims:
 
-        image=pil2tensor(image)
+                image=tensor2pil(im)
+            
+                image=enhance_depth_map(image,contrast)
+
+                image=pil2tensor(image)
+
+                res.append(image)
            
-        return (image,)
+        return (res,)
 
 
 
@@ -1048,7 +1123,7 @@ 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
@@ -1059,29 +1134,93 @@ class ImageCropByAlpha:
                              "RGBA": ("RGBA",),  },
                 }
     
-    RETURN_TYPES = ("IMAGE",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_TYPES = ("IMAGE","MASK","MASK",)
+    RETURN_NAMES = ("IMAGE","MASK","AREA_MASK",)
 
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/image"
 
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (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
+
         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
+        # print(bf_im.size,(x, y, x + w, y + h))
+        b_image = Image.new("RGBA", bf_im.size,(0, 0, 0, 0))
+        # f_image = Image.new("RGBA", bf_im.size,(255, 255, 255, 255))
+
+        draw = ImageDraw.Draw(b_image)
+
+        # 定义区域坐标
+        x1, y1 = x, y  # 左上角坐标
+        x2, y2 =to_x, to_y  # 右下角坐标
+
+        # 绘制白色方块并填充白色
+        draw.rectangle([(x1, y1), (x2, y2)], fill="white")
+
+        b_image=b_image.convert('L')
+        b_image=pil2tensor(b_image)
+        # img=None
+        # b_image=None
+        return ([img],[ori],[b_image],)
 
 
 
@@ -1110,6 +1249,7 @@ class TextImage:
                                 }), 
                     "text_color":("STRING",{"multiline": False,"default": "#000000","dynamicPrompts": False}),
                     "vertical":("BOOLEAN", {"default": True},),
+                    "stroke":("BOOLEAN", {"default": False},),
                              },
                 }
     
@@ -1123,11 +1263,11 @@ class TextImage:
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,)
 
-    def run(self,text,font_path,font_size,spacing,text_color,vertical):
+    def run(self,text,font_path,font_size,spacing,text_color,vertical,stroke):
         
         # text_list=list(text)
-
-        img,mask=generate_text_image(text,font_path,font_size,text_color,vertical,spacing)
+        # stroke=False, stroke_color=(0, 0, 0), stroke_width=1, spacing=0
+        img,mask=generate_text_image(text,font_path,font_size,text_color,vertical,stroke,(0, 0, 0),1,spacing)
         
         img=pil2tensor(img)
         mask=pil2tensor(mask)
@@ -1578,8 +1718,8 @@ class MergeLayers:
 
                 }
     
-    RETURN_TYPES = ("IMAGE",)
-    # RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
+    RETURN_TYPES = ("IMAGE","MASK",)
+    RETURN_NAMES = ("IMAGE","MASK",)
 
     FUNCTION = "run"
 
@@ -1604,7 +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']
@@ -1630,25 +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):
@@ -1818,13 +1968,14 @@ class ResizeImage:
                     "step": 1, #Slider's step
                     "display": "number" # Cosmetic only: display as "number" or "slider"
                 }),
-                "scale_option": (["width","height",'overall'],),
+                "scale_option": (["width","height",'overall','center'],),
 
                              },
 
             "optional":{
                     "image": ("IMAGE",),
                     "average_color": (["on",'off'],),
+                    "fill_color":("STRING",{"multiline": False,"default": "#FFFFFF","dynamicPrompts": False}),
             }
                 }
     
@@ -1838,12 +1989,13 @@ class ResizeImage:
     INPUT_IS_LIST = True
     OUTPUT_IS_LIST = (True,True,)
 
-    def run(self,width,height,scale_option,image=None,average_color=['on']):
+    def run(self,width,height,scale_option,image=None,average_color=['on'],fill_color=["#FFFFFF"]):
         
         w=width[0]
         h=height[0]
         scale_option=scale_option[0]
         average_color=average_color[0]
+        fill_color=fill_color[0]
 
         imgs=[]
         average_images=[]
@@ -1860,7 +2012,7 @@ class ResizeImage:
         else:
             for im in image:
                 im=tensor2pil(im)
-                im=resize_image(im,scale_option,w,h)
+                im=resize_image(im,scale_option,w,h,fill_color)
                 im=im.convert('RGB')
 
                 a_im=get_average_color_image(im)
@@ -1871,4 +2023,50 @@ class ResizeImage:
                 a_im=pil2tensor(a_im)
                 average_images.append(a_im)
         
-        return (imgs,average_images,)
+        return (imgs,average_images,)
+    
+
+
+
+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/_test"
+
+    # 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/PromptNode.py

@@ -1,9 +1,11 @@
 import random
 import comfy.utils
-import json
+import os
+import numpy as np
 from urllib import request, parse
-
-
+import folder_paths
+from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
+from PIL.PngImagePlugin import PngInfo
 # def queue_prompt(prompt_workflow):
 #     p = {"prompt": prompt_workflow}
 #     data = json.dumps(p).encode('utf-8')
@@ -45,12 +47,171 @@ default_prompt1='''Swing
 default_prompt1="\n".join([p.strip() for p in default_prompt1.split('\n') if p.strip()!=''])
 
 
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
 def addWeight(text, weight=1):
     if weight == 1:
         return text
     else:
         return f"({text}:{round(weight,2)})"
 
