v0.22.0

- 优化ImageColorTransfer
- 支持动态提示
- 添加更多节点支持，AppInfo自动填充id
- LoadImagesToBatch加载
- zhipuai 按需安装

README.md

@@ -17,7 +17,9 @@
 - 支持多个web app 切换
 - 发布为app的workflow，可以在右键里再次编辑了
 - web app可以设置分类，在comfyui右键菜单可以编辑更新web app
+- 支持动态提示
 
+![](./assets/微信图片_20240421205440.png)
 
 - Support multiple web app switching.
 - Add the AppInfo node, which allows you to transform the workflow into a web app by simple configuration.
@@ -116,18 +118,23 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 [workflow](./workflow/3D-workflow.json)
 
 
-### LoadImagesFromLocal
+### Image
+
+#### LoadImagesToBatch
+> Upload multiple images for batch input into the IP adapter. 
+
+#### LoadImagesFromLocal
 > Monitor changes to images in a local folder, and trigger real-time execution of workflows, supporting common image formats, especially PSD format, in conjunction with Photoshop. 
 
 ![watch](./assets/4-loadfromlocal-watcher-workflow.svg)
 
 [workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)
 
-### LoadImagesFromURL
+#### LoadImagesFromURL
 > Conveniently load images from a fixed address on the internet to ensure that default images in the workflow can be executed.
 
 
-## Style
+### Style
 > Apply VisualStyle Prompting , Modified from [ComfyUI_VisualStylePrompting](https://github.com/ExponentialML/ComfyUI_VisualStylePrompting) 
 
 ![](./assets/VisualStylePrompting.png)
@@ -135,7 +142,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 > StyleAligned , Modified from [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy) 
 
 
-## Utils
+### Utils
 > The Color node provides a color picker for easy color selection, the Font node offers built-in font selection for use with TextImage to generate text images, and the DynamicDelayByText node allows delayed execution based on the length of the input text.
 
 - [添加了DynamicDelayByText功能，可以根据输入文本的长度进行延迟执行。](./workflow/audio-chatgpt-workflow.json)
@@ -148,7 +155,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 
 
-## Other Nodes
+### Other Nodes
 
 ![main](./assets/all-workflow.svg)
 ![main2](./assets/detect-face-all.png)

__init__.py

@@ -576,9 +576,6 @@ async def post_prompt_result(request):
     
     return web.json_response({"result":res})
 
-
-
-
 # 扩展api接口
 # from server import PromptServer
 # from aiohttp import web
@@ -592,18 +589,18 @@ async def post_prompt_result(request):
 
 # 导入节点
 from .nodes.PromptNode import GLIGENTextBoxApply_Advanced,EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
-from .nodes.ImageNode import GridDisplayAndSave,GridInput,ImagesPrompt,SaveImageAndMetadata,SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.ImageNode import LoadImages_,CompositeImages,GridDisplayAndSave,GridInput,ImagesPrompt,SaveImageAndMetadata,SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 # from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 
 from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.Utils import IncrementingListNode,ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import MaskListReplace,MaskListMerge,OutlineMask,FeatheredMask
 
 from .nodes.Style import ApplyVisualStylePrompting,StyleAlignedReferenceSampler,StyleAlignedBatchAlign,StyleAlignedSampleReferenceLatents
 
-from .nodes.Video import LoadVideoAndSegment
+from .nodes.Video import VideoCombine_Adv,LoadVideoAndSegment,ImageListReplace,VAEEncodeForInpaint_Frames
 
 
 # 要导出的所有节点及其名称的字典
@@ -626,6 +623,7 @@ NODE_CLASS_MAPPINGS = {
     "ResizeImageMixlab":ResizeImage,
     "LoadImagesFromPath":LoadImagesFromPath,
     "LoadImagesFromURL":LoadImagesFromURL,
+    "LoadImagesToBatch":LoadImages_,
     "TextImage":TextImage,
     "EnhanceImage":EnhanceImage,
     "SvgImage":SvgImage,
@@ -633,6 +631,7 @@ NODE_CLASS_MAPPINGS = {
     "ImageColorTransfer":ImageColorTransfer,
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
+    "CompositeImages_":CompositeImages,
     "SplitImage":SplitImage,
     "CenterImage":CenterImage,
     "GridOutput":GridOutput,
@@ -681,9 +680,12 @@ NODE_CLASS_MAPPINGS = {
     "StyleAlignedSampleReferenceLatents_": StyleAlignedSampleReferenceLatents,
     "StyleAlignedBatchAlign_": StyleAlignedBatchAlign,
     "LoadVideoAndSegment_":LoadVideoAndSegment,
+    "VideoCombine_Adv":VideoCombine_Adv,
     "ListSplit_":ListSplit,
-    "MaskListReplace_":MaskListReplace
-    # "LaMaInpainting":LaMaInpainting
+    "MaskListReplace_":MaskListReplace,
+    "ImageListReplace_":ImageListReplace,
+    "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames,
+    "IncrementingListNode_":IncrementingListNode
     # "GamePal":GamePal
 }
 
@@ -710,28 +712,34 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
     "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
     "3DImage":"3DImage ♾️Mixlab",
+    "CompositeImages_":"Composite Images",
     "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
     "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
     "PromptSlide":"Prompt Slide ♾️Mixlab",
     "PromptGenerate_Mix":"Prompt Generate ♾️Mixlab",
     "ChinesePrompt_Mix":"Chinese Prompt ♾️Mixlab",
     "GamePal":"GamePal ♾️Mixlab",
-    "RembgNode_Mix":"Remove Background",
+    "RembgNode_Mix":"Remove Background ♾️Mixlab",
     "LoraNames_":"LoraName",
     "ApplyVisualStylePrompting_":"Apply VisualStyle Prompting",
     "StyleAlignedReferenceSampler_": "StyleAligned Reference Sampler",
     "StyleAlignedSampleReferenceLatents_": "StyleAligned Sample Reference Latents",
     "StyleAlignedBatchAlign_": "StyleAligned Batch Align",
     "LoadVideoAndSegment_":"Load Video And Segment",
+    "VideoCombine_Adv":"Video Combine",
     "MaskListMerge_":"MaskList to Mask",
     "ListSplit_":"Split List",
     "MaskListReplace_":"MaskList Replace",
+    "ImageListReplace_":"ImageList Replace",
     "SwitchByIndex":"List Switch By Index",
     "GLIGENTextBoxApply_Advanced":"GLIGEN TextBox Apply ♾️Mixlab",
     "GridDisplayAndSave":"Grid Display And Save",
     "GridInput":"Grid Input",
     "GridOutput":"Grid Output",
-    "GetImageSize_":"Get Image Size"
+    "GetImageSize_":"Get Image Size",
+    "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames",
+    "IncrementingListNode_":"Create Incrementing Number List",
+    "LoadImagesToBatch":"Load Images(base64) ♾️Mixlab"
 }
 
 # web ui的节点功能

nodes/ChatGPT.py

@@ -4,7 +4,18 @@ import urllib.error
 import re,json,os,string,random
 import folder_paths
 import hashlib
-from zhipuai import ZhipuAI
+import codecs,sys
+import importlib.util
+
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
 def get_unique_hash(string):
     hash_object = hashlib.sha1(string.encode())
     unique_hash = hash_object.hexdigest()
@@ -46,14 +57,36 @@ def openai_client(key,url):
     base_url=url
     )
     return client
+
 def ZhipuAI_client(key):
+
+    try:
+        if is_installed('zhipuai')==False:
+            import subprocess
+
+            # 安装
+            print('#pip install zhipuai')
+
+            result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'zhipuai'], capture_output=True, text=True)
+
+            #检查命令执行结果
+            if result.returncode == 0:
+                print("#install success")
+                from zhipuai import ZhipuAI
+            else:
+                print("#install error")
+            
+        else:
+            from zhipuai import ZhipuAI
+    except:
+        print("#install zhipuai error")
+
     client = ZhipuAI(
         api_key=key, # 填写您的 APIKey
     ) 
     return client
 
 
-
 def chat(client, model_name,messages ):
 
         try_count = 0
@@ -314,8 +347,8 @@ class TextSplitByDelimiter:
     def INPUT_TYPES(s):
         return {
             "required": {
-                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
-                "delimiter":(["newline","comma"],),
+                "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                "delimiter":("STRING", {"multiline": False,"default":",","dynamicPrompts": False}),
                 "start_index": ("INT", {
                     "default": 0,
                     "min": 0, #Minimum value
@@ -349,12 +382,13 @@ class TextSplitByDelimiter:
     CATEGORY = "♾️Mixlab/Text"
 
     def run(self, text,delimiter,start_index,skip_every,max_count):
-        arr=[]
-        if delimiter=='newline':
-            arr = [line for line in text.split('\n') if line.strip()]
-        elif delimiter=='comma':
-            arr = [line for line in text.split(',') if line.strip()]
-        
+         
+        if delimiter=="":
+            arr=[text.strip()]
+        else:
+            delimiter=codecs.decode(delimiter, 'unicode_escape')
+            arr= [line for line in text.split(delimiter) if line.strip()]
+
         arr= arr[start_index:start_index + max_count * (skip_every+1):(skip_every+1)]
 
         return (arr,)

nodes/ImageNode.py

@@ -8,11 +8,48 @@ import base64,os,random
 from io import BytesIO
 import folder_paths
 import json,io
+import comfy.utils
 from comfy.cli_args import args
 import cv2
 import string
 import math,glob
 from .Watcher import FolderWatcher
+import hashlib
+
+def composite_images(foreground, background, mask):
+    width,height=foreground.size
+ 
+    bg_image=background
+    # 按z-index排序
+    layer = {
+            "x":0,
+            "y":0,
+            "width":width,
+            "height":height,
+            "z_index":88,
+            "scale_option":'overall',
+            "image":foreground,
+            "mask":mask
+    }
+                
+    width, height = bg_image.size
+
+    layer_image=layer['image']
+    layer_mask=layer['mask']
+    
+    bg_image=merge_images(bg_image,
+                        layer_image,
+                        layer_mask,
+                                        layer['x'],
+                                        layer['y'],
+                                        layer['width'],
+                                        layer['height'],
+                                        layer['scale_option']
+                                        )
+                   
+    bg_image=bg_image.convert('RGB')
+    
+    return bg_image
 
 
 def count_files_in_directory(directory):
@@ -652,6 +689,17 @@ def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option)
     return bg_image
 
 
+def resize_2(img):
+    # 检查图像的高度是否是2的倍数，如果不是，则调整高度
+    if img.height % 2 != 0:
+        img = img.resize((img.width, img.height + 1))
+
+    # 检查图像的宽度是否是2的倍数，如果不是，则调整宽度
+    if img.width % 2 != 0:
+        img = img.resize((img.width + 1, img.height))
+
+    return img
+
 # 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")
@@ -681,8 +729,10 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
         resized_image = Image.new("RGB", (width, height), color=color)
         resized_image.paste(layer_image.resize((new_width, new_height)), ((width - new_width) // 2, (height - new_height) // 2))
         resized_image = resized_image.convert("RGB")
+        resized_image=resize_2(resized_image)
         return resized_image
     
+    layer_image=resize_2(layer_image)
     return layer_image
 
 
@@ -1082,6 +1132,42 @@ class EnhanceImage:
 
 
 
+class LoadImages_:
+    @classmethod
+    def INPUT_TYPES(s):
+        
+        return {"required":
+                    {"images": ("IMAGEBASE64",), 
+                     },
+                }
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_image"
+    def load_image(self, images):
+
+        # print(images)
+        ims=[]
+        for im in images['base64']:
+            image = base64_to_image(im)
+            image=image.convert('RGB')
+            image=pil2tensor(image)
+            ims.append(image)
+
+        image1 = ims[0]
+        for image2 in ims[1:]:
+            if image1.shape[1:] != image2.shape[1:]:
+                image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
+            image1 = torch.cat((image1, image2), dim=0)
+        return (image1,)
+        
+ 
+
+
 ''' 
 ("STRING",{"multiline": False,"default": "Hello World!"})
 对应 widgets.js 里：
@@ -1431,7 +1517,7 @@ class Image3D:
 
     FUNCTION = "run"
 
-    CATEGORY = "♾️Mixlab/Image"
+    CATEGORY = "♾️Mixlab/3D"
 
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,False,False,False,)
@@ -1541,6 +1627,37 @@ class FaceToMask:
         return (mask,)
 
 
+class CompositeImages:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": { 
+            "foreground": ("IMAGE",),
+            "mask":("MASK",),
+            "background": ("IMAGE",),
+            },
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self, foreground,mask,background):
+        foreground= tensor2pil(foreground)
+        mask= tensor2pil(mask)
+        background= tensor2pil(background)
+        res=composite_images(foreground,background,mask)
+        
+        return (pil2tensor(res),)
+
+
+
+
 class EmptyLayer:
     @classmethod
     def INPUT_TYPES(s):
@@ -2057,6 +2174,9 @@ class GridOutput:
         x,y,w,h=grid
         return (x,y,w,h,)
 
+
+
+
 class ShowLayer:
     @classmethod
     def INPUT_TYPES(s):
@@ -2618,6 +2738,7 @@ class ImageColorTransfer:
         return {"required": {
                 "source": ("IMAGE",),
                 "target": ("IMAGE",),
+                "weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
                 },
                 }
     
@@ -2631,25 +2752,45 @@ class ImageColorTransfer:
     CATEGORY = "♾️Mixlab/Color"
 
