update version and toml file

- Add permissions to allow issue writing
- Update action version from `main` to `v1`
- Add condition to run job only for 'BennyKok' repository owner

Co-authored-by: snomiao <snomiao+comfy-pr@gmail.com>

.github/workflows/publish.yml

@@ -7,15 +7,19 @@ on:
     paths:
       - "pyproject.toml"
 
+permissions:
+  issues: write
+
 jobs:
   publish-node:
     name: Publish Custom Node to registry
     runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'BennyKok' }}
     steps:
       - name: Check out code
         uses: actions/checkout@v4
       - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@main
+        uses: Comfy-Org/publish-node-action@v1
         with:
           ## Add your own personal access token to your Github Repository secrets and reference it here.
-          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

README.md

@@ -96,10 +96,6 @@ Major areas
 
 # Self Hosting with Vercel
 
-[![Video](https://img.mytsi.org/i/nFOG479.png)](https://www.youtube.com/watch?v=hWvsEY1cS2M)
-Tutorial Created by [Ross](https://github.com/rossman22590) and [Syn](https://github.com/mortlsyn)
-
-
 Build command
 
 ```

comfy-nodes/external_audio.py

@@ -7,6 +7,7 @@ class ComfyUIDeployExternalAudio:
     RETURN_TYPES = ("AUDIO",)
     RETURN_NAMES = ("audio",)
     FUNCTION = "load_audio"
+    CATEGORY = "🔗ComfyDeploy"
     
     @classmethod
     def INPUT_TYPES(cls):

comfy-nodes/external_boolean.py

@@ -23,8 +23,9 @@ class ComfyUIDeployExternalBoolean:
 
     RETURN_TYPES = ("BOOLEAN",)
     RETURN_NAMES = ("bool_value",)
-    
+        
     FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
     
     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}")

comfy-nodes/external_checkpoints.py

@@ -36,10 +36,11 @@ class ComfyUIDeployExternalCheckpoint:
 
     RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
+    
 
     FUNCTION = "run"
 
-    CATEGORY = "deploy"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(self, input_id, default_value=None, display_name=None, description=None):
         import requests

comfy-nodes/external_exr.py

@@ -10,6 +10,7 @@ class ComfyUIDeployExternalEXR:
     RETURN_TYPES = ("IMAGE", "MASK")
     RETURN_NAMES = ("image", "mask") 
     FUNCTION = "load_exr"
+    CATEGORY = "🔗ComfyDeploy"
     
     @classmethod
     def INPUT_TYPES(cls):

comfy-nodes/external_face_model.py

@@ -48,10 +48,8 @@ class ComfyUIDeployExternalFaceModel:
 
     RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
-
     FUNCTION = "run"
-
-    CATEGORY = "deploy"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(
         self,

comfy-nodes/external_image.py

@@ -29,10 +29,8 @@ class ComfyUIDeployExternalImage:
 
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("image",)
-
     FUNCTION = "run"
-
-    CATEGORY = "image"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(self, input_id, default_value=None, display_name=None, description=None, default_value_url=None):
         image = default_value

comfy-nodes/external_image_alpha.py

@@ -28,10 +28,8 @@ class ComfyUIDeployExternalImageAlpha:
 
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("image",)
-
     FUNCTION = "run"
-
-    CATEGORY = "image"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(self, input_id, default_value=None, display_name=None, description=None):
         image = default_value

comfy-nodes/external_image_batch.py

@@ -34,10 +34,8 @@ class ComfyUIDeployExternalImageBatch:
 
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("image",)
-
     FUNCTION = "run"
-
-    CATEGORY = "image"
+    CATEGORY = "🔗ComfyDeploy"
     
     def process_image(self, image):
         image = ImageOps.exif_transpose(image)

comfy-nodes/external_lora.py

@@ -46,10 +46,8 @@ class ComfyUIDeployExternalLora:
 
     RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
-
     FUNCTION = "run"
-
-    CATEGORY = "deploy"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(
         self,

comfy-nodes/external_number.py

@@ -31,10 +31,8 @@ class ComfyUIDeployExternalNumber:
 
     RETURN_TYPES = ("FLOAT",)
     RETURN_NAMES = ("value",)
-
     FUNCTION = "run"
-
-    CATEGORY = "number"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(self, input_id, default_value=None, display_name=None, description=None):
         try:

comfy-nodes/external_number_int.py

@@ -31,10 +31,8 @@ class ComfyUIDeployExternalNumberInt:
 
     RETURN_TYPES = ("INT",)
     RETURN_NAMES = ("value",)
-
     FUNCTION = "run"
-
-    CATEGORY = "number"
+    CATEGORY = "🔗ComfyDeploy"
 
     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()):

comfy-nodes/external_number_slider.py

@@ -34,10 +34,8 @@ class ComfyUIDeployExternalNumberSlider:
 
     RETURN_TYPES = ("FLOAT",)
     RETURN_NAMES = ("value",)
-
     FUNCTION = "run"
-
-    CATEGORY = "number"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(self, input_id, default_value=None, min_value=0, max_value=1, display_name=None, description=None):
         try:

comfy-nodes/external_string_combine.py

@@ -18,7 +18,7 @@ class StringFunction:
 
     RETURN_TYPES = ("STRING",)
     FUNCTION = "exec"
-    CATEGORY = "utils"
+    CATEGORY = "🔗ComfyDeploy"
     OUTPUT_NODE = True
 
     def exec(self, action, tidy_tags, text_a="", text_b="", text_c=""):

comfy-nodes/external_text.py

@@ -34,7 +34,7 @@ class ComfyUIDeployExternalText:
 
     FUNCTION = "run"
 
-    CATEGORY = "text"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(self, input_id, default_value=None, display_name=None, description=None):
         return [default_value]

comfy-nodes/external_text_any.py

@@ -36,7 +36,7 @@ class ComfyUIDeployExternalTextAny:
 
     FUNCTION = "run"
 
-    CATEGORY = "text"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(self, input_id, default_value=None, display_name=None, description=None):
         return [default_value]

comfy-nodes/external_vid.py

@@ -36,6 +36,7 @@ class ComfyUIDeployExternalVideo:
     RETURN_NAMES = ("video")
 
     FUNCTION = "load_video"
+    CATEGORY = "🔗ComfyDeploy"
 
     def load_video(self, input_id, default_value):
         input_dir = folder_paths.get_input_directory()

comfy-nodes/external_video.py

@@ -791,6 +791,7 @@ class ComfyUIDeployExternalVideo:
     )
 
     FUNCTION = "load_video"
+    CATEGORY = "🔗ComfyDeploy"
 
     def load_video(self, **kwargs):
         input_id = kwargs.get("input_id")

comfy-nodes/input_websocket_image.py

@@ -33,6 +33,7 @@ class ComfyDeployWebscoketImageInput:
     RETURN_NAMES = ("images",)
 
     FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
 
     @classmethod
     def VALIDATE_INPUTS(s, input_id):

comfy-nodes/model_list.py

@@ -48,7 +48,7 @@ class ComfyUIDeployModalList:
 
     FUNCTION = "run"
 
-    CATEGORY = "model"
+    CATEGORY = "🔗ComfyDeploy"
 
     def run(self, model=""):
         # Split the model path by '/' and select the last item

comfy-nodes/output_image.py

@@ -27,6 +27,8 @@ class ComfyDeployOutputImage:
                 ),
                 "file_type": (["png", "jpg", "webp"], {"default": "webp"}),
                 "quality": ("INT", {"default": 80, "min": 1, "max": 100, "step": 1}),
+            },
+            "optional": {
                 "output_id": (
                     "STRING",
                     {"multiline": False, "default": "output_images"},
@@ -39,8 +41,7 @@ class ComfyDeployOutputImage:
     FUNCTION = "run"
 
     OUTPUT_NODE = True
-
-    CATEGORY = "output"
+    CATEGORY = "🔗ComfyDeploy"
     DESCRIPTION = "Saves the input images to your ComfyUI output directory."
 
     def run(

comfy-nodes/output_websocket_image.py

@@ -33,10 +33,8 @@ class ComfyDeployWebscoketImageOutput:
 
     RETURN_TYPES = ()
     RETURN_NAMES = ("text",)
-
     FUNCTION = "run"
-
-    CATEGORY = "output"
+    CATEGORY = "🔗ComfyDeploy"
     
     @classmethod
     def VALIDATE_INPUTS(s, output_id):

custom_routes.py

@@ -30,6 +30,7 @@ from model_management import get_torch_device
 import torch
 import psutil
 from collections import OrderedDict
+import io
 
 # Global session
 client_session = None
@@ -43,7 +44,22 @@ client_session = None
 async def ensure_client_session():
     global client_session
     if client_session is None:
-        client_session = aiohttp.ClientSession()
+        # Configure TCP connection pooling for better performance
+        connector = aiohttp.TCPConnector(
+            limit=30,  # Maximum number of connections in the pool
+            limit_per_host=10,  # Maximum number of connections per host
+            enable_cleanup_closed=True,  # Clean up closed connections
+            force_close=False,  # Keep connections alive when possible
+            ttl_dns_cache=300,  # Cache DNS results for 5 minutes
+        )
+
+        # Create the session with the connector
+        client_session = aiohttp.ClientSession(
+            connector=connector,
+            timeout=ClientTimeout(total=None, connect=5, sock_read=60, sock_connect=5),
+            raise_for_status=False,  # We'll handle status manually
+        )
+        logger.info("Created global client session with optimized connection pooling")
 
 
 async def cleanup():
@@ -123,7 +139,7 @@ async def async_request_with_retry(
                 logger.error(f"Error response body: {error_body}")
 
             if attempt == max_retries - 1:
-                logger.error(f"Request failed after {max_retries} attempts: {e}")
+                logger.error(f"Request {method} : {url} failed after {max_retries} attempts: {e}")
                 raise
 
         await asyncio.sleep(retry_delay)
@@ -131,7 +147,7 @@ async def async_request_with_retry(
 
     total_time = time.time() - start_time
     raise Exception(
-        f"Request failed after {max_retries} attempts and {total_time:.2f} seconds"
+        f"Request {method} : {url} failed after {max_retries} attempts and {total_time:.2f} seconds"
     )
 
 
@@ -288,6 +304,11 @@ def post_prompt(json_data):
 
 
 def randomSeed(num_digits=15):
+    # Special case for SONICSampler which uses np.int32
+    if num_digits == "sonic":
+        return random.randint(0, 2147483647)  # np.iinfo(np.int32).max
+
+    # Original logic for other cases
     range_start = 10 ** (num_digits - 1)
     range_end = (10**num_digits) - 1
     return random.randint(range_start, range_end)
@@ -306,6 +327,13 @@ def apply_random_seed_to_workflow(workflow_api):
                 # If seed is a list, it's an input from another node (generally `external number int`)
                 if isinstance(workflow_api[key]["inputs"]["seed"], list):
                     continue
+                # Special case for SONICSampler
+                if workflow_api[key]["class_type"] == "SONICSampler":
+                    workflow_api[key]["inputs"]["seed"] = randomSeed("sonic")
+                    logger.info(
+                        f"Applied random seed {workflow_api[key]['inputs']['seed']} to SONICSampler"
+                    )
+                    continue
                 if workflow_api[key]["class_type"] == "PromptExpansion":
                     workflow_api[key]["inputs"]["seed"] = randomSeed(8)
                     logger.info(
@@ -469,7 +497,7 @@ async def comfy_deploy_run(request):
                 },
             ) as response:
                 data = await response.json()
-                print(data)
+                # print(data)
 
     if "cd_token" in data:
         token = data["cd_token"]
@@ -1347,13 +1375,17 @@ async def send_json_override(self, event, data, sid=None):
                 )
             )
 
-            print(node_execution_array)
+            # print(node_execution_array)
 
             # print("\n=== Node Execution Times ===")
-            logger.info("Printing Node Execution Times")
+            # logger.info("Printing Node Execution Times")
             logger.info(format_table(headers, table_data))
             # print("========================\n")
 
+            timeline = format_execution_timeline(NODE_EXECUTION_TIMES)
+            logger.info(f"\nNode Execution Timeline:\n{timeline}")
+            # Clear the execution times for the next run
+
     # the last executing event is none, then the workflow is finished
     if event == "executing" and data.get("node") is None:
         mark_prompt_done(prompt_id=prompt_id)
@@ -1622,40 +1654,75 @@ async def file_sender(file_object, chunk_size):
 chunk_size = 1024 * 1024  # 1MB chunks, adjust as needed
 
 
+class ProgressTracker:
+    def __init__(self, data, callback):
+        self.data = data
+        self.callback = callback
+        self.total = len(data)
+        self.uploaded = 0
+        self._cursor = 0
+
+    async def read(self, n=-1):
+        if n == -1:
+            chunk = self.data[self._cursor :]
+            self._cursor = len(self.data)
+        else:
+            chunk = self.data[self._cursor : self._cursor + n]
+            self._cursor += len(chunk)
+
+        if chunk:
+            self.uploaded += len(chunk)
+            if self.callback:
+                await self.callback(self.uploaded, self.total)
+
+        return chunk
+
+
 async def upload_with_retry(
-    session, url, headers, data, max_retries=3, initial_delay=1
+    session,
+    url,
+    headers,
+    data,
+    max_retries=5,
+    initial_delay=1,
+    timeout=300,
+    progress_callback=None,
 ):
-    start_time = time.time()  # Start timing here
-    for attempt in range(max_retries):
+    """Upload data with retry logic and progress tracking"""
+    retries = 0
+    total_size = len(data)
+
+    while True:
         try:
-            async with session.put(url, headers=headers, data=data) as response:
-                upload_duration = time.time() - start_time
-                logger.info(
-                    f"Upload attempt {attempt + 1} completed in {upload_duration:.2f} seconds"
-                )
-                logger.info(f"Upload response status: {response.status}")
+            async with session.put(
+                url,
+                headers=headers,
+                data=data,
+                timeout=aiohttp.ClientTimeout(total=timeout),
+            ) as response:
+                if progress_callback:
+                    await progress_callback(
+                        total_size, total_size
+                    )  # Mark as complete since we can't track progress
 
-                response.raise_for_status()  # This will raise an exception for 4xx and 5xx status codes
+                if response.status >= 200 and response.status < 300:
+                    return response
+                else:
+                    raise aiohttp.ClientError(
+                        f"Upload failed with status {response.status}"
+                    )
 
-                response_text = await response.text()
-                # logger.info(f"Response body: {response_text[:1000]}...")