+def prompt_delete_words(sentence, new_words_length):
+    # 使用逗号分割句子，并去除空格
+    words = [word.strip() for word in sentence.split(",")]
+    
+    # 计算需要删除的单词数量
+    num_to_delete = len(words) - new_words_length
+    
+    words_to=[w for w in words]
+
+    # 逐个删除单词并存储在新列表中
+    new_words = []
+    for i in range(len(words)):
+        if num_to_delete > 0:
+            num_to_delete -= 1
+        else:
+            words_to.pop()
+            if len(words_to)>0:
+                new_words.append(", ".join(words_to))
+         
+    return new_words
+
+# # 测试方法
+# sentence = "a computer, a glass tablet with a keyboard on a dark background, 3d illustration, reflection, cgi 8k, clear glass, archaic, cut-away, white outline"
+# new_words_length = 5
+# result = prompt_delete_words(sentence, new_words_length)
+# print(result)
+
+class PromptImage:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = "PromptImage"
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompts": ("STRING", 
+                         {
+                            "multiline": True, 
+                            "default": '',
+                            "dynamicPrompts": False
+                          }),
+
+                "images": ("IMAGE",{"default": None}), 
+                 "save_to_image": (["enable", "disable"],),
+                }
+            }
+    
+    RETURN_TYPES = ()
+   
+    OUTPUT_NODE = True
+
+    INPUT_IS_LIST = True
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/prompt"
+
+    # 运行的函数
+    def run(self,prompts,images,save_to_image):
+        filename_prefix="mixlab_"
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+            filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+        
+        results = list()
+
+        save_to_image=save_to_image[0]=='enable'
+
+        for index in range(len(images)):
+            res=[]
+            imgs=images[index]
+
+            for image in imgs:
+                img=tensor2pil(image)
+
+                metadata = None
+                if save_to_image:
+                    metadata = PngInfo()
+                    prompt_text=prompts[index]
+                    if prompt_text is not None:
+                        metadata.add_text("prompt_text", prompt_text)
+                    
+                file = f"{filename}_{index}_{counter:05}_.png"
+                img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
+                res.append({
+                    "filename": file,
+                    "subfolder": subfolder,
+                    "type": self.type
+                })
+                counter += 1
+            results.append(res)
+        
+        return { "ui": { "_images": results,"prompts":prompts } }
+
+
+
+
+class PromptSimplification:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": ("STRING", 
+                         {
+                            "multiline": True, 
+                            "default": '',
+                            "dynamicPrompts": False
+                          }),
+
+                "length":("INT", {"default": 5, "min": 1,"max":100, "step": 1, "display": "number"}),
+
+                # "min_value":("FLOAT", {
+                #         "default": -2, 
+                #         "min": -10, 
+                #         "max": 0xffffffffffffffff,
+                #         "step": 0.01, 
+                #         "display": "number"  
+                #     }),
+                # "max_value":("FLOAT", {
+                #         "default": 2, 
+                #         "min": -10, 
+                #         "max": 0xffffffffffffffff,
+                #         "step": 0.01, 
+                #         "display": "number"  
+                #     }),
+              
+                }
+            }
+    
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("prompts",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/prompt"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+    OUTPUT_NODE = True
+
+    # 运行的函数
+    def run(self,prompt,length):
+        length=length[0]
+        result=[]
+        for p in prompt:
+            nps=prompt_delete_words(p,length)
+            for n in nps:
+                result.append(n)
+
+        result= [elem.strip() for elem in result if elem.strip()]
+
+        return {"ui": {"prompts": result}, "result": (result,)}
+
 
 
 class PromptSlide:
@@ -187,6 +348,8 @@ class RandomPrompt:
         else:
             prompts = prompts[:min(max_count,len(prompts))]
 
+        prompts= [elem.strip() for elem in prompts if elem.strip()]
+
         # return (new_prompt)
         return {"ui": {"prompts": prompts}, "result": (prompts,)}
 

nodes/Utils.py

@@ -34,13 +34,13 @@ def create_temp_file(image):
         ) = folder_paths.get_save_image_path('tmp', output_dir)
 
     
-    image=tensor2pil(image)
+    im=tensor2pil(image)
  
     image_file = f"{filename}_{counter:05}.png"
      
     image_path=os.path.join(full_output_folder, image_file)
 
-    image.save(image_path,compress_level=4)
+    im.save(image_path,compress_level=4)
 
     return [{
                 "filename": image_file,
@@ -188,7 +188,7 @@ class FloatSlider:
                     "number":("FLOAT", {
                         "default": 0, 
                         "min": 0, #Minimum value
-                        "max": 1, #Maximum value
+                        "max": 0xffffffffffffffff, #Maximum value
                         "step": 0.001, #Slider's step
                         "display": "slider" # Cosmetic only: display as "number" or "slider"
                     }),
@@ -225,12 +225,13 @@ class FloatSlider:
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
 
-    def run(self,number,min_value,max_value,step):
+    def run(self, number, min_value, max_value, step):
         if number < min_value:
-            number= min_value
+            number = min_value
         elif number > max_value:
-            number= max_value
-        return (number,)
+            number = max_value
+        scaled_number = (number - min_value) / (max_value - min_value)
+        return (scaled_number,)
     
   
 class IntNumber:
@@ -262,7 +263,7 @@ class IntNumber:
                             "default": 1, 
                             "min": -0xffffffffffffffff,
                             "max": 0xffffffffffffffff,
-                            "step":1, 
+                            "step":1,
                             "display": "number"  
                         }),
                              },
@@ -433,12 +434,12 @@ 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":{
+                    "LOGO": ("IMAGE",),
                     "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
                     "version":("INT", {
                         "default": 1, 
@@ -450,27 +451,43 @@ 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"
 
     CATEGORY = "♾️Mixlab"
 
-    INPUT_IS_LIST = False
-    OUTPUT_IS_LIST = (False,)
+    OUTPUT_NODE = True
+    INPUT_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,LOGO,description,version,share_prefix,link,category,auto_save):
+        name=name[0]
+        
+        im=None
+        if LOGO:
+            im=LOGO[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，
 
-        im=create_temp_file(image)
+        input_ids=input_ids[0]
+        output_ids=output_ids[0]
+        description=description[0]
+        version=version[0]
+        share_prefix=share_prefix[0]
+        link=link[0]
+        category=category[0]
         
