rembgNode update

"briarmbg","u2net","u2netp","u2net_human_seg","u2net_cloth_seg","silueta","isnet-general-use","isnet-anime"

README.md

@@ -1,5 +1,15 @@
 > 适配了最新版comfyui的py3.11 ，torch 2.1.2+cu121
 
+> [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。
 - 支持多个web app 切换
@@ -115,6 +125,11 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
 
 - [Added DynamicDelayByText, enabling delayed execution based on input text length.](./workflow/audio-chatgpt-workflow.json)
 
+- [使用CkptNames 对比不同的模型效果](./workflow/ckpts-image-workflow.json)
+
+- [CkptNames compare the effects of different models.](./workflow/ckpts-image-workflow.json)
+
+
 
 ## Other Nodes
 
@@ -130,15 +145,6 @@ 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.
@@ -151,6 +157,12 @@ 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"
+
+
+
 ### Improvement 
 
 - Add "help" option to the context menu for each node.
@@ -167,8 +179,6 @@ 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
@@ -206,18 +216,18 @@ 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)
-
 #### 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'],
@@ -430,7 +431,7 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
 PromptServer.start=new_start
 
 # 创建路由表
-routes = web.RouteTableDef()
+routes = PromptServer.instance.routes
 
 @routes.post('/mixlab')
 async def mixlab_hander(request):
@@ -516,27 +517,6 @@ async def nodes_map_hander(request):
 
     return web.json_response(result)
 
-# 把插件自定义的路由添加到comfyui server里
-def new_add_routes(self):
-        import nodes
-        try:
-            self.user_manager.add_routes(self.routes)
-        except:
-            print('pls update')
-        
-        self.app.add_routes(routes)
-        self.app.add_routes(self.routes)
-        for name, dir in nodes.EXTENSION_WEB_DIRS.items():
-            self.app.add_routes([
-                web.static('/extensions/' + urllib.parse.quote(name), dir, follow_symlinks=True),
-            ])
-        self.app.add_routes([
-            web.static('/', self.web_root, follow_symlinks=True),
-        ])
-
-PromptServer.add_routes=new_add_routes
-
-
 
 # 扩展api接口
 # from server import PromptServer
@@ -551,13 +531,13 @@ PromptServer.add_routes=new_add_routes
 
 # 导入节点
 from .nodes.PromptNode import EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
-from .nodes.ImageNode import SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
-from .nodes.Vae import VAELoader,VAEDecode
+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.ScreenShareNode import ScreenShareNode,FloatingVideo
-from .nodes.Clipseg import CLIPSeg,CombineMasks
+
 from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import OutlineMask,FeatheredMask
 
 
@@ -586,6 +566,7 @@ NODE_CLASS_MAPPINGS = {
     "ShowLayer":ShowLayer,
     "NewLayer":NewLayer,
     "SplitImage":SplitImage,
+    "CenterImage":CenterImage,
     "GridOutput":GridOutput,
     "MergeLayers":MergeLayers,
     "SplitLongMask":SplitLongMask,
@@ -594,12 +575,11 @@ NODE_CLASS_MAPPINGS = {
     "FaceToMask":FaceToMask,
     "AreaToMask":AreaToMask,
     "ImageCropByAlpha":ImageCropByAlpha,
-    "VAELoaderConsistencyDecoder":VAELoader,
-    "VAEDecodeConsistencyDecoder":VAEDecode,
+    # "VAELoaderConsistencyDecoder":VAELoader,
+    "SaveImageToLocal":SaveImageToLocal,
+    # "VAEDecodeConsistencyDecoder":VAEDecode,
     "ScreenShare":ScreenShareNode,
     "FloatingVideo":FloatingVideo,
-    "CLIPSeg_":CLIPSeg,
-    "CombineMasks_":CombineMasks,
     "ChatGPTOpenAI":ChatGPTNode,
     "ShowTextForGPT":ShowTextForGPT,
     "CharacterInText":CharacterInText,
@@ -617,7 +597,11 @@ NODE_CLASS_MAPPINGS = {
     "SwitchByIndex":SwitchByIndex,
     "LimitNumber":LimitNumber, 
     "OutlineMask":OutlineMask,
-    "JoinWithDelimiter":JoinWithDelimiter
+    "JoinWithDelimiter":JoinWithDelimiter,
+    "Seed_":CreateSeedNode,
+    "CkptNames_":CreateCkptNames,
+    "SamplerNames_":CreateSampler_names,
+    "LoraNames_":CreateLoraNames
     # "LaMaInpainting":LaMaInpainting
     # "GamePal":GamePal
 }
@@ -644,7 +628,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "PromptGenerate_Mix":"PromptGenerate ♾️Mixlab",
     "ChinesePrompt_Mix":"ChinesePrompt ♾️Mixlab",
     "GamePal":"GamePal ♾️Mixlab",
-    "RembgNode_Mix":"Removebg"
+    "RembgNode_Mix":"Removebg",
+    "LoraNames_":"LoraName_TriggerWords.safetensors"
 }
 
 # web ui的节点功能

data/extension-node-map.json

@@ -4773,12 +4773,15 @@
             "ResizeImage",
             "NoiseImage",
             "PromptImage",
+            "SaveImageToLocal",
             "AreaToMask",
-            "CLIPSeg_",
             "CharacterInText",
             "ChatGPTOpenAI",
             "Color",
-            "CombineMasks_",
+            "Seed_",
+            "CkptNames_",
+            "SamplerNames_",
+            "LoraNames_",
             "EnhanceImage",
             "GradientImage",
             "FaceToMask",
@@ -4790,6 +4793,7 @@
             "LoadImagesFromURL",
             "MergeLayers",
             "NewLayer",
+            "CenterImage",
             "RandomPrompt",
             "PromptSlide",
             "PromptSimplification",
@@ -4805,12 +4809,11 @@
             "TextImage",
             "ResizeImageMixlab",
             "TransparentImage",
-            "VAEDecodeConsistencyDecoder",
-            "VAELoaderConsistencyDecoder",
             "TextToNumber",
             "TextInput_",
             "DynamicDelayProcessor",
-            "LaMaInpainting"
+            "LaMaInpainting",
+            "Moondream"
         ],
         {
             "title_aux": "comfyui-mixlab-nodes"