-
-                logger.info("Upload successful")
-                return response  # Successful upload, exit the retry loop
-
-        except (ClientError, ClientResponseError) as e:
-            logger.error(f"Upload attempt {attempt + 1} failed: {str(e)}")
-            if attempt < max_retries - 1:  # If it's not the last attempt
-                delay = initial_delay * (2**attempt)  # Exponential backoff
-                logger.info(f"Retrying in {delay} seconds...")
-                await asyncio.sleep(delay)
-            else:
-                logger.error("Max retries reached. Upload failed.")
-                raise  # Re-raise the last exception if all retries are exhausted
         except Exception as e:
-            logger.error(f"Unexpected error during upload: {str(e)}")
-            logger.error(traceback.format_exc())
-            raise  # Re-raise unexpected exceptions immediately
+            retries += 1
+            if retries > max_retries:
+                raise
+
+            # Calculate delay with exponential backoff
+            delay = initial_delay * (2 ** (retries - 1))
+            logger.warning(
+                f"Upload attempt {retries} failed: {str(e)}. Retrying in {delay}s..."
+            )
+            await asyncio.sleep(delay)
 
 
 async def upload_file(
@@ -1834,7 +1901,7 @@ async def update_file_status(
         else:
             prompt_metadata[prompt_id].uploading_nodes.discard(node_id)
 
-    logger.info(f"Remaining uploads: {prompt_metadata[prompt_id].uploading_nodes}")
+    # logger.info(f"Remaining uploads: {prompt_metadata[prompt_id].uploading_nodes}")
     # Update the remote status
 
     if have_error:
@@ -1921,42 +1988,68 @@ async def upload_in_background(
     prompt_id: str, data, node_id=None, have_upload=True, node_meta=None
 ):
     try:
-        # await handle_upload(prompt_id, data, 'images', "content_type", "image/png")
-        # await handle_upload(prompt_id, data, 'files', "content_type", "image/png")
-        # await handle_upload(prompt_id, data, 'gifs', "format", "image/gif")
-        # await handle_upload(prompt_id, data, 'mesh', "format", "application/octet-stream")
-
         file_upload_endpoint = prompt_metadata[prompt_id].file_upload_endpoint
 
         if file_upload_endpoint is not None and file_upload_endpoint != "":
-            upload_tasks = [
-                handle_upload(prompt_id, data, "images", "content_type", "image/png"),
-                handle_upload(prompt_id, data, "files", "content_type", "image/png"),
-                handle_upload(prompt_id, data, "gifs", "format", "image/gif"),
-                handle_upload(
-                    prompt_id, data, "mesh", "format", "application/octet-stream"
-                ),
-            ]
+            # Flag to track if we need to update status after uploads
+            has_uploads = False
 
-            await asyncio.gather(*upload_tasks)
+            # Flatten all file types into a single list of uploads
+            for file_type, content_type_key, default_content_type in [
+                ("images", "content_type", "image/png"),
+                ("files", "content_type", "image/png"),
+                ("gifs", "format", "image/gif"),
+                ("model_file", "format", "application/octet-stream"),
+            ]:
+                items = data.get(file_type, [])
+
+                for item in items:
+                    # if is model_file, just add it to the data
+                    if file_type == "model_file":
+                        if isinstance(item, str):
+                            filename = os.path.basename(item)
+                            item = {
+                                "filename": filename,
+                                "subfolder": "",
+                                "type": "output",
+                            }
+
+                    # Skip temp files
+                    if item.get("type") == "temp":
+                        continue
+
+                    # Add to the upload queue instead of uploading immediately
+                    await upload_queue.add_upload(prompt_id, item, node_id)
+                    has_uploads = True
+
+            # Mark the prompt as needing uploads but still report data immediately
+            if has_uploads:
+                await update_file_status(prompt_id, data, True, node_id=node_id)
         else:
-            print("No file upload endpoint, skipping file upload")
+            logger.info("No file upload endpoint, skipping file upload")
 
-        status_endpoint = prompt_metadata[prompt_id].status_endpoint
-        token = prompt_metadata[prompt_id].token
-        gpu_event_id = prompt_metadata[prompt_id].gpu_event_id or None
-        if have_upload:
-            if status_endpoint is not None:
-                body = {
-                    "run_id": prompt_id,
-                    "output_data": data,
-                    "node_meta": node_meta,
-                    "gpu_event_id": gpu_event_id,
-                }
-                # pprint(body)
-                await async_request_with_retry(
-                    "POST", status_endpoint, token=token, json=body
-                )
+        # Still update the API with the output data even if we're not uploading files
+        # status_endpoint = prompt_metadata[prompt_id].status_endpoint
+        # token = prompt_metadata[prompt_id].token
+        # gpu_event_id = prompt_metadata[prompt_id].gpu_event_id or None
+
+        # if have_upload and status_endpoint is not None:
+        #     body = {
+        #         "run_id": prompt_id,
+        #         "output_data": data,
+        #         "node_meta": node_meta,
+        #         "gpu_event_id": gpu_event_id,
+        #     }
+        #     await async_request_with_retry(
+        #         "POST", status_endpoint, token=token, json=body
+        #     )
+
+        # If no uploads are needed, update file status immediately
+        if (
+            not have_upload
+            or file_upload_endpoint is None
+            or file_upload_endpoint == ""
+        ):
             await update_file_status(prompt_id, data, False, node_id=node_id)
     except Exception as e:
         await handle_error(prompt_id, data, e)
@@ -1983,7 +2076,10 @@ async def update_run_with_output(
     have_upload_media = False
     if data is not None:
         have_upload_media = (
-            "images" in data or "files" in data or "gifs" in data or "mesh" in data
+            "images" in data
+            or "files" in data
+            or "gifs" in data
+            or "model_file" in data
         )
     if bypass_upload and have_upload_media:
         print(
@@ -2033,7 +2129,7 @@ async def watch_file_changes(file_path, callback):
     global last_read_line
     last_modified_time = os.stat(file_path).st_mtime
     while True:
-        time.sleep(1)  # sleep for a while to reduce CPU usage
+        await asyncio.sleep(1)  # Use asyncio.sleep instead of time.sleep
         modified_time = os.stat(file_path).st_mtime
         if modified_time != last_modified_time:
             last_modified_time = modified_time
@@ -2073,9 +2169,641 @@ if cd_enable_log:
     run_in_new_thread(watch_file_changes(log_file_path, send_logs_to_websocket))
 
 
+# Initialize the upload queue when the module loads
+async def initialize_upload_queue(app=None):
+    """Initialize the upload queue and start the worker process"""
+    logger.info("Initializing upload queue system...")
+    await upload_queue.ensure_worker_running()
+    logger.info(
+        "Upload queue system initialized with max_concurrent=%d",
+        upload_queue.max_concurrent,
+    )
+
+    # Start the queue monitoring task in the same event loop
+    asyncio.create_task(monitor_upload_queue())
+
+
+# Get the server's event loop and initialize there
+server.PromptServer.instance.app.on_startup.append(initialize_upload_queue)
+
+
+async def monitor_upload_queue():
+    """Monitor the upload queue and log statistics periodically"""
+    while True:
+        try:
+            queue_size = upload_queue.queue.qsize()
+            pending_uploads_count = sum(
+                len(uploads) for uploads in upload_queue.pending_uploads.values()
+            )
+            pending_prompts = len(upload_queue.pending_uploads)
+
+            if queue_size > 0 or pending_uploads_count > 0:
+                logger.info(
+                    f"Upload queue status: {queue_size} queued, {pending_uploads_count} pending "
+                    f"uploads across {pending_prompts} prompts"
+                )
+
+                # If queue is getting big, log a warning
+                if queue_size > 20:
+                    logger.warning(
+                        f"Upload queue is large ({queue_size} items). Check for bottlenecks."
+                    )
+
+                # More detailed logging for large queue
+                if queue_size > 50:
+                    for prompt_id, uploads in upload_queue.pending_uploads.items():
+                        logger.warning(
+                            f"Prompt {prompt_id[:8]}... has {len(uploads)} pending uploads"
+                        )
+        except Exception as e:
+            logger.error(f"Error in upload queue monitor: {str(e)}")
+
+        # Check every 30 seconds
+        await asyncio.sleep(30)
+
+
 # use after calling GET /object_info (it populates the `filename_list_cache` variable)
 @server.PromptServer.instance.routes.get("/comfyui-deploy/filename_list_cache")
 async def get_filename_list_cache(_):
     from folder_paths import filename_list_cache
 
     return web.json_response({"filename_list": filename_list_cache})
+
+
+@server.PromptServer.instance.routes.get("/comfyui-deploy/upload-queue-status")
+async def get_upload_queue_status(request):
+    """Get the current status of the upload queue"""
+    prompt_id = request.rel_url.query.get("prompt_id", None)
+
+    queue_size = upload_queue.queue.qsize()
+    pending_uploads_count = sum(
+        len(uploads) for uploads in upload_queue.pending_uploads.values()
+    )
+
+    status_data = {
+        "queue_size": queue_size,
+        "pending_uploads": pending_uploads_count,
+        "max_concurrent": upload_queue.max_concurrent,
+    }
+
+    # If prompt_id is provided, add specific data for that prompt
+    if prompt_id and prompt_id in upload_queue.pending_uploads:
+        prompt_pending = len(upload_queue.pending_uploads[prompt_id])
+        status_data["prompt_pending"] = prompt_pending
+        status_data["prompt_id"] = prompt_id
+
+    return web.json_response(status_data)
+
+
+@server.PromptServer.instance.routes.post("/comfyui-deploy/cancel-uploads")
+async def cancel_prompt_uploads(request):
+    """Cancel all pending uploads for a specific prompt"""
+    data = await request.json()
+    prompt_id = data.get("prompt_id")
+
+    if not prompt_id:
+        return web.json_response({"error": "prompt_id is required"}, status=400)
+
+    success = await upload_queue.cancel_uploads_for_prompt(prompt_id)
+
+    if success:
+        # Also update the prompt status
+        if prompt_id in prompt_metadata:
+            # Mark as SUCCESS since we're not waiting for uploads anymore
+            await update_run(prompt_id, Status.SUCCESS)
+            return web.json_response(
+                {
+                    "success": True,
+                    "message": f"Cancelled pending uploads for prompt {prompt_id}",
+                }
+            )
+
+    return web.json_response(
+        {
+            "success": False,
+            "message": f"No pending uploads found for prompt {prompt_id}",
+        },
+        status=404,
+    )
+
+
+class UploadQueue:
+    def __init__(self, max_concurrent=3):
+        self.queue = asyncio.Queue()
+        self.max_concurrent = max_concurrent
+        self.active_uploads = 0
+        self.worker_task = None
+        self.lock = asyncio.Lock()
+        self.pending_uploads = {}  # prompt_id -> set of pending upload tasks
+        self.node_uploads = {}  # prompt_id -> {node_id -> set of pending upload tasks}
+        self.node_output_data = {}  # prompt_id -> {node_id -> output data}
+        self.upload_stats = {}  # prompt_id -> {filename: {stats}}
+        self.upload_timeline = {}  # prompt_id -> list of upload events with timing
+        self.last_status_update = 0
+        self.status_update_interval = 5
+        self.upload_lock = asyncio.Lock()  # Add lock for upload coordination
+        self.last_upload_time = 0  # Track the last upload start time
+        self.STAGGER_DELAY = 0.05  # Stagger delay in seconds
+
+    def _log_upload_stats(self, prompt_id):
+        """Log upload statistics in a formatted table with waterfall timing"""
+        if prompt_id not in self.upload_stats or not self.upload_stats[prompt_id]:
+            return
+
+        stats = self.upload_stats[prompt_id]
+        timeline = self.upload_timeline[prompt_id]
+
+        # Sort timeline by start time
+        timeline.sort(key=lambda x: x["start_time"])
+        first_start = min(event["start_time"] for event in timeline)
+
+        headers = [
+            "Node",
+            "File",
+            "Size",
+            "Start Time",
+            "Duration",
+            "Speed",
+            "Timeline",
+        ]
+        data = []
+
+        # Calculate timeline scale (80 chars wide)
+        total_duration = max(event["end_time"] for event in timeline) - first_start
+        scale = 80.0 / total_duration if total_duration > 0 else 1.0
+
+        for event in timeline:
+            filename = event["filename"]
+            file_stats = stats[filename]
+
+            # Calculate timeline bar position and width
+            start_offset = event["start_time"] - first_start
+            duration = event["end_time"] - event["start_time"]
+            bar_start = int(start_offset * scale)
+            bar_width = max(1, int(duration * scale))
+
+            # Create timeline bar
+            timeline_bar = " " * bar_start + "=" * bar_width
+
+            # Format size and speed
+            size_mb = file_stats["size"] / (1024 * 1024)
+            speed_mb = file_stats["size"] / (file_stats["upload_time"] * 1024 * 1024)
+
+            # Format relative time
+            start_time = f"+{start_offset:.2f}s"
+
+            data.append(
+                [
+                    event["node_name"] or "-",
+                    filename,
+                    f"{size_mb:.2f}MB",
+                    start_time,
+                    f"{duration:.2f}s",
+                    f"{speed_mb:.2f}MB/s",
+                    timeline_bar,
+                ]
+            )
+
+        logger.info("\nUpload Performance Summary:")
+        logger.info(format_table(headers, data))
+
+        # Calculate and show totals
+        total_size = sum(s["size"] for s in stats.values())