         # 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": ()}
     
 
     
@@ -544,6 +561,7 @@ class SwitchByIndex:
                 C=[C[index]]
             except Exception as e:
                 C=[]
+        
         return (C,)
 
 
@@ -598,3 +616,24 @@ class LimitNumber:
         
         return (nn,)
 
+
+
+class TESTNODE_:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "ANY":(any_type,), },
+                }
+    
+    RETURN_TYPES = (any_type,)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/_test"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self,ANY):
+        print(ANY)
+        
+        return (ANY,)

requirements.txt

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

web/index.html

@@ -276,7 +276,7 @@
 
         .show_text {
             font-size: 14px;
-            /* display: inline-block; */
+            user-select: text;
             margin: 8px;
             padding: 32px;
             min-width: 200px;
@@ -302,7 +302,7 @@
             color: #555;
         }
 
-       
+
 
         select,
         button,
@@ -323,6 +323,15 @@
             height: 56px !important;
             outline: 1px solid white;
         }
+
+
+        /* 给prompt image 节点使用 */
+        .prompt_image {
+            color: #2f2f2f;
+            padding: 0 10px;
+            font-size: 12px;
+            width: 200px;
+        }
     </style>
     <!-- <script src="../../../scripts/api.js" type="module"></script> -->
     <link href="/extensions/comfyui-mixlab-nodes/lib/photoswipe.min.css" rel="stylesheet">
@@ -399,23 +408,71 @@
 
         }
 
+        async function getQueue(clientId) {
+            try {
+
+                const res = await fetch(`${get_url()}/queue`);
+                const data = await res.json();
+                return {
+                    // Running action uses a different endpoint for cancelling
+                    Running: Array.from(data.queue_running, prompt => {
+                        if (prompt[3].client_id === clientId) {
+                            let prompt_id = prompt[1];
+                            return {
+                                prompt_id,
+                                remove: () => interrupt(),
+                            }
+                        }
+                    }),
+                    Pending: data.queue_pending.map((prompt) => ({ prompt })),
+                };
+            } catch (error) {
+                console.error(error);
+                return { Running: [], Pending: [] };
+            }
+        }
+
+
+        async function interrupt() {
+            try {
+                await fetch(`${get_url()}/interrupt`, {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: undefined
+                });
+
+            } catch (error) {
+                console.error(error)
+            }
+            return true
+        }
+
 
         function randomSeed(seed, data) {
             for (const id in data) {
-
                 if (data[id].inputs.seed != undefined
                     && !Array.isArray(data[id].inputs.seed) //如果是数组，则由其他节点控制
                     && ['increment', 'decrement', 'randomize'].includes(seed[id])) {
                     data[id].inputs.seed = Math.round(Math.random() * 1849378600828930)
                     // console.log('new Seed', data[id])
                 }
+                if (data[id].inputs.noise_seed != undefined
+                    && !Array.isArray(data[id].inputs.noise_seed) //如果是数组，则由其他节点控制
+                    && ['increment', 'decrement', 'randomize'].includes(seed[id])) {
+                    data[id].inputs.noise_seed = Math.round(Math.random() * 1849378600828930)
+
+                }
+                console.log('new Seed', data[id])
             }
             return data
         }
 
         function updateSeed(id, val) {
-            console.log(val)
-            if (!Array.isArray(window._appData.data[id].inputs.seed)) window._appData.data[id].inputs.seed = Math.round(val);
+            // console.log(val)
+            if (window._appData.data[id].inputs.seed && !Array.isArray(window._appData.data[id].inputs.seed)) window._appData.data[id].inputs.seed = Math.round(val);
+            if (window._appData.data[id].inputs.noise_seed && !Array.isArray(window._appData.data[id].inputs.noise_seed)) window._appData.data[id].inputs.noise_seed = Math.round(val);
         }
 
 
@@ -555,7 +612,16 @@
                     div.innerText = Array.isArray(node.inputs.text) ? node.inputs.text[0] : node.inputs.text
                     output_card.appendChild(div);
                 };
-                if (["SaveImage", "PreviewImage"].includes(node.class_type)) {
+
+                if (node.class_type == "ClipInterrogator") {
+                    let div = document.createElement('div');
+                    div.className = "show_text";
+                    div.id = `output_${node.id}`;
+                    div.innerText = '#ClipInterrogator: …… '
+                    output_card.appendChild(div);
+                };
+
+                if (["SaveImage", "PreviewImage", "PromptImage"].includes(node.class_type)) {
 
                     let a = document.createElement('a');
                     a.id = `output_${node.id}`
@@ -888,7 +954,7 @@
 
                     const label = data.title;
 
-                    if (!options.keywords.includes(data.inputs.prompt_keyword)) {
+                    if (options.keywords && !options.keywords?.includes(data.inputs.prompt_keyword)) {
                         options.keywords = [data.inputs.prompt_keyword, ...options.keywords]
                     };
 
@@ -918,16 +984,20 @@
                     let silde = createNumSlide(data.title,
                         data.inputs.number,
                         (v) => {
-                            window._appData.data[data.id].inputs.number = v;
+                            // console.log(data.id,window._appData.data[data.id])
+                            window._appData.data[data.id].inputs.number = data.class_type === 'IntNumber' ? parseInt(v) : parseFloat(v);
                         },
                         options.min,
                         options.max,
-                        data.class_type === 'IntNumber' ? 'int' : 'float')
+                        data.class_type === 'IntNumber' ? 'int' : 'float',
+                        null,
+                        data.id
+                    )
                     container.appendChild(silde);
                 }
 