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
+
+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):
@@ -73,8 +92,8 @@ 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", 
                                    {
@@ -168,7 +187,10 @@ 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 +201,60 @@ 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 +287,7 @@ 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,)
 

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

@@ -9,11 +9,22 @@ from io import BytesIO
 import folder_paths
 import json,io
 from comfy.cli_args import args
-import cv2 
-import math
+import cv2
+import string
+import math,glob
 from .Watcher import FolderWatcher
 
+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("*")
 
 
 FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
@@ -129,10 +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()
 
@@ -369,6 +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:
@@ -376,6 +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:
@@ -383,12 +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:
@@ -961,6 +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)}
 
 
@@ -1047,14 +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
@@ -1090,10 +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)
         
@@ -1101,11 +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
@@ -1136,7 +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,)
 
@@ -1151,11 +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)
@@ -1176,7 +1203,7 @@ class ImageCropByAlpha:
                 if a != 0:
                     new_pixel_data[x, y] = (255, 255, 255, 255)
                 else:
-                    new_pixel_data[x, y] = (r, g, b, a)
+                    new_pixel_data[x, y] = (0,0,0,0)
 
         # 保存修改后的图像
         # new_image.save("output.png")
@@ -1250,6 +1277,9 @@ class LoadImagesFromURL:
         return {"required": { 
                     "url": ("STRING",{"multiline": True,"default": "https://","dynamicPrompts": False}),
                              },
+                "optional":{
+                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                }
                 }
     
     RETURN_TYPES = ("IMAGE","MASK",)
@@ -1266,7 +1296,7 @@ class LoadImagesFromURL:
     global urls_image
     urls_image={}
 
-    def run(self,url):
+    def run(self,url,seed=0):
         global urls_image
         print(urls_image)
         def filter_http_urls(urls):
@@ -1599,6 +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
 
@@ -1617,6 +1659,19 @@ def splitImage(image, num):
 
     return grid_coordinates
 
+
+def centerImage(margin,canvas):
+    w,h=canvas.size
+
+    l,t,r,b=margin
+
+    x=l
+    y=t
+    width=w-r-l
+    height=h-t-b
+
+    return (x,y,width,height)
+
 # # 读取图片
 # image = Image.open("path_to_your_image.jpg")
 
@@ -1654,8 +1709,8 @@ class SplitImage:
             }
                 }
     
-    RETURN_TYPES = ("_GRID","_GRID",)
-    RETURN_NAMES = ("grids","grid")
+    RETURN_TYPES = ("_GRID","_GRID","MASK",)
+    RETURN_NAMES = ("grids","grid","mask",)
 
     FUNCTION = "run"
 
@@ -1666,10 +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
@@ -1678,7 +1733,71 @@ 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,)
+
+
+
+class CenterImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": { 
+                "canvas": ("IMAGE",), 
+                "left": ("INT",{
+                    "default":24, 
+                    "min": 0, #Minimum value
+                    "max": 5000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+               "top": ("INT",{
+                    "default":24, 
+                    "min": 0, #Minimum value
+                    "max": 5000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                "right": ("INT",{
+                    "default": 24, 
+                    "min": 0, #Minimum value
+                    "max": 5000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                 "bottom": ("INT",{
+                    "default": 24, 
+                    "min": 0, #Minimum value
+                    "max": 5000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+            }
+                }
+    
+    RETURN_TYPES = ("_GRID","MASK",)
+    RETURN_NAMES = ("grid","mask",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    INPUT_IS_LIST = False
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,canvas,left,top,right,bottom):
+        canvas=tensor2pil(canvas)
+
+        grid=centerImage((left,top,right,bottom),canvas)
+
+        mask=createMask(canvas,left,top,canvas.width-left-right,canvas.height-top-bottom)
+
+        return (grid,pil2tensor(mask),)
+
+
 
 
 class GridOutput:
@@ -2040,14 +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'],),
@@ -2058,20 +2177,21 @@ class ResizeImage:
                     "image": ("IMAGE",),
                     "average_color": (["on",'off'],),
                     "fill_color":("STRING",{"multiline": False,"default": "#FFFFFF","dynamicPrompts": False}),
+                    "mask": ("MASK",),
             }
                 }
     
-    RETURN_TYPES = ("IMAGE","IMAGE","STRING",)
-    RETURN_NAMES = ("image","average_image","average_hex",)
+    RETURN_TYPES = ("IMAGE","IMAGE","STRING","MASK",)
+    RETURN_NAMES = ("image","average_image","average_hex","mask",)
 
     FUNCTION = "run"
 
     CATEGORY = "♾️Mixlab/Image"
 
     INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,True,True,)
+    OUTPUT_IS_LIST = (True,True,True,True,)
 
-    def run(self,width,height,scale_option,image=None,average_color=['on'],fill_color=["#FFFFFF"]):
+    def run(self,width,height,scale_option,image=None,average_color=['on'],fill_color=["#FFFFFF"],mask=None):
         
         w=width[0]
         h=height[0]
@@ -2080,6 +2200,7 @@ class ResizeImage:
         fill_color=fill_color[0]
 
         imgs=[]
+        masks=[]
         average_images=[]
         hexs=[]
 
