Update checkVersion_mixlab.js

fixbug
add TripoSRModel
2024-04-29 00:16:55 +08:00 · 2024-04-29 00:15:47 +08:00 · 2024-04-29 00:15:32 +08:00 · 2024-04-27 23:43:40 +08:00 · 2024-04-27 23:38:00 +08:00 · 2024-04-25 21:23:40 +08:00
44 changed files with 7275 additions and 654 deletions
@@ -5,3 +5,4 @@ workflow/my_workflow.json
 workflow/my_workflow_app.json
 workflow/prompt_result.json
 app/*
+workflow/prompt_result.json
@@ -3,11 +3,15 @@
 > [Mixlab nodes discord](https://discord.gg/cXs9vZSqeK)

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

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

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


 ## 🚀🚗🚚🏃 Workflow-to-APP 
@@ -15,7 +19,9 @@
 - 支持多个web app 切换
 - 发布为app的workflow，可以在右键里再次编辑了
 - web app可以设置分类，在comfyui右键菜单可以编辑更新web app
+- 支持动态提示

+![](./assets/微信图片_20240421205440.png)

 - Support multiple web app switching.
 - Add the AppInfo node, which allows you to transform the workflow into a web app by simple configuration.
@@ -110,28 +116,39 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43


 ### 3D
+![](./assets/3d-workflow.png)
+![](./assets/3d_app.png)
+[workflow](./assets/Image-to-3D_1.json)
+
 ![](./assets/3dimage.png)
 [workflow](./workflow/3D-workflow.json)


-### LoadImagesFromLocal
+### Image
+
+#### LoadImagesToBatch
+> Upload multiple images for batch input into the IP adapter. 
+
+#### LoadImagesFromLocal
 > Monitor changes to images in a local folder, and trigger real-time execution of workflows, supporting common image formats, especially PSD format, in conjunction with Photoshop. 

 ![watch](./assets/4-loadfromlocal-watcher-workflow.svg)

 [workflow-4](./workflow/4-loadfromlocal-watcher-workflow.json)

-### LoadImagesFromURL
+#### LoadImagesFromURL
 > Conveniently load images from a fixed address on the internet to ensure that default images in the workflow can be executed.


-## Style
+### Style
 > Apply VisualStyle Prompting , Modified from [ComfyUI_VisualStylePrompting](https://github.com/ExponentialML/ComfyUI_VisualStylePrompting) 

 ![](./assets/VisualStylePrompting.png)

+> StyleAligned , Modified from [style_aligned_comfy](https://github.com/brianfitzgerald/style_aligned_comfy) 

-## Utils
+
+### Utils
 > The Color node provides a color picker for easy color selection, the Font node offers built-in font selection for use with TextImage to generate text images, and the DynamicDelayByText node allows delayed execution based on the length of the input text.

 - [添加了DynamicDelayByText功能，可以根据输入文本的长度进行延迟执行。](./workflow/audio-chatgpt-workflow.json)
@@ -144,7 +161,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43



-## Other Nodes
+### Other Nodes

 ![main](./assets/all-workflow.svg)
 ![main2](./assets/detect-face-all.png)
@@ -191,15 +208,20 @@ An improvement has been made to directly redirect to GitHub to search for missin

 ### Models

-[Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:models/rembg
+* [Download TripoSR](https://huggingface.co/stabilityai/TripoSR/blob/main/model.ckpt) and place it in ```models/triposr```

-[Download lama](https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt), move to : models/lama
+* [Download facebook/dino-vitb16](https://huggingface.co/facebook/dino-vitb16/tree/main)  and place it in ```models/triposr/facebook/dino-vitb16```

-[Download Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), move to : models/clip_interrogator/Salesforce/blip-image-captioning-base

-[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:prompt_generator/text2image-prompt-generator
+[Download rembg Models](https://github.com/danielgatis/rembg/tree/main#Models),move to:```models/rembg```

-[Download Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main),move to:prompt_generator/opus-mt-zh-en
+[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```
+
+[Download succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator/tree/main),move to:```models/prompt_generator/text2image-prompt-generator```
+
+[Download Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en/tree/main),move to:```models/prompt_generator/opus-mt-zh-en```

 ## Installation

@@ -263,7 +263,7 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
                print("发生异常：", str(e))
    else:
        app_workflow_path=os.path.join(category_path, filename)
-        # print('app_workflow_path: ',app_workflow_path)
+        print('app_workflow_path: ',app_workflow_path)
        try:
            with open(app_workflow_path) as json_file:
                apps = [{
@@ -273,7 +273,8 @@ def get_my_workflow_for_app(filename="my_workflow_app.json",category="",is_all=F
        except Exception as e:
            print("发生异常：", str(e))
        
-        if len(apps)==1 and category!='' and category!=None:
+        # 这个代码不需要
+        # if len(apps)==1 and category!='' and category!=None:
            data=read_workflow_json_files(category_path)
            
            for item in data:
@@ -576,9 +577,6 @@ async def post_prompt_result(request):
    
    return web.json_response({"result":res})

-
-
-
 # 扩展api接口
 # from server import PromptServer
 # from aiohttp import web
@@ -591,17 +589,22 @@ async def post_prompt_result(request):


 # 导入节点
-from .nodes.PromptNode import EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
-from .nodes.ImageNode import SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
+from .nodes.PromptNode import GLIGENTextBoxApply_Advanced,EmbeddingPrompt,RandomPrompt,PromptSlide,PromptSimplification,PromptImage,JoinWithDelimiter
+from .nodes.ImageNode import LoadImages_,CompositeImages,GridDisplayAndSave,GridInput,ImagesPrompt,SaveImageAndMetadata,SaveImageToLocal,SplitImage,GridOutput,GetImageSize_,MirroredImage,ImageColorTransfer,NoiseImage,TransparentImage,GradientImage,LoadImagesFromPath,LoadImagesFromURL,ResizeImage,TextImage,SvgImage,Image3D,ShowLayer,NewLayer,MergeLayers,CenterImage,AreaToMask,SmoothMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
 # from .nodes.Vae import VAELoader,VAEDecode
 from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo

 from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
-from .nodes.Mask import OutlineMask,FeatheredMask
+from .nodes.Utils import IncrementingListNode,ListSplit,CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,TESTNODE_TOKEN,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.Mask import PreviewMask_,MaskListReplace,MaskListMerge,OutlineMask,FeatheredMask
+
+from .nodes.Style import ApplyVisualStylePrompting,StyleAlignedReferenceSampler,StyleAlignedBatchAlign,StyleAlignedSampleReferenceLatents
+
+from .nodes.Video import VideoCombine_Adv,LoadVideoAndSegment,ImageListReplace,VAEEncodeForInpaint_Frames
+
+from .nodes.TripoSR import LoadTripoSRModel,TripoSRSampler,SaveTripoSRMesh

-from .nodes.Style import ApplyVisualStylePrompting

 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
@@ -613,6 +616,7 @@ NODE_CLASS_MAPPINGS = {
    # "LoraPrompt":LoraPrompt,
    "EmbeddingPrompt":EmbeddingPrompt,
    "PromptSlide":PromptSlide,
+    "GLIGENTextBoxApply_Advanced":GLIGENTextBoxApply_Advanced,
    "PromptSimplification":PromptSimplification,
    "PromptImage":PromptImage,
    "MirroredImage":MirroredImage,
@@ -622,6 +626,7 @@ NODE_CLASS_MAPPINGS = {
    "ResizeImageMixlab":ResizeImage,
    "LoadImagesFromPath":LoadImagesFromPath,
    "LoadImagesFromURL":LoadImagesFromURL,
+    "LoadImagesToBatch":LoadImages_,
    "TextImage":TextImage,
    "EnhanceImage":EnhanceImage,
    "SvgImage":SvgImage,
@@ -629,9 +634,12 @@ NODE_CLASS_MAPPINGS = {
    "ImageColorTransfer":ImageColorTransfer,
    "ShowLayer":ShowLayer,
    "NewLayer":NewLayer,
+    "CompositeImages_":CompositeImages,
    "SplitImage":SplitImage,
    "CenterImage":CenterImage,
    "GridOutput":GridOutput,
+    "GridDisplayAndSave":GridDisplayAndSave,
+    "GridInput":GridInput,
    "MergeLayers":MergeLayers,
    "SplitLongMask":SplitLongMask,
    "FeatheredMask":FeatheredMask,
@@ -639,8 +647,10 @@ NODE_CLASS_MAPPINGS = {
    "FaceToMask":FaceToMask,
    "AreaToMask":AreaToMask,
    "ImageCropByAlpha":ImageCropByAlpha,
+    "ImagesPrompt_":ImagesPrompt,
    # "VAELoaderConsistencyDecoder":VAELoader,
    "SaveImageToLocal":SaveImageToLocal,
+    "SaveImageAndMetadata_":SaveImageAndMetadata,
    # "VAEDecodeConsistencyDecoder":VAEDecode,
    "ScreenShare":ScreenShareNode,
    "FloatingVideo":FloatingVideo,
@@ -662,41 +672,85 @@ NODE_CLASS_MAPPINGS = {
    "SwitchByIndex":SwitchByIndex,
    "LimitNumber":LimitNumber, 
    "OutlineMask":OutlineMask,
+    "MaskListMerge_":MaskListMerge,
    "JoinWithDelimiter":JoinWithDelimiter,
    "Seed_":CreateSeedNode,
    "CkptNames_":CreateCkptNames,
    "SamplerNames_":CreateSampler_names,
    "LoraNames_":CreateLoraNames,
-    "ApplyVisualStylePrompting_":ApplyVisualStylePrompting
-    # "LaMaInpainting":LaMaInpainting
+    "ApplyVisualStylePrompting_":ApplyVisualStylePrompting,
+    "StyleAlignedReferenceSampler_": StyleAlignedReferenceSampler,
+    "StyleAlignedSampleReferenceLatents_": StyleAlignedSampleReferenceLatents,
+    "StyleAlignedBatchAlign_": StyleAlignedBatchAlign,
+    "LoadVideoAndSegment_":LoadVideoAndSegment,
+    "VideoCombine_Adv":VideoCombine_Adv,
+    "ListSplit_":ListSplit,
+    "MaskListReplace_":MaskListReplace,
+    "ImageListReplace_":ImageListReplace,
+    "VAEEncodeForInpaint_Frames":VAEEncodeForInpaint_Frames,
+    "IncrementingListNode_":IncrementingListNode,
+    "PreviewMask_":PreviewMask_,
+     "LoadTripoSRModel_": LoadTripoSRModel,
+    "TripoSRSampler_": TripoSRSampler,
+    "SaveTripoSRMesh": SaveTripoSRMesh
    # "GamePal":GamePal
 }

 # 一个包含节点友好/可读的标题的字典
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "AppInfo":"AppInfo ♾️Mixlab",
-    "ResizeImageMixlab":"ResizeImage ♾️Mixlab",
+    "AppInfo":"App Info ♾️MixlabApp",
+    "Color":"Color Input ♾️MixlabApp",
+    "TextInput_":"Text Input ♾️MixlabApp",
+    "FloatSlider":"Float Slider Input ♾️MixlabApp",
+    "IntNumber":"Int Input ♾️MixlabApp",
+    "ImagesPrompt_":"Images Input ♾️MixlabApp",
+    "SaveImageAndMetadata_":"Save Image Output ♾️MixlabApp",
+    "ResizeImageMixlab":"Resize Image ♾️Mixlab",
    "RandomPrompt": "Random Prompt ♾️Mixlab",
+    "PromptImage":"Output Prompt and Image ♾️Mixlab",
    "SplitLongMask":"Splitting a long image into sections",
    "VAELoaderConsistencyDecoder":"Consistency Decoder Loader",
    "VAEDecodeConsistencyDecoder":"Consistency Decoder Decode",
-    "ScreenShare":"ScreenShare ♾️Mixlab",
+    "ScreenShare":"Screen Share ♾️Mixlab",
    "FloatingVideo":"FloatingVideo ♾️Mixlab",
    "ChatGPTOpenAI":"ChatGPT ♾️Mixlab",
-    "ShowTextForGPT":"ShowTextForGPT ♾️Mixlab",
-    "MergeLayers":"MergeLayers ♾️Mixlab",
+    "ShowTextForGPT":"Show Text ♾️MixlabApp",
+    "MergeLayers":"Merge Layers ♾️Mixlab",
    "SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
    "SpeechRecognition":"SpeechRecognition ♾️Mixlab",
    "3DImage":"3DImage ♾️Mixlab",
+    "CompositeImages_":"Composite Images ♾️Mixlab",
    "DynamicDelayProcessor":"DynamicDelayByText ♾️Mixlab",
    "LaMaInpainting":"LaMaInpainting ♾️Mixlab",
-    "PromptSlide":"PromptSlide ♾️Mixlab",
-    "PromptGenerate_Mix":"PromptGenerate ♾️Mixlab",
-    "ChinesePrompt_Mix":"ChinesePrompt ♾️Mixlab",
+    "PromptSlide":"Prompt Slide ♾️Mixlab",
+    "PromptGenerate_Mix":"Prompt Generate ♾️Mixlab",
+    "ChinesePrompt_Mix":"Chinese Prompt ♾️Mixlab",
    "GamePal":"GamePal ♾️Mixlab",
-    "RembgNode_Mix":"Removebg",
-    "LoraNames_":"LoraName",
-    "ApplyVisualStylePrompting_":"Apply VisualStyle Prompting"
+    "RembgNode_Mix":"Remove Background ♾️Mixlab",
+    "LoraNames_":"LoraName ♾️Mixlab",
+    "ApplyVisualStylePrompting_":"Apply VisualStyle Prompting ♾️Mixlab",
+    "StyleAlignedReferenceSampler_": "StyleAligned Reference Sampler ♾️Mixlab",
+    "StyleAlignedSampleReferenceLatents_": "StyleAligned Sample Reference Latents ♾️Mixlab",
+    "StyleAlignedBatchAlign_": "StyleAligned Batch Align ♾️Mixlab",
+    "LoadVideoAndSegment_":"Load Video And Segment ♾️Mixlab",
+    "VideoCombine_Adv":"Video Combine ♾️Mixlab",
+    "MaskListMerge_":"MaskList to Mask ♾️Mixlab",
+    "ListSplit_":"Split List ♾️Mixlab",
+    "MaskListReplace_":"MaskList Replace ♾️Mixlab",
+    "ImageListReplace_":"ImageList Replace ♾️Mixlab",
+    "SwitchByIndex":"List Switch By Index ♾️Mixlab",
+    "GLIGENTextBoxApply_Advanced":"GLIGEN TextBox Apply ♾️Mixlab",
+    "GridDisplayAndSave":"Grid Display And Save ♾️Mixlab",
+    "GridInput":"Grid Input ♾️Mixlab",
+    "GridOutput":"Grid Output ♾️Mixlab",
+    "GetImageSize_":"Get Image Size ♾️Mixlab",
+    "VAEEncodeForInpaint_Frames":"VAE Encode For Inpaint Frames ♾️Mixlab",
+    "IncrementingListNode_":"Create Incrementing Number List ♾️Mixlab",
+    "LoadImagesToBatch":"Load Images(base64) ♾️Mixlab",
+    "PreviewMask_":"Preview Mask",
+    "LoadTripoSRModel_": "Load TripoSR Model",
+    "TripoSRSampler_": "TripoSR Sampler",
+    "SaveTripoSRMesh": "Save TripoSR Mesh"
 }

 # web ui的节点功能
@@ -0,0 +1,10 @@
+[
+    {
+        "keyword":"Dog",
+        "imgurl":"http://127.0.0.1:8188/view?filename=1709966910233.png&type=input&subfolder=&rand=0.2734446552394221"
+    },
+    {
+        "keyword":"x",
+        "imgurl":"http://127.0.0.1:8188/view?filename=image%20(33).png&type=input&subfolder=pasted&rand=0.6984318219852814"
+    }
+]
@@ -4,7 +4,18 @@ import urllib.error
 import re,json,os,string,random
 import folder_paths
 import hashlib
-from zhipuai import ZhipuAI
+import codecs,sys
+import importlib.util
+
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+    return spec is not None
+
+
 def get_unique_hash(string):
    hash_object = hashlib.sha1(string.encode())
    unique_hash = hash_object.hexdigest()
@@ -46,14 +57,36 @@ def openai_client(key,url):
    base_url=url
    )
    return client
+
 def ZhipuAI_client(key):
+
+    try:
+        if is_installed('zhipuai')==False:
+            import subprocess
+
+            # 安装
+            print('#pip install zhipuai')
+
+            result = subprocess.run([sys.executable, '-s', '-m', 'pip', 'install', 'zhipuai'], capture_output=True, text=True)
+
+            #检查命令执行结果
+            if result.returncode == 0:
+                print("#install success")
+                from zhipuai import ZhipuAI
+            else:
+                print("#install error")
+            
+        else:
+            from zhipuai import ZhipuAI
+    except:
+        print("#install zhipuai error")
+
    client = ZhipuAI(
        api_key=key, # 填写您的 APIKey
    ) 
    return client


-
 def chat(client, model_name,messages ):

        try_count = 0
@@ -105,7 +138,15 @@ class ChatGPTNode:
                                       "default": "You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.", 
                                       "multiline": True,"dynamicPrompts": False
                                       }),
-                "model": (["gpt-3.5-turbo","gpt-35-turbo","gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-4-0613","gpt-4-1106-preview","glm-4"], 
+                "model": ([
+                    "gpt-3.5-turbo",
+                    "gpt-3.5-turbo-0125",
+                    "gpt-35-turbo",
+                    "gpt-3.5-turbo-16k", 
+                    "gpt-3.5-turbo-16k-0613", 
+                    "gpt-4-0613",
+                    "gpt-4-1106-preview",
+                    "glm-4"], 
                          {"default": "gpt-3.5-turbo"}),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
                "context_size":("INT", {"default": 1, "min": 0, "max":30, "step": 1}),
@@ -119,7 +160,7 @@ class ChatGPTNode:
    RETURN_TYPES = ("STRING","STRING","STRING",)
    RETURN_NAMES = ("text","messages","session_history",)
    FUNCTION = "generate_contextual_text"
-    CATEGORY = "♾️Mixlab/Prompt/GPT"
+    CATEGORY = "♾️Mixlab/GPT"
    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,False,False,)

@@ -209,7 +250,7 @@ class ShowTextForGPT:
    OUTPUT_NODE = True
    OUTPUT_IS_LIST = (True,)

-    CATEGORY = "♾️Mixlab/Prompt/GPT"
+    CATEGORY = "♾️Mixlab/Text"

    def run(self, text,output_dir=[""]):
        
@@ -293,7 +334,7 @@ class CharacterInText:
    # OUTPUT_NODE = True
    OUTPUT_IS_LIST = (False,)

-    CATEGORY = "♾️Mixlab/Prompt/GPT"
+    CATEGORY = "♾️Mixlab/Text"

    def run(self, text,character,start_index):
        # print(text,character,start_index)
@@ -306,8 +347,8 @@ class TextSplitByDelimiter:
    def INPUT_TYPES(s):
        return {
            "required": {
-                 "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
-                "delimiter":(["newline","comma"],),
+                "text": ("STRING", {"multiline": True,"dynamicPrompts": False}),
+                "delimiter":("STRING", {"multiline": False,"default":",","dynamicPrompts": False}),
                "start_index": ("INT", {
                    "default": 0,
                    "min": 0, #Minimum value
@@ -338,15 +379,16 @@ class TextSplitByDelimiter:
    # OUTPUT_NODE = True
    OUTPUT_IS_LIST = (True,)

-    CATEGORY = "♾️Mixlab/Prompt/GPT"
+    CATEGORY = "♾️Mixlab/Text"

    def run(self, text,delimiter,start_index,skip_every,max_count):
-        arr=[]
-        if delimiter=='newline':
-            arr = [line for line in text.split('\n') if line.strip()]
-        elif delimiter=='comma':
-            arr = [line for line in text.split(',') if line.strip()]
-        
+         
+        if delimiter=="":
+            arr=[text.strip()]
+        else:
+            delimiter=codecs.decode(delimiter, 'unicode_escape')
+            arr= [line for line in text.split(delimiter) if line.strip()]
+
        arr= arr[start_index:start_index + max_count * (skip_every+1):(skip_every+1)]

        return (arr,)
@@ -8,16 +8,90 @@ import base64,os,random
 from io import BytesIO
 import folder_paths
 import json,io
+import comfy.utils
 from comfy.cli_args import args
 import cv2
 import string
 import math,glob
 from .Watcher import FolderWatcher
+import hashlib
+
+
+
+# 将PIL图片转换为OpenCV格式
+def pil_to_opencv(image):
+    open_cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    return open_cv_image
+
+# 将OpenCV格式图片转换为PIL格式
+def opencv_to_pil(image):
+    pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    return pil_image
+
+
+def composite_images(foreground, background, mask):
+    width,height=foreground.size
+ 
+    bg_image=background
+    # 按z-index排序
+    layer = {
+            "x":0,
+            "y":0,
+            "width":width,
+            "height":height,
+            "z_index":88,
+            "scale_option":'overall',
+            "image":foreground,
+            "mask":mask
+    }
+                
+    width, height = bg_image.size
+
+    layer_image=layer['image']
+    layer_mask=layer['mask']
+    
+    bg_image=merge_images(bg_image,
+                        layer_image,
+                        layer_mask,
+                                        layer['x'],
+                                        layer['y'],
+                                        layer['width'],
+                                        layer['height'],
+                                        layer['scale_option']
+                                        )
+                   
+    bg_image=bg_image.convert('RGB')
+    
+    return bg_image
+
+
+def count_files_in_directory(directory):
+    file_count = 0
+    for _, _, files in os.walk(directory):
+        file_count += len(files)
+    return file_count
+
+def save_json_to_file(data, file_path):
+    with open(file_path, 'w') as file:
+        json.dump(data, file)
+
+def draw_rectangle(image, grid, color,width):
+    x, y, w, h = grid
+    draw = ImageDraw.Draw(image)
+    draw.rectangle([(x, y), (x+w, y+h)], outline=color,width=width)

 def generate_random_string(length):
    letters = string.ascii_letters + string.digits
    return ''.join(random.choice(letters) for _ in range(length))

+def padding_rectangle(grid, padding):
+    x, y, w, h = grid
+    x -= padding
+    y -= padding
+    w += 2 * padding
+    h += 2 * padding
+    return (x, y, w, h)
+
 class AnyType(str):
  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""

@@ -559,7 +633,7 @@ def detect_faces(image):

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

    # 创建一个可用于绘制的对象
    draw = ImageDraw.Draw(mask)
@@ -592,7 +666,46 @@ def areaToMask(x,y,w,h,image):
 #     # bg_image.save("output.jpg")
 #     return bg_image

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

@@ -621,13 +734,43 @@ def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option)
    nw, nh = layer_image.size
    mask = mask.resize((nw, nh))

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

    # 输出合成后的图片
    return bg_image


+def resize_2(img):
+    # 检查图像的高度是否是2的倍数，如果不是，则调整高度
+    if img.height % 2 != 0:
+        img = img.resize((img.width, img.height + 1))
+
+    # 检查图像的宽度是否是2的倍数，如果不是，则调整宽度
+    if img.width % 2 != 0:
+        img = img.resize((img.width + 1, img.height))
+
+    return img
+
 # TODO 几个像素点的底
 def resize_image(layer_image, scale_option, width, height,color="white"):
    layer_image = layer_image.convert("RGB")
@@ -657,8 +800,10 @@ def resize_image(layer_image, scale_option, width, height,color="white"):
        resized_image = Image.new("RGB", (width, height), color=color)
        resized_image.paste(layer_image.resize((new_width, new_height)), ((width - new_width) // 2, (height - new_height) // 2))
        resized_image = resized_image.convert("RGB")
+        resized_image=resize_2(resized_image)
        return resized_image
    
+    layer_image=resize_2(layer_image)
    return layer_image


@@ -982,6 +1127,35 @@ class TransparentImage:



+class ImagesPrompt:
+    @classmethod
+    def INPUT_TYPES(s):
+        # input_dir = folder_paths.get_input_directory()
+        # files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {
+            "required": { 
+               "image_base64": ("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+               "text": ("STRING",{"multiline": True,"default": "","dynamicPrompts": True}),
+            }
+            }
+    
+    RETURN_TYPES = ("IMAGE","STRING",)
+    RETURN_NAMES = ("image","text",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Input"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,False,)
+    OUTPUT_NODE = False
+
+    # 运行的函数
+    def run(self,image_base64,text):
+        image = base64_to_image(image_base64)
+        image=image.convert('RGB')
+        image=pil2tensor(image)
+        return (image,text,)


 class EnhanceImage:
@@ -1029,6 +1203,42 @@ class EnhanceImage:



+class LoadImages_:
+    @classmethod
+    def INPUT_TYPES(s):
+        
+        return {"required":
+                    {"images": ("IMAGEBASE64",), 
+                     },
+                }
+
+    CATEGORY = "♾️Mixlab/Image"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_image"
+    def load_image(self, images):
+
+        # print(images)
+        ims=[]
+        for im in images['base64']:
+            image = base64_to_image(im)
+            image=image.convert('RGB')
+            image=pil2tensor(image)
+            ims.append(image)
+
+        image1 = ims[0]
+        for image2 in ims[1:]:
+            if image1.shape[1:] != image2.shape[1:]:
+                image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
+            image1 = torch.cat((image1, image2), dim=0)
+        return (image1,)
+        
+ 
+
+
 ''' 
 ("STRING",{"multiline": False,"default": "Hello World!"})
 对应 widgets.js 里：
@@ -1378,7 +1588,7 @@ class Image3D:

    FUNCTION = "run"

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

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,False,False,False,)
@@ -1488,6 +1698,37 @@ class FaceToMask:
        return (mask,)


+class CompositeImages:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": { 
+            "foreground": ("IMAGE",),
+            "mask":("MASK",),
+            "background": ("IMAGE",),
+            },
+                }
+    
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self, foreground,mask,background):
+        foreground= tensor2pil(foreground)
+        mask= tensor2pil(mask)
+        background= tensor2pil(background)
+        res=composite_images(foreground,background,mask)
+        
+        return (pil2tensor(res),)
+
+
+
+
 class EmptyLayer:
    @classmethod
    def INPUT_TYPES(s):
@@ -1583,7 +1824,7 @@ class NewLayer:
                    "display": "number" # Cosmetic only: display as "number" or "slider"
                }),
                "scale_option": (["width","height",'overall'],),
-                "image": ("IMAGE",),
+                "image": (any_type,),
            },
             "optional":{
                    "mask": ("MASK",{"default": None}),
@@ -1640,21 +1881,20 @@ def createMask(image,x,y,w,h):
    # mask.save("mask.png")
    return mask

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

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

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

    return grid_coordinates
@@ -1804,20 +2044,198 @@ class CenterImage:

        return (grid,pil2tensor(mask),)

+class GridDisplayAndSave:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": {
+                "labels": ("STRING", 
+                                        {
+                                            "multiline": True, 
+                                            "default": "",
+                                            "forceInput": True,
+                                            "dynamicPrompts": False
+                                        }),
+                "grids": ("_GRID",),
+                
+                "image": ("IMAGE",),
+                "filename_prefix": ("STRING", {"default": "mixlab/grids"})
+            }
+                }
+    
+    RETURN_TYPES = ( )
+    RETURN_NAMES = ( )
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Layer"
+
+    INPUT_IS_LIST = True
+    OUTPUT_NODE = True
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,labels,grids,image,filename_prefix):
+
+        # print(image.shape)
+
+        img= tensor2pil(image[0])
+
+        for grid in grids:
+            draw_rectangle(img, grid, 'red',8)
+
+        #获取临时目录：temp
+        output_dir = folder_paths.get_temp_directory()
+
+        (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('tmp_', output_dir)
+        
+        image_file = f"{filename}_{counter:05}.png"
+
+        image_path=os.path.join(full_output_folder, image_file)
+        # 保存图片
+        img.save(image_path,compress_level=6)
+        width, height = img.size
+
+        (
+            full_output_folder,
+            filename,
+            counter,
+            _,
+            _,
+        ) = folder_paths.get_save_image_path(filename_prefix[0], output_dir)


+        data_converted = [{
+            "label":labels[i],
+            "grid":[float(grids[i][0]),
+                           float(grids[i][1]),
+                           float(grids[i][2]),
+                           float(grids[i][3])
+                           ]
+        } for i in range(len(grids))]
+
+        data={
+            "width":int(width),
+            "height":int(height),
+            "grids":data_converted
+        }
+
+        save_json_to_file(data,os.path.join(full_output_folder,f"${filename}_{counter:05}.json"))
+
+        return {"ui":{"image": [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type":"temp"
+            }],
+            "json":[data["width"],data['height'],data["grids"]]
+            },"result": ()}
+        # return {"ui":{"image": [ ],
+             
+        #     },"result": ()}
+
+class GridInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return { 
+            "required": {
+                "grids": ("STRING", 
+                                        {
+                                            "multiline": True, 
+                                            "default": "",
+                                            "dynamicPrompts": False
+                                        }),
+                "padding":("INT",{
+                    "default": 24, 
+                    "min": -500, #Minimum value
+                    "max": 5000, #Maximum value
+                    "step": 1, #Slider's step
+                    "display": "number" # Cosmetic only: display as "number" or "slider"
+                }),
+                
+            },
+            "optional":{
+                    "width":("INT",{
+                        "forceInput": True,
+                    }),
+                     "height":("INT",{
+                        "forceInput": True,
+                    }),
+                }
+
+                }
+    
+    RETURN_TYPES = ("_GRID","STRING","IMAGE",)
+    RETURN_NAMES = ("grids","labels","image",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Input"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,False,)
+    OUTPUT_NODE = True
+
+    def run(self,grids,padding,width=[-1],height=[-1]):
+        # print(padding[0],grids[0])
+        width=width[0]
+        height=height[0]
+
+        grids=grids[0]
+        data=json.loads(grids)
+        grids=data['grids']
+
+        if width>-1:
+            data['width']=width
+        if height>-1:
+            data['height']=height
+    
+        new_grids=[]
+        labels=[]
+        
+        for g in grids:
+            labels.append(g['label'])
+            new_grids.append(padding_rectangle(g['grid'],padding[0]))
+
+        image = Image.new("RGB", (int(data['width']),int(data["height"])), "white")
+        im=pil2tensor(image)
+        # image=create_temp_file(im)
+
+        data_converted = [{
+            "label":labels[i],
+            "grid":[float(new_grids[i][0]),
+                           float(new_grids[i][1]),
+                           float(new_grids[i][2]),
+                           float(new_grids[i][3])
+                           ]
+        } for i in range(len(new_grids))]
+
+        # 传递到前端节点的数据 报错,需要处理成 key:[x,x,x,x]
+        return {"ui":{
+            "json":[data["width"],data["height"],data_converted]
+            },"result": (new_grids,labels,im,)}
+    
+        # return (new_grids,labels,pil2tensor(image),)

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

    FUNCTION = "run"

@@ -1826,9 +2244,29 @@ class GridOutput:
    INPUT_IS_LIST = False
    # OUTPUT_IS_LIST = (True,)

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

 class ShowLayer:
    @classmethod
@@ -1920,10 +2358,14 @@ class MergeLayers:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { 
-            "layers": ("LAYER",),
-            "images": ("IMAGE",),
-                             },
-
+                            "layers": ("LAYER",),
+                            "images": ("IMAGE",),
+                                            },
+            "optional":{
+                           
+                            "is_multiply_blend":  ("BOOLEAN", {"default": False}),
+                            
+                    }
                }
    
    RETURN_TYPES = ("IMAGE","MASK",)
@@ -1936,11 +2378,12 @@ class MergeLayers:
    INPUT_IS_LIST = True
    # OUTPUT_IS_LIST = (False,)

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

        bg_images=[]
        masks=[]
- 
+        
+        is_multiply_blend=is_multiply_blend[0]
        # print(len(images),images[0].shape)
        # 1  torch.Size([2, 512, 512, 3])
        # 4  torch.Size([1, 1024, 768, 3])
@@ -1971,6 +2414,8 @@ class MergeLayers:
                    
                    layer_image=tensor2pil(image)
                    layer_mask=tensor2pil(mask)
+                    # t=layer_image.convert("RGBA")
+                    # t.save('test.png') 如果layerimage传入的是rgba，则是透明的
                    bg_image=merge_images(bg_image,
                                        layer_image,
                                        layer_mask,
@@ -1978,7 +2423,8 @@ class MergeLayers:
                                        layer['y'],
                                        layer['width'],
                                        layer['height'],
-                                        layer['scale_option']
+                                        layer['scale_option'],
+                                        is_multiply_blend
                                        )
                    
                    final_mask=merge_images(final_mask,
@@ -2225,11 +2671,15 @@ class ResizeImage:
            for ims in image:
                for im in ims:
                    im=tensor2pil(im)
-                    im=resize_image(im,scale_option,w,h,fill_color)
-                    im=im.convert('RGB')