+        total_time = total_duration
+        avg_speed = total_size / (total_time * 1024 * 1024) if total_time > 0 else 0
+
+        logger.info(f"\nTotal Stats:")
+        logger.info(f"Total Size: {total_size / (1024 * 1024):.2f}MB")
+        logger.info(f"Total Time: {total_time:.2f}s")
+        logger.info(f"Average Speed: {avg_speed:.2f}MB/s")
+
+    async def _process_upload(self, prompt_id, file_info, node_id):
+        """Process a single file upload"""
+        if prompt_id not in prompt_metadata:
+            logger.warning(f"Cannot upload for unknown prompt ID: {prompt_id}")
+            return
+
+        file_upload_endpoint = prompt_metadata[prompt_id].file_upload_endpoint
+        token = prompt_metadata[prompt_id].token
+
+        if not file_upload_endpoint:
+            logger.warning(f"No upload endpoint for prompt ID: {prompt_id}")
+            return
+
+        filename = file_info.get("filename")
+        subfolder = file_info.get("subfolder")
+        file_type = file_info.get("type", "output")
+
+        # Initialize tracking for this prompt if needed
+        if prompt_id not in self.upload_stats:
+            self.upload_stats[prompt_id] = {}
+            self.upload_timeline[prompt_id] = []
+
+        # Determine content type based on file extension
+        file_extension = os.path.splitext(filename)[1]
+        if file_extension in [".jpg", ".jpeg"]:
+            content_type = "image/jpeg"
+        elif file_extension == ".png":
+            content_type = "image/png"
+        elif file_extension == ".webp":
+            content_type = "image/webp"
+        elif file_extension == ".gif":
+            content_type = "image/gif"
+        else:
+            content_type = file_info.get("content_type", "application/octet-stream")
+
+        # Get node name from metadata if available
+        node_name = None
+        if node_id and prompt_id in prompt_metadata:
+            workflow_api = prompt_metadata[prompt_id].workflow_api
+            node = workflow_api.get(node_id)
+            if node:
+                node_name = node.get("class_type", "")
+
+        # Get the full file path and validate
+        filename, output_dir = folder_paths.annotated_filepath(filename)
+        if filename[0] == "/" or ".." in filename:
+            logger.warning(f"Insecure filename path: {filename}")
+            return
+
+        if output_dir is None:
+            output_dir = folder_paths.get_directory_by_type(file_type)
+
+        if output_dir is None:
+            logger.warning(f"{filename} Upload failed: output_dir is None")
+            return
+
+        if subfolder is not None:
+            full_output_dir = os.path.join(output_dir, subfolder)
+            if (
+                os.path.commonpath((os.path.abspath(full_output_dir), output_dir))
+                != output_dir
+            ):
+                logger.warning(f"Insecure subfolder path: {subfolder}")
+                return
+            output_dir = full_output_dir
+
+        filename_base = os.path.basename(filename)
+        file_path = os.path.join(output_dir, filename_base)
+
+        # Record start time
+        start_time = time.perf_counter()
+
+        try:
+            # Get the signed upload URL
+            filename_quoted = quote(filename_base)
+            prompt_id_quoted = quote(prompt_id)
+            content_type_quoted = quote(content_type)
+            target_url = f"{file_upload_endpoint}?file_name={filename_quoted}&run_id={prompt_id_quoted}&type={content_type_quoted}&version=v2"
+
+            result = await async_request_with_retry(
+                "GET", target_url, disable_timeout=True, token=token
+            )
+            signed_url_data = await result.json()
+
+            # Read and upload the file
+            async with aiofiles.open(file_path, "rb") as f:
+                data = await f.read()
+                size = len(data)
+
+                headers = {
+                    "Content-Type": content_type,
+                    "Content-Length": str(size),
+                }
+
+                if signed_url_data.get("include_acl") is True:
+                    headers["x-amz-acl"] = "public-read"
+
+                async with aiohttp.ClientSession() as session:
+                    response = await upload_with_retry(
+                        session,
+                        signed_url_data.get("url"),
+                        headers,
+                        data,
+                    )
+
+                    # Record upload success and timing
+                    end_time = time.perf_counter()
+                    upload_time = end_time - start_time
+
+                    # Store upload statistics
+                    self.upload_stats[prompt_id][filename_base] = {
+                        "size": size,
+                        "type": content_type,
+                        "upload_time": upload_time,
+                    }
+
+                    # Store timeline event
+                    self.upload_timeline[prompt_id].append(
+                        {
+                            "filename": filename_base,
+                            "node_id": node_id,
+                            "node_name": node_name,
+                            "start_time": start_time
+                            - prompt_metadata[prompt_id].start_time,
+                            "end_time": end_time
+                            - prompt_metadata[prompt_id].start_time,
+                        }
+                    )
+
+                    # Update the file_info with download URL and timing
+                    file_info["url"] = signed_url_data.get("download_url")
+                    file_info["upload_duration"] = upload_time
+                    if signed_url_data.get("is_public") is not None:
+                        file_info["is_public"] = signed_url_data.get("is_public")
+
+                    # Update node output data if this upload is associated with a node
+                    if (
+                        node_id
+                        and prompt_id in self.node_output_data
+                        and node_id in self.node_output_data[prompt_id]
+                    ):
+                        node_data = self.node_output_data[prompt_id][node_id]
+                        file_type_key = (
+                            "images" if content_type.startswith("image/") else "files"
+                        )
+                        if file_type_key not in node_data["data"]:
+                            node_data["data"][file_type_key] = []
+                        node_data["data"][file_type_key].append(file_info)
+
+                    # Send success status to clients
+                    await send(
+                        "upload_success",
+                        {
+                            "prompt_id": prompt_id,
+                            "filename": filename_base,
+                            "url": file_info["url"],
+                            "node_id": node_id,
+                        },
+                    )
+
+                    # If this was the last file for this prompt, show the stats summary
+                    if (
+                        prompt_id in self.pending_uploads
+                        and len(self.pending_uploads[prompt_id]) == 1
+                    ):
+                        self._log_upload_stats(prompt_id)
+                        # Clean up stats
+                        del self.upload_stats[prompt_id]
+                        del self.upload_timeline[prompt_id]
+
+        except Exception as e:
+            logger.error(f"\nUpload failed for {filename_base}: {str(e)}")
+            await send(
+                "upload_failed",
+                {
+                    "prompt_id": prompt_id,
+                    "filename": filename_base,
+                    "error": str(e),
+                    "node_id": node_id,
+                },
+            )
+            raise
+
+    async def add_upload(self, prompt_id, file_info, node_id=None):
+        """Add a file to the upload queue"""
+        # Initialize the pending uploads set for this prompt if needed
+        if prompt_id not in self.pending_uploads:
+            self.pending_uploads[prompt_id] = set()
+            self.node_uploads[prompt_id] = {}
+            self.node_output_data[prompt_id] = {}
+
+        # Initialize node tracking if needed
+        if node_id and node_id not in self.node_uploads[prompt_id]:
+            self.node_uploads[prompt_id][node_id] = set()
+            self.node_output_data[prompt_id][node_id] = {"data": {}}
+
+        # Add a unique identifier for this upload
+        upload_id = str(uuid.uuid4())
+        self.pending_uploads[prompt_id].add(upload_id)
+
+        # Track upload for specific node if provided
+        if node_id:
+            self.node_uploads[prompt_id][node_id].add(upload_id)
+
+        # Add to the queue
+        await self.queue.put(
+            {
+                "prompt_id": prompt_id,
+                "file_info": file_info,
+                "node_id": node_id,
+                "upload_id": upload_id,
+            }
+        )
+
+        # Send status update to clients
+        await self.update_queue_status(prompt_id)
+
+        # Ensure worker is running
+        await self.ensure_worker_running()
+
+        return upload_id
+
+    async def ensure_worker_running(self):
+        """Ensure the worker task is running"""
+        async with self.lock:
+            if self.worker_task is None or self.worker_task.done():
+                self.worker_task = asyncio.create_task(self.worker())
+
+    async def update_queue_status(self, prompt_id=None):
+        """Send queue status updates to clients"""
+        # Throttle updates to avoid flooding clients
+        current_time = time.time()
+        if current_time - self.last_status_update < self.status_update_interval:
+            return
+
+        self.last_status_update = current_time
+
+        queue_size = self.queue.qsize()
+        pending_uploads_count = sum(
+            len(uploads) for uploads in self.pending_uploads.values()
+        )
+
+        status_data = {
+            "queue_size": queue_size,
+            "pending_uploads": pending_uploads_count,
+            "max_concurrent": self.max_concurrent,
+        }
+
+        # If prompt_id is provided, add specific data for that prompt
+        if prompt_id and prompt_id in self.pending_uploads:
+            prompt_pending = len(self.pending_uploads[prompt_id])
+            status_data["prompt_pending"] = prompt_pending
+            status_data["prompt_id"] = prompt_id
+
+            # Send targeted status update to relevant clients
+            await send("upload_queue_status", status_data, prompt_id)
+        else:
+            # Send global update to all clients
+            await send("upload_queue_status", status_data)
+
+    async def worker(self):
+        """Worker process that manages the upload queue"""
+        # Start multiple worker tasks within concurrency limits
+        workers = [
+            asyncio.create_task(self.upload_worker())
+            for _ in range(self.max_concurrent)
+        ]
+
+        # Wait for all workers to complete (should only happen on shutdown)
+        await asyncio.gather(*workers)
+
+    async def upload_worker(self):
+        """Individual worker that processes uploads from the queue"""
+        loop = asyncio.get_event_loop()
+
+        while True:
+            try:
+                # Get next upload task first
+                upload_task = await self.queue.get()
+
+                prompt_id = upload_task["prompt_id"]
+                file_info = upload_task["file_info"]
+                node_id = upload_task["node_id"]
+                upload_id = upload_task["upload_id"]
+
+                try:
+                    # Coordinate the actual start of the upload
+                    async with self.upload_lock:
+                        current_time = time.time()
+                        time_since_last = current_time - self.last_upload_time
+                        if time_since_last < self.STAGGER_DELAY:
+                            await asyncio.sleep(self.STAGGER_DELAY - time_since_last)
+                        self.last_upload_time = time.time()
+                        # Start the actual upload while holding the lock
+                    # to ensure true staggering
+                    await self._process_upload(prompt_id, file_info, node_id)
+                except Exception as e:
+                    logger.error(f"Upload failed: {str(e)}")
+                    logger.error(traceback.format_exc())
+                finally:
+                    # Remove this upload from tracking
+                    if prompt_id in self.pending_uploads:
+                        self.pending_uploads[prompt_id].discard(upload_id)
+                        # Remove from node tracking if applicable
+                        if (
+                            node_id
+                            and prompt_id in self.node_uploads
+                            and node_id in self.node_uploads[prompt_id]
+                        ):
+                            self.node_uploads[prompt_id][node_id].discard(upload_id)
+
+                            # If this was the last upload for this node, clean up node data
+                            if not self.node_uploads[prompt_id][node_id]:
+                                del self.node_uploads[prompt_id][node_id]
+                                if self.node_output_data[prompt_id][node_id]["data"]:
+                                    # Send final node data to API before cleanup
+                                    if prompt_metadata[prompt_id].status_endpoint:
+                                        body = {
+                                            "run_id": prompt_id,
+                                            "output_data": self.node_output_data[
+                                                prompt_id
+                                            ][node_id]["data"],
+                                            "node_meta": {"node_id": node_id},
+                                        }
+                                        try:
+                                            await async_request_with_retry(
+                                                "POST",
+                                                prompt_metadata[
+                                                    prompt_id
+                                                ].status_endpoint,
+                                                token=prompt_metadata[prompt_id].token,
+                                                json=body,
+                                            )
+                                        except Exception as e:
+                                            logger.error(
+                                                f"Failed to send final node data: {str(e)}"
+                                            )
+                                del self.node_output_data[prompt_id][node_id]
+
+                        # Send status update
+                        await self.update_queue_status(prompt_id)
+
+                        # If no more pending uploads for this prompt and it's done, update status
+                        if not self.pending_uploads[prompt_id] and is_prompt_done(
+                            prompt_id
+                        ):
+                            # Clean up all data for this prompt
+                            if prompt_id in self.node_uploads:
+                                del self.node_uploads[prompt_id]
+                            if prompt_id in self.node_output_data:
+                                del self.node_output_data[prompt_id]
+                            del self.pending_uploads[prompt_id]
+
+                            # Use the same event loop for these tasks
+                            loop.create_task(update_run(prompt_id, Status.SUCCESS))
+                            loop.create_task(send("success", {"prompt_id": prompt_id}))
+
+                    # Mark task as done
+                    self.queue.task_done()
+
+            except Exception as e:
+                logger.error(f"Error in upload worker: {str(e)}")
+                logger.error(traceback.format_exc())
+                # Brief pause to prevent tight loop in case of persistent errors
+                await asyncio.sleep(0.5)
+
+    async def cancel_uploads_for_prompt(self, prompt_id):
+        """Cancel all pending uploads for a prompt"""
+        if prompt_id in self.pending_uploads:
+            # Remove all pending uploads for this prompt
+            self.pending_uploads[prompt_id].clear()
+            self.node_uploads[prompt_id].clear()
+            self.node_output_data[prompt_id].clear()
+
+            # Clean up
+            del self.pending_uploads[prompt_id]
+            del self.node_uploads[prompt_id]
+            del self.node_output_data[prompt_id]
+
+            # Send status update
+            await self.update_queue_status(prompt_id)
+
+
+# Create a global instance of the upload queue
+upload_queue = UploadQueue(max_concurrent=3)  # Limit to 3 concurrent uploads
+
+
+def format_execution_timeline(execution_times):
+    """Format node execution times into a table with timeline visualization"""
+    if not execution_times:
+        return "No execution data available"
+
+    # Calculate total time and start times for each node
+    sorted_nodes = sorted(execution_times.items(), key=lambda x: x[1]["time"])
+    total_duration = sum(node["time"] for _, node in execution_times.items())
+
+    # Prepare table data
+    headers = ["Node", "Type", "Duration", "VRAM", "Timeline"]
+    rows = []
+    current_time = 0
+
+    # Calculate timeline width (e.g., 80 chars)
+    TIMELINE_WIDTH = 80
+
+    for node_id, data in sorted_nodes:
+        # Calculate the start position and width for the timeline
+        duration = data["time"]
+        vram_mb = data["vram_used"] / (1024 * 1024)  # Convert to MB
+        start_pos = int((current_time / total_duration) * TIMELINE_WIDTH)
+        width = max(1, int((duration / total_duration) * TIMELINE_WIDTH))
+
+        # Create the timeline visualization
+        timeline = (
+            " " * start_pos + "=" * width + " " * (TIMELINE_WIDTH - start_pos - width)
+        )
+
+        # Add the row
+        rows.append(
+            [
+                f"#{node_id}",
+                data["class_type"],
+                f"{duration:.2f}s",
+                f"{vram_mb:.1f}MB",
+                timeline,
+            ]
+        )
+
+        current_time += duration
+
+    return format_table(headers, rows)

examples/flux-dev.json

@@ -0,0 +1,359 @@
+{
+  "extra": {
+    "ds": {
+      "scale": 0.6010518407212623,
+      "offset": [815.5938895649746, 84.94304700477853]
+    },
+    "node_versions": {
+      "comfy-core": "0.3.19",
+      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
+    }
+  },
+  "links": [
+    [9, 8, 0, 9, 0, "IMAGE"],
+    [45, 30, 1, 6, 0, "CLIP"],
+    [46, 30, 2, 8, 1, "VAE"],
+    [47, 30, 0, 31, 0, "MODEL"],
+    [51, 27, 0, 31, 3, "LATENT"],
+    [52, 31, 0, 8, 0, "LATENT"],
+    [54, 30, 1, 33, 0, "CLIP"],
+    [55, 33, 0, 31, 2, "CONDITIONING"],
+    [56, 6, 0, 35, 0, "CONDITIONING"],
+    [57, 35, 0, 31, 1, "CONDITIONING"],
+    [58, 38, 0, 6, 1, "STRING"],
+    [59, 40, 0, 27, 0, "INT"],
+    [60, 39, 0, 27, 1, "INT"]
+  ],
+  "nodes": [
+    {
+      "id": 6,
+      "pos": [384, 192],
+      "mode": 0,
+      "size": [422.8500061035156, 164.30999755859375],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 7,
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 45, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 58,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [56],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open placing a fancy black forest cake with candles on top of a dinner table of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere there are paintings on the walls"
+      ]
+    },
+    {
+      "id": 8,
+      "pos": [1151, 195],
+      "mode": 0,
+      "size": [210, 46],
+      "type": "VAEDecode",
+      "flags": {},
+      "order": 11,
+      "inputs": [
+        { "link": 52, "name": "samples", "type": "LATENT" },
+        { "link": 46, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [9], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
+    },
+    {
+      "id": 9,
+      "pos": [1375, 194],
+      "mode": 0,
+      "size": [985.2999877929688, 1060.3800048828125],
+      "type": "SaveImage",
+      "flags": {},
+      "order": 12,
+      "inputs": [{ "link": 9, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI"]
+    },
+    {
+      "id": 31,
+      "pos": [816, 192],
+      "mode": 0,
+      "size": [315, 262],
+      "type": "KSampler",
+      "flags": {},
+      "order": 10,
+      "inputs": [
+        { "link": 47, "name": "model", "type": "MODEL" },
+        { "link": 57, "name": "positive", "type": "CONDITIONING" },
+        { "link": 55, "name": "negative", "type": "CONDITIONING" },
+        { "link": 51, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [52],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
+      "widgets_values": [
+        1024035737089801,
+        "randomize",
+        20,
+        1,
+        "euler",
+        "simple",
+        1
+      ]
+    },
+    {
+      "id": 35,
+      "pos": [576, 96],
+      "mode": 0,
+      "size": [211.60000610351562, 58],
+      "type": "FluxGuidance",
+      "flags": {},
+      "order": 9,
+      "inputs": [
+        { "link": 56, "name": "conditioning", "type": "CONDITIONING" }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [57],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "FluxGuidance" },
+      "widgets_values": [3.5]
+    },
+    {
+      "id": 37,
+      "pos": [60, 345],
+      "mode": 0,
+      "size": [225, 88],
+      "type": "MarkdownNote",
+      "color": "#432",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "🛈 [Learn more about this workflow](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-dev-1)"
+      ]
+    },
+    {
+      "id": 34,
+      "pos": [825, 510],
+      "mode": 0,
+      "size": [282.8599853515625, 164.0800018310547],
+      "type": "Note",
+      "color": "#432",
+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": { "text": "" },
+      "widgets_values": [
+        "Note that Flux dev and schnell do not have any negative prompt so CFG should be set to 1.0. Setting CFG to 1.0 means the negative prompt is ignored."
+      ]
+    },
+    {
+      "id": 30,
+      "pos": [48, 192],
+      "mode": 0,
+      "size": [315, 98],
+      "type": "CheckpointLoaderSimple",
+      "flags": {},
+      "order": 2,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [47],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [45, 54],
+          "shape": 3,
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [46],
+          "shape": 3,
+          "slot_index": 2
+        }
+      ],
+      "properties": { "Node name for S&R": "CheckpointLoaderSimple" },
+      "widgets_values": ["FLUX1/flux1-dev-fp8.safetensors"]
+    },
+    {
+      "id": 33,
+      "pos": [390, 400],
+      "mode": 0,
+      "size": [422.8500061035156, 164.30999755859375],
+      "type": "CLIPTextEncode",
+      "color": "#322",
+      "flags": { "collapsed": true },
+      "order": 6,
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "inputs": [
+        { "link": 54, "name": "clip", "type": "CLIP", "slot_index": 0 }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [55],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [""]
+    },
+    {
+      "id": 27,
+      "pos": [461.8629455566406, 460.2491149902344],
+      "mode": 0,
+      "size": [315, 126],
+      "type": "EmptySD3LatentImage",
+      "color": "#323",
+      "flags": {},
+      "order": 8,
+      "inputs": [
+        {
+          "pos": [10, 60],
+          "link": 59,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        },
+        {
+          "pos": [10, 36],
+          "link": 60,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        }
+      ],
+      "bgcolor": "#535",
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [51],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "EmptySD3LatentImage" },
+      "widgets_values": [1024, 1024, 1]
+    },
+    {
+      "id": 38,
+      "pos": [-497.3238525390625, 306.5517578125],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [58] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "prompt",
+        "cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open placing a fancy black forest cake with candles on top of a dinner table of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere there are paintings on the walls",
+        "Prompt",
+        "The prompt to generate an image from."
+      ]
+    },
+    {
+      "id": 39,
+      "pos": [-507.4543762207031, 612.9552001953125],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [60], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 1024, "Width", "The width of the image."]
+    },
+    {
+      "id": 40,
+      "pos": [-497.8695068359375, 864.68994140625],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 5,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [59], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 1024, "Height", "The height of the image."]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Input",
+      "bounding": [
+        -508.010986328125, 186.76593017578125, 453.23040771484375,
+        334.3218994140625
+      ],
+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#A88",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
+        -517.1227416992188, 537.53759765625, 644.4954223632812,
+        599.2609252929688
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 60,
+  "last_node_id": 40
+}

examples/flux-schnell.json

@@ -0,0 +1,423 @@
+{
+  "extra": {
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+      "offset": [750.2474149300585, 120.48924647647146]
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+      "comfyui-deploy": "171a227856bd5f31e97828d89f83f3741004d05e"
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+  "links": [
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+    [10, 11, 0, 6, 0, "CLIP"],
+    [12, 10, 0, 8, 1, "VAE"],
+    [19, 16, 0, 13, 2, "SAMPLER"],
+    [20, 17, 0, 13, 3, "SIGMAS"],
+    [23, 5, 0, 13, 4, "LATENT"],
+    [24, 13, 0, 8, 0, "LATENT"],
+    [30, 22, 0, 13, 1, "GUIDER"],
+    [37, 25, 0, 13, 0, "NOISE"],
+    [38, 12, 0, 17, 0, "MODEL"],
+    [39, 12, 0, 22, 0, "MODEL"],
+    [40, 6, 0, 22, 1, "CONDITIONING"],
+    [41, 28, 0, 6, 1, "STRING"],
+    [44, 29, 0, 5, 0, "INT"],
+    [45, 30, 0, 5, 1, "INT"]
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+        { "link": 30, "name": "guider", "type": "GUIDER", "slot_index": 1 },
+        { "link": 19, "name": "sampler", "type": "SAMPLER", "slot_index": 2 },
+        { "link": 20, "name": "sigmas", "type": "SIGMAS", "slot_index": 3 },
+        {
+          "link": 23,
+          "name": "latent_image",
+          "type": "LATENT",
+          "slot_index": 4
+        }
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+      "outputs": [
+        {
+          "name": "output",
+          "type": "LATENT",
+          "links": [24],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "denoised_output",
+          "type": "LATENT",
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+          "shape": 3
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+      "properties": { "Node name for S&R": "SamplerCustomAdvanced" },
+      "widgets_values": []
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+      "pos": [1248, 192],
+      "mode": 0,
+      "size": [210, 46],
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+        { "name": "IMAGE", "type": "IMAGE", "links": [9], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
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+        {
+          "link": 40,
+          "name": "conditioning",
+          "type": "CONDITIONING",
+          "slot_index": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "GUIDER",
+          "type": "GUIDER",
+          "links": [30],
+          "shape": 3,
+          "slot_index": 0
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+      "pos": [480, 720],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "KSamplerSelect",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "outputs": [
+        { "name": "SAMPLER", "type": "SAMPLER", "links": [19], "shape": 3 }
+      ],
+      "properties": { "Node name for S&R": "KSamplerSelect" },
+      "widgets_values": ["euler"]
+    },
+    {
+      "id": 17,
+      "pos": [480, 816],
+      "mode": 0,
+      "size": [315, 106],
+      "type": "BasicScheduler",
+      "flags": {},
+      "order": 10,
+      "inputs": [
+        { "link": 38, "name": "model", "type": "MODEL", "slot_index": 0 }
+      ],
+      "outputs": [
+        { "name": "SIGMAS", "type": "SIGMAS", "links": [20], "shape": 3 }
+      ],
+      "properties": { "Node name for S&R": "BasicScheduler" },
+      "widgets_values": ["simple", 4, 1]
+    },
+    {
+      "id": 27,
+      "pos": [480, 960],
+      "mode": 0,
+      "size": [311.3529052734375, 131.16229248046875],
+      "type": "Note",
+      "color": "#432",
+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": { "text": "" },
+      "widgets_values": [
+        "The schnell model is a distilled model that can generate a good image with only 4 steps."