     # 输入是否为列表
-    INPUT_IS_LIST = True
+    # INPUT_IS_LIST = True
 
     # 输出是否为列表
-    OUTPUT_IS_LIST = (True,)
+    # OUTPUT_IS_LIST = (True,)
 
-    def run(self,source,target):
+    def run(self,source,target,weight):
 
         res=[]
 
-        target=target[0][0]
-        print(target.shape)
-        target=tensor2pil(target)
+        #batch-list
+        source_list = [source[i:i + 1, ...] for i in range(source.shape[0])]
+        target_list = [target[i:i + 1, ...] for i in range(target.shape[0])]
 
-        for ims in source:
-            for im in ims:
-                image=tensor2pil(im)
-                image=color_transfer(image,target)
-                image=pil2tensor(image)
-                res.append(image)
+        # 长度纠正为相等
+        if len(target_list) != len(source_list):
+            target_list = target_list * (len(source_list) // len(target_list)) + target_list[:len(source_list) % len(target_list)]
+        
+        for i in range(len(source_list)):
+            target=target_list[i]
+            source=source_list[i]
+            target=tensor2pil(target)
+
+            image=tensor2pil(source)
+
+            image_res=color_transfer(image,target)
+
+            # weight Blend image # contributors:@ning
+            blend_mask = Image.new(mode="L", size=image.size,
+                                    color=(round(weight * 255)))
+            blend_mask = ImageOps.invert(blend_mask)
+            img_result = Image.composite(image, image_res, blend_mask)
+            del image, image_res, blend_mask
+            
+            img_result=pil2tensor(img_result)
+
+            res.append(img_result)
+
+        # list - batch
+        res=torch.cat(res, dim=0)
 
         return (res,)
 

nodes/Mask.py

@@ -108,32 +108,32 @@ class MaskListReplace:
             "mask_replace": ("MASK",),
             "start_index":("INT", {"default": 0, "min": 0, "step": 1}),
             "end_index":("INT", {"default": 0, "min": 0, "step": 1}),
-            "reverse": ("BOOLEAN", {"default": False}),
+            "invert": ("BOOLEAN", {"default": False}),
         }
         }
 
     RETURN_TYPES = ("MASK",)
     FUNCTION = "run"
-    CATEGORY = "♾️Mixlab/Mask"
+    CATEGORY = "♾️Mixlab/Video"
 
     INPUT_IS_LIST = True
     OUTPUT_IS_LIST = (True,)
 
-    def run(self, masks,mask_replace,start_index,end_index,reverse):
+    def run(self, masks,mask_replace,start_index,end_index,invert):
         mask_replace=mask_replace[0]
         start_index=start_index[0]
         end_index=end_index[0]
-        reverse=reverse[0]
+        invert=invert[0]
         
         new_masks=[]
         for i in range(len(masks)):
             if i>=start_index and i<=end_index:
-                if reverse:
+                if invert:
                     new_masks.append(masks[i])
                 else:
                     new_masks.append(mask_replace)
             else:
-                if reverse:
+                if invert:
                     new_masks.append(mask_replace)
                 else:
                     new_masks.append(masks[i])

nodes/PromptNode.py

@@ -580,16 +580,17 @@ class GLIGENTextBoxApply_Advanced:
     RETURN_NAMES = ("CONDITIONING","label",)
 
     FUNCTION = "run"
-    INPUT_IS_LIST = True
+    # INPUT_IS_LIST = True
     CATEGORY = "♾️Mixlab/Prompt"
 
     def run(self, conditioning, clip, gligen_textbox_model, grids, labels, index,max_size,random_shuffle,seed=0):
-        conditioning=conditioning[0]
-        clip=clip[0]
-        gligen_textbox_model=gligen_textbox_model[0]
-        index=index[0]
-        max_size=max_size[0]
-        random_shuffle=random_shuffle[0]
+        # print('grids',grids)
+        # conditioning=conditioning[0]
+        # clip=clip[0]
+        # gligen_textbox_model=gligen_textbox_model[0]
+        # index=index[0]
+        # max_size=max_size[0]
+        # random_shuffle=random_shuffle[0]
 
         texts=labels
         
@@ -618,7 +619,7 @@ class GLIGENTextBoxApply_Advanced:
                 text=texts[i]
                 grid=grids[i]
                 x,y,width,height=grid
-                print(text)
+                # print(text)
                 cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
                 position_params =position_params+ [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]
 
@@ -628,6 +629,7 @@ class GLIGENTextBoxApply_Advanced:
                 prev = n[1]['gligen'][2]
 
             n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params)
+            # print('gligen',n)
             c.append(n)
         
         #  下面这个写法有bug

nodes/Utils.py

@@ -8,6 +8,10 @@ import matplotlib.font_manager as fm
 import torch
 import importlib.util
 
+def create_incrementing_list(min_value, max_value, step, count):
+    l1 = [int(min_value + i * step) for i in range(count) if min_value + i * step <= max_value]
+    l2 = [float(min_value + i * step) for i in range(count) if min_value + i * step <= max_value]
+    return (l1,l2)
 
 def split_list(lst, chunk_size, transition_size):
     result = []
@@ -154,7 +158,6 @@ class ColorInput:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { 
-             
                     "color":("TCOLOR",), 
                              },
                 }
@@ -281,7 +284,7 @@ class FloatSlider:
                 }
     
     RETURN_TYPES = ("FLOAT",) 
-
+    RETURN_NAMES = ('weight(0-1)',)
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/Input"
@@ -406,6 +409,61 @@ class TextInput:
        
         return (text,)
     
+
+class IncrementingListNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "min_value": ("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "max_value": ("FLOAT", {
+                        "default": 10, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "step": ("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "count": ("INT", {
+                        "default": 1, 
+                        "min": 1, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step":1, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    })
+                             },
+                             "optional":{
+                    "seed":("INT", {"default": -1, "min": -1, "max": 1000000}), 
+                    
+                },
+                }
+    
+    RETURN_TYPES = ("INT","FLOAT",) 
+    RETURN_NAMES = ('int_list','float_list',)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Video"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,True,)
+
+    def run(self,min_value,max_value,step,count,seed):
+        print('create_incrementing_list',seed)
+        l1,l2=create_incrementing_list(min_value,max_value,step,count)
+        return (l1,l2,)
+    
 # 接收一个值，然后根据字符串或数值长度计算延迟时间，用户可以自定义延迟"字/s"，延迟之后将转化
 
 import comfy.samplers
@@ -585,14 +643,14 @@ class SwitchByIndex:
         }
 
     RETURN_TYPES = (any_type,"INT",)
-    RETURN_NAMES = ("C","count",)
+    RETURN_NAMES = ("list", "count",)
 
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,False,)
+    OUTPUT_IS_LIST = (True, False,)
 
     def run(self, A=[],B=[],index=-1,flat='on'):
 
@@ -613,9 +671,9 @@ class SwitchByIndex:
             try:
                 C=[C[index]]
             except Exception as e:
-                C=[]
+                C=[C[-1]] #最后一个
 
-        return (C,len(C),)
+        return (C, len(C),)
 
 class ListSplit:
     @classmethod
@@ -755,9 +813,13 @@ class TESTNODE_:
 
     def run(self,ANY):
         print(type(ANY))
-        print(ANY[0].shape)
-        img= tensor2pil(ANY[0])
-        print(img.size)
+        try:
+            print(ANY[0].shape)
+            img= tensor2pil(ANY[0])
+            print(img.size)
+        except:
+            print('')
+        
         # data=ANY
         list_stats = ListStatistics()
 

nodes/Video.py

@@ -4,13 +4,13 @@ import json
 import subprocess
 import shutil
 import re
-import time
+import time,math
 import numpy as np
 from typing import List
 import torch
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
-import cv2
+import cv2,random,string
 from pathlib import Path
 
 import folder_paths
@@ -18,6 +18,73 @@ from comfy.k_diffusion.utils import FolderOfImages
 from comfy.utils import common_upscale
 
 
+
+
+def generate_folder_name(directory,video_path):
+    # Get the directory and filename from the video path
+    _, filename = os.path.split(video_path)
+    # Generate a random string of lowercase letters and digits
+    random_string = ''.join(random.choices(string.ascii_lowercase + string.digits, k=8))
+    # Create the folder name by combining the random string and the filename
+    folder_name = random_string + '_' + filename
+    # Create the full folder path by joining the directory and the folder name
+    folder_path = os.path.join(directory, folder_name)
+    return folder_path
+
+def create_folder(directory,video_path):
+    folder_path = generate_folder_name(directory,video_path)
+    os.makedirs(folder_path)
+    return folder_path
+
+
+def split_video(video_path, video_segment_frames, transition_frames, output_dir):
+    # 读取视频文件
+    video_capture = cv2.VideoCapture(video_path)
+    
+    # 获取视频的总帧数和帧率
+    total_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = video_capture.get(cv2.CAP_PROP_FPS)
+    
+    # 计算每个视频片段的总帧数，包括过渡帧
+    segment_total_frames = video_segment_frames + transition_frames
+    
+    # 计算可以分割的片段数量，向上取整
+    num_segments = (total_frames + transition_frames - 1) // segment_total_frames
+    
+    vs=[]
+    # 计算每个片段的起始帧和结束帧
+    start_frame = 0
+    for i in range(num_segments):
+        # 计算当前片段的结束帧，注意最后一个片段可能没有过渡帧
+        end_frame = min(start_frame + segment_total_frames, total_frames)
+        
+        # 打印当前片段的起始帧和结束帧
+        print(f"Segment {i+1}: Start Frame {start_frame}, End Frame {end_frame}")
+        
+        # 保存当前片段为一个视频文件
+        segment_video_path = f"{output_dir}/segment_{i+1}.avi"
+
+        fourcc = cv2.VideoWriter_fourcc(*'XVID')
+        segment_video = cv2.VideoWriter(segment_video_path, fourcc, fps, (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
+                                                              int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))))
+        
+        for frame_num in range(start_frame, end_frame):
+            ret, frame = video_capture.read()
+            if ret:
+                segment_video.write(frame)
+            else:
+                break  # 如果读取失败，则退出循环
+        
+        # 更新起始帧为下一个片段的起始位置
+        start_frame = end_frame + transition_frames
+        vs.append(segment_video_path)
+    
+    # 释放视频捕获对象
+    video_capture.release()
+    # print(vs)
+    return (vs,total_frames,fps)
+
+
 folder_paths.folder_names_and_paths["video_formats"] = (
     [
         os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "video_formats"),
@@ -34,6 +101,48 @@ if ffmpeg_path is None:
     except:
         print("ffmpeg could not be found. Outputs that require it have been disabled")
 
+# 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 count_files(directory):
+    count = 0
+    for root, dirs, files in os.walk(directory):
+        count += len(files)
+    return count
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    c=count_files(output_dir)
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('temp_', output_dir)
+
+    
+    image=tensor2pil(image)
+ 
+    image_file = f"{filename}_{c}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    image.save(image_path,compress_level=4)
+
+    return [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type": "temp"
+                }]
+
 
 def split_list(lst, chunk_size, transition_size):
     result = []
@@ -50,7 +159,96 @@ def split_list(lst, chunk_size, transition_size):
 # result = split_list(images, chunk_size, transition_size)
 # print(result)
 
+class ImageListReplace:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "images": ("IMAGE",),
+            "start_index":("INT", {"default": 0, "min": 0, "step": 1}),
+            "end_index":("INT", {"default": 0, "min": 0, "step": 1}),
+            "invert": ("BOOLEAN", {"default": False}),
+        },
+         "optional":{
+                    "image_replace": ("IMAGE",),
+                    "images_replace": ("IMAGE",),
+                    
+                    }
+        }
 
+    RETURN_TYPES = ("IMAGE","IMAGE",)
+    RETURN_NAMES = ("images","select_images",)
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/Video"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,)
+
+    def run(self, images,start_index=[0],end_index=[0],invert=[False],image_replace=None,images_replace=None):
+        start_index=start_index[0]
+        end_index=end_index[0]
+        invert=invert[0]
+
+        image_rs=[]
+
+        if image_replace!=None:
+            for i in range(end_index-start_index+1):
+                image_rs.append(image_replace[0])
+
+        if images_replace!=None:
+            image_rs=images_replace
+
+        # 如果image replace 为空
+        if image_replace==None and images_replace==None:
+            # print('如果image replace 为空',images[0])
+            # [[tensor(
+            # tensor([[[[0.
+            first_image=tensor2pil(images[0][0])
+            width, height = first_image.size
+            image_replace=Image.new("RGB", (width, height), (0, 0, 0))
+            image_replace=pil2tensor(image_replace)
+            for i in range(end_index-start_index+1):
+                image_rs.append(image_replace)
+
+        
+        new_images=[]
+        select_images=[]
+        k=0
+        for i in range(len(images)):
+            if i>=start_index and i<=end_index:
+                if invert:
+                    new_images.append(images[i])
+                else:
+                    new_images.append(image_rs[k])
+                    select_images.append(images[i])
+                    k+=1
+            else:
+                if invert:
+                    new_images.append(image_rs[k])
+                    select_images.append(images[i])
+                    k+=1
+                else:
+                    new_images.append(images[i])
+
+        imss=[]
+        # print(len(images))
+        for i in range(len(images)):
+            t=images[i][0]
+            t=tensor2pil(t)
+            t = t.convert("RGB")
+            original_width, original_height = t.size
+            scale = 300 / original_width
+            new_height = int(original_height * scale)
+            t = t.resize((300, new_height))
+
+            ims=create_temp_file(pil2tensor(t))
+            imss.append(ims[0])
+
+        # image_replace=create_temp_file(image_replace)
+
+        return {"ui":{"_images": imss},"result": (new_images,select_images,)}
+
+# The code is based on ComfyUI-VideoHelperSuite modification.
 class LoadVideoAndSegment:
     @classmethod
     def INPUT_TYPES(s):
@@ -70,11 +268,11 @@ class LoadVideoAndSegment:
 