+                    im=im.convert('RGB')
                    a_im,hex=get_average_color_image(im)

+                    if average_color=='on':
+                        fill_color=hex
+                        
+                    im=resize_image(im,scale_option,w,h,fill_color)
+
                    im=pil2tensor(im)
                    imgs.append(im)

@@ -2329,7 +2779,57 @@ class GetImageSize_:

        return (width, height,min_width,min_height,)

+class SaveImageAndMetadata:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+        self.compress_level = 4

+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": 
+                    {"images": ("IMAGE", ),
+                     "filename_prefix": ("STRING", {"default": "Mixlab"}),
+                     "metadata": (["disable","enable"],),
+                     },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "♾️Mixlab/Output"
+
+    def save_images(self, images, filename_prefix="Mixlab",metadata="disable", prompt=None, extra_pnginfo=None):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+        results = list()
+        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) and (metadata=="enable"):
+                print('##enable_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"
+            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+        return { "ui": { "images": results } }

 class ImageColorTransfer:
    @classmethod
@@ -2337,6 +2837,7 @@ class ImageColorTransfer:
        return {"required": {
                "source": ("IMAGE",),
                "target": ("IMAGE",),
+                "weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
                },
                }
    
@@ -2347,28 +2848,48 @@ class ImageColorTransfer:
    FUNCTION = "run"

    # 右键菜单目录
-    CATEGORY = "♾️Mixlab/Image"
+    CATEGORY = "♾️Mixlab/Color"

    # 输入是否为列表
-    INPUT_IS_LIST = True
+    # INPUT_IS_LIST = True

    # 输出是否为列表
-    OUTPUT_IS_LIST = (True,)
+    # OUTPUT_IS_LIST = (True,)

-    def run(self,source,target):
+    def run(self,source,target,weight):

        res=[]

-        target=target[0][0]
-        print(target.shape)
-        target=tensor2pil(target)
+        #batch-list
+        source_list = [source[i:i + 1, ...] for i in range(source.shape[0])]
+        target_list = [target[i:i + 1, ...] for i in range(target.shape[0])]

-        for ims in source:
-            for im in ims:
-                image=tensor2pil(im)
-                image=color_transfer(image,target)
-                image=pil2tensor(image)
-                res.append(image)
+        # 长度纠正为相等
+        if len(target_list) != len(source_list):
+            target_list = target_list * (len(source_list) // len(target_list)) + target_list[:len(source_list) % len(target_list)]
+        
+        for i in range(len(source_list)):
+            target=target_list[i]
+            source=source_list[i]
+            target=tensor2pil(target)
+
+            image=tensor2pil(source)
+
+            image_res=color_transfer(image,target)
+
+            # weight Blend image # contributors:@ning
+            blend_mask = Image.new(mode="L", size=image.size,
+                                    color=(round(weight * 255)))
+            blend_mask = ImageOps.invert(blend_mask)
+            img_result = Image.composite(image, image_res, blend_mask)
+            del image, image_res, blend_mask
+            
+            img_result=pil2tensor(img_result)
+
+            res.append(img_result)
+
+        # list - batch
+        res=torch.cat(res, dim=0)

        return (res,)

@@ -2395,7 +2916,7 @@ class SaveImageToLocal:

    OUTPUT_NODE = True

-    CATEGORY = "♾️Mixlab/Image"
+    CATEGORY = "♾️Mixlab/Output"

    def save_images(self, images,file_path , prompt=None, extra_pnginfo=None):
        filename_prefix = os.path.basename(file_path)
@@ -2,16 +2,14 @@
 import scipy.ndimage
 import torch

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

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

 # Tensor to PIL
 def tensor2pil(image):
@@ -22,6 +20,19 @@ def pil2tensor(image):
    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)


+def add_masks(mask1, mask2):
+    mask1 = mask1.cpu()
+    mask2 = mask2.cpu()
+    cv2_mask1 = np.array(mask1) * 255
+    cv2_mask2 = np.array(mask2) * 255
+
+    if cv2_mask1.shape == cv2_mask2.shape:
+        cv2_mask = cv2.add(cv2_mask1, cv2_mask2)
+        return torch.clamp(torch.from_numpy(cv2_mask) / 255.0, min=0, max=1)
+    else:
+        return mask1
+    
+
 def grow(mask, expand, tapered_corners):
    c = 0 if tapered_corners else 1
    kernel = np.array([[c, 1, c],
@@ -58,6 +69,35 @@ def combine(destination, source, x, y):

    return output

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

    @classmethod
@@ -87,6 +127,69 @@ class OutlineMask:
        return (m3,)
    

+class MaskListReplace:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "masks": ("MASK",),
+            "mask_replace": ("MASK",),
+            "start_index":("INT", {"default": 0, "min": 0, "step": 1}),
+            "end_index":("INT", {"default": 0, "min": 0, "step": 1}),
+            "invert": ("BOOLEAN", {"default": False}),
+        }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/Video"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self, masks,mask_replace,start_index,end_index,invert):
+        mask_replace=mask_replace[0]
+        start_index=start_index[0]
+        end_index=end_index[0]
+        invert=invert[0]
+        
+        new_masks=[]
+        for i in range(len(masks)):
+            if i>=start_index and i<=end_index:
+                if invert:
+                    new_masks.append(masks[i])
+                else:
+                    new_masks.append(mask_replace)
+            else:
+                if invert:
+                    new_masks.append(mask_replace)
+                else:
+                    new_masks.append(masks[i])
+
+        return (new_masks,)
+
+
+class MaskListMerge:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "masks": ("MASK",),
+        }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/Mask"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (False,)
+
+    def run(self, masks):
+        mask=masks[0]
+        if isinstance(masks, list):
+            for m in masks:
+                # print(m.shape)
+                mask = add_masks(mask, m)
+        return (mask,)


 class FeatheredMask:
@@ -189,7 +189,7 @@ class PromptImage:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Prompt"
+    CATEGORY = "♾️Mixlab/Output"

    # 运行的函数
    def run(self,prompts,images,save_to_image):
@@ -514,7 +514,7 @@ class RandomPrompt:
 #         return (lora_name,prompt,output_tags.split(','),)


-import folder_paths
+
 class EmbeddingPrompt:
    @classmethod
    def INPUT_TYPES(s):
@@ -546,7 +546,112 @@ class EmbeddingPrompt:
        # return (new_prompt)
        return (prompt,)

-RETURN_TYPES = (any_type,)
+# RETURN_TYPES = (any_type,)
+    
+# conditioning ：提示，正向or负向
+# clip：clip模型
+# gligen_textbox_model：gligen模型
+# grids：矩形框的集合
+# labels：每个矩形框对应的标签的集合
+# index：选取第几个矩形框作为gligen的box
+
+class GLIGENTextBoxApply_Advanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                              "clip": ("CLIP", ),
+                              "gligen_textbox_model": ("GLIGEN", ),
+                              "grids": ("_GRID",),
+                              "labels": ("STRING", 
+                                        {
+                                            "multiline": True, 
+                                            "default": "",
+                                            "forceInput": True
+                                        }),
+                              "index": ("INT", {"default": -1, "min": -1, "max": 300, "step": 1}),
+                              "max_size": ("INT", {"default": 8, "min": 1, "max": 300, "step": 1}),
+                              "random_shuffle":(["on","off"],),
+                             },
+                 "optional":{
+                    "seed": (any_type,  {"default": 0, "min": 0, "max": 0xffffffffffffffff,"step": 1}),
+                } 
+                            }
+    RETURN_TYPES = ("CONDITIONING","STRING",)
+    RETURN_NAMES = ("CONDITIONING","label",)
+
+    FUNCTION = "run"
+    # INPUT_IS_LIST = True
+    CATEGORY = "♾️Mixlab/Prompt"
+
+    def run(self, conditioning, clip, gligen_textbox_model, grids, labels, index,max_size,random_shuffle,seed=0):
+        # print('grids',grids)
+        # conditioning=conditioning[0]
+        # clip=clip[0]
+        # gligen_textbox_model=gligen_textbox_model[0]
+        # index=index[0]
+        # max_size=max_size[0]
+        # random_shuffle=random_shuffle[0]
+
+        texts=labels
+        
+        if index>-1:
+            texts=[labels[index]]
+            grids=[grids[index]]
+
+        if random_shuffle=='on':
+            sss=[[texts[i],grids[i]] for i in range(len(texts))]
+            random.shuffle(sss)
+            texts=[s[0] for s in sss]
+            grids=[s[1] for s in sss]
+
+        if len(texts) > max_size:
+            texts = texts[:max_size]
+
+        c = []
+        
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+
+
+            # 多个
+            position_params=[]
+            for i in range(len(texts)):
+                text=texts[i]
+                grid=grids[i]
+                x,y,width,height=grid
+                # print(text)
+                cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
+                position_params =position_params+ [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]
+
+            # 前一个
+            prev = []
+            if "gligen" in n[1]:
+                prev = n[1]['gligen'][2]
+
+            n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params)
+            # print('gligen',n)
+            c.append(n)
+        
+        #  下面这个写法有bug
+        # for i in range(len(texts)):
+        #     text=texts[i]
+        #     grid=grids[i]
+        #     x,y,width,height=grid
+
+        #     cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
+        #     for t in conditioning:
+        #         n = [t[0], t[1].copy()]
+        #         position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]
+        #         prev = []
+        #         if "gligen" in n[1]:
+        #             prev = n[1]['gligen'][2]
+
+        #         n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params)
+        #         c.append(n)
+
+
+        return (c,texts, )
+    

 class JoinWithDelimiter:
    @classmethod
@@ -561,7 +666,7 @@ class JoinWithDelimiter:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Prompt"
+    CATEGORY = "♾️Mixlab/Text"

    INPUT_IS_LIST = True # 当true的时候，输入时list，当false的时候，如果输入是list，则会自动包一层for循环调用
    OUTPUT_IS_LIST = (False,)
@@ -93,7 +93,7 @@ class ScreenShareNode:
    RETURN_NAMES = ("IMAGE","PROMPT","FLOAT","INT")
    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Image"
+    CATEGORY = "♾️Mixlab/Screen"

    # INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (False,False,False,False)
@@ -118,7 +118,7 @@ class FloatingVideo:
    OUTPUT_NODE = True
    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Image"
+    CATEGORY = "♾️Mixlab/Screen"

    # INPUT_IS_LIST = True
    # OUTPUT_IS_LIST = (False,False,)
@@ -1,6 +1,18 @@
 import comfy
 import torch

+from dataclasses import dataclass
+import torch.nn as nn
+from comfy.model_patcher import ModelPatcher
+import comfy.ops
+from typing import Union
+import comfy.sample
+import latent_preview
+import comfy.utils
+
+T = torch.Tensor
+
+
 from .VisualStylePrompting.attention_functions import VisualStyleProcessor

 class ApplyVisualStylePrompting:
@@ -74,3 +86,418 @@ class ApplyVisualStylePrompting:

        return (model, conditioning_prompt, negative_prompt, {"samples": latents, "noise_mask": denoise_mask})    

+
+
+
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d
+
+
+class StyleAlignedArgs:
+    def __init__(self, share_attn: str) -> None:
+        self.adain_keys = "k" in share_attn
+        self.adain_values = "v" in share_attn
+        self.adain_queries = "q" in share_attn
+
+    share_attention: bool = True
+    adain_queries: bool = True
+    adain_keys: bool = True
+    adain_values: bool = True
+
+
+def expand_first(
+    feat: T,
+    scale=1.0,
+) -> T:
+    """
+    Expand the first element so it has the same shape as the rest of the batch.
+    """
+    b = feat.shape[0]
+    feat_style = torch.stack((feat[0], feat[b // 2])).unsqueeze(1)
+    if scale == 1:
+        feat_style = feat_style.expand(2, b // 2, *feat.shape[1:])
+    else:
+        feat_style = feat_style.repeat(1, b // 2, 1, 1, 1)
+        feat_style = torch.cat([feat_style[:, :1], scale * feat_style[:, 1:]], dim=1)
+    return feat_style.reshape(*feat.shape)
+
+
+def concat_first(feat: T, dim=2, scale=1.0) -> T:
+    """
+    concat the the feature and the style feature expanded above
+    """
+    feat_style = expand_first(feat, scale=scale)
+    return torch.cat((feat, feat_style), dim=dim)
+
+
+def calc_mean_std(feat, eps: float = 1e-5) -> "tuple[T, T]":
+    feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt()
+    feat_mean = feat.mean(dim=-2, keepdims=True)
+    return feat_mean, feat_std
+
+def adain(feat: T) -> T:
+    feat_mean, feat_std = calc_mean_std(feat)
+    feat_style_mean = expand_first(feat_mean)
+    feat_style_std = expand_first(feat_std)
+    feat = (feat - feat_mean) / feat_std
+    feat = feat * feat_style_std + feat_style_mean
+    return feat
+
+class SharedAttentionProcessor:
+    def __init__(self, args: StyleAlignedArgs, scale: float):
+        self.args = args
+        self.scale = scale
+
+    def __call__(self, q, k, v, extra_options):
+        if self.args.adain_queries:
+            q = adain(q)
+        if self.args.adain_keys:
+            k = adain(k)
+        if self.args.adain_values:
+            v = adain(v)
+        if self.args.share_attention:
+            k = concat_first(k, -2, scale=self.scale)
+            v = concat_first(v, -2)
+
+        return q, k, v
+
+
+def get_norm_layers(
+    layer: nn.Module,
+    norm_layers_: "dict[str, list[Union[nn.GroupNorm, nn.LayerNorm]]]",
+    share_layer_norm: bool,
+    share_group_norm: bool,
+):
+    if isinstance(layer, nn.LayerNorm) and share_layer_norm:
+        norm_layers_["layer"].append(layer)
+    if isinstance(layer, nn.GroupNorm) and share_group_norm:
+        norm_layers_["group"].append(layer)
+    else:
+        for child_layer in layer.children():
+            get_norm_layers(
+                child_layer, norm_layers_, share_layer_norm, share_group_norm
+            )
+
+
+def register_norm_forward(
+    norm_layer: Union[nn.GroupNorm, nn.LayerNorm],
+) -> Union[nn.GroupNorm, nn.LayerNorm]:
+    if not hasattr(norm_layer, "orig_forward"):
+        setattr(norm_layer, "orig_forward", norm_layer.forward)
+    orig_forward = norm_layer.orig_forward
+
+    def forward_(hidden_states: T) -> T:
+        n = hidden_states.shape[-2]
+        hidden_states = concat_first(hidden_states, dim=-2)
+        hidden_states = orig_forward(hidden_states)  # type: ignore
+        return hidden_states[..., :n, :]
+
+    norm_layer.forward = forward_  # type: ignore
+    return norm_layer
+
+
+def register_shared_norm(
+    model: ModelPatcher,
+    share_group_norm: bool = True,
+    share_layer_norm: bool = True,
+):
+    norm_layers = {"group": [], "layer": []}
+    get_norm_layers(model.model, norm_layers, share_layer_norm, share_group_norm)
+    print(
+        f"Patching {len(norm_layers['group'])} group norms, {len(norm_layers['layer'])} layer norms."
+    )
+    return [register_norm_forward(layer) for layer in norm_layers["group"]] + [
+        register_norm_forward(layer) for layer in norm_layers["layer"]
+    ]
+
+
+SHARE_NORM_OPTIONS = ["both", "group", "layer", "disabled"]
+SHARE_ATTN_OPTIONS = ["q+k", "q+k+v", "disabled"]
+
+class StyleAlignedSampleReferenceLatents:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    { 
+                    "reference_image": ("IMAGE",),
+                    "positive": ("CONDITIONING",),
+                    "negative": ("CONDITIONING", ),
+                    "model": ("MODEL",),
+                    "vae": ("VAE", ),
+                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+
+                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS.reverse(), ), 
+                    "denoise": ("FLOAT", {"default": 1, "min": 0.0, "max": 1.0, "step": 0.01}),
+                    
+                     }
+                }
+
+    RETURN_TYPES = ("STEP_LATENTS","LATENT")
+    RETURN_NAMES = ("ref_latents", "noised_output")
+
+    FUNCTION = "run"
+
+    # CATEGORY = "style_aligned"
+    CATEGORY = "♾️Mixlab/Style"
+
+    def run(self, reference_image, positive, negative, model, vae, seed, steps, cfg,scheduler,denoise):
+
+        # TODO noise_mask?
+        def vae_encode_crop_pixels(pixels):
+            x = (pixels.shape[1] // 8) * 8
+            y = (pixels.shape[2] // 8) * 8
+            if pixels.shape[1] != x or pixels.shape[2] != y:
+                x_offset = (pixels.shape[1] % 8) // 2
+                y_offset = (pixels.shape[2] % 8) // 2
+                pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
+            return pixels
+
+        pixels=vae_encode_crop_pixels(reference_image)
+        t = vae.encode(pixels[:,:,:,:3])
+        latent_image =  {"samples":t}
+
+        noise_seed=seed
+
+        sampler_name="ddim"
+        
+        sampler = comfy.samplers.sampler_object(sampler_name)
+
+        total_steps = steps
+        if denoise < 1.0:
+            total_steps = int(steps/denoise)
+
+        comfy.model_management.load_models_gpu([model])
+        sigmas = comfy.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu()
+        sigmas = sigmas[-(steps + 1):]
+
+        sigmas = sigmas.flip(0)
+        if sigmas[0] == 0:
+            sigmas[0] = 0.0001
+
+        latent = latent_image
+        latent_image = latent["samples"]
+        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+
+    
+        noise_mask = None
+        if "noise_mask" in latent:
+            noise_mask = latent["noise_mask"]
+
+        ref_latents = []
+        def callback(step: int, x0: T, x: T, steps: int):
+            ref_latents.insert(0, x[0])
+        
+        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+        samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+        out = latent.copy()
+        out["samples"] = samples
+        out_noised = out
+
+        ref_latents = torch.stack(ref_latents)
+
+        return (ref_latents, out_noised)
+
+class StyleAlignedReferenceSampler:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+
+                "ref_latents": ("STEP_LATENTS",),
+                "reference_image_text": ("STRING", {"multiline": True}),
+                "model": ("MODEL",),
+                "clip": ("CLIP", ),
+
+                "positive": ("CONDITIONING",),
+                "negative": ("CONDITIONING",),
+
+                "share_norm": (SHARE_NORM_OPTIONS,),
+                "share_attn": (SHARE_ATTN_OPTIONS,),
+                "scale": ("FLOAT", {"default": 1, "min": 0, "max": 2.0, "step": 0.01}),
+                "batch_size": ("INT", {"default": 2, "min": 1, "max": 8, "step": 1}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), 
+                "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = ("LATENT", "LATENT")
+    RETURN_NAMES = ("output", "denoised_output")
+    FUNCTION = "patch"
+    # CATEGORY = "style_aligned"
+    CATEGORY = "♾️Mixlab/Style"
+    def patch(
+        self,
+        ref_latents,
+        reference_image_text,
+        model,
+        clip,
+        positive,
+        negative,
+        share_norm,
+        share_attn,
+        scale,
+        batch_size,
+        seed,steps,cfg,scheduler,denoise
+
+    ) -> "tuple[dict, dict]":
+        
+        m = model.clone()
+
+        # ref_latents = vae.encode(reference_image[:,:,:,:3])
+
+        tokens = clip.tokenize(reference_image_text)
+        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
+        ref_positive=[[cond, {"pooled_output": pooled}]]
+
+        noise_seed=seed
+
+
+        total_steps = steps
+        if denoise < 1.0:
+            total_steps = int(steps/denoise)
+
+        # comfy.model_management.load_models_gpu([model])
+        sigmas = comfy.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu()
+        sigmas = sigmas[-(steps + 1):]
+
+        sampler_name="ddim"
+        
+        sampler = comfy.samplers.sampler_object(sampler_name)
+
+        args = StyleAlignedArgs(share_attn)
+
+        # Concat batch with style latent
+        style_latent_tensor = ref_latents[0].unsqueeze(0)
+        height, width = style_latent_tensor.shape[-2:]
+        latent_t = torch.zeros(
+            [batch_size, 4, height, width], device=ref_latents.device
+        )
+        latent = {"samples": latent_t}
+        noise = comfy.sample.prepare_noise(latent_t, noise_seed)
+
+        latent_t = torch.cat((style_latent_tensor, latent_t), dim=0)
+        ref_noise = torch.zeros_like(noise[0]).unsqueeze(0)
+        noise = torch.cat((ref_noise, noise), dim=0)
+
+        x0_output = {}
+        preview_callback = latent_preview.prepare_callback(m, sigmas.shape[-1] - 1, x0_output)
+
+        # Replace first latent with the corresponding reference latent after each step
+        def callback(step: int, x0: T, x: T, steps: int):
+            preview_callback(step, x0, x, steps)
+            if (step + 1 < steps):
+                # 当ref_latents的step不够时
+                if step+1>len(ref_latents)-1:
+                    step=len(ref_latents)-2
+                
+                x[0] = ref_latents[step+1]
+                x0[0] = ref_latents[step+1]
+
+        # Register shared norms
+        share_group_norm = share_norm in ["group", "both"]
+        share_layer_norm = share_norm in ["layer", "both"]
+        register_shared_norm(m, share_group_norm, share_layer_norm)
+
+        # Patch cross attn
+        m.set_model_attn1_patch(SharedAttentionProcessor(args, scale))
+
+        # Add reference conditioning to batch 
+        batched_condition = []
+        for i,condition in enumerate(positive):
+            additional = condition[1].copy()
+            batch_with_reference = torch.cat([ref_positive[i][0], condition[0].repeat([batch_size] + [1] * len(condition[0].shape[1:]))], dim=0)
+            if 'pooled_output' in additional and 'pooled_output' in ref_positive[i][1]:
+                # combine pooled output
+                pooled_output = torch.cat([ref_positive[i][1]['pooled_output'], additional['pooled_output'].repeat([batch_size] 
+                    + [1] * len(additional['pooled_output'].shape[1:]))], dim=0)
+                additional['pooled_output'] = pooled_output
+            if 'control' in additional:
+                if 'control' in ref_positive[i][1]:
+                    # combine control conditioning
+                    control_hint = torch.cat([ref_positive[i][1]['control'].cond_hint_original, additional['control'].cond_hint_original.repeat([batch_size] 
+                        + [1] * len(additional['control'].cond_hint_original.shape[1:]))], dim=0)
+                    cloned_controlnet = additional['control'].copy()
+                    cloned_controlnet.set_cond_hint(control_hint, strength=additional['control'].strength, timestep_percent_range=additional['control'].timestep_percent_range)
+                    additional['control'] = cloned_controlnet
+                else:
+                    # add zeros for first in batch
+                    control_hint = torch.cat([torch.zeros_like(additional['control'].cond_hint_original), additional['control'].cond_hint_original.repeat([batch_size] 
+                        + [1] * len(additional['control'].cond_hint_original.shape[1:]))], dim=0)
+                    cloned_controlnet = additional['control'].copy()
+                    cloned_controlnet.set_cond_hint(control_hint, strength=additional['control'].strength, timestep_percent_range=additional['control'].timestep_percent_range)
+                    additional['control'] = cloned_controlnet
+            batched_condition.append([batch_with_reference, additional])
+
+        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+        samples = comfy.sample.sample_custom(
+            m,
+            noise,
+            cfg,
+            sampler,
+            sigmas,
+            batched_condition,
+            negative,
+            latent_t,
+            callback=callback,
+            disable_pbar=disable_pbar,
+            seed=noise_seed,
+        )
+
+        # remove reference image
+        samples = samples[1:]
+
+        out = latent.copy()
+        out["samples"] = samples
+        if "x0" in x0_output:
+            out_denoised = latent.copy()
+            x0 = x0_output["x0"][1:]
+            out_denoised["samples"] = m.model.process_latent_out(x0.cpu())
+        else:
+            out_denoised = out
+        return (out, out_denoised)
+
+
+class StyleAlignedBatchAlign:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "share_norm": (SHARE_NORM_OPTIONS,),
+                "share_attn": (SHARE_ATTN_OPTIONS,),
+                "scale": ("FLOAT", {"default": 1, "min": 0, "max": 1.0, "step": 0.1}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    # CATEGORY = "style_aligned"
+    CATEGORY = "♾️Mixlab/Style"
+    def patch(
+        self,
+        model: ModelPatcher,
+        share_norm: str,
+        share_attn: str,
+        scale: float,
+    ):
+        m = model.clone()
+        share_group_norm = share_norm in ["group", "both"]
+        share_layer_norm = share_norm in ["layer", "both"]
+        register_shared_norm(model, share_group_norm, share_layer_norm)
+        args = StyleAlignedArgs(share_attn)
+        m.set_model_attn1_patch(SharedAttentionProcessor(args, scale))
+        return (m,)
+
+
@@ -12,6 +12,8 @@ import comfy.utils
 # import numpy as np
 import torch
 import random
+from lark import Lark, Transformer, v_args
+

 global _available
 _available=True
@@ -109,7 +111,7 @@ def text_generate(text_pipe,input,seed=None):
    
 import re

-def correct_prompt_syntax(prompt):
+def correct_prompt_syntax(prompt=""):

    # print("input prompt",prompt)
    corrected_elements = []
@@ -191,7 +193,7 @@ NUMBER: /\s*-?\d+(\.\d+)?\s*/
 WORD: /[^,:\(\)\[\]<>]+/
 """

-from lark import Lark, Transformer, v_args
+

@v_args(inline=True)  # Decorator to flatten the tree directly into the function arguments
 class ChinesePromptTranslate(Transformer):
@@ -304,7 +306,6 @@ class ChinesePrompt:
        pbar = comfy.utils.ProgressBar(len(text)+1)
        texts = [correct_prompt_syntax(t) for t in text]

-
        global text_pipe,zh_en_model,zh_en_tokenizer
        if zh_en_model==None:
            zh_en_model = AutoModelForSeq2SeqLM.from_pretrained(zh_en_model_path).eval()
@@ -323,13 +324,14 @@ class ChinesePrompt:
        en_texts=[]

        for t in texts:
-            # translated_text =  translated_word = translate(zh_en_tokenizer,zh_en_model,str(t))
-            parser = Lark(grammar, start="start", parser="lalr", transformer=ChinesePromptTranslate())
-            # print('t',t)
-            result = parser.parse(t).children
-            # print('en_result',result)        
-            # en_text=translate(zh_en_tokenizer,zh_en_model,text_without_syntax)
-            en_texts.append(result[0])
+            if t:
+                # translated_text =  translated_word = translate(zh_en_tokenizer,zh_en_model,str(t))
+                parser = Lark(grammar, start="start", parser="lalr", transformer=ChinesePromptTranslate())
+                # print('t',t)
+                result = parser.parse(t).children
+                # print('en_result',result)        
+                # en_text=translate(zh_en_tokenizer,zh_en_model,text_without_syntax)
+                en_texts.append(result[0])

        zh_en_model.to('cpu')
        print("test en_text",en_texts)
@@ -352,7 +354,8 @@ class ChinesePrompt:

        print('prompt_result',prompt_result,)
        # prompt_result = [','.join(correct_prompt_syntax(p)) for p in prompt_result]
-        
+        if len(prompt_result)==0:
+            prompt_result=[""]
        return {
            "ui":{
                    "prompt": prompt_result
@@ -0,0 +1,173 @@
+import sys
+from os import path
+sys.path.insert(0, path.dirname(__file__))
+from PIL import Image
+import numpy as np
+import torch
+
+from folder_paths import get_filename_list, get_full_path, get_save_image_path, get_output_directory,models_dir
+from comfy.model_management import get_torch_device
+from .tsr.system import TSR
+
+import comfy.utils
+
+triposr_model_path=path.join(models_dir,'triposr/model.ckpt')
+
+
+# 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 fill_background(image):
+    im = np.array(image).astype(np.float32) / 255.0
+    im = im[:, :, :3] * im[:, :, 3:4] + (1 - im[:, :, 3:4]) * 0.5
+    im = Image.fromarray((im * 255.0).astype(np.uint8))
+    return im
+
+
+class LoadTripoSRModel:
+    def __init__(self):
+        self.initialized_model = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                # "model": (get_filename_list("checkpoints"),),
+                "chunk_size": ("INT", {"default": 8192, "min": 0, "max": 10000})
+            }
+        }
+
+    RETURN_TYPES = ("TRIPOSR_MODEL",)
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/3D/TripoSR"
+
+    def run(self, chunk_size):
+        device = get_torch_device()
+
+        if not torch.cuda.is_available():
+            device = "cpu"
+
+        if not self.initialized_model:
+            # triposr_model_path
+            print("#Loading TripoSR model",triposr_model_path)
+            self.initialized_model = TSR.from_pretrained_custom(
+                weight_path=triposr_model_path,
+                config_path=path.join(path.dirname(__file__), "tsr/config.yaml")
+            )
+            self.initialized_model.renderer.set_chunk_size(chunk_size)
+            self.initialized_model.to(device)
+
+        return (self.initialized_model,)
+
+
+class TripoSRSampler:
+    def __init__(self):
+        self.initialized_model = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("TRIPOSR_MODEL",),
+                "image": ("IMAGE",),
+                "resolution": ("INT", {"default": 256, "min": 128, "max": 12288}),
+                "threshold": ("FLOAT", {"default": 25.0, "min": 0.0, "step": 0.01}),
+                "device":(["auto","cpu"],), 
+            },
+            "optional": {
+                "mask": ("MASK",)
+            }
+        }
+
+    RETURN_TYPES = ("MESH",)
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/3D/TripoSR"
+
+    def run(self, model, image, resolution, threshold,device='auto', mask=None):
+
+        reference_image=image
+        reference_mask=mask
+
+        device = get_torch_device()
+
+        if not torch.cuda.is_available():
+            device = "cpu"
+
+        if device=='cpu':
+            device = "cpu"
+
+        print('#TripoSRSampler device',device)
+
+        to_images=[]
+
+        for i in range(len(reference_image)):
+
+            image = reference_image[i]
+            
+            if reference_mask is not None:
+                mask = reference_mask[i].unsqueeze(2)
+                image = torch.cat((image, mask), dim=2).detach().cpu().numpy()
+                image = Image.fromarray(np.clip(255. * image, 0, 255).astype(np.uint8))
+                image = fill_background(image)
+            else:
+                image = tensor2pil(image)
+
+            image = image.convert('RGB')
+
+            to_images.append(image)
+
+        # 进度条
+        pbar = comfy.utils.ProgressBar(len(to_images))
+        def callback(c):
+            pbar.update(1)    
+
+        scene_codes = model(to_images, device)
+        meshes = model.extract_mesh(scene_codes, resolution=resolution, threshold=threshold,callback=callback)
+
+        del model
+        return (meshes,)
+
+
+class SaveTripoSRMesh:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mesh": ("MESH",),
+                # "format":(["glb","obj"],), 
+                "filename_prefix":("STRING", {"multiline": False,"default": "TripoSR_"})
+            }
+        }
+
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/3D/TripoSR"
+
+    def run(self, mesh,filename_prefix):
+        format='glb'
+        saved = list()
+        full_output_folder, filename, counter, subfolder, filename_prefix = get_save_image_path(filename_prefix,
+                                                                                                get_output_directory())
+
+        for (index, single_mesh) in enumerate(mesh):
+            filename_with_batch_num = filename.replace("%batch_num%", str(index))
+            file = f"{filename_with_batch_num}_{counter:05}_.{format}"
+            single_mesh.apply_transform(np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]))
+            single_mesh.export(path.join(full_output_folder, file))
+            saved.append({
+                "filename": file,
+                "type": "output",
+                "subfolder": subfolder
+            })
+
+        return {"ui": {"mesh": saved}}
+
+
+
@@ -8,6 +8,18 @@ import matplotlib.font_manager as fm
 import torch
 import importlib.util

+def create_incrementing_list(min_value, max_value, step, count):
+    l1 = [int(min_value + i * step) for i in range(count) if min_value + i * step <= max_value]
+    l2 = [float(min_value + i * step) for i in range(count) if min_value + i * step <= max_value]
+    return (l1,l2)
+
+def split_list(lst, chunk_size, transition_size):
+    result = []
+    for i in range(0, len(lst), chunk_size):
+        start = i - transition_size
+        end = i + chunk_size + transition_size
+        result.append(lst[max(start, 0):end])
+    return result

 def recursive_search(directory, excluded_dir_names=None):
    if not os.path.isdir(directory):
@@ -146,7 +158,6 @@ class ColorInput:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { 
-             
                    "color":("TCOLOR",), 
                             },
                }
@@ -156,7 +167,7 @@ class ColorInput:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Color"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,False,False,False,False,)
@@ -185,7 +196,7 @@ class FontInput:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Input"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -218,7 +229,7 @@ class TextToNumber:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Text"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -273,10 +284,10 @@ class FloatSlider:
                }
    
