Update README.md

Update checkVersion_mixlab.js
支持正片叠底
2024-04-27 23:43:40 +08:00 · 2024-04-27 23:38:00 +08:00 · 2024-04-25 21:23:40 +08:00 · 2024-04-24 23:10:03 +08:00 · 2024-04-23 12:14:50 +08:00 · 2024-04-22 12:32:48 +08:00
19 changed files with 1304 additions and 282 deletions
@@ -3,13 +3,15 @@
 > [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)

 #### 
+[comfyui-sd-prompt-mixlab](https://github.com/shadowcz007/comfyui-sd-prompt-mixlab)
+
 [comfyui-Image-reward](https://github.com/shadowcz007/comfyui-Image-reward)

 [comfyui-ultralytics-yolo](https://github.com/shadowcz007/comfyui-ultralytics-yolo)

 [comfyui-moondream](https://github.com/shadowcz007/comfyui-moondream)

-[comfyui-CLIPSeg](https://github.com/shadowcz007/comfyui-CLIPSeg)
+<!-- [comfyui-CLIPSeg](https://github.com/shadowcz007/comfyui-CLIPSeg) -->


 ## 🚀🚗🚚🏃 Workflow-to-APP 
@@ -17,7 +19,9 @@
 - 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 +120,23 @@ 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 +144,7 @@ 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 +157,7 @@ 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)
@@ -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
@@ -592,14 +589,14 @@ 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 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.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.Mask import MaskListReplace,MaskListMerge,OutlineMask,FeatheredMask
+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 PreviewMask_,MaskListReplace,MaskListMerge,OutlineMask,FeatheredMask

 from .nodes.Style import ApplyVisualStylePrompting,StyleAlignedReferenceSampler,StyleAlignedBatchAlign,StyleAlignedSampleReferenceLatents

@@ -626,6 +623,7 @@ NODE_CLASS_MAPPINGS = {
    "ResizeImageMixlab":ResizeImage,
    "LoadImagesFromPath":LoadImagesFromPath,
    "LoadImagesFromURL":LoadImagesFromURL,
+    "LoadImagesToBatch":LoadImages_,
    "TextImage":TextImage,
    "EnhanceImage":EnhanceImage,
    "SvgImage":SvgImage,
@@ -686,7 +684,9 @@ NODE_CLASS_MAPPINGS = {
    "ListSplit_":ListSplit,
    "MaskListReplace_":MaskListReplace,
    "ImageListReplace_":ImageListReplace,
-    "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames
+    "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames,
+    "IncrementingListNode_":IncrementingListNode,
+    "PreviewMask_":PreviewMask_
    # "GamePal":GamePal
 }

@@ -701,7 +701,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "SaveImageAndMetadata_":"Save Image Output ♾️MixlabApp",
    "ResizeImageMixlab":"Resize Image ♾️Mixlab",
    "RandomPrompt": "Random Prompt ♾️Mixlab",
-    "PromptImage":"Output Prompt and Image",
+    "PromptImage":"Output Prompt and Image ♾️Mixlab",
    "SplitLongMask":"Splitting a long image into sections",
    "VAELoaderConsistencyDecoder":"Consistency Decoder Loader",
    "VAEDecodeConsistencyDecoder":"Consistency Decoder Decode",
@@ -713,32 +713,35 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
    "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
    "3DImage":"3DImage ♾️Mixlab",
-    "CompositeImages_":"Composite Images",
+    "CompositeImages_":"Composite Images ♾️Mixlab",
    "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",
-    "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",
+    "RembgNode_Mix":"Remove Background ♾️Mixlab",
+    "LoraNames_":"LoraName ♾️Mixlab",
+    "ApplyVisualStylePrompting_":"Apply VisualStyle Prompting ♾️Mixlab",
+    "StyleAlignedReferenceSampler_": "StyleAligned Reference Sampler ♾️Mixlab",
+    "StyleAlignedSampleReferenceLatents_": "StyleAligned Sample Reference Latents ♾️Mixlab",
+    "StyleAlignedBatchAlign_": "StyleAligned Batch Align ♾️Mixlab",
+    "LoadVideoAndSegment_":"Load Video And Segment ♾️Mixlab",
+    "VideoCombine_Adv":"Video Combine ♾️Mixlab",
+    "MaskListMerge_":"MaskList to Mask ♾️Mixlab",
+    "ListSplit_":"Split List ♾️Mixlab",
+    "MaskListReplace_":"MaskList Replace ♾️Mixlab",
+    "ImageListReplace_":"ImageList Replace ♾️Mixlab",
+    "SwitchByIndex":"List Switch By Index ♾️Mixlab",
    "GLIGENTextBoxApply_Advanced":"GLIGEN TextBox Apply ♾️Mixlab",
-    "GridDisplayAndSave":"Grid Display And Save",
-    "GridInput":"Grid Input",
-    "GridOutput":"Grid Output",
-    "GetImageSize_":"Get Image Size",
-    "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames"
+    "GridDisplayAndSave":"Grid Display And Save ♾️Mixlab",
+    "GridInput":"Grid Input ♾️Mixlab",
+    "GridOutput":"Grid Output ♾️Mixlab",
+    "GetImageSize_":"Get Image Size ♾️Mixlab",
+    "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames ♾️Mixlab",
+    "IncrementingListNode_":"Create Incrementing Number List ♾️Mixlab",
+    "LoadImagesToBatch":"Load Images(base64) ♾️Mixlab",
+    "PreviewMask_":"Preview Mask"
 }

