add: name

comfy-nodes/comfy_std_output.py

@@ -0,0 +1,46 @@
+import json
+
+
+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 = AnyType("*")
+
+
+class ComfyDeployStdOutputAny:
+    @classmethod
+    def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+        return {
+            "required": {
+                "name": ("STRING", {"default": "ComfyUI"}),
+                "source": (any, {}),  # Use "*" to accept any input type
+            },
+        }
+
+    CATEGORY = "output"
+    RETURN_TYPES = ()
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+
+    def run(self, name, source=None):
+        value = "None"
+        if source is not None:
+            try:
+                value = json.dumps(source)
+            except Exception:
+                try:
+                    value = str(source)
+                except Exception:
+                    value = "source exists, but could not be serialized."
+
+        return {"ui": {name: (value,)}}
+
+
+NODE_CLASS_MAPPINGS = {"ComfyDeployStdOutputAny": ComfyDeployStdOutputAny}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyDeployStdOutputAny": "Standard Any Output (ComfyDeploy)"
+}

comfy-nodes/comfy_std_output_image.py

@@ -0,0 +1,92 @@
+import os
+import json
+import numpy as np
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+import folder_paths
+
+
+class ComfyDeployStdOutputImage:
+    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", {"tooltip": "The images to save."}),
+                "filename_prefix": (
+                    "STRING",
+                    {
+                        "default": "ComfyUI",
+                        "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
+                    },
+                ),
+                "file_type": (["png", "jpg", "webp"], {"default": "webp"}),
+                "quality": ("INT", {"default": 80, "min": 1, "max": 100, "step": 1}),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "run"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "output"
+    DESCRIPTION = "Saves the input images to your ComfyUI output directory."
+
+    def run(
+        self,
+        images,
+        filename_prefix="ComfyUI",
+        file_type="png",
+        quality=80,
+        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 batch_number, image in enumerate(images):
+            i = 255.0 * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            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]))
+
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.{file_type}"
+            file_path = os.path.join(full_output_folder, file)
+
+            if file_type == "png":
+                img.save(
+                    file_path, pnginfo=metadata, compress_level=self.compress_level
+                )
+            elif file_type == "jpg":
+                img.save(file_path, quality=quality, optimize=True)
+            elif file_type == "webp":
+                img.save(file_path, quality=quality)
+
+            results.append(
+                {"filename": file, "subfolder": subfolder, "type": self.type}
+            )
+            counter += 1
+
+        return {"ui": {"images": results}}
+
+
+NODE_CLASS_MAPPINGS = {"ComfyDeployStdOutputImage": ComfyDeployStdOutputImage}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyDeployStdOutputImage": "Standard Image Output (ComfyDeploy)"
+}

comfy-nodes/external_text_list.py

@@ -1,52 +0,0 @@
-import folder_paths
-from PIL import Image, ImageOps
-import numpy as np
-import torch
-import json
-
-class ComfyUIDeployExternalTextList:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_id": (
-                    "STRING",
-                    {"multiline": False, "default": 'input_text_list'},
-                ),
-                 "text": (
-                    "STRING",
-                    {"multiline": True, "default": "[]"},
-                ),
-            },
-            "optional": {
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("text",)
-
-    OUTPUT_IS_LIST = (True,)
-
-    FUNCTION = "run"
-
-    CATEGORY = "text"
-
-    def run(self, input_id, text=None, display_name=None, description=None):
-        text_list = []
-        try:
-            text_list = json.loads(text)  # Assuming text is a JSON array string
-        except Exception as e:
-            print(f"Error processing images: {e}")
-            pass
-        return ([text_list],)
-
-NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalTextList": ComfyUIDeployExternalTextList}
-NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalTextList": "External Text List (ComfyUI Deploy)"}