     CATEGORY = "♾️Mixlab/Video"
 
-    RETURN_TYPES = ("IMAGE", "INT",)
-    RETURN_NAMES = ("image_batch", "frame_count",)
+    RETURN_TYPES = ("SCENE_VIDEO","INT", "INT","INT",)
+    RETURN_NAMES = ("scenes_video","scenes_count","frame_count","fps",)
     FUNCTION = "load_video"
     OUTPUT_NODE = True
-    OUTPUT_IS_LIST = (True,False,)
+    OUTPUT_IS_LIST = (True,False,False,False,)
 
 
     def is_gif(self, filename):
@@ -131,70 +329,84 @@ class LoadVideoAndSegment:
         return (images, frames_added)
 
     def load_video(self, video,video_segment_frames,transition_frames ):
-        frame_load_cap=0
-        skip_first_frames=0
+        
+        video_path = folder_paths.get_annotated_filepath(video)
+        
         # check if video is a gif - will need to use cv fallback to read frames
         # use cv fallback if ffmpeg not installed or gif
-        if ffmpeg_path is None:
-            return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
+        # if ffmpeg_path is None:
+        #     return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
         # otherwise, continue with ffmpeg
-        video_path = folder_paths.get_annotated_filepath(video)
-        args_dummy = [ffmpeg_path, "-i", video_path, "-f", "null", "-"]
-        try:
-            with subprocess.Popen(args_dummy, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE) as proc:
-                for line in proc.stderr.readlines():
-                    match = re.search(", ([1-9]|\\d{2,})x(\\d+)",line.decode('utf-8'))
-                    if match is not None:
-                        size = [int(match.group(1)), int(match.group(2))]
-                        break
-        except Exception as e:
-            print(f"Retrying with opencv due to ffmpeg error: {e}")
-            return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
-        args_all_frames = [ffmpeg_path, "-i", video_path, "-v", "error",
-                             "-pix_fmt", "rgb24"]
-
-        vfilters = []
-       
-        if skip_first_frames > 0:
-            vfilters.append(f"select=gt(n\\,{skip_first_frames-1})")
-        if frame_load_cap > 0:
-            vfilters.append(f"select=gt({frame_load_cap}\\,n)")
-        #manually calculate aspect ratio to ensure reads remain aligned
         
-        if len(vfilters) > 0:
-            args_all_frames += ["-vf", ",".join(vfilters)]
+        # args_dummy = [ffmpeg_path, "-i", video_path, "-f", "null", "-"]
+        # try:
+        #     with subprocess.Popen(args_dummy, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE) as proc:
+        #         for line in proc.stderr.readlines():
+        #             match = re.search(", ([1-9]|\\d{2,})x(\\d+)",line.decode('utf-8'))
+        #             if match is not None:
+        #                 size = [int(match.group(1)), int(match.group(2))]
+        #                 break
+        # except Exception as e:
+        #     print(f"Retrying with opencv due to ffmpeg error: {e}")
+        #     return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
+        # args_all_frames = [ffmpeg_path, "-i", video_path, "-v", "error",
+        #                      "-pix_fmt", "rgb24"]
 
-        args_all_frames += ["-f", "rawvideo", "-"]
-        images = []
-        try:
-            with subprocess.Popen(args_all_frames, stdout=subprocess.PIPE) as proc:
-                #Manually buffer enough bytes for an image
-                bpi = size[0]*size[1]*3
-                current_bytes = bytearray(bpi)
-                current_offset=0
-                while True:
-                    bytes_read = proc.stdout.read(bpi - current_offset)
-                    if bytes_read is None:#sleep to wait for more data
-                        time.sleep(.2)
-                        continue
-                    if len(bytes_read) == 0:#EOF
-                        break
-                    current_bytes[current_offset:len(bytes_read)] = bytes_read
-                    current_offset+=len(bytes_read)
-                    if current_offset == bpi:
-                        images.append(np.array(current_bytes, dtype=np.float32).reshape(size[1], size[0], 3) / 255.0)
-                        current_offset = 0
-        except Exception as e:
-            print(f"Retrying with opencv due to ffmpeg error: {e}")
-            return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
+        # vfilters = []
+       
+        # if skip_first_frames > 0:
+        #     vfilters.append(f"select=gt(n\\,{skip_first_frames-1})")
+        # if frame_load_cap > 0:
+        #     vfilters.append(f"select=gt({frame_load_cap}\\,n)")
+        # #manually calculate aspect ratio to ensure reads remain aligned
+        
+        # if len(vfilters) > 0:
+        #     args_all_frames += ["-vf", ",".join(vfilters)]
 
-        imgs=split_list(images,video_segment_frames,transition_frames)
+        # args_all_frames += ["-f", "rawvideo", "-"]
+        # images = []
+        # try:
+        #     with subprocess.Popen(args_all_frames, stdout=subprocess.PIPE) as proc:
+        #         #Manually buffer enough bytes for an image
+        #         bpi = size[0]*size[1]*3
+        #         current_bytes = bytearray(bpi)
+        #         current_offset=0
+        #         while True:
+        #             bytes_read = proc.stdout.read(bpi - current_offset)
+        #             if bytes_read is None:#sleep to wait for more data
+        #                 time.sleep(.2)
+        #                 continue
+        #             if len(bytes_read) == 0:#EOF
+        #                 break
+        #             current_bytes[current_offset:len(bytes_read)] = bytes_read
+        #             current_offset+=len(bytes_read)
+        #             if current_offset == bpi:
+        #                 images.append(np.array(current_bytes, dtype=np.float32).reshape(size[1], size[0], 3) / 255.0)
+        #                 current_offset = 0
+        # except Exception as e:
+        #     print(f"Retrying with opencv due to ffmpeg error: {e}")
+        #     return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
 
-        imgs=[torch.from_numpy(np.stack(im)) for im in imgs]
+        # imgs=split_list(images,video_segment_frames,transition_frames)
+
+        # temp path 
+        tp=folder_paths.get_temp_directory()
+        basename = os.path.basename(video_path)  # 获取文件名
+        name_without_extension = os.path.splitext(basename)[0]  # 去掉文件后缀
+
+        folder_path = create_folder(tp,name_without_extension)
+        
+ 
+        # 导出的数据
+        scenes_video,total_frames,fps=split_video(video_path,video_segment_frames,
+                                 transition_frames,folder_path)
+      
+
+        # imgs=[torch.from_numpy(np.stack(im)) for im in imgs]
 
         # images = torch.from_numpy(np.stack(images))
 
-        return (imgs, len(imgs))
+        return (scenes_video,len(scenes_video), total_frames,fps,)
 
     @classmethod
     def IS_CHANGED(s, video, **kwargs):
@@ -212,3 +424,269 @@ class LoadVideoAndSegment:
         return True
     
 
+# The code is based on ComfyUI-VideoHelperSuite modification.
+class VideoCombine_Adv:
+    @classmethod
+    def INPUT_TYPES(s):
+        #Hide ffmpeg formats if ffmpeg isn't available
+        if ffmpeg_path is not None:
+            ffmpeg_formats = ["video/"+x[:-5] for x in folder_paths.get_filename_list("video_formats")]
+        else:
+            ffmpeg_formats = []
+        return {
+            "required": {
+                "image_batch": ("IMAGE",),
+                "frame_rate": (
+                    "INT",
+                    {"default": 8, "min": 1, "step": 1},
+                ),
+                "loop_count": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
+                "filename_prefix": ("STRING", {"default": "Comfyui"}),
+                "format": (["image/gif", "image/webp"] + ffmpeg_formats,),
+                "pingpong": ("BOOLEAN", {"default": False}),
+                "save_image": ("BOOLEAN", {"default": True}),
+                "metadata": ("BOOLEAN", {"default": False}),
+            },
+            "hidden": {
+                "prompt": "PROMPT",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    CATEGORY = "♾️Mixlab/Video"
+    FUNCTION = "run"
+
+    def save_with_tempfile(self, args, metadata, file_path, frames, env):
+        #Ensure temp directory exists
+        os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
+
+        metadata_path = os.path.join(folder_paths.get_temp_directory(), "metadata.txt")
+        #metadata from file should  escape = ; # \ and newline
+        #From my testing, though, only backslashes need escapes and = in particular causes problems
+        #It is likely better to prioritize future compatibility with containers that don't support
+        #or shouldn't use the comment tag for embedding metadata
+        metadata = metadata.replace("\\","\\\\")
+        metadata = metadata.replace(";","\\;")
+        metadata = metadata.replace("#","\\#")
+        #metadata = metadata.replace("=","\\=")
+        metadata = metadata.replace("\n","\\\n")
+        with open(metadata_path, "w") as f:
+            f.write(";FFMETADATA1\n")
+            f.write(metadata)
+        args = args[:1] + ["-i", metadata_path] + args[1:] + [file_path]
+        with subprocess.Popen(args, stdin=subprocess.PIPE, env=env) as proc:
+            for frame in frames:
+                proc.stdin.write(frame.tobytes())
+
+    def run(
+        self,
+        image_batch,
+        frame_rate: int,
+        loop_count: int,
+        filename_prefix="AnimateDiff",
+        format="image/gif",
+        pingpong=False,
+        save_image=True,
+        metadata=False,
+        prompt=None,
+        extra_pnginfo=None,
+    ):
+        images=image_batch
+
+        frames: List[Image.Image] = []
+        for image in images:
+            img = 255.0 * image.cpu().numpy()
+            img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
+            # resize 保证
+            # 检查图像的高度是否是2的倍数，如果不是，则调整高度
+            if img.height % 2 != 0:
+                img = img.resize((img.width, img.height + 1))
+
+            # 检查图像的宽度是否是2的倍数，如果不是，则调整宽度
+            if img.width % 2 != 0:
+                img = img.resize((img.width + 1, img.height))
+                
+            frames.append(img)
+
+        # get output information
+        output_dir = (
+            folder_paths.get_output_directory()
+            if save_image
+            else folder_paths.get_temp_directory()
+        )
+        (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path(filename_prefix, output_dir)
+
+        metadata = PngInfo()
+        video_metadata = {}
+        if prompt is not None:
+            metadata.add_text("prompt", json.dumps(prompt))
+            video_metadata["prompt"] = prompt
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+                video_metadata[x] = extra_pnginfo[x]
+
+        # 取消保存metadata
+        if metadata==False:
+            metadata = PngInfo()
+
+        # save first frame as png to keep metadata
+        file = f"{filename}_{counter:05}_.png"
+        file_path = os.path.join(full_output_folder, file)
+        frames[0].save(
+            file_path,
+            pnginfo=metadata,
+            compress_level=4,
+        )
+        if pingpong:
+            frames = frames + frames[-2:0:-1]
+
+        format_type, format_ext = format.split("/")
+        file = f"{filename}_{counter:05}_.{format_ext}"
+        file_path = os.path.join(full_output_folder, file)
+        if format_type == "image":
+            # Use pillow directly to save an animated image
+            frames[0].save(
+                file_path,
+                format=format_ext.upper(),
+                save_all=True,
+                append_images=frames[1:],
+                duration=round(1000 / frame_rate),
+                loop=loop_count,
+                compress_level=4,
+            )
+        else:
+            # Use ffmpeg to save a video
+            if ffmpeg_path is None:
+                #Should never be reachable
+                raise ProcessLookupError("Could not find ffmpeg")
+
+            video_format_path = folder_paths.get_full_path("video_formats", format_ext + ".json")
+            with open(video_format_path, 'r') as stream:
+                video_format = json.load(stream)
+            file = f"{filename}_{counter:05}_.{video_format['extension']}"
+            file_path = os.path.join(full_output_folder, file)
+            dimensions = f"{frames[0].width}x{frames[0].height}"
+            metadata_args = ["-metadata", "comment=" + json.dumps(video_metadata)]
+            args = [ffmpeg_path, "-v", "error", "-f", "rawvideo", "-pix_fmt", "rgb24",
+                    "-s", dimensions, "-r", str(frame_rate), "-i", "-"] \
+                    + video_format['main_pass']
+            # On linux, max arg length is Pagesize * 32 -> 131072
+            # On windows, this around 32767 but seems to vary wildly by > 500
+            # in a manor not solely related to other arguments
+            if os.name == 'posix':
+                max_arg_length = 4096*32
+            else:
+                max_arg_length = 32767 - len(" ".join(args + [metadata_args[0]] + [file_path])) - 1
+            #test max limit
+            #metadata_args[1] = metadata_args[1] + "a"*(max_arg_length - len(metadata_args[1])-1)
+
+            env=os.environ.copy()
+            if  "environment" in video_format:
+                env.update(video_format["environment"])
+            if len(metadata_args[1]) >= max_arg_length:
+                print(f"Using fallback file for extremely long metadata: {len(metadata_args[1])}/{max_arg_length}")
+                self.save_with_tempfile(args, metadata_args[1], file_path, frames, env)
+            else:
+                try:
+                    with subprocess.Popen(args + metadata_args + [file_path],
+                                          stdin=subprocess.PIPE, env=env) as proc:
+                        for frame in frames:
+                            proc.stdin.write(frame.tobytes())
+                except FileNotFoundError as e:
+                    if "winerror" in dir(e) and e.winerror == 206:
+                        print("Metadata was too long. Retrying with fallback file")
+                        self.save_with_tempfile(args, metadata_args[1], file_path, frames, env)
+                    else:
+                        raise
+                except OSError as e:
+                    if "errno" in dir(e) and e.errno == 7:
+                        print("Metadata was too long. Retrying with fallback file")
+                        self.save_with_tempfile(args, metadata_args[1], file_path, frames, env)
+                    else:
+                        raise
+
+        previews = [
+            {
+                "filename": file,
+                "subfolder": subfolder,
+                "type": "output" if save_image else "temp",
+                "format": format,
+            }
+        ]
+        return {"ui": {"gifs": previews}}
+
+
+class VAEEncodeForInpaint_Frames:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "vae": ("VAE", ), 
+            "images": ("IMAGE", ), 
+            "masks": ("MASK", ), 
+            "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),
+            }}
+    
+    FUNCTION = "encode"
+
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("LATENT",)
+ 
+    CATEGORY = "♾️Mixlab/Video"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+
+    def encode(self, vae, images, masks, grow_mask_by=[6]):
+        vae=vae[0]
+        grow_mask_by=grow_mask_by[0]
+
+        result=[]
+
+        for i in range(len(images)):
+            pixels=images[i]
+            mask=masks[i]
+
+
+            x = (pixels.shape[1] // 8) * 8
+            y = (pixels.shape[2] // 8) * 8
+            mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
+
+            pixels = pixels.clone()
+            if pixels.shape[1] != x or pixels.shape[2] != y:
+                x_offset = (pixels.shape[1] % 8) // 2
+                y_offset = (pixels.shape[2] % 8) // 2
+                pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+                mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
+
+            #grow mask by a few pixels to keep things seamless in latent space
+            if grow_mask_by == 0:
+                mask_erosion = mask
+            else:
+                kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
+                padding = math.ceil((grow_mask_by - 1) / 2)
+
+                mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)
+
+            m = (1.0 - mask.round()).squeeze(1)
+            for i in range(3):
+                pixels[:,:,:,i] -= 0.5
+                pixels[:,:,:,i] *= m
+                pixels[:,:,:,i] += 0.5
+            t = vae.encode(pixels)
+
+            result.append({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())})
+
+
+        return (result, )