                 // Check if the class_type is "CLIPTextEncode"
-                if (["TextInput_", "CLIPTextEncode"].includes(data.class_type)) {
+                if (["TextInput_", "CLIPTextEncode", "PromptSimplification"].includes(data.class_type)) {
                     // Create a container for the upload control
                     const uploadContainer = document.createElement("div");
                     uploadContainer.className = 'card';
@@ -939,9 +1009,34 @@
 
                     // Create an input field for the image name
                     const textInput = document.createElement("textarea");
+                    // textInput.className=;
+                    if (data.class_type == "PromptSimplification") {
+                        textInput.value = data.inputs.prompt;
+                    } else {
+                        textInput.value = data.inputs.text;
+                    }
+
                     // uploadImageInput.type = "text";
-                    textInput.value = data.inputs.text;
+                    let json = localStorage.getItem(`t_${data.id}`)
+                    try {
+                        // 缓存
+                        const { value, height } = JSON.parse(json);
+                        textInput.value = value;
+                        textInput.style.height = height;
+
+                        if (data.class_type == "PromptSimplification") {
+                            window._appData.data[data.id].inputs.prompt = textInput.value;
+                        } else {
+                            window._appData.data[data.id].inputs.text = textInput.value;
+                        }
+
+
+                    } catch (error) {
+
+                    }
+
                     uploadContainer.appendChild(textInput);
+                    // autoResize(textInput);
 
                     function autoResize(textarea) {
                         textarea.style.height = 'auto';
@@ -951,7 +1046,16 @@
                     textInput.addEventListener('input', (event) => {
                         // console.log(textInput.value)
                         autoResize(textInput);
-                        window._appData.data[data.id].inputs.text = textInput.value;
+
+                        if (data.class_type == "PromptSimplification") {
+                            window._appData.data[data.id].inputs.prompt = textInput.value;
+                        } else {
+                            window._appData.data[data.id].inputs.text = textInput.value;
+                        }
+                        localStorage.setItem(`t_${data.id}`, JSON.stringify({
+                            value: textInput.value,
+                            height: textInput.style.height
+                        }));
                     })
 
                     // Append the upload container to the main container
@@ -962,6 +1066,22 @@
                 if (["CheckpointLoaderSimple", "LoraLoader"].includes(data.class_type)) {
                     let value = data.inputs.ckpt_name || data.inputs.lora_name;
 
+                    try {
+                        let v = localStorage.getItem(`_model_${data.id}_${data.class_type}`)
+                        if (v) {
+                            value = v;
+                            if (data.class_type === 'CheckpointLoaderSimple') {
+                                window._appData.data[data.id].inputs.ckpt_name = value;
+                            }
+                            if (data.class_type === 'LoraLoader') {
+                                window._appData.data[data.id].inputs.lora_name = value;
+                            }
+                        }
+                    } catch (error) {
+
+                    }
+
+
                     let [div, selectDom] = createSelectWithOptions(data.title, Array.from(data.options, o => {
                         return {
                             value: o,
@@ -978,6 +1098,8 @@
                         if (data.class_type === 'LoraLoader') {
                             window._appData.data[data.id].inputs.lora_name = selectDom.value;
                         }
+
+                        localStorage.setItem(`_model_${data.id}_${data.class_type}`, selectDom.value)
                     })
 
                     container.appendChild(div);
@@ -1104,6 +1226,15 @@
         function createNumSlide(labelText, value = 0, callback, minValue = 0, maxValue = 1, type = 'float', keywords = null, targetId = null) {
 
             value = 'float' ? parseFloat(value.toFixed(3)) : parseInt(value)
+            try {
+                let i = parseFloat(localStorage.getItem(`_slider_${targetId}`))
+                if (!!i) {
+                    value = i;
+                    callback && callback(value)
+                }
+            } catch (error) {
+
+            }
 
             // 输入div
             let inputDiv = document.createElement('div');
@@ -1126,13 +1257,19 @@
             if (keywords && keywords[0]) {
                 // label.innerHTML = ``
                 // 有备选的关键词
-                let selectTag = createSelect(Array.from(keywords, (k,i) => {
+
+                let defaultValue = (targetId ? localStorage.getItem(`_slide_${targetId}`) : '') || keywords[0];
+
+                window._appData.data[targetId].inputs.prompt_keyword = defaultValue;
+
+                let selectTag = createSelect(Array.from(keywords, (k, i) => {
                     return {
                         value: k,
                         text: k,
-                        selected:i==0
+                        selected: i == 0
                     }
-                }), keywords[0]);
+                }), defaultValue);
+
                 selectTag.style = `background: none;
                                     color: black;    max-width: 300px;
                                     border-bottom: 1px solid #acacac;
@@ -1141,8 +1278,8 @@
                 selectTag.addEventListener('change', e => {
                     e.preventDefault();
                     window._appData.data[targetId].inputs.prompt_keyword = selectTag.value;
-                    // label.querySelector('.label').innerText = selectTag.value
-                    // console.log(window._appData.data[targetId].inputs.prompt_keyword,selectTag.value)
+
+                    targetId ? localStorage.setItem(`_slide_${targetId}`, selectTag.value) : ''
                 })
                 label.appendChild(selectTag);
             }
@@ -1177,6 +1314,8 @@
                 label.setAttribute('data-content', value);
                 // 在这里可以执行其他操作，根据需要进行相应的处理
                 callback && callback(value)
+
+                localStorage.setItem(`_slider_${targetId}`, value)
             });
 
             // 返回容器元素
@@ -1199,7 +1338,7 @@
 
             // 设置默认值
             selectElement.value = defaultValue;
-            console.log(defaultValue,options)
+            // console.log(defaultValue, options)
             return selectElement
         }
 
@@ -1433,7 +1572,6 @@
                     update: async function (type = "image", val, id) {
                         console.log(val, id)
                         if (val && type == "image" && output.querySelector(`#output_${id} img`)) {
-                            // if (output.querySelector(`#output_${id}`)) {
 