    RETURN_TYPES = ("FLOAT",) 
-
+    RETURN_NAMES = ('weight(0-1)',)
    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Input"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -329,7 +340,7 @@ class IntNumber:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Input"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -389,7 +400,7 @@ class TextInput:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Input"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -398,6 +409,61 @@ class TextInput:
       
        return (text,)
    
+
+class IncrementingListNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "min_value": ("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "max_value": ("FLOAT", {
+                        "default": 10, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "step": ("FLOAT", {
+                        "default": 0, 
+                        "min": -2000, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step": 0.01, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    }),
+                    "count": ("INT", {
+                        "default": 1, 
+                        "min": 1, #Minimum value
+                        "max": 0xffffffffffffffff,
+                        "step":1, #Slider's step
+                        "display": "number" # Cosmetic only: display as "number" or "slider"
+                    })
+                             },
+                             "optional":{
+                    "seed":("INT", {"default": -1, "min": -1, "max": 1000000}), 
+                    
+                },
+                }
+    
+    RETURN_TYPES = ("INT","FLOAT",) 
+    RETURN_NAMES = ('int_list','float_list',)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Video"
+
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (True,True,)
+
+    def run(self,min_value,max_value,step,count,seed):
+        print('create_incrementing_list',seed)
+        l1,l2=create_incrementing_list(min_value,max_value,step,count)
+        return (l1,l2,)
+    
 # 接收一个值，然后根据字符串或数值长度计算延迟时间，用户可以自定义延迟"字/s"，延迟之后将转化

 import comfy.samplers
@@ -577,14 +643,14 @@ class SwitchByIndex:
        }

    RETURN_TYPES = (any_type,"INT",)
-    RETURN_NAMES = ("C","count",)
+    RETURN_NAMES = ("list", "count",)

    FUNCTION = "run"

    CATEGORY = "♾️Mixlab/Utils"

    INPUT_IS_LIST = True
-    OUTPUT_IS_LIST = (True,False,)
+    OUTPUT_IS_LIST = (True, False,)

    def run(self, A=[],B=[],index=-1,flat='on'):

@@ -605,10 +671,43 @@ class SwitchByIndex:
            try:
                C=[C[index]]
            except Exception as e:
-                C=[]
+                C=[C[-1]] #最后一个

-        return (C,len(C),)
+        return (C, len(C),)

+class ListSplit:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+             "optional":{
+                  "A":(any_type,), 
+            },
+            "required": { 
+                "chunk_size": ("INT", {"default": 10, "min": 1, "step": 1}),
+                "transition_size": ("INT", {"default": 0, "min": 0, "step": 1}),
+                "index": ("INT", {"default": -1, "min": -1, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("B",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab/Utils"
+
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+    def run(self, A=[],chunk_size=[10],transition_size=[0],index=[-1]):
+        # print(len(A))
+        B=split_list(A,chunk_size[0],transition_size[0])
+
+        if index[0]>-1:
+            B=B[index[0]]
+
+        return (B,)
+    


 class LimitNumber:
@@ -639,7 +738,7 @@ class LimitNumber:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Input"

    INPUT_IS_LIST = False
    OUTPUT_IS_LIST = (False,)
@@ -706,14 +805,21 @@ class TESTNODE_:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/__TEST"
+    CATEGORY = "♾️Mixlab/Test"

    OUTPUT_NODE = True
    INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (True,)

    def run(self,ANY):
-        # print(ANY)
+        print(type(ANY))
+        try:
+            print(ANY[0].shape)
+            img= tensor2pil(ANY[0])
+            print(img.size)
+        except:
+            print('')
+        
        # data=ANY
        list_stats = ListStatistics()

@@ -751,7 +857,7 @@ class TESTNODE_TOKEN:

    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/__TEST"
+    CATEGORY = "♾️Mixlab/Test"

    OUTPUT_NODE = True
    INPUT_IS_LIST = False
@@ -788,7 +894,7 @@ class CreateSeedNode:
    OUTPUT_NODE = True
    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Experiment"

    def run(self, seed):
        return (seed,)
@@ -815,7 +921,7 @@ class CreateCkptNames:
    # OUTPUT_NODE = True
    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Experiment"

    def run(self, ckpt_names):
        ckpt_names=ckpt_names.split('\n')
@@ -844,7 +950,7 @@ class CreateLoraNames:
    # OUTPUT_NODE = True
    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Experiment"

    def run(self, lora_names):
        lora_names=lora_names.split('\n')
@@ -875,7 +981,7 @@ class CreateSampler_names:
    # OUTPUT_NODE = True
    FUNCTION = "run"

-    CATEGORY = "♾️Mixlab/Utils"
+    CATEGORY = "♾️Mixlab/Experiment"