comfy-nodes/model_list.py

@@ -0,0 +1,60 @@
+import folder_paths
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+from os import walk
+
+WILDCARD = AnyType("*")
+
+MODEL_EXTENSIONS = {
+    "safetensors": "SafeTensors file format",
+    "ckpt": "Checkpoint file",
+    "pth": "PyTorch serialized file",
+    "pkl": "Pickle file",
+    "onnx": "ONNX file",
+}
+
+def fetch_files(path):
+    for (dirpath, dirnames, filenames) in walk(path):
+        fs = []
+        if len(dirnames) > 0:
+            for dirname in dirnames:
+                fs.extend(fetch_files(f"{dirpath}/{dirname}"))
+        for filename in filenames:
+            # Remove "./models/" from the beginning of dirpath
+            relative_dirpath = dirpath.replace("./models/", "", 1)
+            file_path = f"{relative_dirpath}/{filename}"
+            
+            # Only add files that are known model extensions
+            file_extension = filename.split('.')[-1].lower()
+            if file_extension in MODEL_EXTENSIONS:
+                fs.append(file_path)
+
+        return fs
+allModels = fetch_files("./models")
+
+class ComfyUIDeployModalList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": (allModels, ),
+            }
+        }
+
+    RETURN_TYPES = (WILDCARD,)
+    RETURN_NAMES = ("model",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "model"
+
+    def run(self, model=""):
+        # Split the model path by '/' and select the last item
+        model_name = model.split('/')[-1]
+        return [model_name]
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployModelList": ComfyUIDeployModalList}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployModelList": "Model List (ComfyUI Deploy)"}

globals.py

@@ -6,10 +6,12 @@ from PIL import Image, ImageOps
 from io import BytesIO
 from pydantic import BaseModel as PydanticBaseModel
 
+
 class BaseModel(PydanticBaseModel):
     class Config:
         arbitrary_types_allowed = True
 
+
 class Status(Enum):
     NOT_STARTED = "not-started"
     RUNNING = "running"
@@ -17,6 +19,7 @@ class Status(Enum):
     FAILED = "failed"
     UPLOADING = "uploading"
 
+
 class StreamingPrompt(BaseModel):
     workflow_api: Any
     auth_token: str
@@ -25,6 +28,8 @@ class StreamingPrompt(BaseModel):
     status_endpoint: Optional[str]
     file_upload_endpoint: Optional[str]
     workflow: Any
+    gpu_event_id: Optional[str] = None
+
 
 class SimplePrompt(BaseModel):
     status_endpoint: Optional[str]
@@ -35,34 +40,39 @@ class SimplePrompt(BaseModel):
     workflow_api: dict
     status: Status = Status.NOT_STARTED
     progress: set = set()
-    last_updated_node: Optional[str] = None,
+    last_updated_node: Optional[str] = None
     uploading_nodes: set = set()
     done: bool = False
-    is_realtime: bool = False,
+    is_realtime: bool = False
-    start_time: Optional[float] = None,
+    start_time: Optional[float] = None
+    gpu_event_id: Optional[str] = None
+
 
 sockets = dict()
 prompt_metadata: dict[str, SimplePrompt] = {}
 streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
 
+
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
     UNENCODED_PREVIEW_IMAGE = 2
 
+
 max_output_id_length = 24
 
-async def send_image(image_data, sid=None, output_id:str = None):
+
+async def send_image(image_data, sid=None, output_id: str = None):
     max_length = max_output_id_length
     output_id = output_id[:max_length]
-    padded_output_id = output_id.ljust(max_length, '\x00')
+    padded_output_id = output_id.ljust(max_length, "\x00")
-    encoded_output_id = padded_output_id.encode('ascii', 'replace')
+    encoded_output_id = padded_output_id.encode("ascii", "replace")
 
     image_type = image_data[0]
     image = image_data[1]
     max_size = image_data[2]
     quality = image_data[3]
     if max_size is not None:
-        if hasattr(Image, 'Resampling'):
+        if hasattr(Image, "Resampling"):
             resampling = Image.Resampling.BILINEAR
         else:
             resampling = Image.ANTIALIAS
@@ -91,12 +101,18 @@ async def send_image(image_data, sid=None, output_id:str = None):
     preview_bytes = bytesIO.getvalue()
     await send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
 
+
 async def send_socket_catch_exception(function, message):
     try:
         await function(message)
-    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
+    except (
+        aiohttp.ClientError,
+        aiohttp.ClientPayloadError,
+        ConnectionResetError,
+    ) as err:
         print("send error:", err)
 
+
 def encode_bytes(event, data):
     if not isinstance(event, int):
         raise RuntimeError(f"Binary event types must be integers, got {event}")
@@ -106,6 +122,7 @@ def encode_bytes(event, data):
     message.extend(data)
     return message
 
