v0.17.1

add chatglm4 model

README.md

@@ -1,6 +1,14 @@
 > 适配了最新版comfyui的py3.11 ，torch 2.1.2+cu121
 
-> [discord](https://discord.gg/cXs9vZSqeK)
+> [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)
+
+#### 
+[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)
+
 
 ## 🚀🚗🚚🏃 Workflow-to-APP 
 - 新增AppInfo节点，可以通过简单的配置，把workflow转变为一个Web APP。
@@ -31,12 +39,19 @@ APP-JSON:
 - [image-to-image](./example/Image-to-Image_2.json)
 - text-to-text
 
-> 暂时支持8种节点作为界面上的输入节点：Load Image、CLIPTextEncode、PromptSlide、TextInput_、Color、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader
+> 暂时支持 9 种节点作为界面上的输入节点：Load Image、VHS_LoadVideo、CLIPTextEncode、PromptSlide、TextInput_、Color、FloatSlider、IntNumber、CheckpointLoaderSimple、LoraLoader
 
 > 输出节点：PreviewImage 、SaveImage、ShowTextForGPT、VHS_VideoCombine、PromptImage
 
 > seed统一输入控件，支持：SamplerCustom、KSampler
 
+> 配套[ps插件](https://github.com/shadowcz007/comfyui-ps-plugin)
+
+> 如果遇到上传图片不成功，请检查下：局域网或者是云服务，请使用https，端口8189这个服务（ 感谢 @Damien 反馈问题）
+
+> If you encounter difficulties in uploading images, please check the following: for local network or cloud services, please use HTTPS and the service on port 8189. (Thanks to @Damien for reporting the issue.)
+
+
 
 ## 🏃🚗🚚🚀  Real-time Design
 > ScreenShareNode & FloatingVideoNode. Now comfyui supports capturing screen pixel streams from any software and can be used for LCM-Lora integration. Let's get started with implementation and design! 💻🌐
@@ -58,7 +73,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 [Voice + Real-time Face Swap Workflow](./workflow/语音+实时换脸workflow.json)
 
 ### GPT
-> Support for calling multiple GPTs.ChatGPT、ChatGLM3 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 
+> Support for calling multiple GPTs.ChatGPT、ChatGLM3 、ChatGLM4 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 .  Azure OpenAI:https://xxxx.openai.azure.com 
 
 ![gpt-workflow.svg](./assets/gpt-workflow.svg)
 
@@ -137,15 +152,6 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 ![TransparentImage](./assets/TransparentImage.png)
 
 
-> Consistency Decoder
-
-[openai Consistency Decoder]( https://github.com/openai/consistencydecoder)
-
-![Consistency](./assets/consistency.png)
-After downloading the OpenAI VAE model, place it in the "model/vae" directory for use.
-https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt
-
-
 > FeatheredMask、SmoothMask
 
 Add edges to an image.
@@ -158,6 +164,13 @@ Add edges to an image.
 from [simple-lama-inpainting](https://github.com/enesmsahin/simple-lama-inpainting)
 
 
+> rembgNode
+
+"briarmbg","u2net","u2netp","u2net_human_seg","u2net_cloth_seg","silueta","isnet-general-use","isnet-anime"
+
+*** briarmbg ***  model was developed by BRlA Al and can be used as an open-source model for non-commercial purposes
+
+
 ### Improvement 
 
 - Add "help" option to the context menu for each node.
@@ -174,8 +187,6 @@ An improvement has been made to directly redirect to GitHub to search for missin
 
 [Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:models/rembg
 
-[Download CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main), move to : models/clipseg
-
 [Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama
 