+      ]
+    },
+    {
+      "id": 9,
+      "pos": [1488, 192],
+      "mode": 0,
+      "size": [985.3012084960938, 1060.3828125],
+      "type": "SaveImage",
+      "flags": {},
+      "order": 16,
+      "inputs": [{ "link": 9, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI"]
+    },
+    {
+      "id": 25,
+      "pos": [480, 587.7890625],
+      "mode": 0,
+      "size": [315, 82],
+      "type": "RandomNoise",
+      "color": "#2a363b",
+      "flags": {},
+      "order": 2,
+      "inputs": [],
+      "bgcolor": "#3f5159",
+      "outputs": [
+        { "name": "NOISE", "type": "NOISE", "links": [37], "shape": 3 }
+      ],
+      "properties": { "Node name for S&R": "RandomNoise" },
+      "widgets_values": [393845863554716, "randomize"]
+    },
+    {
+      "id": 5,
+      "pos": [471.7746276855469, 423.91571044921875],
+      "mode": 0,
+      "size": [315, 126],
+      "type": "EmptyLatentImage",
+      "color": "#323",
+      "flags": {},
+      "order": 12,
+      "inputs": [
+        {
+          "pos": [10, 36],
+          "link": 44,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        },
+        {
+          "pos": [10, 60],
+          "link": 45,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        }
+      ],
+      "bgcolor": "#535",
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [23], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "EmptyLatentImage" },
+      "widgets_values": [1024, 1024, 1]
+    },
+    {
+      "id": 26,
+      "pos": [41.60395812988281, 517.059326171875],
+      "mode": 0,
+      "size": [336, 288],
+      "type": "Note",
+      "color": "#432",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": { "text": "" },
+      "widgets_values": [
+        "If you get an error in any of the nodes above make sure the files are in the correct directories.\n\nSee the top of the examples page for the links : https://comfyanonymous.github.io/ComfyUI_examples/flux/\n\nflux1-schnell.safetensors goes in: ComfyUI/models/unet/\n\nt5xxl_fp16.safetensors and clip_l.safetensors go in: ComfyUI/models/clip/\n\nae.safetensors goes in: ComfyUI/models/vae/\n\n\nTip: You can set the weight_dtype above to one of the fp8 types if you have memory issues."
+      ]
+    },
+    {
+      "id": 11,
+      "pos": [45.584651947021484, 229.13525390625],
+      "mode": 0,
+      "size": [315, 122],
+      "type": "DualCLIPLoader",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [10],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "DualCLIPLoader" },
+      "widgets_values": [
+        "t5xxl_fp16.safetensors",
+        "clip_l.safetensors",
+        "flux",
+        "default"
+      ]
+    },
+    {
+      "id": 12,
+      "pos": [45.92215347290039, 95.6536865234375],
+      "mode": 0,
+      "size": [315, 82],
+      "type": "UNETLoader",
+      "color": "#223",
+      "flags": { "pinned": true },
+      "order": 5,
+      "inputs": [],
+      "bgcolor": "#335",
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [38, 39],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["flux1-schnell.safetensors", "default"]
+    },
+    {
+      "id": 6,
+      "pos": [385.87054443359375, 216.75625610351562],
+      "mode": 0,
+      "size": [422.84503173828125, 164.31304931640625],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 11,
+      "inputs": [
+        { "link": 10, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 41,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [40],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a bottle with a beautiful rainbow galaxy inside it on top of a wooden table in the middle of a modern kitchen beside a plate of vegetables and mushrooms and a wine glasse that contains a planet earth with a plate with a half eaten apple pie on it"
+      ]
+    },
+    {
+      "id": 10,
+      "pos": [42.45906066894531, 405.2187805175781],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "VAELoader",
+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [12],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "VAELoader" },
+      "widgets_values": ["ae.sft"]
+    },
+    {
+      "id": 28,
+      "pos": [-620.3922119140625, 199.8013916015625],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 7,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [41] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "prompt",
+        "a bottle with a beautiful rainbow galaxy inside it on top of a wooden table in the middle of a modern kitchen beside a plate of vegetables and mushrooms and a wine glasse that contains a planet earth with a plate with a half eaten apple pie on it",
+        "Prompt",
+        "The prompt to generate an image from."
+      ]
+    },
+    {
+      "id": 29,
+      "pos": [-677.4290161132812, 597.3389282226562],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 8,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [44], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 1024, "Width", "The width of the image."]
+    },
+    {
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+      "pos": [-670.0703735351562, 848.3707885742188],
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+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 9,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [45], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 1024, "Height", "The height of the image."]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Input",
+      "bounding": [
+        -652.184326171875, 99.11231994628906, 565.7923583984375,
+        362.8800354003906
+      ],
+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#A88",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
+        -720.3274536132812, 508.1206970214844, 703.0422973632812,
+        576.5584716796875
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 45,
+  "last_node_id": 30
+}

examples/hunyuan-video.json

@@ -0,0 +1,482 @@
+{
+  "extra": {
+    "ds": {
+      "scale": 0.6830134553650709,
+      "offset": [1030.9627343190857, 159.73178253107423]
+    },
+    "groupNodes": {},
+    "node_versions": {
+      "comfy-core": "0.3.12",
+      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
+    }
+  },
+  "links": [
+    [19, 16, 0, 13, 2, "SAMPLER"],
+    [20, 17, 0, 13, 3, "SIGMAS"],
+    [30, 22, 0, 13, 1, "GUIDER"],
+    [37, 25, 0, 13, 0, "NOISE"],
+    [129, 26, 0, 22, 1, "CONDITIONING"],
+    [175, 44, 0, 26, 0, "CONDITIONING"],
+    [180, 45, 0, 13, 4, "LATENT"],
+    [181, 13, 0, 8, 0, "LATENT"],
+    [190, 12, 0, 17, 0, "MODEL"],
+    [195, 67, 0, 22, 0, "MODEL"],
+    [205, 11, 0, 44, 0, "CLIP"],
+    [206, 10, 0, 8, 1, "VAE"],
+    [209, 12, 0, 67, 0, "MODEL"],
+    [210, 13, 0, 73, 0, "LATENT"],
+    [211, 10, 0, 73, 1, "VAE"],
+    [215, 73, 0, 75, 0, "IMAGE"],
+    [216, 78, 0, 44, 1, "STRING"],
+    [217, 79, 0, 45, 0, "INT"],
+    [218, 83, 0, 45, 1, "INT"]
+  ],
+  "nodes": [
+    {
+      "id": 16,
+      "pos": [484, 751],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "KSamplerSelect",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "outputs": [
+        { "name": "SAMPLER", "type": "SAMPLER", "links": [19], "shape": 3 }
+      ],
+      "properties": { "Node name for S&R": "KSamplerSelect" },
+      "widgets_values": ["euler"]
+    },
+    {
+      "id": 17,
+      "pos": [478, 860],
+      "mode": 0,
+      "size": [315, 106],
+      "type": "BasicScheduler",
+      "flags": {},
+      "order": 10,
+      "inputs": [
+        { "link": 190, "name": "model", "type": "MODEL", "slot_index": 0 }
+      ],
+      "outputs": [
+        { "name": "SIGMAS", "type": "SIGMAS", "links": [20], "shape": 3 }
+      ],
+      "properties": { "Node name for S&R": "BasicScheduler" },
+      "widgets_values": ["simple", 20, 1]
+    },
+    {
+      "id": 26,
+      "pos": [520, 100],
+      "mode": 0,
+      "size": [317.4000244140625, 58],
+      "type": "FluxGuidance",
+      "color": "#233",
+      "flags": {},
+      "order": 14,
+      "inputs": [
+        { "link": 175, "name": "conditioning", "type": "CONDITIONING" }
+      ],
+      "bgcolor": "#355",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [129],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "FluxGuidance" },
+      "widgets_values": [6]
+    },
+    {
+      "id": 45,
+      "pos": [475.540771484375, 432.673583984375],
+      "mode": 0,
+      "size": [315, 130],
+      "type": "EmptyHunyuanLatentVideo",
+      "flags": {},
+      "order": 13,
+      "inputs": [
+        {
+          "link": 217,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        },
+        {
+          "link": 218,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [180], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "EmptyHunyuanLatentVideo" },
+      "widgets_values": [848, 480, 73, 1]
+    },
+    {
+      "id": 22,
+      "pos": [600, 0],
+      "mode": 0,
+      "size": [222.3482666015625, 46],
+      "type": "BasicGuider",
+      "flags": {},
+      "order": 15,
+      "inputs": [
+        { "link": 195, "name": "model", "type": "MODEL", "slot_index": 0 },
+        {
+          "link": 129,
+          "name": "conditioning",
+          "type": "CONDITIONING",
+          "slot_index": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "GUIDER",
+          "type": "GUIDER",
+          "links": [30],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "BasicGuider" },
+      "widgets_values": []
+    },
+    {
+      "id": 67,
+      "pos": [360, 0],
+      "mode": 0,
+      "size": [210, 58],
+      "type": "ModelSamplingSD3",
+      "flags": {},
+      "order": 11,
+      "inputs": [{ "link": 209, "name": "model", "type": "MODEL" }],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [195], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ModelSamplingSD3" },
+      "widgets_values": [7]
+    },
+    {
+      "id": 73,
+      "pos": [1150, 200],
+      "mode": 0,
+      "size": [210, 150],
+      "type": "VAEDecodeTiled",
+      "flags": {},
+      "order": 18,
+      "inputs": [
+        { "link": 210, "name": "samples", "type": "LATENT" },
+        { "link": 211, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [215], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecodeTiled" },
+      "widgets_values": [256, 64, 64, 8]
+    },
+    {
+      "id": 8,
+      "pos": [1150, 90],
+      "mode": 2,
+      "size": [210, 46],
+      "type": "VAEDecode",
+      "flags": {},
+      "order": 17,
+      "inputs": [
+        { "link": 181, "name": "samples", "type": "LATENT" },
+        { "link": 206, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
+    },
+    {
+      "id": 77,
+      "pos": [0, 0],
+      "mode": 0,
+      "size": [350, 110],
+      "type": "Note",
+      "color": "#432",
+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Select a fp8 weight_dtype if you are running out of  memory."
+      ]
+    },
+    {
+      "id": 13,
+      "pos": [860, 200],
+      "mode": 0,
+      "size": [272.3617858886719, 124.53733825683594],
+      "type": "SamplerCustomAdvanced",
+      "flags": {},
+      "order": 16,
+      "inputs": [
+        { "link": 37, "name": "noise", "type": "NOISE", "slot_index": 0 },
+        { "link": 30, "name": "guider", "type": "GUIDER", "slot_index": 1 },
+        { "link": 19, "name": "sampler", "type": "SAMPLER", "slot_index": 2 },
+        { "link": 20, "name": "sigmas", "type": "SIGMAS", "slot_index": 3 },
+        {
+          "link": 180,
+          "name": "latent_image",
+          "type": "LATENT",
+          "slot_index": 4
+        }
+      ],
+      "outputs": [
+        {
+          "name": "output",
+          "type": "LATENT",
+          "links": [181, 210],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "denoised_output",
+          "type": "LATENT",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": { "Node name for S&R": "SamplerCustomAdvanced" },
+      "widgets_values": []
+    },
+    {
+      "id": 75,
+      "pos": [1410, 200],
+      "mode": 0,
+      "size": [315, 366],
+      "type": "SaveAnimatedWEBP",
+      "flags": {},
+      "order": 19,
+      "inputs": [{ "link": 215, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI", 24, false, 80, "default"]
+    },
+    {
+      "id": 25,
+      "pos": [479, 618],
+      "mode": 0,
+      "size": [315, 82],
+      "type": "RandomNoise",
+      "color": "#2a363b",
+      "flags": {},
+      "order": 2,
+      "inputs": [],
+      "bgcolor": "#3f5159",
+      "outputs": [
+        { "name": "NOISE", "type": "NOISE", "links": [37], "shape": 3 }
+      ],
+      "properties": { "Node name for S&R": "RandomNoise" },
+      "widgets_values": [1, "randomize"]
+    },
+    {
+      "id": 12,
+      "pos": [0, 150],
+      "mode": 0,
+      "size": [350, 82],
+      "type": "UNETLoader",
+      "color": "#223",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "bgcolor": "#335",
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [190, 209],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["hunyuan_video_t2v_720p_bf16.safetensors", "default"]
+    },
+    {
+      "id": 10,
+      "pos": [0, 420],
+      "mode": 0,
+      "size": [350, 60],
+      "type": "VAELoader",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [206, 211],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "VAELoader" },
+      "widgets_values": ["hunyuan_video_vae_bf16.safetensors"]
+    },
+    {
+      "id": 11,
+      "pos": [0, 270],
+      "mode": 0,
+      "size": [350, 106],
+      "type": "DualCLIPLoader",
+      "flags": {},
+      "order": 5,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [205],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "DualCLIPLoader" },
+      "widgets_values": [
+        "clip_l.safetensors",
+        "llava_llama3_fp8_scaled.safetensors",
+        "hunyuan_video",
+        "default"
+      ]
+    },
+    {
+      "id": 44,
+      "pos": [459.0518798828125, 226.60147094726562],
+      "mode": 0,
+      "size": [285.6000061035156, 54],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 12,
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 205, "name": "clip", "type": "CLIP" },
+        {
+          "link": 216,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [175],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "anime style anime girl with massive fennec ears and one big fluffy tail, she has blonde hair long hair blue eyes wearing a pink sweater and a long blue skirt walking in a beautiful outdoor scenery with snow mountains in the background"
+      ]
+    },
+    {
+      "id": 83,
+      "pos": [-591.1870727539062, 751.6737670898438],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [218], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 480, "Height", "The height of the video."]