@@ -2108,8 +2229,20 @@ class ResizeImage:
                     a_im=pil2tensor(a_im)
                     average_images.append(a_im)
                     hexs.append(hex)
+
+            try:
+                for mas in mask:
+                    for ma in mas:
+                        ma=tensor2pil(ma)
+                        ma=ma.convert('RGB')
+                        ma=resize_image(ma,scale_option,w,h,fill_color)
+                        ma=ma.convert('L')
+                        ma=pil2tensor(ma)
+                        masks.append(ma)
+            except:
+                print('')
         
-        return (imgs,average_images,hexs,)
+        return (imgs,average_images,hexs,masks,)
 
 
 class MirroredImage:
@@ -2153,19 +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,)
 
 
 
@@ -2212,3 +2367,104 @@ class ImageColorTransfer:
 
 
 
+class SaveImageToLocal:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": 
+                    {"images": ("IMAGE", ),
+                     "file_path": ("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                     },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    def save_images(self, images,file_path , prompt=None, extra_pnginfo=None):
+        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) and not extension:
+            # 使用os.makedirs函数创建新目录
+            os.makedirs(file_path)
+            print("目录已创建")
+        else:
+            print("目录已存在")
+
+        # 使用glob模块获取当前目录下的所有文件
+        if file_path=="":
+            files = glob.glob(full_output_folder + '/*')
+        else:
+            files = glob.glob(file_path + '/*')
+        # 统计文件数量
+        file_count = len(files)
+        counter+=file_count
+        print('统计文件数量',file_count,counter)
+
+        results = list()
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            metadata = None
+            if not args.disable_metadata:
+                metadata = PngInfo()
+                if prompt is not None:
+                    metadata.add_text("prompt", json.dumps(prompt))
+                if extra_pnginfo is not None:
+                    for x in extra_pnginfo:
+                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+            file = f"{filename}_{counter:05}_.png"
+            
+            if file_path=="":
+                fp=os.path.join(full_output_folder, file)
+                if os.path.exists(fp):
+                    file = f"{filename}_{counter:05}_{generate_random_string(8)}.png"
+                    fp=os.path.join(full_output_folder, file)
+                img.save(fp, pnginfo=metadata, compress_level=self.compress_level)
+                results.append({
+                    "filename": file,
+                    "subfolder": subfolder,
+                    "type": self.type
+                })
+            
+            else:
+
+                fp=os.path.join(file_path, file)
+                if os.path.exists(fp):
+                    file = f"{filename}_{counter:05}_{generate_random_string(8)}.png"
+                    fp=os.path.join(file_path, file)
+
+                img.save(os.path.join(file_path, file), pnginfo=metadata, compress_level=self.compress_level)
+                results.append({
+                    "filename": file,
+                    "subfolder": file_path,
+                    "type": self.type
+                })
+            counter += 1
+
+        return ()

nodes/PromptNode.py

@@ -323,7 +323,9 @@ class RandomPrompt:
                 "random_sample": (["enable", "disable"],),
                 # "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                 },
-           
+            "optional":{
+                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                }
             }
     
     
@@ -339,7 +341,7 @@ class RandomPrompt:
 
 
     # 运行的函数
-    def run(self,max_count,mutable_prompt,immutable_prompt,random_sample):
+    def run(self,max_count,mutable_prompt,immutable_prompt,random_sample,seed=0):
         # print('#运行的函数',mutable_prompt,immutable_prompt,max_count,random_sample)
         
         # Split the text into an array of words

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

@@ -7,6 +7,8 @@ import folder_paths
 import matplotlib.font_manager as fm
 import torch
 
+
+
 def recursive_search(directory, excluded_dir_names=None):
     if not os.path.isdir(directory):
         return [], {}
@@ -198,14 +200,17 @@ class TextToNumber:
         return {"required": { 
                     "text": ("STRING",{"multiline": False,"default": "1"}),
                     "random_number": (["enable", "disable"],),
-                    "number":("INT", {
-                        "default": 0, 
-                        "min": 0, #Minimum value
+                    "max_num":("INT", {
+                        "default": 10, 
+                        "min":2, #Minimum value
                         "max": 10000000000, #Maximum value
                         "step": 1, #Slider's step
                         "display": "number" # Cosmetic only: display as "number" or "slider"
                     }),
                              },
+                 "optional":{
+                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                }
                 }
     
     RETURN_TYPES = ("INT",)
@@ -218,7 +223,7 @@ class TextToNumber:
     INPUT_IS_LIST = False
     OUTPUT_IS_LIST = (False,)
 
-    def run(self,text,random_number,number):
+    def run(self,text,random_number,max_num,seed=0):
         
         numbers = re.findall(r'\d+', text)
         result=0
@@ -227,7 +232,7 @@ class TextToNumber:
             # print(result)
         
         if random_number=='enable' and result>0:
-            result= random.randint(1, 10000000000)
+            result= random.randint(1, max_num)
         return {"ui": {"text": [text],"num":[result]}, "result": (result,)}
     