 [Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base
@@ -213,18 +224,26 @@ If you are using a venv, make sure you have it activated before installation and
 pip3 install -r requirements.txt
 ```
 
+
+
+
+
 #### Chinese community
 访问 [www.mixcomfy.com](https://www.mixcomfy.com)，获得更多内测功能，关注微信公众号：Mixlab无界社区
 
 
-#### Thanks:
-[ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
+
+
+#### 
+File / LoadImagesFromPath SaveImageToLocal LoadImagesFromURL
+
+
+
 
 #### discussions:
 [discussions](https://github.com/shadowcz007/comfyui-mixlab-nodes/discussions)
 
 
-
 <picture>
   <source
     media="(prefers-color-scheme: dark)"

__init__.py

@@ -222,7 +222,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
             # print(item)
             try:
                 x=item["data"]
-                if i==0:
+                # 管理员模式，读取全部数据
+                if i==0 or is_all:
                     apps.append({
                         "filename":item["filename"],
                         # "category":item['category'],
@@ -531,12 +532,12 @@ async def nodes_map_hander(request):
 # 导入节点
 from .nodes.PromptNode import EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
 from .nodes.ImageNode import 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.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
-from .nodes.Clipseg import CLIPSeg,CombineMasks
-from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
+
+from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import OutlineMask,FeatheredMask
 
 
@@ -545,7 +546,9 @@ from .nodes.Mask import OutlineMask,FeatheredMask
 NODE_CLASS_MAPPINGS = {
     "AppInfo":AppInfo,
     "TESTNODE_":TESTNODE_,
+    "TESTNODE_TOKEN":TESTNODE_TOKEN,
     "RandomPrompt":RandomPrompt,
+    # "LoraPrompt":LoraPrompt,
     "EmbeddingPrompt":EmbeddingPrompt,
     "PromptSlide":PromptSlide,
     "PromptSimplification":PromptSimplification,
@@ -574,16 +577,15 @@ NODE_CLASS_MAPPINGS = {
     "FaceToMask":FaceToMask,
     "AreaToMask":AreaToMask,
     "ImageCropByAlpha":ImageCropByAlpha,
-    "VAELoaderConsistencyDecoder":VAELoader,
+    # "VAELoaderConsistencyDecoder":VAELoader,
     "SaveImageToLocal":SaveImageToLocal,
-    "VAEDecodeConsistencyDecoder":VAEDecode,
+    # "VAEDecodeConsistencyDecoder":VAEDecode,
     "ScreenShare":ScreenShareNode,
     "FloatingVideo":FloatingVideo,
-    "CLIPSeg_":CLIPSeg,
-    "CombineMasks_":CombineMasks,
     "ChatGPTOpenAI":ChatGPTNode,
     "ShowTextForGPT":ShowTextForGPT,
     "CharacterInText":CharacterInText,
+    "TextSplitByDelimiter":TextSplitByDelimiter,
     "SpeechRecognition":SpeechRecognition,
     "SpeechSynthesis":SpeechSynthesis,
     "Color":ColorInput,

data/extension-node-map.json

@@ -4775,7 +4775,6 @@
             "PromptImage",
             "SaveImageToLocal",
             "AreaToMask",
-            "CLIPSeg_",
             "CharacterInText",
             "ChatGPTOpenAI",
             "Color",
@@ -4783,7 +4782,6 @@
             "CkptNames_",
             "SamplerNames_",
             "LoraNames_",
-            "CombineMasks_",
             "EnhanceImage",
             "GradientImage",
             "FaceToMask",
@@ -4811,8 +4809,6 @@
             "TextImage",
             "ResizeImageMixlab",
             "TransparentImage",
-            "VAEDecodeConsistencyDecoder",
-            "VAELoaderConsistencyDecoder",
             "TextToNumber",
             "TextInput_",
             "DynamicDelayProcessor",

data/loras_tags.json

@@ -0,0 +1 @@
+{}

nodes/ChatGPT.py

@@ -1,7 +1,26 @@
 import openai 
 import time
 import urllib.error
-import re,json
+import re,json,os,string,random
+import folder_paths
+import hashlib
+from zhipuai import ZhipuAI
+def get_unique_hash(string):
+    hash_object = hashlib.sha1(string.encode())
+    unique_hash = hash_object.hexdigest()
+    return unique_hash
+
+def generate_random_string(length):
+    letters = string.ascii_letters + string.digits
+    return ''.join(random.choice(letters) for _ in range(length))
+
+class AnyType(str):
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+  def __ne__(self, __value: object) -> bool:
+    return False
+
+any_type = AnyType("*")
 
 #  判断是否是azure服务
 def is_azure_url(url):
@@ -27,6 +46,11 @@ def openai_client(key,url):
     base_url=url
     )
     return client
+def ZhipuAI_client(key):
+    client = ZhipuAI(
+        api_key=key, # 填写您的 APIKey
+    ) 
+    return client
 
 
 
@@ -73,15 +97,15 @@ class ChatGPTNode:
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "api_key":("KEY", {"default": "", "multiline": True}),
-                "api_url":("URL", {"default": "", "multiline": True}),
+                "api_key":("KEY", {"default": "", "multiline": True,"dynamicPrompts": False}),
+                "api_url":("URL", {"default": "", "multiline": True,"dynamicPrompts": False}),
                 "prompt": ("STRING", {"multiline": True,"dynamicPrompts": False}),
                 "system_content": ("STRING", 
                                    {
                                        "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
                                        "multiline": True,"dynamicPrompts": False
                                        }),
-                "model": (["gpt-3.5-turbo","gpt-35-turbo","gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-4-0613","gpt-4-1106-preview"], 
+                "model": (["gpt-3.5-turbo","gpt-35-turbo","gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-4-0613","gpt-4-1106-preview","glm-4"], 
                           {"default": "gpt-3.5-turbo"}),
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                 "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
@@ -124,8 +148,13 @@ class ChatGPTNode:
         if is_azure_url(api_url):
             client=azure_client(api_key,api_url)
         else:
-            client=openai_client(api_key,api_url)
-            print('openai url')
+            # 根据用户选择的模型，设置相应的接口和模型名称
+            if model == "glm-4" :
+                client = ZhipuAI_client(api_key)  # 使用 Zhipuai 的接口
+                print('using Zhipuai interface')
+            else :
+                client = openai_client(api_key,api_url)  # 使用 ChatGPT  的接口
+                print('using ChatGPT interface')
 
         # 把用户的提示添加到会话历史中
         # 调用API时传递整个会话历史
@@ -168,7 +197,10 @@ class ShowTextForGPT:
         return {
             "required": {
                 "text": ("STRING", {"forceInput": True,"dynamicPrompts": False}),
-            }
+            },
+            "optional":{ 
+                    "output_dir": ("STRING",{"forceInput": True,"default": "","multiline": True,"dynamicPrompts": False}), 
+                }
         }
 
     INPUT_IS_LIST = True
@@ -179,7 +211,60 @@ class ShowTextForGPT:
 
     CATEGORY = "♾️Mixlab/GPT"
 
-    def run(self, text):
+    def run(self, text,output_dir=[""]):
+        
+        # 类型纠正
+        texts=[]
+        for t in text:
+            if not isinstance(t, str):
+                t = str(t)
+            texts.append(t)
+
+        text=texts
+
+        if len(output_dir)==1 and (output_dir[0]=='' or os.path.dirname(output_dir[0])==''):
+            t='\n'.join(text)
+            output_dir=[
+                os.path.join(folder_paths.get_temp_directory(),
+                             get_unique_hash(t)+'.txt'
+                             )
+            ]
+        elif len(output_dir)==1:
+            base=os.path.basename(output_dir[0])
+            t='\n'.join(text)
+            if base=='' or os.path.splitext(base)[1]=='':
+                base=get_unique_hash(t)+'.txt'
+            output_dir=[
+                os.path.join(output_dir[0],
+                             base
+                             )
+            ]
+        # elif len(output_dir)>1:
+
+        
+
+        if len(output_dir)==1 and len(text)>1:
+            output_dir=[output_dir[0] for _ in range(len(text))]
+        
+        for i in range(len(text)):
+
+            o_fp=output_dir[i]
+            dirp=os.path.dirname(o_fp)
+            if dirp=='':
+                dirp=folder_paths.get_temp_directory()
+                o_fp=os.path.join(folder_paths.get_temp_directory(),o_fp
+                             )
+
+            if not os.path.exists(dirp):
+                os.mkdir(dirp)
+
+            if not os.path.splitext(o_fp)[1].lower()=='.txt':
+                o_fp=o_fp+'.txt'
+
+            t=text[i]
+            with open(o_fp, 'w') as file:
+                file.write(t)
+
         # print(text)
         return {"ui": {"text": text}, "result": (text,)}
         
@@ -212,7 +297,56 @@ class CharacterInText:
 
     def run(self, text,character,start_index):
         # print(text,character,start_index)
-        b=1 if character in text else 0
+        b=1 if character.lower() in text.lower() else 0
         
         return (b+start_index,)
 
+class TextSplitByDelimiter:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                "delimiter":(["newline","comma"],),
+                "start_index": ("INT", {
+                    "default": 0,
+                    "min": 0, #Minimum value
+                    "max": 1000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                 "skip_every": ("INT", {
+                    "default": 0,
+                    "min": 0, #Minimum value
+                    "max": 10, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                "max_count": ("INT", {
+                    "default": 10,
+                    "min": 1, #Minimum value
+                    "max": 1000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+            }
+        }
+
+    INPUT_IS_LIST = False
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "run"
+    # OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "♾️Mixlab/GPT"
+
+    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()]
+        
+        arr= arr[start_index:start_index + max_count * (skip_every+1):(skip_every+1)]
+
+        return (arr,)

nodes/Clipseg.py

@@ -1,272 +0,0 @@
-#### Thanks:
-# [ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
-
-from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
-
-from PIL import Image
-import torch
-import torchvision.transforms as T
-import numpy as np
-
-from torchvision.transforms.functional import to_pil_image
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-
-
-import cv2
-
-from scipy.ndimage import gaussian_filter
-
-from typing import Optional, Tuple
-
-import warnings,os
-warnings.filterwarnings("ignore", category=UserWarning, module="torch")
-warnings.filterwarnings("ignore", category=UserWarning, module="safetensors")
-
-import folder_paths
-import logging
-
-logger = logging.getLogger('CLIPSeg nodes')
-
-clipseg_model_dir = os.path.join(folder_paths.models_dir, "clipseg")
-
-if not os.path.exists(clipseg_model_dir):
-    print(f"## clipseg model not found: {clipseg_model_dir},pls download from https://huggingface.co/CIDAS/clipseg-rd64-refined/tree/main")
-    clipseg_model_dir='CIDAS/clipseg-rd64-refined'
-
-"""Helper methods for CLIPSeg nodes"""
-
-# 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 tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
-    """Convert a tensor to a numpy array and scale its values to 0-255."""
-    array = tensor.numpy().squeeze()
-    return (array * 255).astype(np.uint8)
-
-def numpy_to_tensor(array: np.ndarray) -> torch.Tensor:
-    """Convert a numpy array to a tensor and scale its values from 0-255 to 0-1."""
-    array = array.astype(np.float32) / 255.0
-    return torch.from_numpy(array)[None,]
-
-def apply_colormap(mask: torch.Tensor, colormap) -> np.ndarray:
-    """Apply a colormap to a tensor and convert it to a numpy array."""
-    colored_mask = colormap(mask.numpy())[:, :, :3]
-    return (colored_mask * 255).astype(np.uint8)
-
-def resize_image(image: np.ndarray, dimensions: Tuple[int, int]) -> np.ndarray:
-    """Resize an image to the given dimensions using linear interpolation."""
-    return cv2.resize(image, dimensions, interpolation=cv2.INTER_LINEAR)
-
-def overlay_image(background: np.ndarray, foreground: np.ndarray, alpha: float) -> np.ndarray:
-    """Overlay the foreground image onto the background with a given opacity (alpha)."""
-    return cv2.addWeighted(background, 1 - alpha, foreground, alpha, 0)
-
-def dilate_mask(mask: torch.Tensor, dilation_factor: float) -> torch.Tensor:
-    """Dilate a mask using a square kernel with a given dilation factor."""
-    kernel_size = int(dilation_factor * 2) + 1
-    kernel = np.ones((kernel_size, kernel_size), np.uint8)
-    mask_dilated = cv2.dilate(mask.numpy(), kernel, iterations=1)
-    return torch.from_numpy(mask_dilated)
-
-
-
-class CLIPSeg:
-
-    def __init__(self):
-        pass
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        """
-            Return a dictionary which contains config for all input fields.
-            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
-            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
-            The type can be a list for selection.
-
-            Returns: `dict`:
-                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
-                - Value input_fields (`dict`): Contains input fields config:
-                    * Key field_name (`string`): Name of a entry-point method's argument
-                    * Value field_config (`tuple`):
-                        + First value is a string indicate the type of field or a list for selection.
-                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
-        """
-        return {"required":