 # web ui的节点功能
@@ -4,8 +4,17 @@ import urllib.error
 import re,json,os,string,random
 import folder_paths
 import hashlib
-import codecs
-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())
@@ -48,14 +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
@@ -8,11 +8,25 @@ 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
+
+
+
+# 将PIL图片转换为OpenCV格式
+def pil_to_opencv(image):
+    open_cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    return open_cv_image
+
+# 将OpenCV格式图片转换为PIL格式
+def opencv_to_pil(image):
+    pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    return pil_image


 def composite_images(foreground, background, mask):
@@ -619,7 +633,7 @@ def detect_faces(image):

 def areaToMask(x,y,w,h,image):
    # 创建一个与原图片大小相同的空白图片
-    mask = Image.new('1', image.size)
+    mask = Image.new('L', image.size)

    # 创建一个可用于绘制的对象
    draw = ImageDraw.Draw(mask)
@@ -652,7 +666,46 @@ def areaToMask(x,y,w,h,image):
 #     # bg_image.save("output.jpg")
 #     return bg_image

-def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option):
+
+import cv2
+import numpy as np
+
+# ps的正片叠底
+# 可以基于https://www.cnblogs.com/jsxyhelu/p/16947810.html ，用gpt写python代码
+def multiply_blend(image1, image2):
+    image1=pil_to_opencv(image1)
+    image2=pil_to_opencv(image2)
+    # 将图像转换为浮点型
+    image1 = image1.astype(float)
+    image2 = image2.astype(float)
+    if image1.shape != image2.shape:
+        image1 = cv2.resize(image1, (image2.shape[1], image2.shape[0]))
+        
+    # 归一化图像
+    image1 /= 255.0
+    image2 /= 255.0
+
+    # 正片叠底混合
+    blended = image1 * image2
+
+    # 将图像还原为8位无符号整数
+    blended = (blended * 255).astype(np.uint8)
+
+    blended=opencv_to_pil(blended)
+    return blended
+
+# # 读取图像
+# image1 = cv2.imread('1.png')
+# image2 = cv2.imread('3.png')
+
+# # 进行正片叠底混合
+# result = multiply_blend(image1, image2)
+
+# cv2.imwrite('result.jpg', result)
+
+
+
+def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option,is_multiply_blend=False):
    # 打开底图
    bg_image = bg_image.convert("RGBA")

@@ -681,8 +734,27 @@ def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option)
    nw, nh = layer_image.size
    mask = mask.resize((nw, nh))

-    # 在底图上粘贴图层
-    bg_image.paste(layer_image, (x, y), mask=mask)
+    # 分离出a通道
+    r, g, b, alpha = layer_image.split()
+    alpha = ImageOps.invert(alpha)
+    # 创建一个新的RGB图像
+    new_rgb_image = Image.new("RGB", layer_image.size)
+    # 将透明通道粘贴到新的RGB图像上
+    new_rgb_image.paste(layer_image, (0, 0), mask=alpha)
+   
+    new_rgb_image.paste(layer_image, (x, y), mask=mask)
+    mask=new_rgb_image.convert('L')
+    mask = ImageOps.invert(mask)
+
+    if is_multiply_blend:
+        bg_image_white=Image.new("RGB", bg_image.size,(255, 255, 255))
+
+        bg_image_white.paste(layer_image, (x, y), mask=mask)
+        bg_image=multiply_blend(bg_image_white,bg_image)
+        bg_image=bg_image.convert("RGBA")
+    else:
+        # 在底图上粘贴图层
+        bg_image.paste(layer_image, (x, y), mask=mask)