    def run(self, sampler_names):
        sampler_names=sampler_names.split('\n')
@@ -0,0 +1,692 @@
+import os
+import hashlib
+import json
+import subprocess
+import shutil
+import re
+import time,math
+import numpy as np
+from typing import List
+import torch
+from PIL import Image, ImageOps
+from PIL.PngImagePlugin import PngInfo
+import cv2,random,string
+from pathlib import Path
+
+import folder_paths
+from comfy.k_diffusion.utils import FolderOfImages
+from comfy.utils import common_upscale
+
+
+
+
+def generate_folder_name(directory,video_path):
+    # Get the directory and filename from the video path
+    _, filename = os.path.split(video_path)
+    # Generate a random string of lowercase letters and digits
+    random_string = ''.join(random.choices(string.ascii_lowercase + string.digits, k=8))
+    # Create the folder name by combining the random string and the filename
+    folder_name = random_string + '_' + filename
+    # Create the full folder path by joining the directory and the folder name
+    folder_path = os.path.join(directory, folder_name)
+    return folder_path
+
+def create_folder(directory,video_path):
+    folder_path = generate_folder_name(directory,video_path)
+    os.makedirs(folder_path)
+    return folder_path
+
+
+def split_video(video_path, video_segment_frames, transition_frames, output_dir):
+    # 读取视频文件
+    video_capture = cv2.VideoCapture(video_path)
+    
+    # 获取视频的总帧数和帧率
+    total_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = video_capture.get(cv2.CAP_PROP_FPS)
+    
+    # 计算每个视频片段的总帧数，包括过渡帧
+    segment_total_frames = video_segment_frames + transition_frames
+    
+    # 计算可以分割的片段数量，向上取整
+    num_segments = (total_frames + transition_frames - 1) // segment_total_frames
+    
+    vs=[]
+    # 计算每个片段的起始帧和结束帧
+    start_frame = 0
+    for i in range(num_segments):
+        # 计算当前片段的结束帧，注意最后一个片段可能没有过渡帧
+        end_frame = min(start_frame + segment_total_frames, total_frames)
+        
+        # 打印当前片段的起始帧和结束帧
+        print(f"Segment {i+1}: Start Frame {start_frame}, End Frame {end_frame}")
+        
+        # 保存当前片段为一个视频文件
+        segment_video_path = f"{output_dir}/segment_{i+1}.avi"
+
+        fourcc = cv2.VideoWriter_fourcc(*'XVID')
+        segment_video = cv2.VideoWriter(segment_video_path, fourcc, fps, (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
+                                                              int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))))
+        
+        for frame_num in range(start_frame, end_frame):
+            ret, frame = video_capture.read()
+            if ret:
+                segment_video.write(frame)
+            else:
+                break  # 如果读取失败，则退出循环
+        
+        # 更新起始帧为下一个片段的起始位置
+        start_frame = end_frame + transition_frames
+        vs.append(segment_video_path)
+    
+    # 释放视频捕获对象
+    video_capture.release()
+    # print(vs)
+    return (vs,total_frames,fps)
+
+
+folder_paths.folder_names_and_paths["video_formats"] = (
+    [
+        os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "video_formats"),
+    ],
+    [".json"]
+)
+
+ffmpeg_path = shutil.which("ffmpeg")
+if ffmpeg_path is None:
+    print("ffmpeg could not be found. Using ffmpeg from imageio-ffmpeg.")
+    from imageio_ffmpeg import get_ffmpeg_exe
+    try:
+        ffmpeg_path = get_ffmpeg_exe()
+    except:
+        print("ffmpeg could not be found. Outputs that require it have been disabled")
+
+# Tensor to PIL
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+def count_files(directory):
+    count = 0
+    for root, dirs, files in os.walk(directory):
+        count += len(files)
+    return count
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    c=count_files(output_dir)
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('temp_', output_dir)
+
+    
+    image=tensor2pil(image)
+ 
+    image_file = f"{filename}_{c}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    image.save(image_path,compress_level=4)
+
+    return [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type": "temp"
+                }]
+
+
+def split_list(lst, chunk_size, transition_size):
+    result = []
+    for i in range(0, len(lst), chunk_size):
+        start = i - transition_size
+        end = i + chunk_size + transition_size
+        result.append(lst[max(start, 0):end])
+    return result
+
+# images = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+# chunk_size = 3
+# transition_size = 1
+
+# result = split_list(images, chunk_size, transition_size)
+# print(result)
+
+class ImageListReplace:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "images": ("IMAGE",),
+            "start_index":("INT", {"default": 0, "min": 0, "step": 1}),
+            "end_index":("INT", {"default": 0, "min": 0, "step": 1}),
+            "invert": ("BOOLEAN", {"default": False}),
+        },
+         "optional":{
+                    "image_replace": ("IMAGE",),
+                    "images_replace": ("IMAGE",),
+                    
+                    }
+        }
+
+    RETURN_TYPES = ("IMAGE","IMAGE",)
+    RETURN_NAMES = ("images","select_images",)
+    FUNCTION = "run"
+    CATEGORY = "♾️Mixlab/Video"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,True,)
+
+    def run(self, images,start_index=[0],end_index=[0],invert=[False],image_replace=None,images_replace=None):
+        start_index=start_index[0]
+        end_index=end_index[0]
+        invert=invert[0]
+
+        image_rs=[]
+
+        if image_replace!=None:
+            for i in range(end_index-start_index+1):
+                image_rs.append(image_replace[0])
+
+        if images_replace!=None:
+            image_rs=images_replace
+
+        # 如果image replace 为空
+        if image_replace==None and images_replace==None:
+            # print('如果image replace 为空',images[0])
+            # [[tensor(
+            # tensor([[[[0.
+            first_image=tensor2pil(images[0][0])
+            width, height = first_image.size
+            image_replace=Image.new("RGB", (width, height), (0, 0, 0))
+            image_replace=pil2tensor(image_replace)
+            for i in range(end_index-start_index+1):
+                image_rs.append(image_replace)
+
+        
+        new_images=[]
+        select_images=[]
+        k=0
+        for i in range(len(images)):
+            if i>=start_index and i<=end_index:
+                if invert:
+                    new_images.append(images[i])
+                else:
+                    new_images.append(image_rs[k])
+                    select_images.append(images[i])
+                    k+=1
+            else:
+                if invert:
+                    new_images.append(image_rs[k])
+                    select_images.append(images[i])
+                    k+=1
+                else:
+                    new_images.append(images[i])
+
+        imss=[]
+        # print(len(images))
+        for i in range(len(images)):
+            t=images[i][0]
+            t=tensor2pil(t)
+            t = t.convert("RGB")
+            original_width, original_height = t.size
+            scale = 300 / original_width
+            new_height = int(original_height * scale)
+            t = t.resize((300, new_height))
+
+            ims=create_temp_file(pil2tensor(t))
+            imss.append(ims[0])
+
+        # image_replace=create_temp_file(image_replace)
+
+        return {"ui":{"_images": imss},"result": (new_images,select_images,)}
+
+# The code is based on ComfyUI-VideoHelperSuite modification.
+class LoadVideoAndSegment:
+    @classmethod
+    def INPUT_TYPES(s):
+        video_extensions = ['webm', 'mp4', 'mkv', 'gif']
+        input_dir = folder_paths.get_input_directory()
+        files = []
+        for f in os.listdir(input_dir):
+            if os.path.isfile(os.path.join(input_dir, f)):
+                file_parts = f.split('.')
+                if len(file_parts) > 1 and (file_parts[-1] in video_extensions):
+                    files.append(f)
+        return {"required": {
+                    "video": (sorted(files), {"video_upload": True}),
+                     "video_segment_frames": ("INT", {"default": 10, "min": 1, "step": 1}),
+                     "transition_frames": ("INT", {"default": 0, "min": 0, "step": 1}), 
+                     },}
+
+    CATEGORY = "♾️Mixlab/Video"
+
+    RETURN_TYPES = ("SCENE_VIDEO","INT", "INT","INT",)
+    RETURN_NAMES = ("scenes_video","scenes_count","frame_count","fps",)
+    FUNCTION = "load_video"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,False,False,False,)
+
+
+    def is_gif(self, filename):
+        file_parts = filename.split('.')
+        return len(file_parts) > 1 and file_parts[-1] == "gif"
+
+    def load_video_cv_fallback(self, video, frame_load_cap, skip_first_frames):
+        try:
+            video_cap = cv2.VideoCapture(folder_paths.get_annotated_filepath(video))
+            if not video_cap.isOpened():
+                raise ValueError(f"{video} could not be loaded with cv fallback.")
+            # set video_cap to look at start_index frame
+            images = []
+            total_frame_count = 0
+            frames_added = 0
+            base_frame_time = 1/video_cap.get(cv2.CAP_PROP_FPS)
+            
+            target_frame_time = base_frame_time
+
+            time_offset=0.0
+            while video_cap.isOpened():
+                if time_offset < target_frame_time:
+                    is_returned, frame = video_cap.read()
+                    # if didn't return frame, video has ended
+                    if not is_returned:
+                        break
+                    time_offset += base_frame_time
+                if time_offset < target_frame_time:
+                    continue
+                time_offset -= target_frame_time
+                # if not at start_index, skip doing anything with frame
+                total_frame_count += 1
+                if total_frame_count <= skip_first_frames:
+                    continue
+                # TODO: do whatever operations need to happen, like force_size, etc
+
+                # opencv loads images in BGR format (yuck), so need to convert to RGB for ComfyUI use
+                # follow up: can videos ever have an alpha channel?
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                # convert frame to comfyui's expected format (taken from comfy's load image code)
+                image = Image.fromarray(frame)
+                image = ImageOps.exif_transpose(image)
+                image = np.array(image, dtype=np.float32) / 255.0
+                image = torch.from_numpy(image)[None,]
+                images.append(image)
+                frames_added += 1
+                # if cap exists and we've reached it, stop processing frames
+                if frame_load_cap > 0 and frames_added >= frame_load_cap:
+                    break
+        finally:
+            video_cap.release()
+        images = torch.cat(images, dim=0)
+       
+        return (images, frames_added)
+
+    def load_video(self, video,video_segment_frames,transition_frames ):
+        
+        video_path = folder_paths.get_annotated_filepath(video)
+        
+        # check if video is a gif - will need to use cv fallback to read frames
+        # use cv fallback if ffmpeg not installed or gif
+        # if ffmpeg_path is None:
+        #     return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
+        # otherwise, continue with ffmpeg
+        
+        # args_dummy = [ffmpeg_path, "-i", video_path, "-f", "null", "-"]
+        # try:
+        #     with subprocess.Popen(args_dummy, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE) as proc:
+        #         for line in proc.stderr.readlines():
+        #             match = re.search(", ([1-9]|\\d{2,})x(\\d+)",line.decode('utf-8'))
+        #             if match is not None:
+        #                 size = [int(match.group(1)), int(match.group(2))]
+        #                 break
+        # except Exception as e:
+        #     print(f"Retrying with opencv due to ffmpeg error: {e}")
+        #     return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
+        # args_all_frames = [ffmpeg_path, "-i", video_path, "-v", "error",
+        #                      "-pix_fmt", "rgb24"]
+
+        # vfilters = []
+       
+        # if skip_first_frames > 0:
+        #     vfilters.append(f"select=gt(n\\,{skip_first_frames-1})")
+        # if frame_load_cap > 0:
+        #     vfilters.append(f"select=gt({frame_load_cap}\\,n)")
+        # #manually calculate aspect ratio to ensure reads remain aligned
+        
+        # if len(vfilters) > 0:
+        #     args_all_frames += ["-vf", ",".join(vfilters)]
+
+        # args_all_frames += ["-f", "rawvideo", "-"]
+        # images = []
+        # try:
+        #     with subprocess.Popen(args_all_frames, stdout=subprocess.PIPE) as proc:
+        #         #Manually buffer enough bytes for an image
+        #         bpi = size[0]*size[1]*3
+        #         current_bytes = bytearray(bpi)
+        #         current_offset=0
+        #         while True:
+        #             bytes_read = proc.stdout.read(bpi - current_offset)
+        #             if bytes_read is None:#sleep to wait for more data
+        #                 time.sleep(.2)
+        #                 continue
+        #             if len(bytes_read) == 0:#EOF
+        #                 break
+        #             current_bytes[current_offset:len(bytes_read)] = bytes_read
+        #             current_offset+=len(bytes_read)
+        #             if current_offset == bpi:
+        #                 images.append(np.array(current_bytes, dtype=np.float32).reshape(size[1], size[0], 3) / 255.0)
+        #                 current_offset = 0
+        # except Exception as e:
+        #     print(f"Retrying with opencv due to ffmpeg error: {e}")
+        #     return self.load_video_cv_fallback(video, frame_load_cap, skip_first_frames)
+
+        # imgs=split_list(images,video_segment_frames,transition_frames)
+
+        # temp path 
+        tp=folder_paths.get_temp_directory()
+        basename = os.path.basename(video_path)  # 获取文件名
+        name_without_extension = os.path.splitext(basename)[0]  # 去掉文件后缀
+
+        folder_path = create_folder(tp,name_without_extension)
+        
+ 
+        # 导出的数据
+        scenes_video,total_frames,fps=split_video(video_path,video_segment_frames,
+                                 transition_frames,folder_path)
+      
+
+        # imgs=[torch.from_numpy(np.stack(im)) for im in imgs]
+
+        # images = torch.from_numpy(np.stack(images))
+
+        return (scenes_video,len(scenes_video), total_frames,fps,)
+
+    @classmethod
+    def IS_CHANGED(s, video, **kwargs):
+        image_path = folder_paths.get_annotated_filepath(video)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, video, **kwargs):
+        if not folder_paths.exists_annotated_filepath(video):
+            return "Invalid image file: {}".format(video)
+
+        return True
+    
+
+# The code is based on ComfyUI-VideoHelperSuite modification.
+class VideoCombine_Adv:
+    @classmethod
+    def INPUT_TYPES(s):
+        #Hide ffmpeg formats if ffmpeg isn't available
+        if ffmpeg_path is not None:
+            ffmpeg_formats = ["video/"+x[:-5] for x in folder_paths.get_filename_list("video_formats")]
+        else:
+            ffmpeg_formats = []
+        return {
+            "required": {
+                "image_batch": ("IMAGE",),
+                "frame_rate": (
+                    "INT",
+                    {"default": 8, "min": 1, "step": 1},
+                ),
+                "loop_count": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}),
+                "filename_prefix": ("STRING", {"default": "Comfyui"}),
+                "format": (["image/gif", "image/webp"] + ffmpeg_formats,),
+                "pingpong": ("BOOLEAN", {"default": False}),
+                "save_image": ("BOOLEAN", {"default": True}),
+                "metadata": ("BOOLEAN", {"default": False}),
+            },
+            "hidden": {
+                "prompt": "PROMPT",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ()
+    OUTPUT_NODE = True
+    CATEGORY = "♾️Mixlab/Video"
+    FUNCTION = "run"
+
+    def save_with_tempfile(self, args, metadata, file_path, frames, env):
+        #Ensure temp directory exists
+        os.makedirs(folder_paths.get_temp_directory(), exist_ok=True)
+
+        metadata_path = os.path.join(folder_paths.get_temp_directory(), "metadata.txt")
+        #metadata from file should  escape = ; # \ and newline
+        #From my testing, though, only backslashes need escapes and = in particular causes problems
+        #It is likely better to prioritize future compatibility with containers that don't support
+        #or shouldn't use the comment tag for embedding metadata
+        metadata = metadata.replace("\\","\\\\")
+        metadata = metadata.replace(";","\\;")
+        metadata = metadata.replace("#","\\#")
+        #metadata = metadata.replace("=","\\=")
+        metadata = metadata.replace("\n","\\\n")
+        with open(metadata_path, "w") as f:
+            f.write(";FFMETADATA1\n")
+            f.write(metadata)
+        args = args[:1] + ["-i", metadata_path] + args[1:] + [file_path]
+        with subprocess.Popen(args, stdin=subprocess.PIPE, env=env) as proc:
+            for frame in frames:
+                proc.stdin.write(frame.tobytes())
+
+    def run(
+        self,
+        image_batch,
+        frame_rate: int,
+        loop_count: int,
+        filename_prefix="AnimateDiff",
+        format="image/gif",
+        pingpong=False,
+        save_image=True,
+        metadata=False,
+        prompt=None,
+        extra_pnginfo=None,
+    ):
+        images=image_batch
+
+        frames: List[Image.Image] = []
+        for image in images:
+            img = 255.0 * image.cpu().numpy()
+            img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
+            # resize 保证
+            # 检查图像的高度是否是2的倍数，如果不是，则调整高度
+            if img.height % 2 != 0:
+                img = img.resize((img.width, img.height + 1))
+
+            # 检查图像的宽度是否是2的倍数，如果不是，则调整宽度
+            if img.width % 2 != 0:
+                img = img.resize((img.width + 1, img.height))
+                
+            frames.append(img)
+
+        # get output information
+        output_dir = (
+            folder_paths.get_output_directory()
+            if save_image
+            else folder_paths.get_temp_directory()
+        )
+        (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path(filename_prefix, output_dir)
+
+        metadata = PngInfo()
+        video_metadata = {}
+        if prompt is not None:
+            metadata.add_text("prompt", json.dumps(prompt))
+            video_metadata["prompt"] = prompt
+        if extra_pnginfo is not None:
+            for x in extra_pnginfo:
+                metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+                video_metadata[x] = extra_pnginfo[x]
+
+        # 取消保存metadata
+        if metadata==False:
+            metadata = PngInfo()
+
+        # save first frame as png to keep metadata
+        file = f"{filename}_{counter:05}_.png"
+        file_path = os.path.join(full_output_folder, file)
+        frames[0].save(
+            file_path,
+            pnginfo=metadata,
+            compress_level=4,
+        )
+        if pingpong:
+            frames = frames + frames[-2:0:-1]
+
+        format_type, format_ext = format.split("/")
+        file = f"{filename}_{counter:05}_.{format_ext}"
+        file_path = os.path.join(full_output_folder, file)
+        if format_type == "image":
+            # Use pillow directly to save an animated image
+            frames[0].save(
+                file_path,
+                format=format_ext.upper(),
+                save_all=True,
+                append_images=frames[1:],
+                duration=round(1000 / frame_rate),
+                loop=loop_count,
+                compress_level=4,
+            )
+        else:
+            # Use ffmpeg to save a video
+            if ffmpeg_path is None:
+                #Should never be reachable
+                raise ProcessLookupError("Could not find ffmpeg")
+
+            video_format_path = folder_paths.get_full_path("video_formats", format_ext + ".json")
+            with open(video_format_path, 'r') as stream:
+                video_format = json.load(stream)
+            file = f"{filename}_{counter:05}_.{video_format['extension']}"
+            file_path = os.path.join(full_output_folder, file)
+            dimensions = f"{frames[0].width}x{frames[0].height}"
+            metadata_args = ["-metadata", "comment=" + json.dumps(video_metadata)]
+            args = [ffmpeg_path, "-v", "error", "-f", "rawvideo", "-pix_fmt", "rgb24",
+                    "-s", dimensions, "-r", str(frame_rate), "-i", "-"] \
+                    + video_format['main_pass']
+            # On linux, max arg length is Pagesize * 32 -> 131072
+            # On windows, this around 32767 but seems to vary wildly by > 500
+            # in a manor not solely related to other arguments
+            if os.name == 'posix':
+                max_arg_length = 4096*32
+            else:
+                max_arg_length = 32767 - len(" ".join(args + [metadata_args[0]] + [file_path])) - 1
+            #test max limit
+            #metadata_args[1] = metadata_args[1] + "a"*(max_arg_length - len(metadata_args[1])-1)
+
+            env=os.environ.copy()
+            if  "environment" in video_format:
+                env.update(video_format["environment"])
+            if len(metadata_args[1]) >= max_arg_length:
+                print(f"Using fallback file for extremely long metadata: {len(metadata_args[1])}/{max_arg_length}")
+                self.save_with_tempfile(args, metadata_args[1], file_path, frames, env)
+            else:
+                try:
+                    with subprocess.Popen(args + metadata_args + [file_path],
+                                          stdin=subprocess.PIPE, env=env) as proc:
+                        for frame in frames:
+                            proc.stdin.write(frame.tobytes())
+                except FileNotFoundError as e:
+                    if "winerror" in dir(e) and e.winerror == 206:
+                        print("Metadata was too long. Retrying with fallback file")
+                        self.save_with_tempfile(args, metadata_args[1], file_path, frames, env)
+                    else:
+                        raise
+                except OSError as e:
+                    if "errno" in dir(e) and e.errno == 7:
+                        print("Metadata was too long. Retrying with fallback file")
+                        self.save_with_tempfile(args, metadata_args[1], file_path, frames, env)
+                    else:
+                        raise
+
+        previews = [
+            {
+                "filename": file,
+                "subfolder": subfolder,
+                "type": "output" if save_image else "temp",
+                "format": format,
+            }
+        ]
+        return {"ui": {"gifs": previews}}
+
+
+class VAEEncodeForInpaint_Frames:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+            "vae": ("VAE", ), 
+            "images": ("IMAGE", ), 
+            "masks": ("MASK", ), 
+            "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),
+            }}
+    
+    FUNCTION = "encode"
+
+
+    RETURN_TYPES = ("LATENT",)
+    RETURN_NAMES = ("LATENT",)
+ 
+    CATEGORY = "♾️Mixlab/Video"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    OUTPUT_IS_LIST = (True,)
+
+
+    def encode(self, vae, images, masks, grow_mask_by=[6]):
+        vae=vae[0]
+        grow_mask_by=grow_mask_by[0]
+
+        result=[]
+
+        for i in range(len(images)):
+            pixels=images[i]
+            mask=masks[i]
+
+
+            x = (pixels.shape[1] // 8) * 8
+            y = (pixels.shape[2] // 8) * 8
+            mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
+
+            pixels = pixels.clone()
+            if pixels.shape[1] != x or pixels.shape[2] != y:
+                x_offset = (pixels.shape[1] % 8) // 2
+                y_offset = (pixels.shape[2] % 8) // 2
+                pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+                mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
+
+            #grow mask by a few pixels to keep things seamless in latent space
+            if grow_mask_by == 0:
+                mask_erosion = mask
+            else:
+                kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
+                padding = math.ceil((grow_mask_by - 1) / 2)
+
+                mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1)
+
+            m = (1.0 - mask.round()).squeeze(1)
+            for i in range(3):
+                pixels[:,:,:,i] -= 0.5
+                pixels[:,:,:,i] *= m
+                pixels[:,:,:,i] += 0.5
+            t = vae.encode(pixels)
+
+            result.append({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())})
+
+
+        return (result, )
@@ -0,0 +1,38 @@
+cond_image_size: 512
+
+image_tokenizer_cls: tsr.models.tokenizers.image.DINOSingleImageTokenizer
+image_tokenizer:
+  pretrained_model_name_or_path: "facebook/dino-vitb16"
+
+tokenizer_cls: tsr.models.tokenizers.triplane.Triplane1DTokenizer
+tokenizer:
+  plane_size: 32
+  num_channels: 1024
+
+backbone_cls: tsr.models.transformer.transformer_1d.Transformer1D
+backbone:
+  in_channels: ${tokenizer.num_channels}
+  num_attention_heads: 16
+  attention_head_dim: 64
+  num_layers: 16
+  cross_attention_dim: 768
+
+post_processor_cls: tsr.models.network_utils.TriplaneUpsampleNetwork
+post_processor:
+  in_channels: 1024
+  out_channels: 40
+
+decoder_cls: tsr.models.network_utils.NeRFMLP
+decoder:
+  in_channels: 120 # 3 * 40
+  n_neurons: 64
+  n_hidden_layers: 9
+  activation: silu
+
+renderer_cls: tsr.models.nerf_renderer.TriplaneNeRFRenderer
+renderer:
+  radius: 0.87 # slightly larger than 0.5 * sqrt(3)
+  feature_reduction: concat
+  density_activation: exp
+  density_bias: -1.0
+  num_samples_per_ray: 128
@@ -0,0 +1,51 @@
+from typing import Callable, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+from skimage import measure
+
+
+class IsosurfaceHelper(nn.Module):
+    points_range: Tuple[float, float] = (0, 1)
+
+    @property
+    def grid_vertices(self) -> torch.FloatTensor:
+        raise NotImplementedError
+
+
+class MarchingCubeHelper(IsosurfaceHelper):
+    def __init__(self, resolution: int) -> None:
+        super().__init__()
+        self.resolution = resolution
+        #self.mc_func: Callable = marching_cubes
+        self._grid_vertices: Optional[torch.FloatTensor] = None
+
+    @property
+    def grid_vertices(self) -> torch.FloatTensor:
+        if self._grid_vertices is None:
+            # keep the vertices on CPU so that we can support very large resolution
+            x, y, z = (
+                torch.linspace(*self.points_range, self.resolution),
+                torch.linspace(*self.points_range, self.resolution),
+                torch.linspace(*self.points_range, self.resolution),
+            )
+            x, y, z = torch.meshgrid(x, y, z, indexing="ij")
+            verts = torch.cat(
+                [x.reshape(-1, 1), y.reshape(-1, 1), z.reshape(-1, 1)], dim=-1
+            ).reshape(-1, 3)
+            self._grid_vertices = verts
+        return self._grid_vertices
+
+    def forward(
+        self,
+        level: torch.FloatTensor,
+    ) -> Tuple[torch.FloatTensor, torch.LongTensor]:
+        level = -level.view(self.resolution, self.resolution, self.resolution)
+        v_pos, t_pos_idx, _, __ = measure.marching_cubes((level.detach().cpu() if level.is_cuda else level.detach()).numpy(), 0.0) #self.mc_func(level.detach(), 0.0)
+        v_pos = torch.from_numpy(v_pos.copy()).type(torch.FloatTensor).to(level.device)
+        t_pos_idx = torch.from_numpy(t_pos_idx.copy()).type(torch.LongTensor).to(level.device)
+        v_pos = v_pos[..., [0, 1, 2]]
+        t_pos_idx = t_pos_idx[..., [1, 0, 2]]
+        v_pos = v_pos / (self.resolution - 1.0)
+        return v_pos, t_pos_idx
@@ -0,0 +1,180 @@
+from dataclasses import dataclass
+from typing import Dict
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, reduce
+
+from ..utils import (
+    BaseModule,
+    chunk_batch,
+    get_activation,
+    rays_intersect_bbox,
+    scale_tensor,
+)
+
+
+class TriplaneNeRFRenderer(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        radius: float
+
+        feature_reduction: str = "concat"
+        density_activation: str = "trunc_exp"
+        density_bias: float = -1.0
+        color_activation: str = "sigmoid"
+        num_samples_per_ray: int = 128
+        randomized: bool = False
+
+    cfg: Config
+
+    def configure(self) -> None:
+        assert self.cfg.feature_reduction in ["concat", "mean"]
+        self.chunk_size = 0
+
+    def set_chunk_size(self, chunk_size: int):
+        assert (
+            chunk_size >= 0
+        ), "chunk_size must be a non-negative integer (0 for no chunking)."
+        self.chunk_size = chunk_size
+
+    def query_triplane(
+        self,
+        decoder: torch.nn.Module,
+        positions: torch.Tensor,
+        triplane: torch.Tensor,
+    ) -> Dict[str, torch.Tensor]:
+        input_shape = positions.shape[:-1]
+        positions = positions.view(-1, 3)
+
+        # positions in (-radius, radius)
+        # normalized to (-1, 1) for grid sample
+        positions = scale_tensor(
+            positions, (-self.cfg.radius, self.cfg.radius), (-1, 1)
+        )
+
+        def _query_chunk(x):
+            indices2D: torch.Tensor = torch.stack(
+                (x[..., [0, 1]], x[..., [0, 2]], x[..., [1, 2]]),
+                dim=-3,
+            )
+            out: torch.Tensor = F.grid_sample(
+                rearrange(triplane, "Np Cp Hp Wp -> Np Cp Hp Wp", Np=3),
+                rearrange(indices2D, "Np N Nd -> Np () N Nd", Np=3),
+                align_corners=False,
+                mode="bilinear",
+            )
+            if self.cfg.feature_reduction == "concat":
+                out = rearrange(out, "Np Cp () N -> N (Np Cp)", Np=3)
+            elif self.cfg.feature_reduction == "mean":
+                out = reduce(out, "Np Cp () N -> N Cp", Np=3, reduction="mean")
+            else:
+                raise NotImplementedError
+
+            net_out: Dict[str, torch.Tensor] = decoder(out)
+            return net_out
+
+        if self.chunk_size > 0:
+            net_out = chunk_batch(_query_chunk, self.chunk_size, positions)
+        else:
+            net_out = _query_chunk(positions)
+
+        net_out["density_act"] = get_activation(self.cfg.density_activation)(
+            net_out["density"] + self.cfg.density_bias
+        )
+        net_out["color"] = get_activation(self.cfg.color_activation)(
+            net_out["features"]
+        )
+
+        net_out = {k: v.view(*input_shape, -1) for k, v in net_out.items()}
+
+        return net_out
+
+    def _forward(
+        self,
+        decoder: torch.nn.Module,
+        triplane: torch.Tensor,
+        rays_o: torch.Tensor,
+        rays_d: torch.Tensor,
+        **kwargs,
+    ):
+        rays_shape = rays_o.shape[:-1]
+        rays_o = rays_o.view(-1, 3)
+        rays_d = rays_d.view(-1, 3)
+        n_rays = rays_o.shape[0]
+
+        t_near, t_far, rays_valid = rays_intersect_bbox(rays_o, rays_d, self.cfg.radius)
+        t_near, t_far = t_near[rays_valid], t_far[rays_valid]
+
+        t_vals = torch.linspace(
+            0, 1, self.cfg.num_samples_per_ray + 1, device=triplane.device
+        )
+        t_mid = (t_vals[:-1] + t_vals[1:]) / 2.0
+        z_vals = t_near * (1 - t_mid[None]) + t_far * t_mid[None]  # (N_rays, N_samples)
+
+        xyz = (
+            rays_o[:, None, :] + z_vals[..., None] * rays_d[..., None, :]
+        )  # (N_rays, N_sample, 3)
+
+        mlp_out = self.query_triplane(
+            decoder=decoder,
+            positions=xyz,
+            triplane=triplane,
+        )
+
+        eps = 1e-10
+        # deltas = z_vals[:, 1:] - z_vals[:, :-1] # (N_rays, N_samples)
+        deltas = t_vals[1:] - t_vals[:-1]  # (N_rays, N_samples)
+        alpha = 1 - torch.exp(
+            -deltas * mlp_out["density_act"][..., 0]
+        )  # (N_rays, N_samples)
+        accum_prod = torch.cat(
+            [
+                torch.ones_like(alpha[:, :1]),
+                torch.cumprod(1 - alpha[:, :-1] + eps, dim=-1),
+            ],
+            dim=-1,
+        )
+        weights = alpha * accum_prod  # (N_rays, N_samples)
+        comp_rgb_ = (weights[..., None] * mlp_out["color"]).sum(dim=-2)  # (N_rays, 3)
+        opacity_ = weights.sum(dim=-1)  # (N_rays)
+
+        comp_rgb = torch.zeros(
+            n_rays, 3, dtype=comp_rgb_.dtype, device=comp_rgb_.device
+        )
+        opacity = torch.zeros(n_rays, dtype=opacity_.dtype, device=opacity_.device)
+        comp_rgb[rays_valid] = comp_rgb_
+        opacity[rays_valid] = opacity_
+
+        comp_rgb += 1 - opacity[..., None]
+        comp_rgb = comp_rgb.view(*rays_shape, 3)
+
+        return comp_rgb
+
+    def forward(
+        self,
+        decoder: torch.nn.Module,
+        triplane: torch.Tensor,
+        rays_o: torch.Tensor,
+        rays_d: torch.Tensor,
+    ) -> Dict[str, torch.Tensor]:
+        if triplane.ndim == 4:
+            comp_rgb = self._forward(decoder, triplane, rays_o, rays_d)
+        else:
+            comp_rgb = torch.stack(
+                [
+                    self._forward(decoder, triplane[i], rays_o[i], rays_d[i])
+                    for i in range(triplane.shape[0])
+                ],
+                dim=0,
+            )
+
+        return comp_rgb
+
+    def train(self, mode=True):
+        self.randomized = mode and self.cfg.randomized
+        return super().train(mode=mode)
+
+    def eval(self):
+        self.randomized = False
+        return super().eval()
@@ -0,0 +1,124 @@
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+from ..utils import BaseModule
+
+
+class TriplaneUpsampleNetwork(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        in_channels: int
+        out_channels: int
+
+    cfg: Config
+
+    def configure(self) -> None:
+        self.upsample = nn.ConvTranspose2d(
+            self.cfg.in_channels, self.cfg.out_channels, kernel_size=2, stride=2
+        )
+
+    def forward(self, triplanes: torch.Tensor) -> torch.Tensor:
+        triplanes_up = rearrange(
+            self.upsample(
+                rearrange(triplanes, "B Np Ci Hp Wp -> (B Np) Ci Hp Wp", Np=3)
+            ),
+            "(B Np) Co Hp Wp -> B Np Co Hp Wp",
+            Np=3,
+        )
+        return triplanes_up
+
+
+class NeRFMLP(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        in_channels: int
+        n_neurons: int
+        n_hidden_layers: int
+        activation: str = "relu"
+        bias: bool = True
+        weight_init: Optional[str] = "kaiming_uniform"
+        bias_init: Optional[str] = None
+
+    cfg: Config
+
+    def configure(self) -> None:
+        layers = [
+            self.make_linear(
+                self.cfg.in_channels,
+                self.cfg.n_neurons,
+                bias=self.cfg.bias,
+                weight_init=self.cfg.weight_init,
+                bias_init=self.cfg.bias_init,
+            ),
+            self.make_activation(self.cfg.activation),
+        ]
+        for i in range(self.cfg.n_hidden_layers - 1):
+            layers += [
+                self.make_linear(
+                    self.cfg.n_neurons,
+                    self.cfg.n_neurons,
+                    bias=self.cfg.bias,
+                    weight_init=self.cfg.weight_init,
+                    bias_init=self.cfg.bias_init,
+                ),
+                self.make_activation(self.cfg.activation),
+            ]
+        layers += [
+            self.make_linear(
+                self.cfg.n_neurons,
+                4,  # density 1 + features 3
+                bias=self.cfg.bias,
+                weight_init=self.cfg.weight_init,
+                bias_init=self.cfg.bias_init,
+            )
+        ]
+        self.layers = nn.Sequential(*layers)
+
+    def make_linear(
+        self,
+        dim_in,
+        dim_out,
+        bias=True,
+        weight_init=None,
+        bias_init=None,
+    ):
+        layer = nn.Linear(dim_in, dim_out, bias=bias)
+
+        if weight_init is None:
+            pass
+        elif weight_init == "kaiming_uniform":
+            torch.nn.init.kaiming_uniform_(layer.weight, nonlinearity="relu")
+        else:
+            raise NotImplementedError
+
+        if bias:
+            if bias_init is None:
+                pass
+            elif bias_init == "zero":
+                torch.nn.init.zeros_(layer.bias)
+            else:
+                raise NotImplementedError
+
+        return layer
+
+    def make_activation(self, activation):
+        if activation == "relu":
+            return nn.ReLU(inplace=True)
+        elif activation == "silu":
+            return nn.SiLU(inplace=True)
+        else:
+            raise NotImplementedError
+
+    def forward(self, x):
+        inp_shape = x.shape[:-1]
+        x = x.reshape(-1, x.shape[-1])
+
+        features = self.layers(x)
+        features = features.reshape(*inp_shape, -1)
+        out = {"density": features[..., 0:1], "features": features[..., 1:4]}
+
+        return out
@@ -0,0 +1,72 @@
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from huggingface_hub import hf_hub_download
+from transformers.models.vit.modeling_vit import ViTModel
+
+from ...utils import BaseModule
+import os
+import folder_paths
+model_path=os.path.join(folder_paths.models_dir,'triposr')
+
+
+class DINOSingleImageTokenizer(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        pretrained_model_name_or_path: str = "facebook/dino-vitb16"
+        enable_gradient_checkpointing: bool = False
+
+    cfg: Config
+
+    def configure(self) -> None:
+        print('#Loading ViTModel:',os.path.join(model_path,self.cfg.pretrained_model_name_or_path))
+        self.model: ViTModel = ViTModel(
+            ViTModel.config_class.from_pretrained(
+                hf_hub_download(
+                    repo_id=self.cfg.pretrained_model_name_or_path,
+                    filename="config.json",
+                    local_dir=model_path,
+                    endpoint='https://hf-mirror.com'
+                )
+            )
+        )
+
+        if self.cfg.enable_gradient_checkpointing:
+            self.model.encoder.gradient_checkpointing = True
+
+        self.register_buffer(
+            "image_mean",
+            torch.as_tensor([0.485, 0.456, 0.406]).reshape(1, 1, 3, 1, 1),
+            persistent=False,
+        )
+        self.register_buffer(
+            "image_std",
+            torch.as_tensor([0.229, 0.224, 0.225]).reshape(1, 1, 3, 1, 1),
+            persistent=False,
+        )
+
+    def forward(self, images: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
+        packed = False
+        if images.ndim == 4:
+            packed = True
+            images = images.unsqueeze(1)
+
+        batch_size, n_input_views = images.shape[:2]
+        images = (images - self.image_mean) / self.image_std
+        out = self.model(
+            rearrange(images, "B N C H W -> (B N) C H W"), interpolate_pos_encoding=True
+        )
+        local_features, global_features = out.last_hidden_state, out.pooler_output
+        local_features = local_features.permute(0, 2, 1)
+        local_features = rearrange(
+            local_features, "(B N) Ct Nt -> B N Ct Nt", B=batch_size
+        )
+        if packed:
+            local_features = local_features.squeeze(1)
+
+        return local_features
+
+    def detokenize(self, *args, **kwargs):
+        raise NotImplementedError
@@ -0,0 +1,45 @@
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from einops import rearrange, repeat
+
+from ...utils import BaseModule
+
+
+class Triplane1DTokenizer(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        plane_size: int
+        num_channels: int
+
+    cfg: Config
+
+    def configure(self) -> None:
+        self.embeddings = nn.Parameter(
+            torch.randn(
+                (3, self.cfg.num_channels, self.cfg.plane_size, self.cfg.plane_size),
+                dtype=torch.float32,
+            )
+            * 1
+            / math.sqrt(self.cfg.num_channels)
+        )
+
+    def forward(self, batch_size: int) -> torch.Tensor:
+        return rearrange(
+            repeat(self.embeddings, "Np Ct Hp Wp -> B Np Ct Hp Wp", B=batch_size),
+            "B Np Ct Hp Wp -> B Ct (Np Hp Wp)",
+        )
+
+    def detokenize(self, tokens: torch.Tensor) -> torch.Tensor:
+        batch_size, Ct, Nt = tokens.shape
+        assert Nt == self.cfg.plane_size**2 * 3
+        assert Ct == self.cfg.num_channels
+        return rearrange(
+            tokens,
+            "B Ct (Np Hp Wp) -> B Np Ct Hp Wp",
+            Np=3,
+            Hp=self.cfg.plane_size,
+            Wp=self.cfg.plane_size,
+        )
@@ -0,0 +1,653 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# --------
+#
+# Modified 2024 by the Tripo AI and Stability AI Team.
+#
+# Copyright (c) 2024 Tripo AI & Stability AI
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class Attention(nn.Module):
+    r"""
+    A cross attention layer.
+
+    Parameters:
+        query_dim (`int`):
+            The number of channels in the query.
+        cross_attention_dim (`int`, *optional*):
+            The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
+        heads (`int`,  *optional*, defaults to 8):
+            The number of heads to use for multi-head attention.
+        dim_head (`int`,  *optional*, defaults to 64):
+            The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability to use.
+        bias (`bool`, *optional*, defaults to False):
+            Set to `True` for the query, key, and value linear layers to contain a bias parameter.
+        upcast_attention (`bool`, *optional*, defaults to False):
+            Set to `True` to upcast the attention computation to `float32`.
+        upcast_softmax (`bool`, *optional*, defaults to False):
+            Set to `True` to upcast the softmax computation to `float32`.
+        cross_attention_norm (`str`, *optional*, defaults to `None`):
+            The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
+        cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups to use for the group norm in the cross attention.
+        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
+            The number of channels to use for the added key and value projections. If `None`, no projection is used.
+        norm_num_groups (`int`, *optional*, defaults to `None`):
+            The number of groups to use for the group norm in the attention.
+        spatial_norm_dim (`int`, *optional*, defaults to `None`):
+            The number of channels to use for the spatial normalization.
+        out_bias (`bool`, *optional*, defaults to `True`):
+            Set to `True` to use a bias in the output linear layer.
+        scale_qk (`bool`, *optional*, defaults to `True`):
+            Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
+        only_cross_attention (`bool`, *optional*, defaults to `False`):
+            Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
+            `added_kv_proj_dim` is not `None`.
+        eps (`float`, *optional*, defaults to 1e-5):
+            An additional value added to the denominator in group normalization that is used for numerical stability.
+        rescale_output_factor (`float`, *optional*, defaults to 1.0):
+            A factor to rescale the output by dividing it with this value.
+        residual_connection (`bool`, *optional*, defaults to `False`):
+            Set to `True` to add the residual connection to the output.
+        _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
+            Set to `True` if the attention block is loaded from a deprecated state dict.
+        processor (`AttnProcessor`, *optional*, defaults to `None`):
+            The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
+            `AttnProcessor` otherwise.
+    """
+
+    def __init__(
+        self,
+        query_dim: int,
+        cross_attention_dim: Optional[int] = None,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        bias: bool = False,
+        upcast_attention: bool = False,
+        upcast_softmax: bool = False,
+        cross_attention_norm: Optional[str] = None,
+        cross_attention_norm_num_groups: int = 32,
+        added_kv_proj_dim: Optional[int] = None,
+        norm_num_groups: Optional[int] = None,
+        out_bias: bool = True,
+        scale_qk: bool = True,
+        only_cross_attention: bool = False,
+        eps: float = 1e-5,
+        rescale_output_factor: float = 1.0,
+        residual_connection: bool = False,
+        _from_deprecated_attn_block: bool = False,
+        processor: Optional["AttnProcessor"] = None,
+        out_dim: int = None,
+    ):
+        super().__init__()
+        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
+        self.query_dim = query_dim
+        self.cross_attention_dim = (
+            cross_attention_dim if cross_attention_dim is not None else query_dim
+        )
+        self.upcast_attention = upcast_attention
+        self.upcast_softmax = upcast_softmax
+        self.rescale_output_factor = rescale_output_factor
+        self.residual_connection = residual_connection
+        self.dropout = dropout
+        self.fused_projections = False
+        self.out_dim = out_dim if out_dim is not None else query_dim
+
+        # we make use of this private variable to know whether this class is loaded
+        # with an deprecated state dict so that we can convert it on the fly
+        self._from_deprecated_attn_block = _from_deprecated_attn_block
+
+        self.scale_qk = scale_qk
+        self.scale = dim_head**-0.5 if self.scale_qk else 1.0
+
+        self.heads = out_dim // dim_head if out_dim is not None else heads
+        # for slice_size > 0 the attention score computation
+        # is split across the batch axis to save memory
+        # You can set slice_size with `set_attention_slice`
+        self.sliceable_head_dim = heads
+
+        self.added_kv_proj_dim = added_kv_proj_dim
+        self.only_cross_attention = only_cross_attention
+
+        if self.added_kv_proj_dim is None and self.only_cross_attention:
+            raise ValueError(
+                "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
+            )
+
+        if norm_num_groups is not None:
+            self.group_norm = nn.GroupNorm(
+                num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True
+            )
+        else:
+            self.group_norm = None
+
+        self.spatial_norm = None
+
+        if cross_attention_norm is None:
+            self.norm_cross = None
+        elif cross_attention_norm == "layer_norm":
+            self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
+        elif cross_attention_norm == "group_norm":
+            if self.added_kv_proj_dim is not None:
+                # The given `encoder_hidden_states` are initially of shape
+                # (batch_size, seq_len, added_kv_proj_dim) before being projected
+                # to (batch_size, seq_len, cross_attention_dim). The norm is applied
+                # before the projection, so we need to use `added_kv_proj_dim` as
+                # the number of channels for the group norm.
+                norm_cross_num_channels = added_kv_proj_dim
+            else:
+                norm_cross_num_channels = self.cross_attention_dim
+
+            self.norm_cross = nn.GroupNorm(
+                num_channels=norm_cross_num_channels,
+                num_groups=cross_attention_norm_num_groups,
+                eps=1e-5,
+                affine=True,
+            )
+        else:
+            raise ValueError(
+                f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
+            )
+
+        linear_cls = nn.Linear
+
+        self.linear_cls = linear_cls
+        self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)
+
+        if not self.only_cross_attention:
+            # only relevant for the `AddedKVProcessor` classes
+            self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
+            self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
+        else:
+            self.to_k = None
+            self.to_v = None
+
+        if self.added_kv_proj_dim is not None:
+            self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
+            self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
+
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(linear_cls(self.inner_dim, self.out_dim, bias=out_bias))
+        self.to_out.append(nn.Dropout(dropout))
+
+        # set attention processor
+        # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
+        # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
+        # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
+        if processor is None:
+            processor = (
+                AttnProcessor2_0()
+                if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
+                else AttnProcessor()
+            )
+        self.set_processor(processor)
+
+    def set_processor(self, processor: "AttnProcessor") -> None:
+        self.processor = processor
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        **cross_attention_kwargs,
+    ) -> torch.Tensor:
+        r"""
+        The forward method of the `Attention` class.
+
+        Args:
+            hidden_states (`torch.Tensor`):
+                The hidden states of the query.
+            encoder_hidden_states (`torch.Tensor`, *optional*):
+                The hidden states of the encoder.
+            attention_mask (`torch.Tensor`, *optional*):
+                The attention mask to use. If `None`, no mask is applied.
+            **cross_attention_kwargs:
+                Additional keyword arguments to pass along to the cross attention.
+
+        Returns:
+            `torch.Tensor`: The output of the attention layer.
+        """
+        # The `Attention` class can call different attention processors / attention functions
+        # here we simply pass along all tensors to the selected processor class
+        # For standard processors that are defined here, `**cross_attention_kwargs` is empty
+        return self.processor(
+            self,
+            hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+
+    def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
+        r"""
+        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
+        is the number of heads initialized while constructing the `Attention` class.
+
+        Args:
+            tensor (`torch.Tensor`): The tensor to reshape.
+
+        Returns:
+            `torch.Tensor`: The reshaped tensor.
+        """
+        head_size = self.heads
+        batch_size, seq_len, dim = tensor.shape
+        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(
+            batch_size // head_size, seq_len, dim * head_size
+        )
+        return tensor
+
+    def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
+        r"""
+        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
+        the number of heads initialized while constructing the `Attention` class.
+
+        Args:
+            tensor (`torch.Tensor`): The tensor to reshape.
+            out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
+                reshaped to `[batch_size * heads, seq_len, dim // heads]`.
+
+        Returns:
+            `torch.Tensor`: The reshaped tensor.
+        """
+        head_size = self.heads
+        batch_size, seq_len, dim = tensor.shape
+        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
+        tensor = tensor.permute(0, 2, 1, 3)
+
+        if out_dim == 3:
+            tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
+
+        return tensor
+
+    def get_attention_scores(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+    ) -> torch.Tensor:
+        r"""
+        Compute the attention scores.
+
+        Args:
+            query (`torch.Tensor`): The query tensor.
+            key (`torch.Tensor`): The key tensor.
+            attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
+
+        Returns:
+            `torch.Tensor`: The attention probabilities/scores.
+        """
+        dtype = query.dtype
+        if self.upcast_attention:
+            query = query.float()
+            key = key.float()
+
+        if attention_mask is None:
+            baddbmm_input = torch.empty(
+                query.shape[0],
+                query.shape[1],
+                key.shape[1],
+                dtype=query.dtype,
+                device=query.device,
+            )
+            beta = 0
+        else:
+            baddbmm_input = attention_mask
+            beta = 1
+
+        attention_scores = torch.baddbmm(
+            baddbmm_input,
+            query,
+            key.transpose(-1, -2),
+            beta=beta,
+            alpha=self.scale,
+        )
+        del baddbmm_input
+
+        if self.upcast_softmax:
+            attention_scores = attention_scores.float()
+
+        attention_probs = attention_scores.softmax(dim=-1)
+        del attention_scores
+
+        attention_probs = attention_probs.to(dtype)
+
+        return attention_probs
+
+    def prepare_attention_mask(
+        self,
+        attention_mask: torch.Tensor,
+        target_length: int,
+        batch_size: int,
+        out_dim: int = 3,
+    ) -> torch.Tensor:
+        r"""
+        Prepare the attention mask for the attention computation.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                The attention mask to prepare.
+            target_length (`int`):
+                The target length of the attention mask. This is the length of the attention mask after padding.
+            batch_size (`int`):
+                The batch size, which is used to repeat the attention mask.
+            out_dim (`int`, *optional*, defaults to `3`):
+                The output dimension of the attention mask. Can be either `3` or `4`.
+
+        Returns:
+            `torch.Tensor`: The prepared attention mask.
+        """
+        head_size = self.heads
+        if attention_mask is None:
+            return attention_mask
+
+        current_length: int = attention_mask.shape[-1]
+        if current_length != target_length:
+            if attention_mask.device.type == "mps":
+                # HACK: MPS: Does not support padding by greater than dimension of input tensor.
+                # Instead, we can manually construct the padding tensor.
+                padding_shape = (
+                    attention_mask.shape[0],
+                    attention_mask.shape[1],
+                    target_length,
+                )
+                padding = torch.zeros(
+                    padding_shape,
+                    dtype=attention_mask.dtype,
+                    device=attention_mask.device,
+                )
+                attention_mask = torch.cat([attention_mask, padding], dim=2)
+            else:
+                # TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
+                #       we want to instead pad by (0, remaining_length), where remaining_length is:
+                #       remaining_length: int = target_length - current_length
+                # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
+                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+
+        if out_dim == 3:
+            if attention_mask.shape[0] < batch_size * head_size:
+                attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
+        elif out_dim == 4:
+            attention_mask = attention_mask.unsqueeze(1)
+            attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
+
+        return attention_mask
+
+    def norm_encoder_hidden_states(
+        self, encoder_hidden_states: torch.Tensor
+    ) -> torch.Tensor:
+        r"""
+        Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
+        `Attention` class.
+
+        Args:
+            encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
+
+        Returns:
+            `torch.Tensor`: The normalized encoder hidden states.
+        """
+        assert (
+            self.norm_cross is not None
+        ), "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
+
+        if isinstance(self.norm_cross, nn.LayerNorm):
+            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
+        elif isinstance(self.norm_cross, nn.GroupNorm):
+            # Group norm norms along the channels dimension and expects
+            # input to be in the shape of (N, C, *). In this case, we want
+            # to norm along the hidden dimension, so we need to move
+            # (batch_size, sequence_length, hidden_size) ->
+            # (batch_size, hidden_size, sequence_length)
+            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
+            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
+            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
+        else:
+            assert False
+
+        return encoder_hidden_states
+
+    @torch.no_grad()
+    def fuse_projections(self, fuse=True):
+        is_cross_attention = self.cross_attention_dim != self.query_dim
+        device = self.to_q.weight.data.device
+        dtype = self.to_q.weight.data.dtype
+
+        if not is_cross_attention:
+            # fetch weight matrices.
+            concatenated_weights = torch.cat(
+                [self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data]
+            )
+            in_features = concatenated_weights.shape[1]
+            out_features = concatenated_weights.shape[0]
+
+            # create a new single projection layer and copy over the weights.
+            self.to_qkv = self.linear_cls(
+                in_features, out_features, bias=False, device=device, dtype=dtype
+            )
+            self.to_qkv.weight.copy_(concatenated_weights)
+
+        else:
+            concatenated_weights = torch.cat(
+                [self.to_k.weight.data, self.to_v.weight.data]
+            )
+            in_features = concatenated_weights.shape[1]
+            out_features = concatenated_weights.shape[0]
+
+            self.to_kv = self.linear_cls(
+                in_features, out_features, bias=False, device=device, dtype=dtype
+            )
+            self.to_kv.weight.copy_(concatenated_weights)
+
+        self.fused_projections = fuse
+
+
+class AttnProcessor:
+    r"""
+    Default processor for performing attention-related computations.
+    """
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(
+            attention_mask, sequence_length, batch_size
+        )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+class AttnProcessor2_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(
+                batch_size, attn.heads, -1, attention_mask.shape[-1]
+            )
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
@@ -0,0 +1,334 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# --------
+#
+# Modified 2024 by the Tripo AI and Stability AI Team.
+#
+# Copyright (c) 2024 Tripo AI & Stability AI
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from .attention import Attention
+
+
+class BasicTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
+            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+
+        assert norm_type == "layer_norm"
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )
+
+        # 2. Cross-Attn
+        if cross_attention_dim is not None or double_self_attention:
+            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+            # the second cross attention block.
+            self.norm2 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=(
+                    cross_attention_dim if not double_self_attention else None
+                ),
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )  # is self-attn if encoder_hidden_states is none
+        else:
+            self.norm2 = None
+            self.attn2 = None
+
+        # 3. Feed-forward
+        self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+        )
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        norm_hidden_states = self.norm1(hidden_states)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=(
+                encoder_hidden_states if self.only_cross_attention else None
+            ),
+            attention_mask=attention_mask,
+        )
+
+        hidden_states = attn_output + hidden_states
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            norm_hidden_states = self.norm2(hidden_states)
+
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+            )
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+                raise ValueError(
+                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+                )
+
+            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+            ff_output = torch.cat(
+                [
+                    self.ff(hid_slice)
+                    for hid_slice in norm_hidden_states.chunk(
+                        num_chunks, dim=self._chunk_dim
+                    )
+                ],
+                dim=self._chunk_dim,
+            )
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = ff_output + hidden_states
+
+        return hidden_states
+
+
+class FeedForward(nn.Module):
+    r"""
+    A feed-forward layer.
+
+    Parameters:
+        dim (`int`): The number of channels in the input.
+        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        dropout: float = 0.0,
+        activation_fn: str = "geglu",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+        linear_cls = nn.Linear
+
+        if activation_fn == "gelu":
+            act_fn = GELU(dim, inner_dim)
+        if activation_fn == "gelu-approximate":
+            act_fn = GELU(dim, inner_dim, approximate="tanh")
+        elif activation_fn == "geglu":
+            act_fn = GEGLU(dim, inner_dim)
+        elif activation_fn == "geglu-approximate":
+            act_fn = ApproximateGELU(dim, inner_dim)
+
+        self.net = nn.ModuleList([])
+        # project in
+        self.net.append(act_fn)
+        # project dropout
+        self.net.append(nn.Dropout(dropout))
+        # project out
+        self.net.append(linear_cls(inner_dim, dim_out))
+        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
+        if final_dropout:
+            self.net.append(nn.Dropout(dropout))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
+
+
+class GELU(nn.Module):
+    r"""
+    GELU activation function with tanh approximation support with `approximate="tanh"`.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, approximate: str = "none"):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out)
+        self.approximate = approximate
+
+    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
+        if gate.device.type != "mps":
+            return F.gelu(gate, approximate=self.approximate)
+        # mps: gelu is not implemented for float16
+        return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(
+            dtype=gate.dtype
+        )
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = self.gelu(hidden_states)
+        return hidden_states
+
+
+class GEGLU(nn.Module):
+    r"""
+    A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int):
+        super().__init__()
+        linear_cls = nn.Linear
+
+        self.proj = linear_cls(dim_in, dim_out * 2)
+
+    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
+        if gate.device.type != "mps":
+            return F.gelu(gate)
+        # mps: gelu is not implemented for float16
+        return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
+
+    def forward(self, hidden_states, scale: float = 1.0):
+        args = ()
+        hidden_states, gate = self.proj(hidden_states, *args).chunk(2, dim=-1)
+        return hidden_states * self.gelu(gate)
+
+
+class ApproximateGELU(nn.Module):
+    r"""
+    The approximate form of Gaussian Error Linear Unit (GELU). For more details, see section 2:
+    https://arxiv.org/abs/1606.08415.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        return x * torch.sigmoid(1.702 * x)
@@ -0,0 +1,219 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# --------
+#
+# Modified 2024 by the Tripo AI and Stability AI Team.
+#
+# Copyright (c) 2024 Tripo AI & Stability AI
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ...utils import BaseModule
+from .basic_transformer_block import BasicTransformerBlock
+
+
+class Transformer1D(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        num_attention_heads: int = 16
+        attention_head_dim: int = 88
+        in_channels: Optional[int] = None
+        out_channels: Optional[int] = None
+        num_layers: int = 1
+        dropout: float = 0.0
+        norm_num_groups: int = 32
+        cross_attention_dim: Optional[int] = None
+        attention_bias: bool = False
+        activation_fn: str = "geglu"
+        only_cross_attention: bool = False
+        double_self_attention: bool = False
+        upcast_attention: bool = False
+        norm_type: str = "layer_norm"
+        norm_elementwise_affine: bool = True
+        gradient_checkpointing: bool = False
+
+    cfg: Config
+
+    def configure(self) -> None:
+        self.num_attention_heads = self.cfg.num_attention_heads
+        self.attention_head_dim = self.cfg.attention_head_dim
+        inner_dim = self.num_attention_heads * self.attention_head_dim
+
+        linear_cls = nn.Linear
+
+        # 2. Define input layers
+        self.in_channels = self.cfg.in_channels
+
+        self.norm = torch.nn.GroupNorm(
+            num_groups=self.cfg.norm_num_groups,
+            num_channels=self.cfg.in_channels,
+            eps=1e-6,
+            affine=True,
+        )
+        self.proj_in = linear_cls(self.cfg.in_channels, inner_dim)
+
+        # 3. Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    self.num_attention_heads,
+                    self.attention_head_dim,
+                    dropout=self.cfg.dropout,
+                    cross_attention_dim=self.cfg.cross_attention_dim,
+                    activation_fn=self.cfg.activation_fn,
+                    attention_bias=self.cfg.attention_bias,
+                    only_cross_attention=self.cfg.only_cross_attention,
+                    double_self_attention=self.cfg.double_self_attention,
+                    upcast_attention=self.cfg.upcast_attention,
+                    norm_type=self.cfg.norm_type,
+                    norm_elementwise_affine=self.cfg.norm_elementwise_affine,
+                )
+                for d in range(self.cfg.num_layers)
+            ]
+        )
+
+        # 4. Define output layers
+        self.out_channels = (
+            self.cfg.in_channels
+            if self.cfg.out_channels is None
+            else self.cfg.out_channels
+        )
+
+        self.proj_out = linear_cls(inner_dim, self.cfg.in_channels)
+
+        self.gradient_checkpointing = self.cfg.gradient_checkpointing
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+    ):
+        """
+        The [`Transformer1DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
+                Input `hidden_states`.
+            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            attention_mask ( `torch.Tensor`, *optional*):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+            encoder_attention_mask ( `torch.Tensor`, *optional*):
+                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
+
+                    * Mask `(batch, sequence_length)` True = keep, False = discard.
+                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
+
+                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
+                above. This bias will be added to the cross-attention scores.
+
+        Returns:
+            torch.FloatTensor
+        """
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
+        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
+        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None and attention_mask.ndim == 2:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
+            encoder_attention_mask = (
+                1 - encoder_attention_mask.to(hidden_states.dtype)
+            ) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 1. Input
+        batch, _, seq_len = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 1).reshape(
+            batch, seq_len, inner_dim
+        )
+        hidden_states = self.proj_in(hidden_states)
+
+        # 2. Blocks
+        for block in self.transformer_blocks:
+            if self.training and self.gradient_checkpointing:
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    block,
+                    hidden_states,
+                    attention_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+
+        # 3. Output
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = (
+            hidden_states.reshape(batch, seq_len, inner_dim)
+            .permute(0, 2, 1)
+            .contiguous()
+        )
+
+        output = hidden_states + residual
+
+        return output
@@ -0,0 +1,218 @@
+import math
+import os
+from dataclasses import dataclass, field
+from typing import List, Union
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+import trimesh
+from einops import rearrange
+from huggingface_hub import hf_hub_download
+from omegaconf import OmegaConf
+from PIL import Image
+
+from .models.isosurface import MarchingCubeHelper
+from .utils import (
+    BaseModule,
+    ImagePreprocessor,
+    find_class,
+    get_spherical_cameras,
+    scale_tensor,
+)
+
+
+class TSR(BaseModule):
+    @dataclass
+    class Config(BaseModule.Config):
+        cond_image_size: int
+
+        image_tokenizer_cls: str
+        image_tokenizer: dict
+
+        tokenizer_cls: str
+        tokenizer: dict
+
+        backbone_cls: str
+        backbone: dict
+
+        post_processor_cls: str
+        post_processor: dict
+
+        decoder_cls: str
+        decoder: dict
+
+        renderer_cls: str
+        renderer: dict
+
+    cfg: Config
+
+    @classmethod
+    def from_pretrained(
+        cls, pretrained_model_name_or_path: str, config_name: str, weight_name: str
+    ):
+        if os.path.isdir(pretrained_model_name_or_path):
+            config_path = os.path.join(pretrained_model_name_or_path, config_name)
+            weight_path = os.path.join(pretrained_model_name_or_path, weight_name)
+        else:
+            config_path = hf_hub_download(
+                repo_id=pretrained_model_name_or_path, filename=config_name
+            )
+            weight_path = hf_hub_download(
+                repo_id=pretrained_model_name_or_path, filename=weight_name
+            )
+
+        cfg = OmegaConf.load(config_path)
+        OmegaConf.resolve(cfg)
+        model = cls(cfg)
+        ckpt = torch.load(weight_path, map_location="cpu")
+        model.load_state_dict(ckpt)
+        return model
+
+    @classmethod
+    def from_pretrained_custom(
+            cls, weight_path: str, config_path: str
+    ):
+        cfg = OmegaConf.load(config_path)
+        OmegaConf.resolve(cfg)
+        model = cls(cfg)
+        ckpt = torch.load(weight_path, map_location="cpu")
+        model.load_state_dict(ckpt)
+        return model
+
+    def configure(self):
+        self.image_tokenizer = find_class(self.cfg.image_tokenizer_cls)(
+            self.cfg.image_tokenizer
+        )
+        self.tokenizer = find_class(self.cfg.tokenizer_cls)(self.cfg.tokenizer)
+        self.backbone = find_class(self.cfg.backbone_cls)(self.cfg.backbone)
+        self.post_processor = find_class(self.cfg.post_processor_cls)(
+            self.cfg.post_processor
+        )
+        self.decoder = find_class(self.cfg.decoder_cls)(self.cfg.decoder)
+        self.renderer = find_class(self.cfg.renderer_cls)(self.cfg.renderer)
+        self.image_processor = ImagePreprocessor()
+        self.isosurface_helper = None
+
+    def forward(
+        self,
+        image: Union[
+            PIL.Image.Image,
+            np.ndarray,
+            torch.FloatTensor,
+            List[PIL.Image.Image],
+            List[np.ndarray],
+            List[torch.FloatTensor],
+        ],
+        device: str,
+    ) -> torch.FloatTensor:
+        rgb_cond = self.image_processor(image, self.cfg.cond_image_size)[:, None].to(
+            device
+        )
+        batch_size = rgb_cond.shape[0]
+
+        input_image_tokens: torch.Tensor = self.image_tokenizer(
+            rearrange(rgb_cond, "B Nv H W C -> B Nv C H W", Nv=1),
+        )
+
+        input_image_tokens = rearrange(
+            input_image_tokens, "B Nv C Nt -> B (Nv Nt) C", Nv=1
+        )
+
+        tokens: torch.Tensor = self.tokenizer(batch_size)
+
+        tokens = self.backbone(
+            tokens,
+            encoder_hidden_states=input_image_tokens,
+        )
+
+        scene_codes = self.post_processor(self.tokenizer.detokenize(tokens))
+        return scene_codes
+
+    def render(
+        self,
+        scene_codes,
+        n_views: int,
+        elevation_deg: float = 0.0,
+        camera_distance: float = 1.9,
+        fovy_deg: float = 40.0,
+        height: int = 256,
+        width: int = 256,
+        return_type: str = "pil",
+    ):
+        rays_o, rays_d = get_spherical_cameras(
+            n_views, elevation_deg, camera_distance, fovy_deg, height, width
+        )
+        rays_o, rays_d = rays_o.to(scene_codes.device), rays_d.to(scene_codes.device)
+
+        def process_output(image: torch.FloatTensor):
+            if return_type == "pt":
+                return image
+            elif return_type == "np":
+                return image.detach().cpu().numpy()
+            elif return_type == "pil":
+                return Image.fromarray(
+                    (image.detach().cpu().numpy() * 255.0).astype(np.uint8)
+                )
+            else:
+                raise NotImplementedError
+
+        images = []
+        for scene_code in scene_codes:
+            images_ = []
+            for i in range(n_views):
+                with torch.no_grad():
+                    image = self.renderer(
+                        self.decoder, scene_code, rays_o[i], rays_d[i]
+                    )
+                images_.append(process_output(image))
+            images.append(images_)
+
+        return images
+
+    def set_marching_cubes_resolution(self, resolution: int):
+        if (
+            self.isosurface_helper is not None
+            and self.isosurface_helper.resolution == resolution
+        ):
+            return
+        self.isosurface_helper = MarchingCubeHelper(resolution)
+
+    def extract_mesh(self, scene_codes, resolution: int = 256, threshold: float = 25.0,callback=None):
+        self.set_marching_cubes_resolution(resolution)
+        meshes = []
+        for scene_code in scene_codes:
+            with torch.no_grad():
+                density = self.renderer.query_triplane(
+                    self.decoder,
+                    scale_tensor(
+                        self.isosurface_helper.grid_vertices.to(scene_codes.device),
+                        self.isosurface_helper.points_range,
+                        (-self.renderer.cfg.radius, self.renderer.cfg.radius),
+                    ),
+                    scene_code,
+                )["density_act"]
+            v_pos, t_pos_idx = self.isosurface_helper(-(density - threshold))
+            v_pos = scale_tensor(
+                v_pos,
+                self.isosurface_helper.points_range,
+                (-self.renderer.cfg.radius, self.renderer.cfg.radius),
+            )
+            with torch.no_grad():
+                color = self.renderer.query_triplane(
+                    self.decoder,
+                    v_pos,
+                    scene_code,
+                )["color"]
+            mesh = trimesh.Trimesh(
+                vertices=v_pos.cpu().numpy(),
+                faces=t_pos_idx.cpu().numpy(),
+                vertex_colors=color.cpu().numpy(),
+            )
+            meshes.append(mesh)
+
+            if callback:
+                callback(len(meshes))
+
+        return meshes
@@ -0,0 +1,475 @@
+import importlib
+import math
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import imageio
+import numpy as np
+import PIL.Image
+#import rembg
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import trimesh
+from omegaconf import DictConfig, OmegaConf
+#from PIL import Image
+
+
+def parse_structured(fields: Any, cfg: Optional[Union[dict, DictConfig]] = None) -> Any:
+    scfg = OmegaConf.merge(OmegaConf.structured(fields), cfg)
+    return scfg
+
+
+def find_class(cls_string):
+    module_string = ".".join(cls_string.split(".")[:-1])
+    cls_name = cls_string.split(".")[-1]
+    module = importlib.import_module(module_string, package=None)
+    cls = getattr(module, cls_name)
+    return cls
+
+
+def get_intrinsic_from_fov(fov, H, W, bs=-1):
+    focal_length = 0.5 * H / np.tan(0.5 * fov)
+    intrinsic = np.identity(3, dtype=np.float32)
+    intrinsic[0, 0] = focal_length
+    intrinsic[1, 1] = focal_length
+    intrinsic[0, 2] = W / 2.0
+    intrinsic[1, 2] = H / 2.0
+
+    if bs > 0:
+        intrinsic = intrinsic[None].repeat(bs, axis=0)
+
+    return torch.from_numpy(intrinsic)
+
+
+class BaseModule(nn.Module):
+    @dataclass
+    class Config:
+        pass
+
+    cfg: Config  # add this to every subclass of BaseModule to enable static type checking
+
+    def __init__(
+        self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs
+    ) -> None:
+        super().__init__()
+        self.cfg = parse_structured(self.Config, cfg)
+        self.configure(*args, **kwargs)
+
+    def configure(self, *args, **kwargs) -> None:
+        raise NotImplementedError
+
+
+class ImagePreprocessor:
+    def convert_and_resize(
+        self,
+        image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
+        size: int,
+    ):
+        if isinstance(image, PIL.Image.Image):
+            image = torch.from_numpy(np.array(image).astype(np.float32) / 255.0)
+        elif isinstance(image, np.ndarray):
+            if image.dtype == np.uint8:
+                image = torch.from_numpy(image.astype(np.float32) / 255.0)
+            else:
+                image = torch.from_numpy(image)
+        elif isinstance(image, torch.Tensor):
+            pass
+
+        batched = image.ndim == 4
+
+        if not batched:
+            image = image[None, ...]
+        image = F.interpolate(
+            image.permute(0, 3, 1, 2),
+            (size, size),
+            mode="bilinear",
+            align_corners=False,
+            antialias=True,
+        ).permute(0, 2, 3, 1)
+        if not batched:
+            image = image[0]
+        return image
+
+    def __call__(
+        self,
+        image: Union[
+            PIL.Image.Image,
+            np.ndarray,
+            torch.FloatTensor,
+            List[PIL.Image.Image],
+            List[np.ndarray],
+            List[torch.FloatTensor],
+        ],
+        size: int,
+    ) -> Any:
+        if isinstance(image, (np.ndarray, torch.FloatTensor)) and image.ndim == 4:
+            image = self.convert_and_resize(image, size)
+        else:
+            if not isinstance(image, list):
+                image = [image]
+            image = [self.convert_and_resize(im, size) for im in image]
+            image = torch.stack(image, dim=0)
+        return image
+
+
+def rays_intersect_bbox(
+    rays_o: torch.Tensor,
+    rays_d: torch.Tensor,
+    radius: float,
+    near: float = 0.0,
+    valid_thresh: float = 0.01,
+):
+    input_shape = rays_o.shape[:-1]
+    rays_o, rays_d = rays_o.view(-1, 3), rays_d.view(-1, 3)
+    rays_d_valid = torch.where(
+        rays_d.abs() < 1e-6, torch.full_like(rays_d, 1e-6), rays_d
+    )
+    if type(radius) in [int, float]:
+        radius = torch.FloatTensor(
+            [[-radius, radius], [-radius, radius], [-radius, radius]]
+        ).to(rays_o.device)
+    radius = (
+        1.0 - 1.0e-3
+    ) * radius  # tighten the radius to make sure the intersection point lies in the bounding box
+    interx0 = (radius[..., 1] - rays_o) / rays_d_valid
+    interx1 = (radius[..., 0] - rays_o) / rays_d_valid
+    t_near = torch.minimum(interx0, interx1).amax(dim=-1).clamp_min(near)
+    t_far = torch.maximum(interx0, interx1).amin(dim=-1)
+
+    # check wheter a ray intersects the bbox or not
+    rays_valid = t_far - t_near > valid_thresh
+
+    t_near[torch.where(~rays_valid)] = 0.0
+    t_far[torch.where(~rays_valid)] = 0.0
+
+    t_near = t_near.view(*input_shape, 1)
+    t_far = t_far.view(*input_shape, 1)
+    rays_valid = rays_valid.view(*input_shape)
+
+    return t_near, t_far, rays_valid
+
+
+def chunk_batch(func: Callable, chunk_size: int, *args, **kwargs) -> Any:
+    if chunk_size <= 0:
+        return func(*args, **kwargs)
+    B = None
+    for arg in list(args) + list(kwargs.values()):
+        if isinstance(arg, torch.Tensor):
+            B = arg.shape[0]
+            break
+    assert (
+        B is not None
+    ), "No tensor found in args or kwargs, cannot determine batch size."
+    out = defaultdict(list)
+    out_type = None
+    # max(1, B) to support B == 0
+    for i in range(0, max(1, B), chunk_size):
+        out_chunk = func(
+            *[
+                arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
+                for arg in args
+            ],
+            **{
+                k: arg[i : i + chunk_size] if isinstance(arg, torch.Tensor) else arg
+                for k, arg in kwargs.items()
+            },
+        )
+        if out_chunk is None:
+            continue
+        out_type = type(out_chunk)
+        if isinstance(out_chunk, torch.Tensor):
+            out_chunk = {0: out_chunk}
+        elif isinstance(out_chunk, tuple) or isinstance(out_chunk, list):
+            chunk_length = len(out_chunk)
+            out_chunk = {i: chunk for i, chunk in enumerate(out_chunk)}
+        elif isinstance(out_chunk, dict):
+            pass
+        else:
+            print(
+                f"Return value of func must be in type [torch.Tensor, list, tuple, dict], get {type(out_chunk)}."
+            )
+            exit(1)
+        for k, v in out_chunk.items():
+            v = v if torch.is_grad_enabled() else v.detach()
+            out[k].append(v)
+
+    if out_type is None:
+        return None
+
+    out_merged: Dict[Any, Optional[torch.Tensor]] = {}
+    for k, v in out.items():
+        if all([vv is None for vv in v]):
+            # allow None in return value
+            out_merged[k] = None
+        elif all([isinstance(vv, torch.Tensor) for vv in v]):
+            out_merged[k] = torch.cat(v, dim=0)
+        else:
+            raise TypeError(
+                f"Unsupported types in return value of func: {[type(vv) for vv in v if not isinstance(vv, torch.Tensor)]}"
+            )
+
+    if out_type is torch.Tensor:
+        return out_merged[0]
+    elif out_type in [tuple, list]:
+        return out_type([out_merged[i] for i in range(chunk_length)])
+    elif out_type is dict:
+        return out_merged
+
+
+ValidScale = Union[Tuple[float, float], torch.FloatTensor]
+
+
+def scale_tensor(dat: torch.FloatTensor, inp_scale: ValidScale, tgt_scale: ValidScale):
+    if inp_scale is None:
+        inp_scale = (0, 1)
+    if tgt_scale is None:
+        tgt_scale = (0, 1)
+    if isinstance(tgt_scale, torch.FloatTensor):
+        assert dat.shape[-1] == tgt_scale.shape[-1]
+    dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])
+    dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]
+    return dat
+
+
+def get_activation(name) -> Callable:
+    if name is None:
+        return lambda x: x
+    name = name.lower()
+    if name == "none":
+        return lambda x: x
+    elif name == "exp":
+        return lambda x: torch.exp(x)
+    elif name == "sigmoid":
+        return lambda x: torch.sigmoid(x)
+    elif name == "tanh":
+        return lambda x: torch.tanh(x)
+    elif name == "softplus":
+        return lambda x: F.softplus(x)
+    else:
+        try:
+            return getattr(F, name)
+        except AttributeError:
+            raise ValueError(f"Unknown activation function: {name}")
+
+
+def get_ray_directions(
+    H: int,
+    W: int,
+    focal: Union[float, Tuple[float, float]],
+    principal: Optional[Tuple[float, float]] = None,
+    use_pixel_centers: bool = True,
+    normalize: bool = True,
+) -> torch.FloatTensor:
+    """
+    Get ray directions for all pixels in camera coordinate.
+    Reference: https://www.scratchapixel.com/lessons/3d-basic-rendering/
+               ray-tracing-generating-camera-rays/standard-coordinate-systems
+
+    Inputs:
+        H, W, focal, principal, use_pixel_centers: image height, width, focal length, principal point and whether use pixel centers
+    Outputs:
+        directions: (H, W, 3), the direction of the rays in camera coordinate
+    """
+    pixel_center = 0.5 if use_pixel_centers else 0
+
+    if isinstance(focal, float):
+        fx, fy = focal, focal
+        cx, cy = W / 2, H / 2
+    else:
+        fx, fy = focal
+        assert principal is not None
+        cx, cy = principal
+
+    i, j = torch.meshgrid(
+        torch.arange(W, dtype=torch.float32) + pixel_center,
+        torch.arange(H, dtype=torch.float32) + pixel_center,
+        indexing="xy",
+    )
+
+    directions = torch.stack([(i - cx) / fx, -(j - cy) / fy, -torch.ones_like(i)], -1)
+
+    if normalize:
+        directions = F.normalize(directions, dim=-1)
+
+    return directions
+
+
+def get_rays(
+    directions,
+    c2w,
+    keepdim=False,
+    normalize=False,
+) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
+    # Rotate ray directions from camera coordinate to the world coordinate
+    assert directions.shape[-1] == 3
+
+    if directions.ndim == 2:  # (N_rays, 3)
+        if c2w.ndim == 2:  # (4, 4)
+            c2w = c2w[None, :, :]
+        assert c2w.ndim == 3  # (N_rays, 4, 4) or (1, 4, 4)
+        rays_d = (directions[:, None, :] * c2w[:, :3, :3]).sum(-1)  # (N_rays, 3)
+        rays_o = c2w[:, :3, 3].expand(rays_d.shape)
+    elif directions.ndim == 3:  # (H, W, 3)
+        assert c2w.ndim in [2, 3]
+        if c2w.ndim == 2:  # (4, 4)
+            rays_d = (directions[:, :, None, :] * c2w[None, None, :3, :3]).sum(
+                -1
+            )  # (H, W, 3)
+            rays_o = c2w[None, None, :3, 3].expand(rays_d.shape)
+        elif c2w.ndim == 3:  # (B, 4, 4)
+            rays_d = (directions[None, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
+                -1
+            )  # (B, H, W, 3)
+            rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)
+    elif directions.ndim == 4:  # (B, H, W, 3)
+        assert c2w.ndim == 3  # (B, 4, 4)
+        rays_d = (directions[:, :, :, None, :] * c2w[:, None, None, :3, :3]).sum(
+            -1
+        )  # (B, H, W, 3)
+        rays_o = c2w[:, None, None, :3, 3].expand(rays_d.shape)
+
+    if normalize:
+        rays_d = F.normalize(rays_d, dim=-1)
+    if not keepdim:
+        rays_o, rays_d = rays_o.reshape(-1, 3), rays_d.reshape(-1, 3)
+
+    return rays_o, rays_d
+
+
+def get_spherical_cameras(
+    n_views: int,
+    elevation_deg: float,
+    camera_distance: float,
+    fovy_deg: float,
+    height: int,
+    width: int,
+):
+    azimuth_deg = torch.linspace(0, 360.0, n_views + 1)[:n_views]
+    elevation_deg = torch.full_like(azimuth_deg, elevation_deg)
+    camera_distances = torch.full_like(elevation_deg, camera_distance)
+
+    elevation = elevation_deg * math.pi / 180
+    azimuth = azimuth_deg * math.pi / 180
+
+    # convert spherical coordinates to cartesian coordinates
+    # right hand coordinate system, x back, y right, z up
+    # elevation in (-90, 90), azimuth from +x to +y in (-180, 180)
+    camera_positions = torch.stack(
+        [
+            camera_distances * torch.cos(elevation) * torch.cos(azimuth),
+            camera_distances * torch.cos(elevation) * torch.sin(azimuth),
+            camera_distances * torch.sin(elevation),
+        ],
+        dim=-1,
+    )
+
+    # default scene center at origin
+    center = torch.zeros_like(camera_positions)
+    # default camera up direction as +z
+    up = torch.as_tensor([0, 0, 1], dtype=torch.float32)[None, :].repeat(n_views, 1)
+
+    fovy = torch.full_like(elevation_deg, fovy_deg) * math.pi / 180
+
+    lookat = F.normalize(center - camera_positions, dim=-1)
+    right = F.normalize(torch.cross(lookat, up), dim=-1)
+    up = F.normalize(torch.cross(right, lookat), dim=-1)
+    c2w3x4 = torch.cat(
+        [torch.stack([right, up, -lookat], dim=-1), camera_positions[:, :, None]],
+        dim=-1,
+    )
+    c2w = torch.cat([c2w3x4, torch.zeros_like(c2w3x4[:, :1])], dim=1)
+    c2w[:, 3, 3] = 1.0
+
+    # get directions by dividing directions_unit_focal by focal length
+    focal_length = 0.5 * height / torch.tan(0.5 * fovy)
+    directions_unit_focal = get_ray_directions(
+        H=height,
+        W=width,
+        focal=1.0,
+    )
+    directions = directions_unit_focal[None, :, :, :].repeat(n_views, 1, 1, 1)
+    directions[:, :, :, :2] = (
+        directions[:, :, :, :2] / focal_length[:, None, None, None]
+    )
+    # must use normalize=True to normalize directions here
+    rays_o, rays_d = get_rays(directions, c2w, keepdim=True, normalize=True)
+
+    return rays_o, rays_d
+
+
+# def remove_background(
+#     image: PIL.Image.Image,
+#     rembg_session: Any = None,
+#     force: bool = False,
+#     **rembg_kwargs,
+# ) -> PIL.Image.Image:
+#     do_remove = True
+#     if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
+#         do_remove = False
+#     do_remove = do_remove or force
+#     if do_remove:
+#         image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
+#     return image
+
+
+def resize_foreground(
+    image: PIL.Image.Image,
+    ratio: float,
+) -> PIL.Image.Image:
+    image = np.array(image)
+    assert image.shape[-1] == 4
+    alpha = np.where(image[..., 3] > 0)
+    y1, y2, x1, x2 = (
+        alpha[0].min(),
+        alpha[0].max(),
+        alpha[1].min(),
+        alpha[1].max(),
+    )
+    # crop the foreground
+    fg = image[y1:y2, x1:x2]
+    # pad to square
+    size = max(fg.shape[0], fg.shape[1])
+    ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
+    ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
+    new_image = np.pad(
+        fg,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+
+    # compute padding according to the ratio
+    new_size = int(new_image.shape[0] / ratio)
+    # pad to size, double side
+    ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
+    ph1, pw1 = new_size - size - ph0, new_size - size - pw0
+    new_image = np.pad(
+        new_image,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+    new_image = PIL.Image.fromarray(new_image)
+    return new_image
+
+
+def save_video(
+    frames: List[PIL.Image.Image],
+    output_path: str,
+    fps: int = 30,
+):
+    # use imageio to save video
+    frames = [np.array(frame) for frame in frames]
+    writer = imageio.get_writer(output_path, fps=fps)
+    for frame in frames:
+        writer.append_data(frame)
+    writer.close()
+
+
+def to_gradio_3d_orientation(mesh):
+    mesh.apply_transform(trimesh.transformations.rotation_matrix(-np.pi/2, [1, 0, 0]))
+    mesh.apply_scale([1, 1, -1])
+    mesh.apply_transform(trimesh.transformations.rotation_matrix(np.pi/2, [0, 1, 0]))
+    return mesh
@@ -7,5 +7,12 @@ openai
 simple-lama-inpainting
 clip-interrogator==0.6.0
 transformers>=4.36.0
-zhipuai
-lark-parser
+lark-parser
+imageio-ffmpeg
+rembg[gpu]
+omegaconf==2.3.0
+Pillow>=9.5.0
+einops==0.7.0
+trimesh>=4.0.5
+huggingface-hub
+scikit-image
@@ -202,7 +202,7 @@
            width: fit-content;
            max-width: 100%;
            margin-left: 12px;
-            min-height: 200px;
+            /*min-height: 200px;*/
        }

