更新 builder/modal-builder/src/template/app2.py

This reverts commit 67f25b2353cde0c318f0450d5c3222091a988625.

.github/workflows/publish.yml

@@ -0,0 +1,21 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.gitignore

@@ -1,2 +1,3 @@
 __pycache__
 .DS_Store
+file-hash-cache.json

builder/modal-builder/src/main.py

@@ -1,504 +0,0 @@
-from typing import Union, Optional, Dict, List
-from pydantic import BaseModel, Field, field_validator
-from fastapi import FastAPI, HTTPException, WebSocket, BackgroundTasks, WebSocketDisconnect
-from fastapi.responses import JSONResponse
-from fastapi.logger import logger as fastapi_logger
-import os
-from enum import Enum
-import json
-import subprocess
-import time
-from contextlib import asynccontextmanager
-import asyncio
-import threading
-import signal
-import logging
-from fastapi.logger import logger as fastapi_logger
-import requests
-from urllib.parse import parse_qs
-from starlette.middleware.base import BaseHTTPMiddleware
-from starlette.types import ASGIApp, Scope, Receive, Send
-
-from concurrent.futures import ThreadPoolExecutor
-
-# executor = ThreadPoolExecutor(max_workers=5)
-
-gunicorn_error_logger = logging.getLogger("gunicorn.error")
-gunicorn_logger = logging.getLogger("gunicorn")
-uvicorn_access_logger = logging.getLogger("uvicorn.access")
-uvicorn_access_logger.handlers = gunicorn_error_logger.handlers
-
-fastapi_logger.handlers = gunicorn_error_logger.handlers
-
-if __name__ != "__main__":
-    fastapi_logger.setLevel(gunicorn_logger.level)
-else:
-    fastapi_logger.setLevel(logging.DEBUG)
-
-logger = logging.getLogger("uvicorn")
-logger.setLevel(logging.INFO)
-
-last_activity_time = time.time()
-global_timeout = 60 * 4
-
-machine_id_websocket_dict = {}
-machine_id_status = {}
-
-fly_instance_id = os.environ.get('FLY_ALLOC_ID', 'local').split('-')[0]
-
-
-class FlyReplayMiddleware(BaseHTTPMiddleware):
-    """
-    If the wrong instance was picked by the fly.io load balancer we use the fly-replay header
-    to repeat the request again on the right instance.
-
-    This only works if the right instance is provided as a query_string parameter.
-    """
-
-    def __init__(self, app: ASGIApp) -> None:
-        self.app = app
-
-    async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
-        query_string = scope.get('query_string', b'').decode()
-        query_params = parse_qs(query_string)
-        target_instance = query_params.get(
-            'fly_instance_id', [fly_instance_id])[0]
-
-        async def send_wrapper(message):
-            if target_instance != fly_instance_id:
-                if message['type'] == 'websocket.close' and 'Invalid session' in message['reason']:
-                    # fly.io only seems to look at the fly-replay header if websocket is accepted
-                    message = {'type': 'websocket.accept'}
-                if 'headers' not in message:
-                    message['headers'] = []
-                message['headers'].append(
-                    [b'fly-replay', f'instance={target_instance}'.encode()])
-            await send(message)
-        await self.app(scope, receive, send_wrapper)
-
-
-async def check_inactivity():
-    global last_activity_time
-    while True:
-        # logger.info("Checking inactivity...")
-        if time.time() - last_activity_time > global_timeout:
-            if len(machine_id_status) == 0:
-                # The application has been inactive for more than 60 seconds.
-                # Scale it down to zero here.
-                logger.info(
-                    f"No activity for {global_timeout} seconds, exiting...")
-                # os._exit(0)
-                os.kill(os.getpid(), signal.SIGINT)
-                break
-            else:
-                pass
-                # logger.info(f"Timeout but still in progress")
-
-        await asyncio.sleep(1)  # Check every second
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    thread = run_in_new_thread(check_inactivity())
-    yield
-    logger.info("Cancelling")
-
-#
-app = FastAPI(lifespan=lifespan)
-app.add_middleware(FlyReplayMiddleware)
-# MODAL_ORG = os.environ.get("MODAL_ORG")
-
-
-@app.get("/")
-def read_root():
-    global last_activity_time
-    last_activity_time = time.time()
-    logger.info(f"Extended inactivity time to {global_timeout}")
-    return {"Hello": "World"}
-
-# create a post route called /create takes in a json of example
-# {
-#     name: "my first image",
-#     deps: {
-#         "comfyui": "d0165d819afe76bd4e6bdd710eb5f3e571b6a804",
-#         "git_custom_nodes": {
-#             "https://github.com/cubiq/ComfyUI_IPAdapter_plus": {
-#                 "hash": "2ca0c6dd0b2ad64b1c480828638914a564331dcd",
-#                 "disabled": true
-#             },
-#             "https://github.com/ltdrdata/ComfyUI-Manager.git": {
-#                 "hash": "9c86f62b912f4625fe2b929c7fc61deb9d16f6d3",
-#                 "disabled": false
-#             },
-#         },
-#         "file_custom_nodes": []
-#     }
-# }
-
-
-class GitCustomNodes(BaseModel):
-    hash: str
-    disabled: bool
-
-class FileCustomNodes(BaseModel):
-    filename: str
-    disabled: bool
-
-
-class Snapshot(BaseModel):
-    comfyui: str
-    git_custom_nodes: Dict[str, GitCustomNodes]
-    file_custom_nodes: List[FileCustomNodes]
-
-class Model(BaseModel):
-    name: str
-    type: str
-    base: str
-    save_path: str
-    description: str
-    reference: str
-    filename: str
-    url: str
-
-
-class GPUType(str, Enum):
-    T4 = "T4"
-    A10G = "A10G"
-    A100 = "A100"
-    L4 = "L4"
-
-
-class Item(BaseModel):
-    machine_id: str
-    name: str
-    snapshot: Snapshot
-    models: List[Model]
-    callback_url: str
-    gpu: GPUType = Field(default=GPUType.T4)
-
-    @field_validator('gpu')
-    @classmethod
-    def check_gpu(cls, value):
-        if value not in GPUType.__members__:
-            raise ValueError(
-                f"Invalid GPU option. Choose from: {', '.join(GPUType.__members__.keys())}")
-        return GPUType(value)
-
-
-@app.websocket("/ws/{machine_id}")
-async def websocket_endpoint(websocket: WebSocket, machine_id: str):
-    await websocket.accept()
-    machine_id_websocket_dict[machine_id] = websocket
-    # Send existing logs
-    if machine_id in machine_logs_cache:
-        combined_logs = "\n".join(
-            log_entry['logs'] for log_entry in machine_logs_cache[machine_id])
-        await websocket.send_text(json.dumps({"event": "LOGS", "data": {
-            "machine_id": machine_id,
-            "logs": combined_logs,
-            "timestamp": time.time()
-        }}))
-    try:
-        while True:
-            data = await websocket.receive_text()
-            global last_activity_time
-            last_activity_time = time.time()
-            logger.info(f"Extended inactivity time to {global_timeout}")
-            # You can handle received messages here if needed
-    except WebSocketDisconnect:
-        if machine_id in machine_id_websocket_dict:
-            machine_id_websocket_dict.pop(machine_id)
-
-# @app.get("/test")
-# async def test():
-#     machine_id_status["123"] = True
-#     global last_activity_time
-#     last_activity_time = time.time()
-#     logger.info(f"Extended inactivity time to {global_timeout}")
-
-#     await asyncio.sleep(10)
-
-#     machine_id_status["123"] = False
-#     machine_id_status.pop("123")
-
-#     return {"Hello": "World"}
-
-
-@app.post("/create")
-async def create_machine(item: Item):
-    global last_activity_time
-    last_activity_time = time.time()
-    logger.info(f"Extended inactivity time to {global_timeout}")
-
-    if item.machine_id in machine_id_status and machine_id_status[item.machine_id]:
-        return JSONResponse(status_code=400, content={"error": "Build already in progress."})
-
-    # Run the building logic in a separate thread
-    # future = executor.submit(build_logic, item)
-    task = asyncio.create_task(build_logic(item))
-
-    return JSONResponse(status_code=200, content={"message": "Build Queued", "build_machine_instance_id": fly_instance_id})
-
-
-class StopAppItem(BaseModel):
-    machine_id: str
-
-
-def find_app_id(app_list, app_name):
-    for app in app_list:
-        if app['Name'] == app_name:
-            return app['App ID']
-    return None
-
-@app.post("/stop-app")
-async def stop_app(item: StopAppItem):
-    # cmd = f"modal app list | grep {item.machine_id} | awk -F '│' '{{print $2}}'"
-    cmd = f"modal app list --json"
-
-    env = os.environ.copy()
-    env["COLUMNS"] = "10000"  # Set the width to a large value
-    find_id_process = await asyncio.subprocess.create_subprocess_shell(cmd,
-                                                                      stdout=asyncio.subprocess.PIPE,
-                                                                      stderr=asyncio.subprocess.PIPE,
-                                                                      env=env)
-    await find_id_process.wait()
-
-    stdout, stderr = await find_id_process.communicate()
-    if stdout:
-        app_id = stdout.decode().strip()
-        app_list = json.loads(app_id)
-        app_id = find_app_id(app_list, item.machine_id)
-        logger.info(f"cp_process stdout: {app_id}")
-    if stderr:
-        logger.info(f"cp_process stderr: {stderr.decode()}")
-
-    cp_process = await asyncio.subprocess.create_subprocess_exec("modal", "app", "stop", app_id,
-                                                                 stdout=asyncio.subprocess.PIPE,
-                                                                 stderr=asyncio.subprocess.PIPE,)
-    await cp_process.wait()
-    logger.info(f"Stopping app {item.machine_id}")
-    stdout, stderr = await cp_process.communicate()
-    if stdout:
-        logger.info(f"cp_process stdout: {stdout.decode()}")
-    if stderr:
-        logger.info(f"cp_process stderr: {stderr.decode()}")
-
-    if cp_process.returncode == 0:
-        return JSONResponse(status_code=200, content={"status": "success"})
-    else:
-        return JSONResponse(status_code=500, content={"status": "error", "error": stderr.decode()})
-
-# Initialize the logs cache
-machine_logs_cache = {}
-
-
-async def build_logic(item: Item):
-    # Deploy to modal
-    folder_path = f"/app/builds/{item.machine_id}"
-    machine_id_status[item.machine_id] = True
-
-    # Ensure the os path is same as the current directory
-    # os.chdir(os.path.dirname(os.path.realpath(__file__)))
-    # print(
-    #     f"builder - Current working directory: {os.getcwd()}"
-    # )
-
-    # Copy the app template
-    # os.system(f"cp -r template {folder_path}")
-    cp_process = await asyncio.subprocess.create_subprocess_exec("cp", "-r", "/app/src/template", folder_path)
-    await cp_process.wait()
-
-    # Write the config file
-    config = {
-        "name": item.name,
-        "deploy_test": os.environ.get("DEPLOY_TEST_FLAG", "False"),
-        "gpu": item.gpu,
-        "civitai_token": os.environ.get("CIVITAI_TOKEN", "")
-    }
-    with open(f"{folder_path}/config.py", "w") as f:
-        f.write("config = " + json.dumps(config))
-
-    with open(f"{folder_path}/data/snapshot.json", "w") as f:
-        f.write(item.snapshot.json())
-
-    with open(f"{folder_path}/data/models.json", "w") as f:
-        models_json_list = [model.dict() for model in item.models]
-        models_json_string = json.dumps(models_json_list)
-        f.write(models_json_string)
-
-    # os.chdir(folder_path)
-    # process = subprocess.Popen(f"modal deploy {folder_path}/app.py", stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
-    process = await asyncio.subprocess.create_subprocess_shell(
-        f"modal deploy app.py",
-        stdout=asyncio.subprocess.PIPE,
-        stderr=asyncio.subprocess.PIPE,
-        cwd=folder_path,
-        env={**os.environ, "COLUMNS": "10000"}
-    )
-
-    url = None
-
-    if item.machine_id not in machine_logs_cache:
-        machine_logs_cache[item.machine_id] = []
-
-    machine_logs = machine_logs_cache[item.machine_id]
-
-    url_queue = asyncio.Queue()
-
-    async def read_stream(stream, isStderr, url_queue: asyncio.Queue):
-        while True:
-            line = await stream.readline()
-            if line:
-                l = line.decode('utf-8').strip()
-
-                if l == "":
-                    continue
-
-                if not isStderr:
-                    logger.info(l)
-                    machine_logs.append({
-                        "logs": l,
-                        "timestamp": time.time()
-                    })
-
-                    if item.machine_id in machine_id_websocket_dict:
-                        await machine_id_websocket_dict[item.machine_id].send_text(json.dumps({"event": "LOGS", "data": {
-                            "machine_id": item.machine_id,
-                            "logs": l,
-                            "timestamp": time.time()
-                        }}))
-
-                    if "Created comfyui_api =>" in l or ((l.startswith("https://") or l.startswith("│")) and l.endswith(".modal.run")):
-                        if "Created comfyui_api =>" in l:
-                            url = l.split("=>")[1].strip()
-                        # making sure it is a url
-                        elif "comfyui-api" in l:
-                            # Some case it only prints the url on a blank line
-                            if l.startswith("│"):
-                                url = l.split("│")[1].strip()
-                            else:
-                                url = l
-
-                        if url:
-                            machine_logs.append({
-                                "logs": f"App image built, url: {url}",
-                                "timestamp": time.time()
-                            })
-
-                            await url_queue.put(url)
-
-                            if item.machine_id in machine_id_websocket_dict:
-                                await machine_id_websocket_dict[item.machine_id].send_text(json.dumps({"event": "LOGS", "data": {
-                                    "machine_id": item.machine_id,
-                                    "logs": f"App image built, url: {url}",
-                                    "timestamp": time.time()
-                                }}))
-                                await machine_id_websocket_dict[item.machine_id].send_text(json.dumps({"event": "FINISHED", "data": {
-                                    "status": "succuss",
-                                }}))
-
-                else:
-                    # is error
-                    logger.error(l)
-                    machine_logs.append({
-                        "logs": l,
-                        "timestamp": time.time()
-                    })
-
-                    if item.machine_id in machine_id_websocket_dict:
-                        await machine_id_websocket_dict[item.machine_id].send_text(json.dumps({"event": "LOGS", "data": {
-                            "machine_id": item.machine_id,
-                            "logs": l,
-                            "timestamp": time.time()
-                        }}))
-                        await machine_id_websocket_dict[item.machine_id].send_text(json.dumps({"event": "FINISHED", "data": {
-                            "status": "failed",
-                        }}))
-            else:
-                break
-
-    stdout_task = asyncio.create_task(
-        read_stream(process.stdout, False, url_queue))
-    stderr_task = asyncio.create_task(
-        read_stream(process.stderr, True, url_queue))
-
-    await asyncio.wait([stdout_task, stderr_task])
-
-    # Wait for the subprocess to finish
-    await process.wait()
-
-    if not url_queue.empty():
-        # The queue is not empty, you can get an item
-        url = await url_queue.get()
-
-    # Close the ws connection and also pop the item
-    if item.machine_id in machine_id_websocket_dict and machine_id_websocket_dict[item.machine_id] is not None:
-        await machine_id_websocket_dict[item.machine_id].close()
-
-    if item.machine_id in machine_id_websocket_dict:
-        machine_id_websocket_dict.pop(item.machine_id)
-
-    if item.machine_id in machine_id_status:
-        machine_id_status[item.machine_id] = False
-
-    # Check for errors
-    if process.returncode != 0:
-        logger.info("An error occurred.")
-        # Send a post request with the json body machine_id to the callback url
-        machine_logs.append({
-            "logs": "Unable to build the app image.",
-            "timestamp": time.time()
-        })
-        requests.post(item.callback_url, json={
-                      "machine_id": item.machine_id, "build_log": json.dumps(machine_logs)})
-
-        if item.machine_id in machine_logs_cache:
-            del machine_logs_cache[item.machine_id]
-
-        return
-        # return JSONResponse(status_code=400, content={"error": "Unable to build the app image."})
-
-    # app_suffix = "comfyui-app"
-
-    if url is None:
-        machine_logs.append({
-            "logs": "App image built, but url is None, unable to parse the url.",
-            "timestamp": time.time()
-        })
-        requests.post(item.callback_url, json={
-                      "machine_id": item.machine_id, "build_log": json.dumps(machine_logs)})
-
-        if item.machine_id in machine_logs_cache:
-            del machine_logs_cache[item.machine_id]
-
-        return
-        # return JSONResponse(status_code=400, content={"error": "App image built, but url is None, unable to parse the url."})
-    # example https://bennykok--my-app-comfyui-app.modal.run/
-    # my_url = f"https://{MODAL_ORG}--{item.container_id}-{app_suffix}.modal.run"
-
-    requests.post(item.callback_url, json={
-                  "machine_id": item.machine_id, "endpoint": url, "build_log": json.dumps(machine_logs)})
-    if item.machine_id in machine_logs_cache:
-        del machine_logs_cache[item.machine_id]
-
-    logger.info("done")
-    logger.info(url)
-
-
-def start_loop(loop):
-    asyncio.set_event_loop(loop)
-    loop.run_forever()
-
-
-def run_in_new_thread(coroutine):
-    new_loop = asyncio.new_event_loop()
-    t = threading.Thread(target=start_loop, args=(new_loop,), daemon=True)
-    t.start()
-    asyncio.run_coroutine_threadsafe(coroutine, new_loop)
-    return t
-
-
-if __name__ == "__main__":
-    import uvicorn
-    # , log_level="debug"
-    uvicorn.run("main:app", host="0.0.0.0", port=8080, lifespan="on")