                             let im = await createImage(val)
 
@@ -1445,10 +1583,52 @@
                             a.setAttribute('target', "_blank");
                             a.setAttribute('href', val);
 
-                            // }
-                            //  else {
-                            //     output.querySelector(`#output_${id}`).src = val;
-                            // }
+                        }
+
+                        if (val && (type == "images" || type == 'images_prompts') && output.querySelector(`#output_${id} img`)) {
+                            let imgDiv = output.querySelector(`#output_${id}`)
+                            imgDiv.style.display = 'none';
+
+                            // 清空
+                            // Array.from(imgDiv.parentElement.querySelectorAll('.output_images'), im => im.remove());
+
+                            for (const v of val) {
+
+                                let url = v, prompt = ''
+
+                                if (type == 'images_prompts') {
+                                    // 是个数组，多了对应的prompt
+                                    url = v[0];
+                                    prompt = v[1];
+                                }
+
+                                let im = await createImage(url);
+
+                                // 构建新的
+                                let a = document.createElement('a');
+                                a.className = `${imgDiv.id} output_images`
+                                a.setAttribute('data-pswp-width', im.naturalWidth);
+                                a.setAttribute('data-pswp-height', im.naturalHeight);
+                                a.setAttribute('target', "_blank");
+                                a.setAttribute('href', url);
+
+
+                                let img = new Image();
+                                // img;
+                                img.src = url;
+                                a.appendChild(img);
+
+                                if (prompt) {
+                                    a.style.textDecoration = 'none';
+                                    let p = document.createElement('p')
+                                    p.className = 'prompt_image'
+                                    p.innerText = prompt;
+                                    a.appendChild(p)
+                                }
+
+                                // imgDiv.parentElement.appendChild(a);
+                                imgDiv.parentElement.insertBefore(a, imgDiv.parentElement.firstChild);
+                            }
 
                         }
 
@@ -1478,19 +1658,42 @@
                 },
                 submitButton: {
                     element: submitButton,
-                    update: function (callback) {
+                    update: function (runFn, cancelFn) {
                         submitButton.addEventListener('dblclick', (e) => {
+                            e.preventDefault()
                             submitButton.classList.remove('disabled');
                         });
                         submitButton.addEventListener('click', (e) => {
+                            e.preventDefault();
+
+                            if (submitButton.classList.contains('data-click')) {
+                                return
+                            } else {
+                                submitButton.classList.add('data-click')
+                                setTimeout(() => submitButton.classList.remove('data-click'), 500)
+                            }
 
                             if (!submitButton.classList.contains('disabled')) {
-                                callback && callback();
+                                runFn && runFn();
                                 submitButton.classList.add('disabled');
-                                setTimeout(() => submitButton.classList.remove('disabled'), 500)
+                                submitButton.innerText = 'Cancel'
+                            } else {
+                                // 如果能取消
+                                let canCancel = cancelFn && cancelFn();
+                                if (canCancel) submitButton.classList.remove('disabled');
+                                if (canCancel) submitButton.innerText = 'Create';
                             }
 
                         });
+                    },
+                    running: () => {
+                        submitButton.innerText = 'Cancel';
+                        if (!submitButton.classList.contains('disabled')) submitButton.classList.add('disabled');
+                    },
+                    reset: () => {
+                        submitButton.innerText = 'Create';
+                        submitButton.classList.remove('disabled');
+                        submitButton.classList.remove('data-click');
                     }
                 },
             };
@@ -1522,7 +1725,6 @@
                     Array.from(detail.querySelectorAll('.card'), c => c.classList.remove('selected'));
                     div.className = 'upload_btn card selected'
 
-
                     let { output, app } = jsonData;
 
                     window._appData = {
@@ -1561,10 +1763,19 @@
             ui.status.update(appData ? 'READY' : '-');
 
             // 添加提交按钮点击事件
-            ui.submitButton.update(function () {
-                // 在提交按钮点击时执行的逻辑
-                queuePrompt(window._appData.data, window._appData.seed, api.clientId)
-            });
+            ui.submitButton.update(
+                () => {
+                    // 在提交按钮点击时执行的逻辑
+                    queuePrompt(window._appData.data, window._appData.seed, api.clientId);
+                }, () => {
+                    // 取消
+                    if (api.runningCancel) {
+                        api.runningCancel();
+                        api.runningCancel = null;
+                        return true
+                    }
+
+                });
 
 
             const show = (src, id, type = "image") => {
@@ -1573,52 +1784,105 @@
             };
 
             api.addEventListener("status", ({ detail }) => {
-                console.log("status", detail);
+                console.log("status", detail, detail.exec_info?.queue_remaining);
                 try {
-                    ui.status.update(`queue#${detail.exec_info.queue_remaining}`);
+                    ui.status.update(`queue#${detail.exec_info?.queue_remaining}`);
+                    if (detail.exec_info?.queue_remaining === 0) {
+                        // 运行按钮重设
+                        ui.submitButton.reset()
+                        console.log('运行按钮重设')
+                    }
                 } catch (error) {
-
+                    console.log(error)
                 }
 
             });
 
             api.addEventListener("progress", ({ detail }) => {
                 console.log("progress", detail);
+                const class_type = window._appData.data[detail?.node]?.class_type || ''
                 try {
-                    ui.status.update(`${detail.value}/${detail.max}`);
+                    ui.status.update(`${parseFloat(100 * detail.value / detail.max).toFixed(1)}% ${class_type}`);
+                    ui.submitButton.running()
                 } catch (error) {
 
                 }
             });
 
-            api.addEventListener("executed", ({ detail }) => {
+            api.addEventListener("executed", async ({ detail }) => {
                 console.log("executed", detail)
                 // if (!enabled) return;
                 const images = detail?.output?.images;
                 const text = detail?.output?.text;
                 const gifs = detail?.output?.gifs;
 