requirements.txt

@@ -7,6 +7,6 @@ openai
 simple-lama-inpainting
 clip-interrogator==0.6.0
 transformers>=4.36.0
-zhipuai
 lark-parser
-imageio-ffmpeg
+imageio-ffmpeg
+rembg[gpu]

web/index.html

@@ -202,7 +202,7 @@
             width: fit-content;
             max-width: 100%;
             margin-left: 12px;
-            min-height: 200px;
+            /*min-height: 200px;*/
         }
 
         .input_card {
@@ -278,8 +278,11 @@
         }
 
         button:hover {
-            border-color: yellow !important;
-            color: yellow !important;
+            /* border-color: yellow !important; */
+            color: #ffffff !important;
+            /* font-weight: 400; */
+            background: #232222;
+            cursor: pointer;
         }
 
         .disabled {
@@ -400,12 +403,53 @@
         .hidden-caption-content {
             display: none;
         }
+
+         /* 定义滚动条轨道的背景颜色 */
+
+         ::-webkit-scrollbar-track {
+            background-color: #f1f1f1;
+            /* 轨道背景颜色 */
+        }
+
+        /* 定义滚动条滑块的颜色 */
+
+        ::-webkit-scrollbar-thumb {
+            background-color: #888;
+            /* 滑块颜色 */
+            border-radius: 4px;
+            /* 滑块圆角 */
+        }
+
+        /* 鼠标悬停时滚动条滑块的颜色 */
+
+        ::-webkit-scrollbar-thumb:hover {
+            background-color: #555;
+            /* 悬停时滑块颜色 */
+        }
+
+        /* 定义滚动条角落的颜色 */
+
+        ::-webkit-scrollbar-corner {
+            background-color: transparent;
+            /* 角落颜色 */
+        }
+
+
+        .dynamic_prompt::after {
+            content: attr(title);
+            position: absolute; 
+            color: black;
+            padding: 4px;
+            padding-left: 25px;
+            border-radius: 4px;
+        }
     </style>
     <!-- <script src="../../../scripts/api.js" type="module"></script> -->
     <link href="/extensions/comfyui-mixlab-nodes/lib/photoswipe.min.css" rel="stylesheet">
     <link href="/extensions/comfyui-mixlab-nodes/lib/classic.min.css" rel="stylesheet">
     <script src="/extensions/comfyui-mixlab-nodes/lib/pickr.min.js"></script>
     <script src="/extensions/comfyui-mixlab-nodes/lib/filerobot-image-editor.min.js"></script>
+    <script type="module" src="/extensions/comfyui-mixlab-nodes/lib/model-viewer.min.js"></script>
     <link rel="stylesheet" href="/extensions/comfyui-mixlab-nodes/lib/login.css">
 </head>
 
@@ -439,8 +483,39 @@
     <script type="module">
         import PhotoSwipeLightbox from '/extensions/comfyui-mixlab-nodes/lib/photoswipe-lightbox.esm.min.js'
         // console.log(Lightbox)
-
         import { api } from "../../../scripts/api.js";
+
+
+        // ComfyUI\web\extensions\core\dynamicPrompts.js
+        // 官方实现修改
+        // Allows for simple dynamic prompt replacement
+        // Inputs in the format {a|b} will have a random value of a or b chosen when the prompt is queued.
+
+        /*
+         * Strips C-style line and block comments from a string
+         */
+        function stripComments(str) {
+            return str.replace(/\/\*[\s\S]*?\*\/|\/\/.*/g, '');
+        }
+
+        function dynamicPrompts(prompt) {
+            prompt = stripComments(prompt);
+            while (prompt.replace("\\{", "").includes("{") && prompt.replace("\\}", "").includes("}")) {
+                const startIndex = prompt.replace("\\{", "00").indexOf("{");
+                const endIndex = prompt.replace("\\}", "00").indexOf("}");
+
+                const optionsString = prompt.substring(startIndex + 1, endIndex);
+                const options = optionsString.split("|");
+
+                const randomIndex = Math.floor(Math.random() * options.length);
+                const randomOption = options[randomIndex];
+
+                prompt = prompt.substring(0, startIndex) + randomOption + prompt.substring(endIndex + 1);
+            }
+            return prompt
+        }
+
+
         // console.log('api', api)
         const base64Df =
             'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
@@ -498,6 +573,39 @@
             })
         }
 
+        //给load image to batch节点使用的输入
+        function createBase64ImageForLoadImageToBatch(imageElement, nodeId, bs) {
+            let im = new Image()
+            im.src = bs;
+            im.className = "base64"
+            imageElement.appendChild(im);
+
+            let base64s = imageElement.querySelectorAll('.base64')
+            //更新输入
+            window._appData.data[nodeId].inputs.images.base64 = Array.from(base64s, (b) => b.src)
+
+            // 删除
+            im.addEventListener('click', e => {
+                e.preventDefault();
+                im.remove();
+                let base64s = imageElement.querySelectorAll('.base64')
+                //更新输入
+                window._appData.data[nodeId].inputs.images.base64 = Array.from(base64s, (b) => b.src)
+            })
+        }
+
+        const blobToBase64 = blob => {
+            return new Promise((res, rej) => {
+                const reader = new FileReader()
+                reader.onloadend = () => {
+                    const base64data = reader.result
+                    res(base64data)
+                    // 在这里可以将base64数据用于进一步处理或显示图片
+                }
+                reader.readAsDataURL(blob)
+            })
+        }
+
 
         function base64ToBlob(base64) {
             // 去除base64编码中的前缀
@@ -712,7 +820,7 @@
                     data[id].inputs.noise_seed = Math.round(Math.random() * max_seed)
                 }
                 // class_type:"Seed_"
-                if (data[id].class_type == "Seed_") {
+                if (data[id].class_type == "Seed_" && ['increment', 'decrement', 'randomize'].includes(seed[id])) {
                     data[id].inputs.seed = Math.round(Math.random() * max_seed)
                 }
                 console.log('new Seed', data[id])
@@ -737,6 +845,21 @@
             // 随机seed
             promptWorkflow = randomSeed(seed, promptWorkflow);
 
+            // //动态提示,改为输入的时候，手动触发
+            // for (const id in promptWorkflow) {
+            //     let node = promptWorkflow[id]
+            //     if (["TextInput_", "CLIPTextEncode", "PromptSimplification", "ChinesePrompt_Mix"].includes(
+            //         node.class_type
+            //     )) {
+            //         if (node.class_type == "PromptSimplification") {
+            //             promptWorkflow[id].inputs.prompt = dynamicPrompts(node.inputs.prompt);
+            //         } else {
+            //             promptWorkflow[id].inputs.text = dynamicPrompts(node.inputs.text);
+            //         }
+            //         console.log('#动态提示', promptWorkflow[id].inputs)
+            //     }
+            // }
+
             let url = get_url()
             const data = JSON.stringify({ prompt: promptWorkflow, client_id });
             fetch(`${url}/prompt`, {
@@ -912,6 +1035,9 @@
                 // copyImagesToClipboard(output_card.outerHTML)
             })
 
+            //是否显示复制图片，复制html两个按钮
+            let isShowImageFn = false;
+
             for (const node of outputData) {
                 // console.log('output', node)
                 if (node.class_type == "ShowTextForGPT") {
@@ -930,8 +1056,31 @@
                     output_card.appendChild(div);
                 };
 
-                if (["SaveImage", "PreviewImage", "PromptImage", "Image Save", "SaveImageAndMetadata_"].includes(node.class_type)) {
+                if (["SaveImage",
+                    "PreviewImage",
+                    "PromptImage",
+                    "Image Save",
+                    "SaveImageAndMetadata_",
+                    "TransparentImage"].includes(node.class_type)) {
+                    console.log('output#image', node)
+                    let a = document.createElement('a');
+                    a.id = `output_${node.id}`
+                    a.setAttribute('data-pswp-width', "200");
+                    a.setAttribute('data-pswp-height', "200");
+                    a.setAttribute('target', "_blank");
+                    a.setAttribute('href', base64Df);
+                    a.setAttribute('title', node.title);
 
+                    let img = new Image();
+                    // img;
+                    img.src = window._appData?.icon || base64Df;
+                    a.appendChild(img)
+                    output_card.appendChild(a);
+                    isShowImageFn = true;
+                }
+
+                //3d 
+                if (["SaveTripoSRMesh"].includes(node.class_type)) {
                     let a = document.createElement('a');
                     a.id = `output_${node.id}`
                     a.setAttribute('data-pswp-width', "200");
@@ -947,7 +1096,7 @@
                 }
 
                 // video ,gif
-                if (["VHS_VideoCombine"].includes(node.class_type)) {
+                if (["VHS_VideoCombine", "VideoCombine_Adv"].includes(node.class_type)) {
 
                     let a = document.createElement('a');
                     a.id = `output_${node.id}`
@@ -974,6 +1123,12 @@
                 }
 
             }
+
+            if (isShowImageFn === false) {
+                copyImage.remove();
+                copyHTML.remove();
+            }
+
             return container
         }
 
@@ -1030,6 +1185,7 @@
         }
 
         async function handleClipboardImage(imageElement, data) {
+            //data.class_type === 'LoadImagesToBatch'
             try {
                 const clipboardItems = await navigator.clipboard.read();
                 for (const clipboardItem of clipboardItems) {
@@ -1042,18 +1198,17 @@
 
                             if (hashId == window._appData.data[data.id].hashId) return
 
-                            let { url, name } = await uploadImage(fileBlob);
-                            // 在这里可以对 Blob 对象进行进一步处理
-                            imageElement.src = url;
-                            window._appData.data[data.id].inputs.image = name;
-                            window._appData.data[data.id].hashId = hashId;
-
-                            console.log("上传的文件：", url, data.id, name);
-
-                            // const img = document.createElement('img');
-                            // img.src = URL.createObjectURL(blob);
-                            // document.body.appendChild(img);
-                            // console.log( URL.createObjectURL(blob));
+                            if (data.class_type === 'LoadImagesToBatch') {
+                                let base64 = await blobToBase64(fileBlob)
+                                createBase64ImageForLoadImageToBatch(imageElement, data.id, base64)
+                            } else {
+                                let { url, name } = await uploadImage(fileBlob);
+                                // 在这里可以对 Blob 对象进行进一步处理
+                                imageElement.src = url;
+                                window._appData.data[data.id].inputs.image = name;
+                                window._appData.data[data.id].hashId = hashId;
+                                console.log("上传的文件：", url, data.id, name);
+                            }
                         }
                     }
                 }
@@ -1217,10 +1372,16 @@
                 console.log('inputData', data);
 