builder/modal-builder/src/main1.py

@@ -0,0 +1,448 @@
+import modal
+from typing import Union, Optional, Dict, List
+from pydantic import BaseModel, Field, field_validator
+from fastapi import FastAPI, HTTPException, WebSocket, BackgroundTasks, WebSocketDisconnect
+from fastapi.responses import JSONResponse
+from fastapi.logger import logger as fastapi_logger
+import os
+from enum import Enum
+import json
+import subprocess
+import time
+from contextlib import asynccontextmanager
+import asyncio
+import threading
+import signal
+import logging
+from fastapi.logger import logger as fastapi_logger
+import requests
+from urllib.parse import parse_qs
+from starlette.middleware.base import BaseHTTPMiddleware
+from starlette.types import ASGIApp, Scope, Receive, Send
+
+# Modal应用实例
+modal_app = modal.App(name="comfyui-deploy")
+
+gunicorn_error_logger = logging.getLogger("gunicorn.error")
+gunicorn_logger = logging.getLogger("gunicorn")
+uvicorn_access_logger = logging.getLogger("uvicorn.access")
+uvicorn_access_logger.handlers = gunicorn_error_logger.handlers
+
+fastapi_logger.handlers = gunicorn_error_logger.handlers
+
+if __name__ != "__main__":
+    fastapi_logger.setLevel(gunicorn_logger.level)
+else:
+    fastapi_logger.setLevel(logging.DEBUG)
+
+logger = logging.getLogger("uvicorn")
+logger.setLevel(logging.INFO)
+
+last_activity_time = time.time()
+global_timeout = 60 * 4
+
+machine_id_websocket_dict = {}
+machine_id_status = {}
+machine_logs_cache = {}
+
+fly_instance_id = os.environ.get('FLY_ALLOC_ID', 'local').split('-')[0]
+
+class FlyReplayMiddleware(BaseHTTPMiddleware):
+    def __init__(self, app: ASGIApp) -> None:
+        super().__init__(app)
+
+    async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
+        query_string = scope.get('query_string', b'').decode()
+        query_params = parse_qs(query_string)
+        target_instance = query_params.get('fly_instance_id', [fly_instance_id])[0]
+
+        async def send_wrapper(message):
+            if target_instance != fly_instance_id:
+                if message['type'] == 'websocket.close' and 'Invalid session' in message.get('reason', ''):
+                    message = {'type': 'websocket.accept'}
+                if 'headers' not in message:
+                    message['headers'] = []
+                message['headers'].append([b'fly-replay', f'instance={target_instance}'.encode()])
+            await send(message)
+        await self.app(scope, receive, send_wrapper)
+
+async def check_inactivity():
+    global last_activity_time
+    while True:
+        if time.time() - last_activity_time > global_timeout:
+            if len(machine_id_status) == 0:
+                logger.info(f"No activity for {global_timeout} seconds, exiting...")
+                os.kill(os.getpid(), signal.SIGINT)
+                break
+        await asyncio.sleep(1)
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    thread = run_in_new_thread(check_inactivity())
+    yield
+    logger.info("Cancelling")
+
+# FastAPI实例
+fastapi_app = FastAPI(lifespan=lifespan)
+fastapi_app.add_middleware(FlyReplayMiddleware)
+
+class GitCustomNodes(BaseModel):
+    hash: str
+    disabled: bool
+
+class FileCustomNodes(BaseModel):
+    filename: str
+    disabled: bool
+
+class Snapshot(BaseModel):
+    comfyui: str
+    git_custom_nodes: Dict[str, GitCustomNodes]
+    file_custom_nodes: List[FileCustomNodes]
+
+class Model(BaseModel):
+    name: str
+    type: str
+    base: str
+    save_path: str
+    description: str
+    reference: str
+    filename: str
+    url: str
+
+class GPUType(str, Enum):
+    T4 = "T4"
+    A10G = "A10G"
+    A100 = "A100"
+    L4 = "L4"
+
+class Item(BaseModel):
+    machine_id: str
+    name: str
+    snapshot: Snapshot
+    models: List[Model]
+    callback_url: str
+    gpu: GPUType = Field(default=GPUType.T4)
+
+    @field_validator('gpu')
+    @classmethod
+    def check_gpu(cls, value):
+        if value not in GPUType.__members__:
+            raise ValueError(f"Invalid GPU option. Choose from: {', '.join(GPUType.__members__.keys())}")
+        return GPUType(value)
+
+class StopAppItem(BaseModel):
+    machine_id: str
+
+@fastapi_app.get("/")
+def read_root():
+    global last_activity_time
+    last_activity_time = time.time()
+    logger.info(f"Extended inactivity time to {global_timeout}")
+    return {"Hello": "World"}
+
+@fastapi_app.websocket("/ws/{machine_id}")
+async def websocket_endpoint(websocket: WebSocket, machine_id: str):
+    await websocket.accept()
+    machine_id_websocket_dict[machine_id] = websocket
+    if machine_id in machine_logs_cache:
+        combined_logs = "\n".join(log_entry['logs'] for log_entry in machine_logs_cache[machine_id])
+        await websocket.send_text(json.dumps({
+            "event": "LOGS", 
+            "data": {
+                "machine_id": machine_id,
+                "logs": combined_logs,
+                "timestamp": time.time()
+            }
+        }))
+    try:
+        while True:
+            data = await websocket.receive_text()
+            global last_activity_time
+            last_activity_time = time.time()
+            logger.info(f"Extended inactivity time to {global_timeout}")
+    except WebSocketDisconnect:
+        if machine_id in machine_id_websocket_dict:
+            del machine_id_websocket_dict[machine_id]
+
+@fastapi_app.post("/create")
+async def create_machine(item: Item):
+    global last_activity_time
+    last_activity_time = time.time()
+    logger.info(f"Extended inactivity time to {global_timeout}")
+
+    if item.machine_id in machine_id_status and machine_id_status[item.machine_id]:
+        return JSONResponse(status_code=400, content={"error": "Build already in progress."})
+
+    task = asyncio.create_task(build_logic(item))
+    return JSONResponse(
+        status_code=200, 
+        content={
+            "message": "Build Queued",
+            "build_machine_instance_id": fly_instance_id
+        }
+    )
+
+def find_app_id(app_list, app_name):
+    for app in app_list:
+        if app['Name'] == app_name:
+            return app['App ID']
+    return None
+
+@fastapi_app.post("/stop-app")
+async def stop_app(item: StopAppItem):
+    cmd = f"modal app list --json"
+    env = os.environ.copy()
+    env["COLUMNS"] = "10000"
+    
+    find_id_process = await asyncio.subprocess.create_subprocess_shell(
+        cmd,
+        stdout=asyncio.subprocess.PIPE,
+        stderr=asyncio.subprocess.PIPE,
+        env=env
+    )
+    
+    stdout, stderr = await find_id_process.communicate()
+    if stdout:
+        app_list = json.loads(stdout.decode().strip())
+        app_id = find_app_id(app_list, item.machine_id)
+        logger.info(f"cp_process stdout: {app_id}")
+    if stderr:
+        logger.info(f"cp_process stderr: {stderr.decode()}")
+
+    cp_process = await asyncio.subprocess.create_subprocess_exec(
+        "modal", "app", "stop", app_id,
+        stdout=asyncio.subprocess.PIPE,
+        stderr=asyncio.subprocess.PIPE,
+    )
+    
+    await cp_process.wait()
+    stdout, stderr = await cp_process.communicate()
+    
+    if stdout:
+        logger.info(f"cp_process stdout: {stdout.decode()}")
+    if stderr:
+        logger.info(f"cp_process stderr: {stderr.decode()}")
+
+    if cp_process.returncode == 0:
+        return JSONResponse(status_code=200, content={"status": "success"})
+    else:
+        return JSONResponse(
+            status_code=500, 
+            content={"status": "error", "error": stderr.decode()}
+        )
+
+async def build_logic(item: Item):
+    folder_path = f"/app/builds/{item.machine_id}"
+    machine_id_status[item.machine_id] = True
+
+    cp_process = await asyncio.subprocess.create_subprocess_exec(
+        "cp", "-r", "/app/src/template", folder_path
+    )
+    await cp_process.wait()
+
+    config = {
+        "name": item.name,
+        "deploy_test": os.environ.get("DEPLOY_TEST_FLAG", "False"),
+        "gpu": item.gpu,
+        "civitai_token": os.environ.get("CIVITAI_TOKEN", "833b4ded5c7757a06a803763500bab58")
+    }
+    
+    with open(f"{folder_path}/config.py", "w") as f:
+        f.write("config = " + json.dumps(config))
+
+    with open(f"{folder_path}/data/snapshot.json", "w") as f:
+        f.write(item.snapshot.json())
+
+    with open(f"{folder_path}/data/models.json", "w") as f:
+        models_json_list = [model.dict() for model in item.models]
+        f.write(json.dumps(models_json_list))
+
+    process = await asyncio.subprocess.create_subprocess_shell(
+        f"modal deploy app.py",
+        stdout=asyncio.subprocess.PIPE,
+        stderr=asyncio.subprocess.PIPE,
+        cwd=folder_path,
+        env={**os.environ, "COLUMNS": "10000"}
+    )
+
+    if item.machine_id not in machine_logs_cache:
+        machine_logs_cache[item.machine_id] = []
+
+    machine_logs = machine_logs_cache[item.machine_id]
+    url_queue = asyncio.Queue()
+
+    async def read_stream(stream, isStderr, url_queue: asyncio.Queue):
+        while True:
+            line = await stream.readline()
+            if not line:
+                break
+
+            l = line.decode('utf-8').strip()
+            if not l:
+                continue
+
+            if not isStderr:
+                logger.info(l)
+                machine_logs.append({
+                    "logs": l,
+                    "timestamp": time.time()
+                })
+
+                if item.machine_id in machine_id_websocket_dict:
+                    await machine_id_websocket_dict[item.machine_id].send_text(
+                        json.dumps({
+                            "event": "LOGS",
+                            "data": {
+                                "machine_id": item.machine_id,
+                                "logs": l,
+                                "timestamp": time.time()
+                            }
+                        })
+                    )
+
+                if "Created comfyui_api =>" in l or ((l.startswith("https://") or l.startswith("│")) and l.endswith(".modal.run")):
+                    if "Created comfyui_api =>" in l:
+                        url = l.split("=>")[1].strip()
+                    elif "comfyui-api" in l:
+                        url = l.split("│")[1].strip() if l.startswith("│") else l
+
+                    if url:
+                        machine_logs.append({
+                            "logs": f"App image built, url: {url}",
+                            "timestamp": time.time()
+                        })
+
+                        await url_queue.put(url)
+
+                        if item.machine_id in machine_id_websocket_dict:
+                            await machine_id_websocket_dict[item.machine_id].send_text(
+                                json.dumps({
+                                    "event": "LOGS",
+                                    "data": {
+                                        "machine_id": item.machine_id,
+                                        "logs": f"App image built, url: {url}",
+                                        "timestamp": time.time()
+                                    }
+                                })
+                            )
+                            await machine_id_websocket_dict[item.machine_id].send_text(
+                                json.dumps({
+                                    "event": "FINISHED",
+                                    "data": {
+                                        "status": "success",
+                                    }
+                                })
+                            )
+
+            else:
+                logger.error(l)
+                machine_logs.append({
+                    "logs": l,
+                    "timestamp": time.time()
+                })
+
+                if item.machine_id in machine_id_websocket_dict:
+                    await machine_id_websocket_dict[item.machine_id].send_text(
+                        json.dumps({
+                            "event": "LOGS",
+                            "data": {
+                                "machine_id": item.machine_id,
+                                "logs": l,
+                                "timestamp": time.time()
+                            }
+                        })
+                    )
+                    await machine_id_websocket_dict[item.machine_id].send_text(
+                        json.dumps({
+                            "event": "FINISHED",
+                            "data": {
+                                "status": "failed",
+                            }
+                        })
+                    )
+
+    stdout_task = asyncio.create_task(read_stream(process.stdout, False, url_queue))
+    stderr_task = asyncio.create_task(read_stream(process.stderr, True, url_queue))
+
+    await asyncio.wait([stdout_task, stderr_task])
+    await process.wait()
+
+    url = await url_queue.get() if not url_queue.empty() else None
+
+    if item.machine_id in machine_id_websocket_dict and machine_id_websocket_dict[item.machine_id] is not None:
+        await machine_id_websocket_dict[item.machine_id].close()
+
+    if item.machine_id in machine_id_websocket_dict:
+        del machine_id_websocket_dict[item.machine_id]
+
+    if item.machine_id in machine_id_status:
+        machine_id_status[item.machine_id] = False
+
+    if process.returncode != 0:
+        logger.info("An error occurred.")
+        machine_logs.append({
+            "logs": "Unable to build the app image.",
+            "timestamp": time.time()
+        })
+        requests.post(
+            item.callback_url,
+            json={
+                "machine_id": item.machine_id,
+                "build_log": json.dumps(machine_logs)
+            }
+        )
+        if item.machine_id in machine_logs_cache:
+            del machine_logs_cache[item.machine_id]
+        return
+
+    if url is None:
+        machine_logs.append({
+            "logs": "App image built, but url is None, unable to parse the url.",
+            "timestamp": time.time()
+        })
+        requests.post(
+            item.callback_url,
+            json={
+                "machine_id": item.machine_id,
+                "build_log": json.dumps(machine_logs)
+            }
+        )
+        if item.machine_id in machine_logs_cache:
+            del machine_logs_cache[item.machine_id]
+        return
+
+    requests.post(
+        item.callback_url,
+        json={
+            "machine_id": item.machine_id,
+            "endpoint": url,
+            "build_log": json.dumps(machine_logs)
+        }
+    )
+    
+    if item.machine_id in machine_logs_cache:
+        del machine_logs_cache[item.machine_id]
+
+    logger.info("done")
+    logger.info(url)
+
+def start_loop(loop):
+    asyncio.set_event_loop(loop)
+    loop.run_forever()
+
+def run_in_new_thread(coroutine):
+    new_loop = asyncio.new_event_loop()
+    t = threading.Thread(target=start_loop, args=(new_loop,), daemon=True)
+    t.start()
+    asyncio.run_coroutine_threadsafe(coroutine, new_loop)
+    return t
+
+# Modal endpoint
+@modal_app.function()
+@modal.asgi_app()
+def app():
+    return fastapi_app
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(fastapi_app, host="0.0.0.0", port=8080, lifespan="on")

builder/modal-builder/src/template/app2.py

@@ -307,4 +307,5 @@ def comfyui_app():
         },
     )()
 
-    return make_simple_proxy_app(ProxyContext(config))
+    proxy_app = make_simple_proxy_app(ProxyContext(config)) # Assign to variable
+    return proxy_app # Return the variable

comfy-nodes/external_audio.py

@@ -0,0 +1,57 @@
+import os
+import io
+import torchaudio
+from folder_paths import get_annotated_filepath
+
+class ComfyUIDeployExternalAudio:
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("audio",)
+    FUNCTION = "load_audio"
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_audio"},
+                ),
+                "audio_file": ("STRING", {"default": ""}),
+            },
+            "optional": {
+                "default_value": ("AUDIO",),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+            }
+        }
+    
+    @classmethod
+    def VALIDATE_INPUTS(s, audio_file, **kwargs):
+        return True
+
+    def load_audio(self, input_id, audio_file, default_value=None, display_name=None, description=None):
+        if audio_file and audio_file != "":
+            if audio_file.startswith(('http://', 'https://')):
+                # Handle URL input
+                import requests
+                response = requests.get(audio_file)
+                audio_data = io.BytesIO(response.content)
+                waveform, sample_rate = torchaudio.load(audio_data)
+            else:
+                # Handle local file
+                audio_path = get_annotated_filepath(audio_file)
+                waveform, sample_rate = torchaudio.load(audio_path)
+            
+            audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
+            return (audio,)
+        else:
+            return (default_value,)
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalAudio": ComfyUIDeployExternalAudio}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalAudio": "External Audio (ComfyUI Deploy)"}

comfy-nodes/external_boolean.py

@@ -0,0 +1,35 @@
+class ComfyUIDeployExternalBoolean:
+    @classmethod    
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_bool"},
+                ),
+                "default_value": ("BOOLEAN", {"default": False})
+            },
+            "optional": {
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("bool_value",)
+    
+    FUNCTION = "run"
+    
+    def run(self, input_id, default_value=None, display_name=None, description=None):
+        print(f"Node '{input_id}' processing with switch set to {default_value}")
+        return [default_value]
+    
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalBoolean": ComfyUIDeployExternalBoolean}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalBoolean": "External Boolean (ComfyUI Deploy)"}

comfy-nodes/external_checkpoints.py

@@ -5,6 +5,12 @@ import torch
 import folder_paths
 from tqdm import tqdm
 
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+        
+WILDCARD = AnyType("*")
+
 class ComfyUIDeployExternalCheckpoint:
     @classmethod
     def INPUT_TYPES(s):
@@ -16,23 +22,31 @@ class ComfyUIDeployExternalCheckpoint:
                 ),
             },
             "optional": {
-                "default_checkpoint_name": (folder_paths.get_filename_list("checkpoints"), ),
+                "default_value": (folder_paths.get_filename_list("checkpoints"), ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
             }
         }
 