 
@@ -425,7 +430,7 @@ class DynamicDelayProcessor:
             },
             "optional":{
                 "any_input":(any_type,),
-                "delay_by_text":("STRING",{"multiline":True,}),
+                "delay_by_text":("STRING",{"multiline":True,"dynamicPrompts": False,}),
                 "words_per_seconds":("FLOAT",{ "default":1.50,"min": 0.0,"max": 1000.00,"display":"Chars per second?"}),
                 "replace_output": (["disable","enable"],),
                 "replace_value":("INT",{ "default":-1,"min": 0,"max": 1000000,"display":"Replacement value"})
@@ -712,3 +717,117 @@ class TESTNODE_:
         result = list_stats.count_types(ANY)
             
         return {"ui": {"data": result,"type":[str(type(ANY[0]))]}, "result": (ANY,)}
+
+
+
+class CreateSeedNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            }
+        }
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("seed",)
+  
+    OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, seed):
+        return (seed,)
+    
+
+class CreateCkptNames:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "ckpt_names": ("STRING",{"multiline": True,"default": "\n".join(folder_paths.get_filename_list("checkpoints")),"dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("ckpt_names",)
+    
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,)
+
+    # OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, ckpt_names):
+        ckpt_names=ckpt_names.split('\n')
+        ckpt_names = [name for name in ckpt_names if name.strip()]
+        return (ckpt_names,)
+
+
+class CreateLoraNames:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "lora_names": ("STRING",{"multiline": True,"default": "\n".join(folder_paths.get_filename_list("loras")),"dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,"STRING",)
+    RETURN_NAMES = ("lora_names","prompt",)
+    
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,True,)
+
+    # OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, lora_names):
+        lora_names=lora_names.split('\n')
+        lora_names = [name for name in lora_names if name.strip()]
+        prompts=[os.path.splitext(n)[0] for n in lora_names]
+        return (lora_names,prompts,)
+   
+
+
+class CreateSampler_names:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "sampler_names": ("STRING",{"multiline": True,"default": "\n".join(comfy.samplers.KSampler.SAMPLERS),"dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("sampler_names",)
+    
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,)
+
+    # OUTPUT_NODE = True
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    def run(self, sampler_names):
+        sampler_names=sampler_names.split('\n')
+        sampler_names = [name for name in sampler_names if name.strip()]
+        return (sampler_names,)

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, )

requirements.txt

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

web/index.html

@@ -643,7 +643,7 @@
             return true
         }
 
-
+        // 种子的处理
         function randomSeed(seed, data) {
             for (const id in data) {
                 if (data[id].inputs.seed != undefined
@@ -1316,6 +1316,7 @@
                         }
                     }), value);
 
+                    // 选择事件绑定
                     selectDom.addEventListener('change', e => {
                         e.preventDefault();
                         // console.log(selectDom.value)
@@ -1550,6 +1551,7 @@
 
         }
 
+        // 创建下拉选择
         function createSelect(options, defaultValue) {
             var selectElement = document.createElement("select");
             selectElement.className = "select"
@@ -1558,7 +1560,7 @@
             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 +1571,7 @@
             return selectElement
         }
 
+        // 创建下拉选择 - 带说明
         function createSelectWithOptions(title, options, defaultValue) {
 
             const div = document.createElement("div");

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
     )
@@ -399,7 +416,7 @@ app.registerExtension({
     }
   },
   async loadedGraphNode (node, app) {
-    console.log('#loadedGraphNode1111')
+    // console.log('#loadedGraphNode1111')
     window._mixlab_app_json = null //切换workflow需要清空
     if (node.type === 'AppInfo') {
       let auto_save = node.widgets.filter(w => w.name == 'auto_save')[0]
@@ -408,6 +425,9 @@ app.registerExtension({
           auto_save.value = 'enable'
         }
       }
+
+      // app.canvas.centerOnNode(node)
+      // app.canvas.setZoom(0.45)
     }
   }
 })

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 = 'v0.14.0'
+const version = 'v0.16.0'
 
 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/layer_mixlab.js

@@ -156,6 +156,32 @@ const parseSvg = async svgContent => {
   return { data, image: base64, svgElement }
 }
 
+
+function findImages(nodeId) {
+  // 检查当前节点是否有 imgs 字段
+  const n = app.graph.getNodeById(nodeId)
+  if (n.imgs) {
+    return n.imgs;
+  }
+
+  // 检查当前节点的 inputs 是否有 image 字段
+  if (n.inputs) {
+    for (let i = 0; i < n.inputs.length; i++) {
+      if (n.inputs[i].name==='image'||n.inputs[i].name==='images') {
+        // 获取新的 nodeId，并递归调用 findImages 函数
+        var linkId = n.inputs[i]?.link;
+        var origin_id = app.graph.links[linkId].origin_id
+        return findImages(origin_id);
+      }
+    }
+  }
+
+  // 如果没有找到 imgs 字段或者 image 字段，则返回 null
+  return null;
+}
+
+ 
+
 async function setArea (cw, ch, topBase64, base64, data, fn) {
   let displayHeight = Math.round(window.screen.availHeight * 0.8)
   let div = document.createElement('div')
@@ -571,15 +597,19 @@ app.registerExtension({
               }
             }
             try {
-              console.log('this.inputs', this.inputs)
-              let topLinkId = this.inputs[0].link
-              let topNodeId = app.graph.links[topLinkId].origin_id
-              let topIm = app.graph.getNodeById(topNodeId).imgs[0]
+              console.log('this.inputs', this.id)
+              let imgs=findImages(this.id)
+              
+              // let topLinkId = this.inputs[0].link
+              // let topNodeId = app.graph.links[topLinkId].origin_id
+              let topIm = imgs[0]
 
               let linkId = this.inputs[3].link
               let nodeId = app.graph.links[linkId].origin_id
               // console.log(linkId,this.inputs)
-              let im = app.graph.getNodeById(nodeId).imgs[0]
+              let imgs2=findImages(nodeId)
+              let im = imgs2[0]
+              console.log(topIm,im)
               // let src = im.src
               setArea(
                 im.naturalWidth,
@@ -589,7 +619,9 @@ app.registerExtension({
                 data,
                 updateValue
               )
-            } catch (error) {}
+            } catch (error) {
+              console.log(error)
+            }
           })
         }
       }

