test appnode

__init__.py

@@ -192,7 +192,7 @@ def read_workflow_json_files(folder_path ):
 def get_workflows():
     # print("#####path::", current_path)
     workflow_path=os.path.join(current_path, "workflow")
-    # print('workflow_path: ',workflow_path)
+    # print('##workflow_path: ',workflow_path)
     if not os.path.exists(workflow_path):
         # 使用mkdir()方法创建新目录
         os.mkdir(workflow_path)
@@ -391,6 +391,9 @@ async def new_start(self, address, port, verbose=True, call_on_start=None):
         site = web.TCPSite(runner, address, port)
         await site.start()
 
+        PromptServer.instance.port=port
+
+
         import ssl
         crt, key = create_for_https()
         ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
@@ -493,6 +496,7 @@ async def mixlab_workflow_hander(request):
                     'status':'success',
                 }
             elif data['task']=='list':
+                # 暂时没有用到
                 result={
                     'data':get_workflows(),
                     'status':'success',
@@ -536,14 +540,15 @@ from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
 from .nodes.Clipseg import CLIPSeg,CombineMasks
 from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
 from .nodes.Audio import GamePal,SpeechRecognition,SpeechSynthesis
-from .nodes.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,AppInfo,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
+from .nodes.Utils import CreateLoraNames,CreateSampler_names,CreateCkptNames,CreateSeedNode,TESTNODE_,IntNumber,FloatSlider,TextInput,ColorInput,FontInput,TextToNumber,DynamicDelayProcessor,LimitNumber,SwitchByIndex,MultiplicationNode
 from .nodes.Mask import OutlineMask,FeatheredMask
-
+from .nodes.App import AppInfo,AppNode
 
 # 要导出的所有节点及其名称的字典
 # 注意：名称应全局唯一
 NODE_CLASS_MAPPINGS = {
     "AppInfo":AppInfo,
+    "AppNode":AppNode,
     "TESTNODE_":TESTNODE_,
     "RandomPrompt":RandomPrompt,
     "EmbeddingPrompt":EmbeddingPrompt,

data/extension-node-map.json

@@ -4768,6 +4768,7 @@
             "ChinesePrompt_Mix",
             "3DImage",
             "AppInfo",
+            "AppNode",
             "IntNumber",
             "FloatSlider",
             "ResizeImage",