-    RETURN_TYPES = (folder_paths.get_filename_list("checkpoints"),)
+    RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
 
     FUNCTION = "run"
 
     CATEGORY = "deploy"
 
-    def run(self, input_id, default_checkpoint_name=None):
+    def run(self, input_id, default_value=None, display_name=None, description=None):
         import requests
         import os
         import uuid
 
-        if input_id and input_id.startswith('http'):
+        if default_value.startswith('http'):
             unique_filename = str(uuid.uuid4()) + ".safetensors"
             print(unique_filename)
             print(folder_paths.folder_names_and_paths["checkpoints"][0][0])
@@ -59,7 +73,7 @@ class ComfyUIDeployExternalCheckpoint:
                     out_file.write(chunk)
             return (unique_filename,)
         else:
-            return (default_checkpoints_name,)
+            return (default_value,)
 
 
 NODE_CLASS_MAPPINGS = {

comfy-nodes/external_face_model.py

@@ -0,0 +1,108 @@
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+import folder_paths
+
+
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+WILDCARD = AnyType("*")
+
+
+class ComfyUIDeployExternalFaceModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_reactor_face_model"},
+                ),
+            },
+            "optional": {
+                "default_face_model_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "face_model_save_name": (  # if `default_face_model_name` is a link to download a file, we will attempt to save it with this name
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+                "face_model_url": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+            },
+        }
+
+    RETURN_TYPES = (WILDCARD,)
+    RETURN_NAMES = ("path",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "deploy"
+
+    def run(
+        self,
+        input_id,
+        default_face_model_name=None,
+        face_model_save_name=None,
+        display_name=None,
+        description=None,
+        face_model_url=None,
+    ):
+        import requests
+        import os
+        import uuid
+
+        if face_model_url and face_model_url.startswith("http"):
+            if face_model_save_name:
+                existing_face_models = folder_paths.get_filename_list("reactor/faces")
+                # Check if face_model_save_name exists in the list
+                if face_model_save_name in existing_face_models:
+                    print(f"using face model: {face_model_save_name}")
+                    return (face_model_save_name,)
+            else:
+                face_model_save_name = str(uuid.uuid4()) + ".safetensors"
+            print(face_model_save_name)
+            print(folder_paths.folder_names_and_paths["reactor/faces"][0][0])
+            destination_path = os.path.join(
+                folder_paths.folder_names_and_paths["reactor/faces"][0][0],
+                face_model_save_name,
+            )
+
+            print(destination_path)
+            print(
+                "Downloading external face model - "
+                + face_model_url
+                + " to "
+                + destination_path
+            )
+            response = requests.get(
+                face_model_url,
+                headers={"User-Agent": "Mozilla/5.0"},
+                allow_redirects=True,
+            )
+            with open(destination_path, "wb") as out_file:
+                out_file.write(response.content)
+            return (face_model_save_name,)
+        else:
+            print(f"using face model: {default_face_model_name}")
+            return (default_face_model_name,)
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalFaceModel": ComfyUIDeployExternalFaceModel}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalFaceModel": "External Face Model (ComfyUI Deploy)"
+}

comfy-nodes/external_image.py

@@ -15,6 +15,15 @@ class ComfyUIDeployExternalImage:
             },
             "optional": {
                 "default_value": ("IMAGE",),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "default_value_url": ("STRING", {"image_preview": True, "default": ""}),
             }
         }
 
@@ -25,32 +34,44 @@ class ComfyUIDeployExternalImage:
 
     CATEGORY = "image"
 
-    def run(self, input_id, default_value=None):
+    def run(self, input_id, default_value=None, display_name=None, description=None, default_value_url=None):
         image = default_value
-        try:
-            if input_id.startswith('http'):
-                import requests
-                from io import BytesIO
-                print("Fetching image from url: ", input_id)
-                response = requests.get(input_id)
-                image = Image.open(BytesIO(response.content))
-            elif input_id.startswith('data:image/png;base64,') or input_id.startswith('data:image/jpeg;base64,') or input_id.startswith('data:image/jpg;base64,'):
-                import base64
-                from io import BytesIO
-                print("Decoding base64 image")
-                base64_image = input_id[input_id.find(",")+1:]
-                decoded_image = base64.b64decode(base64_image)
-                image = Image.open(BytesIO(decoded_image))
-            else:
-                raise ValueError("Invalid image url provided.")
         
-            image = ImageOps.exif_transpose(image)
-            image = image.convert("RGB")
-            image = np.array(image).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[None,]
-            return [image]
-        except:
-            return [image]
+        # Try both input_id and default_value_url
+        urls_to_try = [url for url in [input_id, default_value_url] if url]
+        
+        print(default_value_url)
+        
+        for url in urls_to_try:
+            try:
+                if url.startswith('http'):
+                    import requests
+                    from io import BytesIO
+                    print(f"Fetching image from url: {url}")
+                    response = requests.get(url)
+                    image = Image.open(BytesIO(response.content))
+                    break
+                elif url.startswith(('data:image/png;base64,', 'data:image/jpeg;base64,', 'data:image/jpg;base64,')):
+                    import base64
+                    from io import BytesIO
+                    print("Decoding base64 image")
+                    base64_image = url[url.find(",")+1:]
+                    decoded_image = base64.b64decode(base64_image)
+                    image = Image.open(BytesIO(decoded_image))
+                    break
+            except:
+                continue
+        
+        if image is not None:
+            try:
+                image = ImageOps.exif_transpose(image)
+                image = image.convert("RGB")
+                image = np.array(image).astype(np.float32) / 255.0
+                image = torch.from_numpy(image)[None,]
+            except:
+                pass
+                
+        return [image]
 
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalImage": ComfyUIDeployExternalImage}

comfy-nodes/external_image_alpha.py

@@ -15,6 +15,14 @@ class ComfyUIDeployExternalImageAlpha:
             },
             "optional": {
                 "default_value": ("IMAGE",),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
             }
         }
 
@@ -25,7 +33,7 @@ class ComfyUIDeployExternalImageAlpha:
 
     CATEGORY = "image"
 
-    def run(self, input_id, default_value=None):
+    def run(self, input_id, default_value=None, display_name=None, description=None):
         image = default_value
         try:
             if input_id.startswith('http'):

comfy-nodes/external_image_batch.py

@@ -0,0 +1,113 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+import json
+import comfy
+
+class ComfyUIDeployExternalImageBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_images"},
+                ),
+                "images": (
+                    "STRING",
+                    {"multiline": False, "default": "[]"},
+                ),
+            },
+            "optional": {
+                "default_value": ("IMAGE",),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "image"
+    
+    def process_image(self, image):
+        image = ImageOps.exif_transpose(image)
+        image = image.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image_tensor = torch.from_numpy(image)[None,]
+        return image_tensor
+    
+    def run(self, input_id, images=None, default_value=None, display_name=None, description=None):
+        import requests
+        import zipfile
+        import io
+        
+        processed_images = []
+        try:
+            images_list = json.loads(images)  # Assuming images is a JSON array string
+            print(images_list)
+            for img_input in images_list:
+                if img_input.startswith('http') and img_input.endswith('.zip'):
+                    print("Fetching zip file from url: ", img_input)
+                    response = requests.get(img_input)
+                    zip_file = zipfile.ZipFile(io.BytesIO(response.content))
+                    for file_name in zip_file.namelist():
+                        if file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
+                            with zip_file.open(file_name) as file:
+                                image = Image.open(file)
+                                image = self.process_image(image)
+                                processed_images.append(image)
+                elif  img_input.startswith('http'):
+                    from io import BytesIO
+                    print("Fetching image from url: ", img_input)
+                    response = requests.get(img_input)
+                    image = Image.open(BytesIO(response.content))
+                elif img_input.startswith('data:image/png;base64,') or img_input.startswith('data:image/jpeg;base64,') or img_input.startswith('data:image/jpg;base64,'):
+                    import base64
+                    from io import BytesIO
+                    print("Decoding base64 image")
+                    base64_image = img_input[img_input.find(",")+1:]
+                    decoded_image = base64.b64decode(base64_image)
+                    image = Image.open(BytesIO(decoded_image))
+                else:
+                    raise ValueError("Invalid image url or base64 data provided.")
+
+                image = ImageOps.exif_transpose(image)
+                image = image.convert("RGB")
+                image = np.array(image).astype(np.float32) / 255.0
+                image_tensor = torch.from_numpy(image)[None,]
+                processed_images.append(image_tensor)
+        except Exception as e:
+            print(f"Error processing images: {e}")
+            pass
+            
+        if default_value is not None and len(images_list) == 0:
+            processed_images.append(default_value)  # Assuming default_value is a pre-processed image tensor
+
+        # Resize images if necessary and concatenate from MakeImageBatch in ImpactPack
+        if processed_images:
+            base_shape = processed_images[0].shape[1:]  # Get the shape of the first image for comparison
+            batch_tensor = processed_images[0]
+            for i in range(1, len(processed_images)):
+                if processed_images[i].shape[1:] != base_shape:
+                    # Resize to match the first image's dimensions
+                    processed_images[i] = comfy.utils.common_upscale(processed_images[i].movedim(-1, 1), base_shape[1], base_shape[0], "lanczos", "center").movedim(1, -1)
+
+                batch_tensor = torch.cat((batch_tensor, processed_images[i]), dim=0)
+            # Concatenate using torch.cat
+        else:
+            batch_tensor = None  # or handle the empty case as needed
+        return (batch_tensor, )
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalImageBatch": ComfyUIDeployExternalImageBatch}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalImageBatch": "External Image Batch (ComfyUI Deploy)"}

comfy-nodes/external_lora.py

@@ -5,6 +5,14 @@ import torch
 import folder_paths
 
 
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+WILDCARD = AnyType("*")
+
+
 class ComfyUIDeployExternalLora:
     @classmethod
     def INPUT_TYPES(s):
@@ -16,36 +24,86 @@ class ComfyUIDeployExternalLora:
                 ),
             },
             "optional": {
-                "default_lora_name": (folder_paths.get_filename_list("loras"), ),
-            }
+                "default_lora_name": (folder_paths.get_filename_list("loras"),),
+                "lora_save_name": (  # if `default_lora_name` is a link to download a file, we will attempt to save it with this name
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+                "lora_url": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+            },
         }
 
-    RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
+    RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
 
     FUNCTION = "run"
 
     CATEGORY = "deploy"
 
-    def run(self, input_id, default_lora_name=None):
+    def run(
+        self,
+        input_id,
+        default_lora_name=None,
+        lora_save_name=None,
+        display_name=None,
+        description=None,
+        lora_url=None,
+    ):
         import requests
         import os
         import uuid
 