-                    {
-                        "image": ("IMAGE",),
-                        "text": ("STRING", {"multiline": False,"dynamicPrompts": False}),
-                        
-                     },
-                "optional":
-                    {
-                        "blur": ("FLOAT", {"min": 0, "max": 15, "step": 0.1, "default": 3}),
-                        "threshold": ("FLOAT", {"min": 0, "max": 1, "step": 0.05, "default": 0.3}),
-                        "dilation_factor": ("INT", {"min": 0, "max": 10, "step": 1, "default": 4}),
-                    }
-                }
-
-    CATEGORY = "♾️Mixlab/Mask"
-    RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
-    RETURN_NAMES = ("Mask","Heatmap Mask", "BW Mask")
-
-    # INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (False,False,False,)
-
-    FUNCTION = "segment_image"
-    def segment_image(self, image: torch.Tensor, text: str, blur: float, threshold: float, dilation_factor: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """Create a segmentation mask from an image and a text prompt using CLIPSeg.
-
-        Args:
-            image (torch.Tensor): The image to segment.
-            text (str): The text prompt to use for segmentation.
-            blur (float): How much to blur the segmentation mask.
-            threshold (float): The threshold to use for binarizing the segmentation mask.
-            dilation_factor (int): How much to dilate the segmentation mask.
-
-        Returns:
-            Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: The segmentation mask, the heatmap mask, and the binarized mask.
-        """
-            
-        # Convert the Tensor to a PIL image
-        image_np = image.numpy().squeeze()  # Remove the first dimension (batch size of 1)
-        # Convert the numpy array back to the original range (0-255) and data type (uint8)
-        image_np = (image_np * 255).astype(np.uint8)
-        # Create a PIL image from the numpy array
-        i = Image.fromarray(image_np, mode="RGB")
-
-        processor = CLIPSegProcessor.from_pretrained(clipseg_model_dir)
-        model = CLIPSegForImageSegmentation.from_pretrained(clipseg_model_dir)
-        
-        prompt = text
-        
-        input_prc = processor(text=prompt, images=i, padding="max_length", return_tensors="pt")
-        
-        # Predict the segemntation mask
-        with torch.no_grad():
-            outputs = model(**input_prc)
-        
-        tensor = torch.sigmoid(outputs[0]) # get the mask
-        
-        # Apply a threshold to the original tensor to cut off low values
-        thresh = threshold
-        tensor_thresholded = torch.where(tensor > thresh, tensor, torch.tensor(0, dtype=torch.float))
-
-        # Apply Gaussian blur to the thresholded tensor
-        sigma = blur
-        tensor_smoothed = gaussian_filter(tensor_thresholded.numpy(), sigma=sigma)
-        tensor_smoothed = torch.from_numpy(tensor_smoothed)
-
-        # Normalize the smoothed tensor to [0, 1]
-        mask_normalized = (tensor_smoothed - tensor_smoothed.min()) / (tensor_smoothed.max() - tensor_smoothed.min())
-
-        # Dilate the normalized mask
-        mask_dilated = dilate_mask(mask_normalized, dilation_factor)
-
-        # Convert the mask to a heatmap and a binary mask
-        heatmap = apply_colormap(mask_dilated, cm.viridis)
-        binary_mask = apply_colormap(mask_dilated, cm.Greys_r)
-
-        # Overlay the heatmap and binary mask on the original image
-        dimensions = (image_np.shape[1], image_np.shape[0])
-        heatmap_resized = resize_image(heatmap, dimensions)
-        binary_mask_resized = resize_image(binary_mask, dimensions)
-
-        alpha_heatmap, alpha_binary = 0.5, 1
-        overlay_heatmap = overlay_image(image_np, heatmap_resized, alpha_heatmap)
-        overlay_binary = overlay_image(image_np, binary_mask_resized, alpha_binary)
-
-        # Convert the numpy arrays to tensors
-        image_out_heatmap = numpy_to_tensor(overlay_heatmap)
-        image_out_binary = numpy_to_tensor(overlay_binary)
-
-        # Save or display the resulting binary mask
-        binary_mask_image = Image.fromarray(binary_mask_resized[..., 0])
-
-        # convert PIL image to numpy array
-        tensor_bw = binary_mask_image.convert("L")
-        tensor_bw=pil2tensor(tensor_bw)
-        # tensor_bw = np.array(tensor_bw).astype(np.float32) / 255.0
-        # tensor_bw = torch.from_numpy(tensor_bw)[None,]
-        # tensor_bw = tensor_bw.squeeze(0)[..., 0]
-
-        return (tensor_bw, image_out_heatmap, image_out_binary,)
-
-    #OUTPUT_NODE = False
-
-class CombineMasks:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {
-                        "input_image": ("IMAGE", ),
-                        "mask_1": ("MASK", ), 
-                        "mask_2": ("MASK", ),
-                    },
-                "optional": 
-                    {
-                        "mask_3": ("MASK",), 
-                    },
-                }
-        
-    CATEGORY = "♾️Mixlab/Mask"
-    RETURN_TYPES = ("MASK", "IMAGE", "IMAGE",)
-    RETURN_NAMES = ("Combined Mask","Heatmap Mask", "BW Mask")
-
-    FUNCTION = "combine_masks"
-            
-    def combine_masks(self, input_image: torch.Tensor, mask_1: torch.Tensor, mask_2: torch.Tensor, mask_3: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """A method that combines two or three masks into one mask. Takes in tensors and returns the mask as a tensor, as well as the heatmap and binary mask as tensors."""
-        
-        # Combine masks
-        if mask_1 is not None:
-            mask_1 = mask_1.squeeze()
-        if mask_2 is not None:
-            mask_2 = mask_2.squeeze()
-        if mask_3 is not None:
-            mask_3 = mask_3.squeeze()
-        
-        print(mask_1.shape,mask_2.shape , mask_3.shape)
-        combined_mask = mask_1 + mask_2 + mask_3 if mask_3 is not None else mask_1 + mask_2
-        # print(combined_mask)
-
-        # Convert image and masks to numpy arrays
-        image_np = tensor_to_numpy(input_image)
-        heatmap = apply_colormap(combined_mask, cm.viridis)
-        binary_mask = apply_colormap(combined_mask, cm.Greys_r)
-        
-        # Resize heatmap and binary mask to match the original image dimensions
-        dimensions = (image_np.shape[1], image_np.shape[0])
-        # print('heatmap',heatmap)
-        if dimensions is None or dimensions[0] == 0 or dimensions[1] == 0:
-            raise ValueError("Invalid dimensions")
-        
-        heatmap_resized = resize_image(heatmap, dimensions)
-        binary_mask_resized = resize_image(binary_mask, dimensions)
-
-        # Overlay the heatmap and binary mask onto the original image
-        alpha_heatmap, alpha_binary = 0.5, 1
-        overlay_heatmap = overlay_image(image_np, heatmap_resized, alpha_heatmap)
-        overlay_binary = overlay_image(image_np, binary_mask_resized, alpha_binary)
-
-        # Convert overlays to tensors
-        image_out_heatmap = numpy_to_tensor(overlay_heatmap)
-        image_out_binary = numpy_to_tensor(overlay_binary)
-
-        return combined_mask, image_out_heatmap, image_out_binary
-
-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-# NODE_CLASS_MAPPINGS = {
-#     "CLIPSeg": CLIPSeg,
-#     "CombineSegMasks": CombineMasks,
-# }

nodes/ImageNode.py

@@ -140,10 +140,10 @@ def naive_cutout(img, mask,invert=True):
     image using the mask.
     """
 
-    img=img.convert("RGBA")
+    # img=img.convert("RGBA")
     mask=mask.convert("RGBA")
 
-    empty = Image.new("RGBA", (img.size), 0)
+    empty = Image.new("RGBA", (mask.size), 0)
 
     red, green, blue, alpha = mask.split()
 
@@ -380,6 +380,7 @@ def get_images_filepath(f,white_bg=False):
         for root, dirs, files in os.walk(f):
             for file in files:
                 file_path = os.path.join(root, file)
+                file_name=os.path.basename(file_path)
                 try:
                     imgs=load_image(file_path,white_bg)
                     for img in imgs:
@@ -387,6 +388,7 @@ def get_images_filepath(f,white_bg=False):
                             "image":img['image'],
                             "mask":img['mask'],
                             "file_path":file_path,
+                            "file_name":file_name,
                             "psd":len(imgs)>1
                         })
                 except:
@@ -394,12 +396,15 @@ def get_images_filepath(f,white_bg=False):
  
     elif os.path.isfile(f):
         try:
+            file_path = os.path.join(root, f)
+            file_name=os.path.basename(file_path)
             imgs=load_image(f,white_bg)
             for img in imgs:
                 images.append({
                     "image":img['image'],
                     "mask":img['mask'],
                     "file_path":file_path,
+                    "file_name":file_name,
                     "psd":len(imgs)>1
                 })
         except:
@@ -972,6 +977,7 @@ class TransparentImage:
         # ui.images 节点里显示图片，和 传参，image_path自定义的数据，需要写节点的自定义ui
         # result 里输出给下个节点的数据 
         # print('TransparentImage',len(images_rgb))
+        
         return {"ui":{"images": ui_images,"image_paths":image_paths},"result": (image_paths,images_rgb,images_rgba)}
 
 
@@ -1058,14 +1064,15 @@ class LoadImagesFromPath:
                 }
             }
     
-    RETURN_TYPES = ('IMAGE','MASK','STRING',)
+    RETURN_TYPES = ('IMAGE','MASK','STRING','STRING',)
+    RETURN_NAMES = ("IMAGE","MASK","prompt_for_FloatingVideo","filepaths",)
 
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/Image"
 
     # INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,False,)
+    OUTPUT_IS_LIST = (True,True,False,True,)
   
     global watcher_folder
     watcher_folder=None
@@ -1101,10 +1108,12 @@ class LoadImagesFromPath:
 
         imgs=[]
         masks=[]
+        file_names=[]
 
         for im in sorted_files:
             imgs.append(im['image'])
             masks.append(im['mask'])
+            file_names.append(im['file_name'])
         
         # print('index_variable',index_variable)
         
@@ -1112,11 +1121,12 @@ class LoadImagesFromPath:
             if index_variable!=-1:
                 imgs=[imgs[index_variable]] if index_variable < len(imgs) else None
                 masks=[masks[index_variable]] if index_variable < len(masks) else None
+                file_names=[file_names[index_variable]] if index_variable < len(file_names) else None
         except Exception as e:
             print("发生了一个未知的错误：", str(e))
 
         # print('#prompt::::',prompt)
-        return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,)}
+        return  {"ui": {"seed": [1]}, "result":(imgs,masks,prompt,file_names,)}
 
 
 # TODO 扩大选区的功能,重新输出mask
@@ -1147,7 +1157,12 @@ class ImageCropByAlpha:
 
         # print(RGBA)
         im=tensor2pil(RGBA)
-        im=naive_cutout(im,im)
+
+        # 要把im的alpha通道转为mask
+        im=im.convert('RGBA')
+        red, green, blue, alpha = im.split()
+
+        im=naive_cutout(bf_im,alpha)
         x, y, w, h=get_not_transparent_area(im)
         # print('#ForImageCrop:',w, h,x, y,)
 
@@ -1162,11 +1177,12 @@ class ImageCropByAlpha:
         height_1=h
 
         img = image[:,y:to_y, x:to_x, :]
-
+        # tensor2pil(img).save('test2.png')
 
         # 原图的mask
         ori=RGBA[:,y:to_y, x:to_x, :]
         ori=tensor2pil(ori)
+        # ori.save('test.png')
 
         # 创建一个新的图像对象，大小和模式与原始图像相同
         new_image = Image.new("RGBA", ori.size)
@@ -1613,6 +1629,18 @@ class NewLayer:
         return (layer_n,)
 
 
+
+def createMask(image,x,y,w,h):
+    mask = Image.new("L", image.size)
+    pixels = mask.load()
+    # 遍历指定区域的像素，将其设置为黑色（0 表示黑色）
+    for i in range(int(x), int(x + w)):
+        for j in range(int(y), int(y + h)):
+            pixels[i, j] = 255
+    # mask.save("mask.png")
+    return mask
+
+
 def splitImage(image, num):
     width, height = image.size
 
@@ -1681,8 +1709,8 @@ class SplitImage:
             }
                 }
     
-    RETURN_TYPES = ("_GRID","_GRID",)
-    RETURN_NAMES = ("grids","grid")
+    RETURN_TYPES = ("_GRID","_GRID","MASK",)
+    RETURN_NAMES = ("grids","grid","mask",)
 
     FUNCTION = "run"
 
@@ -1693,10 +1721,10 @@ class SplitImage:
 
     def run(self,image,num,seed):
         image=tensor2pil(image)
-
+        
         grids=splitImage(image,num)
 
-        if seed>=num:
+        if seed>num:
             num=int(seed / 500 * num)-1
         else:
             num=seed-1
@@ -1705,7 +1733,11 @@ class SplitImage:
 
         g=grids[num]
 
-        return (grids,g,)
+        x,y,w,h=g
+        mask=createMask(image, x,y,w,h)
+        mask=pil2tensor(mask)
+
+        return (grids,g,mask,)
 
 
 
@@ -1746,8 +1778,8 @@ class CenterImage:
             }
                 }
     
-    RETURN_TYPES = ("_GRID",)
-    RETURN_NAMES = ("grid",)
+    RETURN_TYPES = ("_GRID","MASK",)
+    RETURN_NAMES = ("grid","mask",)
 
     FUNCTION = "run"
 
@@ -1761,7 +1793,9 @@ class CenterImage:
 
         grid=centerImage((left,top,right,bottom),canvas)
 
-        return (grid,)
+        mask=createMask(canvas,left,top,canvas.width-left-right,canvas.height-top-bottom)
+
+        return (grid,pil2tensor(mask),)
 
 
 
@@ -2125,14 +2159,14 @@ class ResizeImage:
                     "default": 512, 
                     "min": 1, #Minimum value
                     "max": 8192, #Maximum value
-                    "step": 1, #Slider's step
+                    "step": 8, #Slider's step
                     "display": "number" # Cosmetic only: display as "number" or "slider"