nodes/App.py

@@ -0,0 +1,366 @@
+import os
+# import re,random,json
+from PIL import Image
+import numpy as np
+# FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
+import folder_paths
+#
+import os
+import json
+import datetime
+
+import folder_paths
+from server import PromptServer
+
+class AnyType(str):
+  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+  def __ne__(self, __value: object) -> bool:
+    return False
+
+any_type = AnyType("*")
+
+app_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'../app'))
+
+# workflow  目录下的所有json
+def read_workflow_json_files_all(folder_path):
+    print('#read_workflow_json_files_all',folder_path)
+    json_files = []
+    for root, dirs, files in os.walk(folder_path):
+        for file in files:
+            if file.endswith('.json'):
+                json_files.append(os.path.join(root, file))
+
+    data = []
+    for file_path in json_files:
+        try:
+            with open(file_path) as json_file:
+                json_data = json.load(json_file)
+                creation_time = datetime.datetime.fromtimestamp(os.path.getctime(file_path))
+                numeric_timestamp = creation_time.timestamp()
+
+                option=os.path.basename(os.path.dirname(file_path))+'/'+os.path.basename(file_path)
+                if os.path.dirname(file_path) == folder_path:
+                    option=os.path.basename(file_path)
+
+                file_info = {
+                    'filename': os.path.basename(file_path),
+                    'category': os.path.dirname(file_path),
+                    'data': json_data,
+                    'date': numeric_timestamp,
+                    "option":option
+                }
+                data.append(file_info)
+        except Exception as e:
+            print(e)
+    
+    sorted_data = sorted(data, key=lambda x: x['date'], reverse=True)
+    return sorted_data
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+
+def create_temp_file(image):
+    output_dir = folder_paths.get_temp_directory()
+
+    (
+            full_output_folder,
+            filename,
+            counter,
+            subfolder,
+            _,
+        ) = folder_paths.get_save_image_path('tmp', output_dir)
+
+    
+    im=tensor2pil(image)
+ 
+    image_file = f"{filename}_{counter:05}.png"
+     
+    image_path=os.path.join(full_output_folder, image_file)
+
+    im.save(image_path,compress_level=4)
+
+    return [{
+                "filename": image_file,
+                "subfolder": subfolder,
+                "type": "temp"
+                }]
+
+
+
+#This is an example that uses the websockets api to know when a prompt execution is done
+#Once the prompt execution is done it downloads the images using the /history endpoint
+
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import json
+import urllib.request
+import urllib.parse
+
+server_address = "127.0.0.1:8188"
+client_id = str(uuid.uuid4())
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt, "client_id": client_id}
+    data = json.dumps(p).encode('utf-8')
+    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
+    return json.loads(urllib.request.urlopen(req).read())
+
+def get_image(filename, subfolder, folder_type):
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
+        return response.read()
+
+def get_history(prompt_id):
+    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
+        return json.loads(response.read())
+
+def get_images(ws, prompt):
+    prompt_id = queue_prompt(prompt)['prompt_id']
+    output_images = {}
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message['type'] == 'executing':
+                data = message['data']
+                if data['node'] is None and data['prompt_id'] == prompt_id:
+                    break #Execution is done
+        else:
+            continue #previews are binary data
+
+    history = get_history(prompt_id)[prompt_id]
+    for o in history['outputs']:
+        for node_id in history['outputs']:
+            node_output = history['outputs'][node_id]
+            if 'images' in node_output:
+                images_output = []
+                for image in node_output['images']:
+                    image_data = get_image(image['filename'], image['subfolder'], image['type'])
+                    images_output.append(image_data)
+            output_images[node_id] = images_output
+
+    return output_images
+
+prompt_text = """
+{
+    "3": {
+        "class_type": "KSampler",
+        "inputs": {
+            "cfg": 8,
+            "denoise": 1,
+            "latent_image": [
+                "5",
+                0
+            ],
+            "model": [
+                "4",
+                0
+            ],
+            "negative": [
+                "7",
+                0
+            ],
+            "positive": [
+                "6",
+                0
+            ],
+            "sampler_name": "euler",
+            "scheduler": "normal",
+            "seed": 8566257,
+            "steps": 20
+        }
+    },
+    "4": {
+        "class_type": "CheckpointLoaderSimple",
+        "inputs": {
+            "ckpt_name": "v1-5-pruned-emaonly.ckpt"
+        }
+    },
+    "5": {
+        "class_type": "EmptyLatentImage",
+        "inputs": {
+            "batch_size": 1,
+            "height": 512,
+            "width": 512
+        }
+    },
+    "6": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "masterpiece best quality girl"
+        }
+    },
+    "7": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "bad hands"
+        }
+    },
+    "8": {
+        "class_type": "VAEDecode",
+        "inputs": {
+            "samples": [
+                "3",
+                0
+            ],
+            "vae": [
+                "4",
+                2
+            ]
+        }
+    },
+    "9": {
+        "class_type": "SaveImage",
+        "inputs": {
+            "filename_prefix": "ComfyUI",
+            "images": [
+                "8",
+                0
+            ]
+        }
+    }
+}
+"""
+
+# prompt = json.loads(prompt_text)
+# #set the text prompt for our positive CLIPTextEncode
+# prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
+
+# #set the seed for our KSampler node
+# prompt["3"]["inputs"]["seed"] = 5
+
+# ws = websocket.WebSocket()
+# ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+# images = get_images(ws, prompt)
+
+# #Commented out code to display the output images:
+
+# # for node_id in images:
+# #     for image_data in images[node_id]:
+# #         from PIL import Image
+# #         import io
+# #         image = Image.open(io.BytesIO(image_data))
+# #         image.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# app 配置节点
+class AppInfo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
+                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
+                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
+                             },
+
+                "optional":{
+                    "IMAGE": ("IMAGE",),
+                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
+                    "version":("INT", {
+                        "default": 1, 
+                        "min": 1, 
+                        "max": 10000, 
+                        "step": 1, 
+                        "display": "number"  
+                    }),
+                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
+                    "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
+                    "auto_save": (["enable","disable"],),
+                }
+
+                }
+    
+    RETURN_TYPES = ()
+    # RETURN_NAMES = ("IMAGE",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = True
+    # OUTPUT_IS_LIST = (True,)
+
+    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
+        name=name[0]
+        
+        im=None
+        if IMAGE:
+            im=IMAGE[0][0]
+            #TODO batch 的方式需要处理
+            im=create_temp_file(im)
+        # image [img,] img[batch,w,h,a] 列表里面是batch，
+
+        input_ids=input_ids[0]
+        output_ids=output_ids[0]
+        description=description[0]
+        version=version[0]
+        share_prefix=share_prefix[0]
+        link=link[0]
+        category=category[0]
+        
+        # id=get_json_hash([name,im,input_ids,output_ids,description,version])
+
+        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": ()}
+    
+
+
+#  app可以当成节点运行
+class AppNode:
+   
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { 
+                    "name": ([x['option'] for x in read_workflow_json_files_all(app_path)],),
+                    "image":("IMAGE",),
+                             }
+                }
+    
+    RETURN_TYPES = (any_type,)
+    RETURN_NAMES = ("output",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "♾️Mixlab"
+
+    OUTPUT_NODE = True
+    INPUT_IS_LIST = False
+    OUTPUT_IS_LIST = (False,)
+    
+
+    def run(self,name,input):
+        print('#app_path',input)
+        print(PromptServer.instance.port)
+
+        return (name,)
+    
+