+
 async def send_bytes(event, data, sid=None):
     message = encode_bytes(event, data)
 

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-deploy"
 description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
-version = "1.0.0"
+version = "1.1.0"
 license = "LICENSE"
 dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
 

web-plugin/index.js

@@ -92,8 +92,30 @@ const context = {
 //   native_run_api_endpoint: "http://localhost:3011/api/run",
 // };
 
-function getSelectedWorkflowInfo() {
+async function getSelectedWorkflowInfo() {
-  return context.selectedWorkflowInfo;
+  const workflow_info_promise = new Promise((resolve) => {
+    try {
+      const handleMessage = (event) => {
+        try {
+          const message = JSON.parse(event.data);
+          if (message.type === "workflow_info") {
+            resolve(message.data);
+            window.removeEventListener("message", handleMessage);
+          }
+        } catch (error) {
+          console.error(error);
+          resolve(undefined);
+        }
+      };
+      window.addEventListener("message", handleMessage);
+      sendEventToCD("workflow_info");
+    } catch (error) {
+      console.error(error);
+      resolve(undefined);
+    }
+  });
+
+  return workflow_info_promise;
 }
 
 function setSelectedWorkflowInfo(info) {
@@ -105,6 +127,8 @@ function setSelectedWorkflowInfo(info) {
 const ext = {
   name: "BennyKok.ComfyUIDeploy",
 
+  native_mode: false,
+
   init(app) {
     addButton();
 
@@ -114,6 +138,7 @@ const ext = {
     const org_display = queryParams.get("org_display");
     const origin = queryParams.get("origin");
     const workspace_mode = queryParams.get("workspace_mode");
+    this.native_mode = queryParams.get("native_mode") === "true";
 
     if (workspace_mode) {
       document.querySelector(".comfy-menu").style.display = "none";
@@ -319,7 +344,10 @@ const ext = {
                 false,
               );
             } catch (error) {
-              console.warning("Error setting validation to false, is fine to ignore this", error);
+              console.warning(
+                "Error setting validation to false, is fine to ignore this",
+                error,
+              );
             }
             console.log("loadGraphData");
             app.loadGraphData(comfyUIWorkflow);
@@ -429,6 +457,25 @@ const ext = {
       //   }
     });
 
+    if (this.native_mode) {
+      // console.log("native mode", window, window.app);
+      try {
+        await app.ui.settings.setSettingValueAsync("Comfy.UseNewMenu", "Top");
+        await app.ui.settings.setSettingValueAsync(
+          "Comfy.Sidebar.Size",
+          "small"
+        );
+        await app.ui.settings.setSettingValueAsync(
+          "Comfy.Sidebar.Location",
+          "right"
+        );
+        localStorage.setItem("Comfy.MenuPosition.Docked", "true");
+        console.log("native mode manmanman");
+      } catch (error) {
+        console.error("Error setting validation to false", error);
+      }
+    }
+
     app.graph.onAfterChange = ((originalFunction) =>
       async function () {
         const prompt = await app.graphToPrompt();
@@ -1527,10 +1574,12 @@ async function loadWorkflowApi(versionId) {
 
 const orginal_fetch_api = api.fetchApi;
 api.fetchApi = async (route, options) => {
-  console.log("Fetch API called with args:", route, options);
+  console.log("Fetch API called with args:", route, options, ext.native_mode);
 
-  const info = getSelectedWorkflowInfo();
+  if (route.startsWith("/prompt") && ext.native_mode) {
-  if (info && route.startsWith("/prompt")) {
+    const info = await getSelectedWorkflowInfo();
+    console.log("info", info);
+    if (info) {
       const body = JSON.parse(options.body);
 
       const data = {
@@ -1541,6 +1590,7 @@ api.fetchApi = async (route, options) => {
         machine_id: info.machine_id,
         workflow_id: info.workflow_id,
         native_run_api_endpoint: info.native_run_api_endpoint,
+        gpu_event_id: info.gpu_event_id,
       };
 
       return await fetch("/comfyui-deploy/run", {
@@ -1552,6 +1602,7 @@ api.fetchApi = async (route, options) => {
         body: JSON.stringify(data),
       });
     }
+  }
 
   return await orginal_fetch_api.call(api, route, options);
 };