    # 输出合成后的图片
    return bg_image
@@ -1131,6 +1203,42 @@ 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：
@@ -1480,7 +1588,7 @@ class Image3D:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Image"
+    CATEGORY = "♾️Mixlab/3D"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,False,False,False,)
@@ -1716,7 +1824,7 @@ class NewLayer:
                    "display": "number" # Cosmetic only: display as "number" or "slider"
                }),
                "scale_option": (["width","height",'overall'],),
-                "image": ("IMAGE",),
+                "image": (any_type,),
            },
             "optional":{
                    "mask": ("MASK",{"default": None}),
@@ -1773,21 +1881,20 @@ def createMask(image,x,y,w,h):
    # mask.save("mask.png")
    return mask

-
 def splitImage(image, num):
    width, height = image.size

    num_rows = int(num ** 0.5)
    num_cols = int(num / num_rows)
    
-    grid_width = width // num_cols
-    grid_height = height // num_rows
+    grid_width = int(width // num_cols)
+    grid_height = int(height // num_rows)

    grid_coordinates = []
    for i in range(num_rows):
        for j in range(num_cols):
-            x = j * grid_width
-            y = i * grid_height
+            x = int(j * grid_width)
+            y = int(i * grid_height)
            grid_coordinates.append((x, y, grid_width, grid_height))

    return grid_coordinates
@@ -2119,12 +2226,16 @@ class GridOutput:
    def INPUT_TYPES(s):
        return { 
            "required": {
-                "grid": ("_GRID",)
-            }
+                "grid": ("_GRID",),
+                
+            },
+            "optional":{
+                  "bg_image":("IMAGE",)
+                }
                }
    
-    RETURN_TYPES = ("INT","INT","INT","INT",)
-    RETURN_NAMES = ("x","y","width","height",)
+    RETURN_TYPES = ("INT","INT","INT","INT","MASK",)
+    RETURN_NAMES = ("x","y","width","height","mask",)

    FUNCTION = "run"

@@ -2133,9 +2244,26 @@ class GridOutput:
    INPUT_IS_LIST = False
    # OUTPUT_IS_LIST = (True,)

-    def run(self,grid):
+    def run(self,grid,bg_image=None):
        x,y,w,h=grid
-        return (x,y,w,h,)
+        x=int(x)
+        y=int(y)
+        w=int(w)
+        h=int(h)
+
+        masks=[]
+        if bg_image!=None:
+            for i in range(len(bg_image)):
+                im=bg_image[i]
+                #增加输出mask
+                im=tensor2pil(im)
+                mask=areaToMask(x,y,w,h,im)
+                mask=pil2tensor(mask)
+                masks.append(mask)
+        out=None
+        if len(masks)>0:
+            out = torch.cat(masks, dim=0)
+        return (x,y,w,h,out,)



@@ -2230,10 +2358,14 @@ class MergeLayers:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { 
-            "layers": ("LAYER",),
-            "images": ("IMAGE",),
-                             },
-
+                            "layers": ("LAYER",),
+                            "images": ("IMAGE",),
+                                            },
+            "optional":{
+                           
+                            "is_multiply_blend":  ("BOOLEAN", {"default": False}),
+                            
+                    }
                }
    
    RETURN_TYPES = ("IMAGE","MASK",)
@@ -2246,11 +2378,12 @@ class MergeLayers:
    INPUT_IS_LIST = True
    # OUTPUT_IS_LIST = (False,)

-    def run(self,layers,images):
+    def run(self,layers,images,is_multiply_blend):

        bg_images=[]
        masks=[]
- 
+        
+        is_multiply_blend=is_multiply_blend[0]
        # print(len(images),images[0].shape)
        # 1  torch.Size([2, 512, 512, 3])
        # 4  torch.Size([1, 1024, 768, 3])
@@ -2281,6 +2414,8 @@ class MergeLayers:
                    
                    layer_image=tensor2pil(image)
                    layer_mask=tensor2pil(mask)
+                    # t=layer_image.convert("RGBA")
+                    # t.save('test.png') 如果layerimage传入的是rgba，则是透明的
                    bg_image=merge_images(bg_image,
                                        layer_image,
                                        layer_mask,
@@ -2288,7 +2423,8 @@ class MergeLayers:
                                        layer['y'],
                                        layer['width'],
                                        layer['height'],
-                                        layer['scale_option']
+                                        layer['scale_option'],
+                                        is_multiply_blend
                                        )
                    
                    final_mask=merge_images(final_mask,
@@ -2701,6 +2837,7 @@ class ImageColorTransfer:
        return {"required": {
                "source": ("IMAGE",),
                "target": ("IMAGE",),
+                "weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
                },
                }
    