                 }),
                 "height": ("INT",{
                     "default": 512, 
                     "min": 1, #Minimum value
                     "max": 8192, #Maximum value
-                    "step": 1, #Slider's step
+                    "step": 8, #Slider's step
                     "display": "number" # Cosmetic only: display as "number" or "slider"
                 }),
                 "scale_option": (["width","height",'overall','center'],),
@@ -2252,19 +2286,41 @@ class GetImageSize_:
         return {
             "required": {
                 "image": ("IMAGE",),
-            }
+            },
+             "optional":{
+                    "min_width":("INT", {
+                        "default": 512, 
+                        "min":1, #Minimum value
+                        "max": 2048, #Maximum value
+                        "step": 8, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    })
+                },
         }
 
-    RETURN_TYPES = ("INT", "INT")
-    RETURN_NAMES = ("width", "height")
+    RETURN_TYPES = ("INT", "INT","INT", "INT",)
+    RETURN_NAMES = ("width", "height","min_width", "min_height",)
 
     FUNCTION = "get_size"
 
     CATEGORY = "♾️Mixlab/Image"
 
-    def get_size(self, image):
+    def get_size(self, image,min_width):
         _, height, width, _ = image.shape
-        return (width, height)
+        
+        # 如果比min_widht,还小，则输出 min width
+        if min_width>width:
+            im=tensor2pil(image)
+            im=resize_image(im,'width',min_width,min_width,"white")
+            im=im.convert('RGB')
+
+            min_width,min_height=im.size
+
+        else:
+            min_width=width
+            min_height=height
+
+        return (width, height,min_width,min_height,)
 
 
 
@@ -2338,9 +2394,20 @@ class SaveImageToLocal:
         filename_prefix = os.path.basename(file_path)
         if file_path=='':
             filename_prefix="ComfyUI"
+        
+        filename_prefix, _ = os.path.splitext(filename_prefix)
+
+        _, extension = os.path.splitext(file_path)
+
+        if extension:
+            # 是文件名，需要处理
+            file_path=os.path.dirname(file_path)
+            # filename_prefix=
+
             
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
         
+    
         if not os.path.exists(file_path):
             # 使用os.makedirs函数创建新目录
             os.makedirs(file_path)
@@ -2386,6 +2453,7 @@ class SaveImageToLocal:
                 })
             
             else:
+
                 fp=os.path.join(file_path, file)
                 if os.path.exists(fp):
                     file = f"{filename}_{counter:05}_{generate_random_string(8)}.png"

nodes/PromptNode.py

@@ -6,6 +6,12 @@ from urllib import request, parse
 import folder_paths
 from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
 from PIL.PngImagePlugin import PngInfo
+
+import hashlib
+import requests
+import json
+
+
 # def queue_prompt(prompt_workflow):
 #     p = {"prompt": prompt_workflow}
 #     data = json.dumps(p).encode('utf-8')
@@ -28,6 +34,50 @@ def join_with_(text_list,delimiter):
     return joined_text
 
 
+
+def load_json(file_path):
+    try:
+        with open(file_path, 'r') as json_file:
+            data = json.load(json_file)
+            return data
+    except FileNotFoundError:
+        print(f"File not found: {file_path}")
+        return None
+    except json.JSONDecodeError:
+        print(f"Error decoding JSON in file: {file_path}")
+        return None
+
+def save_json(data_dict, file_path):
+    try:
+        with open(file_path, 'w') as json_file:
+            json.dump(data_dict, json_file, indent=4)
+            print(f"Data saved to {file_path}")
+    except Exception as e:
+        print(f"Error saving JSON to file: {e}")
+
+# pysss的lora加载器
+# def get_model_version_info(hash_value):
+#     # http://127.0.0.1:1082
+#     proxies = {'http': 'http://127.0.0.1:1082', 'https': 'https://127.0.0.1:1082'}
+#     api_url = f"https://civitai.com/api/v1/model-versions/by-hash/{hash_value}"
+#     print(api_url)
+#     response = requests.get(api_url,proxies=proxies, verify=False)
+    
+#     if response.status_code == 200:
+#         return response.json()
+#     else:
+#         return None
+    
+# def calculate_sha256(file_path):
+#     sha256_hash = hashlib.sha256()
+#     with open(file_path, "rb") as f:
+#         for chunk in iter(lambda: f.read(4096), b""):
+#             sha256_hash.update(chunk)
+#     return sha256_hash.hexdigest()
+
+
+
+
 class AnyType(str):
   """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
 
@@ -385,7 +435,82 @@ class RandomPrompt:
         return {"ui": {"prompts": prompts}, "result": (prompts,)}
 
 
+# class LoraPrompt:
+#     @classmethod
+#     def INPUT_TYPES(s):
+#         return {
+#             "required": {
+#                 "lora_name":(sorted(folder_paths.get_filename_list("loras"), key=str.lower),),
+#                 "weight": ("FLOAT", {"default": 1, "min": -2, "max": 2,"step":0.01 ,"display": "slider"}),
+#                 "force_update": ("BOOLEAN", {"default": False}),
+#                 },
+                
+#             }
+    
+#     RETURN_TYPES = ("STRING","STRING",any_type)
+#     RETURN_NAMES = ("lora_name","prompt","tags",)
 
+#     FUNCTION = "run"
+
+#     CATEGORY = "♾️Mixlab/Prompt"
+
+#     OUTPUT_IS_LIST = (False,False,True,)
+#     # OUTPUT_NODE = True
+
+#     # 运行的函数
+#     def run(self,lora_name,weight,force_update=False):
+       
+#         # print('##LoraPrompt',__file__)
+#         # 从本地数据库读取
+#         json_tags_path = os.path.join(os.path.dirname(os.path.dirname(__file__)),r'data/loras_tags.json')
+
+#         if not os.path.exists(json_tags_path):
+#             save_json({},json_tags_path)
+
+#         lora_tags = load_json(json_tags_path)
+#         output_tags = lora_tags.get(lora_name, None) if lora_tags is not None else None
+#         if output_tags is not None:
+#             output_tags = ",".join(output_tags)
+#             print("trainedWords:",output_tags)
+#         else:
+#             output_tags = ""
+
+
+#         lora_path = folder_paths.get_full_path("loras", lora_name)
+#         if output_tags == "" or force_update:
+#             print("calculating lora hash")
+#             LORAsha256 = calculate_sha256(lora_path)
+#             print("requesting infos")
+#             model_info = get_model_version_info(LORAsha256)
+#             if model_info is not None:
+#                 if "trainedWords" in model_info:
+#                     print("tags found!")
+#                     if lora_tags is None:
+#                         lora_tags = {}
+#                     lora_tags[lora_name] = model_info["trainedWords"]
+#                     save_json(lora_tags,json_tags_path)
+#                     output_tags = ",".join(model_info["trainedWords"])
+#                     print("trainedWords:",output_tags)
+#             else:
+#                 print("No informations found.")
+#                 if lora_tags is None:
+#                     lora_tags = {}
+#                 lora_tags[lora_name] = []
+#                 save_json(lora_tags,json_tags_path)
+
+
+#         weight = round(weight, 3)
+#         prompt=[]
+#         for p in output_tags.split(','):
+            
+#             if weight!=1:
+#                 prompt.append('('+p+':'+str(weight)+')')
+#             else:
+#                 prompt.append(p)
+
+#         prompt=",".join(prompt)
+
+#         return (lora_name,prompt,output_tags.split(','),)
 
 
 
@@ -426,7 +551,7 @@ class JoinWithDelimiter:
     def INPUT_TYPES(s):
         return {"required": {
                     "text_list": (any_type,),
-                    "delimiter":(["newline","comma"],),
+                    "delimiter":(["newline","comma","backslash","space"],),
                              },
                 }
     
@@ -445,6 +570,10 @@ class JoinWithDelimiter:
             delimiter='\n'
         elif delimiter=='comma':
             delimiter=','
+        elif delimiter=='backslash':
+            delimiter='\\'
+        elif delimiter=='space':
+            delimiter=' '
         t=''
         if isinstance(text_list, list):
             t=join_with_(text_list,delimiter)

nodes/RembgNode.py

@@ -8,6 +8,467 @@ import comfy.utils
 import numpy as np
 import torch
 
+from huggingface_hub import hf_hub_download
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision.transforms.functional import normalize
+# BRIA-RMBG-1.4 / briarmbg.py
+class REBNCONV(nn.Module):
+    def __init__(self,in_ch=3,out_ch=3,dirate=1,stride=1):
+        super(REBNCONV,self).__init__()
+
+        self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate,stride=stride)
+        self.bn_s1 = nn.BatchNorm2d(out_ch)
+        self.relu_s1 = nn.ReLU(inplace=True)
+
+    def forward(self,x):
+
+        hx = x
+        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
+
+        return xout
+
+## upsample tensor 'src' to have the same spatial size with tensor 'tar'
+def _upsample_like(src,tar):
+
+    src = F.interpolate(src,size=tar.shape[2:],mode='bilinear')
+
+    return src
+
+
+### RSU-7 ###
+class RSU7(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
+        super(RSU7,self).__init__()
+
+        self.in_ch = in_ch
+        self.mid_ch = mid_ch
+        self.out_ch = out_ch
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) ## 1 -> 1/2
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+        b, c, h, w = x.shape
+
+        hx = x
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+        hx = self.pool5(hx5)
+
+        hx6 = self.rebnconv6(hx)
+
+        hx7 = self.rebnconv7(hx6)
+
+        hx6d =  self.rebnconv6d(torch.cat((hx7,hx6),1))
+        hx6dup = _upsample_like(hx6d,hx5)
+
+        hx5d =  self.rebnconv5d(torch.cat((hx6dup,hx5),1))
+        hx5dup = _upsample_like(hx5d,hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+
+### RSU-6 ###
+class RSU6(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU6,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+        hx = self.pool4(hx4)
+
+        hx5 = self.rebnconv5(hx)
+
+        hx6 = self.rebnconv6(hx5)
+
+
+        hx5d =  self.rebnconv5d(torch.cat((hx6,hx5),1))
+        hx5dup = _upsample_like(hx5d,hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-5 ###
+class RSU5(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU5,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+        hx = self.pool3(hx3)
+
+        hx4 = self.rebnconv4(hx)
+
+        hx5 = self.rebnconv5(hx4)
+
+        hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-4 ###
+class RSU4(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
+        self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
+
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx = self.pool1(hx1)
+
+        hx2 = self.rebnconv2(hx)
+        hx = self.pool2(hx2)
+
+        hx3 = self.rebnconv3(hx)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
+
+        return hx1d + hxin
+
+### RSU-4F ###
+class RSU4F(nn.Module):
+
+    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
+        super(RSU4F,self).__init__()
+
+        self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
+
+        self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
+        self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
+        self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
+
+        self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
+
+        self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
+        self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
+        self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.rebnconvin(hx)
+
+        hx1 = self.rebnconv1(hxin)
+        hx2 = self.rebnconv2(hx1)
+        hx3 = self.rebnconv3(hx2)
+
+        hx4 = self.rebnconv4(hx3)
+
+        hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
+        hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
+        hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
+
+        return hx1d + hxin
+
+
+class myrebnconv(nn.Module):
+    def __init__(self, in_ch=3,
+                       out_ch=1,
+                       kernel_size=3,
+                       stride=1,
+                       padding=1,
+                       dilation=1,
+                       groups=1):
+        super(myrebnconv,self).__init__()
+
+        self.conv = nn.Conv2d(in_ch,
+                              out_ch,
+                              kernel_size=kernel_size,
+                              stride=stride,
+                              padding=padding,
+                              dilation=dilation,
+                              groups=groups)
+        self.bn = nn.BatchNorm2d(out_ch)
+        self.rl = nn.ReLU(inplace=True)
+
+    def forward(self,x):
+        return self.rl(self.bn(self.conv(x)))
+
+
+class BriaRMBG(nn.Module):
+
+    def __init__(self,in_ch=3,out_ch=1):
+        super(BriaRMBG,self).__init__()
+
+        self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1)
+        self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage1 = RSU7(64,32,64)
+        self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage2 = RSU6(64,32,128)
+        self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage3 = RSU5(128,64,256)
+        self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage4 = RSU4(256,128,512)
+        self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage5 = RSU4F(512,256,512)
+        self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
+
+        self.stage6 = RSU4F(512,256,512)