web/javascript/smart_connect.js

@@ -0,0 +1,302 @@
+const smart_connect_config_input = [
+  {
+    node_type: 'CLIPTextEncode',
+    node_widget_name: 'text',
+    inputNodeName: 'RandomPrompt',
+    inputNode_output_name: 'STRING'
+  },
+  {
+    node_type: 'CLIPTextEncode',
+    node_widget_name: 'text',
+    inputNodeName: 'EmbeddingPrompt',
+    inputNode_output_name: 'STRING'
+  },
+  {
+    node_type: 'CLIPTextEncode',
+    node_widget_name: 'text',
+    inputNodeName: 'ChinesePrompt_Mix',
+    inputNode_output_name: 'prompt'
+  },
+  {
+    node_type: 'CheckpointLoaderSimple',
+    node_widget_name: 'ckpt_name',
+    inputNodeName: 'CkptNames_',
+    inputNode_output_name: 'ckpt_names'
+  },
+  {
+    node_type: 'KSampler',
+    node_widget_name: 'sampler_name',
+    inputNodeName: 'SamplerNames_',
+    inputNode_output_name: 'sampler_names'
+  },
+  {
+    node_type: 'LoraLoaderModelOnly',
+    node_widget_name: 'lora_name',
+    inputNodeName: 'LoraNames_',
+    inputNode_output_name: 'lora_names'
+  },
+  {
+    node_type: 'LoadLoRA',
+    node_widget_name: 'lora_name',
+    inputNodeName: 'LoraNames_',
+    inputNode_output_name: 'lora_names'
+  },
+  {
+    node_type: 'Moondream',
+    node_widget_name: 'image',
+    inputNodeName: 'LoadImage',
+    inputNode_output_name: 'IMAGE'
+  }
+]
+
+const smart_connect_config_output = [
+  {
+    node_type: 'LoadImage',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'ClipInterrogator',
+    outputNode_input_name: 'image'
+  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'PromptImage',
+    outputNode_input_name: 'images'
+  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'PreviewImage',
+    outputNode_input_name: 'images'
+  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'SaveImage',
+    outputNode_input_name: 'images'
+  },
+  {
+    node_type: 'Moondream',
+    node_output_name: 'STRING',
+    outputNodeName: 'ShowTextForGPT',
+    outputNode_input_name: 'text'
+  }
+]
+
+// import {
+//   convertToInput,
+//   getConfig,
+//   isConvertableWidget
+// } from '../../../extensions/core/widgetInputs.js'
+
+const CONVERTED_TYPE = 'converted-widget'
+const GET_CONFIG = Symbol()
+
+function getConfig (widgetName) {
+  const { nodeData } = this.constructor
+  return (
+    nodeData?.input?.required[widgetName] ??
+    nodeData?.input?.optional?.[widgetName]
+  )
+}
+
+function hideWidget (node, widget, suffix = '') {
+  widget.origType = widget.type
+  widget.origComputeSize = widget.computeSize
+  widget.origSerializeValue = widget.serializeValue
+  widget.computeSize = () => [0, -4] // -4 is due to the gap litegraph adds between widgets automatically
+  widget.type = CONVERTED_TYPE + suffix
+  widget.serializeValue = () => {
+    // Prevent serializing the widget if we have no input linked
+    if (!node.inputs) {
+      return undefined
+    }
+    let node_input = node.inputs.find(i => i.widget?.name === widget.name)
+
+    if (!node_input || !node_input.link) {
+      return undefined
+    }
+    return widget.origSerializeValue
+      ? widget.origSerializeValue()
+      : widget.value
+  }
+
+  // Hide any linked widgets, e.g. seed+seedControl
+  if (widget.linkedWidgets) {
+    for (const w of widget.linkedWidgets) {
+      hideWidget(node, w, ':' + widget.name)
+    }
+  }
+}
+
+function convertToInput (node, widget, config) {
+  hideWidget(node, widget)
+
+  const type = config[0]
+
+  // Add input and store widget config for creating on primitive node
+  const sz = node.size
+  node.addInput(widget.name, type, {
+    widget: { name: widget.name, [GET_CONFIG]: () => config }
+  })
+
+  for (const widget of node.widgets) {
+    widget.last_y += LiteGraph.NODE_SLOT_HEIGHT
+  }
+
+  // Restore original size but grow if needed
+  node.setSize([Math.max(sz[0], node.size[0]), Math.max(sz[1], node.size[1])])
+}
+
+export function smart_init () {
+  LGraphCanvas.prototype._createNodeForInput = function (
+    node,
+    widget,
+    inputNodeName,
+    inputNode_slot
+  ) {
+    // console.log(node.pos)
+
+    // var widget = node.widgets.filter(w => w.name === node_widget_name)[0]
+    if (widget) {
+      // 如果有存在的，没有连线输出的，自动连，不新建
+      let input_node = null
+
+      Array.from(app.graph.findNodesByType(inputNodeName), n => {
+        var links = n.outputs.filter(o => o.name === inputNode_slot)[0].links
+        //  console.log(links)
+        if (!links || links?.length === 0) input_node = n
+      })
+      // 新建
+      if (!input_node) {
+        input_node = LiteGraph.createNode(inputNodeName)
+        input_node.pos = [node.pos[0] - node.size[0] - 24, node.pos[1] - 48]
+        app.canvas.graph.add(input_node, false)
+      } else {
+        input_node.pos = [node.pos[0] - node.size[0] - 24, node.pos[1] - 48]
+      }
+
+      const config = getConfig.call(node, widget.name) ?? [
+        widget.type,
+        widget.options || {}
+      ]
+      let node_slotType = config[0]
+      // 如果input没有，则创建
+      if (!node.inputs?.filter(inp => inp.name === widget.name)[0]||!node.inputs)
+        convertToInput(node, widget, config)
+      input_node.connectByType(inputNode_slot, node, node_slotType)
+    }
+  }
+
+  LGraphCanvas.prototype._createNodeForOutput = function (
+    node,
+    widget,
+    outputNodeName,
+    outputNode_slot
+  ) {
+    if (widget) {
+      let output_node
+      Array.from(app.graph.findNodesByType(outputNodeName), n => {
+        var links = n.inputs.filter(o => o.name === outputNode_slot)[0].links
+        //  console.log(links)
+        if (!links || links?.length === 0) output_node = n
+      })
+      console.log('output_node', output_node, widget.name)
+
+      if (!output_node) {
+        // 新建
+        output_node = LiteGraph.createNode(outputNodeName)
+        output_node.pos = [node.pos[0] + node.size[0] + 24, node.pos[1] - 48]
+        app.canvas.graph.add(output_node, false)
+      } else {
+        output_node.pos = [node.pos[0] + node.size[0] + 24, node.pos[1] - 48]
+      }
+
+      const config = getConfig.call(node, widget.name) ?? [
+        widget.type,
+        widget.options || {}
+      ]
+      let node_slotType = config[0]
+      console.log(node_slotType, output_node, outputNode_slot)
+      let type = output_node.inputs.filter(
+        inp => inp.name == outputNode_slot
+      )[0].type
+      node.connectByType(node_slotType, output_node, type)
+    }
+  }
+}
+
+export function addSmartMenu (options, node) {
+  let sopts = []
+
+  for (const sc of smart_connect_config_input) {
+    // 有智能推荐，则出现
+    if (node.type === sc.node_type) {
+      // console.log('smart',node)
+      // 则出现 randomPrompt
+      // CLIPTextEncode 的widget ，name== 'text'
+      let node_widget_name = sc.node_widget_name
+      let widget = node.widgets.filter(w => w.name === node_widget_name)[0]
+      if (!widget) {
+        // 控件没有，则查找inputs
+        widget = node.inputs.filter(w => w.name === node_widget_name)[0]
+      }
+
+      let isLinkNull = true
+      // 如果input里已经有，但是link为空
+      if (node.inputs?.filter(inp => inp.name === node_widget_name)[0]) {
+        isLinkNull =
+          node.inputs.filter(inp => inp.name === node_widget_name)[0].link ===
+          null
+      }
+
+      if (widget && isLinkNull) {
+        sopts.push({
+          content: sc.inputNodeName.split('_')[0] + '➡️',
+          callback: () => {
+            LGraphCanvas.prototype._createNodeForInput(
+              node, //当前node
+              widget, //当前node里需要自动连线的widget
+              sc.inputNodeName, //作为input的node type
+              sc.inputNode_output_name // 作为input的node的outputs的name. the input slot type of the target node
+            )
+          }
+        })
+      }
+    }
+  }
+
+  for (const sc of smart_connect_config_output) {
+    if (node.type === sc.node_type) {
+      let node_output_name = sc.node_output_name
+      const widget = node.outputs.filter(w => w.name === node_output_name)[0]
+
+      let isLinkNull = true
+      // 如果output里 link为空
+      if (node.outputs?.filter(inp => inp.name === node_output_name)[0]) {
+        isLinkNull =
+          node.outputs.filter(inp => inp.name === node_output_name)[0].links
+            ?.length === 0
+        if (!node.outputs.filter(inp => inp.name === node_output_name)[0].links)
+          isLinkNull = true
+      }
+
+      if (widget && isLinkNull) {
+        sopts.push({
+          content: '➡️' + sc.outputNodeName.split('_')[0],
+          callback: () => {
+            LGraphCanvas.prototype._createNodeForOutput(
+              node, //当前node
+              widget, //当前node里需要自动连线的widget
+              sc.outputNodeName, //作为input的node type
+              sc.outputNode_input_name // 作为input的node的outputs的name. the input slot type of the target node
+            )
+          }
+        })
+      }
+    }
+  }
+
+  if (sopts.length > 0) options = [...sopts, null, ...options]
+
+  return options
+}