                 // 图片 or 视频输入
-                if (data.class_type === "LoadImage" || data.class_type === "VHS_LoadVideo" || data.class_type === 'ImagesPrompt_') {
+
+                if (["LoadImage",
+                    "VHS_LoadVideo",
+                    "ImagesPrompt_",
+                    "LoadImagesToBatch"].includes(data.class_type)) {
 
                     let isVideoUpload = data.class_type === "VHS_LoadVideo";
 
+                    let isBase64Upload = data.class_type === "LoadImagesToBatch";
+
                     // Create a container for the upload control
                     const uploadContainer = document.createElement("div");
                     uploadContainer.className = 'card';
@@ -1252,7 +1413,7 @@
                     const btnForImageEdit = document.createElement("button");
                     btnForImageEdit.style = ` width: 32px; background: none;margin-left: 18px;`
                     btnForImageEdit.innerHTML = '<?xml version="1.0" ?><svg version="1.1" style="width: 24px;" viewBox="0 0 50 50" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g id="Layer_1_1_"><path d="M18.293,31.707h6.414l24-24l-6.414-6.414l-24,24V31.707z M45.879,7.707l-3.586,3.586l-3.586-3.586l3.586-3.586   L45.879,7.707z M20.293,26.121l17-17l3.586,3.586l-17,17h-3.586V26.121z"/><polygon points="43.293,19.707 41.293,19.707 41.293,46.707 3.293,46.707 3.293,8.707 31.293,8.707 31.293,6.707 1.293,6.707    1.293,48.707 43.293,48.707  "/></g></svg>'
-                    if (!isVideoUpload && data.class_type !== 'ImagesPrompt_') actionDiv.appendChild(btnForImageEdit);
+                    if ((!isVideoUpload && !isBase64Upload) && data.class_type !== 'ImagesPrompt_') actionDiv.appendChild(btnForImageEdit);
 
 
                     uploadContainer.appendChild(actionDiv)
@@ -1293,11 +1454,22 @@
                             data.title,
                             data.options.images,
                             data.inputs.imageIndex,
-                            (base64) => {
+                            (base64, text) => {
                                 window._appData.data[data.id].inputs.image_base64 = base64;
+                                window._appData.data[data.id].inputs.text = text;
                             })
                         uploadContainer.appendChild(imgDiv);
                         window._appData.data[data.id].inputs.image_base64 = mainImage.querySelector('.images_prompt_main').src;
+                    } else if (data.class_type === 'LoadImagesToBatch') {
+                        // 多张base64 图片
+                        let base64 = data.inputs.images.base64
+
+                        imageElement = document.createElement('div');
+                        imageElement.className = "images"
+                        for (const bs of base64) {
+                            createBase64ImageForLoadImageToBatch(imageElement, data.id, bs)
+                        }
+
                     }
 
 
@@ -1305,7 +1477,7 @@
 
                     if (!isVideoUpload) btnFromClipboard.addEventListener('click', (event) => handleClipboardImage(imageElement, data));
 
-                    if (!isVideoUpload) btnForImageEdit.addEventListener('click', e => editImage(imageElement, data))
+                    if (!isVideoUpload && !isBase64Upload) btnForImageEdit.addEventListener('click', e => editImage(imageElement, data))
 
 
                     uploadImageInput.addEventListener('click', (event) => {
@@ -1328,33 +1500,43 @@
 
                             if (hashId == window._appData.data[data.id].hashId) return
 
-                            let { url, name } = await uploadImage(fileBlob, '.' + file.type.split('/')[1])
-
-                            if (data.class_type === 'ImagesPrompt_') {
-                                // 
-                                let base64 = await parseImageToBase64(url);
-                                uploadContainer.querySelector('.images_prompt_main').src = base64
-                                window._appData.data[data.id].inputs.image_base64 = base64;
+                            if (data.class_type === 'LoadImagesToBatch') {
+                                // 上传 ，转为base64
+                                let base64 = await blobToBase64(fileBlob)
+                                createBase64ImageForLoadImageToBatch(imageElement, data.id, base64)
                             } else {
-                                if (isVideoUpload) {
-                                    imageElement.srcObject = null;
-                                }
-                                // 在这里可以对 Blob 对象进行进一步处理
-                                imageElement.src = url;
+                                //上传，返回url
+                                let { url, name } = await uploadImage(fileBlob, '.' + file.type.split('/')[1])
 
-
-                                if (isVideoUpload) {
-                                    window._appData.data[data.id].inputs.video = name;
+                                if (data.class_type === 'ImagesPrompt_') {
+                                    // 
+                                    let base64 = await parseImageToBase64(url);
+                                    uploadContainer.querySelector('.images_prompt_main').src = base64
+                                    window._appData.data[data.id].inputs.image_base64 = base64;
                                 } else {
-                                    window._appData.data[data.id].inputs.image = name;
+                                    if (isVideoUpload) {
+                                        imageElement.srcObject = null;
+                                    }
+                                    // 在这里可以对 Blob 对象进行进一步处理
+                                    imageElement.src = url;
+
+
+                                    if (isVideoUpload) {
+                                        window._appData.data[data.id].inputs.video = name;
+                                    } else {
+                                        window._appData.data[data.id].inputs.image = name;
+                                    }
+
                                 }
 
+
+                                window._appData.data[data.id].hashId = hashId;
+
+                                console.log("上传的文件：", url, data.id, name);
                             }
 
 
-                            window._appData.data[data.id].hashId = hashId;
 
-                            console.log("上传的文件：", url, data.id, name);
                         };
 
                         // 开始读取文件
@@ -1441,7 +1623,7 @@
                         textInput.value = data.inputs.prompt;
                     } else {
                         textInput.value = data.inputs.text;
-                    }
+                    };
 
                     // uploadImageInput.type = "text";
                     let json = localStorage.getItem(`t_${data.id}`)
@@ -1457,7 +1639,6 @@
                             window._appData.data[data.id].inputs.text = textInput.value;
                         }
 
-
                     } catch (error) {
 
                     }
@@ -1465,6 +1646,27 @@
                     uploadContainer.appendChild(textInput);
                     // autoResize(textInput);
 
+
+                    //动态提示功能
+                    const dynamicPromptsBtn = document.createElement('button');
+                    dynamicPromptsBtn.className = "dynamic_prompt"
+                    dynamicPromptsBtn.innerText = 'dynamic';
+                    dynamicPromptsBtn.style.width = '88px'
+                    uploadContainer.appendChild(dynamicPromptsBtn);
+                    dynamicPromptsBtn.addEventListener('click', e => {
+                        e.preventDefault();
+                        e.stopPropagation();
+                        let prompt = dynamicPrompts(textInput.value)
+                        textInput.setAttribute('title', prompt)
+                        dynamicPromptsBtn.setAttribute('title', prompt)
+                        if (data.class_type == "PromptSimplification") {
+                            window._appData.data[data.id].inputs.prompt = prompt;
+                        } else {
+                            window._appData.data[data.id].inputs.text = prompt;
+                        }
+                    })
+
+
                     function autoResize(textarea) {
                         textarea.style.height = 'auto';
                         textarea.style.height = textarea.scrollHeight + 'px';
@@ -1836,7 +2038,7 @@
                         if (!opt.imgurl.match('data:image')) {
                             opt.imgurl = await parseImageToBase64(opt.imgurl)
                         }
-                        callback(opt.imgurl);
+                        callback(opt.imgurl, opt.keyword);
                     }
                 })
             }
@@ -1907,6 +2109,7 @@
 
         function createUI(data, share = true) {
             // appData.input, appData.output, appData.seed, share, appData.link
+            if (!data) return
             const { input: inputData, output: outputData, data: workflow, seed, seedTitle, link, name } = data;
 
             let mainDiv = document.createElement('div');
@@ -2199,6 +2402,51 @@
 
                         if (val && type == "text" && output.querySelector(`#output_${id}`)) output.querySelector(`#output_${id}`).innerText = val;
 
+                        // 3d meshes
+                        if (val && type == 'meshes' && output.querySelector(`#output_${id}`)) {
+
+                            let threeD = output.querySelector('.threeD')
+                            //判断默认的图片，需要去掉后创建model-viewer
+                            let imgDf = output.querySelector(`#output_${id} img`);
+                            if (!threeD) {
+                                if (imgDf) imgDf.parentElement.remove();
+                                threeD = document.createElement('div');
+                                threeD.className = 'threeD'
+                                threeD.id = `output_${id}`
+                                output.querySelector('.output_card').appendChild(threeD)
+                                // output.insertBefore(threeD, output.firstChild);
+                            };
+
+
+                            for (const meshUrl of val) {
+                                const modelViewer = document.createElement('div');
+                                modelViewer.style = `width:300px;margin:4px;height:300px;display:block`
+                                modelViewer.innerHTML = `<model-viewer  src="${meshUrl}" 
+                                    min-field-of-view="0deg" max-field-of-view="180deg"
+                                    shadow-intensity="1" 
+                                    camera-controls 
+                                    touch-action="pan-y"
+                                    style="width:300px;height:300px;"
+                                    > 
+                                    <div class="controls">
+                                        <button class="export">Save As</button>
+                                    </div>
+                                </model-viewer>`
+
+                                const btn = modelViewer.querySelector('.export');
+                                btn.addEventListener('click', async e => {
+                                    e.preventDefault();
+                                    const glTF = await (modelViewer.querySelector('model-viewer')).exportScene()
+                                    const file = new File([glTF], 'mixlab.glb')
+                                    const link = document.createElement('a')
+                                    link.download = file.name
+                                    link.href = URL.createObjectURL(file)
+                                    link.click()
+                                })
+                                threeD.appendChild(modelViewer)
+                            }
+
+                        }
                     }
                 },
                 submitButton: {
@@ -2314,6 +2562,9 @@
             const _images = detail?.output?._images;
             const prompts = detail?.output?.prompts;
 
+            // 3d模型
+            const meshes = detail?.output?.mesh;
+
             if (images) {
                 // if (!images) return;
 
@@ -2323,6 +2574,14 @@
                     return `${url}/view?filename=${encodeURIComponent(img.filename)}&type=${img.type}&subfolder=${encodeURIComponent(img.subfolder)}&t=${+new Date()}`;
                 }), detail.node, 'images');
 
+            } else if (meshes) {
+                //多个
+                let url = get_url();
+
+                show(Array.from(meshes, mesh => {
+                    return `${url}/view?filename=${encodeURIComponent(mesh.filename)}&type=${mesh.type}&subfolder=${encodeURIComponent(mesh.subfolder)}&t=${+new Date()}`;
+                }), detail.node, 'meshes');
+
             } else if (_images && prompts) {
                 let url = get_url();
 
@@ -2556,6 +2815,9 @@
         // 创建app的选择菜单
         function createAppList(apps = [], innerApp = false) {
             window.prompt_ids = {};
+            if (document.body.querySelector('.apps')) {
+                document.body.querySelector('.apps').remove()
+            }
             let details = document.createElement('details');
             details.className = 'apps';
 
@@ -2756,20 +3018,21 @@
                 card.addEventListener('click', async e => {
                     e.preventDefault();
                     // console.log(c)
+
+                    const { category, filename } = c.appInfo;
+                    window._appData = (await get_my_app(category, filename))[0];
+
+                    await createApp(window._appData);
+
+                    executed(c.data, window._show);
+
                     try {
                         document.body.querySelector('#app_container').setAttribute('open', true)
                         document.body.querySelector('#app_input_pannel').removeAttribute('open')
                         document.body.querySelector('.apps').removeAttribute('open')
                     } catch (error) {
-
+                        console.log(error)
                     }
-
-                    const { category, filename } = c.appInfo;
-                    window._appData = (await get_my_app(category, filename))[0];
-
-                    createApp(window._appData);
-
-                    executed(c.data, window._show);
                 })
             }
 

web/javascript/app_mixlab.js

@@ -2,6 +2,24 @@ import { app } from '../../../scripts/app.js'
 import { $el } from '../../../scripts/ui.js'
 import { api } from '../../../scripts/api.js'
 
+//本机安装的插件节点全集
+window._nodesAll = null
+
+//获取当前系统的插件，节点清单
+function getObjectInfo () {
+  return new Promise(async (resolve, reject) => {
+    let url = getUrl()
+
+    try {
+      const response = await fetch(`${url}/object_info`)
+      const data = await response.json()
+      resolve(data)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+
 const base64Df =
   'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
 
@@ -197,7 +215,8 @@ async function extractInputAndOutputData (
       if (
         node.type === 'KSampler' ||
         node.type == 'SamplerCustom' ||
-        node.type === 'ChinesePrompt_Mix'
+        node.type === 'ChinesePrompt_Mix' ||
+        node.type === 'Seed_'
       ) {
         // seed 的类型收集
         try {
@@ -266,7 +285,10 @@ function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
 }
 
 async function save (json, download = false, showInfo = true) {
-  console.log('####SAVE', json[0])
+  let nodesAll = window._nodesAll || (await getObjectInfo())
+
+  console.log('####SAVE', nodesAll, json[0])
+
   const name = json[0],
     version = json[5],
     share_prefix = json[6], //用于分享的功能扩展
@@ -287,6 +309,13 @@ async function save (json, download = false, showInfo = true) {
   try {
     let data = await app.graphToPrompt()
 