+                const prompt = detail?.output?.prompt;
+                const analysis = detail?.output?.analysis;
+
+                const _images = detail?.output?._images;
+                const prompts = detail?.output?.prompts;
+
                 if (images) {
-                    // if (!images) return; 
-                    const src = `${get_url()}/view?filename=${encodeURIComponent(images[0].filename)}&type=${images[0].type}&subfolder=${encodeURIComponent(images[0].subfolder)}&t=${+new Date()}`;
-                    show(src, detail.node, 'image');
+                    // if (!images) return;
+
+                    let url = get_url();
+
+                    show(Array.from(images, img => {
+                        return `${url}/view?filename=${encodeURIComponent(img.filename)}&type=${img.type}&subfolder=${encodeURIComponent(img.subfolder)}&t=${+new Date()}`;
+                    }), detail.node, 'images');
+
+                } else if (_images && prompts) {
+                    let url = get_url();
+
+                    let items = [];
+                    // 支持图片的batch
+                    Array.from(_images, (imgs, i) => {
+
+                        for (const img of imgs) {
+                            items.push([`${url}/view?filename=${encodeURIComponent(img.filename)
+                                }&type=${img.type}&subfolder=${encodeURIComponent(img.subfolder)
+                                }&t=${+new Date()}`, prompts[i]])
+                        }
+
+                    })
+
+                    show(items, detail.node, 'images_prompts');
+
                 } else if (text) {
-                    ui.output.update("text", Array.isArray(text) ? text[0] : text, detail.node)
+                    ui.output.update("text", Array.isArray(text) ? text.join('\n\n') : text, detail.node)
                 } else if (gifs && gifs[0]) {
                     // if (!images) return; 
                     const src = `${get_url()}/view?filename=${encodeURIComponent(gifs[0].filename)}&type=${gifs[0].type}&subfolder=${encodeURIComponent(gifs[0].subfolder)
                         }&&format=${gifs[0].format}&t=${+new Date()}`;
 
                     show(src, detail.node, gifs[0].format.match('video') ? 'video' : 'image');
+                } else if (prompt && analysis) {
+                    // #ClipInterrogator: …… 
+                    ui.output.update("text", `${prompt.join('\n\n')}\n${JSON.stringify(analysis, null, 2)}`, detail.node)
                 }
 
 
+
                 try {
-                    ui.status.update(`executed_#${detail.node}`);
+                    ui.status.update(`executed_#${window._appData.data[detail.node]?.class_type}`);
+                    ui.submitButton.reset()
                 } catch (error) {
 
                 }
 
+                // console.log(Running, Pending);
+                try {
+                    const { Running, Pending } = await getQueue(api.clientId);
+                    if (Running && Running[0]) {
+                        api.runningCancel = Running[0].remove;
+                        ui.submitButton.running()
+                    } else {
+                        api.runningCancel = null;
+                    }
+                } catch (error) {
+                    api.runningCancel = null;
+                }
+
             });
 
             // api.addEventListener("b_preview", ({ detail }) => {
@@ -1626,15 +1890,32 @@
             //     console.log("b_preview", detail)
             //     show(URL.createObjectURL(detail));
             // });
+            api.addEventListener("execution_error", ({ detail }) => {
+
+                console.log("execution_error", detail)
+                // show(URL.createObjectURL(detail));
+            });
 
 
-            api.addEventListener('execution_start', ({ detail }) => {
+            api.addEventListener('execution_start', async ({ detail }) => {
                 console.log("execution_start", detail)
                 try {
                     ui.status.update(`execution_start`);
+                    ui.submitButton.running()
                 } catch (error) {
 
                 }
+
+                try {
+                    const { Running, Pending } = await getQueue(api.clientId);
+                    if (Running && Running[0]) {
+                        api.runningCancel = Running[0].remove;
+                    } else {
+                        api.runningCancel = null;
+                    }
+                } catch (error) {
+                    api.runningCancel = null;
+                }
             })
 
             api.api_base = ""

web/javascript/3d_mixlab.js

@@ -196,6 +196,9 @@ app.registerExtension({
               }
               if (bg) {
                 data.bg_image = await parseImage(bg)
+                if (!data.bg_image.match('data:image/')) {
+                  delete data.bg_image
+                }
               }
 
               if (material) {

web/javascript/app_mixlab.js

@@ -108,8 +108,7 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
           }
         }
 
-        if(node.type=='Color'){
-          
+        if (node.type == 'Color') {
         }
 
         input[inputIds.indexOf(id)] = {
@@ -126,11 +125,11 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
         output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
       }
 
-      if (node.type === 'KSampler') {
+      if (node.type === 'KSampler' || node.type == 'SamplerCustom') {
         // seed 的类型收集
         try {
           seed[id] = node.widgets.filter(
-            w => w.name === 'seed'
+            w => w.name === 'seed' || w.name == 'noise_seed'
           )[0].linkedWidgets[0].value
         } catch (error) {}
       }
@@ -188,7 +187,7 @@ function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
   }, 0)
 }
 
-async function save (json, download = false) {
+async function save (json, download = false, showInfo = true) {
   const name = json[0],
     version = json[5],
     share_prefix = json[6], //用于分享的功能扩展
@@ -237,22 +236,53 @@ 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 +290,16 @@ 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',
@@ -339,6 +378,12 @@ 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 +392,31 @@ app.registerExtension({
         } catch (error) {}
       }
     }
+  },
+  async loadedGraphNode (node, app) {
+    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('executed', async ({ detail }) => {
+  console.log('#executed', detail)
+  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 = 'v0.9.1'
+const version = 'v0.11.4'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/clipInterrogator_mixlab.js