web/javascript/ui_mixlab.js

@@ -1,14 +1,247 @@
 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 { closeIcon } from './svg_icons.js'
+import { api } from '../../../scripts/api.js'
 
 import {
   GroupNodeConfig,
   GroupNodeHandler
 } from '../../../extensions/core/groupNode.js'
 
+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
+
+  // copy input connections
+
+  for (let i in src.inputs) {
+    let input = src.inputs[i]
+    if (input.link) {
+      let link = app.graph.links[input.link]
+      let src_node = app.graph.getNodeById(link.origin_id)
+      if (dest.inputs.filter(inp => inp.name === input.name).length === 0) {
+        // 没有，name换了
+        let dInp = dest.inputs.filter(inp => inp.type === input.type)
+        if (dInp.length === 1) {
+          src_node.connect(link.origin_slot, dest.id, dInp[0].name)
+        }
+      } else {
+        src_node.connect(link.origin_slot, dest.id, input.name)
+      }
+    }
+  }
+
+  // copy output connections
+  let output_links = {}
+  for (let i in src.outputs) {
+    let output = src.outputs[i]
+    if (output.links) {
+      let links = []
+      for (let j in output.links) {
+        links.push(app.graph.links[output.links[j]])
+      }
+      output_links[output.name] = links
+    }
+  }
+
+  for (let i in dest.outputs) {
+    let links = output_links[dest.outputs[i].name]
+    if (links) {
+      for (let j in links) {
+        let link = links[j]
+        let target_node = app.graph.getNodeById(link.target_id)
+        dest.connect(parseInt(i), target_node, link.target_slot)
+      }
+    }
+  }
+
+  // copy  widgets
+  for (const w of src.widgets) {
+    for (const d of dest.widgets) {
+      if (w.name === d.name) {
+        d.value = w.value
+      }
+    }
+  }
+
+  app.graph.afterChange()
+}
+
 function deepEqual (obj1, obj2) {
   if (typeof obj1 !== typeof obj2) {
     return false
@@ -558,6 +791,39 @@ 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 ?? []
+}
+
 app.registerExtension({
   name: 'Comfy.Mixlab.ui',
   init () {
@@ -575,22 +841,71 @@ app.registerExtension({
       }
     }
 