nodes/ClipInterrogator.py

@@ -77,7 +77,8 @@ if not os.path.exists(caption_model_path):
     caption_model_path='Salesforce/blip-image-captioning-base'
 
 cache_path=os.path.join(folder_paths.models_dir, "clip_interrogator")
-
+if not os.path.exists(cache_path):
+    os.mkdir(cache_path)
 
 # Tensor to PIL
 def tensor2pil(image):

nodes/Lama.py

@@ -42,6 +42,9 @@ else:
     _available=True
 
 
+lama_path=os.path.join(folder_paths.models_dir, "lama")
+if not os.path.exists(lama_path):
+    os.mkdir(lama_path)
  
 llma_model_path=os.path.join(folder_paths.models_dir, "lama/big-lama.pt")
 if not os.path.exists(llma_model_path):

nodes/Utils.py

@@ -1,10 +1,9 @@
 import os,platform
 import re,random,json
-from PIL import Image
-import numpy as np
+
 # FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
 import folder_paths
-import matplotlib.font_manager as fm
+# import matplotlib.font_manager as fm
 import torch
 
 
@@ -65,37 +64,7 @@ def get_system_font_path():
 #     return hash_value
     
 
-
-def tensor2pil(image):
-    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-
-
-def create_temp_file(image):
-    output_dir = folder_paths.get_temp_directory()
-
-    (
-            full_output_folder,
-            filename,
-            counter,
-            subfolder,
-            _,
-        ) = folder_paths.get_save_image_path('tmp', output_dir)
-
-    
-    im=tensor2pil(image)
  
-    image_file = f"{filename}_{counter:05}.png"
-     
-    image_path=os.path.join(full_output_folder, image_file)
-
-    im.save(image_path,compress_level=4)
-
-    return [{
-                "filename": image_file,
-                "subfolder": subfolder,
-                "type": "temp"
-                }]
-
 def get_font_files(directory):
     font_files = {}
 
@@ -413,6 +382,7 @@ class AnyType(str):
     return False
 
 any_type = AnyType("*")
+
 import time
 
 class DynamicDelayProcessor:
@@ -490,69 +460,6 @@ class DynamicDelayProcessor:
         
 
 