+    },
+    {
+      "id": 74,
+      "pos": [1151.89599609375, 402.439697265625],
+      "mode": 0,
+      "size": [210, 170],
+      "type": "Note",
+      "color": "#432",
+      "flags": {},
+      "order": 7,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Use the tiled decode node by default because most people will need it.\n\nLower the tile_size and overlap if you run out of memory."
+      ]
+    },
+    {
+      "id": 78,
+      "pos": [-560.058837890625, 155.3986358642578],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 8,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [216] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "prompt",
+        "anime style anime girl with massive fennec ears and one big fluffy tail, she has blonde hair long hair blue eyes wearing a pink sweater and a long blue skirt walking in a beautiful outdoor scenery with snow mountains in the background",
+        "Prompt",
+        "The prompt to generate the video from."
+      ]
+    },
+    {
+      "id": 79,
+      "pos": [-588.2138061523438, 493.3861389160156],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 9,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [217], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 848, "Width", "The width of the video."]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Input",
+      "bounding": [
+        -662.9779052734375, 14.93773078918457, 575.2218627929688,
+        384.41424560546875
+      ],
+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#b06634",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
+        -659.7621459960938, 414.3213195800781, 570.7182006835938,
+        594.72216796875
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 218,
+  "last_node_id": 83
+}

examples/stable-diffusion-v15.json

@@ -0,0 +1,240 @@
+{
+  "extra": {
+    "ds": { "scale": 1, "offset": { "0": 0, "1": 0 } },
+    "node_versions": {
+      "comfy-core": "0.3.12",
+      "comfyui-deploy": "171a227856bd5f31e97828d89f83f3741004d05e"
+    }
+  },
+  "links": [
+    [1, 4, 0, 3, 0, "MODEL"],
+    [2, 5, 0, 3, 3, "LATENT"],
+    [3, 4, 1, 6, 0, "CLIP"],
+    [4, 6, 0, 3, 1, "CONDITIONING"],
+    [5, 4, 1, 7, 0, "CLIP"],
+    [6, 7, 0, 3, 2, "CONDITIONING"],
+    [7, 3, 0, 8, 0, "LATENT"],
+    [8, 4, 2, 8, 1, "VAE"],
+    [9, 8, 0, 9, 0, "IMAGE"],
+    [10, 12, 0, 6, 1, "STRING"],
+    [11, 13, 0, 7, 1, "STRING"]
+  ],
+  "nodes": [
+    {
+      "id": 5,
+      "pos": [473, 609],
+      "mode": 0,
+      "size": [315, 106],
+      "type": "EmptyLatentImage",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [2], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "EmptyLatentImage" },
+      "widgets_values": [512, 512, 1]
+    },
+    {
+      "id": 3,
+      "pos": [863, 186],
+      "mode": 0,
+      "size": [315, 262],
+      "type": "KSampler",
+      "flags": {},
+      "order": 7,
+      "inputs": [
+        { "link": 1, "name": "model", "type": "MODEL" },
+        { "link": 4, "name": "positive", "type": "CONDITIONING" },
+        { "link": 6, "name": "negative", "type": "CONDITIONING" },
+        { "link": 2, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [7], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
+      "widgets_values": [
+        156680208700286,
+        "randomize",
+        20,
+        8,
+        "euler",
+        "normal",
+        1
+      ]
+    },
+    {
+      "id": 8,
+      "pos": [1209, 188],
+      "mode": 0,
+      "size": [210, 46],
+      "type": "VAEDecode",
+      "flags": {},
+      "order": 8,
+      "inputs": [
+        { "link": 7, "name": "samples", "type": "LATENT" },
+        { "link": 8, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [9], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
+    },
+    {
+      "id": 9,
+      "pos": [1451, 189],
+      "mode": 0,
+      "size": [210, 58],
+      "type": "SaveImage",
+      "flags": {},
+      "order": 9,
+      "inputs": [{ "link": 9, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI"]
+    },
+    {
+      "id": 4,
+      "pos": [26, 474],
+      "mode": 0,
+      "size": [315, 98],
+      "type": "CheckpointLoaderSimple",
+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [1], "slot_index": 0 },
+        { "name": "CLIP", "type": "CLIP", "links": [3, 5], "slot_index": 1 },
+        { "name": "VAE", "type": "VAE", "links": [8], "slot_index": 2 }
+      ],
+      "properties": { "Node name for S&R": "CheckpointLoaderSimple" },
+      "widgets_values": ["v1-5-pruned-emaonly.ckpt"]
+    },
+    {
+      "id": 6,
+      "pos": [415, 186],
+      "mode": 0,
+      "size": [422.84503173828125, 164.31304931640625],
+      "type": "CLIPTextEncode",
+      "flags": {},
+      "order": 6,
+      "inputs": [
+        { "link": 3, "name": "clip", "type": "CLIP" },
+        {
+          "link": 10,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [4],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,"
+      ]
+    },
+    {
+      "id": 7,
+      "pos": [413, 389],
+      "mode": 0,
+      "size": [425.27801513671875, 180.6060791015625],
+      "type": "CLIPTextEncode",
+      "flags": {},
+      "order": 5,
+      "inputs": [
+        { "link": 5, "name": "clip", "type": "CLIP" },
+        {
+          "link": 11,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [6],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": ["text, watermark"]
+    },
+    {
+      "id": 13,
+      "pos": [-70, -60],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 2,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": [11],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": ["negative_prompt", "text, watermark", "", ""]
+    },
+    {
+      "id": 12,
+      "pos": [-72, 193],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "links": [10],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "positive_prompt",
+        "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
+        "",
+        ""
+      ]
+    },
+    {
+      "id": 14,
+      "pos": [419, 23],
+      "mode": 0,
+      "size": [210, 96],
+      "type": "ComfyDeploy",
+      "color": "#432",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": { "version": "", "workflow_id": "", "workflow_name": "" },
+      "widgets_values": ["", "", "1"]
+    }
+  ],
+  "config": {},
+  "groups": [],
+  "version": 0.4,
+  "last_link_id": 11,
+  "last_node_id": 14
+}

examples/stable-diffusion-v35-large.json

@@ -0,0 +1,409 @@
+{
+  "extra": {
+    "ds": {
+      "scale": 0.620921323059155,
+      "offset": [1535.1422819333457, 285.0502099350181]
+    },
+    "node_versions": {
+      "comfy-core": "0.3.12",
+      "comfyui-deploy": "171a227856bd5f31e97828d89f83f3741004d05e"
+    }
+  },
+  "links": [
+    [7, 3, 0, 8, 0, "LATENT"],
+    [21, 16, 0, 3, 1, "CONDITIONING"],
+    [51, 8, 0, 9, 0, "IMAGE"],
+    [53, 4, 2, 8, 1, "VAE"],
+    [80, 40, 0, 3, 2, "CONDITIONING"],
+    [99, 4, 0, 3, 0, "MODEL"],
+    [100, 53, 0, 3, 3, "LATENT"],
+    [103, 43, 0, 16, 0, "CLIP"],
+    [104, 43, 0, 40, 0, "CLIP"],
+    [105, 54, 0, 16, 1, "STRING"],
+    [106, 55, 0, 40, 1, "STRING"],
+    [107, 56, 0, 53, 0, "INT"],
+    [108, 57, 0, 53, 1, "INT"]
+  ],
+  "nodes": [
+    {
+      "id": 8,
+      "pos": [1200, 96],
+      "mode": 0,
+      "size": [210, 46],
+      "type": "VAEDecode",
+      "flags": {},
+      "order": 14,
+      "inputs": [
+        { "link": 7, "name": "samples", "type": "LATENT" },
+        { "link": 53, "name": "vae", "type": "VAE", "slot_index": 1 }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [51], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
+    },
+    {
+      "id": 9,
+      "pos": [1440, 96],
+      "mode": 0,
+      "size": [952.5112915039062, 1007.9328002929688],
+      "type": "SaveImage",
+      "flags": {},
+      "order": 15,
+      "inputs": [
+        { "link": 51, "name": "images", "type": "IMAGE", "slot_index": 0 }
+      ],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI"]
+    },
+    {
+      "id": 53,
+      "pos": [480, 576],
+      "mode": 0,
+      "size": [315, 106],
+      "type": "EmptySD3LatentImage",
+      "flags": {},
+      "order": 10,
+      "inputs": [
+        {
+          "link": 107,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        },
+        {
+          "link": 108,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [100],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "EmptySD3LatentImage" },
+      "widgets_values": [1024, 1024, 1]
+    },
+    {
+      "id": 50,
+      "pos": [-384, 144],
+      "mode": 0,
+      "size": [223.34756469726562, 254.37765502929688],
+      "type": "Note",
+      "color": "#432",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": { "text": "" },
+      "widgets_values": [
+        "SD3 supports different text encoder configurations, you can see how to load them here.\n\n\nMake sure to put these files:\nclip_g.safetensors\nclip_l.safetensors\nt5xxl_fp16.safetensors\n\n\nIn the ComfyUI/models/clip directory"
+      ]
+    },
+    {
+      "id": 41,
+      "pos": [-96, 0],
+      "mode": 0,
+      "size": [315, 82],
+      "type": "CLIPLoader",
+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPLoader" },
+      "widgets_values": ["t5xxl_fp16.safetensors", "sd3", "default"]
+    },
+    {
+      "id": 4,
+      "pos": [-96, 480],
+      "mode": 0,
+      "size": [384.75592041015625, 98],
+      "type": "CheckpointLoaderSimple",
+      "flags": {},
+      "order": 2,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [99], "slot_index": 0 },
+        { "name": "CLIP", "type": "CLIP", "links": [], "slot_index": 1 },
+        { "name": "VAE", "type": "VAE", "links": [53], "slot_index": 2 }
+      ],
+      "properties": { "Node name for S&R": "CheckpointLoaderSimple" },
+      "widgets_values": ["sd3.5_large.safetensors"]
+    },
+    {
+      "id": 3,
+      "pos": [864, 96],
+      "mode": 0,
+      "size": [315, 474],
+      "type": "KSampler",
+      "flags": {},
+      "order": 13,
+      "inputs": [
+        { "link": 99, "name": "model", "type": "MODEL", "slot_index": 0 },
+        { "link": 21, "name": "positive", "type": "CONDITIONING" },
+        { "link": 80, "name": "negative", "type": "CONDITIONING" },
+        { "link": 100, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [7], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
+      "widgets_values": [
+        797639488625463,
+        "randomize",
+        20,
+        5.45,
+        "euler",
+        "sgm_uniform",
+        1
+      ]
+    },
+    {
+      "id": 51,
+      "pos": [-96, 624],
+      "mode": 0,
+      "size": [384, 192],
+      "type": "Note",
+      "color": "#432",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": { "text": "" },
+      "widgets_values": [
+        "sd3.5_large.safetensors and sd3.5_medium.safetensors are files that do not contain any CLIP/text encoder weights so you need to load them separately.\n\nThey go in the ComfyUI/models/checkpoints directory."
+      ]
+    },
+    {
+      "id": 43,
+      "pos": [-96, 288],
+      "mode": 0,
+      "size": [315, 106],
+      "type": "TripleCLIPLoader",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [103, 104],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "TripleCLIPLoader" },
+      "widgets_values": [
+        "clip_l.safetensors",
+        "clip_g.safetensors",
+        "t5xxl_fp16.safetensors"
+      ]
+    },
+    {
+      "id": 42,
+      "pos": [-96, 144],
+      "mode": 0,
+      "size": [315, 106],
+      "type": "DualCLIPLoader",
+      "flags": {},
+      "order": 5,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "DualCLIPLoader" },
+      "widgets_values": [
+        "clip_l.safetensors",
+        "clip_g.safetensors",
+        "sd3",
+        "default"
+      ]
+    },
+    {
+      "id": 40,
+      "pos": [399.0046081542969, 337.009765625],
+      "mode": 0,
+      "size": [397.45977783203125, 151.2898406982422],
+      "type": "CLIPTextEncode",
+      "color": "#322",
+      "flags": {},
+      "order": 11,
+      "title": "Negative Prompt",
+      "inputs": [
+        { "link": 104, "name": "clip", "type": "CLIP" },
+        {
+          "link": 106,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [80],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [""]
+    },
+    {
+      "id": 16,
+      "pos": [398.5880432128906, 108.27188110351562],
+      "mode": 0,
+      "size": [397.21484375, 158.671875],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 12,
+      "title": "Positive Prompt",
+      "inputs": [
+        { "link": 103, "name": "clip", "type": "CLIP" },
+        {
+          "link": 105,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [21],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a bottle with a rainbow galaxy inside it on top of a wooden table on a snowy mountain top with the ocean and clouds in the background with a shot glass beside containing darkness beside a snow sculpture in the shape of a fox"
+      ]
+    },
+    {
+      "id": 57,
+      "pos": [-1015.1683349609375, 980.396240234375],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [108], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 1024, "Height", "The height of the image. "]
+    },
+    {
+      "id": 56,
+      "pos": [-1014.532470703125, 732.8568725585938],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 7,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [107], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 1024, "Width", "The width of the image. "]
+    },
+    {
+      "id": 55,
+      "pos": [-1010.8035278320312, 487.404052734375],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 8,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [106] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": ["negative_prompt", "", "Negative Prompt", ""]
+    },
+    {
+      "id": 54,
+      "pos": [-1059.1072998046875, 127.12823486328125],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 9,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [105] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "prompt",
+        "a bottle with a rainbow galaxy inside it on top of a wooden table on a snowy mountain top with the ocean and clouds in the background with a shot glass beside containing darkness beside a snow sculpture in the shape of a fox",
+        "Prompt",
+        "The prompt to generate an image from."