@@ -2714,25 +2851,45 @@ 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,)

@@ -2,16 +2,14 @@
 import scipy.ndimage
 import torch

-from nodes import MAX_RESOLUTION
-
 import numpy as np 
 # from PIL import Image, ImageDraw
 from PIL import Image, ImageOps 

 from comfy.cli_args import args
-import cv2
-
-
+import cv2,os
+from nodes import MAX_RESOLUTION, SaveImage, common_ksampler
+import folder_paths,random

 # Tensor to PIL
 def tensor2pil(image):
@@ -71,6 +69,35 @@ def combine(destination, source, x, y):

    return output

+
+class PreviewMask_(SaveImage):
+    def __init__(self):
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+        self.prefix_append =''.join(random.choice("abcdehijklmnopqrstupvxyzfg") for x in range(5))
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                                "mask": ("MASK",), 
+                            }
+            }
+    
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/Mask"
+
+    # 运行的函数
+    def run(self, mask ):
+        img=tensor2pil(mask)
+        img=img.convert('RGB')
+        img=pil2tensor(img) 
+        return self.save_images(img, 'temp_', None,  None)
+
+
 class OutlineMask:

    @classmethod
@@ -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
        
@@ -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",), 
                             },
                }
@@ -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
@@ -10,7 +10,7 @@ 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"),
@@ -201,11 +268,11 @@ class LoadVideoAndSegment:

    CATEGORY = "♾️Mixlab/Video"

-    RETURN_TYPES = ("IMAGE","IMAGE", "INT",)
-    RETURN_NAMES = ("segment_batch","frame_count","segment_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,False,)
+    OUTPUT_IS_LIST = (True,False,False,False,)


    def is_gif(self, filename):
@@ -262,73 +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
        
-        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_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 = []
+        # 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 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)]
+        # if len(vfilters) > 0:
+        #     args_all_frames += ["-vf", ",".join(vfilters)]

-        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)
+        # 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=split_list(images,video_segment_frames,transition_frames)
+        # imgs=split_list(images,video_segment_frames,transition_frames)

-        imgs=[torch.from_numpy(np.stack(im)) for im in imgs]
+        # 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(images),len(imgs),)
+        return (scenes_video,len(scenes_video), total_frames,fps,)

    @classmethod
    def IS_CHANGED(s, video, **kwargs):
@@ -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]
@@ -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);
                })
            }

@@ -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
@@ -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 = 'v0.22.1'