        .input_card {
@@ -278,8 +278,11 @@
        }

        button:hover {
-            border-color: yellow !important;
-            color: yellow !important;
+            /* border-color: yellow !important; */
+            color: #ffffff !important;
+            /* font-weight: 400; */
+            background: #232222;
+            cursor: pointer;
        }

        .disabled {
@@ -400,12 +403,53 @@
        .hidden-caption-content {
            display: none;
        }
+
+         /* 定义滚动条轨道的背景颜色 */
+
+         ::-webkit-scrollbar-track {
+            background-color: #f1f1f1;
+            /* 轨道背景颜色 */
+        }
+
+        /* 定义滚动条滑块的颜色 */
+
+        ::-webkit-scrollbar-thumb {
+            background-color: #888;
+            /* 滑块颜色 */
+            border-radius: 4px;
+            /* 滑块圆角 */
+        }
+
+        /* 鼠标悬停时滚动条滑块的颜色 */
+
+        ::-webkit-scrollbar-thumb:hover {
+            background-color: #555;
+            /* 悬停时滑块颜色 */
+        }
+
+        /* 定义滚动条角落的颜色 */
+
+        ::-webkit-scrollbar-corner {
+            background-color: transparent;
+            /* 角落颜色 */
+        }
+
+
+        .dynamic_prompt::after {
+            content: attr(title);
+            position: absolute; 
+            color: black;
+            padding: 4px;
+            padding-left: 25px;
+            border-radius: 4px;
+        }
    </style>
    <!-- <script src="../../../scripts/api.js" type="module"></script> -->
    <link href="/extensions/comfyui-mixlab-nodes/lib/photoswipe.min.css" rel="stylesheet">
    <link href="/extensions/comfyui-mixlab-nodes/lib/classic.min.css" rel="stylesheet">
    <script src="/extensions/comfyui-mixlab-nodes/lib/pickr.min.js"></script>
    <script src="/extensions/comfyui-mixlab-nodes/lib/filerobot-image-editor.min.js"></script>
+    <script type="module" src="/extensions/comfyui-mixlab-nodes/lib/model-viewer.min.js"></script>
    <link rel="stylesheet" href="/extensions/comfyui-mixlab-nodes/lib/login.css">
 </head>

@@ -413,7 +457,7 @@

    <div id="editor_container"></div>
    <div class="header">
-        <div style="margin: 0 24px;
+        <div id="logo" style="margin: 0 24px;
            margin-bottom: 24px;
            padding: 8px;
            color: #4a4a4a;
@@ -439,8 +483,39 @@
    <script type="module">
        import PhotoSwipeLightbox from '/extensions/comfyui-mixlab-nodes/lib/photoswipe-lightbox.esm.min.js'
        // console.log(Lightbox)
-
        import { api } from "../../../scripts/api.js";
+
+
+        // ComfyUI\web\extensions\core\dynamicPrompts.js
+        // 官方实现修改
+        // Allows for simple dynamic prompt replacement
+        // Inputs in the format {a|b} will have a random value of a or b chosen when the prompt is queued.
+
+        /*
+         * Strips C-style line and block comments from a string
+         */
+        function stripComments(str) {
+            return str.replace(/\/\*[\s\S]*?\*\/|\/\/.*/g, '');
+        }
+
+        function dynamicPrompts(prompt) {
+            prompt = stripComments(prompt);
+            while (prompt.replace("\\{", "").includes("{") && prompt.replace("\\}", "").includes("}")) {
+                const startIndex = prompt.replace("\\{", "00").indexOf("{");
+                const endIndex = prompt.replace("\\}", "00").indexOf("}");
+
+                const optionsString = prompt.substring(startIndex + 1, endIndex);
+                const options = optionsString.split("|");
+
+                const randomIndex = Math.floor(Math.random() * options.length);
+                const randomOption = options[randomIndex];
+
+                prompt = prompt.substring(0, startIndex) + randomOption + prompt.substring(endIndex + 1);
+            }
+            return prompt
+        }
+
+
        // console.log('api', api)
        const base64Df =
            'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
@@ -479,6 +554,59 @@
        };


+        const parseImageToBase64 = url => {
+            return new Promise((res, rej) => {
+                fetch(url)
+                    .then(response => response.blob())
+                    .then(blob => {
+                        const reader = new FileReader()
+                        reader.onloadend = () => {
+                            const base64data = reader.result
+                            res(base64data)
+                            // 在这里可以将base64数据用于进一步处理或显示图片
+                        }
+                        reader.readAsDataURL(blob)
+                    })
+                    .catch(error => {
+                        console.log('发生错误:', error)
+                    })
+            })
+        }
+
+        //给load image to batch节点使用的输入
+        function createBase64ImageForLoadImageToBatch(imageElement, nodeId, bs) {
+            let im = new Image()
+            im.src = bs;
+            im.className = "base64"
+            imageElement.appendChild(im);
+
+            let base64s = imageElement.querySelectorAll('.base64')
+            //更新输入
+            window._appData.data[nodeId].inputs.images.base64 = Array.from(base64s, (b) => b.src)
+
+            // 删除
+            im.addEventListener('click', e => {
+                e.preventDefault();
+                im.remove();
+                let base64s = imageElement.querySelectorAll('.base64')
+                //更新输入
+                window._appData.data[nodeId].inputs.images.base64 = Array.from(base64s, (b) => b.src)
+            })
+        }
+
+        const blobToBase64 = blob => {
+            return new Promise((res, rej) => {
+                const reader = new FileReader()
+                reader.onloadend = () => {
+                    const base64data = reader.result
+                    res(base64data)
+                    // 在这里可以将base64数据用于进一步处理或显示图片
+                }
+                reader.readAsDataURL(blob)
+            })
+        }
+
+
        function base64ToBlob(base64) {
            // 去除base64编码中的前缀
            const base64WithoutPrefix = base64.replace(/^data:image\/\w+;base64,/, '');
@@ -677,18 +805,23 @@

        // 种子的处理
        function randomSeed(seed, data) {
+            let max_seed = 4294967295
+            //1849378600828930
            for (const id in data) {
                if (data[id].inputs.seed != undefined
                    && !Array.isArray(data[id].inputs.seed) //如果是数组，则由其他节点控制
                    && ['increment', 'decrement', 'randomize'].includes(seed[id])) {
-                    data[id].inputs.seed = Math.round(Math.random() * 1849378600828930)
+                    data[id].inputs.seed = Math.round(Math.random() * max_seed)
                    // console.log('new Seed', data[id])
                }
                if (data[id].inputs.noise_seed != undefined
                    && !Array.isArray(data[id].inputs.noise_seed) //如果是数组，则由其他节点控制
                    && ['increment', 'decrement', 'randomize'].includes(seed[id])) {
-                    data[id].inputs.noise_seed = Math.round(Math.random() * 1849378600828930)
-
+                    data[id].inputs.noise_seed = Math.round(Math.random() * max_seed)
+                }
+                // class_type:"Seed_"
+                if (data[id].class_type == "Seed_" && ['increment', 'decrement', 'randomize'].includes(seed[id])) {
+                    data[id].inputs.seed = Math.round(Math.random() * max_seed)
                }
                console.log('new Seed', data[id])
            }
@@ -712,6 +845,21 @@
            // 随机seed
            promptWorkflow = randomSeed(seed, promptWorkflow);

+            // //动态提示,改为输入的时候，手动触发
+            // for (const id in promptWorkflow) {
+            //     let node = promptWorkflow[id]
+            //     if (["TextInput_", "CLIPTextEncode", "PromptSimplification", "ChinesePrompt_Mix"].includes(
+            //         node.class_type
+            //     )) {
+            //         if (node.class_type == "PromptSimplification") {
+            //             promptWorkflow[id].inputs.prompt = dynamicPrompts(node.inputs.prompt);
+            //         } else {
+            //             promptWorkflow[id].inputs.text = dynamicPrompts(node.inputs.text);
+            //         }
+            //         console.log('#动态提示', promptWorkflow[id].inputs)
+            //     }
+            // }
+
            let url = get_url()
            const data = JSON.stringify({ prompt: promptWorkflow, client_id });
            fetch(`${url}/prompt`, {
@@ -747,6 +895,10 @@
            let url = get_url()
            const res = await fetch(`${url}/mixlab/workflow`, {
                method: 'POST',
+                mode: 'cors', // 允许跨域请求
+                headers: {
+                    'Content-Type': 'application/json'
+                },
                body: JSON.stringify({
                    task: 'my_app',
                    filename,
@@ -811,11 +963,6 @@
            action.appendChild(copyImage)
            copyImage.style.marginLeft = '18px';

-            const copyText = document.createElement('button');
-            copyText.innerText = 'copy text for share'
-            action.appendChild(copyText)
-            copyText.style.marginLeft = '18px';
-
            let isURL = false;
            try {
                new URL(link)
@@ -865,12 +1012,16 @@
            output_card.className = 'output_card'
            container.appendChild(output_card)

-
-            copyText.addEventListener('click', e => {
-                e.preventDefault();
-                copyTextToClipboard((window._appData.share_prefix || '') + " " + output_card.outerHTML, (r) => success(r, copyText, 'copy text for share'))
-
-            })
+            if (window._appData.share_prefix) {
+                const copyText = document.createElement('button');
+                copyText.innerText = 'copy text for share'
+                action.appendChild(copyText)
+                copyText.style.marginLeft = '18px';
+                copyText.addEventListener('click', e => {
+                    e.preventDefault();
+                    copyTextToClipboard((window._appData.share_prefix || '') + " " + output_card.outerHTML, (r) => success(r, copyText, 'copy text for share'))
+                })
+            }

            copyImage.addEventListener('click', e => {
                e.preventDefault();
@@ -884,6 +1035,9 @@
                // copyImagesToClipboard(output_card.outerHTML)
            })

+            //是否显示复制图片，复制html两个按钮
+            let isShowImageFn = false;
+
            for (const node of outputData) {
                // console.log('output', node)
                if (node.class_type == "ShowTextForGPT") {
@@ -902,8 +1056,31 @@
                    output_card.appendChild(div);
                };