+      ]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Different Text Encoder Configurations",
+      "bounding": [-144, -96, 480, 528],
+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#b06634",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
+        -1194.5233154296875, 388.3871154785156, 722.5393676757812,
+        824.6746215820312
+      ],
+      "font_size": 24
+    },
+    {
+      "id": 3,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Input",
+      "bounding": [
+        -1148.487060546875, 30.449382781982422, 552.3358764648438,
+        329.3829040527344
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 108,
+  "last_node_id": 57
+}

examples/wan-21-i2v-480p.json

@@ -0,0 +1,452 @@
+{
+  "extra": {
+    "ds": {
+      "scale": 0.6934334949441662,
+      "offset": [276.7941771713029, -7.923387380923855]
+    },
+    "node_versions": {
+      "comfy-core": "0.3.18",
+      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
+    }
+  },
+  "links": [
+    [35, 3, 0, 8, 0, "LATENT"],
+    [56, 8, 0, 28, 0, "IMAGE"],
+    [74, 38, 0, 6, 0, "CLIP"],
+    [75, 38, 0, 7, 0, "CLIP"],
+    [76, 39, 0, 8, 1, "VAE"],
+    [93, 8, 0, 47, 0, "IMAGE"],
+    [94, 49, 0, 51, 0, "CLIP_VISION"],
+    [97, 6, 0, 50, 0, "CONDITIONING"],
+    [98, 7, 0, 50, 1, "CONDITIONING"],
+    [99, 39, 0, 50, 2, "VAE"],
+    [101, 50, 0, 3, 1, "CONDITIONING"],
+    [102, 50, 1, 3, 2, "CONDITIONING"],
+    [103, 50, 2, 3, 3, "LATENT"],
+    [107, 51, 0, 50, 3, "CLIP_VISION_OUTPUT"],
+    [110, 37, 0, 54, 0, "MODEL"],
+    [111, 54, 0, 3, 0, "MODEL"],
+    [112, 55, 0, 6, 1, "STRING"],
+    [113, 56, 0, 7, 1, "STRING"],
+    [115, 57, 0, 50, 4, "IMAGE"],
+    [116, 57, 0, 51, 1, "IMAGE"],
+    [117, 58, 0, 50, 5, "INT"],
+    [118, 59, 0, 50, 6, "INT"]
+  ],
+  "nodes": [
+    {
+      "id": 8,
+      "pos": [1210, 190],
+      "mode": 0,
+      "size": [210, 46],
+      "type": "VAEDecode",
+      "flags": {},
+      "order": 15,
+      "inputs": [
+        { "link": 35, "name": "samples", "type": "LATENT" },
+        { "link": 76, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [56, 93], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
+    },
+    {
+      "id": 39,
+      "pos": [866.3932495117188, 499.18597412109375],
+      "mode": 0,
+      "size": [306.36004638671875, 58],
+      "type": "VAELoader",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "outputs": [
+        { "name": "VAE", "type": "VAE", "links": [76, 99], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAELoader" },
+      "widgets_values": ["wan_2.1_vae.safetensors"]
+    },
+    {
+      "id": 28,
+      "pos": [1460, 190],
+      "mode": 0,
+      "size": [870.8511352539062, 643.7430419921875],
+      "type": "SaveAnimatedWEBP",
+      "flags": {},
+      "order": 16,
+      "inputs": [{ "link": 56, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI", 16, false, 90, "default", ""]
+    },
+    {
+      "id": 47,
+      "pos": [2367.213134765625, 193.6114959716797],
+      "mode": 4,
+      "size": [315, 130],
+      "type": "SaveWEBM",
+      "flags": {},
+      "order": 17,
+      "inputs": [{ "link": 93, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": { "Node name for S&R": "SaveWEBM" },
+      "widgets_values": ["ComfyUI", "vp9", 24, 32]
+    },
+    {
+      "id": 50,
+      "pos": [673.0507202148438, 627.272705078125],
+      "mode": 0,
+      "size": [342.5999755859375, 250],
+      "type": "WanImageToVideo",
+      "flags": {},
+      "order": 13,
+      "inputs": [
+        { "link": 97, "name": "positive", "type": "CONDITIONING" },
+        { "link": 98, "name": "negative", "type": "CONDITIONING" },
+        { "link": 99, "name": "vae", "type": "VAE" },
+        {
+          "link": 107,
+          "name": "clip_vision_output",
+          "type": "CLIP_VISION_OUTPUT",
+          "shape": 7
+        },
+        { "link": 115, "name": "start_image", "type": "IMAGE", "shape": 7 },
+        {
+          "pos": [10, 116],
+          "link": 117,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        },
+        {
+          "pos": [10, 140],
+          "link": 118,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [101],
+          "slot_index": 0
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [102],
+          "slot_index": 1
+        },
+        { "name": "latent", "type": "LATENT", "links": [103], "slot_index": 2 }
+      ],
+      "properties": { "Node name for S&R": "WanImageToVideo" },
+      "widgets_values": [512, 512, 33, 1]
+    },
+    {
+      "id": 3,
+      "pos": [863, 187],
+      "mode": 0,
+      "size": [315, 262],
+      "type": "KSampler",
+      "flags": {},
+      "order": 14,
+      "inputs": [
+        { "link": 111, "name": "model", "type": "MODEL" },
+        { "link": 101, "name": "positive", "type": "CONDITIONING" },
+        { "link": 102, "name": "negative", "type": "CONDITIONING" },
+        { "link": 103, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [35], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
+      "widgets_values": [
+        55104841546759,
+        "randomize",
+        20,
+        6,
+        "uni_pc",
+        "simple",
+        1
+      ]
+    },
+    {
+      "id": 51,
+      "pos": [360, 640],
+      "mode": 0,
+      "size": [253.60000610351562, 78],
+      "type": "CLIPVisionEncode",
+      "flags": {},
+      "order": 12,
+      "inputs": [
+        { "link": 94, "name": "clip_vision", "type": "CLIP_VISION" },
+        { "link": 116, "name": "image", "type": "IMAGE" }
+      ],
+      "outputs": [
+        {
+          "name": "CLIP_VISION_OUTPUT",
+          "type": "CLIP_VISION_OUTPUT",
+          "links": [107],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionEncode" },
+      "widgets_values": ["none"]
+    },
+    {
+      "id": 54,
+      "pos": [510, 70],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "ModelSamplingSD3",
+      "flags": {},
+      "order": 9,
+      "inputs": [{ "link": 110, "name": "model", "type": "MODEL" }],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [111], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ModelSamplingSD3" },
+      "widgets_values": [8]
+    },
+    {
+      "id": 6,
+      "pos": [415, 186],
+      "mode": 0,
+      "size": [422.84503173828125, 164.31304931640625],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 10,
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 74, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 112,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [97],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around"
+      ]
+    },
+    {
+      "id": 55,
+      "pos": [-672.6411743164062, 140.167724609375],
+      "mode": 0,
+      "size": [400, 200],
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+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [112] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "positive_prompt",
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around",
+        "Prompt",
+        "The text prompt to guide video generation."
+      ]
+    },
+    {
+      "id": 57,
+      "pos": [-664.3828125, 405.5113525390625],
+      "mode": 0,
+      "size": [390.5999755859375, 366],
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+      "flags": {},
+      "order": 2,
+      "inputs": [
+        { "link": null, "name": "default_value", "type": "IMAGE", "shape": 7 }
+      ],
+      "outputs": [{ "name": "image", "type": "IMAGE", "links": [115, 116] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalImage" },
+      "widgets_values": [
+        "image_url",
+        "Image Url",
+        "URL of the input image.",
+        "https://comfy-deploy-output.s3.us-east-2.amazonaws.com/assets/img_GZMJYXDnLbYjWybu.png",
+        ""
+      ]
+    },
+    {
+      "id": 56,
+      "pos": [-674.42333984375, 953.0498046875],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [113] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "negative_prompt",
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+        "Negative Prompt",
+        "The negative prompt to use. Use it to address details that you don't want in the video. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
+      ]
+    },
+    {
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+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 512, "Width", "The width of the video. "]
+    },
+    {
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+      "pos": [-665.8831787109375, 1461.255126953125],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
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+      "order": 5,
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+      "outputs": [
+        { "name": "value", "type": "INT", "links": [118], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 512, "Height", "The Height of the video."]
+    },
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+      "pos": [-3.3700287342071533, 81.69654846191406],
+      "mode": 0,
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+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [110], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["wan2.1_i2v_480p_14B_bf16.safetensors", "default"]
+    },
+    {
+      "id": 38,
+      "pos": [-3.7316977977752686, 230.76495361328125],
+      "mode": 0,
+      "size": [390, 98],
+      "type": "CLIPLoader",
+      "flags": {},
+      "order": 7,
+      "inputs": [],
+      "outputs": [
+        { "name": "CLIP", "type": "CLIP", "links": [74, 75], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "CLIPLoader" },
+      "widgets_values": [
+        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "wan",
+        "default"
+      ]
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+    {
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+      "mode": 0,
+      "size": [315, 58],
+      "type": "CLIPVisionLoader",
+      "flags": {},
+      "order": 8,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP_VISION",
+          "type": "CLIP_VISION",
+          "links": [94],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionLoader" },
+      "widgets_values": ["clip_vision_h.safetensors"]
+    },
+    {
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+      "color": "#322",
+      "flags": {},
+      "order": 11,
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "inputs": [
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+        {
+          "pos": [10, 36],
+          "link": 113,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [98],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
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+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+      ]
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+  ],
+  "config": {},
+  "groups": [
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+      "color": "#3f789e",
+      "flags": {},
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+    {
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+      "flags": {},
+      "title": "Additional",
+      "bounding": [
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+        845.0675048828125
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+      "font_size": 24
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+  "version": 0.4,
+  "last_link_id": 118,
+  "last_node_id": 59
+}

examples/wav-21-i2v-720p.json

@@ -0,0 +1,452 @@
+{
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+    [6, 18, 0, 5, 1, "CONDITIONING"],
+    [7, 2, 0, 5, 2, "VAE"],
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+    [11, 14, 0, 5, 6, "INT"],
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+    [15, 5, 2, 6, 3, "LATENT"],
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+    [19, 16, 0, 9, 0, "CLIP"],
+    [20, 10, 0, 9, 1, "STRING"],
+    [21, 16, 0, 18, 0, "CLIP"],
+    [22, 12, 0, 18, 1, "STRING"]
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+        { "link": 2, "name": "vae", "type": "VAE" }
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+        { "link": 14, "name": "negative", "type": "CONDITIONING" },
+        { "link": 15, "name": "latent_image", "type": "LATENT" }
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+      ],
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+        { "link": 17, "name": "image", "type": "IMAGE" }
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+          "type": "CLIP_VISION_OUTPUT",
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+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 19, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 20,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [5],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around"
+      ]
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+      "flags": {},
+      "order": 1,
+      "inputs": [
+        { "link": null, "name": "default_value", "type": "IMAGE", "shape": 7 }
+      ],
+      "outputs": [{ "name": "image", "type": "IMAGE", "links": [9, 17] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalImage" },
+      "widgets_values": [
+        "image_url",
+        "Image Url",
+        "URL of the input image.",
+        "https://comfy-deploy-output.s3.us-east-2.amazonaws.com/assets/img_GZMJYXDnLbYjWybu.png",
+        ""
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+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "negative_prompt",
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+        "Negative Prompt",
+        "The negative prompt to use. Use it to address details that you don't want in the video. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
+      ]
+    },
+    {
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+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 512, "Width", "The width of the video. "]
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+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 512, "Height", "The Height of the video."]
+    },
+    {
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+      "inputs": [],
+      "outputs": [
+        { "name": "CLIP", "type": "CLIP", "links": [19, 21], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "CLIPLoader" },
+      "widgets_values": [
+        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "wan",
+        "default"
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+      "outputs": [
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+          "type": "CLIP_VISION",
+          "links": [16],
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+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionLoader" },
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+      "inputs": [
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+        {
+          "pos": [10, 36],
+          "link": 22,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [6],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+      ]
+    },
+    {
+      "id": 15,
+      "pos": [610.6187744140625, 594.3209838867188],
+      "mode": 0,
+      "size": [346.7470703125, 82],
+      "type": "UNETLoader",
+      "flags": {},
+      "order": 7,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [18], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["wan2.1_i2v_720p_14B_bf16.safetensors", "default"]
+    },
+    {
+      "id": 10,
+      "pos": [-60.696964263916016, 649.5515747070312],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 8,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [20] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "positive_prompt",
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit running towards front happily",
+        "Prompt",
+        "The text prompt to guide video generation."