-        if input_id and input_id.startswith('http'):
-            unique_filename = str(uuid.uuid4()) + ".safetensors"
-            print(unique_filename)
-            print(folder_paths.folder_names_and_paths["loras"][0][0])
-            destination_path = os.path.join(folder_paths.folder_names_and_paths["loras"][0][0], unique_filename)
-            print(destination_path)
-            print("Downloading external lora - " + input_id + " to " + destination_path)
-            response = requests.get(input_id, headers={'User-Agent': 'Mozilla/5.0'}, allow_redirects=True)
-            with open(destination_path, 'wb') as out_file:
-                out_file.write(response.content)
-            return (unique_filename,)
+        if lora_url:
+            if lora_url.startswith("http"):
+                if lora_save_name:
+                    existing_loras = folder_paths.get_filename_list("loras")
+                    # Check if lora_save_name exists in the list
+                    if lora_save_name in existing_loras:
+                        print(f"using lora: {lora_save_name}")
+                        return (lora_save_name,)
+                else:
+                    lora_save_name = str(uuid.uuid4()) + ".safetensors"
+                print(lora_save_name)
+                print(folder_paths.folder_names_and_paths["loras"][0][0])
+                destination_path = os.path.join(
+                    folder_paths.folder_names_and_paths["loras"][0][0], lora_save_name
+                )
+                print(destination_path)
+                print(
+                    "Downloading external lora - "
+                    + lora_url
+                    + " to "
+                    + destination_path
+                )
+                response = requests.get(
+                    lora_url,
+                    headers={"User-Agent": "Mozilla/5.0"},
+                    allow_redirects=True,
+                )
+                with open(destination_path, "wb") as out_file:
+                    out_file.write(response.content)
+                print(f"Ext Lora loading: {lora_url} to {lora_save_name}")
+                return (lora_save_name,)
+            else:
+                print(f"Ext Lora loading: {lora_url}")
+                return (lora_url,)
         else:
+            print(f"Ext Lora loading: {default_lora_name}")
             return (default_lora_name,)
 
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalLora": ComfyUIDeployExternalLora}
-NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalLora": "External Lora (ComfyUI Deploy)"}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalLora": "External Lora (ComfyUI Deploy)"
+}

comfy-nodes/external_number.py

@@ -16,7 +16,15 @@ class ComfyUIDeployExternalNumber:
             "optional": {
                 "default_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "default": 0},
+                    {"multiline": True, "display": "number", "default": 0, "min": -2147483647, "max": 2147483647, "step": 0.01},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
                 ),
             }
         }
@@ -28,10 +36,13 @@ class ComfyUIDeployExternalNumber:
 
     CATEGORY = "number"
 
-    def run(self, input_id, default_value=None):
-        if not input_id or not input_id.strip().isdigit():
+    def run(self, input_id, default_value=None, display_name=None, description=None):
+        try:
+            float_value = float(input_id)
+            print("my number", float_value)
+            return [float_value]
+        except ValueError:
             return [default_value]
-        return [int(input_id)]
 
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalNumber": ComfyUIDeployExternalNumber}

comfy-nodes/external_number_int.py

@@ -16,7 +16,15 @@ class ComfyUIDeployExternalNumberInt:
             "optional": {
                 "default_value": (
                     "INT",
-                    {"multiline": True, "display": "number", "default": 0},
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
                 ),
             }
         }
@@ -28,8 +36,8 @@ class ComfyUIDeployExternalNumberInt:
 
     CATEGORY = "number"
 
-    def run(self, input_id, default_value=None):
-        if not input_id or not input_id.strip().isdigit():
+    def run(self, input_id, default_value=None, display_name=None, description=None):
+        if not input_id or (isinstance(input_id, str) and not input_id.strip().isdigit()):
             return [default_value]
         return [int(input_id)]
 

comfy-nodes/external_number_slider.py

@@ -0,0 +1,56 @@
+class ComfyUIDeployExternalNumberSlider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_number_slider"},
+                ),
+            },
+            "optional": {
+                "default_value": (
+                    "FLOAT",
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0.5, "step": 0.01},
+                ),
+                "min_value": (
+                    "FLOAT",
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0, "step": 0.01},
+                ),
+                "max_value": (
+                    "FLOAT",
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 1, "step": 0.01},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("value",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "number"
+
+    def run(self, input_id, default_value=None, min_value=0, max_value=1, display_name=None, description=None):
+        try:
+            float_value = float(input_id)
+            if min_value <= float_value <= max_value:
+                print("my number", float_value)
+                return [float_value]
+            else:
+                print("Number out of range. Returning default value:", default_value)
+                return [default_value]
+        except ValueError:
+            print("Invalid input. Returning default value:", default_value)
+            return [default_value]
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalNumberSlider": ComfyUIDeployExternalNumberSlider}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalNumberSlider": "External Number Slider (ComfyUI Deploy)"}

comfy-nodes/external_string_combine.py

@@ -0,0 +1,53 @@
+import re
+
+
+class StringFunction:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "action": (["append", "replace"], {}),
+                "tidy_tags": (["yes", "no"], {}),
+            },
+            "optional": {
+                "text_a": ("STRING", {"multiline": True, "dynamicPrompts": False}),
+                "text_b": ("STRING", {"multiline": True, "dynamicPrompts": False}),
+                "text_c": ("STRING", {"multiline": True, "dynamicPrompts": False}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "exec"
+    CATEGORY = "utils"
+    OUTPUT_NODE = True
+
+    def exec(self, action, tidy_tags, text_a="", text_b="", text_c=""):
+        tidy_tags = tidy_tags == "yes"
+        out = ""
+        if action == "append":
+            out = (", " if tidy_tags else "").join(
+                filter(None, [text_a, text_b, text_c])
+            )
+        else:
+            if text_c is None:
+                text_c = ""
+            if text_b.startswith("/") and text_b.endswith("/"):
+                regex = text_b[1:-1]
+                out = re.sub(regex, text_c, text_a)
+            else:
+                out = text_a.replace(text_b, text_c)
+        if tidy_tags:
+            out = re.sub(r"\s{2,}", " ", out)
+            out = out.replace(" ,", ",")
+            out = re.sub(r",{2,}", ",", out)
+            out = out.strip()
+        return {"ui": {"text": (out,)}, "result": (out,)}
+
+
+NODE_CLASS_MAPPINGS = {
+    "ComfyUIDeployStringCombine": StringFunction,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployStringCombine": "String Combine (ComfyUI Deploy)",
+}

comfy-nodes/external_text.py

@@ -18,6 +18,14 @@ class ComfyUIDeployExternalText:
                     "STRING",
                     {"multiline": True, "default": ""},
                 ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
             }
         }
 
@@ -28,7 +36,7 @@ class ComfyUIDeployExternalText:
 
     CATEGORY = "text"
 
-    def run(self, input_id, default_value=None):
+    def run(self, input_id, default_value=None, display_name=None, description=None):
         return [default_value]
 
 

comfy-nodes/external_text_any.py

@@ -0,0 +1,46 @@
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+WILDCARD = AnyType("*")
+
+class ComfyUIDeployExternalTextAny:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_text"},
+                ),
+            },
+            "optional": {
+                "default_value": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+            }
+        }
+
+    RETURN_TYPES = (WILDCARD,)
+    RETURN_NAMES = ("text",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "text"
+
+    def run(self, input_id, default_value=None, display_name=None, description=None):
+        return [default_value]
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalTextAny": ComfyUIDeployExternalTextAny}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalTextAny": "External Text Any (ComfyUI Deploy)"}

comfy-nodes/external_vid.py

@@ -0,0 +1,78 @@
+import os
+import folder_paths
+import uuid
+
+from tqdm import tqdm
+
+video_extensions = ["webm", "mp4", "mkv", "gif"]
+
+
+class ComfyUIDeployExternalVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        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": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_video"},
+                ),
+            },
+            "optional": {
+                "meta_batch": ("VHS_BatchManager",),
+                "default_value": (sorted(files),),
+            },
+        }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("video")
+
+    FUNCTION = "load_video"
+
+    def load_video(self, input_id, default_value):
+        input_dir = folder_paths.get_input_directory()
+        if input_id.startswith("http"):
+            import requests
+
+            print("Fetching video from URL: ", input_id)
+            response = requests.get(input_id, stream=True)
+            file_size = int(response.headers.get("Content-Length", 0))
+            file_extension = input_id.split(".")[-1].split("?")[
+                0
+            ]  # Extract extension and handle URLs with parameters
+            if file_extension not in video_extensions:
+                file_extension = ".mp4"
+
+            unique_filename = str(uuid.uuid4()) + "." + file_extension
+            video_path = os.path.join(input_dir, unique_filename)
+            chunk_size = 1024  # 1 Kibibyte
+
+            num_bars = int(file_size / chunk_size)
+
+            with open(video_path, "wb") as out_file:
+                for chunk in tqdm(
+                    response.iter_content(chunk_size=chunk_size),
+                    total=num_bars,
+                    unit="KB",
+                    desc="Downloading",
+                    leave=True,
+                ):
+                    out_file.write(chunk)
+        else:
+            video_path = os.path.abspath(os.path.join(input_dir, default_value))
+
+        return (video_path,)
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalVid": ComfyUIDeployExternalVideo}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalVid": "External Video (ComfyUI Deploy) path"
+}