@@ -0,0 +1,119 @@
+import { app } from '../../../scripts/app.js'
+// import { api } from '../../../scripts/api.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, random_samples) {
+        if (this.widgets) {
+          for (let i = 0; i < this.widgets.length; i++) {
+            if (this.widgets[i].type !== 'combo') this.widgets[i].onRemove?.()
+          }
+          this.widgets.length = 2
+        }
+
+        createPrompt(this, prompts, items, random_samples)
+
+        // console.log('ClipInterrogator', w, w2)
+        requestAnimationFrame(() => {
+          const sz = this.computeSize()
+          if (sz[0] < this.size[0]) {
+            sz[0] = this.size[0]
+          }
+          if (sz[1] < this.size[1]) {
+            sz[1] = this.size[1]
+          }
+          this.onResize?.(sz)
+          app.graph.setDirtyCanvas(true, false)
+        })
+      }
+
+      // When the node is executed we will be sent the input text, display this in the widget
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        // 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/gpt_mixlab.js

@@ -256,6 +256,7 @@ app.registerExtension({
 			const onExecuted = nodeType.prototype.onExecuted;
 			nodeType.prototype.onExecuted = function (message) {
 				onExecuted?.apply(this, arguments);
+        console.log('##',message.text)
 				populate.call(this, message.text);
 			};
 

web/javascript/prompt_mixlab.js

@@ -35,6 +35,24 @@ function get_position_style (ctx, widget_width, y, node_height) {
     justifyContent: 'space-between'
   }
 }
+function createImage (url) {
+  let im = new Image()
+  return new Promise((res, rej) => {
+    im.onload = () => res(im)
+    im.src = url
+  })
+}
+
+async function fetchImage (url) {
+  try {
+    const response = await fetch(url)
+    const blob = await response.blob()
+
+    return blob
+  } catch (error) {
+    console.error('出现错误:', error)
+  }
+}
 
 const getLocalData = key => {
   let data = {}
@@ -72,6 +90,116 @@ const createSelect = (select, opts, targetWidget) => {
   })
 }
 
+app.registerExtension({
+  name: 'Mixlab.prompt.RandomPrompt',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'RandomPrompt') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+        name
+
+        const mutable_prompt = this.widgets.filter(
+          w => w.name == 'mutable_prompt'
+        )[0]
+        // console.log('PromptSlide nodeData', prompt_keyword)
+
+        const widget = {
+          type: 'div',
+          name: 'upload',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, y, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Upload Keywords'
+
+        btn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;    height: 30px;min-width: 122px;
+       `
+
+        // const btn=document.createElement('button');
+        // btn.innerText='Upload'
+        btn.addEventListener('click', () => {
+          let inp = document.createElement('input')
+          inp.type = 'file'
+          inp.accept = '.txt'
+          inp.click()
+          inp.addEventListener('change', event => {
+            // 获取选择的文件
+            const file = event.target.files[0]
+            this.title = file.name.split('.')[0]
+
+            // console.log(file.name.split('.')[0])
+            // 创建文件读取器
+            const reader = new FileReader()
+
+            // 定义读取完成事件的回调函数
+            reader.onload = event => {
+              // 读取完成后的文本内容
+              const fileContent = event.target.result.split('\n')
+              const keywords = Array.from(fileContent, f => f.trim()).filter(
+                f => f
+              )
+              // 打印文件内容
+              //   console.log(keywords)
+
+              mutable_prompt.value = keywords.join('\n')
+
+              inp.remove()
+            }
+
+            // 以文本方式读取文件
+            reader.readAsText(file)
+          })
+        })
+
+        widget.div.appendChild(btn)
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'RandomPrompt') {
+      // try {
+      //   let mutable_prompt = node.widgets.filter(w => w.name === 'mutable_prompt')[0]
+      //   // let ks = getLocalData(`_mixlab_PromptSlide`)
+      //   let uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
+      //   // console.log('##widget', uploadWidget.value)
+      //   let keywords = JSON.parse(uploadWidget.value)
+      //   if (keywords && keywords[0]) {
+      //     mutable_prompt.value=keywords.join('\n')
+      //   }
+      // } catch (error) {}
+    }
+  }
+})
+
 app.registerExtension({
   name: 'Mixlab.prompt.PromptSlide',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
@@ -133,7 +261,9 @@ app.registerExtension({
           inp.addEventListener('change', event => {
             // 获取选择的文件
             const file = event.target.files[0]
+            this.title = file.name.split('.')[0]
 
+            // console.log(file.name.split('.')[0])
             // 创建文件读取器
             const reader = new FileReader()
 
@@ -147,10 +277,11 @@ app.registerExtension({
               // 打印文件内容
               //   console.log(keywords)
 
-              // widget.value = keywords
-              let ks = getLocalData(`_mixlab_PromptSlide`)
-              ks[this.id] = keywords
-              setLocalDataOfWin(`_mixlab_PromptSlide`, ks)
+              widget.value = JSON.stringify(keywords)
+
+              // let ks = getLocalData(`_mixlab_PromptSlide`)
+              // ks[this.id] = keywords
+              // setLocalDataOfWin(`_mixlab_PromptSlide`, ks)
 
               createSelect(select, keywords, prompt_keyword)
 