+
+        # decoder
+        self.stage5d = RSU4F(1024,256,512)
+        self.stage4d = RSU4(1024,128,256)
+        self.stage3d = RSU5(512,64,128)
+        self.stage2d = RSU6(256,32,64)
+        self.stage1d = RSU7(128,16,64)
+
+        self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
+        self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
+        self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
+        self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
+        self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
+        self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
+
+        # self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
+
+    def forward(self,x):
+
+        hx = x
+
+        hxin = self.conv_in(hx)
+        #hx = self.pool_in(hxin)
+
+        #stage 1
+        hx1 = self.stage1(hxin)
+        hx = self.pool12(hx1)
+
+        #stage 2
+        hx2 = self.stage2(hx)
+        hx = self.pool23(hx2)
+
+        #stage 3
+        hx3 = self.stage3(hx)
+        hx = self.pool34(hx3)
+
+        #stage 4
+        hx4 = self.stage4(hx)
+        hx = self.pool45(hx4)
+
+        #stage 5
+        hx5 = self.stage5(hx)
+        hx = self.pool56(hx5)
+
+        #stage 6
+        hx6 = self.stage6(hx)
+        hx6up = _upsample_like(hx6,hx5)
+
+        #-------------------- decoder --------------------
+        hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
+        hx5dup = _upsample_like(hx5d,hx4)
+
+        hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
+        hx4dup = _upsample_like(hx4d,hx3)
+
+        hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
+        hx3dup = _upsample_like(hx3d,hx2)
+
+        hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
+        hx2dup = _upsample_like(hx2d,hx1)
+
+        hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
+
+
+        #side output
+        d1 = self.side1(hx1d)
+        d1 = _upsample_like(d1,x)
+
+        d2 = self.side2(hx2d)
+        d2 = _upsample_like(d2,x)
+
+        d3 = self.side3(hx3d)
+        d3 = _upsample_like(d3,x)
+
+        d4 = self.side4(hx4d)
+        d4 = _upsample_like(d4,x)
+
+        d5 = self.side5(hx5d)
+        d5 = _upsample_like(d5,x)
+
+        d6 = self.side6(hx6)
+        d6 = _upsample_like(d6,x)
+
+        return [F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)],[hx1d,hx2d,hx3d,hx4d,hx5d,hx6]
+    
+
+
+
+
+
 
 U2NET_HOME=os.path.join(folder_paths.models_dir, "rembg")
 os.environ["U2NET_HOME"] = U2NET_HOME
@@ -48,6 +509,70 @@ except:
     _available=False
 
 
+def briarmbg_run(images=[]):
+    mroot=os.path.join(folder_paths.models_dir, "rembg")
+    m=os.path.join(mroot,'briarmbg.pth')
+    if os.path.exists(m)==False:
+        # 下载
+        m1=hf_hub_download("briaai/RMBG-1.4",
+                       local_dir=mroot,
+                       filename='model.pth',
+                       local_dir_use_symlinks=False,
+                       endpoint='https://hf-mirror.com')
+        os.rename(m1, m)
+    
+    net=BriaRMBG()
+    if torch.cuda.is_available():
+        net.load_state_dict(torch.load(m))
+        net=net.cuda()
+    else:
+        net.load_state_dict(torch.load(m,map_location="cpu"))
+    net.eval() 
+
+    masks=[]
+    rgba_images=[]
+    rgb_images=[]
+    for orig_image in images:
+
+        w,h = orig_im_size = orig_image.size
+
+        image = orig_image.convert('RGB')
+        model_input_size = (1024, 1024)
+        image = image.resize(model_input_size, Image.BILINEAR)
+
+        im_np = np.array(image)
+        im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2,0,1)
+        im_tensor = torch.unsqueeze(im_tensor,0)
+        im_tensor = torch.divide(im_tensor,255.0)
+        im_tensor = normalize(im_tensor,[0.5,0.5,0.5],[1.0,1.0,1.0])
+        if torch.cuda.is_available():
+            im_tensor=im_tensor.cuda()
+
+        result=net(im_tensor)
+        result = torch.squeeze(F.interpolate(result[0][0], size=(h,w), mode='bilinear') ,0)
+        ma = torch.max(result)
+        mi = torch.min(result)
+        result = (result-mi)/(ma-mi)
+        im_array = (result*255).cpu().data.numpy().astype(np.uint8)
+        mask = Image.fromarray(np.squeeze(im_array))
+        # mask.save('test.png')
+        # mask=tensor2pil(result)
+        mask=mask.convert('L') 
+
+        masks.append(mask)
+
+        # rgba图
+        image_rgba =orig_image.convert("RGBA")
+        image_rgba.putalpha(mask)
+        rgba_images.append(image_rgba)
+
+        #rgb
+        rgb_image = Image.new("RGB", image_rgba.size, (0, 0, 0))
+        rgb_image.paste(image_rgba, mask=image_rgba.split()[3])
+        rgb_images.append(rgb_image)
+    return (masks,rgba_images,rgb_images)   
+
+
 def run_bg(model_name= "unet",images=[]):
     # model_name = "unet" # "isnet-general-use"
     rembg_session = new_session(model_name)
@@ -118,14 +643,16 @@ class RembgNode_:
     def INPUT_TYPES(s):
         return {"required": {
             "image": ("IMAGE",),
-            "model_name": (["u2net",
+            "model_name": ([
+                            "briarmbg",
+                            "u2net",
                             "u2netp",
                             "u2net_human_seg",
                             "u2net_cloth_seg",
                             "silueta",
                             "isnet-general-use",
                             "isnet-anime",
-                            # "sam"
+                            
                             ],),
             
                              },
@@ -153,7 +680,10 @@ class RembgNode_:
                 im=tensor2pil(im)
                 images.append(im)
 
-        masks,rgba_images,rgb_images=run_bg(model_name,images)
+        if model_name=='briarmbg':
+            masks,rgba_images,rgb_images=briarmbg_run(images)
+        else:
+            masks,rgba_images,rgb_images=run_bg(model_name,images)
 
         masks=[pil2tensor(m) for m in masks]
 

nodes/Utils.py

@@ -6,7 +6,7 @@ import numpy as np
 import folder_paths
 import matplotlib.font_manager as fm
 import torch
-
+import importlib.util
 
 
 def recursive_search(directory, excluded_dir_names=None):
@@ -347,7 +347,7 @@ class MultiplicationNode:
         return {"required": {
                     "numberA":(any_type,),
                     "multiply_by":("FLOAT", {
-                        "default": 0, 
+                        "default": 1, 
                         "min": -2, #Minimum value
                         "max": 0xffffffffffffffff,
                         "step": 0.01, #Slider's step
@@ -381,7 +381,7 @@ class TextInput:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
-                    "text": ("STRING",{"multiline": True,"default": ""}),
+                    "text": ("STRING",{"multiline": True,"default": ""})
                              },
                 }
     
@@ -560,9 +560,11 @@ class SwitchByIndex:
     @classmethod
     def INPUT_TYPES(cls):
         return {
-            "required": {
-                "A":(any_type,),
-                "B":(any_type,),
+             "optional":{
+                  "A":(any_type,),
+                  "B":(any_type,),
+            },
+            "required": { 
                 "index":("INT", {
                         "default": -1, 
                         "min": -1, 
@@ -574,17 +576,17 @@ class SwitchByIndex:
             }
         }
 
-    RETURN_TYPES = (any_type,)
-    RETURN_NAMES = ("C",)
+    RETURN_TYPES = (any_type,"INT",)
+    RETURN_NAMES = ("C","count",)
 
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/Utils"
 
     INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,)
+    OUTPUT_IS_LIST = (True,False,)
 
-    def run(self, A,B,index,flat):
+    def run(self, A=[],B=[],index=-1,flat='on'):
 
         flat=flat[0]
 
@@ -604,8 +606,8 @@ class SwitchByIndex:
                 C=[C[index]]
             except Exception as e:
                 C=[]
-        
-        return (C,)
+
+        return (C,len(C),)
 
 
 
@@ -695,7 +697,9 @@ class ListStatistics:
 class TESTNODE_:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "ANY":(any_type,), },
+        return {"required": { 
+            "ANY":(any_type,),                
+                              },
                 }
     
     RETURN_TYPES = (any_type,)
@@ -709,16 +713,62 @@ class TESTNODE_:
     OUTPUT_IS_LIST = (True,)
 
     def run(self,ANY):
-        print(ANY)
+        # print(ANY)
         # data=ANY
         list_stats = ListStatistics()
 
         # 调用count_types方法进行统计
         result = list_stats.count_types(ANY)
-            
+ 
+
+        # 假设我们有一个模块文件名为 my_module.py，它位于 'importables' 目录下
+        module_path = os.path.join(os.path.dirname(__file__),'test.py')
+
+        # 使用 spec_from_file_location 获取模块的元数据（名称、定义等）
+        spec = importlib.util.spec_from_file_location('test', module_path)
+
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+
+        functions = getattr(module, 'run')  # 获取函数
+        
+        functions(ANY)
+
+
         return {"ui": {"data": result,"type":[str(type(ANY[0]))]}, "result": (ANY,)}
 
 
+class TESTNODE_TOKEN:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "text":("STRING", {"forceInput": True,}),
+            "clip": ("CLIP", )                 
+                              },
+                }
+    
+    RETURN_TYPES = ("STRING",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/__TEST"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self,text,clip=None):
+        # print(text)
+        
+        tokens = clip.tokenize(text)
+        
+        tokens=[v for v in tokens.values()][0][0]
+
+        tokens=json.dumps(tokens)
+        
+        return (tokens,)
+
+
 
 class CreateSeedNode:
     def __init__(self):

nodes/Vae.py

@@ -1,179 +0,0 @@
-# https://github.com/openai/consistencydecoder/blob/main/consistencydecoder/__init__.py
-
-import folder_paths
-from comfy import model_management
-
-import math
-import torch
-import numpy as np
-from PIL import Image 
-
-
-class ConsistencyDecoderWrapper:
-    def __init__(self, decoder):
-        self.decoder = decoder
-    def decode(self, x):
-        return self.decoder(x)
-
-def _extract_into_tensor(arr, timesteps, broadcast_shape):
-    
-    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895    """
-    res = arr[timesteps].float()
-    dims_to_append = len(broadcast_shape) - len(res.shape)
-    return res[(...,) + (None,) * dims_to_append]
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L45
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return torch.tensor(betas)
-
-class ConsistencyDecoder:
-    def __init__(self, device="cuda:0", download_target=""):
-        self.n_distilled_steps = 64
-        # download_target = _download("https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt", download_root)
-        self.ckpt = torch.jit.load(download_target).to(device)
-        self.device = device 
-        sigma_data = 0.5
-        betas = betas_for_alpha_bar(
-            1024, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-        ).to(device)
-        alphas = 1.0 - betas
-        alphas_cumprod = torch.cumprod(alphas, dim=0)
-        self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
-        self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
-        sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
-        sigmas = torch.sqrt(1.0 / alphas_cumprod - 1)
-        self.c_skip = (
-            sqrt_recip_alphas_cumprod
-            * sigma_data**2
-            / (sigmas**2 + sigma_data**2)
-        )
-        self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5
-        self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5
-
-    @staticmethod
-    def round_timesteps(
-        timesteps, total_timesteps, n_distilled_steps, truncate_start=True
-    ):
-        with torch.no_grad():
-            space = torch.div(total_timesteps, n_distilled_steps, rounding_mode="floor")
-            rounded_timesteps = (
-                torch.div(timesteps, space, rounding_mode="floor") + 1
-            ) * space
-            if truncate_start:
-                rounded_timesteps[rounded_timesteps == total_timesteps] -= space
-            else:
-                rounded_timesteps[rounded_timesteps == total_timesteps] -= space
-                rounded_timesteps[rounded_timesteps == 0] += space
-            return rounded_timesteps
-
-    @staticmethod
-    def ldm_transform_latent(z, extra_scale_factor=1):
-        channel_means = [0.38862467, 0.02253063, 0.07381133, -0.0171294]
-        channel_stds = [0.9654121, 1.0440036, 0.76147926, 0.77022034]
-
-        if len(z.shape) != 4:
-            raise ValueError()
-
-        z = z * 0.18215
-        channels = [z[:, i] for i in range(z.shape[1])]
-
-        channels = [
-            extra_scale_factor * (c - channel_means[i]) / channel_stds[i]
-            for i, c in enumerate(channels)
-        ]
-        return torch.stack(channels, dim=1)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        features: torch.Tensor,
-        schedule=[1.0, 0.5],
-    ):
-        features = self.ldm_transform_latent(features)
-  
-        ts = self.round_timesteps(
-            torch.arange(0, 1024),
-            1024,
-            self.n_distilled_steps,
-            truncate_start=False,
-        )
-        shape = (
-            features.size(0),
-            3,
-            8 * features.size(2),
-            8 * features.size(3),
-        )
-        
-        x_start = torch.zeros(shape, device=features.device, dtype=features.dtype)