+    //从output数据里把工作流的节点，插件数据统计出来
+    data.nodesMap = {}
+    for (const id in data.output) {
+      data.nodesMap[data.output[id].class_type] =
+        nodesAll[data.output[id].class_type]
+    }
+
     let { input, output, seed, seedTitle } = await extractInputAndOutputData(
       data,
       inputIds,
@@ -349,11 +378,11 @@ async function save (json, download = false, showInfo = true) {
 
 function getInputsAndOutputs () {
   const inputs =
-      `LoadImage ImagesPrompt_ VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+      `LoadImage LoadImagesToBatch ImagesPrompt_ VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
         ' '
       ),
     outputs =
-      `PreviewImage,SaveImage,ShowTextForGPT,VHS_VideoCombine,Image Save,SaveImageAndMetadata_`.split(
+      `SaveTripoSRMesh,PreviewImage,SaveImage,TransparentImage,ShowTextForGPT,VHS_VideoCombine,VideoCombine_Adv,Image Save,SaveImageAndMetadata_,ClipInterrogator`.split(
         ','
       )
 
@@ -378,6 +407,11 @@ function getInputsAndOutputs () {
 
 app.registerExtension({
   name: 'Mixlab.utils.AppInfo',
+  init () {
+    if (!window._nodesAll) {
+      getObjectInfo().then(r => (window._nodesAll = r))
+    }
+  },
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == 'AppInfo') {
       const orig_nodeCreated = nodeType.prototype.onNodeCreated

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 = 'v0.19.0'
+const version = 'v0.22.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/gpt_mixlab.js

@@ -61,7 +61,7 @@ app.registerExtension({
   async getCustomWidgets (app) {
     return {
       KEY (node, inputName, inputData, app) {
-        console.log('##inputData', inputData)
+        // console.log('##inputData', inputData)
         const widget = {
           type: inputData[0], // the type, CHEESE
           name: inputName, // the name, slice

web/javascript/image_mixlab.js

@@ -1,7 +1,39 @@
 import { app } from '../../../scripts/app.js'
 import { api } from '../../../scripts/api.js'
-import { ComfyWidgets } from '../../../scripts/widgets.js'
+// import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
+import { applyTextReplacements } from '../../../scripts/utils.js'
+
+function loadImageToCanvas (base64Image) {
+  var img = new Image()
+  var canvas = document.createElement('canvas')
+  var ctx = canvas.getContext('2d')
+  return new Promise((res, rej) => {
+    img.onload = function () {
+      // 等比例缩放图片
+      var width = img.width
+      var height = img.height
+      var max_width = 1024
+      if (width > max_width) {
+        height *= max_width / width
+        width = max_width
+      }
+
+      // 设置canvas尺寸
+      canvas.width = width
+      canvas.height = height
+
+      // 在canvas上绘制图片
+      ctx.drawImage(img, 0, 0, width, height)
+
+      // 将canvas转换为base64图片数据
+      var canvasData = canvas.toDataURL()
+      res(canvasData) //  canvas转换后的base64图片数据
+    }
+
+    img.src = base64Image
+  })
+}
 
 async function uploadImage (blob, fileType = '.svg', filename) {
   // const blob = await (await fetch(src)).blob();
@@ -618,9 +650,194 @@ app.registerExtension({
 
         if (json && json[0]) {
           uploadWidget.select.style.display = 'block'
-          createSelect(img, uploadWidget.select, json, prompt,text)
+          createSelect(img, uploadWidget.select, json, prompt, text)
         }
       } catch (error) {}
     }
   }
 })
+
+const createInputImageForBatch = (base64, widget) => {
+  let im = new Image()
+  im.src = base64
+  im.style = `width: 88px;`
+
+  im.addEventListener('click', e => {
+    let newValue = []
+    let items = widget.value?.base64 || []
+    for (const v of items) {
+      if (v != base64) newValue.push(v)
+    }
+    widget.value.base64 = newValue
+    im.remove()
+  })
+
+  return im
+}
+
+app.registerExtension({
+  name: 'Mixlab.Comfy.LoadImagesToBatch',
+  async getCustomWidgets (app) {
+    return {
+      IMAGEBASE64 (node, inputName, inputData, app) {
+        // console.log('##node', node)
+        const widget = {
+          value: {
+            base64: []
+          }, // 不能[x,x,x]
+          type: inputData[0], // the type
+          name: inputName, // the name, slice
+          size: [128, 32], // a default size
+          draw (ctx, node, width, y) {},
+          computeSize (...args) {
+            return [128, 32] // a method to compute the current size of the widget
+          }
+          // serializeValue (nodeId, widgetIndex) {
+          //   return widget.value
+          // },
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'LoadImagesToBatch') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        let imagesWidget = this.widgets.filter(w => w.name == 'images')[0]
+
+        const widget = {
+          type: 'div',
+          name: 'image_base64',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 44, node.size[1])
+            )
+          },
+          serialize: false
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        let imagePreview = document.createElement('div')
+        let imagesDiv = document.createElement('div') //显示图片
+        imagesDiv.className = 'images_preview'
+        imagesDiv.style = `width: calc(100% - 14px);
+        display: flex;
+        flex-wrap: wrap;
+        padding: 7px;    justify-content: space-between;
+        align-items: center;`
+
+        let inputImage = document.createElement('input')
+        inputImage.type = 'file'
+        inputImage.style.display = 'none'
+        inputImage.addEventListener('change', e => {
+          e.preventDefault()
+          const file = e.target.files[0]
+          const reader = new FileReader()
+          reader.onload = async event => {
+            let base64 = event.target.result
+            //压缩图片，控制1024以内
+            base64 = await loadImageToCanvas(base64)
+            // console.log(base64)
+            if (!imagesWidget.value) imagesWidget.value = { base64: [] }
+            imagesWidget.value.base64.push(base64)
+            let im = createInputImageForBatch(base64, imagesWidget)
+            imagesDiv.appendChild(im)
+          }
+          reader.readAsDataURL(file)
+        })
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Upload Image'
+
+        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;
+       `
+
+        btn.addEventListener('click', e => {
+          e.preventDefault()
+          inputImage.click()
+        })
+
+        widget.div.appendChild(imagePreview)
+        imagePreview.appendChild(imagesDiv)
+        imagePreview.appendChild(btn)
+        imagePreview.appendChild(inputImage)
+
+        this.addCustomWidget(widget)
+
+        // document.addEventListener('wheel', handleMouseWheel)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          inputImage.remove()
+          widget.div.remove()
+          try {
+            // document.removeEventListener('wheel', handleMouseWheel)
+          } catch (error) {
+            console.log(error)
+          }
+
+          return onRemoved?.()
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+
+    if (nodeData.name === 'SaveImageAndMetadata_') {
+      const onNodeCreated = nodeType.prototype.onNodeCreated
+      // /web/extensions/core/saveImageExtraOutput.js
+      nodeType.prototype.onNodeCreated = function () {
+        const r = onNodeCreated
+          ? onNodeCreated.apply(this, arguments)
+          : undefined
+        const widget = this.widgets.find(w => w.name === 'filename_prefix')
+        widget.serializeValue = () => {
+          return applyTextReplacements(app, widget.value)
+        }
+
+        return r
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log('##onExecuted', this, message)
+        //TODO 是否 保存base64
+        if (message.base64) {
+          if (Array.isArray(message.base64)) {
+          }
+        }
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'LoadImagesToBatch') {
+      // await sleep(0)
+      let imagesWidget = node.widgets.filter(w => w.name === 'images')[0]
+      let imagePreview = node.widgets.filter(w => w.name == 'image_base64')[0]
+
+      let pre = imagePreview.div.querySelector('.images_preview')
+      for (const d of imagesWidget.value?.base64 || []) {
+        let im = createInputImageForBatch(d, imagesWidget)
+        pre.appendChild(im)
+      }
+    }
+  }
+})

web/javascript/smart_connect.js

@@ -46,6 +46,18 @@ const smart_connect_config_input = [
     node_widget_name: 'image',
     inputNodeName: 'LoadImage',
     inputNode_output_name: 'IMAGE'
+  },
+  {
+    node_type: 'TripoSRSampler_',
+    node_widget_name: 'image',
+    inputNodeName: 'LoadImagesToBatch',
+    inputNode_output_name: 'IMAGE'
+  },
+  {
+    node_type: 'TripoSRSampler_',
+    node_widget_name: 'mask',
+    inputNodeName: 'RembgNode_Mix',
+    inputNode_output_name: 'masks'
   }
 ]
 
@@ -74,6 +86,18 @@ const smart_connect_config_output = [
     outputNodeName: 'SaveImage',
     outputNode_input_name: 'images'
   },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'AppInfo',
+    outputNode_input_name: 'IMAGE'
+  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'SaveImageAndMetadata_',
+    outputNode_input_name: 'images'
+  },
   {
     node_type: 'Moondream',
     node_output_name: 'STRING',
@@ -181,7 +205,10 @@ export function smart_init () {
       ]
       let node_slotType = config[0]
       // 如果input没有，则创建
-      if (!node.inputs?.filter(inp => inp.name === widget.name)[0]||!node.inputs)
+      if (
+        !node.inputs?.filter(inp => inp.name === widget.name)[0] ||
+        !node.inputs
+      )
         convertToInput(node, widget, config)
       input_node.connectByType(inputNode_slot, node, node_slotType)
     }

web/javascript/ui_mixlab.js

@@ -299,15 +299,16 @@ async function get_my_app (filename = null, category = '') {
     data = []
 
     for (const res of result.data) {
-      let { app, workflow } = res.data
-      if (app.filename)
-        data.push({
+      let { app, workflow } = res.data;
+      if (app?.filename) data.push({
           ...app,
           data: workflow,
           date: res.date
         })
     }
-  } catch (error) {}
+  } catch (error) {
+    console.log(error)
+  }
   return data
 }
 
@@ -1137,6 +1138,57 @@ app.registerExtension({
         (this.canvas.height * 0.5) / (this.ds.scale * dpr) // 考虑设备像素比
       this.setDirty(true, true)
     }
+
+    // 支持app模式的json
+    const loadAppJson = async data => {
+      let workflow
+      try {
+        let w = JSON.parse(data)
+        if (w.app && w.output) workflow = w.workflow
+      } catch (err) {}
+
+      if (workflow && workflow.version && workflow.nodes && workflow.extra) {
+        await app.loadGraphData(workflow)
+      }
+    }
+
+    if (!window._mixlab_app_paste_listener) {
+      window._mixlab_app_paste_listener = true
+      //粘贴json的事件
+      document.addEventListener('paste', async e => {
+        // ctrl+shift+v is used to paste nodes with connections
+        // this is handled by litegraph
+        if (this.shiftDown) return
+
+        let data = e.clipboardData || window.clipboardData
+
+        // No image found. Look for node data
+        data = data.getData('text/plain')
+
+        loadAppJson(data)
+      })
+
+      // 把json往里 拖
+      document.addEventListener('drop', async event => {
+        event.preventDefault()
+        event.stopPropagation()
+
+       
+
+        // Dragging from Chrome->Firefox there is a file but its a bmp, so ignore that
+        if (
+          event.dataTransfer.files.length &&
+          event.dataTransfer.files[0].type == 'application/json'
+        ) {
+          const reader = new FileReader()
+          reader.onload = async () => {
+       
+            loadAppJson(reader.result)
+          }
+          reader.readAsText(event.dataTransfer.files[0])
+        }
+      })
+    }
   },
   setup () {
     setTimeout(async () => {
@@ -1146,6 +1198,8 @@ app.registerExtension({
       const apps = await get_my_app()
       if (!apps) return
 
+      console.log('apps',apps)
+
       let apps_map = { 0: [] }
 
       for (const app of apps) {
@@ -1159,7 +1213,7 @@ app.registerExtension({
 
       let apps_opts = []
       for (const category in apps_map) {
-        console.log('category', typeof category)
+        // console.log('category', typeof category)
         if (category === '0') {
           apps_opts.push(
             ...Array.from(apps_map[category], a => {
@@ -1511,14 +1565,16 @@ app.registerExtension({
                 document.body.appendChild(div)
             }
           },
-          {
-            content: 'Workflow App ♾️Mixlab',
-            has_submenu: true,
-            disabled: false,
-            submenu: {
-              options: apps_opts
-            }
-          }
+          apps_opts.length > 0
+            ? {
+                content: 'Workflow App ♾️Mixlab',
+                has_submenu: true,
+                disabled: false,
+                submenu: {
+                  options: apps_opts
+                }
+              }
+            : null
         )
 
         return options
@@ -1587,3 +1643,41 @@ app.registerExtension({
     } catch (error) {}
   }
 })
+
+//获取当前显存
+function fetchSystemStats () {
+  return new Promise(async (resolve, reject) => {
+    try {
+      const response = await fetch('/system_stats')
+      const data = await response.json()
+      resolve(data)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+//清理显存
+function postFreeData () {
+  return new Promise(async (resolve, reject) => {
+    try {
+      const postData = {
+        unload_models: true,
+        free_memory: true
+      }
+      const response = await fetch('/free', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json'
+        },
+        body: JSON.stringify(postData)
+      })
+      if (response.ok) {
+        resolve()
+      } else {
+        reject(new Error('Request failed'))
+      }
+    } catch (error) {
+      reject(error)
+    }
+  })
+}