-                if (["SaveImage", "PreviewImage", "PromptImage"].includes(node.class_type)) {
+                if (["SaveImage",
+                    "PreviewImage",
+                    "PromptImage",
+                    "Image Save",
+                    "SaveImageAndMetadata_",
+                    "TransparentImage"].includes(node.class_type)) {
+                    console.log('output#image', node)
+                    let a = document.createElement('a');
+                    a.id = `output_${node.id}`
+                    a.setAttribute('data-pswp-width', "200");
+                    a.setAttribute('data-pswp-height', "200");
+                    a.setAttribute('target', "_blank");
+                    a.setAttribute('href', base64Df);
+                    a.setAttribute('title', node.title);

+                    let img = new Image();
+                    // img;
+                    img.src = window._appData?.icon || base64Df;
+                    a.appendChild(img)
+                    output_card.appendChild(a);
+                    isShowImageFn = true;
+                }
+
+                //3d 
+                if (["SaveTripoSRMesh"].includes(node.class_type)) {
                    let a = document.createElement('a');
                    a.id = `output_${node.id}`
                    a.setAttribute('data-pswp-width', "200");
@@ -919,7 +1096,7 @@
                }

                // video ,gif
-                if (["VHS_VideoCombine"].includes(node.class_type)) {
+                if (["VHS_VideoCombine", "VideoCombine_Adv"].includes(node.class_type)) {

                    let a = document.createElement('a');
                    a.id = `output_${node.id}`
@@ -946,6 +1123,12 @@
                }

            }
+
+            if (isShowImageFn === false) {
+                copyImage.remove();
+                copyHTML.remove();
+            }
+
            return container
        }

@@ -1002,6 +1185,7 @@
        }

        async function handleClipboardImage(imageElement, data) {
+            //data.class_type === 'LoadImagesToBatch'
            try {
                const clipboardItems = await navigator.clipboard.read();
                for (const clipboardItem of clipboardItems) {
@@ -1014,18 +1198,17 @@

                            if (hashId == window._appData.data[data.id].hashId) return

-                            let { url, name } = await uploadImage(fileBlob);
-                            // 在这里可以对 Blob 对象进行进一步处理
-                            imageElement.src = url;
-                            window._appData.data[data.id].inputs.image = name;
-                            window._appData.data[data.id].hashId = hashId;
-
-                            console.log("上传的文件：", url, data.id, name);
-
-                            // const img = document.createElement('img');
-                            // img.src = URL.createObjectURL(blob);
-                            // document.body.appendChild(img);
-                            // console.log( URL.createObjectURL(blob));
+                            if (data.class_type === 'LoadImagesToBatch') {
+                                let base64 = await blobToBase64(fileBlob)
+                                createBase64ImageForLoadImageToBatch(imageElement, data.id, base64)
+                            } else {
+                                let { url, name } = await uploadImage(fileBlob);
+                                // 在这里可以对 Blob 对象进行进一步处理
+                                imageElement.src = url;
+                                window._appData.data[data.id].inputs.image = name;
+                                window._appData.data[data.id].hashId = hashId;
+                                console.log("上传的文件：", url, data.id, name);
+                            }
                        }
                    }
                }
@@ -1186,13 +1369,19 @@
            inputData = inputData.filter(inp => inp);
            // console.log('inputData',inputData)
            inputData.forEach(async data => {
-                console.log(data)
+                console.log('inputData', data);

                // 图片 or 视频输入
-                if (data.class_type === "LoadImage" || data.class_type === "VHS_LoadVideo") {
+
+                if (["LoadImage",
+                    "VHS_LoadVideo",
+                    "ImagesPrompt_",
+                    "LoadImagesToBatch"].includes(data.class_type)) {

                    let isVideoUpload = data.class_type === "VHS_LoadVideo";

+                    let isBase64Upload = data.class_type === "LoadImagesToBatch";
+
                    // Create a container for the upload control
                    const uploadContainer = document.createElement("div");
                    uploadContainer.className = 'card';
@@ -1224,7 +1413,7 @@
                    const btnForImageEdit = document.createElement("button");
                    btnForImageEdit.style = ` width: 32px; background: none;margin-left: 18px;`
                    btnForImageEdit.innerHTML = '<?xml version="1.0" ?><svg version="1.1" style="width: 24px;" viewBox="0 0 50 50" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g id="Layer_1_1_"><path d="M18.293,31.707h6.414l24-24l-6.414-6.414l-24,24V31.707z M45.879,7.707l-3.586,3.586l-3.586-3.586l3.586-3.586   L45.879,7.707z M20.293,26.121l17-17l3.586,3.586l-17,17h-3.586V26.121z"/><polygon points="43.293,19.707 41.293,19.707 41.293,46.707 3.293,46.707 3.293,8.707 31.293,8.707 31.293,6.707 1.293,6.707    1.293,48.707 43.293,48.707  "/></g></svg>'
-                    if (!isVideoUpload) actionDiv.appendChild(btnForImageEdit);
+                    if ((!isVideoUpload && !isBase64Upload) && data.class_type !== 'ImagesPrompt_') actionDiv.appendChild(btnForImageEdit);


                    uploadContainer.appendChild(actionDiv)
@@ -1246,7 +1435,7 @@

                        // imageElement.innerHTML=`<img src="${base64Df}"/>`

-                    } else {
+                    } else if (data.class_type === 'LoadImage') {
                        // 图片
                        let [subfolder, name] = data.inputs.image.split('/');
                        if (!name) {
@@ -1259,15 +1448,36 @@
                        imageElement.src = data.options?.defaultImage || url;
                        imageElement.setAttribute('onerror', `this.src='${base64Df}'`)

+                    } else if (data.class_type === 'ImagesPrompt_') {
+                        // 图片库模式
+                        const [imgDiv, mainImage] = createSelectForImages(
+                            data.title,
+                            data.options.images,
+                            data.inputs.imageIndex,
+                            (base64, text) => {
+                                window._appData.data[data.id].inputs.image_base64 = base64;
+                                window._appData.data[data.id].inputs.text = text;
+                            })
+                        uploadContainer.appendChild(imgDiv);
+                        window._appData.data[data.id].inputs.image_base64 = mainImage.querySelector('.images_prompt_main').src;
+                    } else if (data.class_type === 'LoadImagesToBatch') {
+                        // 多张base64 图片
+                        let base64 = data.inputs.images.base64
+
+                        imageElement = document.createElement('div');
+                        imageElement.className = "images"
+                        for (const bs of base64) {
+                            createBase64ImageForLoadImageToBatch(imageElement, data.id, bs)
+                        }
+
                    }

+
                    imageElement.style.maxWidth = '200px';

-
-
                    if (!isVideoUpload) btnFromClipboard.addEventListener('click', (event) => handleClipboardImage(imageElement, data));

-                    if (!isVideoUpload) btnForImageEdit.addEventListener('click', e => editImage(imageElement, data))
+                    if (!isVideoUpload && !isBase64Upload) btnForImageEdit.addEventListener('click', e => editImage(imageElement, data))


                    uploadImageInput.addEventListener('click', (event) => {
@@ -1290,22 +1500,43 @@

                            if (hashId == window._appData.data[data.id].hashId) return

-                            let { url, name } = await uploadImage(fileBlob, '.' + file.type.split('/')[1])
-                            if (isVideoUpload) {
-                                imageElement.srcObject = null;
-                            }
-                            // 在这里可以对 Blob 对象进行进一步处理
-                            imageElement.src = url;
-
-                            if (isVideoUpload) {
-                                window._appData.data[data.id].inputs.video = name;
+                            if (data.class_type === 'LoadImagesToBatch') {
+                                // 上传 ，转为base64
+                                let base64 = await blobToBase64(fileBlob)
+                                createBase64ImageForLoadImageToBatch(imageElement, data.id, base64)
                            } else {
-                                window._appData.data[data.id].inputs.image = name;
+                                //上传，返回url
+                                let { url, name } = await uploadImage(fileBlob, '.' + file.type.split('/')[1])
+
+                                if (data.class_type === 'ImagesPrompt_') {
+                                    // 
+                                    let base64 = await parseImageToBase64(url);
+                                    uploadContainer.querySelector('.images_prompt_main').src = base64
+                                    window._appData.data[data.id].inputs.image_base64 = base64;
+                                } else {
+                                    if (isVideoUpload) {
+                                        imageElement.srcObject = null;
+                                    }
+                                    // 在这里可以对 Blob 对象进行进一步处理
+                                    imageElement.src = url;
+
+
+                                    if (isVideoUpload) {
+                                        window._appData.data[data.id].inputs.video = name;
+                                    } else {
+                                        window._appData.data[data.id].inputs.image = name;
+                                    }
+
+                                }
+
+
+                                window._appData.data[data.id].hashId = hashId;
+
+                                console.log("上传的文件：", url, data.id, name);
                            }

-                            window._appData.data[data.id].hashId = hashId;

-                            console.log("上传的文件：", url, data.id, name);
+
                        };

                        // 开始读取文件
@@ -1315,7 +1546,7 @@
                    })

                    // imageElement.src = `${get_url()}/view?filename=${encodeURIComponent(data.inputs.image)}&type=${type}&subfolder=${subfolder}`;
-                    uploadContainer.appendChild(imageElement);
+                    if (data.class_type !== 'ImagesPrompt_') uploadContainer.appendChild(imageElement);

                    // Append the upload container to the main container
                    container.appendChild(uploadContainer);
@@ -1392,7 +1623,7 @@
                        textInput.value = data.inputs.prompt;
                    } else {
                        textInput.value = data.inputs.text;
-                    }
+                    };

                    // uploadImageInput.type = "text";
                    let json = localStorage.getItem(`t_${data.id}`)
@@ -1408,7 +1639,6 @@
                            window._appData.data[data.id].inputs.text = textInput.value;
                        }

-
                    } catch (error) {

                    }
@@ -1416,6 +1646,27 @@
                    uploadContainer.appendChild(textInput);
                    // autoResize(textInput);

+
+                    //动态提示功能
+                    const dynamicPromptsBtn = document.createElement('button');
+                    dynamicPromptsBtn.className = "dynamic_prompt"
+                    dynamicPromptsBtn.innerText = 'dynamic';
+                    dynamicPromptsBtn.style.width = '88px'
+                    uploadContainer.appendChild(dynamicPromptsBtn);
+                    dynamicPromptsBtn.addEventListener('click', e => {
+                        e.preventDefault();
+                        e.stopPropagation();
+                        let prompt = dynamicPrompts(textInput.value)
+                        textInput.setAttribute('title', prompt)
+                        dynamicPromptsBtn.setAttribute('title', prompt)
+                        if (data.class_type == "PromptSimplification") {
+                            window._appData.data[data.id].inputs.prompt = prompt;
+                        } else {
+                            window._appData.data[data.id].inputs.text = prompt;
+                        }
+                    })
+
+
                    function autoResize(textarea) {
                        textarea.style.height = 'auto';
                        textarea.style.height = textarea.scrollHeight + 'px';
@@ -1729,6 +1980,73 @@

        }

+        function createImageForSelect(isMain, imgurl, keyword) {
+            let im = new Image();
+            im.className = isMain ? 'images_prompt_main' : ''
+            im.src = imgurl;
+            im.title = keyword
+            im.style = `
+            width:${isMain ? 120 : 56}px;
+            height:auto;
+            min-height:${isMain ? 120 : 56}px;
+            filter: brightness(${isMain ? 1 : 0.8});
+            ${isMain ? 'filter: drop-shadow(1px 1px 4px black);' : ''}
+            `
+            let p = document.createElement('p');
+            p.innerText = keyword;
+            p.style = `position: absolute;
+    top: 14px;
+    left: 20px;
+    background-color: #00000075;
+    padding: 2px 4px;
+    color: white;
+    font-size: 12px;`
+            const div = document.createElement("div");
+            // div.className = 'card';
+            div.appendChild(im)
+            if (isMain) div.appendChild(p)
+            im.setAttribute('onerror', `this.src='${base64Df}'`)
+            return div
+        }
+
+        // 创建图库选择
+        function createSelectForImages(title, options, index = 0, callback) {
+
+            const div = document.createElement("div");
+            // div.className = 'card';
+            div.style = `margin-top: 24px;`
+
+            // Create a label for the upload control
+            // const nameLabel = document.createElement("label");
+            // nameLabel.textContent = title;
+            // div.appendChild(nameLabel);
+
+            var mainImg = createImageForSelect(true, options[index].imgurl, options[index].keyword);
+            div.appendChild(mainImg);
+
+            let imgs = document.createElement('div');
+            div.appendChild(imgs);
+            imgs.style = `display:flex; flex-wrap: wrap;`
+            for (const opt of options) {
+                var selectElement = createImageForSelect(false, opt.imgurl, opt.keyword);
+                imgs.appendChild(selectElement);
+                selectElement.addEventListener('click', async e => {
+                    e.preventDefault();
+                    mainImg.querySelector('p').innerText = opt.keyword;
+                    mainImg.querySelector('img').src = opt.imgurl;
+                    if (callback) {
+                        if (!opt.imgurl.match('data:image')) {
+                            opt.imgurl = await parseImageToBase64(opt.imgurl)
+                        }
+                        callback(opt.imgurl, opt.keyword);
+                    }
+                })
+            }
+
+            return [div, mainImg];
+        }
+
+
        // 创建下拉选择
        function createSelect(options, defaultValue) {
            var selectElement = document.createElement("select");
@@ -1791,7 +2109,8 @@

        function createUI(data, share = true) {
            // appData.input, appData.output, appData.seed, share, appData.link
-            const { input: inputData, output: outputData, data: workflow, seed, link, name } = data;
+            if (!data) return
+            const { input: inputData, output: outputData, data: workflow, seed, seedTitle, link, name } = data;

            let mainDiv = document.createElement('div');

@@ -1889,7 +2208,7 @@

                            let em = document.createElement('em');
                            let emText = document.createElement('span');
-                            emText.innerText = `#${id} ${s.toUpperCase()}`;
+                            emText.innerText = `#${seedTitle && seedTitle[id] ? seedTitle[id] : id} ${s.toUpperCase()}`;
                            em.appendChild(emText);

                            seedInput.appendChild(em)
@@ -1910,7 +2229,7 @@
                                        data.seed[id] = 'randomize';
                                        inSeed.style.display = 'none'
                                    }
-                                    emText.innerText = `#${id} ${data.seed[id].toUpperCase()}`;
+                                    emText.innerText = `#${seedTitle && seedTitle[id] ? seedTitle[id] : id} ${data.seed[id].toUpperCase()}`;
                                });

                                seedInput.appendChild(inSeed);
@@ -2083,6 +2402,51 @@

                        if (val && type == "text" && output.querySelector(`#output_${id}`)) output.querySelector(`#output_${id}`).innerText = val;

+                        // 3d meshes
+                        if (val && type == 'meshes' && output.querySelector(`#output_${id}`)) {
+
+                            let threeD = output.querySelector('.threeD')
+                            //判断默认的图片，需要去掉后创建model-viewer
+                            let imgDf = output.querySelector(`#output_${id} img`);
+                            if (!threeD) {
+                                if (imgDf) imgDf.parentElement.remove();
+                                threeD = document.createElement('div');
+                                threeD.className = 'threeD'
+                                threeD.id = `output_${id}`
+                                output.querySelector('.output_card').appendChild(threeD)
+                                // output.insertBefore(threeD, output.firstChild);
+                            };
+
+
+                            for (const meshUrl of val) {
+                                const modelViewer = document.createElement('div');
+                                modelViewer.style = `width:300px;margin:4px;height:300px;display:block`
+                                modelViewer.innerHTML = `<model-viewer  src="${meshUrl}" 
+                                    min-field-of-view="0deg" max-field-of-view="180deg"
+                                    shadow-intensity="1" 
+                                    camera-controls 
+                                    touch-action="pan-y"
+                                    style="width:300px;height:300px;"
+                                    > 
+                                    <div class="controls">
+                                        <button class="export">Save As</button>
+                                    </div>
+                                </model-viewer>`
+
+                                const btn = modelViewer.querySelector('.export');
+                                btn.addEventListener('click', async e => {
+                                    e.preventDefault();
+                                    const glTF = await (modelViewer.querySelector('model-viewer')).exportScene()
+                                    const file = new File([glTF], 'mixlab.glb')
+                                    const link = document.createElement('a')
+                                    link.download = file.name
+                                    link.href = URL.createObjectURL(file)
+                                    link.click()
+                                })
+                                threeD.appendChild(modelViewer)
+                            }
+
+                        }
                    }
                },
                submitButton: {
@@ -2198,6 +2562,9 @@
            const _images = detail?.output?._images;
            const prompts = detail?.output?.prompts;

+            // 3d模型
+            const meshes = detail?.output?.mesh;
+
            if (images) {
                // if (!images) return;

@@ -2207,6 +2574,14 @@
                    return `${url}/view?filename=${encodeURIComponent(img.filename)}&type=${img.type}&subfolder=${encodeURIComponent(img.subfolder)}&t=${+new Date()}`;
                }), detail.node, 'images');

+            } else if (meshes) {
+                //多个
+                let url = get_url();
+
+                show(Array.from(meshes, mesh => {
+                    return `${url}/view?filename=${encodeURIComponent(mesh.filename)}&type=${mesh.type}&subfolder=${encodeURIComponent(mesh.subfolder)}&t=${+new Date()}`;
+                }), detail.node, 'meshes');
+
            } else if (_images && prompts) {
                let url = get_url();

@@ -2440,6 +2815,9 @@
        // 创建app的选择菜单
        function createAppList(apps = [], innerApp = false) {
            window.prompt_ids = {};
+            if (document.body.querySelector('.apps')) {
+                document.body.querySelector('.apps').remove()
+            }
            let details = document.createElement('details');
            details.className = 'apps';

@@ -2640,20 +3018,21 @@
                card.addEventListener('click', async e => {
                    e.preventDefault();
                    // console.log(c)
+
+                    const { category, filename } = c.appInfo;
+                    window._appData = (await get_my_app(category, filename))[0];
+
+                    await createApp(window._appData);
+
+                    executed(c.data, window._show);
+
                    try {
                        document.body.querySelector('#app_container').setAttribute('open', true)
                        document.body.querySelector('#app_input_pannel').removeAttribute('open')
                        document.body.querySelector('.apps').removeAttribute('open')
                    } catch (error) {
-
+                        console.log(error)
                    }
-
-                    const { category, filename } = c.appInfo;
-                    window._appData = (await get_my_app(category, filename))[0];
-
-                    createApp(window._appData);
-
-                    executed(c.data, window._show);
                })
            }

@@ -2,9 +2,46 @@ import { app } from '../../../scripts/app.js'
 import { $el } from '../../../scripts/ui.js'
 import { api } from '../../../scripts/api.js'

+//本机安装的插件节点全集
+window._nodesAll = null
+
+//获取当前系统的插件，节点清单
+function getObjectInfo () {
+  return new Promise(async (resolve, reject) => {
+    let url = getUrl()
+
+    try {
+      const response = await fetch(`${url}/object_info`)
+      const data = await response.json()
+      resolve(data)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+
 const base64Df =
  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'

+const parseImageToBase64 = url => {
+  return new Promise((res, rej) => {
+    fetch(url)
+      .then(response => response.blob())
+      .then(blob => {
+        const reader = new FileReader()
+        reader.onloadend = () => {
+          const base64data = reader.result
+          res(base64data)
+          // 在这里可以将base64数据用于进一步处理或显示图片
+        }
+        reader.readAsDataURL(blob)
+      })
+      .catch(error => {
+        console.log('发生错误:', error)
+      })
+  })
+}
+
 function get_position_style (ctx, widget_width, y, node_height) {
  const MARGIN = 12 // the margin around the html element

@@ -82,6 +119,7 @@ async function extractInputAndOutputData (
  let input = []
  let output = []
  const seed = {}
+  const seedTitle = {}

  for (const id in data) {
    if (data.hasOwnProperty(id)) {
@@ -121,6 +159,28 @@ async function extractInputAndOutputData (
          }
        }

+        if (node.type == 'ImagesPrompt_') {
+          //图库
+          // console.log('ImagesPrompt_', data[id])
+          let image_base64 = data[id].inputs.image_base64
+          let img_index = 0
+          let imgsData = JSON.parse(data[id].inputs.upload)
+          for (let index = 0; index < imgsData.length; index++) {
+            const imgd = imgsData[index].imgurl
+            imgsData[index].index = index
+            //TODO缩放大小
+            imgsData[index].imgurl = await parseImageToBase64(imgd)
+            if (image_base64 == imgsData[index].imgurl) {
+              img_index = index
+            }
+          }
+          options.images = imgsData
+          delete data[id].inputs.upload
+          delete data[id].inputs.image_base64
+
+          data[id].inputs.imageIndex = img_index
+        }
+
        if (node.type == 'Color') {
        }

@@ -133,9 +193,9 @@ async function extractInputAndOutputData (
          }
          // loadImage的默认图，转为base64
          let imgurl = app.graph.getNodeById(id).imgs[0].src
-          
+
          options.defaultImage = await drawImageToCanvas(imgurl, 512)
-          console.log('#loadImage的默认图',options)
+          console.log('#loadImage的默认图', options)
        }

        input[inputIds.indexOf(id)] = {
@@ -152,12 +212,18 @@ async function extractInputAndOutputData (
        output[outputIds.indexOf(id)] = { ...data[id], title: node.title, id }
      }

-      if (node.type === 'KSampler' || node.type == 'SamplerCustom') {
+      if (
+        node.type === 'KSampler' ||
+        node.type == 'SamplerCustom' ||
+        node.type === 'ChinesePrompt_Mix' ||
+        node.type === 'Seed_'
+      ) {
        // seed 的类型收集
        try {
          seed[id] = node.widgets.filter(
            w => w.name === 'seed' || w.name == 'noise_seed'
          )[0].linkedWidgets[0].value
+          seedTitle[id] = node.title
        } catch (error) {}
      }
    }
@@ -167,7 +233,7 @@ async function extractInputAndOutputData (
  input = input.filter(i => i)
  output = output.filter(i => i)

-  return { input, output, seed }
+  return { input, output, seed, seedTitle }
 }

 function getUrl () {
@@ -219,7 +285,10 @@ function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
 }

 async function save (json, download = false, showInfo = true) {
-  console.log('####SAVE', json[0])
+  let nodesAll = window._nodesAll || (await getObjectInfo())
+
+  console.log('####SAVE', nodesAll, json[0])
+
  const name = json[0],
    version = json[5],
    share_prefix = json[6], //用于分享的功能扩展
@@ -240,7 +309,14 @@ async function save (json, download = false, showInfo = true) {
  try {
    let data = await app.graphToPrompt()

-    let { input, output, seed } = await extractInputAndOutputData(
+    //从output数据里把工作流的节点，插件数据统计出来
+    data.nodesMap = {}
+    for (const id in data.output) {
+      data.nodesMap[data.output[id].class_type] =
+        nodesAll[data.output[id].class_type]
+    }
+
+    let { input, output, seed, seedTitle } = await extractInputAndOutputData(
      data,
      inputIds,
      outputIds
@@ -260,6 +336,7 @@ async function save (json, download = false, showInfo = true) {
      input,
      output,
      seed, //控制是fixed 还是random
+      seedTitle,
      share_prefix,
      link,
      category,
@@ -301,12 +378,13 @@ async function save (json, download = false, showInfo = true) {

 function getInputsAndOutputs () {
  const inputs =
-      `LoadImage VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
+      `LoadImage LoadImagesToBatch ImagesPrompt_ VHS_LoadVideo CLIPTextEncode PromptSlide TextInput_ Color FloatSlider IntNumber CheckpointLoaderSimple LoraLoader`.split(
        ' '
      ),
-    outputs = `PreviewImage SaveImage ShowTextForGPT VHS_VideoCombine`.split(
-      ' '
-    )
+    outputs =
+      `SaveTripoSRMesh,PreviewImage,SaveImage,TransparentImage,ShowTextForGPT,VHS_VideoCombine,VideoCombine_Adv,Image Save,SaveImageAndMetadata_,ClipInterrogator`.split(
+        ','
+      )

  let inputsId = [],
    outputsId = []
@@ -329,6 +407,11 @@ function getInputsAndOutputs () {

 app.registerExtension({
  name: 'Mixlab.utils.AppInfo',
+  init () {
+    if (!window._nodesAll) {
+      getObjectInfo().then(r => (window._nodesAll = r))
+    }
+  },
  async beforeRegisterNodeDef (nodeType, nodeData, app) {
    if (nodeType.comfyClass == 'AppInfo') {
      const orig_nodeCreated = nodeType.prototype.onNodeCreated
@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
 const repoOwner = 'shadowcz007' // 替换为仓库的所有者
 const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称

-const version = 'v0.18.0'
+const version = 'v0.23.0'

 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
  .then(response => response.json())
@@ -17,7 +17,13 @@ fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
      return
    if (latestVersion && latestVersion != version) {
      localStorage.setItem('_mixlab_nodes_vesion', latestVersion)
-      app.ui.dialog.show(`<h4 style="font-size: 18px;">${repoName} <br>
+      app.ui.dialog.show(`<a style="color: white;
+      font-size: 18px;
+      font-weight: 800;
+      letter-spacing: 2px;
+    }"
+    href="https://discord.gg/cXs9vZSqeK">Welcome to Mixlab nodes discord</a>
+    <h4 style="font-size: 18px;">${repoName} <br>
      Latest release version: ${latestVersion}</h4>
      <p>Please proceed to the official repository to download the latest version.</p>
      <a style="color: #2196F3;
@@ -61,7 +61,7 @@ app.registerExtension({
  async getCustomWidgets (app) {
    return {
      KEY (node, inputName, inputData, app) {
-        console.log('##inputData', inputData)
+        // console.log('##inputData', inputData)
        const widget = {
          type: inputData[0], // the type, CHEESE
          name: inputName, // the name, slice
@@ -1,7 +1,39 @@
 import { app } from '../../../scripts/app.js'
 import { api } from '../../../scripts/api.js'
-import { ComfyWidgets } from '../../../scripts/widgets.js'
+// import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
+import { applyTextReplacements } from '../../../scripts/utils.js'
+
+function loadImageToCanvas (base64Image) {
+  var img = new Image()
+  var canvas = document.createElement('canvas')
+  var ctx = canvas.getContext('2d')
+  return new Promise((res, rej) => {
+    img.onload = function () {
+      // 等比例缩放图片
+      var width = img.width
+      var height = img.height
+      var max_width = 1024
+      if (width > max_width) {
+        height *= max_width / width
+        width = max_width
+      }
+
+      // 设置canvas尺寸
+      canvas.width = width
+      canvas.height = height
+
+      // 在canvas上绘制图片
+      ctx.drawImage(img, 0, 0, width, height)
+
+      // 将canvas转换为base64图片数据
+      var canvasData = canvas.toDataURL()
+      res(canvasData) //  canvas转换后的base64图片数据
+    }
+
+    img.src = base64Image
+  })
+}

 async function uploadImage (blob, fileType = '.svg', filename) {
  // const blob = await (await fetch(src)).blob();
@@ -28,6 +60,9 @@ async function uploadImage (blob, fileType = '.svg', filename) {
  return src
 }

+const base64Df =
+  'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAwAAAAMCAYAAABWdVznAAAAAXNSR0IArs4c6QAAALZJREFUKFOFkLERwjAQBPdbgBkInECGaMLUQDsE0AkRVRAYWqAByxldPPOWHwnw4OBGye1p50UDSoA+W2ABLPN7i+C5dyC6R/uiAUXRQCs0bXoNIu4QPQzAxDKxHoALOrZcqtiyR/T6CXw7+3IGHhkYcy6BOR2izwT8LptG8rbMiCRAUb+CQ6WzQVb0SNOi5Z2/nX35DRyb/ENazhpWKoGwrpD6nICp5c2qogc4of+c7QcrhgF4Aa/aoAFHiL+RAAAAAElFTkSuQmCC'
+
 function base64ToBlobFromURL (base64URL, contentType) {
  return fetch(base64URL).then(response => response.blob())
 }
@@ -108,7 +143,7 @@ function createImage (url) {
  })
 }

-const parseImage = url => {
+const parseImageToBase64 = url => {
  return new Promise((res, rej) => {
    fetch(url)
      .then(response => response.blob())
@@ -406,9 +441,7 @@ app.registerExtension({

        this.serialize_widgets = true //需要保存参数
      }
-    };
-
- 
+    }
  },
  async loadedGraphNode (node, app) {
    // Fires every time a node is constructed
@@ -442,3 +475,369 @@ app.registerExtension({
    }
  }
 })
+
+const createSelect = (imgDiv, select, opts, targetWidget, textWidget) => {
+  select.style.display = 'block'
+  let html = ''
+  let isMatch = false
+  for (const opt of opts) {
+    html += `<option value='${opt.keyword}' ${opt.selected ? 'selected' : ''}>${
+      opt.keyword
+    }</option>`
+    if (opt.selected) {
+      isMatch = true
+      imgDiv.src = opt.imgurl
+      // targetWidget.value = opt.keyword
+    }
+  }
+  select.innerHTML = html
+  if (!isMatch) {
+    // targetWidget.value = opts[0].keyword
+    imgDiv.src = opts[0].imgurl
+  }
+
+  // 添加change事件监听器
+  select.addEventListener('change', async function () {
+    // 获取选中的选项的值
+    var selectedOption = select.options[select.selectedIndex].value
+    let t = opts.filter(opt => opt.keyword === selectedOption)[0]
+
+    targetWidget.value = await parseImageToBase64(t.imgurl)
+    imgDiv.src = targetWidget.value
+    textWidget.value = t.keyword
+  })
+  // console.log(select)
+}
+
+app.registerExtension({
+  name: 'Mixlab.prompt.ImagesPrompt_',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'ImagesPrompt_') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const image_prompt = this.widgets.filter(
+          w => w.name == 'image_base64'
+        )[0]
+        const image_text = this.widgets.filter(w => w.name == 'text')[0]
+
+        const node = this
+
+        const widget = {
+          type: 'div',
+          name: 'upload',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, y, node.size[1])
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        // console.log('image_prompt',image_prompt)
+        const img = new Image()
+        img.src = image_prompt?.value || base64Df
+        widget.div.appendChild(img)
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Upload Images JSON'
+
+        btn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;height: 30px;min-width: 122px;
+       `
+
+        const select = document.createElement('select')
+        select.style = `display:none;cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;height: 30px;min-width: 100px;
+       `
+        widget.select = select
+
+        // const btn=document.createElement('button');
+        // btn.innerText='Upload'
+        btn.addEventListener('click', () => {
+          let inp = document.createElement('input')
+          inp.type = 'file'
+          inp.accept = '.json'
+          inp.click()
+          inp.addEventListener('change', event => {
+            // 获取选择的文件
+            // [{title,imageUrl}]
+            const file = event.target.files[0]
+            this.title = file.name.split('.')[0]
+
+            // console.log(file.name.split('.')[0])
+            // 创建文件读取器
+            const reader = new FileReader()
+
+            // 定义读取完成事件的回调函数
+            reader.onload = async event => {
+              // 读取完成后的文本内容
+              const json = JSON.parse(event.target.result)
+              console.log(node, json)
+
+              widget.value = JSON.stringify(json)
+
+              let img = widget.div.querySelector('img')
+
+              createSelect(img, select, json, image_prompt, image_text)
+
+              image_prompt.value = await parseImageToBase64(json[0].imgurl)
+              image_text.value = json[0].keyword
+
+              if (img) {
+                img.src = image_prompt.value
+              }
+
+              inp.remove()
+            }
+
+            // 以文本方式读取文件
+            reader.readAsText(file)
+          })
+        })
+
+        widget.div.appendChild(btn)
+        widget.div.appendChild(select)
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'ImagesPrompt_') {
+      try {
+        let prompt = node.widgets.filter(w => w.name === 'image_base64')[0]
+        let text = node.widgets.filter(w => w.name === 'text')[0]
+        let uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
+        // console.log('##prompt',prompt.value)
+        let img = uploadWidget.div.querySelector('img')
+        let json = JSON.parse(uploadWidget.value)
+
+        for (let index = 0; index < json.length; index++) {
+          const j = json[index]
+          let base64 = await parseImageToBase64(j.imgurl)
+          if (base64 === prompt.value) {
+            json[index].selected = true
+          }
+        }
+
+        if (json && json[0]) {
+          uploadWidget.select.style.display = 'block'
+          createSelect(img, uploadWidget.select, json, prompt, text)
+        }
+      } catch (error) {}
+    }
+  }
+})
+
+const createInputImageForBatch = (base64, widget) => {
+  let im = new Image()
+  im.src = base64
+  im.style = `width: 88px;`
+
+  im.addEventListener('click', e => {
+    let newValue = []
+    let items = widget.value?.base64 || []
+    for (const v of items) {
+      if (v != base64) newValue.push(v)
+    }
+    widget.value.base64 = newValue
+    im.remove()
+  })
+
+  return im
+}
+
+app.registerExtension({
+  name: 'Mixlab.Comfy.LoadImagesToBatch',
+  async getCustomWidgets (app) {
+    return {
+      IMAGEBASE64 (node, inputName, inputData, app) {
+        // console.log('##node', node)
+        const widget = {
+          value: {
+            base64: []
+          }, // 不能[x,x,x]
+          type: inputData[0], // the type
+          name: inputName, // the name, slice
+          size: [128, 32], // a default size
+          draw (ctx, node, width, y) {},
+          computeSize (...args) {
+            return [128, 32] // a method to compute the current size of the widget
+          }
+          // serializeValue (nodeId, widgetIndex) {
+          //   return widget.value
+          // },
+        }
+        //  widget.something = something;          // maybe adds stuff to it
+        node.addCustomWidget(widget) // adds it to the node
+        return widget // and returns it.
+      }
+    }
+  },
+
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'LoadImagesToBatch') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        let imagesWidget = this.widgets.filter(w => w.name == 'images')[0]
+
+        const widget = {
+          type: 'div',
+          name: 'image_base64',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 44, node.size[1])
+            )
+          },
+          serialize: false
+        }
+
+        widget.div = $el('div', {})
+
+        document.body.appendChild(widget.div)
+
+        let imagePreview = document.createElement('div')
+        let imagesDiv = document.createElement('div') //显示图片
+        imagesDiv.className = 'images_preview'
+        imagesDiv.style = `width: calc(100% - 14px);
+        display: flex;
+        flex-wrap: wrap;
+        padding: 7px;    justify-content: space-between;
+        align-items: center;`
+
+        let inputImage = document.createElement('input')
+        inputImage.type = 'file'
+        inputImage.style.display = 'none'
+        inputImage.addEventListener('change', e => {
+          e.preventDefault()
+          const file = e.target.files[0]
+          const reader = new FileReader()
+          reader.onload = async event => {
+            let base64 = event.target.result
+            //压缩图片，控制1024以内
+            base64 = await loadImageToCanvas(base64)
+            // console.log(base64)
+            if (!imagesWidget.value) imagesWidget.value = { base64: [] }
+            imagesWidget.value.base64.push(base64)
+            let im = createInputImageForBatch(base64, imagesWidget)
+            imagesDiv.appendChild(im)
+          }
+          reader.readAsDataURL(file)
+        })
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Upload Image'
+
+        btn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;height: 30px;min-width: 122px;
+       `
+
+        btn.addEventListener('click', e => {
+          e.preventDefault()
+          inputImage.click()
+        })
+
+        widget.div.appendChild(imagePreview)
+        imagePreview.appendChild(imagesDiv)
+        imagePreview.appendChild(btn)
+        imagePreview.appendChild(inputImage)
+
+        this.addCustomWidget(widget)
+
+        // document.addEventListener('wheel', handleMouseWheel)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          inputImage.remove()
+          widget.div.remove()
+          try {
+            // document.removeEventListener('wheel', handleMouseWheel)
+          } catch (error) {
+            console.log(error)
+          }
+
+          return onRemoved?.()
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+
+    if (nodeData.name === 'SaveImageAndMetadata_') {
+      const onNodeCreated = nodeType.prototype.onNodeCreated
+      // /web/extensions/core/saveImageExtraOutput.js
+      nodeType.prototype.onNodeCreated = function () {
+        const r = onNodeCreated
+          ? onNodeCreated.apply(this, arguments)
+          : undefined
+        const widget = this.widgets.find(w => w.name === 'filename_prefix')
+        widget.serializeValue = () => {
+          return applyTextReplacements(app, widget.value)
+        }
+
+        return r
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+        console.log('##onExecuted', this, message)
+        //TODO 是否 保存base64
+        if (message.base64) {
+          if (Array.isArray(message.base64)) {
+          }
+        }
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'LoadImagesToBatch') {
+      // await sleep(0)
+      let imagesWidget = node.widgets.filter(w => w.name === 'images')[0]
+      let imagePreview = node.widgets.filter(w => w.name == 'image_base64')[0]
+
+      let pre = imagePreview.div.querySelector('.images_preview')
+      for (const d of imagesWidget.value?.base64 || []) {
+        let im = createInputImageForBatch(d, imagesWidget)
+        pre.appendChild(im)
+      }
+    }
+  }
+})
@@ -1,8 +1,70 @@
 import { app } from '../../../scripts/app.js'
-// import { api } from '../../../scripts/api.js'
+import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'