comfy-nodes/external_video.py

@@ -0,0 +1,864 @@
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
+# Intended to work with https://github.com/NicholasKao1029/ComfyUI-VideoHelperSuite/tree/main
+import os
+import itertools
+import numpy as np
+import torch
+from typing import Union
+from torch import Tensor
+import cv2
+import psutil
+
+from collections.abc import Mapping
+import folder_paths
+from comfy.utils import common_upscale
+
+### Utils
+import hashlib
+from typing import Iterable
+import shutil
+import subprocess
+import re
+import uuid
+
+import server
+from tqdm import tqdm
+
+BIGMIN = -(2**53 - 1)
+BIGMAX = 2**53 - 1
+
+DIMMAX = 8192
+
+
+def ffmpeg_suitability(path):
+    try:
+        version = subprocess.run(
+            [path, "-version"], check=True, capture_output=True
+        ).stdout.decode("utf-8")
+    except:
+        return 0
+    score = 0
+    # rough layout of the importance of various features
+    simple_criterion = [
+        ("libvpx", 20),
+        ("264", 10),
+        ("265", 3),
+        ("svtav1", 5),
+        ("libopus", 1),
+    ]
+    for criterion in simple_criterion:
+        if version.find(criterion[0]) >= 0:
+            score += criterion[1]
+    # obtain rough compile year from copyright information
+    copyright_index = version.find("2000-2")
+    if copyright_index >= 0:
+        copyright_year = version[copyright_index + 6 : copyright_index + 9]
+        if copyright_year.isnumeric():
+            score += int(copyright_year)
+    return score
+
+
+if "VHS_FORCE_FFMPEG_PATH" in os.environ:
+    ffmpeg_path = os.environ.get("VHS_FORCE_FFMPEG_PATH")
+else:
+    ffmpeg_paths = []
+    try:
+        from imageio_ffmpeg import get_ffmpeg_exe
+
+        imageio_ffmpeg_path = get_ffmpeg_exe()
+        ffmpeg_paths.append(imageio_ffmpeg_path)
+    except:
+        if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
+            raise
+    if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
+        ffmpeg_path = imageio_ffmpeg_path
+    else:
+        system_ffmpeg = shutil.which("ffmpeg")
+        if system_ffmpeg is not None:
+            ffmpeg_paths.append(system_ffmpeg)
+        if os.path.isfile("ffmpeg"):
+            ffmpeg_paths.append(os.path.abspath("ffmpeg"))
+        if os.path.isfile("ffmpeg.exe"):
+            ffmpeg_paths.append(os.path.abspath("ffmpeg.exe"))
+        if len(ffmpeg_paths) == 0:
+            ffmpeg_path = None
+        elif len(ffmpeg_paths) == 1:
+            # Evaluation of suitability isn't required, can take sole option
+            # to reduce startup time
+            ffmpeg_path = ffmpeg_paths[0]
+        else:
+            ffmpeg_path = max(ffmpeg_paths, key=ffmpeg_suitability)
+gifski_path = os.environ.get("VHS_GIFSKI", None)
+if gifski_path is None:
+    gifski_path = os.environ.get("JOV_GIFSKI", None)
+    if gifski_path is None:
+        gifski_path = shutil.which("gifski")
+
+
+def is_safe_path(path):
+    if "VHS_STRICT_PATHS" not in os.environ:
+        return True
+    basedir = os.path.abspath(".")
+    try:
+        common_path = os.path.commonpath([basedir, path])
+    except:
+        # Different drive on windows
+        return False
+    return common_path == basedir
+
+
+def get_sorted_dir_files_from_directory(
+    directory: str,
+    skip_first_images: int = 0,
+    select_every_nth: int = 1,
+    extensions: Iterable = None,
+):
+    directory = strip_path(directory)
+    dir_files = os.listdir(directory)
+    dir_files = sorted(dir_files)
+    dir_files = [os.path.join(directory, x) for x in dir_files]
+    dir_files = list(filter(lambda filepath: os.path.isfile(filepath), dir_files))
+    # filter by extension, if needed
+    if extensions is not None:
+        extensions = list(extensions)
+        new_dir_files = []
+        for filepath in dir_files:
+            ext = "." + filepath.split(".")[-1]
+            if ext.lower() in extensions:
+                new_dir_files.append(filepath)
+        dir_files = new_dir_files
+    # start at skip_first_images
+    dir_files = dir_files[skip_first_images:]
+    dir_files = dir_files[0::select_every_nth]
+    return dir_files
+
+
+# modified from https://stackoverflow.com/questions/22058048/hashing-a-file-in-python
+def calculate_file_hash(filename: str, hash_every_n: int = 1):
+    # Larger video files were taking >.5 seconds to hash even when cached,
+    # so instead the modified time from the filesystem is used as a hash
+    h = hashlib.sha256()
+    h.update(filename.encode())
+    h.update(str(os.path.getmtime(filename)).encode())
+    return h.hexdigest()
+
+
+prompt_queue = server.PromptServer.instance.prompt_queue
+
+
+def requeue_workflow_unchecked():
+    """Requeues the current workflow without checking for multiple requeues"""
+    currently_running = prompt_queue.currently_running
+    (_, _, prompt, extra_data, outputs_to_execute) = next(
+        iter(currently_running.values())
+    )
+
+    # Ensure batch_managers are marked stale
+    prompt = prompt.copy()
+    for uid in prompt:
+        if prompt[uid]["class_type"] == "VHS_BatchManager":
+            prompt[uid]["inputs"]["requeue"] = (
+                prompt[uid]["inputs"].get("requeue", 0) + 1
+            )
+
+    # execution.py has guards for concurrency, but server doesn't.
+    # TODO: Check that this won't be an issue
+    number = -server.PromptServer.instance.number
+    server.PromptServer.instance.number += 1
+    prompt_id = str(server.uuid.uuid4())
+    prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+
+
+requeue_guard = [None, 0, 0, {}]
+
+
+def requeue_workflow(requeue_required=(-1, True)):
+    assert len(prompt_queue.currently_running) == 1
+    global requeue_guard
+    (run_number, _, prompt, _, _) = next(iter(prompt_queue.currently_running.values()))
+    if requeue_guard[0] != run_number:
+        # Calculate a count of how many outputs are managed by a batch manager
+        managed_outputs = 0
+        for bm_uid in prompt:
+            if prompt[bm_uid]["class_type"] == "VHS_BatchManager":
+                for output_uid in prompt:
+                    if prompt[output_uid]["class_type"] in ["VHS_VideoCombine"]:
+                        for inp in prompt[output_uid]["inputs"].values():
+                            if inp == [bm_uid, 0]:
+                                managed_outputs += 1
+        requeue_guard = [run_number, 0, managed_outputs, {}]
+    requeue_guard[1] = requeue_guard[1] + 1
+    requeue_guard[3][requeue_required[0]] = requeue_required[1]
+    if requeue_guard[1] == requeue_guard[2] and max(requeue_guard[3].values()):
+        requeue_workflow_unchecked()
+
+
+def get_audio(file, start_time=0, duration=0):
+    args = [ffmpeg_path, "-i", file]
+    if start_time > 0:
+        args += ["-ss", str(start_time)]
+    if duration > 0:
+        args += ["-t", str(duration)]
+    try:
+        # TODO: scan for sample rate and maintain
+        res = subprocess.run(
+            args + ["-f", "f32le", "-"], capture_output=True, check=True
+        )
+        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
+        match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
+    except subprocess.CalledProcessError as e:
+        raise Exception(
+            f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
+        )
+    if match:
+        ar = int(match.group(1))
+        # NOTE: Just throwing an error for other channel types right now
+        # Will deal with issues if they come
+        ac = {"mono": 1, "stereo": 2}[match.group(2)]
+    else:
+        ar = 44100
+        ac = 2
+    audio = audio.reshape((-1, ac)).transpose(0, 1).unsqueeze(0)
+    return {"waveform": audio, "sample_rate": ar}
+
+
+class LazyAudioMap(Mapping):
+    def __init__(self, file, start_time, duration):
+        self.file = file
+        self.start_time = start_time
+        self.duration = duration
+        self._dict = None
+
+    def __getitem__(self, key):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return self._dict[key]
+
+    def __iter__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return iter(self._dict)
+
+    def __len__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return len(self._dict)
+
+
+def lazy_get_audio(file, start_time=0, duration=0):
+    return LazyAudioMap(file, start_time, duration)
+
+
+def lazy_eval(func):
+    class Cache:
+        def __init__(self, func):
+            self.res = None
+            self.func = func
+
+        def get(self):
+            if self.res is None:
+                self.res = self.func()
+            return self.res
+
+    cache = Cache(func)
+    return lambda: cache.get()
+
+
+def is_url(url):
+    return url.split("://")[0] in ["http", "https"]
+
+
+def validate_sequence(path):
+    # Check if path is a valid ffmpeg sequence that points to at least one file
+    (path, file) = os.path.split(path)
+    if not os.path.isdir(path):
+        return False
+    match = re.search("%0?\d+d", file)
+    if not match:
+        return False
+    seq = match.group()
+    if seq == "%d":
+        seq = "\\\\d+"
+    else:
+        seq = "\\\\d{%s}" % seq[1:-1]
+    file_matcher = re.compile(re.sub("%0?\d+d", seq, file))
+    for file in os.listdir(path):
+        if file_matcher.fullmatch(file):
+            return True
+    return False
+
+
+def strip_path(path):
+    # This leaves whitespace inside quotes and only a single "
+    # thus ' ""test"' -> '"test'
+    # consider path.strip(string.whitespace+"\"")
+    # or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1)
+    path = path.strip()
+    if path.startswith('"'):
+        path = path[1:]
+    if path.endswith('"'):
+        path = path[:-1]
+    return path
+
+
+def hash_path(path):
+    if path is None:
+        return "input"
+    if is_url(path):
+        return "url"
+    return calculate_file_hash(path.strip('"'))
+
+
+def validate_path(path, allow_none=False, allow_url=True):
+    if path is None:
+        return allow_none
+    if is_url(path):
+        # Probably not feasible to check if url resolves here
+        return True if allow_url else "URLs are unsupported for this path"
+    if not os.path.isfile(path.strip('"')):
+        return "Invalid file path: {}".format(path)
+    return True
+
+
+### Utils
+
+video_extensions = ["webm", "mp4", "mkv", "gif"]
+
+
+def is_gif(filename) -> bool:
+    file_parts = filename.split(".")
+    return len(file_parts) > 1 and file_parts[-1] == "gif"
+
+
+def target_size(
+    width, height, force_size, custom_width, custom_height
+) -> tuple[int, int]:
+    if force_size == "Custom":
+        return (custom_width, custom_height)
+    elif force_size == "Custom Height":
+        force_size = "?x" + str(custom_height)
+    elif force_size == "Custom Width":
+        force_size = str(custom_width) + "x?"
+
+    if force_size != "Disabled":
+        force_size = force_size.split("x")
+        if force_size[0] == "?":
+            width = (width * int(force_size[1])) // height
+            # Limit to a multple of 8 for latent conversion
+            width = int(width) + 4 & ~7
+            height = int(force_size[1])
+        elif force_size[1] == "?":
+            height = (height * int(force_size[0])) // width
+            height = int(height) + 4 & ~7
+            width = int(force_size[0])
+        else:
+            width = int(force_size[0])
+            height = int(force_size[1])
+    return (width, height)
+
+
+def validate_index(
+    index: int,
+    length: int = 0,
+    is_range: bool = False,
+    allow_negative=False,
+    allow_missing=False,
+) -> int:
+    # if part of range, do nothing
+    if is_range:
+        return index
+    # otherwise, validate index
+    # validate not out of range - only when latent_count is passed in
+    if length > 0 and index > length - 1 and not allow_missing:
+        raise IndexError(f"Index '{index}' out of range for {length} item(s).")
+    # if negative, validate not out of range
+    if index < 0:
+        if not allow_negative:
+            raise IndexError(f"Negative indeces not allowed, but was '{index}'.")
+        conv_index = length + index
+        if conv_index < 0 and not allow_missing:
+            raise IndexError(
+                f"Index '{index}', converted to '{conv_index}' out of range for {length} item(s)."
+            )
+        index = conv_index
+    return index
+
+
+def convert_to_index_int(
+    raw_index: str,
+    length: int = 0,
+    is_range: bool = False,
+    allow_negative=False,
+    allow_missing=False,
+) -> int:
+    try:
+        return validate_index(
+            int(raw_index),
+            length=length,
+            is_range=is_range,
+            allow_negative=allow_negative,
+            allow_missing=allow_missing,
+        )
+    except ValueError as e:
+        raise ValueError(f"Index '{raw_index}' must be an integer.", e)
+
+
+def convert_str_to_indexes(
+    indexes_str: str, length: int = 0, allow_missing=False
+) -> list[int]:
+    if not indexes_str:
+        return []
+    int_indexes = list(range(0, length))
+    allow_negative = length > 0
+    chosen_indexes = []
+    # parse string - allow positive ints, negative ints, and ranges separated by ':'
+    groups = indexes_str.split(",")
+    groups = [g.strip() for g in groups]
+    for g in groups:
+        # parse range of indeces (e.g. 2:16)
+        if ":" in g:
+            index_range = g.split(":", 2)
+            index_range = [r.strip() for r in index_range]
+
+            start_index = index_range[0]
+            if len(start_index) > 0:
+                start_index = convert_to_index_int(
+                    start_index,
+                    length=length,
+                    is_range=True,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            else:
+                start_index = 0
+            end_index = index_range[1]
+            if len(end_index) > 0:
+                end_index = convert_to_index_int(
+                    end_index,
+                    length=length,
+                    is_range=True,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            else:
+                end_index = length
+            # support step as well, to allow things like reversing, every-other, etc.
+            step = 1
+            if len(index_range) > 2:
+                step = index_range[2]