web/javascript/video_mixlab.js

@@ -0,0 +1,515 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+
+import { $el } from '../../../scripts/ui.js'
+
+// The code is based on ComfyUI-VideoHelperSuite modification.
+
+
+
+function injectCSS (css) {
+  // 检查页面中是否已经存在具有相同内容的style标签
+  const existingStyle = document.querySelector('style')
+  if (existingStyle && existingStyle.textContent === css) {
+    return // 如果已经存在相同的样式，则不进行注入
+  }
+
+  // 创建一个新的style标签，并将CSS内容注入其中
+  const style = document.createElement('style')
+  style.textContent = css
+
+  // 将style标签插入到页面的head元素中
+  const head = document.querySelector('head')
+  head.appendChild(style)
+}
+
+injectCSS(`
+.hidden{
+  display:none !important
+}`)
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+function videoUpload (node, inputName, inputData, app) {
+  const imageWidget = node.widgets.find(w => w.name === 'video')
+  let uploadWidget
+
+  const widget = {
+    type: 'div',
+    name: 'upload-preview',
+    draw (ctx, node, widget_width, y, widget_height) {
+      Object.assign(
+        this.div.style,
+        get_position_style(ctx, widget_width, 220, node.size[1]),
+        {
+          outline: '1px solid'
+        }
+      )
+    }
+  }
+
+  widget.div = $el('div', {})
+  widget.div.style.width = `120px`
+  document.body.appendChild(widget.div)
+  node.addCustomWidget(widget)
+  // console.log('#imageWidget', imageWidget)
+  const displayDiv = document.createElement('video')
+  displayDiv.controls = true
+  // displayDiv.style=`width:200px;height:200px`
+  imageWidget.callback = () => {
+    displayDiv.src = `/view?filename=${
+      imageWidget.value
+    }&type=input&subfolder=${''}&rand=${Math.random()}`
+
+    // displayDiv.onloadedmetadata = function () {
+    //   var frameCount = displayDiv.duration * displayDiv.webkitDecodedFrameCount
+    //   console.log('视频帧数：' + frameCount)
+    //   node.widgets.filter(w => w.name == 'video_segment_frames')[0].value =
+    //     frameCount
+    // }
+  }
+
+  if (imageWidget.value) {
+    // console.log(imageWidget.value)
+    displayDiv.src = `/view?filename=${
+      imageWidget.value
+    }&type=input&subfolder=${''}&rand=${Math.random()}`
+  }
+
+  widget.div.appendChild(displayDiv)
+
+  const onRemoved = node.onRemoved
+  node.onRemoved = () => {
+    widget.div.remove()
+    return onRemoved?.()
+  }
+
+  var default_value = imageWidget.value
+  Object.defineProperty(imageWidget, 'value', {
+    set: function (value) {
+      this._real_value = value
+    },
+
+    get: function () {
+      let value = ''
+      if (this._real_value) {
+        value = this._real_value
+      } else {
+        return default_value
+      }
+
+      if (value.filename) {
+        let real_value = value
+        value = ''
+        if (real_value.subfolder) {
+          value = real_value.subfolder + '/'
+        }
+
+        value += real_value.filename
+
+        if (real_value.type && real_value.type !== 'input')
+          value += ` [${real_value.type}]`
+      }
+      return value
+    }
+  })
+  async function uploadFile (file, updateNode, pasted = false) {
+    try {
+      // Wrap file in formdata so it includes filename
+      const body = new FormData()
+      body.append('image', file)
+      if (pasted) body.append('subfolder', 'pasted')
+      const resp = await api.fetchApi('/upload/image', {
+        method: 'POST',
+        body
+      })
+
+      if (resp.status === 200) {
+        const data = await resp.json()
+        // Add the file to the dropdown list and update the widget value
+        let path = data.name
+        if (data.subfolder) path = data.subfolder + '/' + path
+
+        if (!imageWidget.options.values.includes(path)) {
+          imageWidget.options.values.push(path)
+        }
+
+        if (updateNode) {
+          imageWidget.value = path
+        }
+
+        return `/view?filename=${path}&type=input&subfolder=${
+          pasted ? 'pasted' : ''
+        }&rand=${Math.random()}`
+      } else {
+        alert(resp.status + ' - ' + resp.statusText)
+      }
+    } catch (error) {
+      alert(error)
+    }
+  }
+
+  const fileInput = document.createElement('input')
+  Object.assign(fileInput, {
+    type: 'file',
+    accept: 'video/*,.mkv,video/webm,video/mp4,video/x-matroska,image/gif',
+    style: 'display: none',
+    onchange: async () => {
+      if (fileInput.files.length) {
+        let file = fileInput.files[0]
+
+        const url = await uploadFile(file, true)
+
+        // console.log('fileInput', file)
+        var reader = new FileReader()
+        reader.onload = function () {
+          displayDiv.src = url
+          displayDiv.onloadedmetadata = function () {
+            // var frameCount =
+            //   displayDiv.duration * displayDiv.webkitDecodedFrameCount
+            // console.log('视频帧数：' + frameCount)
+            // node.widgets.filter(
+            //   w => w.name == 'video_segment_frames'
+            // )[0].value = frameCount
+          }
+        }
+        reader.readAsDataURL(file)
+      }
+    }
+  })
+  document.body.append(fileInput)
+
+  // Create the button widget for selecting the files
+  uploadWidget = node.addWidget('button', 'upload file', 'video', () => {
+    fileInput.click()
+  })
+  uploadWidget.serialize = false
+  return { widget: uploadWidget }
+}
+ComfyWidgets.VIDEOUPLOAD_ = videoUpload
+
+app.registerExtension({
+  name: 'Mixlab.Video.LoadVideoAndSegment_',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData?.name == 'LoadVideoAndSegment_') {
+      nodeData.input.required.upload = ['VIDEOUPLOAD_']
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'LoadVideoAndSegment_') {
+      const imageWidget = node.widgets.find(w => w.name === 'video')
+      const uploadPreview = node.widgets.find(w => w.name === 'upload-preview')
+      if (imageWidget.value) {
+        // console.log(imageWidget.value)
+        uploadPreview.div.querySelector('video').src = `/view?filename=${
+          imageWidget.value
+        }&type=input&subfolder=${''}&rand=${Math.random()}`
+      }
+    }
+  }
+})
+
+
+function offsetDOMWidget(
+  widget,
+  ctx,
+  node,
+  widgetWidth,
+  widgetY,
+  height
+) {
+  const margin = 10
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(0, widgetY + margin)
+
+  const scale = new DOMMatrix().scaleSelf(transform.a, transform.d)
+  Object.assign(widget.inputEl.style, {
+    transformOrigin: '0 0',
+    transform: scale,
+    left: `${transform.e}px`,
+    top: `${transform.d + transform.f}px`,
+    width: `${widgetWidth}px`,
+    height: `${(height || widget.parent?.inputHeight || 32) - margin}px`,
+    position: 'absolute',
+    background: !node.color ? '' : node.color,
+    color: !node.color ? '' : 'white',
+    zIndex: 5, //app.graph._nodes.indexOf(node),
+  })
+}
+
+export const hasWidgets = (node) => {
+  if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
+    return false
+  }
+  return true
+}
+
+export const cleanupNode = (node) => {
+  if (!hasWidgets(node)) {
+    return
+  }
+
+  for (const w of node.widgets) {
+    if (w.canvas) {
+      w.canvas.remove()
+    }
+    if (w.inputEl) {
+      w.inputEl.remove()
+    }
+    // calls the widget remove callback
+    w.onRemoved?.()
+  }
+}
+
+const CreatePreviewElement = (name, val, format) => {
+  const [type] = format.split('/')  
+  const w = {
+      name,
+      type,
+      value: val,
+      draw: function (ctx, node, widgetWidth, widgetY, height) {
+        const [cw, ch] = this.computeSize(widgetWidth)
+        offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, ch)
+      },
+      computeSize: function (_) {
+        const ratio = this.inputRatio || 1
+        const width = Math.max(220, this.parent.size[0])
+        return [width, (width / ratio + 10)]
+      },
+      onRemoved: function () {
+        if (this.inputEl) {
+          this.inputEl.remove()
+        }
+      },
+    }
+
+    w.inputEl = document.createElement(type === 'video' ? 'video' : 'img')
+    w.inputEl.src = w.value
+    if (type === 'video') {
+      w.inputEl.setAttribute('type', 'video/webm');
+      w.inputEl.autoplay = true
+      w.inputEl.loop = true
+      w.inputEl.controls = false;
+    }
+    w.inputEl.onload = function () {
+      w.inputRatio = w.inputEl.naturalWidth / w.inputEl.naturalHeight
+    }
+    document.body.appendChild(w.inputEl)
+    return w
+  }
+
+
+app.registerExtension({
+  name: 'Mixlab.Video.ImageListReplace',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData?.name == 'ImageListReplace_') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        const widget = {
+          type: 'div',
+          name: 'preview',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 188, node.size[1]),
+              {
+                outline: '1px solid',
+                display: 'flex',
+                flexWrap: 'wrap',
+                flexDirection: 'row',
+                justifyContent: 'flex-start'
+              }
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+        widget.div.style.width = `120px`
+        widget.div.className = 'hidden'
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+        // console.log('#ImageListReplace', widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+
+        // let _image_replace = message._image_replace[0]
+        // _image_replace = `/view?filename=${_image_replace.filename}&type=${
+        //   _image_replace.type
+        // }&subfolder=${_image_replace.subfolder}&rand=${Math.random()}`
+
+        let preview = this.widgets.filter(w => w.name == 'preview')[0]
+
+        if (message._images.length > 0) {
+          preview.div.className = ''
+          // console.log('#ImageListReplace', preview.div)
+        }
+
+        preview.div.innerHTML = ''
+        for (const img_ of message._images) {
+          let img = new Image()
+          img.style = `width: 100px;
+          margin: 4px;`
+          img.src = `/view?filename=${img_.filename}&type=${
+            img_.type
+          }&subfolder=${img_.subfolder}&rand=${Math.random()}`
+          preview.div.appendChild(img)
+        }
+
+        let start_index = this.widgets.filter(w => w.name == 'start_index')[0]
+        let end_index = this.widgets.filter(w => w.name == 'end_index')[0]
+        let invert = this.widgets.filter(w => w.name == 'invert')[0]
+        let _sc = start_index.callback.bind(start_index)
+        let _ec = end_index.callback.bind(end_index)
+
+        const selectImages = () => {
+          // console.log(v)
+          let s = start_index.value,
+            e = end_index.value
+          let imgs = preview.div.querySelectorAll('img')
+          for (let index = 0; index < imgs.length; index++) {
+            if (invert.value) {
+              imgs[index].style.outline =
+                index >= s && index <= e ? 'none' : '4px solid #cbd3fe'
+            } else {
+              imgs[index].style.outline =
+                index >= s && index <= e ? '4px solid #cbd3fe' : 'none'
+            }
+          }
+        }
+
+        selectImages()
+
+        start_index.callback = v => {
+          let s = v,
+            e = end_index.value
+          let imgs = preview.div.querySelectorAll('img')
+          for (let index = 0; index < imgs.length; index++) {
+            if (invert.value) {
+              imgs[index].style.outline =
+                index >= s && index <= e ? 'none' : '4px solid #cbd3fe'
+            } else {
+              imgs[index].style.outline =
+                index >= s && index <= e ? '4px solid #cbd3fe' : 'none'
+            }
+          }
+
+          _sc(v)
+        }
+
+        end_index.callback = v => {
+          let s = start_index.value,
+            e = v
+          let imgs = preview.div.querySelectorAll('img')
+          for (let index = 0; index < imgs.length; index++) {
+            if (invert.value) {
+              imgs[index].style.outline =
+                index >= s && index <= e ? 'none' : '4px solid #cbd3fe'
+            } else {
+              imgs[index].style.outline =
+                index >= s && index <= e ? '4px solid #cbd3fe' : 'none'
+            }
+          }
+
+          _ec(v)
+        }
+
+        invert.callback = v => {
+          selectImages()
+        }
+
+        try {
+        } catch (error) {}
+      }
+    }
+
+    if (nodeData?.name == 'VideoCombine_Adv') {
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        const prefix = 'vhs_gif_preview_'
+        const r = onExecuted ? onExecuted.apply(this, message) : undefined
+
+        if (this.widgets) {
+          const pos = this.widgets.findIndex(w => w.name === `${prefix}_0`)
+          if (pos !== -1) {
+            for (let i = pos; i < this.widgets.length; i++) {
+              this.widgets[i].onRemoved?.()
+            }
+            this.widgets.length = pos
+          }
+          if (message?.gifs) {
+            message.gifs.forEach((params, i) => {
+              const previewUrl = api.apiURL(
+                '/view?' + new URLSearchParams(params).toString()
+              )
+              const w = this.addCustomWidget(
+                CreatePreviewElement(
+                  `${prefix}_${i}`,
+                  previewUrl,
+                  params.format || 'image/gif'
+                )
+              )
+              w.parent = this
+            })
+          }
+          const onRemoved = this.onRemoved
+          this.onRemoved = () => {
+            cleanupNode(this)
+            return onRemoved?.()
+          }
+        }
+        this.setSize([
+          this.size[0],
+          this.computeSize([this.size[0], this.size[1]])[1]
+        ])
+        return r
+      }
+    }
+  }
+})