+function downloadJsonFile (jsonData, fileName = 'grid.json') {
+  const dataString = JSON.stringify(jsonData)
+  const blob = new Blob([dataString], { type: 'application/json' })
+  const url = URL.createObjectURL(blob)
+
+  const link = document.createElement('a')
+  link.href = url
+  link.download = fileName
+  link.click()
+
+  // 释放URL对象
+  setTimeout(() => {
+    URL.revokeObjectURL(url)
+  }, 0)
+}
+
+function createSelectWithOptions (options) {
+  const select = document.createElement('select')
+
+  options.forEach(option => {
+    const optionElement = document.createElement('option')
+    optionElement.text = option
+    optionElement.value = option
+    select.appendChild(optionElement)
+  })
+
+  select.style = `cursor: pointer;
+  font-weight: 300; 
+  height: 30px;
+  min-width: 122px;
+  position: absolute;
+  top: 24px;
+  left: 88px;
+  z-index: 999999999999999;
+       `
+
+  return select
+}
+
+function drawCanvasWithText (w, h, tag, color = 'rgba(255,255,255,0.4)') {
+  const canvas = document.createElement('canvas')
+  const ctx = canvas.getContext('2d')
+
+  // 设置画布大小
+  canvas.width = w
+  canvas.height = h
+
+  // 绘制白色背景
+  ctx.fillStyle = color
+  ctx.fillRect(0, 0, canvas.width, canvas.height)
+
+  // 绘制文字
+  ctx.fillStyle = '#000000'
+  ctx.font = '20px Arial'
+  ctx.fillText(tag, 50, 50)
+
+  // 导出为Base64
+  const base64 = canvas.toDataURL()
+
+  return base64
+}
+
 function get_position_style (ctx, widget_width, y, node_height) {
  const MARGIN = 4 // the margin around the html element

@@ -156,32 +218,29 @@ const parseSvg = async svgContent => {
  return { data, image: base64, svgElement }
 }

-
-function findImages(nodeId) {
+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;
+    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') {
+      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 linkId = n.inputs[i]?.link
        var origin_id = app.graph.links[linkId].origin_id
-        return findImages(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;
+  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')
@@ -353,6 +412,196 @@ async function setArea (cw, ch, topBase64, base64, data, fn) {
  }
 }

+async function setAreaTags (cw, ch, grids, fn) {
+  let base64 = drawCanvasWithText(cw, ch, '', 'white')
+  let displayHeight = Math.round(window.screen.availHeight * 0.8)
+  let div = document.createElement('div')
+  div.innerHTML = `
+    <div id='ml_overlay' style='position: absolute;top:0;background: #251f1fc4;
+    height: 100vh;
+    z-index:999999;
+    width: 100%;'>
+      <img id='ml_video' style='position: absolute; 
+      height: ${displayHeight}px;user-select: none; 
+      -webkit-user-drag: none;
+      outline: 2px solid #eaeaea;
+      box-shadow: 8px 9px 17px #575757;' />
+      ${Array.from(grids, g => {
+        const { label: tag, grid } = g
+        const [dx, dy, dw, dh] = grid
+        const base64Data = drawCanvasWithText(dw, dh, tag)
+
+        let x = 0,
+          y = 0,
+          width = (cw * displayHeight) / ch,
+          height = displayHeight
+
+        let imgWidth = cw
+        let imgHeight = ch
+
+        if (dw > 0 && dh > 0) {
+          // 相同尺寸窗口，恢复选区
+          x = (width * dx) / imgWidth
+          y = (height * dy) / imgHeight
+          width = (width * dw) / imgWidth
+          height = (height * dh) / imgHeight
+        }
+
+        return `<div class='ml_selection' 
+          data-tag="${tag}"
+          style='position:absolute; 
+          border: 2px dashed red;
+          pointer-events: none;
+          background-image: url("${base64Data}");
+                background-repeat: no-repeat;
+                background-size: cover;
+                left:${x}px;
+                top:${y}px;
+                width:${width}px;
+                height:${height}px;
+                '></div>`
+      })}
+      <div class="mx_close"> X </div>
+    </div>`
+  // document.body.querySelector('#ml_overlay')
+  document.body.appendChild(div)
+
+  const tags = Array.from(grids, g => g.label)
+  let select = createSelectWithOptions(tags)
+  document.body.appendChild(select)
+
+  let img = div.querySelector('#ml_video')
+  // let overlay = div.querySelector('#ml_overlay')
+  let selections = [...div.querySelectorAll('.ml_selection')]
+
+  let selection = selections.filter(
+    s => s.getAttribute('data-tag') === select.value
+  )[0]
+
+  select.addEventListener('change', e => {
+    selection = selections.filter(
+      s => s.getAttribute('data-tag') === select.value
+    )[0]
+  })
+
+  // console.log(select.value,selection)
+  let close = div.querySelector('.mx_close')
+  let startX, startY, endX, endY
+  let start = false
+  let setDone = false
+  // Set video source
+  img.src = base64
+  // canvas.toDataURL();
+  close.style = `cursor: pointer;
+  position: fixed;
+  left: 12px;
+  top: 12px;
+  z-index: 99999999;
+  background: black;
+  width: 44px;
+  height: 44px;
+  text-align: center;
+  line-height: 44px;`
+
+  // Add mouse events
+  img.addEventListener('mousedown', startSelection)
+  img.addEventListener('mousemove', updateSelection)
+  img.addEventListener('mouseup', endSelection)
+
+  const removeDiv = () => {
+    div.remove()
+    select?.remove()
+    close.removeEventListener('click', removeDiv)
+    img.removeEventListener('mousedown', startSelection)
+    img.removeEventListener('mousemove', updateSelection)
+    img.removeEventListener('mouseup', endSelection)
+    img.removeEventListener('mousedown', setDoneCheck)
+  }
+  close.addEventListener('click', removeDiv)
+
+  const setDoneCheck = event => {
+    console.log(setDone)
+    if (setDone) {
+      img.addEventListener('mousedown', startSelection)
+      img.addEventListener('mousemove', updateSelection)
+      img.addEventListener('mouseup', endSelection)
+      setDone = false
+      start = false
+      startX = event.clientX
+      startY = event.clientY
+    }
+  }
+  img.addEventListener('mousedown', setDoneCheck)
+
+  function remove () {
+    img.removeEventListener('mousedown', startSelection)
+    img.removeEventListener('mousemove', updateSelection)
+    img.removeEventListener('mouseup', endSelection)
+    setDone = true
+    // select?.remove()
+  }
+
+  function startSelection (event) {
+    if (start == false) {
+      startX = event.clientX
+      startY = event.clientY
+      updateSelection(event)
+      start = true
+    } else {
+    }
+  }
+
+  function updateSelection (event) {
+    endX = event.clientX
+    endY = event.clientY
+
+    // Calculate width, height, and coordinates
+    let width = Math.abs(endX - startX)
+    let height = Math.abs(endY - startY)
+    let left = Math.min(startX, endX)
+    let top = Math.min(startY, endY)
+
+    // Set selection style
+    selection.style.left = left + 'px'
+    selection.style.top = top + 'px'
+    selection.style.width = width + 'px'
+    selection.style.height = height + 'px'
+  }
+
+  function endSelection (event) {
+    endX = event.clientX
+    endY = event.clientY
+
+    // 获取img元素的真实宽度和高度
+    let imgWidth = img.naturalWidth
+    let imgHeight = img.naturalHeight
+
+    // 换算起始坐标
+    let realStartX = (startX / img.offsetWidth) * imgWidth
+    let realStartY = (startY / img.offsetHeight) * imgHeight
+
+    // 换算起始坐标
+    let realEndX = (endX / img.offsetWidth) * imgWidth
+    let realEndY = (endY / img.offsetHeight) * imgHeight
+
+    startX = realStartX
+    startY = realStartY
+    endX = realEndX
+    endY = realEndY
+    // Calculate width, height, and coordinates
+    let width = Math.round(Math.abs(endX - startX))
+    let height = Math.round(Math.abs(endY - startY))
+    let left = Math.round(Math.min(startX, endX))
+    let top = Math.round(Math.min(startY, endY))
+
+    if (width <= 0 && height <= 0) return remove()
+
+    if (!!fn) fn(select.value, left, top, width, height)
+
+    remove()
+  }
+}
+
 app.registerExtension({
  name: 'Mixlab.layer.ShowLayer',
  async getCustomWidgets (app) {
@@ -598,8 +847,8 @@ app.registerExtension({
            }
            try {
              console.log('this.inputs', this.id)
-              let imgs=findImages(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]
@@ -607,9 +856,9 @@ app.registerExtension({
              let linkId = this.inputs[3].link
              let nodeId = app.graph.links[linkId].origin_id
              // console.log(linkId,this.inputs)
-              let imgs2=findImages(nodeId)
+              let imgs2 = findImages(nodeId)
              let im = imgs2[0]
-              console.log(topIm,im)
+              console.log(topIm, im)
              // let src = im.src
              setArea(
                im.naturalWidth,
@@ -641,3 +890,306 @@ app.registerExtension({
    }
  }
 })
+
+app.registerExtension({
+  name: 'Mixlab.layer.GridInput',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'GridInput') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const grids_widget = this.widgets.filter(w => w.name == 'grids')[0]
+
+        const widget = {
+          type: 'div',
+          name: 'upload',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, y, node.size[1]),
+              {
+                justifyContent: 'flex-start'
+              }
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        const addBtn = document.createElement('button')
+        addBtn.innerText = 'Add Box'
+        addBtn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;height: 30px;min-width: 122px;
+       `
+
+        const vbtn = document.createElement('button')
+        vbtn.innerText = 'Set Box'
+        vbtn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;height: 30px;min-width: 122px;
+       `
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Upload JSON'
+
+        btn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;height: 30px;min-width: 122px;
+       `
+
+        addBtn.addEventListener('click', () => {
+          const { width, height, grids } = JSON.parse(grids_widget.value)
+          grids.push({
+            label: 'background',
+            grid: [12, 12, width - 24, height - 24]
+          })
+          grids_widget.value = JSON.stringify(
+            {
+              width,
+              height,
+              grids
+            },
+            null,
+            2
+          )
+        })
+
+        vbtn.addEventListener('click', () => {
+          const { width, height, grids } = JSON.parse(grids_widget.value)
+
+          setAreaTags(width, height, grids, (tag, x, y, w, h) => {
+            grids_widget.value = JSON.stringify(
+              {
+                width,
+                height,
+                grids: Array.from(grids, g => {
+                  if (g.label === tag) {
+                    g.grid = [x, y, w, h]
+                  }
+                  return g
+                })
+              },
+              null,
+              2
+            )
+          })
+        })
+
+        btn.addEventListener('click', () => {
+          let inp = document.createElement('input')
+          inp.type = 'file'
+          inp.accept = '.json'
+          inp.click()
+          inp.addEventListener('change', event => {
+            // 获取选择的文件
+            const file = event.target.files[0]
+            this.title = file.name.split('.')[0]
+
+            // console.log(file.name.split('.')[0])
+            // 创建文件读取器
+            const reader = new FileReader()
+
+            // 定义读取完成事件的回调函数
+            reader.onload = event => {
+              // 读取完成后的文本内容
+              const fileContent = JSON.parse(event.target.result)
+              const grids = fileContent
+              grids_widget.value = JSON.stringify(grids, null, 2)
+              // widget.value = grids
+
+              inp.remove()
+            }
+
+            // 以文本方式读取文件
+            reader.readAsText(file)
+          })
+        })
+
+        widget.div.appendChild(addBtn)
+        widget.div.appendChild(vbtn)
+        widget.div.appendChild(btn)
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+
+        const onExecuted = nodeType.prototype.onExecuted
+        nodeType.prototype.onExecuted = function (message) {
+          const r = onExecuted?.apply?.(this, arguments)
+
+          let json = message.json
+          if (json) {
+            json = {
+              width: json[0],
+              height: json[1],
+              grids: json[2]
+            }
+            grids_widget.value = JSON.stringify(json, null, 2)
+            // widget.value = json
+          }
+
+          return r
+        }
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'GridInput') {
+      try {
+        const grids_widget = node.widgets.filter(w => w.name == 'grids')[0]
+        const { width, height, grids } = JSON.parse(grids_widget.value)
+        console.log('#GridInput', node, grids)
+
+        const div = node.widgets.filter(w => w.name == 'upload')[0]
+        div.div.querySelector('select').innerHTML = Array.from(
+          grids,
+          g => `<option value="${g.label}">${g.label}</option>`
+        ).join('')
+      } catch (error) {}
+    }
+  }
+})
+
+app.registerExtension({
+  name: 'Mixlab.layer.GridDisplayAndSave',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'GridDisplayAndSave') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const grids_widget = this.widgets.filter(w => w.name == 'grids')[0]
+        console.log('GridDisplayAndSave', grids_widget)
+        const widget = {
+          type: 'div',
+          name: 'save_json',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, y, node.size[1]),
+              {
+                justifyContent: 'flex-start',
+                flexDirection: 'column'
+              }
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+
+        const btn = document.createElement('button')
+        btn.innerText = 'Save JSON'
+
+        btn.style = `cursor: pointer;
+        font-weight: 300;
+        margin: 2px; 
+        color: var(--descrip-text);
+        background-color: var(--comfy-input-bg);
+        border-radius: 8px;
+        border-color: var(--border-color);
+        border-style: solid;height: 30px;min-width: 122px;
+        max-width: 122px;
+       `
+
+        btn.addEventListener('click', () => {
+          if (window._mixlab_grid)
+            downloadJsonFile(
+              window._mixlab_grid,
+              this.widgets.filter(w => w.name == 'filename_prefix')[0]?.value +
+                '_grid.json'
+            )
+        })
+
+        widget.div.appendChild(btn)
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+
+        const onExecuted = nodeType.prototype.onExecuted
+        nodeType.prototype.onExecuted = function (message) {
+          const r = onExecuted?.apply?.(this, arguments)
+          let save_json = this.widgets.filter(d => d.name == 'save_json')[0]
+          let div = save_json?.div
+          // console.log('Test',message)
+
+          let image = message.image[0]
+          let json = message.json
+          if (image) {
+            const { filename, subfolder, type } = image
+
+            if (!div.querySelector('img')) {
+              let im = new Image()
+              div.appendChild(im)
+              im.style.width = '100%'
+            }
+            div.querySelector('img').src = api.apiURL(
+              `/view?filename=${encodeURIComponent(
+                filename
+              )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+            )
+
+            window._mixlab_grid = {
+              width: json[0],
+              height: json[1],
+              grids: json[2]
+            }
+            // console.log(src)
+          }
+
+          this.onResize?.(this.size)
+
+          return r
+        }
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'GridDisplayAndSave') {
+      try {
+        let grids_widget = node.widgets.filter(w => w.name === 'grids')[0]
+        // let ks = getLocalData(`_mixlab_PromptSlide`)
+        let uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
+        // console.log('##widget', uploadWidget.value)
+        let grids = JSON.parse(uploadWidget.value)
+      } catch (error) {}
+    }
+  }
+})
@@ -0,0 +1,203 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { $el } from '../../../scripts/ui.js'
+
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 14 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    // outline: '1px solid red',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+
+app.registerExtension({
+  name: 'Mixlab.3D.SaveTripoSRMesh',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'SaveTripoSRMesh') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = async function () {
+        orig_nodeCreated?.apply(this, arguments)
+
+        const widget = {
+          type: 'div',
+          name: 'preview',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 88, node.size[1])
+            )
+          }
+          //   value: [],
+          //   async serializeValue (nodeId, widgetIndex) {
+          //     return widget.value
+          //   }
+        }
+
+        widget.div = $el('div', {})
+        widget.div.style.width = `120px`
+
+        document.body.appendChild(widget.div)
+
+        // preview.style = `margin-top: 12px;display: flex;
+        //   justify-content: center;
+        //   align-items: center;background-repeat: no-repeat;background-size: contain;`
+
+        this.addCustomWidget(widget)
+
+        const onResize = this.onResize
+        this.onResize = () => {
+          widget.div.style.width = `${this.size[0]}px`
+          widget.div.style.height = `${this.size[1] - 112}px`
+          let mvs = widget.div.querySelectorAll('model-viewer')
+          for (const m of mvs) {
+            m.style.height = `${Math.round(
+              (this.size[1] - 112) / mvs.length
+            )}px`
+            // console.log(m.style.height)
+          }
+          //   console.log('resize', this.size)
+          return onResize?.apply(this, arguments)
+        }
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+
+        if (this.onResize) {
+          this.onResize(this.size)
+        }
+        // this.isVirtualNode = true
+        this.serialize_widgets = false //需要保存参数
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        const r = onExecuted?.apply?.(this, arguments)
+
+        let widget = this.widgets.filter(d => d.name == 'preview')[0]
+        console.log('Test', widget, message)
+
+        let meshes = message.mesh
+        widget.div.innerHTML = ''
+
+        for (const mesh of meshes) {
+          if (mesh) {
+            const { filename, subfolder, type } = mesh
+            const fileURL = api.apiURL(
+              `/view?filename=${encodeURIComponent(
+                filename
+              )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+            )
+
+            let modelViewer = document.createElement('div')
+            modelViewer.innerHTML = `<model-viewer  src="${fileURL}" 
+              min-field-of-view="0deg" max-field-of-view="180deg"
+              shadow-intensity="1" 
+              camera-controls 
+              touch-action="pan-y"
+                  style="width:100%;margin:4px;min-height:88px"
+              >
+              
+              <div class="controls">
+            
+              <div><button class="export" style="
+              background-color: var(--comfy-input-bg);
+              border-radius: 8px;
+              border-color: var(--border-color);
+              border-style: solid;
+              color: var(--descrip-text);cursor: pointer;">Export GLB</button></div>
+              
+              </div></model-viewer>`
+            widget.div.appendChild(modelViewer)
+            let modelViewerVariants= modelViewer
+            .querySelector('model-viewer');
+            
+            modelViewer
+              .querySelector('.export')
+              .addEventListener('click', async e => {
+                e.preventDefault()
+                const glTF = await modelViewerVariants.exportScene()
+                const file = new File([glTF], filename)
+                const link = document.createElement('a')
+                link.download = file.name
+                link.href = URL.createObjectURL(file)
+                link.click()
+              })
+          }
+        }
+
+        // widget.value = [meshes]
+
+        this.onResize?.(this.size)
+
+        return r
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {
+    const sleep = (t = 1000) => {
+      return new Promise((res, rej) => {
+        setTimeout(() => res(1), t)
+      })
+    }
+    // if (node.type === 'SaveTripoSRMesh') {
+    //   await sleep(0)
+    //   let widget = node.widgets.filter(w => w.name === 'preview')[0]
+    //   widget.div.innerHTML = ''
+
+    //   for (const mesh of widget.value) {
+    //     if (mesh) {
+    //       const { filename, subfolder, type } = mesh
+    //       const fileURL = api.apiURL(
+    //         `/view?filename=${encodeURIComponent(
+    //           filename
+    //         )}&type=${type}&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
+    //       )
+
+    //       let modelViewer = document.createElement('div')
+    //       modelViewer.innerHTML = `<model-viewer  src="${fileURL}"
+    // 	 min-field-of-view="0deg" max-field-of-view="180deg"
+    // 	 shadow-intensity="1"
+    // 	 camera-controls
+    // 	 touch-action="pan-y">
+
+    // 	 <div class="controls">
+
+    // 	  <div><button class="export">Export GLB</button></div>
+
+    // 	</div></model-viewer>`
+    //       widget.div.appendChild(modelViewer)
+    //     }
+    //   }
+    // }
+  }
+})
@@ -46,6 +46,18 @@ const smart_connect_config_input = [
    node_widget_name: 'image',
    inputNodeName: 'LoadImage',
    inputNode_output_name: 'IMAGE'
+  },
+  {
+    node_type: 'TripoSRSampler_',
+    node_widget_name: 'image',
+    inputNodeName: 'LoadImagesToBatch',
+    inputNode_output_name: 'IMAGE'
+  },
+  {
+    node_type: 'TripoSRSampler_',
+    node_widget_name: 'mask',
+    inputNodeName: 'RembgNode_Mix',
+    inputNode_output_name: 'masks'
  }
 ]

@@ -74,6 +86,18 @@ const smart_connect_config_output = [
    outputNodeName: 'SaveImage',
    outputNode_input_name: 'images'
  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'AppInfo',
+    outputNode_input_name: 'IMAGE'
+  },
+  {
+    node_type: 'VAEDecode',
+    node_output_name: 'IMAGE',
+    outputNodeName: 'SaveImageAndMetadata_',
+    outputNode_input_name: 'images'
+  },
  {
    node_type: 'Moondream',
    node_output_name: 'STRING',
@@ -181,7 +205,10 @@ export function smart_init () {
      ]
      let node_slotType = config[0]
      // 如果input没有，则创建
-      if (!node.inputs?.filter(inp => inp.name === widget.name)[0]||!node.inputs)
+      if (
+        !node.inputs?.filter(inp => inp.name === widget.name)[0] ||
+        !node.inputs
+      )
        convertToInput(node, widget, config)
      input_node.connectByType(inputNode_slot, node, node_slotType)
    }
@@ -299,15 +299,16 @@ async function get_my_app (filename = null, category = '') {
    data = []

    for (const res of result.data) {
-      let { app, workflow } = res.data
-      if (app.filename)
-        data.push({
+      let { app, workflow } = res.data;
+      if (app?.filename) data.push({
          ...app,
          data: workflow,
          date: res.date
        })
    }
-  } catch (error) {}
+  } catch (error) {
+    console.log(error)
+  }
  return data
 }

@@ -943,6 +944,20 @@ app.registerExtension({
        }
      ]

+      if (node.widgets) {
+        // let text_widget = node.widgets.filter(
+        //   w => w.name === 'text' && typeof w.value == 'string'
+        // )
+        // if (text_widget && text_widget.length == 1) {
+        //   opts.push({
+        //     content: 'Text-to-Text ♾️Mixlab', // with a name
+        //     callback: () => {
+        //       LGraphCanvas.prototype.text2text(node)
+        //     } // and the callback
+        //   })
+        // }
+      }
+
      opts = addSmartMenu(opts, node)

      // if (node.type == 'CLIPTextEncode') {
@@ -1123,6 +1138,57 @@ app.registerExtension({
        (this.canvas.height * 0.5) / (this.ds.scale * dpr) // 考虑设备像素比
      this.setDirty(true, true)
    }
+
+    // 支持app模式的json
+    const loadAppJson = async data => {
+      let workflow
+      try {
+        let w = JSON.parse(data)
+        if (w.app && w.output) workflow = w.workflow
+      } catch (err) {}
+
+      if (workflow && workflow.version && workflow.nodes && workflow.extra) {
+        await app.loadGraphData(workflow)
+      }
+    }
+
+    if (!window._mixlab_app_paste_listener) {
+      window._mixlab_app_paste_listener = true
+      //粘贴json的事件
+      document.addEventListener('paste', async e => {
+        // ctrl+shift+v is used to paste nodes with connections
+        // this is handled by litegraph
+        if (this.shiftDown) return
+
+        let data = e.clipboardData || window.clipboardData
+
+        // No image found. Look for node data
+        data = data.getData('text/plain')
+
+        loadAppJson(data)
+      })
+
+      // 把json往里 拖
+      document.addEventListener('drop', async event => {
+        event.preventDefault()
+        event.stopPropagation()
+
+       
+
+        // Dragging from Chrome->Firefox there is a file but its a bmp, so ignore that
+        if (
+          event.dataTransfer.files.length &&
+          event.dataTransfer.files[0].type == 'application/json'
+        ) {
+          const reader = new FileReader()
+          reader.onload = async () => {
+       
+            loadAppJson(reader.result)
+          }
+          reader.readAsText(event.dataTransfer.files[0])
+        }
+      })
+    }
  },
  setup () {
    setTimeout(async () => {
@@ -1132,6 +1198,8 @@ app.registerExtension({
      const apps = await get_my_app()
      if (!apps) return