+                if len(step) > 0:
+                    step = convert_to_index_int(
+                        step,
+                        length=length,
+                        is_range=True,
+                        allow_negative=True,
+                        allow_missing=True,
+                    )
+                else:
+                    step = 1
+            # if latents were passed in, base indeces on known latent count
+            if len(int_indexes) > 0:
+                chosen_indexes.extend(int_indexes[start_index:end_index][::step])
+            # otherwise, assume indeces are valid
+            else:
+                chosen_indexes.extend(list(range(start_index, end_index, step)))
+        # parse individual indeces
+        else:
+            chosen_indexes.append(
+                convert_to_index_int(
+                    g,
+                    length=length,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            )
+    return chosen_indexes
+
+
+def select_indexes(input_obj: Union[Tensor, list], idxs: list):
+    if type(input_obj) == Tensor:
+        return input_obj[idxs]
+    else:
+        return [input_obj[i] for i in idxs]
+
+
+def select_indexes_from_str(
+    input_obj: Union[Tensor, list], indexes: str, err_if_missing=True, err_if_empty=True
+):
+    real_idxs = convert_str_to_indexes(
+        indexes, len(input_obj), allow_missing=not err_if_missing
+    )
+    if err_if_empty and len(real_idxs) == 0:
+        raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
+    return select_indexes(input_obj, real_idxs)
+
+
+###
+
+
+def cv_frame_generator(
+    video,
+    force_rate,
+    frame_load_cap,
+    skip_first_frames,
+    select_every_nth,
+    meta_batch=None,
+    unique_id=None,
+):
+    video_cap = cv2.VideoCapture(strip_path(video))
+    if not video_cap.isOpened():
+        raise ValueError(f"{video} could not be loaded with cv.")
+    pbar = None
+
+    # extract video metadata
+    fps = video_cap.get(cv2.CAP_PROP_FPS)
+    width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(video_cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    duration = total_frames / fps
+
+    # set video_cap to look at start_index frame
+    total_frame_count = 0
+    total_frames_evaluated = -1
+    frames_added = 0
+    base_frame_time = 1 / fps
+    prev_frame = None
+
+    if force_rate == 0:
+        target_frame_time = base_frame_time
+    else:
+        target_frame_time = 1 / force_rate
+
+    yield (width, height, fps, duration, total_frames, target_frame_time)
+    if meta_batch is not None:
+        yield min(frame_load_cap, total_frames)
+
+    time_offset = target_frame_time - base_frame_time
+    while video_cap.isOpened():
+        if time_offset < target_frame_time:
+            is_returned = video_cap.grab()
+            # 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
+        else:
+            total_frames_evaluated += 1
+
+        # if should not be selected, skip doing anything with frame
+        if total_frames_evaluated % select_every_nth != 0:
+            continue
+
+        # 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?
+        # To my testing: No. opencv has no support for alpha
+        unused, frame = video_cap.retrieve()
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # convert frame to comfyui's expected format
+        # TODO: frame contains no exif information. Check if opencv2 has already applied
+        frame = np.array(frame, dtype=np.float32)
+        torch.from_numpy(frame).div_(255)
+        if prev_frame is not None:
+            inp = yield prev_frame
+            if inp is not None:
+                # ensure the finally block is called
+                return
+        prev_frame = frame
+        frames_added += 1
+        if pbar is not None:
+            pbar.update_absolute(frames_added, frame_load_cap)
+        # if cap exists and we've reached it, stop processing frames
+        if frame_load_cap > 0 and frames_added >= frame_load_cap:
+            break
+    if meta_batch is not None:
+        meta_batch.inputs.pop(unique_id)
+        meta_batch.has_closed_inputs = True
+    if prev_frame is not None:
+        yield prev_frame
+
+
+def batched(it, n):
+    while batch := tuple(itertools.islice(it, n)):
+        yield batch
+
+
+def batched_vae_encode(images, vae, frames_per_batch):
+    for batch in batched(images, frames_per_batch):
+        image_batch = torch.from_numpy(np.array(batch))
+        yield from vae.encode(image_batch).numpy()
+
+
+def load_video_cv(
+    video: str,
+    force_rate: int,
+    force_size: str,
+    custom_width: int,
+    custom_height: int,
+    frame_load_cap: int,
+    skip_first_frames: int,
+    select_every_nth: int,
+    meta_batch=None,
+    unique_id=None,
+    memory_limit_mb=None,
+    vae=None,
+):
+    if meta_batch is None or unique_id not in meta_batch.inputs:
+        gen = cv_frame_generator(
+            video,
+            force_rate,
+            frame_load_cap,
+            skip_first_frames,
+            select_every_nth,
+            meta_batch,
+            unique_id,
+        )
+        (width, height, fps, duration, total_frames, target_frame_time) = next(gen)
+
+        if meta_batch is not None:
+            meta_batch.inputs[unique_id] = (
+                gen,
+                width,
+                height,
+                fps,
+                duration,
+                total_frames,
+                target_frame_time,
+            )
+            meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
+
+    else:
+        (gen, width, height, fps, duration, total_frames, target_frame_time) = (
+            meta_batch.inputs[unique_id]
+        )
+
+    memory_limit = None
+    if memory_limit_mb is not None:
+        memory_limit *= 2**20
+    else:
+        # TODO: verify if garbage collection should be performed here.
+        # leaves ~128 MB unreserved for safety
+        try:
+            memory_limit = (
+                psutil.virtual_memory().available + psutil.swap_memory().free
+            ) - 2**27
+        except:
+            print(
+                "Failed to calculate available memory. Memory load limit has been disabled"
+            )
+    if memory_limit is not None:
+        if vae is not None:
+            # space required to load as f32, exist as latent with wiggle room, decode to f32
+            max_loadable_frames = int(
+                memory_limit // (width * height * 3 * (4 + 4 + 1 / 10))
+            )
+        else:
+            # TODO: use better estimate for when vae is not None
+            # Consider completely ignoring for load_latent case?
+            max_loadable_frames = int(memory_limit // (width * height * 3 * (0.1)))
+        if meta_batch is not None:
+            if meta_batch.frames_per_batch > max_loadable_frames:
+                raise RuntimeError(
+                    f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory"
+                )
+            gen = itertools.islice(gen, meta_batch.frames_per_batch)
+        else:
+            original_gen = gen
+            gen = itertools.islice(gen, max_loadable_frames)
+    downscale_ratio = getattr(vae, "downscale_ratio", 8)
+    frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
+    if force_size != "Disabled" or vae is not None:
+        new_size = target_size(
+            width, height, force_size, custom_width, custom_height, downscale_ratio
+        )
+        if new_size[0] != width or new_size[1] != height:
+
+            def rescale(frame):
+                s = torch.from_numpy(
+                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
+                )
+                s = s.movedim(-1, 1)
+                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+                return s.movedim(1, -1).numpy()
+
+            gen = itertools.chain.from_iterable(
+                map(rescale, batched(gen, frames_per_batch))
+            )
+    else:
+        new_size = width, height
+    if vae is not None:
+        gen = batched_vae_encode(gen, vae, frames_per_batch)
+        vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
+        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
+    else:
+        # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+        images = torch.from_numpy(
+            np.fromiter(gen, np.dtype((np.float32, (new_size[1], new_size[0], 3))))
+        )
+    if meta_batch is None and memory_limit is not None:
+        try:
+            next(original_gen)
+            raise RuntimeError(
+                f"Memory limit hit after loading {len(images)} frames. Stopping execution."
+            )
+        except StopIteration:
+            pass
+    if len(images) == 0:
+        raise RuntimeError("No frames generated")
+
+    # Setup lambda for lazy audio capture
+    audio = lazy_get_audio(
+        video,
+        skip_first_frames * target_frame_time,
+        frame_load_cap * target_frame_time * select_every_nth,
+    )
+    # Adjust target_frame_time for select_every_nth
+    target_frame_time *= select_every_nth
+    video_info = {
+        "source_fps": fps,
+        "source_frame_count": total_frames,
+        "source_duration": duration,
+        "source_width": width,
+        "source_height": height,
+        "loaded_fps": 1 / target_frame_time,
+        "loaded_frame_count": len(images),
+        "loaded_duration": len(images) * target_frame_time,
+        "loaded_width": new_size[0],
+        "loaded_height": new_size[1],
+    }
+    if vae is None:
+        return (images, len(images), audio, video_info, None)
+    else:
+        return (None, len(images), audio, video_info, {"samples": images})
+
+
+# modeled after Video upload node
+class ComfyUIDeployExternalVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        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": {
+                    "input_id": (
+                        "STRING",
+                        {"multiline": False, "default": "input_video"},
+                    ),
+                     "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
+                     "force_size": (["Disabled", "Custom Height", "Custom Width", "Custom", "256x?", "?x256", "256x256", "512x?", "?x512", "512x512"],),
+                     "custom_width": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
+                     "custom_height": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
+                     "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                     "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                     "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                     },
+                "optional": {
+                    "meta_batch": ("VHS_BatchManager",),
+                    "vae": ("VAE",),
+                    "default_video": (sorted(files),),
+                    "display_name": (
+                        "STRING",
+                        {"multiline": False, "default": ""},
+                    ),
+                    "description": (
+                        "STRING",
+                        {"multiline": True, "default": ""},
+                    ),
+                },
+                "hidden": {
+                    "unique_id": "UNIQUE_ID"
+                },
+                }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
+    RETURN_NAMES = (
+        "IMAGE",
+        "frame_count",
+        "audio",
+        "video_info",
+        "LATENT",
+    )
+
+    FUNCTION = "load_video"
+
+    def load_video(self, **kwargs):
+        input_id = kwargs.get("input_id")
+        force_rate = kwargs.get("force_rate")
+        force_size = kwargs.get("force_size", "Disabled")
+        custom_width = kwargs.get("custom_width")
+        custom_height = kwargs.get("custom_height")
+        frame_load_cap = kwargs.get("frame_load_cap")
+        skip_first_frames = kwargs.get("skip_first_frames")
+        select_every_nth = kwargs.get("select_every_nth")
+        meta_batch = kwargs.get("meta_batch")
+        unique_id = kwargs.get("unique_id")
+
+
+        input_dir = folder_paths.get_input_directory()
+        if input_id.startswith("http"):
+            import requests
+
+            print("Fetching video from URL: ", input_id)
+            response = requests.get(input_id, stream=True)
+            file_size = int(response.headers.get("Content-Length", 0))
+            file_extension = input_id.split(".")[-1].split("?")[
+                0
+            ]  # Extract extension and handle URLs with parameters
+            if file_extension not in video_extensions:
+                file_extension = ".mp4"
+
+            unique_filename = str(uuid.uuid4()) + "." + file_extension
+            video_path = os.path.join(input_dir, unique_filename)
+            chunk_size = 1024  # 1 Kibibyte
+
+            num_bars = int(file_size / chunk_size)
+
+            with open(video_path, "wb") as out_file:
+                for chunk in tqdm(
+                    response.iter_content(chunk_size=chunk_size),
+                    total=num_bars,
+                    unit="KB",
+                    desc="Downloading",
+                    leave=True,
+                ):
+                    out_file.write(chunk)
+        else:
+            video = kwargs.get("default_video", None)
+            if video is None:
+                raise "No default video given and no external video provided"
+            video_path = folder_paths.get_annotated_filepath(video.strip('"'))
+
+        return load_video_cv(
+            video=video_path,
+            force_rate=force_rate,
+            force_size=force_size,
+            custom_width=custom_width,
+            custom_height=custom_height,
+            frame_load_cap=frame_load_cap,
+            skip_first_frames=skip_first_frames,
+            select_every_nth=select_every_nth,
+            meta_batch=meta_batch,
+            unique_id=unique_id,
+        )
+
+    @classmethod
+    def IS_CHANGED(s, video, **kwargs):
+        image_path = folder_paths.get_annotated_filepath(video)
+        return calculate_file_hash(image_path)
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalVideo": ComfyUIDeployExternalVideo}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalVideo": "External Video (ComfyUI Deploy x VHS)"
+}