 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
  .then(response => response.json())
@@ -17,7 +17,13 @@ fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
      return
    if (latestVersion && latestVersion != version) {
      localStorage.setItem('_mixlab_nodes_vesion', latestVersion)
-      app.ui.dialog.show(`<h4 style="font-size: 18px;">${repoName} <br>
+      app.ui.dialog.show(`<a style="color: white;
+      font-size: 18px;
+      font-weight: 800;
+      letter-spacing: 2px;
+    }"
+    href="https://discord.gg/cXs9vZSqeK">Welcome to Mixlab nodes discord</a>
+    <h4 style="font-size: 18px;">${repoName} <br>
      Latest release version: ${latestVersion}</h4>
      <p>Please proceed to the official repository to download the latest version.</p>
      <a style="color: #2196F3;
@@ -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
@@ -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)
+      }
+    }
+  }
+})
@@ -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)
    }
@@ -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)
+    }
+  })
+}
Author	SHA1	Message	Date
shadowcz007	dd816e95cd	Update README.md	2024-04-27 23:43:40 +08:00
shadowcz007	6d1b51890d	Update checkVersion_mixlab.js	2024-04-27 23:38:00 +08:00
shadowcz007	11f03ec99a	支持正片叠底	2024-04-25 21:23:40 +08:00
shadowcz007	bd192f43e7	优化	2024-04-24 23:10:03 +08:00
shadowcz007	36e4b11983	gridout can export mask	2024-04-23 12:14:50 +08:00
shadowcz007	97397ba8c2	Update ImageNode.py	2024-04-22 12:32:48 +08:00
shadowcz007	052eee4111	Update __init__.py	2024-04-22 12:31:40 +08:00
shadowcz007	e319496044	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	2024-04-22 09:52:45 +08:00
shadowcz007	b83b63c362	添加支持的节点自动填充id	2024-04-21 21:51:07 +08:00
shadowcz007	4d6b1675bb	Load Images to Batch加载文件最大宽度1024	2024-04-21 21:50:56 +08:00
shadowcz007	13110fab39	支持动态提示	2024-04-21 20:58:44 +08:00
shadowcz007	74ea509848	fixbug	2024-04-20 23:05:59 +08:00
shadowcz007	192bff9d2c	optimize ImageColorTransfer and support batching,	2024-04-20 22:23:34 +08:00
shadowcz007	44ed8812dc	output add TransparentImage	2024-04-19 22:27:53 +08:00
shadowcz007	200696ba21	Update ui_mixlab.js	2024-04-19 20:44:27 +08:00
shadowcz007	51cf3b0c04	install Zhipuai as needed	2024-04-19 11:41:56 +08:00
shadowcz007	6a4831c83b	add nodes map for appinfo	2024-04-18 18:24:58 +08:00
shadowcz007	c5e7ed95a3	Update index.html	2024-04-18 16:10:02 +08:00
shadowcz007	42a97fa4d9	Update image_mixlab.js	2024-04-18 16:00:43 +08:00
shadowcz007	45240d0012	Update image_mixlab.js	2024-04-18 16:00:18 +08:00
shadowcz007	8ed085febd	Update image_mixlab.js	2024-04-18 15:57:34 +08:00
shadowcz007	37803ea61b	Update smart_connect.js	2024-04-18 09:06:42 +08:00
shadowcz007	acd416952c	Update image_mixlab.js	2024-04-18 07:48:50 +08:00
shadowcz007	6ec46cbc44	Update ui_mixlab.js	2024-04-17 22:55:16 +08:00
shadowcz007	a9d971e476	Update __init__.py	2024-04-17 22:55:12 +08:00
shadowcz007	9fe064675d	paste appinfo data / 支持粘贴appinfo导出的数据	2024-04-17 16:28:32 +08:00
shadowcz007	c84fa467d0	Update requirements.txt	2024-04-17 15:36:49 +08:00
shadowcz007	3d7a55f6d3	SaveImageAndMetadata支持格式化文件名@bakkhos8	2024-04-17 15:01:05 +08:00
shadowcz007	d49baa1540	Update checkVersion_mixlab.js	2024-04-15 23:40:01 +08:00
shadowcz007	5c686af842	Update ImageNode.py	2024-04-15 09:02:49 +08:00
shadowcz007	22425b5bc6	Update index.html	2024-04-13 00:12:05 +08:00
shadowcz007	b6d9b338d2	Update index.html	2024-04-12 21:55:56 +08:00
shadowcz007	a191a13751	Update index.html	2024-04-12 21:48:31 +08:00
shadowcz007	3eccdbcc9b	Update index.html	2024-04-12 21:43:21 +08:00
shadowcz007	50063903f9	mixlab app add 3D	2024-04-12 16:10:05 +08:00
shadowcz007	95a1b70533	Update app_mixlab.js	2024-04-08 17:14:30 +08:00
shadowcz007	c3679ac90b	Update image_mixlab.js	2024-04-07 11:49:26 +08:00
shadowcz007	29e48eb6a2	Update ui_mixlab.js	2024-04-07 10:32:13 +08:00
shadowcz007	71d02e9651	v0.20.0	2024-04-06 21:47:07 +08:00
shadowcz007	f5193f3eec	add LoadImagesToBatch	2024-04-06 21:39:55 +08:00
shadowcz007	480c4d6919	fixbug	2024-04-06 19:56:43 +08:00
shadowcz007	1c5e030540	Update index.html	2024-04-06 18:41:06 +08:00
shadowcz007	27e83a5908	Incrementing List	2024-03-30 23:46:32 +08:00
shadowcz007	d3cbf8fa8d	Update Video.py	2024-03-30 22:46:50 +08:00
shadowcz007	74b1f8129b	Update PromptNode.py	2024-03-29 15:35:41 +08:00