+    LGraphCanvas.prototype.fixTheNode = function (node) {
+      let new_node = LiteGraph.createNode(node.comfyClass)
+      new_node.pos = [node.pos[0], node.pos[1]]
+      app.canvas.graph.add(new_node, false)
+      copyNodeValues(node, new_node)
+      app.canvas.graph.remove(node)
+    }
+
+    smart_init()
+
     const getNodeMenuOptions = LGraphCanvas.prototype.getNodeMenuOptions // store the existing method
     LGraphCanvas.prototype.getNodeMenuOptions = function (node) {
       // replace it
       const options = getNodeMenuOptions.apply(this, arguments) // start by calling the stored one
       node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)
+      console.log('getNodeMenuOptions', node.type == 'CLIPTextEncode')
 
-      return [
+      let opts = [
         {
           content: 'Help ♾️Mixlab', // with a name
           callback: () => {
             LGraphCanvas.prototype.helpAboutNode(node)
           } // and the callback
         },
-        null,
-        ...options
-      ] // and return the options
+        {
+          content: 'Fix node v2', // with a name
+          callback: () => {
+            LGraphCanvas.prototype.fixTheNode(node)
+          }
+        }
+      ]
+
+      opts = addSmartMenu(opts, node)
+
+      // if (node.type == 'CLIPTextEncode') {
+      //   // 则出现 randomPrompt
+      //   // CLIPTextEncode 的widget ，name== 'text'
+      //   let node_widget_name = 'text'
+      //   const widget = node.widgets.filter(w => w.name === node_widget_name)[0]
+
+      //  let mixlab_nodes_smart_connect= [{node_type:'CLIPTextEncode',
+      //   node_widget_name:'text',
+      //   inputNodeName:'RandomPrompt',
+      //   inputNode_output_type:'STRING'}]
+
+      //   if (widget) {
+      //     opts = [
+      //       {
+      //         content: 'RandomPrompt',
+      //         callback: () => {
+      //           LGraphCanvas.prototype._createNodeForInput(
+      //             node, //当前node
+      //             widget,//当前node里需要自动连线的widget
+      //             'RandomPrompt',//作为input的node type
+      //             'STRING'// 作为input的node的outputs的type. the input slot type of the target node
+      //           )
+      //         }
+      //       },
+      //       null,
+      //       ...opts
+      //     ]
+      //   }
+      // }
+
+      return [...opts, null, ...options] // and return the options
     }
 
     const getGroupMenuOptions = LGraphCanvas.prototype.getGroupMenuOptions // store the existing method
@@ -599,16 +914,6 @@ 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
@@ -666,7 +971,7 @@ app.registerExtension({
               localStorage.setItem('litegrapheditor_clipboard', old)
             }
 
-            clipboardAction(() => {
+            clipboardAction(async () => {
               let name = group.title + ' ♾️Mixlab'
               let nodes = group._nodes
 
@@ -679,6 +984,8 @@ 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) {
@@ -689,14 +996,46 @@ 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
         },
+        {
+          content: `Remove Group&Nodes ♾️Mixlab`, // with a name
+          callback: async (value, opts, e, menu, group) => {
+            // console.log(group)
+            let nodes = group._nodes
+            for (const node of nodes) {
+              app.graph.remove(node)
+            }
+            app.graph.remove(group)
+          } // and the callback
+        },
         null,
         ...options
       ] // and return the options
@@ -722,7 +1061,7 @@ app.registerExtension({
 
       const apps = await get_my_app()
 
-      let apps_map = { '0': [] }
+      let apps_map = { 0: [] }
 
       for (const app of apps) {
         if (app.category) {
@@ -735,7 +1074,7 @@ app.registerExtension({
 
       let apps_opts = []
       for (const category in apps_map) {
-        console.log('category',typeof(category))
+        console.log('category', typeof category)
         if (category === '0') {
           apps_opts.push(
             ...Array.from(apps_map[category], a => {
@@ -764,7 +1103,7 @@ app.registerExtension({
         } else {
           // 二级
           apps_opts.push({
-            content:  '🚀 '+category,
+            content: '🚀 ' + category,
             has_submenu: true,
             disabled: false,
             submenu: {
@@ -1047,7 +1386,7 @@ app.registerExtension({
             has_submenu: true,
             disabled: false,
             submenu: {
-              options:apps_opts
+              options: apps_opts
             }
           }
         )
@@ -1055,5 +1394,21 @@ app.registerExtension({
         return options
       }
     }, 1000)
+
+    // createNodesCharts()
+  },
+  async loadedGraphNode (node, app) {
+    // console.log(
+    //   '#ui init',
+    //   app.graph._nodes[app.graph._nodes.length - 1].id,
+    //   node.id
+    // )
+    try {
+      // 用来居中显示节点
+      if ((app.graph._nodes[app.graph._nodes.length - 1].id, node.id)) {
+        app.canvas.centerOnNode(node)
+        app.canvas.setZoom(0.45)
+      }
+    } catch (error) {}
   }
 })