comfy-nodes/input_websocket_image.py

@@ -0,0 +1,66 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+from server import PromptServer, BinaryEventTypes
+import asyncio
+
+from globals import streaming_prompt_metadata, max_output_id_length
+
+class ComfyDeployWebscoketImageInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_id"},
+                ),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "default_value": ("IMAGE", ),
+                "client_id": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+            }
+        }
+
+    OUTPUT_NODE = True
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("images",)
+
+    FUNCTION = "run"
+
+    @classmethod
+    def VALIDATE_INPUTS(s, input_id):
+        try:
+            if len(input_id.encode('ascii')) > max_output_id_length:
+                raise ValueError(f"input_id size is greater than {max_output_id_length} bytes")
+        except UnicodeEncodeError:
+            raise ValueError("input_id is not ASCII encodable")
+
+        return True
+
+    def run(self, input_id, seed, default_value=None ,client_id=None):
+        # print(streaming_prompt_metadata[client_id].inputs)
+        if client_id in streaming_prompt_metadata and input_id in streaming_prompt_metadata[client_id].inputs:
+            if isinstance(streaming_prompt_metadata[client_id].inputs[input_id], Image.Image):
+                print("Returning image from websocket input")
+                
+                image = streaming_prompt_metadata[client_id].inputs[input_id]
+                
+                image = ImageOps.exif_transpose(image)
+                image = image.convert("RGB")
+                image = np.array(image).astype(np.float32) / 255.0
+                image = torch.from_numpy(image)[None,]
+                
+                return [image]
+
+        print("Returning default value")
+        return [default_value]
+
+NODE_CLASS_MAPPINGS = {"ComfyDeployWebscoketImageInput": ComfyDeployWebscoketImageInput}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployWebscoketImageInput": "Image Websocket Input (ComfyDeploy)"}

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)"}

comfy-nodes/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 ComfyDeployOutputImage:
+    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 = {"ComfyDeployOutputImage": ComfyDeployOutputImage}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyDeployOutputImage": "Image Output (ComfyDeploy)"
+}

comfy-nodes/output_websocket_image.py

@@ -0,0 +1,71 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+from server import PromptServer, BinaryEventTypes
+import asyncio
+
+from globals import send_image, max_output_id_length
+
+class ComfyDeployWebscoketImageOutput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "output_id": (
+                    "STRING",
+                    {"multiline": False, "default": "output_id"},
+                ),
+                "images": ("IMAGE", ),
+                "file_type": (["WEBP", "PNG", "JPEG"], ),
+                "quality": ("INT", {"default": 80, "min": 1, "max": 100, "step": 1}),
+            },
+            "optional": {
+                    "client_id": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+            }
+            # "hidden": {"client_id": "CLIENT_ID"},
+        }
+
+    OUTPUT_NODE = True
+
+    RETURN_TYPES = ()
+    RETURN_NAMES = ("text",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "output"
+    
+    @classmethod
+    def VALIDATE_INPUTS(s, output_id):
+        try:
+            if len(output_id.encode('ascii')) > max_output_id_length:
+                raise ValueError(f"output_id size is greater than {max_output_id_length} bytes")
+        except UnicodeEncodeError:
+            raise ValueError("output_id is not ASCII encodable")
+
+        return True
+
+    def run(self, output_id, images, file_type, quality, client_id):
+        prompt_server = PromptServer.instance
+        loop = prompt_server.loop
+        
+        def schedule_coroutine_blocking(target, *args):
+            future = asyncio.run_coroutine_threadsafe(target(*args), loop)
+            return future.result()  # This makes the call blocking
+        
+        for tensor in images:
+            array = 255.0 * tensor.cpu().numpy()
+            image = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8))
+
+            schedule_coroutine_blocking(send_image, [file_type, image, None, quality], client_id, output_id)
+            print("Image sent")
+
+        return {"ui": {}}
+        
+
+
+NODE_CLASS_MAPPINGS = {"ComfyDeployWebscoketImageOutput": ComfyDeployWebscoketImageOutput}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployWebscoketImageOutput": "Image Websocket Output (ComfyDeploy)"}

globals.py

@@ -0,0 +1,136 @@
+import struct
+from enum import Enum
+import aiohttp
+from typing import List, Union, Any, Optional
+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"
+    SUCCESS = "success"
+    FAILED = "failed"
+    UPLOADING = "uploading"
+
+
+class StreamingPrompt(BaseModel):
+    workflow_api: Any
+    auth_token: str
+    inputs: dict[str, Union[str, bytes, Image.Image]]
+    running_prompt_ids: set[str] = set()
+    status_endpoint: Optional[str]
+    file_upload_endpoint: Optional[str]
+    workflow: Any
+    gpu_event_id: Optional[str] = None
+
+
+class SimplePrompt(BaseModel):
+    status_endpoint: Optional[str]
+    file_upload_endpoint: Optional[str]
+
+    token: Optional[str]
+
+    workflow_api: dict
+    status: Status = Status.NOT_STARTED
+    progress: set = set()
+    last_updated_node: Optional[str] = None
+    uploading_nodes: set = set()
+    done: bool = False
+    is_realtime: bool = False
+    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):
+    max_length = max_output_id_length
+    output_id = output_id[:max_length]
+    padded_output_id = output_id.ljust(max_length, "\x00")
+    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"):
+            resampling = Image.Resampling.BILINEAR
+        else:
+            resampling = Image.ANTIALIAS
+
+        image = ImageOps.contain(image, (max_size, max_size), resampling)
+    type_num = 1
+    if image_type == "JPEG":
+        type_num = 1
+    elif image_type == "PNG":
+        type_num = 2
+    elif image_type == "WEBP":
+        type_num = 3
+
+    bytesIO = BytesIO()
+    header = struct.pack(">I", type_num)
+    # 4 bytes for the type
+    bytesIO.write(header)
+    # 10 bytes for the output_id
+    position_before = bytesIO.tell()
+    bytesIO.write(encoded_output_id)
+    position_after = bytesIO.tell()
+    bytes_written = position_after - position_before
+    print(f"Bytes written: {bytes_written}")
+
+    image.save(bytesIO, format=image_type, quality=quality, compress_level=1)
+    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:
+        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}")
+
+    packed = struct.pack(">I", event)
+    message = bytearray(packed)
+    message.extend(data)
+    return message
+
+
+async def send_bytes(event, data, sid=None):
+    message = encode_bytes(event, data)
+
+    print("sending image to ", event, sid)
+
+    if sid is None:
+        _sockets = list(sockets.values())
+        for ws in _sockets:
+            await send_socket_catch_exception(ws.send_bytes, message)
+    elif sid in sockets:
+        await send_socket_catch_exception(sockets[sid].send_bytes, message)

prestartup_script.py

@@ -58,6 +58,9 @@ if cd_enable_log:
    print("** Comfy Deploy logging enabled")
    setup() 
 
+
+# Store the original working directory
+original_cwd = os.getcwd()
 try:
     # Get the absolute path of the script's directory
     script_dir = os.path.dirname(os.path.abspath(__file__))
@@ -67,3 +70,6 @@ try:
     print(f"** Comfy Deploy Revision: {current_git_commit}")
 except Exception as e:
     print(f"** Comfy Deploy failed to get current git commit: {str(e)}")
+finally:
+    # Change back to the original directory
+    os.chdir(original_cwd)

pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "comfyui-deploy"
+description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
+version = "1.1.0"
+license = { file = "LICENSE" }
+dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
+
+[project.urls]
+Repository = "https://github.com/BennyKok/comfyui-deploy"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "comfydeploy"
+DisplayName = "comfyui-deploy"
+Icon = ""

requirements.txt

@@ -1 +1,7 @@
 aiofiles
+pydantic
+opencv-python
+imageio-ffmpeg
+brotli
+tabulate
+# logfire

web-plugin/api.js

@@ -1,4 +0,0 @@
-/** @typedef {import('../../../web/scripts/api.js').api} API*/
-import { api as _api } from '../../scripts/api.js';
-/** @type {API} */
-export const api = _api;

web-plugin/app.js

@@ -1,4 +0,0 @@
-/** @typedef {import('../../../web/scripts/app.js').ComfyApp} ComfyApp*/
-import { app as _app } from '../../scripts/app.js';
-/** @type {ComfyApp} */
-export const app = _app;

web-plugin/widgets.js

@@ -1,18 +0,0 @@
-// /** @typedef {import('../../../web/scripts/api.js').api} API*/
-// import { api as _api } from "../../scripts/api.js";
-// /** @type {API} */
-// export const api = _api;
-
-/** @typedef {typeof import('../../../web/scripts/widgets.js').ComfyWidgets} Widgets*/
-import { ComfyWidgets as _ComfyWidgets } from "../../scripts/widgets.js";
-
-/**
- * @type {Widgets}
- */
-export const ComfyWidgets = _ComfyWidgets;
-
-// import { LGraphNode as _LGraphNode } from "../../types/litegraph.js";
-
-/** @typedef {typeof import('../../../web/types/litegraph.js').LGraphNode} LGraphNode*/
-/** @type {LGraphNode}*/
-export const LGraphNode = LiteGraph.LGraphNode;

web/package.json

@@ -74,7 +74,7 @@
     "mitata": "^0.1.6",
     "ms": "^2.1.3",
     "nanoid": "^5.0.4",
-    "next": "14.1",
+    "next": "14.2",
     "next-plausible": "^3.12.0",
     "next-themes": "^0.2.1",
     "next-usequerystate": "^1.13.2",

web/src/components/customInputNodes.tsx

@@ -6,4 +6,5 @@ export const customInputNodes: Record<string, string> = {
   ComfyUIDeployExternalNumberInt: "integer",
   ComfyUIDeployExternalLora: "string - (public lora download url)",
   ComfyUIDeployExternalCheckpoint: "string - (public checkpoints download url)",
+  ComfyUIDeployExternalFaceModel: "string - (public face model download url)",
 };

web/src/routes/registerCreateRunRoute.ts

@@ -51,7 +51,9 @@ const createRunRoute = createRoute({
 export const registerCreateRunRoute = (app: App) => {
   app.openapi(createRunRoute, async (c) => {
     const data = c.req.valid("json");
-    const origin = new URL(c.req.url).origin;
+    const proto = c.req.headers.get('x-forwarded-proto')  || "http";
+    const host = c.req.headers.get('x-forwarded-host') || c.req.headers.get('host');
+    const origin = `${proto}://${host}` || new URL(c.req.url).origin;
     const apiKeyTokenData = c.get("apiKeyTokenData")!;
 
     const { deployment_id, inputs } = data;

web/src/server/createRun.ts

@@ -102,7 +102,7 @@ export const createRun = withServerPromise(
 
     let prompt_id: string | undefined = undefined;
     const shareData = {
-      workflow_api: workflow_api,
+      workflow_api_raw: workflow_api,
       status_endpoint: `${origin}/api/update-run`,
       file_upload_endpoint: `${origin}/api/file-upload`,
     };