web/javascript/videoupload.js

@@ -1,126 +0,0 @@
-import { app } from '../../../scripts/app.js'
-import { api } from '../../../scripts/api.js'
-import { ComfyWidgets } from '../../../scripts/widgets.js'
-
-function videoUpload (node, inputName, inputData, app) {
-  const imageWidget = node.widgets.find(w => w.name === 'video')
-  let uploadWidget
-
-  const displayDiv = document.createElement('video')
-  console.log('imageWidget', node)
-
-  var default_value = imageWidget.value
-  Object.defineProperty(imageWidget, 'value', {
-    set: function (value) {
-      this._real_value = value
-    },
-
-    get: function () {
-      let value = ''
-      if (this._real_value) {
-        value = this._real_value
-      } else {
-        return default_value
-      }
-
-      if (value.filename) {
-        let real_value = value
-        value = ''
-        if (real_value.subfolder) {
-          value = real_value.subfolder + '/'
-        }
-
-        value += real_value.filename
-
-        if (real_value.type && real_value.type !== 'input')
-          value += ` [${real_value.type}]`
-      }
-      return value
-    }
-  })
-  async function uploadFile (file, updateNode, pasted = false) {
-    try {
-      // Wrap file in formdata so it includes filename
-      const body = new FormData()
-      body.append('image', file)
-      if (pasted) body.append('subfolder', 'pasted')
-      const resp = await api.fetchApi('/upload/image', {
-        method: 'POST',
-        body
-      })
-
-      if (resp.status === 200) {
-        const data = await resp.json()
-        // Add the file to the dropdown list and update the widget value
-        let path = data.name
-        if (data.subfolder) path = data.subfolder + '/' + path
-
-        if (!imageWidget.options.values.includes(path)) {
-          imageWidget.options.values.push(path)
-        }
-
-        if (updateNode) {
-          imageWidget.value = path
-        }
-      } else {
-        alert(resp.status + ' - ' + resp.statusText)
-      }
-    } catch (error) {
-      alert(error)
-    }
-  }
-
-  const fileInput = document.createElement('input')
-  Object.assign(fileInput, {
-    type: 'file',
-    accept: 'video/webm,video/mp4,video/mkv,image/gif',
-    style: 'display: none',
-    onchange: async () => {
-      if (fileInput.files.length) {
-        let file = fileInput.files[0]
-        console.log(file)
-        await uploadFile(file, true)
-      }
-    }
-  })
-  document.body.append(fileInput)
-
-  // Create the button widget for selecting the files
-  uploadWidget = node.addWidget('button', 'upload file', 'video', () => {
-    fileInput.click()
-  })
-  uploadWidget.serialize = false
-  return { widget: uploadWidget }
-}
-ComfyWidgets.VIDEOUPLOAD_ = videoUpload
-
-
-app.registerExtension({
-  name: 'Mixlab.Video.LoadVideoAndSegment_',
-  async beforeRegisterNodeDef (nodeType, nodeData, app) {
-    if (nodeData?.name == 'LoadVideoAndSegment_') {
-      nodeData.input.required.upload = ['VIDEOUPLOAD_'];
-
-    //   const onExecuted = nodeType.prototype.onExecuted
-    //   nodeType.prototype.onExecuted = function (message) {
-    //     onExecuted?.apply(this, arguments)
-    //     console.log(message)
-        
-    //     // 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'),
-    //     //     b => (b.style.background = 'yellow')
-    //     //   )
-    //     // } catch (error) {}
-    //   }
-
-    }
-  }
-})

web/lib/login.css

@@ -1,277 +0,0 @@
-* {
-    transition: all 0.6s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-#app-login {
-    width: 480px;
-    height: 90vh;
-    padding: 6vh;
-    background: white;
-    box-shadow: 0 0 2rem rgba(0, 0, 0, 0.1);
-    z-index: 999;
-    position: fixed;
-    top: 5vh;
-    left: calc(50vw - 240px);
-}
-
-.login-app-view {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    z-index: 999;
-}
-
-.login-background {
-    background-color: #202020e6;
-    position: fixed;
-    width: 100%;
-    height: 100vh;
-    left: 0;
-    top: 0;
-    z-index: 998;
-}
-
-.app-header {
-    padding: 6vh;
-}
-
-.app-header,
-.app-header>* {
-    font-size: 1.2em;
-    margin: 0;
-    font-weight: 300;
-}
-
-.app-header>h1 {
-    font-size: 4.8vh;
-    font-weight: 400;
-    margin-bottom: 4.8vh;
-}
-
-.app-header>h2 {
-    font-size: 3vh;
-}
-
-.app-subheading {
-    color: rgba(0, 0, 0, 0.45);
-}
-
-.app-register {
-    position: absolute;
-    bottom: 0;
-    height: 10vh;
-    line-height: 10vh;
-    padding: 0 6vh;
-    color: rgba(0, 0, 0, 0.45);
-}
-
-.app-register>a {
-    font-weight: 400;
-}
-
-
-#app-login input {
-    font-size: 2.5vh;
-    width: calc(100% - 13vh);
-    height: 7.5vh;
-    margin-bottom: 2vh;
-    background: transparent;
-    position: absolute;
-    top: 0;
-    left: 6.5vh;
-    z-index: 2;
-    border: none;
-    box-shadow: inset 0 -0.5vh rgba(0, 0, 0, 0.1);
-}
-
-#app-login input:focus {
-    outline: none;
-    box-shadow: inset 0 -0.5vh transparent;
-}
-
-#app-login input[type=email] {
-    top: 58%;
-}
-
-#app-login input[type=password] {
-    top: calc(58% + 7.5vh);
-}
-
-#app-login input[type=email]:valid~* .st1 {
-    transition-timing-function: ease-in-out;
-    stroke-dasharray: 50, 153;
-    stroke-dashoffset: 25;
-}
-
-#app-login input[type=password]:focus~* .st0,
-#app-login input[type=password]:valid~* .st0,
-#login_run:focus~* .st0 {
-    stroke-dasharray: 210, 900;
-    stroke-dashoffset: -305;
-}
-
-#app-login input[type=email]:focus~* .st0 {
-    stroke-dasharray: 210, 900;
-    stroke-dashoffset: 0;
-}
-
-#app-login input:not(:valid)~#login_run {
-    /* pointer-events: none; */
-    opacity: 0.6;
-}
-
-#login_run {
-    text-decoration: none;
-    color: #0f9ede;
-    font-size: 1.5em;
-    padding: 0 6vh;
-    position: absolute;
-    bottom: 10vh;
-    font-weight: 400;
-    z-index: 998;
-    cursor: pointer;
-}
-
-#login_run:focus {
-    outline: none;
-}
-
-.login-app-view:nth-child(2) {
-    display: flex;
-    flex-direction: column;
-    pointer-events: none;
-}
-
-.login-app-view:nth-child(2)>.app-header {
-    font-size: 1rem;
-    flex-basis: 25%;
-    display: flex;
-    flex-direction: column;
-    justify-content: space-between;
-    padding: 4vh;
-    padding-bottom: 1rem;
-}
-
-.login-app-view:nth-child(2)>.app-header>h2 {
-    transform: translateY(1rem);
-}
-
-.login-app-view:nth-child(2)>.app-header>h2>em {
-    color: #0f9ede;
-    font-style: normal;
-}
-
-.login-app-view:nth-child(2)>.app-header>h2,
-.login-app-view:nth-child(2) .app-item>*:not(.app-graphic) {
-    transition-duration: 0.9s;
-    opacity: 0;
-}
-
-
-.st0,
-.st1,
-.svg-loader-segment {
-    fill: none;
-    stroke: #0f9ede;
-    stroke-width: 0.5vh;
-    stroke-alignment: inside;
-    opacity: 1;
-    transition: all 0.6s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-.svg-loader {
-    opacity: 0;
-}
-
-.st0 {
-    stroke-dasharray: 0, 900;
-    stroke-dashoffset: 0;
-}
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-.st1 {
-    transition-delay: 0.3s;
-    stroke-dasharray: 50, 153;
-    stroke-dashoffset: -153;
-}
-
-.svg-loader-segment {
-    transition: transform 1.2s cubic-bezier(0.77, 0, 0.175, 1), opacity 0.85s cubic-bezier(0.77, 0, 0.175, 1), stroke 0.85s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-#svg-lines {
-    position: absolute;
-    top: 45%;
-    left: 0;
-    width: 100%;
-    z-index: 0;
-    overflow: visible;
-    transform-origin: center 4vh;
-}
-
-.svg-data {
-    fill: none;
-    stroke-width: 0.5vh;
-}
-
-.svg-data.-temp {
-    stroke: #f4814b;
-    stroke-dasharray: 20, 118;
-}
-
-.svg-data.-cal {
-    stroke: #08b5cf;
-    stroke-dasharray: 20, 113;
-}
-
-.svg-data.-steps-bg {
-    stroke: #e0e1e0;
-    stroke-dasharray: 40, 100;
-    stroke-dashoffset: -60;
-}
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-.svg-data.-steps {
-    stroke: #0f9ede;
-    stroke-dasharray: 20, 73;
-    stroke-dashoffset: -53;
-}
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-.svg-data.-heart {
-    stroke: #9965aa;
-    stroke-dasharray: 50, 200;
-    stroke-dashoffset: -150;
-}
-
-.svg-activity-fill {
-    fill: #c4e4f8;
-}
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-.svg-activity-line {
-    fill: none;
-    stroke: #65bcea;
-    stroke-miterlimit: 10;
-    stroke-width: 0.25vh;
-}
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-.svg-activity-indicator {
-    fill: none;
-    stroke: #d0dff0;
-    stroke-width: 0.25vh;
-    mix-blend-mode: multiply;
-}
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-.svg-activity-fill,
-.svg-activity-line {
-    transform: translateY(10vh);
-    opacity: 0;
-}
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-*,
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-    box-sizing: border-box;
-    position: relative;
-}

web/lib/login.js

@@ -1,67 +0,0 @@
-;(() => {
-  let div = document.createElement('div')
-
-  div.innerHTML = ` 
-    <div id="app-login">
-        <div class="login-app-view">
-            <header class="app-header">
-                <h1>Hi</h1>
-                Welcome back,<br />
-                <span class="app-subheading">
-                    sign in to continue<br />
-    
-                </span>
-            </header>
-            <input class="email" type="email" required pattern=".*\.\w{2,}" placeholder="Email Address" />
-            <input class="password" type="password" required placeholder="Password" />
-            <a class="app-button" id="login_run">登录</a>
-            <!-- <div class="app-register">
-                Don't have an account? <a>Sign Up</a>
-            </div> -->
-            <svg id="svg-lines" version="1.1" xmlns="http://www.w3.org/2000/svg"
-                xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 284.2 152.7"
-                xml:space="preserve">
-                <path class="st0"
-                    d="M37.7,107.3h222.6c12,0,21.8,9.7,21.8,21.7s-9.7,21.8-21.8,21.8c0,0-203.6,0-222.6,0S2.2,138.6,2.2,103.3   c0-52,113.5-101.5,141-101.5c13.5,0,21.8,9.7,21.8,21.8s-9.7,21.7-21.8,21.7s-21.8-9.7-21.8-21.7s9.7-21.8,21.8-21.8" />
-                <path class="st1"
-                    d="M260.2,76.3L250,87.8l-9-9c-6.2-6.2,2-24.7,17.2-24.7c15.2,0,23.9,17.7,23.9,29.7s-11.7,23.5-23.9,23.5h-10.2">
-                </path>
-                <g class="svg-loader" xmlns="http://www.w3.org/2000/svg">
-                    <path class="svg-loader-segment -cal" d="M164.7,23.5c0-12-9.7-21.8-21.8-21.8" />
-                    <path class="svg-loader-segment -heart" d="M143,45.2c12,0,21.8-9.7,21.8-21.7" />
-                    <path class="svg-loader-segment -steps" d="M121.2,23.5c0,12,9.7,21.7,21.8,21.7" />
-                    <path class="svg-loader-segment -temp" d="M143,1.7c-12,0-21.8,9.7-21.8,21.8" />
-                </g>
-            </svg>
-        </div>
-    </div>
-    <div class="login-background"></div>
-     `
-
-  document.body.appendChild(div)
-  let bg = div.querySelector('.login-background')
-  bg.addEventListener('click', e => {
-    div.style.display = 'none'
-  })
-  let login_btn = document.body.querySelector('#login_btn')
-  // login_btn.href="";
-  if (login_btn) {
-    login_btn.innerHTML =
-      '<svg stroke="currentColor" fill="none" stroke-width="0" viewBox="0 0 24 24" height="40px" width="40px" xmlns="http://www.w3.org/2000/svg"><path d="M12 17C14.2091 17 16 15.2091 16 13H8C8 15.2091 9.79086 17 12 17Z" fill="currentColor"></path><path d="M10 10C10 10.5523 9.55228 11 9 11C8.44772 11 8 10.5523 8 10C8 9.44772 8.44772 9 9 9C9.55228 9 10 9.44772 10 10Z" fill="currentColor"></path><path d="M15 11C15.5523 11 16 10.5523 16 10C16 9.44772 15.5523 9 15 9C14.4477 9 14 9.44772 14 10C14 10.5523 14.4477 11 15 11Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12ZM20 12C20 16.4183 16.4183 20 12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12Z" fill="currentColor"></path></svg>LOGIN'
-    login_btn.addEventListener('click', e => {
-      e.preventDefault()
-      div.style.display = 'block'
-    })
-  }
-
-  let login_run = div.querySelector('#login_run')
-  if (login_run) {
-    login_run.addEventListener('click', e => {
-      e.preventDefault()
-      let ps = div.querySelector('.password')
-      let email = div.querySelector('.email')
-      div.style.display = 'none'
-      console.log(ps.value, email.value)
-    })
-  }
-})()