-
-# app 配置节点
-class AppInfo:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "name": ("STRING",{"multiline": False,"default": "Mixlab-App","dynamicPrompts": False}),
-                    "input_ids":("STRING",{"multiline": True,"default": "\n".join(["1","2","3"]),"dynamicPrompts": False}),
-                    "output_ids":("STRING",{"multiline": True,"default": "\n".join(["5","9"]),"dynamicPrompts": False}),
-                             },
-
-                "optional":{
-                    "IMAGE": ("IMAGE",),
-                    "description":("STRING",{"multiline": True,"default": "","dynamicPrompts": False}),
-                    "version":("INT", {
-                        "default": 1, 
-                        "min": 1, 
-                        "max": 10000, 
-                        "step": 1, 
-                        "display": "number"  
-                    }),
-                    "share_prefix":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
-                    "link":("STRING",{"multiline": False,"default": "https://","dynamicPrompts": False}),
-                    "category":("STRING",{"multiline": False,"default": "","dynamicPrompts": False}),
-                    "auto_save": (["enable","disable"],),
-                }
-
-                }
-    
-    RETURN_TYPES = ()
-    # RETURN_NAMES = ("IMAGE",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "♾️Mixlab"
-
-    OUTPUT_NODE = True
-    INPUT_IS_LIST = True
-    # OUTPUT_IS_LIST = (True,)
-
-    def run(self,name,input_ids,output_ids,IMAGE,description,version,share_prefix,link,category,auto_save):
-        name=name[0]
-        
-        im=None
-        if IMAGE:
-            im=IMAGE[0][0]
-            #TODO batch 的方式需要处理
-            im=create_temp_file(im)
-        # image [img,] img[batch,w,h,a] 列表里面是batch，
-
-        input_ids=input_ids[0]
-        output_ids=output_ids[0]
-        description=description[0]
-        version=version[0]
-        share_prefix=share_prefix[0]
-        link=link[0]
-        category=category[0]
-        
-        # id=get_json_hash([name,im,input_ids,output_ids,description,version])
-
-        return {"ui": {"json": [name,im,input_ids,output_ids,description,version,share_prefix,link,category]}, "result": ()}
-    
-
     
 
 

web/javascript/app_mixlab.js

@@ -287,7 +287,7 @@ function getInputsAndOutputs () {
 }
 
 app.registerExtension({
-  name: 'Mixlab.utils.AppInfo',
+  name: 'Mixlab.app.AppInfo',
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == 'AppInfo') {
       const orig_nodeCreated = nodeType.prototype.onNodeCreated
@@ -436,3 +436,67 @@ api.addEventListener('executed', async ({ detail }) => {
     }
   }
 })
+
+app.registerExtension({
+  name: 'Mixlab.app.AppNode',
+  async beforeRegisterNodeDef (nodeType, nodeData, app) {
+    if (nodeType.comfyClass == 'AppNode') {
+      const orig_nodeCreated = nodeType.prototype.onNodeCreated
+      nodeType.prototype.onNodeCreated = function () {
+        orig_nodeCreated?.apply(this, arguments)
+        console.log('#orig_nodeCreated', this)
+        let node=this;
+
+        const name = this.widgets.filter(w => w.name === 'name')[0]
+
+        name.callback = async e => {
+          let es = e.split('/')
+          let filename = '',
+            category = ''
+          if (es.length == 2) {
+            category = es[0]
+            filename = es[1]
+          } else if (es.length === 1) {
+            filename = e
+          }
+          const res = await api.fetchApi('/mixlab/workflow', {
+            method: 'POST',
+            headers: {
+              'Content-Type': 'application/json'
+            },
+            body: JSON.stringify({
+              task: 'my_app',
+              filename,
+              category
+            })
+          })
+
+          if (res.status !== 200) {
+            throw {
+              response: await res.json()
+            }
+          }
+          let result=await res.json()
+          const {app,output:workflow,}=result.data[0].data;
+          let input=app.input
+          console.log(input)
+
+          if(input.length==1){
+            let widget = node.inputs.filter(w => w.name === 'input')[0];
+            let sc=input[0];
+            LGraphCanvas.prototype._createNodeForInput(
+              node, //当前node
+              widget, //当前node里需要自动连线的widget
+              sc.class_type, //作为input的node type
+              'IMAGE' // 作为input的node的outputs的name. the input slot type of the target node
+            )
+          }
+
+        }
+
+        this.serialize_widgets = true //需要保存参数
+      }
+    }
+  },
+  async loadedGraphNode (node, app) {}
+})