-        schedule_timesteps = [int((1024 - 1) * s) for s in schedule]
-        for i in schedule_timesteps:
-            t = ts[i].item()
-            t_ = torch.tensor([t] * features.shape[0]).to(self.device)
-            noise = torch.randn_like(x_start)
-            
-            x_start = (
-                _extract_into_tensor(self.sqrt_alphas_cumprod, t_, x_start.shape)
-                * x_start
-                + _extract_into_tensor(
-                    self.sqrt_one_minus_alphas_cumprod, t_, x_start.shape
-                )
-                * noise
-            )
-            c_in = _extract_into_tensor(self.c_in, t_, x_start.shape)
-            model_output = self.ckpt(c_in * x_start, t_, features=features)
-            B, C = x_start.shape[:2]
-            model_output, _ = torch.split(model_output, C, dim=1)
-            pred_xstart = (
-                _extract_into_tensor(self.c_out, t_, x_start.shape) * model_output
-                + _extract_into_tensor(self.c_skip, t_, x_start.shape) * x_start
-            ).clamp(-1, 1)
-            x_start = pred_xstart
-        return x_start
-
-
-
-class VAELoader:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "vae_name": (folder_paths.get_filename_list("vae"), )}}
-    RETURN_TYPES = ("VAE",)
-    FUNCTION = "load_vae"
-
-    CATEGORY = "♾️Mixlab/__TEST"
-
-    #TODO: scale factor?
-    def load_vae(self, vae_name):
-        vae_path = folder_paths.get_full_path("vae", vae_name)
-        device = 'cuda:0'
-        # print('device',device)
-        consistencyDecoder = ConsistencyDecoder(device=device,
-                                 download_target=vae_path) # Model size: 2.49 GB
-        vae = ConsistencyDecoderWrapper(consistencyDecoder)
-        return (vae,)
-    
-
-class VAEDecode:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "decode"
-
-    CATEGORY = "♾️Mixlab/__TEST"
-
-    def decode(self, vae, samples):
-        image = vae.decode(samples["samples"].to("cuda:0"))
-        image = image[0].cpu().numpy()
-        image = (image + 1.0) * 127.5
-        image = image.clip(0, 255).astype(np.uint8)
-        image = Image.fromarray(image.transpose(1, 2, 0))
-        image = image.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image)[None,]
-        return (image, )

nodes/test.py

@@ -0,0 +1,8 @@
+import folder_paths
+
+# 外挂一个文件，用来编写新的节点
+def run(v):
+
+    output_dir = folder_paths.get_temp_directory()
+
+    print('1323',v,output_dir)

requirements.txt

@@ -5,4 +5,6 @@ opencv-python-headless
 matplotlib
 openai
 simple-lama-inpainting
-clip-interrogator==0.6.0
+clip-interrogator==0.6.0
+transformers>=4.36.0
+zhipuai

web/index.html

@@ -11,6 +11,12 @@
             padding: 0;
         }
 
+        .header {
+            display: flex;
+            align-items: center;
+            justify-content: space-around;
+        }
+
         .app {
             display: flex;
             width: 90%;
@@ -142,6 +148,10 @@
             word-wrap: break-word;
         }
 
+        .description img {
+            min-height: unset !important;
+        }
+
         .panel {
             display: flex;
             flex-direction: column;
@@ -172,6 +182,7 @@
             width: fit-content;
             max-width: 100%;
             margin-left: 12px;
+            min-height: 200px;
         }
 
         .input_card {
@@ -378,9 +389,7 @@
 
 <body>
     <div id="editor_container"></div>
-    <div style="display: flex;
-    align-items: center;
-    justify-content: space-around;">
+    <div class="header">
         <div style="margin: 0 24px;
             margin-bottom: 24px;
             padding: 8px;
@@ -435,8 +444,9 @@
                 body
             })
 
-            // console.log(resp)
+
             let data = await resp.json()
+            // console.log(data)
             let { name, subfolder } = data
             let src = `${url}/view?filename=${encodeURIComponent(
                 name
@@ -643,7 +653,7 @@
             return true
         }
 
-
+        // 种子的处理
         function randomSeed(seed, data) {
             for (const id in data) {
                 if (data[id].inputs.seed != undefined
@@ -888,6 +898,49 @@
         }
 
 
+        function generateRainbowVideo() {
+
+            // 创建一个canvas元素
+            const canvas = document.createElement('canvas');
+            canvas.width = 640; // 设置canvas宽度
+            canvas.height = 480; // 设置canvas高度
+            const context = canvas.getContext('2d');
+
+            // 绘制第一帧彩虹
+            context.fillStyle = 'red';
+            context.fillRect(0, 0, canvas.width / 2, canvas.height);
+            context.fillStyle = 'orange';
+            context.fillRect(canvas.width / 2, 0, canvas.width / 2, canvas.height);
+
+            // 绘制第二帧彩虹
+            context.fillStyle = 'yellow';
+            context.fillRect(0, 0, canvas.width / 2, canvas.height);
+            context.fillStyle = 'green';
+            context.fillRect(canvas.width / 2, 0, canvas.width / 2, canvas.height);
+
+            const stream = canvas.captureStream();
+
+            return new Promise((res, rej) => {
+                // 导出视频
+                const mediaRecorder = new MediaRecorder(stream);
+                const chunks = [];
+                mediaRecorder.ondataavailable = function (event) {
+                    chunks.push(event.data);
+                };
+                mediaRecorder.onstop = function () {
+                    const blob = new Blob(chunks, { type: 'video/mp4' });
+                    const url = URL.createObjectURL(blob);
+                    res(url)
+                };
+                mediaRecorder.start();
+                setTimeout(function () {
+                    mediaRecorder.stop();
+                }, 1000); // 设置录制时长，这里设置为1秒
+            })
+
+        }
+
+
         async function calculateImageHash(blob) {
             const buffer = await blob.arrayBuffer();
             const hashBuffer = await crypto.subtle.digest('SHA-256', buffer);
@@ -1081,20 +1134,25 @@
             inputData = inputData.filter(inp => inp);
             // console.log('inputData',inputData)
             inputData.forEach(data => {
-                // console.log(data)
-                // Check if the class_type is "LoadImage"
-                if (data.class_type === "LoadImage") {
+                console.log(data)
+
+                // 图片 or 视频输入
+                if (data.class_type === "LoadImage" || data.class_type === "VHS_LoadVideo") {
+
+                    let isVideoUpload = data.class_type === "VHS_LoadVideo";
+
                     // Create a container for the upload control
                     const uploadContainer = document.createElement("div");
                     uploadContainer.className = 'card';
 
                     // Create a label for the upload control
                     const nameLabel = document.createElement("label");
-                    nameLabel.textContent = data.title || "LoadImage: ";
+                    nameLabel.textContent = data.title || (isVideoUpload ? "LoadVideo: " : "LoadImage: ");
                     nameLabel.style.marginBottom = '12px'
                     uploadContainer.appendChild(nameLabel);
 
                     let actionDiv = document.createElement('div');
+                    actionDiv.style = `padding: 0 8px;`
 
                     // Create an input field for the image name
                     const uploadImageInput = document.createElement("button");
@@ -1109,25 +1167,55 @@
                     const btnFromClipboard = document.createElement("button");
                     btnFromClipboard.style = `width: 156px; margin-left: 18px;`
                     btnFromClipboard.innerText = 'paste from clipboard'
-                    actionDiv.appendChild(btnFromClipboard);
+                    if (!isVideoUpload) actionDiv.appendChild(btnFromClipboard);
 
                     const btnForImageEdit = document.createElement("button");
                     btnForImageEdit.style = ` width: 32px; background: none;margin-left: 18px;`
                     btnForImageEdit.innerHTML = '<?xml version="1.0" ?><svg version="1.1" style="width: 24px;" viewBox="0 0 50 50" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g id="Layer_1_1_"><path d="M18.293,31.707h6.414l24-24l-6.414-6.414l-24,24V31.707z M45.879,7.707l-3.586,3.586l-3.586-3.586l3.586-3.586   L45.879,7.707z M20.293,26.121l17-17l3.586,3.586l-17,17h-3.586V26.121z"/><polygon points="43.293,19.707 41.293,19.707 41.293,46.707 3.293,46.707 3.293,8.707 31.293,8.707 31.293,6.707 1.293,6.707    1.293,48.707 43.293,48.707  "/></g></svg>'
-                    actionDiv.appendChild(btnForImageEdit);
+                    if (!isVideoUpload) actionDiv.appendChild(btnForImageEdit);
 
 
                     uploadContainer.appendChild(actionDiv)
 
                     // Create an image element to display the uploaded image
-                    const imageElement = document.createElement("img");
-                    imageElement.src = base64Df
+                    let imageElement = document.createElement("img");
+                    if (isVideoUpload) {
+                        // 视频
+                        imageElement = document.createElement('video');
+                        imageElement.setAttribute('controls', true)
+                        let [subfolder, name] = data.inputs.video.split('/');
+                        //    console.log(subfolder,name)
+                        if (!name) {
+                            subfolder = "";
+                            name = data.inputs.video;
+                        }
+                        let url = `${get_url()}/view?filename=${encodeURIComponent(name)}&type=input&subfolder=${subfolder}&rand=${Math.random()}`
+                        imageElement.src = url;
+
+                        // imageElement.innerHTML=`<img src="${base64Df}"/>`
+
+                    } else {
+                        // 图片
+                        let [subfolder, name] = data.inputs.image.split('/');
+                        if (!name) {
+                            subfolder = "";
+                            name = data.inputs.image;
+                        }
+                        // imageElement.src = base64Df
+                        let url = `${get_url()}/view?filename=${encodeURIComponent(name)}&type=input&subfolder=${subfolder}&rand=${Math.random()}`
+                        imageElement.src = url;
+                        imageElement.setAttribute('onerror',`this.src='${base64Df}'`)
+                       
+                        
+                    }
+
                     imageElement.style.maxWidth = '200px';
 
 
-                    btnFromClipboard.addEventListener('click', (event) => handleClipboardImage(imageElement, data));
 
-                    btnForImageEdit.addEventListener('click', e => editImage(imageElement, data))
+                    if (!isVideoUpload) btnFromClipboard.addEventListener('click', (event) => handleClipboardImage(imageElement, data));
+
+                    if (!isVideoUpload) btnForImageEdit.addEventListener('click', e => editImage(imageElement, data))
 
 
                     uploadImageInput.addEventListener('click', (event) => {
@@ -1145,15 +1233,24 @@
                         reader.onloadend = async function () {
                             // 获取读取的文件内容，即 Blob 对象
                             const fileBlob = new Blob([reader.result], { type: file.type });
-
+                            // console.log( file.type.split('/')[1])
                             let hashId = await calculateImageHash(fileBlob)
 
                             if (hashId == window._appData.data[data.id].hashId) return
 
-                            let { url, name } = await uploadImage(fileBlob)
+                            let { url, name } = await uploadImage(fileBlob, '.' + file.type.split('/')[1])
+                            if (isVideoUpload) {
+                                imageElement.srcObject = null;
+                            }
                             // 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;
+
+                            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);
@@ -1172,6 +1269,7 @@
                     container.appendChild(uploadContainer);
                 }
 
+                // 滑块输入
                 if (["PromptSlide"].includes(data.class_type)) {
                     // 滑块输入
                     let options = data.options || {
@@ -1201,6 +1299,7 @@
                 }
 
 
+                // 数字输入支持
                 if (['FloatSlider', 'IntNumber'].includes(data.class_type)) {
                     // console.log('data.options',data.options)
                     // 滑块输入
@@ -1223,8 +1322,8 @@
                     container.appendChild(silde);
                 }
 
-                // Check if the class_type is "CLIPTextEncode"
-                if (["TextInput_", "CLIPTextEncode", "PromptSimplification"].includes(data.class_type)) {
+                // 文本输入支持
+                if (["TextInput_", "CLIPTextEncode", "PromptSimplification","ChinesePrompt_Mix"].includes(data.class_type)) {
                     // Create a container for the upload control
                     const uploadContainer = document.createElement("div");
                     uploadContainer.className = 'card';
@@ -1289,7 +1388,7 @@
                     container.appendChild(uploadContainer);
                 }
 
-
+                // lora的输入支持
                 if (["CheckpointLoaderSimple", "LoraLoader"].includes(data.class_type)) {
                     let value = data.inputs.ckpt_name || data.inputs.lora_name;
 