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/ckpts-image-workflow.json

@@ -0,0 +1,558 @@
+{
+  "last_node_id": 69,
+  "last_link_id": 72,
+  "nodes": [
+    {
+      "id": 37,
+      "type": "CLIPTextEncode",
+      "pos": [
+        6705,
+        -216
+      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 33
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            34
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "CLIPTextEncode",
+      "pos": [
+        6693,
+        61
+      ],
+      "size": {
+        "0": 425.27801513671875,
+        "1": 180.6060791015625
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 6
+        },
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 25,
+          "widget": {
+            "name": "text"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            3
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "text, watermark"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "EmptyLatentImage",
+      "pos": [
+        6689,
+        306
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            4
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        512,
+        1
+      ]
+    },
+    {
+      "id": 27,
+      "type": "EmbeddingPrompt",
+      "pos": [
+        6104,
+        23
+      ],
+      "size": {
+        "0": 399.6408996582031,
+        "1": 82
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            25
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmbeddingPrompt"
+      },
+      "widgets_values": [
+        "negative-embed-verybadimagenegative_v1.3",
+        1
+      ]
+    },
+    {
+      "id": 67,
+      "type": "VAEDecode",
+      "pos": [
+        7551,
+        -184
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 70
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 69
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            71
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 1,
+      "type": "KSampler",
+      "pos": [
+        7187,
+        -145
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 1
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 34
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 3
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 4
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            70
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        408562451564429,
+        "fixed",
+        20,
+        8,
+        "euler",
+        "normal",
+        1
+      ]
+    },
+    {
+      "id": 61,
+      "type": "PromptImage",
+      "pos": [
+        7853,
+        -238
+      ],
+      "size": [
+        465.6378949342379,
+        760.4568424013569
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 71
+        },
+        {
+          "name": "prompts",
+          "type": "STRING",
+          "link": 72,
+          "widget": {
+            "name": "prompts"
+          }
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PromptImage"
+      },
+      "widgets_values": [
+        "",
+        "disable",
+        {
+          "_images": [
+            [
+              {
+                "filename": "mixlab_PromptImage_0_00027_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_1_00028_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_2_00029_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_3_00030_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_4_00031_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ],
+            [
+              {
+                "filename": "mixlab_PromptImage_5_00032_.png",
+                "subfolder": "",
+                "type": "output"
+              }
+            ]
+          ],
+          "prompts": [
+            "512-inpainting-ema.safetensors",
+            "SSD-1B.safetensors",
+            "awportrait_v12.safetensors",
+            "cardosAnime_v20.safetensors",
+            "deliberate_v2.safetensors",
+            "gameIconInstitute_v40.safetensors"
+          ]
+        }
+      ]
+    },
+    {
+      "id": 2,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        6105,
+        -139
+      ],
+      "size": {
+        "0": 315,
+        "1": 98
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "ckpt_name",
+          "type": [
+            "512-inpainting-ema.safetensors",
+            "SSD-1B.safetensors",
+            "awportrait_v12.safetensors",
+            "cardosAnime_v20.safetensors",
+            "deliberate_v2.safetensors",
+            "gameIconInstitute_v40.safetensors",
+            "illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors",
+            "sd_xl_turbo_1.0_fp16.safetensors",
+            "svd.safetensors"
+          ],
+          "link": 64,
+          "widget": {
+            "name": "ckpt_name"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            1
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            6,
+            33
+          ],
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            69
+          ],
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "deliberate_v2.safetensors"
+      ]
+    },
+    {
+      "id": 56,
+      "type": "CkptNames_",
+      "pos": [
+        7860,
+        -494
+      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "ckpt_names",
+          "type": "*",
+          "links": [
+            64,
+            72
+          ],
+          "shape": 6,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CkptNames_"
+      },
+      "widgets_values": [
+        "512-inpainting-ema.safetensors\nSSD-1B.safetensors\nawportrait_v12.safetensors\ncardosAnime_v20.safetensors\ndeliberate_v2.safetensors\ngameIconInstitute_v40.safetensors"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      1,
+      0,
+      "MODEL"
+    ],
+    [
+      3,
+      5,
+      0,
+      1,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      4,
+      3,
+      0,
+      1,
+      3,
+      "LATENT"
+    ],
+    [
+      6,
+      2,
+      1,
+      5,
+      0,
+      "CLIP"
+    ],
+    [
+      25,
+      27,
+      0,
+      5,
+      1,
+      "STRING"
+    ],
+    [
+      33,
+      2,
+      1,
+      37,
+      0,
+      "CLIP"
+    ],
+    [
+      34,
+      37,
+      0,
+      1,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      64,
+      56,
+      0,
+      2,
+      0,
+      [
+        "512-inpainting-ema.safetensors",
+        "SSD-1B.safetensors",
+        "awportrait_v12.safetensors",
+        "cardosAnime_v20.safetensors",
+        "deliberate_v2.safetensors",
+        "gameIconInstitute_v40.safetensors",
+        "illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors",
+        "sd_xl_turbo_1.0_fp16.safetensors",
+        "svd.safetensors"
+      ]
+    ],
+    [
+      69,
+      2,
+      2,
+      67,
+      1,
+      "VAE"
+    ],
+    [
+      70,
+      1,
+      0,
+      67,
+      0,
+      "LATENT"
+    ],
+    [
+      71,
+      67,
+      0,
+      61,
+      0,
+      "IMAGE"
+    ],
+    [
+      72,
+      56,
+      0,
+      61,
+      1,
+      "STRING"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}