+      console.log('apps',apps)
+
      let apps_map = { 0: [] }

      for (const app of apps) {
@@ -1145,7 +1213,7 @@ app.registerExtension({

      let apps_opts = []
      for (const category in apps_map) {
-        console.log('category', typeof category)
+        // console.log('category', typeof category)
        if (category === '0') {
          apps_opts.push(
            ...Array.from(apps_map[category], a => {
@@ -1497,14 +1565,16 @@ app.registerExtension({
                document.body.appendChild(div)
            }
          },
-          {
-            content: 'Workflow App ♾️Mixlab',
-            has_submenu: true,
-            disabled: false,
-            submenu: {
-              options: apps_opts
-            }
-          }
+          apps_opts.length > 0
+            ? {
+                content: 'Workflow App ♾️Mixlab',
+                has_submenu: true,
+                disabled: false,
+                submenu: {
+                  options: apps_opts
+                }
+              }
+            : null
        )

        return options
@@ -1573,3 +1643,41 @@ app.registerExtension({
    } catch (error) {}
  }
 })
+
+//获取当前显存
+function fetchSystemStats () {
+  return new Promise(async (resolve, reject) => {
+    try {
+      const response = await fetch('/system_stats')
+      const data = await response.json()
+      resolve(data)
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
+//清理显存
+function postFreeData () {
+  return new Promise(async (resolve, reject) => {
+    try {
+      const postData = {
+        unload_models: true,
+        free_memory: true
+      }
+      const response = await fetch('/free', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json'
+        },
+        body: JSON.stringify(postData)
+      })
+      if (response.ok) {
+        resolve()
+      } else {
+        reject(new Error('Request failed'))
+      }
+    } catch (error) {
+      reject(error)
+    }
+  })
+}
@@ -0,0 +1,515 @@
+import { app } from '../../../scripts/app.js'
+import { api } from '../../../scripts/api.js'
+import { ComfyWidgets } from '../../../scripts/widgets.js'
+
+import { $el } from '../../../scripts/ui.js'
+
+// The code is based on ComfyUI-VideoHelperSuite modification.
+
+
+
+function injectCSS (css) {
+  // 检查页面中是否已经存在具有相同内容的style标签
+  const existingStyle = document.querySelector('style')
+  if (existingStyle && existingStyle.textContent === css) {
+    return // 如果已经存在相同的样式，则不进行注入
+  }
+
+  // 创建一个新的style标签，并将CSS内容注入其中
+  const style = document.createElement('style')
+  style.textContent = css
+
+  // 将style标签插入到页面的head元素中
+  const head = document.querySelector('head')
+  head.appendChild(style)
+}
+
+injectCSS(`
+.hidden{
+  display:none !important
+}`)
+
+function get_position_style (ctx, widget_width, y, node_height) {
+  const MARGIN = 4 // the margin around the html element
+
+  /* Create a transform that deals with all the scrolling and zooming */
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(MARGIN, MARGIN + y)
+
+  return {
+    transformOrigin: '0 0',
+    transform: transform,
+    left: `0`,
+    top: `0`,
+    cursor: 'pointer',
+    position: 'absolute',
+    maxWidth: `${widget_width - MARGIN * 2}px`,
+    // maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
+    width: `${widget_width - MARGIN * 2}px`,
+    // height: `${node_height * 0.3 - MARGIN * 2}px`,
+    // background: '#EEEEEE',
+    display: 'flex',
+    flexDirection: 'column',
+    // alignItems: 'center',
+    justifyContent: 'space-around'
+  }
+}
+
+function videoUpload (node, inputName, inputData, app) {
+  const imageWidget = node.widgets.find(w => w.name === 'video')
+  let uploadWidget
+
+  const widget = {
+    type: 'div',
+    name: 'upload-preview',
+    draw (ctx, node, widget_width, y, widget_height) {
+      Object.assign(
+        this.div.style,
+        get_position_style(ctx, widget_width, 220, node.size[1]),
+        {
+          outline: '1px solid'
+        }
+      )
+    }
+  }
+
+  widget.div = $el('div', {})
+  widget.div.style.width = `120px`
+  document.body.appendChild(widget.div)
+  node.addCustomWidget(widget)
+  // console.log('#imageWidget', imageWidget)
+  const displayDiv = document.createElement('video')
+  displayDiv.controls = true
+  // displayDiv.style=`width:200px;height:200px`
+  imageWidget.callback = () => {
+    displayDiv.src = `/view?filename=${
+      imageWidget.value
+    }&type=input&subfolder=${''}&rand=${Math.random()}`
+
+    // displayDiv.onloadedmetadata = function () {
+    //   var frameCount = displayDiv.duration * displayDiv.webkitDecodedFrameCount
+    //   console.log('视频帧数：' + frameCount)
+    //   node.widgets.filter(w => w.name == 'video_segment_frames')[0].value =
+    //     frameCount
+    // }
+  }
+
+  if (imageWidget.value) {
+    // console.log(imageWidget.value)
+    displayDiv.src = `/view?filename=${
+      imageWidget.value
+    }&type=input&subfolder=${''}&rand=${Math.random()}`
+  }
+
+  widget.div.appendChild(displayDiv)
+
+  const onRemoved = node.onRemoved
+  node.onRemoved = () => {
+    widget.div.remove()
+    return onRemoved?.()
+  }
+
+  var default_value = imageWidget.value
+  Object.defineProperty(imageWidget, 'value', {
+    set: function (value) {
+      this._real_value = value
+    },
+
+    get: function () {
+      let value = ''
+      if (this._real_value) {
+        value = this._real_value
+      } else {
+        return default_value
+      }
+
+      if (value.filename) {
+        let real_value = value
+        value = ''
+        if (real_value.subfolder) {
+          value = real_value.subfolder + '/'
+        }
+
+        value += real_value.filename
+
+        if (real_value.type && real_value.type !== 'input')
+          value += ` [${real_value.type}]`
+      }
+      return value
+    }
+  })
+  async function uploadFile (file, updateNode, pasted = false) {
+    try {
+      // Wrap file in formdata so it includes filename
+      const body = new FormData()
+      body.append('image', file)
+      if (pasted) body.append('subfolder', 'pasted')
+      const resp = await api.fetchApi('/upload/image', {
+        method: 'POST',
+        body
+      })
+
+      if (resp.status === 200) {
+        const data = await resp.json()
+        // Add the file to the dropdown list and update the widget value
+        let path = data.name
+        if (data.subfolder) path = data.subfolder + '/' + path
+
+        if (!imageWidget.options.values.includes(path)) {
+          imageWidget.options.values.push(path)
+        }
+
+        if (updateNode) {
+          imageWidget.value = path
+        }
+
+        return `/view?filename=${path}&type=input&subfolder=${
+          pasted ? 'pasted' : ''
+        }&rand=${Math.random()}`
+      } else {
+        alert(resp.status + ' - ' + resp.statusText)
+      }
+    } catch (error) {
+      alert(error)
+    }
+  }
+
+  const fileInput = document.createElement('input')
+  Object.assign(fileInput, {
+    type: 'file',
+    accept: 'video/*,.mkv,video/webm,video/mp4,video/x-matroska,image/gif',
+    style: 'display: none',
+    onchange: async () => {
+      if (fileInput.files.length) {
+        let file = fileInput.files[0]
+
+        const url = await uploadFile(file, true)
+
+        // console.log('fileInput', file)
+        var reader = new FileReader()
+        reader.onload = function () {
+          displayDiv.src = url
+          displayDiv.onloadedmetadata = function () {
+            // var frameCount =
+            //   displayDiv.duration * displayDiv.webkitDecodedFrameCount
+            // console.log('视频帧数：' + frameCount)
+            // node.widgets.filter(
+            //   w => w.name == 'video_segment_frames'
+            // )[0].value = frameCount
+          }
+        }
+        reader.readAsDataURL(file)
+      }
+    }
+  })
+  document.body.append(fileInput)
+
+  // Create the button widget for selecting the files
+  uploadWidget = node.addWidget('button', 'upload file', 'video', () => {
+    fileInput.click()
+  })
+  uploadWidget.serialize = false
+  return { widget: uploadWidget }
+}
+ComfyWidgets.VIDEOUPLOAD_ = videoUpload
+
+app.registerExtension({
+  name: 'Mixlab.Video.LoadVideoAndSegment_',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData?.name == 'LoadVideoAndSegment_') {
+      nodeData.input.required.upload = ['VIDEOUPLOAD_']
+    }
+  },
+  async loadedGraphNode (node, app) {
+    if (node.type === 'LoadVideoAndSegment_') {
+      const imageWidget = node.widgets.find(w => w.name === 'video')
+      const uploadPreview = node.widgets.find(w => w.name === 'upload-preview')
+      if (imageWidget.value) {
+        // console.log(imageWidget.value)
+        uploadPreview.div.querySelector('video').src = `/view?filename=${
+          imageWidget.value
+        }&type=input&subfolder=${''}&rand=${Math.random()}`
+      }
+    }
+  }
+})
+
+
+function offsetDOMWidget(
+  widget,
+  ctx,
+  node,
+  widgetWidth,
+  widgetY,
+  height
+) {
+  const margin = 10
+  const elRect = ctx.canvas.getBoundingClientRect()
+  const transform = new DOMMatrix()
+    .scaleSelf(
+      elRect.width / ctx.canvas.width,
+      elRect.height / ctx.canvas.height
+    )
+    .multiplySelf(ctx.getTransform())
+    .translateSelf(0, widgetY + margin)
+
+  const scale = new DOMMatrix().scaleSelf(transform.a, transform.d)
+  Object.assign(widget.inputEl.style, {
+    transformOrigin: '0 0',
+    transform: scale,
+    left: `${transform.e}px`,
+    top: `${transform.d + transform.f}px`,
+    width: `${widgetWidth}px`,
+    height: `${(height || widget.parent?.inputHeight || 32) - margin}px`,
+    position: 'absolute',
+    background: !node.color ? '' : node.color,
+    color: !node.color ? '' : 'white',
+    zIndex: 5, //app.graph._nodes.indexOf(node),
+  })
+}
+
+export const hasWidgets = (node) => {
+  if (!node.widgets || !node.widgets?.[Symbol.iterator]) {
+    return false
+  }
+  return true
+}
+
+export const cleanupNode = (node) => {
+  if (!hasWidgets(node)) {
+    return
+  }
+
+  for (const w of node.widgets) {
+    if (w.canvas) {
+      w.canvas.remove()
+    }
+    if (w.inputEl) {
+      w.inputEl.remove()
+    }
+    // calls the widget remove callback
+    w.onRemoved?.()
+  }
+}
+
+const CreatePreviewElement = (name, val, format) => {
+  const [type] = format.split('/')  
+  const w = {
+      name,
+      type,
+      value: val,
+      draw: function (ctx, node, widgetWidth, widgetY, height) {
+        const [cw, ch] = this.computeSize(widgetWidth)
+        offsetDOMWidget(this, ctx, node, widgetWidth, widgetY, ch)
+      },
+      computeSize: function (_) {
+        const ratio = this.inputRatio || 1
+        const width = Math.max(220, this.parent.size[0])
+        return [width, (width / ratio + 10)]
+      },
+      onRemoved: function () {
+        if (this.inputEl) {
+          this.inputEl.remove()
+        }
+      },
+    }
+
+    w.inputEl = document.createElement(type === 'video' ? 'video' : 'img')
+    w.inputEl.src = w.value
+    if (type === 'video') {
+      w.inputEl.setAttribute('type', 'video/webm');
+      w.inputEl.autoplay = true
+      w.inputEl.loop = true
+      w.inputEl.controls = false;
+    }
+    w.inputEl.onload = function () {
+      w.inputRatio = w.inputEl.naturalWidth / w.inputEl.naturalHeight
+    }
+    document.body.appendChild(w.inputEl)
+    return w
+  }
+
+
+app.registerExtension({
+  name: 'Mixlab.Video.ImageListReplace',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeData?.name == 'ImageListReplace_') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        const widget = {
+          type: 'div',
+          name: 'preview',
+          draw (ctx, node, widget_width, y, widget_height) {
+            Object.assign(
+              this.div.style,
+              get_position_style(ctx, widget_width, 188, node.size[1]),
+              {
+                outline: '1px solid',
+                display: 'flex',
+                flexWrap: 'wrap',
+                flexDirection: 'row',
+                justifyContent: 'flex-start'
+              }
+            )
+          }
+        }
+
+        widget.div = $el('div', {})
+        widget.div.style.width = `120px`
+        widget.div.className = 'hidden'
+        document.body.appendChild(widget.div)
+        this.addCustomWidget(widget)
+        // console.log('#ImageListReplace', widget)
+
+        const onRemoved = this.onRemoved
+        this.onRemoved = () => {
+          widget.div.remove()
+          return onRemoved?.()
+        }
+      }
+
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        onExecuted?.apply(this, arguments)
+
+        // let _image_replace = message._image_replace[0]
+        // _image_replace = `/view?filename=${_image_replace.filename}&type=${
+        //   _image_replace.type
+        // }&subfolder=${_image_replace.subfolder}&rand=${Math.random()}`
+
+        let preview = this.widgets.filter(w => w.name == 'preview')[0]
+
+        if (message._images.length > 0) {
+          preview.div.className = ''
+          // console.log('#ImageListReplace', preview.div)
+        }
+
+        preview.div.innerHTML = ''
+        for (const img_ of message._images) {
+          let img = new Image()
+          img.style = `width: 100px;
+          margin: 4px;`
+          img.src = `/view?filename=${img_.filename}&type=${
+            img_.type
+          }&subfolder=${img_.subfolder}&rand=${Math.random()}`
+          preview.div.appendChild(img)
+        }
+
+        let start_index = this.widgets.filter(w => w.name == 'start_index')[0]
+        let end_index = this.widgets.filter(w => w.name == 'end_index')[0]
+        let invert = this.widgets.filter(w => w.name == 'invert')[0]
+        let _sc = start_index.callback.bind(start_index)
+        let _ec = end_index.callback.bind(end_index)
+
+        const selectImages = () => {
+          // console.log(v)
+          let s = start_index.value,
+            e = end_index.value
+          let imgs = preview.div.querySelectorAll('img')
+          for (let index = 0; index < imgs.length; index++) {
+            if (invert.value) {
+              imgs[index].style.outline =
+                index >= s && index <= e ? 'none' : '4px solid #cbd3fe'
+            } else {
+              imgs[index].style.outline =
+                index >= s && index <= e ? '4px solid #cbd3fe' : 'none'
+            }
+          }
+        }
+
+        selectImages()
+
+        start_index.callback = v => {
+          let s = v,
+            e = end_index.value
+          let imgs = preview.div.querySelectorAll('img')
+          for (let index = 0; index < imgs.length; index++) {
+            if (invert.value) {
+              imgs[index].style.outline =
+                index >= s && index <= e ? 'none' : '4px solid #cbd3fe'
+            } else {
+              imgs[index].style.outline =
+                index >= s && index <= e ? '4px solid #cbd3fe' : 'none'
+            }
+          }
+
+          _sc(v)
+        }
+
+        end_index.callback = v => {
+          let s = start_index.value,
+            e = v
+          let imgs = preview.div.querySelectorAll('img')
+          for (let index = 0; index < imgs.length; index++) {
+            if (invert.value) {
+              imgs[index].style.outline =
+                index >= s && index <= e ? 'none' : '4px solid #cbd3fe'
+            } else {
+              imgs[index].style.outline =
+                index >= s && index <= e ? '4px solid #cbd3fe' : 'none'
+            }
+          }
+
+          _ec(v)
+        }
+
+        invert.callback = v => {
+          selectImages()
+        }
+
+        try {
+        } catch (error) {}
+      }
+    }
+
+    if (nodeData?.name == 'VideoCombine_Adv') {
+      const onExecuted = nodeType.prototype.onExecuted
+      nodeType.prototype.onExecuted = function (message) {
+        const prefix = 'vhs_gif_preview_'
+        const r = onExecuted ? onExecuted.apply(this, message) : undefined
+
+        if (this.widgets) {
+          const pos = this.widgets.findIndex(w => w.name === `${prefix}_0`)
+          if (pos !== -1) {
+            for (let i = pos; i < this.widgets.length; i++) {
+              this.widgets[i].onRemoved?.()
+            }
+            this.widgets.length = pos
+          }
+          if (message?.gifs) {
+            message.gifs.forEach((params, i) => {
+              const previewUrl = api.apiURL(
+                '/view?' + new URLSearchParams(params).toString()
+              )
+              const w = this.addCustomWidget(
+                CreatePreviewElement(
+                  `${prefix}_${i}`,
+                  previewUrl,
+                  params.format || 'image/gif'
+                )
+              )
+              w.parent = this
+            })
+          }
+          const onRemoved = this.onRemoved
+          this.onRemoved = () => {
+            cleanupNode(this)
+            return onRemoved?.()
+          }
+        }
+        this.setSize([
+          this.size[0],
+          this.computeSize([this.size[0], this.size[1]])[1]
+        ])
+        return r
+      }
+    }
+  }
+})
@@ -1,277 +0,0 @@
-* {
-    transition: all 0.6s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-#app-login {
-    width: 480px;
-    height: 90vh;
-    padding: 6vh;
-    background: white;
-    box-shadow: 0 0 2rem rgba(0, 0, 0, 0.1);
-    z-index: 999;
-    position: fixed;
-    top: 5vh;
-    left: calc(50vw - 240px);
-}
-
-.login-app-view {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    z-index: 999;
-}
-
-.login-background {
-    background-color: #202020e6;
-    position: fixed;
-    width: 100%;
-    height: 100vh;
-    left: 0;
-    top: 0;
-    z-index: 998;
-}
-
-.app-header {
-    padding: 6vh;
-}
-
-.app-header,
-.app-header>* {
-    font-size: 1.2em;
-    margin: 0;
-    font-weight: 300;
-}
-
-.app-header>h1 {
-    font-size: 4.8vh;
-    font-weight: 400;
-    margin-bottom: 4.8vh;
-}
-
-.app-header>h2 {
-    font-size: 3vh;
-}
-
-.app-subheading {
-    color: rgba(0, 0, 0, 0.45);
-}
-
-.app-register {
-    position: absolute;
-    bottom: 0;
-    height: 10vh;
-    line-height: 10vh;
-    padding: 0 6vh;
-    color: rgba(0, 0, 0, 0.45);
-}
-
-.app-register>a {
-    font-weight: 400;
-}
-
-
-#app-login input {
-    font-size: 2.5vh;
-    width: calc(100% - 13vh);
-    height: 7.5vh;
-    margin-bottom: 2vh;
-    background: transparent;
-    position: absolute;
-    top: 0;
-    left: 6.5vh;
-    z-index: 2;
-    border: none;
-    box-shadow: inset 0 -0.5vh rgba(0, 0, 0, 0.1);
-}
-
-#app-login input:focus {
-    outline: none;
-    box-shadow: inset 0 -0.5vh transparent;
-}
-
-#app-login input[type=email] {
-    top: 58%;
-}
-
-#app-login input[type=password] {
-    top: calc(58% + 7.5vh);
-}
-
-#app-login input[type=email]:valid~* .st1 {
-    transition-timing-function: ease-in-out;
-    stroke-dasharray: 50, 153;
-    stroke-dashoffset: 25;
-}
-
-#app-login input[type=password]:focus~* .st0,
-#app-login input[type=password]:valid~* .st0,
-#login_run:focus~* .st0 {
-    stroke-dasharray: 210, 900;
-    stroke-dashoffset: -305;
-}
-
-#app-login input[type=email]:focus~* .st0 {
-    stroke-dasharray: 210, 900;
-    stroke-dashoffset: 0;
-}
-
-#app-login input:not(:valid)~#login_run {
-    /* pointer-events: none; */
-    opacity: 0.6;
-}
-
-#login_run {
-    text-decoration: none;
-    color: #0f9ede;
-    font-size: 1.5em;
-    padding: 0 6vh;
-    position: absolute;
-    bottom: 10vh;
-    font-weight: 400;
-    z-index: 998;
-    cursor: pointer;
-}
-
-#login_run:focus {
-    outline: none;
-}
-
-.login-app-view:nth-child(2) {
-    display: flex;
-    flex-direction: column;
-    pointer-events: none;
-}
-
-.login-app-view:nth-child(2)>.app-header {
-    font-size: 1rem;
-    flex-basis: 25%;
-    display: flex;
-    flex-direction: column;
-    justify-content: space-between;
-    padding: 4vh;
-    padding-bottom: 1rem;
-}
-
-.login-app-view:nth-child(2)>.app-header>h2 {
-    transform: translateY(1rem);
-}
-
-.login-app-view:nth-child(2)>.app-header>h2>em {
-    color: #0f9ede;
-    font-style: normal;
-}
-
-.login-app-view:nth-child(2)>.app-header>h2,
-.login-app-view:nth-child(2) .app-item>*:not(.app-graphic) {
-    transition-duration: 0.9s;
-    opacity: 0;
-}
-
-
-.st0,
-.st1,
-.svg-loader-segment {
-    fill: none;
-    stroke: #0f9ede;
-    stroke-width: 0.5vh;
-    stroke-alignment: inside;
-    opacity: 1;
-    transition: all 0.6s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-.svg-loader {
-    opacity: 0;
-}
-
-.st0 {
-    stroke-dasharray: 0, 900;
-    stroke-dashoffset: 0;
-}
-
-.st1 {
-    transition-delay: 0.3s;
-    stroke-dasharray: 50, 153;
-    stroke-dashoffset: -153;
-}
-
-.svg-loader-segment {
-    transition: transform 1.2s cubic-bezier(0.77, 0, 0.175, 1), opacity 0.85s cubic-bezier(0.77, 0, 0.175, 1), stroke 0.85s cubic-bezier(0.77, 0, 0.175, 1);
-}
-
-#svg-lines {
-    position: absolute;
-    top: 45%;
-    left: 0;
-    width: 100%;
-    z-index: 0;
-    overflow: visible;
-    transform-origin: center 4vh;
-}
-
-.svg-data {
-    fill: none;
-    stroke-width: 0.5vh;
-}
-
-.svg-data.-temp {
-    stroke: #f4814b;
-    stroke-dasharray: 20, 118;
-}
-
-.svg-data.-cal {
-    stroke: #08b5cf;
-    stroke-dasharray: 20, 113;
-}
-
-.svg-data.-steps-bg {
-    stroke: #e0e1e0;
-    stroke-dasharray: 40, 100;
-    stroke-dashoffset: -60;
-}
-
-.svg-data.-steps {
-    stroke: #0f9ede;
-    stroke-dasharray: 20, 73;
-    stroke-dashoffset: -53;
-}
-
-.svg-data.-heart {
-    stroke: #9965aa;
-    stroke-dasharray: 50, 200;
-    stroke-dashoffset: -150;
-}
-
-.svg-activity-fill {
-    fill: #c4e4f8;
-}
-
-.svg-activity-line {
-    fill: none;
-    stroke: #65bcea;
-    stroke-miterlimit: 10;
-    stroke-width: 0.25vh;
-}
-
-.svg-activity-avg,
-.svg-activity-indicator {
-    fill: none;
-    stroke: #d0dff0;
-    stroke-width: 0.25vh;
-    mix-blend-mode: multiply;
-}
-
-.svg-activity-fill,
-.svg-activity-line {
-    transform: translateY(10vh);
-    opacity: 0;
-}
-
-
-*,
-*:before,
-*:after {
-    box-sizing: border-box;
-    position: relative;
-}
@@ -1,67 +0,0 @@
-;(() => {
-  let div = document.createElement('div')
-
-  div.innerHTML = ` 
-    <div id="app-login">
-        <div class="login-app-view">
-            <header class="app-header">
-                <h1>Hi</h1>
-                Welcome back,<br />
-                <span class="app-subheading">
-                    sign in to continue<br />
-    
-                </span>
-            </header>
-            <input class="email" type="email" required pattern=".*\.\w{2,}" placeholder="Email Address" />
-            <input class="password" type="password" required placeholder="Password" />
-            <a class="app-button" id="login_run">登录</a>
-            <!-- <div class="app-register">
-                Don't have an account? <a>Sign Up</a>
-            </div> -->
-            <svg id="svg-lines" version="1.1" xmlns="http://www.w3.org/2000/svg"
-                xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 284.2 152.7"
-                xml:space="preserve">
-                <path class="st0"
-                    d="M37.7,107.3h222.6c12,0,21.8,9.7,21.8,21.7s-9.7,21.8-21.8,21.8c0,0-203.6,0-222.6,0S2.2,138.6,2.2,103.3   c0-52,113.5-101.5,141-101.5c13.5,0,21.8,9.7,21.8,21.8s-9.7,21.7-21.8,21.7s-21.8-9.7-21.8-21.7s9.7-21.8,21.8-21.8" />
-                <path class="st1"
-                    d="M260.2,76.3L250,87.8l-9-9c-6.2-6.2,2-24.7,17.2-24.7c15.2,0,23.9,17.7,23.9,29.7s-11.7,23.5-23.9,23.5h-10.2">
-                </path>
-                <g class="svg-loader" xmlns="http://www.w3.org/2000/svg">
-                    <path class="svg-loader-segment -cal" d="M164.7,23.5c0-12-9.7-21.8-21.8-21.8" />
-                    <path class="svg-loader-segment -heart" d="M143,45.2c12,0,21.8-9.7,21.8-21.7" />
-                    <path class="svg-loader-segment -steps" d="M121.2,23.5c0,12,9.7,21.7,21.8,21.7" />
-                    <path class="svg-loader-segment -temp" d="M143,1.7c-12,0-21.8,9.7-21.8,21.8" />
-                </g>
-            </svg>
-        </div>
-    </div>
-    <div class="login-background"></div>
-     `
-
-  document.body.appendChild(div)
-  let bg = div.querySelector('.login-background')
-  bg.addEventListener('click', e => {
-    div.style.display = 'none'
-  })
-  let login_btn = document.body.querySelector('#login_btn')
-  // login_btn.href="";
-  if (login_btn) {
-    login_btn.innerHTML =
-      '<svg stroke="currentColor" fill="none" stroke-width="0" viewBox="0 0 24 24" height="40px" width="40px" xmlns="http://www.w3.org/2000/svg"><path d="M12 17C14.2091 17 16 15.2091 16 13H8C8 15.2091 9.79086 17 12 17Z" fill="currentColor"></path><path d="M10 10C10 10.5523 9.55228 11 9 11C8.44772 11 8 10.5523 8 10C8 9.44772 8.44772 9 9 9C9.55228 9 10 9.44772 10 10Z" fill="currentColor"></path><path d="M15 11C15.5523 11 16 10.5523 16 10C16 9.44772 15.5523 9 15 9C14.4477 9 14 9.44772 14 10C14 10.5523 14.4477 11 15 11Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M22 12C22 17.5228 17.5228 22 12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12ZM20 12C20 16.4183 16.4183 20 12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12Z" fill="currentColor"></path></svg>LOGIN'
-    login_btn.addEventListener('click', e => {
-      e.preventDefault()
-      div.style.display = 'block'
-    })
-  }
-
-  let login_run = div.querySelector('#login_run')
-  if (login_run) {
-    login_run.addEventListener('click', e => {
-      e.preventDefault()
-      let ps = div.querySelector('.password')
-      let email = div.querySelector('.email')
-      div.style.display = 'none'
-      console.log(ps.value, email.value)
-    })
-  }
-})()
Author	SHA1	Message	Date
shadowcz007	c92e43b920	Update checkVersion_mixlab.js	2024-04-29 00:16:55 +08:00
shadowcz007	be6c32b0e0	fixbug	2024-04-29 00:15:47 +08:00
shadowcz007	3d2062e810	add TripoSRModel	2024-04-29 00:15:32 +08:00
shadowcz007	dd816e95cd	Update README.md	2024-04-27 23:43:40 +08:00
shadowcz007	6d1b51890d	Update checkVersion_mixlab.js	2024-04-27 23:38:00 +08:00
shadowcz007	11f03ec99a	支持正片叠底	2024-04-25 21:23:40 +08:00
shadowcz007	bd192f43e7	优化	2024-04-24 23:10:03 +08:00
shadowcz007	36e4b11983	gridout can export mask	2024-04-23 12:14:50 +08:00
shadowcz007	97397ba8c2	Update ImageNode.py	2024-04-22 12:32:48 +08:00
shadowcz007	052eee4111	Update __init__.py	2024-04-22 12:31:40 +08:00
shadowcz007	e319496044	v0.22.0 - 优化ImageColorTransfer - 支持动态提示 - 添加更多节点支持，AppInfo自动填充id - LoadImagesToBatch加载 - zhipuai 按需安装	2024-04-22 09:52:45 +08:00
shadowcz007	b83b63c362	添加支持的节点自动填充id	2024-04-21 21:51:07 +08:00
shadowcz007	4d6b1675bb	Load Images to Batch加载文件最大宽度1024	2024-04-21 21:50:56 +08:00
shadowcz007	13110fab39	支持动态提示	2024-04-21 20:58:44 +08:00
shadowcz007	74ea509848	fixbug	2024-04-20 23:05:59 +08:00
shadowcz007	192bff9d2c	optimize ImageColorTransfer and support batching,	2024-04-20 22:23:34 +08:00
shadowcz007	44ed8812dc	output add TransparentImage	2024-04-19 22:27:53 +08:00
shadowcz007	200696ba21	Update ui_mixlab.js	2024-04-19 20:44:27 +08:00
shadowcz007	51cf3b0c04	install Zhipuai as needed	2024-04-19 11:41:56 +08:00
shadowcz007	6a4831c83b	add nodes map for appinfo	2024-04-18 18:24:58 +08:00
shadowcz007	c5e7ed95a3	Update index.html	2024-04-18 16:10:02 +08:00
shadowcz007	42a97fa4d9	Update image_mixlab.js	2024-04-18 16:00:43 +08:00
shadowcz007	45240d0012	Update image_mixlab.js	2024-04-18 16:00:18 +08:00
shadowcz007	8ed085febd	Update image_mixlab.js	2024-04-18 15:57:34 +08:00
shadowcz007	37803ea61b	Update smart_connect.js	2024-04-18 09:06:42 +08:00
shadowcz007	acd416952c	Update image_mixlab.js	2024-04-18 07:48:50 +08:00
shadowcz007	6ec46cbc44	Update ui_mixlab.js	2024-04-17 22:55:16 +08:00
shadowcz007	a9d971e476	Update __init__.py	2024-04-17 22:55:12 +08:00
shadowcz007	9fe064675d	paste appinfo data / 支持粘贴appinfo导出的数据	2024-04-17 16:28:32 +08:00
shadowcz007	c84fa467d0	Update requirements.txt	2024-04-17 15:36:49 +08:00
shadowcz007	3d7a55f6d3	SaveImageAndMetadata支持格式化文件名@bakkhos8	2024-04-17 15:01:05 +08:00
shadowcz007	d49baa1540	Update checkVersion_mixlab.js	2024-04-15 23:40:01 +08:00
shadowcz007	5c686af842	Update ImageNode.py	2024-04-15 09:02:49 +08:00
shadowcz007	22425b5bc6	Update index.html	2024-04-13 00:12:05 +08:00
shadowcz007	b6d9b338d2	Update index.html	2024-04-12 21:55:56 +08:00
shadowcz007	a191a13751	Update index.html	2024-04-12 21:48:31 +08:00
shadowcz007	3eccdbcc9b	Update index.html	2024-04-12 21:43:21 +08:00
shadowcz007	50063903f9	mixlab app add 3D	2024-04-12 16:10:05 +08:00
shadowcz007	95a1b70533	Update app_mixlab.js	2024-04-08 17:14:30 +08:00
shadowcz007	c3679ac90b	Update image_mixlab.js	2024-04-07 11:49:26 +08:00
shadowcz007	29e48eb6a2	Update ui_mixlab.js	2024-04-07 10:32:13 +08:00
shadowcz007	71d02e9651	v0.20.0	2024-04-06 21:47:07 +08:00
shadowcz007	f5193f3eec	add LoadImagesToBatch	2024-04-06 21:39:55 +08:00
shadowcz007	480c4d6919	fixbug	2024-04-06 19:56:43 +08:00
shadowcz007	1c5e030540	Update index.html	2024-04-06 18:41:06 +08:00
shadowcz007	27e83a5908	Incrementing List	2024-03-30 23:46:32 +08:00
shadowcz007	d3cbf8fa8d	Update Video.py	2024-03-30 22:46:50 +08:00
shadowcz007	74b1f8129b	Update PromptNode.py	2024-03-29 15:35:41 +08:00
shadowcz007	56ed513cfd	update	2024-03-27 17:03:28 +08:00
shadowcz007	5f412371c4	Update Utils.py	2024-03-27 13:10:30 +08:00
shadowcz007	c6b0b67585	update	2024-03-26 21:44:25 +08:00
shadowcz007	16d18e681a	add composite_images	2024-03-26 18:23:11 +08:00
shadowcz007	1fe99f33b2	add VAEEncodeForInpaint_Frames	2024-03-26 16:48:45 +08:00
shadowcz007	a2ece25ac0	update	2024-03-26 15:43:43 +08:00
shadowcz007	4865f4d148	fixbug	2024-03-26 15:14:01 +08:00
shadowcz007	41e88824cf	Update Video.py	2024-03-26 13:38:00 +08:00
shadowcz007	0961ab138e	Update videoupload.js	2024-03-26 13:35:43 +08:00
shadowcz007	fe8271a12f	fixbug	2024-03-26 13:34:52 +08:00
shadowcz007	f3866ede89	add ImageListReplace	2024-03-26 00:29:21 +08:00
shadowcz007	d938adf3cc	update TextSplitByDelimiter	2024-03-25 20:24:54 +08:00
shadowcz007	9908cff64b	Update Utils.py	2024-03-25 13:21:47 +08:00
shadowcz007	1b9b0bb4e6	fixbug	2024-03-25 13:20:30 +08:00
shadow	03acd9bea5	v0.19.0 SaveImageAndMetadata: Controls whether metadata is saved with the image Image Prompt: Custom image gallery that can be used as input for the app mode MaskList to Mask: Combines a list of masks into a single mask MaskListReplace: Replaces certain segments in a mask list GLIGENTextBoxApply_Advanced: Improved version of gligen, used in conjunction with detect by label for greater convenience Grid Input: Visual selection area and label settings, used in conjunction with gligen.	2024-03-25 08:19:05 +08:00
shadowcz007	4c42949023	Update ui_mixlab.js	2024-03-24 22:28:37 +08:00
shadowcz007	2e4d9836e5	fixbug	2024-03-24 20:52:19 +08:00
shadowcz007	43c6b58354	Update README.md	2024-03-24 20:39:25 +08:00
shadowcz007	df637e8196	update	2024-03-24 20:38:55 +08:00
shadowcz007	8e78f9786c	Update ui_mixlab.js	2024-03-24 15:26:59 +08:00
shadowcz007	f4130f06ed	update Grid Input	2024-03-23 22:32:31 +08:00
shadowcz007	b766e714a4	fixbug	2024-03-23 18:11:14 +08:00
shadowcz007	1100a90be3	fixbug & grid input	2024-03-23 18:06:25 +08:00
shadowcz007	33aaf80c82	add GLIGENTextBoxApply_Advanced	2024-03-23 11:50:02 +08:00
shadowcz007	b5861dbc24	add MaskListReplace 这个节点是把masks list中的某些替换为新的mask masks mask_replace start_index end_index	2024-03-23 00:20:30 +08:00
shadowcz007	93731416fc	Update ChatGPT.py	2024-03-22 23:03:03 +08:00
shadowcz007	a305e736ca	Update requirements.txt	2024-03-22 22:58:18 +08:00
shadowcz007	a046ebbafb	add MaskList to Mask	2024-03-22 12:00:44 +08:00
shadowcz007	af65e96723	update	2024-03-22 09:45:39 +08:00
shadowcz007	c9eb0ab5f0	Update index.html	2024-03-21 23:41:23 +08:00
shadowcz007	9802e841a8	add load video	2024-03-21 23:02:08 +08:00
shadowcz007	b896df8d54	Update index.html	2024-03-21 21:53:10 +08:00
shadowcz007	720b8c237b	Update index.html	2024-03-21 20:54:31 +08:00
shadowcz007	371f9f813f	Update index.html	2024-03-21 17:00:53 +08:00
shadowcz007	33e229c41c	Update index.html	2024-03-21 16:58:37 +08:00
shadowcz007	2c33c0d801	add image prompt node	2024-03-21 14:47:57 +08:00
shadowcz007	d64fee5954	Update index.html	2024-03-21 11:11:16 +08:00
shadowcz007	0217678c8c	Update .gitignore	2024-03-21 11:03:53 +08:00
shadowcz007	2959a9c31f	fixbug	2024-03-21 10:19:55 +08:00
shadowcz007	1928a18992	Update __init__.py	2024-03-21 08:51:30 +08:00
shadowcz007	a168171009	add SaveImageAndMetadata	2024-03-21 08:39:13 +08:00
shadowcz007	6f767f9700	Update index.html	2024-03-21 00:17:43 +08:00
shadowcz007	e5459f63fd	Update README.md	2024-03-19 18:34:43 +08:00
shadowcz007	d0ab85a8c4	add StyleAligned	2024-03-19 18:33:37 +08:00