@@ -1316,6 +1415,7 @@
                         }
                     }), value);
 
+                    // 选择事件绑定
                     selectDom.addEventListener('change', e => {
                         e.preventDefault();
                         // console.log(selectDom.value)
@@ -1332,6 +1432,7 @@
                     container.appendChild(div);
                 }
 
+                // 色彩选择器
                 if (["Color"].includes(data.class_type)) {
                     let value = data.inputs.color.hex || '#000000';
                     let d = document.createElement('div');
@@ -1550,6 +1651,7 @@
 
         }
 
+        // 创建下拉选择
         function createSelect(options, defaultValue) {
             var selectElement = document.createElement("select");
             selectElement.className = "select"
@@ -1558,7 +1660,7 @@
             for (var i = 0; i < options.length; i++) {
                 var option = document.createElement("option");
                 option.value = options[i].value;
-                option.text = options[i].text;
+                option.innerText = options[i].text;
                 selectElement.appendChild(option);
                 // if(options[i].selected) 
             }
@@ -1569,6 +1671,7 @@
             return selectElement
         }
 
+        // 创建下拉选择 - 带说明
         function createSelectWithOptions(title, options, defaultValue) {
 
             const div = document.createElement("div");
@@ -2012,9 +2115,10 @@
             };
 
             api.addEventListener("status", ({ detail }) => {
-                console.log("status", detail, detail.exec_info?.queue_remaining);
+                console.log("status", detail, detail?.exec_info?.queue_remaining);
                 try {
                     ui.status.update(`queue#${detail.exec_info?.queue_remaining}`);
+                    window.parent.postMessage({ cmd: 'status', data: `queue#${detail.exec_info?.queue_remaining}` }, '*');
                     if (detail.exec_info?.queue_remaining === 0) {
                         // 运行按钮重设
                         ui.submitButton.reset()
@@ -2022,6 +2126,7 @@
                     }
                 } catch (error) {
                     console.log(error)
+                    window.parent.postMessage({ cmd: 'status' }, '*');
                 }
 
             });
@@ -2121,6 +2226,7 @@
             api.addEventListener("execution_error", ({ detail }) => {
 
                 console.log("execution_error", detail)
+                window.parent.postMessage({ cmd: 'status', data: `execution_error:${JSON.stringify(detail)}` }, '*');
                 // show(URL.createObjectURL(detail));
             });
 
@@ -2150,7 +2256,6 @@
             api.init();
 
             // 外挂的UI
-
             createAllColorInput();
 
 
@@ -2201,7 +2306,7 @@
         }
 
         // 创建app的选择菜单
-        function createAppList(apps = []) {
+        function createAppList(apps = [], innerApp = false) {
             let details = document.createElement('details');
             details.className = 'apps';
 
@@ -2247,27 +2352,72 @@
                 // console.log(div)
             };
 
-            let uploadApp = createUploadJson(details);
-            div.appendChild(uploadApp);
+            if (!innerApp) {
+                let uploadApp = createUploadJson(details);
+                div.appendChild(uploadApp);
+            }
 
             document.body.appendChild(details);
         }
-
         async function init_app() {
 
-            const { category, filename } = getFilenameAndCategoryFromUrl(location.href);
-            window._apps = await get_my_app(category, filename);
+            const innerApp = checkIsInnerApp();
 
-            window._appData = window._apps[0];
+            if (!innerApp) {
+                const { category, filename } = getFilenameAndCategoryFromUrl(location.href);
+                window._apps = await get_my_app(category, filename);
 
-            createAppList(window._apps);
+                window._appData = window._apps[0];
 
-            createApp(window._appData);
+                createAppList(window._apps);
+
+                createApp(window._appData);
+            }
 
         };
 
         init_app();
 
+        // 支持内嵌app
+        function checkIsInnerApp() {
+            const url = new URL(window.location.href);
+            const params = new URLSearchParams(url.search);
+            const innerApp = params.get("innerApp");
+            // console.log(window.location.href, innerApp == 1, document.body);
+            if (innerApp == 1) {
+                document.body.querySelector('.header').style.display = 'none';
+                window.parent.postMessage({ innerApp, cmd: 'init' }, '*');
+
+                // 在iframe中监听来自父窗口的消息
+                window.addEventListener("message", function (event) {
+                    console.log("Received message from parent:", event.data);
+                    const { init, url } = event.data;
+
+                    window._hostUrl = url;
+
+                    window._apps = init;
+
+                    window._appData = window._apps[0];
+
+                    if (window._appData) {
+                        createAppList(window._apps, innerApp);
+
+                        createApp(window._appData);
+                    } else {
+                        // todo welcome页面
+                        document.body.innerHTML = `<h3 style="padding: 99px;">Welcome to Mixlab Nodes App!</h3>`
+                    }
+
+
+
+                });
+
+
+            }
+
+            return innerApp == 1
+
+        }
 
 
     </script>

web/javascript/app_mixlab.js

@@ -67,9 +67,13 @@ async function drawImageToCanvas (imageUrl) {
 }
 
 function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
-  const data = jsonData
-  const input = []
-  const output = []
+  // workflow
+  // const workflow=jsonData.workflow;
+  // const nodes=workflow.nodes;
+
+  const data = jsonData.output
+  let input = []
+  let output = []
   const seed = {}
 
   for (const id in data) {
@@ -77,7 +81,7 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
       let node = app.graph.getNodeById(id)
       if (inputIds.includes(id)) {
         // let node = app.graph.getNodeById(id)
-        let options = []
+        let options = {}
         // 模型
         try {
           if (node.type === 'CheckpointLoaderSimple') {
@@ -113,6 +117,15 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
         if (node.type == 'Color') {
         }
 
+        // loadImage的mask支持
+        if (node.type === 'LoadImage') {
+          let output = node.outputs.filter(ot => ot.type == 'MASK')[0]
+          if (output.links) {
+            // 有输出
+            options.hasMask = true
+          }
+        }
+
         input[inputIds.indexOf(id)] = {
           ...data[id],
           title: node.title,
@@ -138,6 +151,10 @@ function extractInputAndOutputData (jsonData, inputIds = [], outputIds = []) {
     }
   }
 
+  // 修复bug，当节点不存在时
+  input = input.filter(i => i)
+  output = output.filter(i => i)
+
   return { input, output, seed }
 }
 
@@ -211,8 +228,8 @@ async function save (json, download = false, showInfo = true) {
   try {
     let data = await app.graphToPrompt()
 
-    const { input, output, seed } = extractInputAndOutputData(
-      data.output,
+    let { input, output, seed } = extractInputAndOutputData(
+      data,
       inputIds,
       outputIds
     )
@@ -260,7 +277,7 @@ async function save (json, download = false, showInfo = true) {
 
 function getInputsAndOutputs () {
   const inputs =
-      `LoadImage CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+      `LoadImage VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
         ' '
       ),
     outputs = `PreviewImage SaveImage ShowTextForGPT VHS_VideoCombine`.split(

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 = 'v0.15.0'
+const version = 'v0.17.1'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/gpt_mixlab.js

@@ -68,7 +68,7 @@ app.registerExtension({
           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
+            return [128, 32] // a method to compute the current size of the widget
           },
           async serializeValue (nodeId, widgetIndex) {
             let data = getLocalData('_mixlab_api_key')
@@ -203,76 +203,82 @@ app.registerExtension({
 
 app.registerExtension({
   name: 'Mixlab.GPT.ShowTextForGPT',
-  async beforeRegisterNodeDef(nodeType, nodeData, app) {
-		if (nodeData.name === "ShowTextForGPT") {
-			function populate(text) {
-				if (this.widgets) {
-          
-					const pos = this.widgets.findIndex((w) => w.name === "text");
-					if (pos !== -1) {
-						for (let i = pos; i < this.widgets.length; i++) {
-							this.widgets[i].onRemove?.();
-						}
-						this.widgets.length = pos;
-					}
-				}
-        // console.log('ShowTextForGPT',text)
-				for (let list of text) {
-					const w = ComfyWidgets["STRING"](this, "text", ["STRING", { multiline: true }], app).widget;
-					w.inputEl.readOnly = true;
-					w.inputEl.style.opacity = 0.6;
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData.name === 'ShowTextForGPT') {
+      function populate (text) {
+        text = text.filter(t => t && t?.trim())
 
-          try {
-            let data=JSON.parse(list);
-            data=Array.from(data,d=>{
-              return {
-                ...d,
-                content:decodeURIComponent(d.content)
-              }
-            })
-            list=JSON.stringify(data,null,2)
-          } catch (error) {
-            // console.log(error)
+        if (this.widgets) {
+          // const pos = this.widgets.findIndex(w => w.name === 'text')
+          for (let i = 0; i < this.widgets.length; i++) {
+            if (this.widgets[i].name == 'show_text') this.widgets[i].onRemove?.()
           }
+          this.widgets.length = 1
+        }
+        // console.log('ShowTextForGPT',text)
+        for (let list of text) {
+          if (list) {
+            // console.log('#####', list)
+            const w = ComfyWidgets['STRING'](
+              this,
+              'show_text',
+              ['STRING', { multiline: true }],
+              app
+            ).widget
+            w.inputEl.readOnly = true
+            w.inputEl.style.opacity = 0.6
 
-					w.value =list;
-        
-				}
+            try {
+              if (typeof list != 'string') {
+                let data = JSON.parse(list)
+                data = Array.from(data, d => {
+                  return {
+                    ...d,
+                    content: decodeURIComponent(d.content)
+                  }
+                })
+                list = JSON.stringify(data, null, 2)
+              }
+            } catch (error) {
+              console.log(error)
+            }
+
+            w.value = list
+          }
+        }
         // console.log('ShowTextForGPT',this.widgets.length)
-				requestAnimationFrame(() => {
-					const sz = this.computeSize();
-					if (sz[0] < this.size[0]) {
-						sz[0] = this.size[0];
-					}
-					if (sz[1] < this.size[1]) {
-						sz[1] = this.size[1];
-					}
-					this.onResize?.(sz);
-					app.graph.setDirtyCanvas(true, false);
-				});
-			}
+        requestAnimationFrame(() => {
+          if (this) {
+            const sz = this.computeSize()
+            if (sz[0] < this.size[0]) {
+              sz[0] = this.size[0]
+            }
+            if (sz[1] < this.size[1]) {
+              sz[1] = this.size[1]
+            }
+            this.onResize?.(sz)
+            app.graph.setDirtyCanvas(true, false)
+          }
+        })
+      }
 
-			// When the node is executed we will be sent the input text, display this in the widget
-			const onExecuted = nodeType.prototype.onExecuted;
-			nodeType.prototype.onExecuted = function (message) {
-				onExecuted?.apply(this, arguments);
-        console.log('##',message.text)
-				populate.call(this, message.text);
-			};
+      // When the node is executed we will be sent the input text, display this in the widget
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        // console.log('##onExecuted', this, message)
+        if (message.text) populate.call(this, message.text)
+      }
 
-			const onConfigure = nodeType.prototype.onConfigure;
-			nodeType.prototype.onConfigure = function () {
-				onConfigure?.apply(this, arguments);
-				if (this.widgets_values?.length) {
-          
-					populate.call(this, this.widgets_values);
-				}
-			};
+      const onConfigure = nodeType.prototype.onConfigure
+      nodeType.prototype.onConfigure = function () {
+        onConfigure?.apply(this, arguments)
+        if (this.widgets_values?.length) {
+          populate.call(this, this.widgets_values)
+        }
+      }
 
       this.serialize_widgets = true //需要保存参数
-
-		}
-
-
-	},
+    }
+  }
 })

web/javascript/prompt_mixlab.js

@@ -408,7 +408,7 @@ const _createResult = async (node, widget, message) => {
   const width = node.size[0] * 0.5 - 12
 
   let height_add = 0
-
+  
   for (let index = 0; index < message._images.length; index++) {
     const imgs = message._images[index]
 
@@ -559,8 +559,7 @@ app.registerExtension({
 
       let cards = widget.div.querySelectorAll('.card')
       if (cards.length == 0) node.size = [280, 120]
-
-      _createResult(node, widget, widget.value)
+      if(widget.value) _createResult(node, widget, widget.value)
     }
   }
 })

web/javascript/ui_mixlab.js

@@ -1,5 +1,6 @@
 import { app } from '../../../scripts/app.js'
 import { closeIcon } from './svg_icons.js'
+import { api } from '../../../scripts/api.js'
 
 import {
   GroupNodeConfig,