@@ -185,14 +316,13 @@ app.registerExtension({
     if (node.type === 'PromptSlide') {
       try {
         let prompt = node.widgets.filter(w => w.name === 'prompt_keyword')[0]
-        let ks = getLocalData(`_mixlab_PromptSlide`)
-
-        let keywords = ks[node.id]
+        // let ks = getLocalData(`_mixlab_PromptSlide`)
+        let uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
+        // console.log('##widget', uploadWidget.value)
+        let keywords = JSON.parse(uploadWidget.value)
         // console.log('keywords',keywords)
         let widget = node.widgets.filter(w => w.select)[0]
         if (keywords && keywords[0]) {
-          // let widget = node.widgets.filter(w => w.select)[0]
-          // console.log('select',widget,widget.value)
           widget.select.style.display = 'block'
           createSelect(widget.select, keywords, prompt)
         }
@@ -200,3 +330,152 @@ app.registerExtension({
     }
   }
 })
+
+const _createResult = async (node, widget, message) => {
+  widget.div.innerHTML = ``
+
+  const width = node.size[0] * 0.5 - 12
+
+  let height_add = 0
+
+  for (let index = 0; index < message._images.length; index++) {
+    const imgs = message._images[index]
+
+    for (const img of imgs) {
+      let url = api.apiURL(
+        `/view?filename=${encodeURIComponent(img.filename)}&type=${
+          img.type
+        }&subfolder=${
+          img.subfolder
+        }${app.getPreviewFormatParam()}${app.getRandParam()}`
+      )
+
+      let image = await createImage(url)
+
+      // 创建card
+      let div = document.createElement('div')
+      div.className = 'card'
+      div.draggable = true
+
+      div.ondragend = async event => {
+        console.log('拖动停止')
+        let url = div.querySelector('img').src
+
+        let blob = await fetchImage(url)
+
+        let imageNode = null
+        // No image node selected: add a new one
+        if (!imageNode) {
+          const newNode = LiteGraph.createNode('LoadImage')
+          newNode.pos = [...app.canvas.graph_mouse]
+          imageNode = app.graph.add(newNode)
+          app.graph.change()
+        }
+
+        // const blob = item.getAsFile();
+        imageNode.pasteFile(blob)
+      }
+
+      div.setAttribute('data-scale', image.naturalHeight / image.naturalWidth)
+
+      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="${url}" style='width: 100%'/>
+            <p style="position: absolute;
+            bottom: 0;
+            left: 0; 
+            background:#444444c2;
+            margin: 0;
+            font-size: 12px;
+            padding: 5px;
+            text-align: left;">${message.prompts[index]}</p>`
+      widget.div.appendChild(div)
+    }
+  }
+
+  node.size[1] = 98 + height_add
+}
+
+app.registerExtension({
+  name: 'Mixlab.prompt.PromptImage',
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'PromptImage') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        console.log('#orig_nodeCreated', this)
+        const widget = {
+          type: 'div',
+          name: 'result',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(this.div.style, {
+              ...get_position_style(ctx, widget_width, y, node.size[1]),
+              flexWrap: 'wrap',
+              justifyContent: 'space-between',
+              // outline: '1px solid red',
+              paddingLeft: '0px',
+              width: widget_width + 'px'
+            })
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        const onResize = this.onResize
+        this.onResize = function () {
+          // 缩放发生
+          // console.log('##缩放发生', this.size)
+          let w = this.size[0] * 0.5 - 12
+          Array.from(widget.div.querySelectorAll('.card'), card => {
+            card.style.width = `${w}px`
+            card.style.height = `${
+              w * parseFloat(card.getAttribute('data-scale'))
+            }px`
+          })
+          return onResize?.apply(this, arguments)
+        }
+
+        // this.serialize_widgets = true //需要保存参数
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = async function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log('#PromptImage', message.prompts, message._images)
+        // window._mixlab_app_json = message.json
+        try {
+          let widget = this.widgets.filter(w => w.name === 'result')[0]
+          widget.value = message
+          _createResult(this, widget, { ...message })
+        } catch (error) {
+          console.log(error)
+        }
+      }
+
+      this.serialize_widgets = true //需要保存参数
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'PromptImage') {
+      // await sleep(0)
+      let widget = node.widgets.filter(w => w.name === 'result')[0]
+      console.log('widget.value', widget.value)
+      let cards = widget.div.querySelectorAll('.card')
+      if (cards.length == 0) node.size = [280, 120]
+
+      _createResult(node, widget, widget.value)
+    }
+  }
+})

web/javascript/utils_mixlab.js

@@ -159,7 +159,7 @@ app.registerExtension({
           el: `#${inputColor.id}`,
           theme: 'classic', // or 'monolith', or 'nano'
           // closeOnScroll: true,
-          default:'#000000',
+          default: '#000000',
           swatches: [
             'rgba(244, 67, 54, 1)',
             'rgba(233, 30, 99, 0.95)',
@@ -257,7 +257,6 @@ app.registerExtension({
 
 app.registerExtension({
   name: 'Mixlab.utils.TextToNumber',
-
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == 'TextToNumber') {
       const onExecuted = nodeType.prototype.onExecuted
@@ -270,9 +269,76 @@ app.registerExtension({
           const n = this.widgets.filter(w => w.name === 'number')[0]
           n.value = message.num[0]
         }
-
         console.log('TextToNumber', random_number.value)
       }
     }
   }
 })
+
+const min_max = node => {
+  if(node.widgets){
+    const min_value = node.widgets.filter(w => w.name === 'min_value')[0]
+    const max_value = node.widgets.filter(w => w.name === 'max_value')[0]
+  
+    const number = node.widgets.filter(w => w.name === 'number')[0]
+    if (number) {
+      number.options.min = min_value.value
+      number.options.max = max_value.value
+  
+      number.value = Math.min(number.options.max, number.value)
+      number.value = Math.max(number.options.min, number.value)
+    }
+  
+    if (min_value)
+      min_value.callback = e => {
+        number.options.min = e
+        number.value = e
+      }
+    if (max_value)
+      max_value.callback = e => {
+        number.options.max = e
+        number.value = e
+      }
+  }
+  
+}
+
+app.registerExtension({
+  name: 'Mixlab.utils.FloatSlider',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+
+    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') {
+      min_max(node)
+    }
+  }
+})
+app.registerExtension({
+  name: 'Mixlab.utils.IntNumber',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    
+    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') {
+      min_max(node)
+    }
+  }
+})

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) {}
     }
   }
 })