+      ]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Input",
+      "bounding": [
+        -94.30522155761719, 560.1735229492188, 617.7969360351562,
+        761.303955078125
+      ],
+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#b06634",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
+        -89.63090515136719, 1373.9351806640625, 625.998779296875,
+        845.0675048828125
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 22,
+  "last_node_id": 18
+}

examples/wav-21-t2v-13b.json

@@ -0,0 +1,359 @@
+{
+  "extra": {
+    "ds": {
+      "scale": 0.8390545288824369,
+      "offset": [814.3725295729478, -347.90757575249455]
+    },
+    "node_versions": {
+      "comfy-core": "0.3.18",
+      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
+    }
+  },
+  "links": [
+    [35, 3, 0, 8, 0, "LATENT"],
+    [46, 6, 0, 3, 1, "CONDITIONING"],
+    [52, 7, 0, 3, 2, "CONDITIONING"],
+    [56, 8, 0, 28, 0, "IMAGE"],
+    [74, 38, 0, 6, 0, "CLIP"],
+    [75, 38, 0, 7, 0, "CLIP"],
+    [76, 39, 0, 8, 1, "VAE"],
+    [91, 40, 0, 3, 3, "LATENT"],
+    [93, 8, 0, 47, 0, "IMAGE"],
+    [94, 37, 0, 48, 0, "MODEL"],
+    [95, 48, 0, 3, 0, "MODEL"],
+    [96, 49, 0, 6, 1, "STRING"],
+    [97, 50, 0, 7, 1, "STRING"],
+    [99, 52, 0, 40, 1, "INT"],
+    [100, 51, 0, 40, 0, "INT"]
+  ],
+  "nodes": [
+    {
+      "id": 8,
+      "pos": [1210, 190],
+      "mode": 0,
+      "size": [210, 46],
+      "type": "VAEDecode",
+      "flags": {},
+      "order": 12,
+      "inputs": [
+        { "link": 35, "name": "samples", "type": "LATENT" },
+        { "link": 76, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [56, 93], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
+    },
+    {
+      "id": 39,
+      "pos": [866.3932495117188, 499.18597412109375],
+      "mode": 0,
+      "size": [306.36004638671875, 58],
+      "type": "VAELoader",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "outputs": [
+        { "name": "VAE", "type": "VAE", "links": [76], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAELoader" },
+      "widgets_values": ["wan_2.1_vae.safetensors"]
+    },
+    {
+      "id": 47,
+      "pos": [2367.213134765625, 193.6114959716797],
+      "mode": 4,
+      "size": [315, 130],
+      "type": "SaveWEBM",
+      "flags": {},
+      "order": 14,
+      "inputs": [{ "link": 93, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": { "Node name for S&R": "SaveWEBM" },
+      "widgets_values": ["ComfyUI", "vp9", 24, 32]
+    },
+    {
+      "id": 3,
+      "pos": [863, 187],
+      "mode": 0,
+      "size": [315, 262],
+      "type": "KSampler",
+      "flags": {},
+      "order": 11,
+      "inputs": [
+        { "link": 95, "name": "model", "type": "MODEL" },
+        { "link": 46, "name": "positive", "type": "CONDITIONING" },
+        { "link": 52, "name": "negative", "type": "CONDITIONING" },
+        { "link": 91, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [35], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
+      "widgets_values": [
+        577746309562741,
+        "randomize",
+        30,
+        6,
+        "uni_pc",
+        "simple",
+        1
+      ]
+    },
+    {
+      "id": 48,
+      "pos": [440, 50],
+      "mode": 0,
+      "size": [210, 58],
+      "type": "ModelSamplingSD3",
+      "flags": {},
+      "order": 7,
+      "inputs": [{ "link": 94, "name": "model", "type": "MODEL" }],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [95], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ModelSamplingSD3" },
+      "widgets_values": [8]
+    },
+    {
+      "id": 37,
+      "pos": [20, 40],
+      "mode": 0,
+      "size": [346.7470703125, 82],
+      "type": "UNETLoader",
+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [94], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["wan2.1_t2v_1.3B_fp16.safetensors", "default"]
+    },
+    {
+      "id": 6,
+      "pos": [415, 186],
+      "mode": 0,
+      "size": [422.84503173828125, 164.31304931640625],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 8,
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 74, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 96,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [46],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a fox moving quickly in a beautiful winter scenery nature trees mountains daytime tracking camera"
+      ]
+    },
+    {
+      "id": 7,
+      "pos": [413, 389],
+      "mode": 0,
+      "size": [425.27801513671875, 180.6060791015625],
+      "type": "CLIPTextEncode",
+      "color": "#322",
+      "flags": {},
+      "order": 9,
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "inputs": [
+        { "link": 75, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 97,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [52],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+      ]
+    },
+    {
+      "id": 38,
+      "pos": [-10.047812461853027, 187.37384033203125],
+      "mode": 0,
+      "size": [390, 98],
+      "type": "CLIPLoader",
+      "flags": {},
+      "order": 2,
+      "inputs": [],
+      "outputs": [
+        { "name": "CLIP", "type": "CLIP", "links": [74, 75], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "CLIPLoader" },
+      "widgets_values": [
+        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "wan",
+        "default"
+      ]
+    },
+    {
+      "id": 49,
+      "pos": [-535.2967529296875, 342.3277587890625],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [96] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "positive_prompt",
+        "a fox moving quickly in a beautiful winter scenery nature trees mountains daytime tracking camera",
+        "Prompt",
+        "The text prompt to guide video generation. "
+      ]
+    },
+    {
+      "id": 50,
+      "pos": [-526.2716064453125, 703.8343505859375],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [97] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "negative_prompt",
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+        "Negative Prompt",
+        "The negative prompt to use. Use it to address details that you don't want in the image. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
+      ]
+    },
+    {
+      "id": 40,
+      "pos": [516.926513671875, 619.59716796875],
+      "mode": 0,
+      "size": [315, 150],
+      "type": "EmptyHunyuanLatentVideo",
+      "flags": {},
+      "order": 10,
+      "inputs": [
+        {
+          "pos": [10, 36],
+          "link": 100,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        },
+        {
+          "pos": [10, 60],
+          "link": 99,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [91], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "EmptyHunyuanLatentVideo" },
+      "widgets_values": [832, 480, 33, 1]
+    },
+    {
+      "id": 28,
+      "pos": [1460, 190],
+      "mode": 0,
+      "size": [870.8511352539062, 643.7430419921875],
+      "type": "SaveAnimatedWEBP",
+      "flags": {},
+      "order": 13,
+      "inputs": [{ "link": 56, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI", 16, false, 90, "default"]
+    },
+    {
+      "id": 51,
+      "pos": [-522.7415161132812, 959.3386840820312],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 5,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [100], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 832, "Width", "The Width of the Video. "]
+    },
+    {
+      "id": 52,
+      "pos": [-518.9917602539062, 1207.9444580078125],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [99], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 480, "Height", "The Height of the Video. "]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Inputs",
+      "bounding": [
+        -560.9110717773438, 255.1485595703125, 500.94989013671875,
+        333.4786682128906
+      ],
+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#b06634",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
+        -556.5305786132812, 619.87548828125, 761.2673950195312,
+        811.6837768554688
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 100,
+  "last_node_id": 52
+}

pyproject.toml

@@ -1,9 +1,9 @@
 [project]
 name = "comfyui-deploy"
 description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
-version = "1.1.0"
+version = "2.1.0"
 license = { file = "LICENSE" }
-dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
+dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg", "tabulate", "brotli"]
 
 [project.urls]
 Repository = "https://github.com/BennyKok/comfyui-deploy"

web-plugin/index.js

@@ -635,7 +635,7 @@ const ext = {
           "string",
           inputName,
           /* value=*/ "",
-          () => {},
+          () => { },
           { serialize: true },
         );
 
@@ -716,7 +716,7 @@ const ext = {
             try {
               await window["app"].ui.settings.setSettingValueAsync(
                 "Comfy.Validation.Workflows",
-                false,
+                true,
               );
             } catch (error) {
               console.warning(
@@ -920,10 +920,10 @@ function createDynamicUIHtml(data) {
           <h3 style="font-size: 14px; font-weight: semibold; margin-bottom: 8px;">Missing Nodes</h3>
           <p style="font-size: 12px;">These nodes are not found with any matching custom_nodes in the ComfyUI Manager Database</p>
           ${data.missing_nodes
-            .map((node) => {
-              return `<p style="font-size: 14px; color: #d69e2e;">${node}</p>`;
-            })
-            .join("")}
+        .map((node) => {
+          return `<p style="font-size: 14px; color: #d69e2e;">${node}</p>`;
+        })
+        .join("")}
       </div>
   `;
   }
@@ -931,17 +931,14 @@ function createDynamicUIHtml(data) {
   Object.values(data.custom_nodes).forEach((node) => {
     html += `
           <div style="border-bottom: 1px solid #e2e8f0; padding-top: 16px;">
-              <a href="${
-                node.url
-              }" target="_blank" style="font-size: 18px; font-weight: semibold; color: white; text-decoration: none;">${
-                node.name
-              }</a>
+              <a href="${node.url
+      }" target="_blank" style="font-size: 18px; font-weight: semibold; color: white; text-decoration: none;">${node.name
+      }</a>
               <p style="font-size: 14px; color: #4b5563;">${node.hash}</p>
-              ${
-                node.warning
-                  ? `<p style="font-size: 14px; color: #d69e2e;">${node.warning}</p>`
-                  : ""
-              }
+              ${node.warning
+        ? `<p style="font-size: 14px; color: #d69e2e;">${node.warning}</p>`
+        : ""
+      }
           </div>
       `;
   });
@@ -955,9 +952,8 @@ function createDynamicUIHtml(data) {
   Object.entries(data.models).forEach(([section, items]) => {
     html += `
     <div style="border-bottom: 1px solid #e2e8f0; padding-top: 8px; padding-bottom: 8px;">
-        <h3 style="font-size: 18px; font-weight: semibold; margin-bottom: 8px;">${
-          section.charAt(0).toUpperCase() + section.slice(1)
-        }</h3>`;
+        <h3 style="font-size: 18px; font-weight: semibold; margin-bottom: 8px;">${section.charAt(0).toUpperCase() + section.slice(1)
+      }</h3>`;
     items.forEach((item) => {
       html += `<p style="font-size: 14px; color: ${textColor};">${item.name}</p>`;
     });
@@ -973,9 +969,8 @@ function createDynamicUIHtml(data) {
   Object.entries(data.files).forEach(([section, items]) => {
     html += `
     <div style="border-bottom: 1px solid #e2e8f0; padding-top: 8px; padding-bottom: 8px;">
-        <h3 style="font-size: 18px; font-weight: semibold; margin-bottom: 8px;">${
-          section.charAt(0).toUpperCase() + section.slice(1)
-        }</h3>`;
+        <h3 style="font-size: 18px; font-weight: semibold; margin-bottom: 8px;">${section.charAt(0).toUpperCase() + section.slice(1)
+      }</h3>`;
     items.forEach((item) => {
       html += `<p style="font-size: 14px; color: ${textColor};">${item.name}</p>`;
     });
@@ -1493,14 +1488,12 @@ export class LoadingDialog extends ComfyDialog {
   showLoading(title, message) {
     this.show(`
       <div style="width: 400px; display: flex; gap: 18px; flex-direction: column; overflow: unset">
-        <h3 style="margin: 0px; display: flex; align-items: center; justify-content: center; gap: 12px;">${title} ${
-          this.loadingIcon
-        }</h3>
-          ${
-            message
-              ? `<label style="max-width: 100%; white-space: pre-wrap; word-wrap: break-word;">${message}</label>`
-              : ""
-          }
+        <h3 style="margin: 0px; display: flex; align-items: center; justify-content: center; gap: 12px;">${title} ${this.loadingIcon
+      }</h3>
+          ${message
+        ? `<label style="max-width: 100%; white-space: pre-wrap; word-wrap: break-word;">${message}</label>`
+        : ""
+      }
         </div>
       `);
   }
@@ -1766,21 +1759,17 @@ export class ConfigDialog extends ComfyDialog {
     </label>
       <label style="color: white; width: 100%;">
         Endpoint:
-        <input id="endpoint" style="margin-top: 8px; width: 100%; height:40px; box-sizing: border-box; padding: 0px 6px;" type="text" value="${
-          data.endpoint
-        }">
+        <input id="endpoint" style="margin-top: 8px; width: 100%; height:40px; box-sizing: border-box; padding: 0px 6px;" type="text" value="${data.endpoint
+      }">
       </label>
       <div style="color: white;">
-        API Key: User / Org <button style="font-size: 18px;">${
-          data.displayName ?? ""
-        }</button>
-        <input id="apiKey" style="margin-top: 8px; width: 100%; height:40px; box-sizing: border-box; padding: 0px 6px;" type="password" value="${
-          data.apiKey
-        }">
+        API Key: User / Org <button style="font-size: 18px;">${data.displayName ?? ""
+      }</button>
+        <input id="apiKey" style="margin-top: 8px; width: 100%; height:40px; box-sizing: border-box; padding: 0px 6px;" type="password" value="${data.apiKey
+      }">
         <button id="loginButton" style="margin-top: 8px; width: 100%; height:40px; box-sizing: border-box; padding: 0px 6px;">
-          ${
-            data.apiKey ? "Re-login with ComfyDeploy" : "Login with ComfyDeploy"
-          }
+          ${data.apiKey ? "Re-login with ComfyDeploy" : "Login with ComfyDeploy"
+      }
         </button>
       </div>
       </div>
@@ -1960,7 +1949,7 @@ async function loadWorkflowApi(versionId) {
     console.log("Workflow API loaded:", response);
     await window["app"].ui.settings.setSettingValueAsync(
       "Comfy.Validation.Workflows",
-      false,
+      true,
     );
     app.loadGraphData(response.workflow);
     // You might want to update the UI or trigger some action in ComfyUI here