@@ -8,6 +9,172 @@ import {
 
 import { smart_init, addSmartMenu } from './smart_connect.js'
 
+let isScriptLoaded = {}
+
+function loadExternalScript (url) {
+  return new Promise((resolve, reject) => {
+    if (isScriptLoaded[url]) {
+      resolve()
+      return
+    }
+
+    const script = document.createElement('script')
+    script.src = url
+    script.onload = () => {
+      isScriptLoaded[url] = true
+      resolve()
+    }
+    script.onerror = reject
+    document.head.appendChild(script)
+  })
+}
+
+//
+
+function createChart (chartDom, nodes) {
+  var myChart = echarts.init(chartDom)
+  var option
+
+  console.log(nodes)
+  option = {
+    series: [
+      {
+        type: 'treemap',
+        data: [
+          {
+            name: 'nodeA',
+            value: 10,
+            children: Array.from(nodes, n => {
+              return {
+                name: n.type,
+                value: n.count
+              }
+            })
+          }
+        ]
+      }
+    ]
+  }
+
+  option && myChart.setOption(option)
+}
+
+async function createNodesCharts () {
+  await loadExternalScript(
+    '/extensions/comfyui-mixlab-nodes/lib/echarts.min.js'
+  )
+  const templates = await loadTemplate()
+  var nodes = {}
+  Array.from(templates, t => {
+    let j = JSON.parse(t.data)
+    for (let node of j.nodes) {
+      if (!nodes[node.type]) nodes[node.type] = { type: node.type, count: 0 }
+      nodes[node.type].count++
+    }
+  })
+  nodes = Object.values(nodes).sort((a, b) => b.count - a.count)
+
+  const menu = document.querySelector('.comfy-menu')
+  const separator = document.createElement('div')
+  separator.style = `margin: 20px 0px;
+  width: 100%;
+  height: 1px;
+  background: var(--border-color);
+  `
+  menu.append(separator)
+
+  const appsButton = document.createElement('button')
+  appsButton.textContent = 'Nodes'
+
+  appsButton.onclick = () => {
+    let div = document.querySelector('#mixlab_apps')
+    if (!div) {
+      div = document.createElement('div')
+      div.id = 'mixlab_apps'
+      document.body.appendChild(div)
+
+      let btn = document.createElement('div')
+      btn.style = `display: flex;
+     width: calc(100% - 24px);
+     justify-content: space-between;
+     align-items: center;
+     padding: 0 12px;
+     height: 44px;`
+      let btnB = document.createElement('button')
+      let textB = document.createElement('p')
+      btn.appendChild(textB)
+      btn.appendChild(btnB)
+      textB.style.fontSize = '12px'
+      textB.innerText = `Nodes`
+
+      btnB.style = `float: right; border: none; color: var(--input-text);
+     background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
+      btnB.addEventListener('click', () => {
+        div.style.display = 'none'
+      })
+      btnB.innerText = 'X'
+
+      // 悬浮框拖动事件
+      div.addEventListener('mousedown', function (e) {
+        var startX = e.clientX
+        var startY = e.clientY
+        var offsetX = div.offsetLeft
+        var offsetY = div.offsetTop
+
+        function moveBox (e) {
+          var newX = e.clientX
+          var newY = e.clientY
+          var deltaX = newX - startX
+          var deltaY = newY - startY
+          div.style.left = offsetX + deltaX + 'px'
+          div.style.top = offsetY + deltaY + 'px'
+          localStorage.setItem(
+            'mixlab_app_pannel',
+            JSON.stringify({ x: div.style.left, y: div.style.top })
+          )
+        }
+
+        function stopMoving () {
+          document.removeEventListener('mousemove', moveBox)
+          document.removeEventListener('mouseup', stopMoving)
+        }
+
+        document.addEventListener('mousemove', moveBox)
+        document.addEventListener('mouseup', stopMoving)
+      })
+
+      div.appendChild(btn)
+
+      let chartDom = document.createElement('div')
+      chartDom.style = `height:80vh;width:450px`
+      chartDom.className = 'chart'
+      div.appendChild(chartDom)
+    }
+    if (div.style.display == 'flex') {
+      div.style.display = 'none'
+    } else {
+      let pos = JSON.parse(
+        localStorage.getItem('mixlab_app_pannel') ||
+          JSON.stringify({ x: 0, y: 0 })
+      )
+
+      div.style = `
+      flex-direction: column;
+      align-items: end;
+      display:flex;
+      position: absolute; 
+      top: ${pos.y}; left: ${pos.x}; width: 450px; 
+      color: var(--descrip-text);
+      background-color: var(--comfy-menu-bg);
+      padding: 10px; 
+      border: 1px solid black;z-index: 999999999;padding-top: 0;`
+    }
+
+    createChart(div.querySelector('.chart'), nodes)
+  }
+  menu.append(appsButton)
+}
+
 function copyNodeValues (src, dest) {
   // title
   dest.title = src.title
@@ -191,7 +358,14 @@ injectCSS(`::-webkit-scrollbar {
   animation-name: loading_mixlab;
   animation-duration: 2s;
   animation-iteration-count: infinite;
-}`)
+}
+
+.dynamic_prompt{
+  border-left: 2px solid var(--input-text); 
+}
+ 
+
+`)
 
 async function getCustomnodeMappings (mode = 'url') {
   // mode = "local";
@@ -616,6 +790,106 @@ function createModal (url, markdown, title) {
   div.appendChild(bgElement)
 }
 
+const loadTemplate = async () => {
+  const id = 'Comfy.NodeTemplates'
+  const file = 'comfy.templates.json'
+
+  let templates = []
+  if (app.storageLocation === 'server') {
+    if (app.isNewUserSession) {
+      // New user so migrate existing templates
+      const json = localStorage.getItem(id)
+      if (json) {
+        templates = JSON.parse(json)
+      }
+      await api.storeUserData(file, json, { stringify: false })
+    } else {
+      const res = await api.getUserData(file)
+      if (res.status === 200) {
+        try {
+          templates = await res.json()
+        } catch (error) {}
+      } else if (res.status !== 404) {
+        console.error(res.status + ' ' + res.statusText)
+      }
+    }
+  } else {
+    const json = localStorage.getItem(id)
+    if (json) {
+      templates = JSON.parse(json)
+    }
+  }
+
+  return templates ?? []
+}
+
+function drawBadge (node, orig, restArgs) {
+  let ctx = restArgs[0]
+  const r = orig?.apply?.(node, restArgs)
+
+  if (
+    !node.flags.collapsed &&
+    node.constructor.title_mode != LiteGraph.NO_TITLE
+  ) {
+    let text = `#${node.id} `
+
+    let nick = node.getNickname()
+    if (nick) {
+      if (nick == 'ComfyUI') {
+        nick = '🦊'
+      }
+
+      if (nick.length > 25) {
+        text += nick.substring(0, 23) + '..'
+      } else {
+        text += nick
+      }
+    }
+
+    if (text != '') {
+      let fgColor = 'white'
+      let bgColor = '#0F1F0F'
+      let visible = true
+
+      ctx.save()
+      ctx.font = '12px sans-serif'
+      const sz = ctx.measureText(text)
+      ctx.fillStyle = bgColor
+      ctx.beginPath()
+      ctx.roundRect(
+        node.size[0] - sz.width - 12,
+        -LiteGraph.NODE_TITLE_HEIGHT - 20,
+        sz.width + 12,
+        20,
+        5
+      )
+      ctx.fill()
+
+      ctx.fillStyle = fgColor
+      ctx.fillText(
+        text,
+        node.size[0] - sz.width - 6,
+        -LiteGraph.NODE_TITLE_HEIGHT - 6
+      )
+      ctx.restore()
+
+      if (node.has_errors) {
+        ctx.save()
+        ctx.font = 'bold 14px sans-serif'
+        const sz2 = ctx.measureText(node.type)
+        ctx.fillStyle = 'white'
+        ctx.fillText(
+          node.type,
+          node.size[0] / 2 - sz2.width / 2,
+          node.size[1] / 2
+        )
+        ctx.restore()
+      }
+    }
+  }
+  return r
+}
+
 app.registerExtension({
   name: 'Comfy.Mixlab.ui',
   init () {
@@ -665,7 +939,7 @@ app.registerExtension({
         }
       ]
 
-      opts = addSmartMenu(opts,node)
+      opts = addSmartMenu(opts, node)
 
       // if (node.type == 'CLIPTextEncode') {
       //   // 则出现 randomPrompt
@@ -706,16 +980,6 @@ app.registerExtension({
       const options = getGroupMenuOptions.apply(this, arguments) // start by calling the stored one
       node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)
 
-      // templete
-      const key = 'Comfy.NodeTemplates'
-      let templates = localStorage.getItem(key)
-      if (templates) {
-        templates = JSON.parse(templates)
-      } else {
-        templates = []
-      }
-      const store = () => localStorage.setItem(key, JSON.stringify(templates))
-
       return [
         {
           content: 'Clone Group ♾️Mixlab', // with a name
@@ -773,7 +1037,7 @@ app.registerExtension({
               localStorage.setItem('litegrapheditor_clipboard', old)
             }
 
-            clipboardAction(() => {
+            clipboardAction(async () => {
               let name = group.title + ' ♾️Mixlab'
               let nodes = group._nodes
 
@@ -786,6 +1050,8 @@ app.registerExtension({
                 const nodeData = node.serialize()
 
                 let groupData = GroupNodeHandler.getGroupData(node)
+
+                // console.log('groupData',GroupNodeHandler.isGroupNode(node),groupData)
                 if (groupData) {
                   groupData = groupData.nodeData
                   if (!data.groupNodes) {
@@ -796,11 +1062,32 @@ app.registerExtension({
                 }
               }
 
-              templates.push({
+              // templete
+              const store = async nt => {
+                const id = 'Comfy.NodeTemplates'
+                const file = 'comfy.templates.json'
+                let templates = await loadTemplate()
+                templates.push(nt)
+                if (app.storageLocation === 'server') {
+                  const ts = JSON.stringify(templates, undefined, 4)
+                  localStorage.setItem(id, ts) // Backwards compatibility
+                  try {
+                    await api.storeUserData(file, ts, {
+                      stringify: false
+                    })
+                  } catch (error) {
+                    console.error(error)
+                    alert(error.message)
+                  }
+                } else {
+                  localStorage.setItem(id, JSON.stringify(templates))
+                }
+              }
+              console.log('data', data)
+              store({
                 name,
                 data: JSON.stringify(data)
               })
-              store()
             })
           } // and the callback
         },
@@ -1173,6 +1460,53 @@ app.registerExtension({
         return options
       }
     }, 1000)
+
+    // createNodesCharts()
+  },
+  nodeCreated (node) {
+    if (node.widgets) {
+      // Locate dynamic prompt text widgets
+      // Include any widgets with dynamicPrompts set to true, and customtext
+
+      for (let index = 0; index < node.widgets.length; index++) {
+        const widget = node.widgets[index]
+        if (
+          (widget.type === 'customtext' && widget.dynamicPrompts !== false) ||
+          widget.dynamicPrompts
+        ) {
+          widget.element.classList.add('dynamic_prompt')
+
+          widget.element.addEventListener('mouseover', e => {
+            // console.log(node.widgets_values[index])
+            if (node.widgets_values&&node.widgets_values[index])
+              widget.element.setAttribute('title', node.widgets_values[index])
+          })
+        }
+      }
+    }
+
+    fetch('manager/badge_mode').then(r => {
+      if (r.status === 404) {
+        // 右上角的badge是否已经绘制
+        if (!node.badge_enabled) {
+          if (!node.getNickname) {
+            node.getNickname = function () {
+              if (node.nickname) {
+                return node.nickname
+              }
+              return
+              // return getNickname(node, node.comfyClass.trim())
+            }
+          }
+
+          const orig = node.__proto__.onDrawForeground
+          node.onDrawForeground = function (ctx) {
+            drawBadge(node, orig, arguments)
+          }
+          node.badge_enabled = true
+        }
+      }
+    })
   },
   async loadedGraphNode (node, app) {
     // console.log(

web/javascript/utils_mixlab.js

@@ -1,8 +1,5 @@
 import { app } from '../../../scripts/app.js'
-import { api } from '../../../scripts/api.js'
-import { ComfyWidgets } from '../../../scripts/widgets.js'
-import { $el } from '../../../scripts/ui.js'
-import { addValueControlWidget } from '../../../scripts/widgets.js'
+import { $el } from '../../../scripts/ui.js' 
 
 const getLocalData = key => {
   let data = {}

workflow/Prompt-weight-workflow.json

@@ -1,6 +1,6 @@
 {
-  "last_node_id": 23,
-  "last_link_id": 25,
+  "last_node_id": 24,
+  "last_link_id": 26,
   "nodes": [
     {
       "id": 9,
@@ -203,6 +203,7 @@
         "Node name for S&R": "ShowTextForGPT"
       },
       "widgets_values": [
+        "a girl face,super,(Pop Art:1.26),(Black and White:1.26)",
         "a girl face,super,(Pop Art:1.26),(Black and White:1.26)"
       ]
     },
@@ -537,7 +538,7 @@
           "type": "IMAGE",
           "links": [
             24,
-            25
+            26
           ],
           "slot_index": 0
         }
@@ -547,7 +548,7 @@
       }
     },
     {
-      "id": 23,
+      "id": 24,
       "type": "AppInfo",
       "pos": [
         3363.0014990624995,
@@ -562,9 +563,9 @@
       "mode": 0,
       "inputs": [
         {
-          "name": "LOGO",
+          "name": "IMAGE",
           "type": "IMAGE",
-          "link": 25
+          "link": 26
         }
       ],
       "properties": {
@@ -580,7 +581,7 @@
         "https://",
         "",
         "enable",
-        1
+        2
       ]
     }
   ],
@@ -706,10 +707,10 @@
       "IMAGE"
     ],
     [
-      25,
+      26,
       10,
       0,
-      23,
+      24,
       0,
       "IMAGE"
     ]