merge

* feat(text): create node for external text list 

This is to send a list of texts to other nodes

* refactor: remove prints and rename variable

* style: update comment

* refactor: remove unused optional inputs

.github/workflows/publish.yml

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

LICENSE

@@ -1,21 +1,208 @@
-MIT License
+GNU AFFERO GENERAL PUBLIC LICENSE
+Version 3, 19 November 2007
 
-Copyright (c) 2023 BennyKok
+Copyright © 2007 Free Software Foundation, Inc. <https://fsf.org/>
+Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
 
-Permission is hereby granted, free of charge, to any person obtaining a copy
+Preamble
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+17. Interpretation of Sections 15 and 16.
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+END OF TERMS AND CONDITIONS
+
+How to Apply These Terms to Your New Programs
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+
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+    Copyright (C) <year>  <name of author>
+
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+    it under the terms of the GNU Affero General Public License as
+    published by the Free Software Foundation, either version 3 of the
+    License, or (at your option) any later version.
+
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+Also add information on how to contact you by electronic and paper mail.
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comfy-nodes/external_boolean.py

@@ -0,0 +1,25 @@
+class ComfyUIDeployExternalBoolean:
+    @classmethod    
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_bool"},
+                ),
+                "default_value": ("BOOLEAN", {"default": False})
+            }
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("bool_value",)
+    
+    FUNCTION = "run"
+    
+    def run(self, input_id, default_value=None):
+        print(f"Node '{input_id}' processing with switch set to {default_value}")
+        return [default_value]
+    
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalBoolean": ComfyUIDeployExternalBoolean}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalBoolean": "External Boolean (ComfyUI Deploy)"}

comfy-nodes/external_checkpoints.py

@@ -5,6 +5,12 @@ import torch
 import folder_paths
 from tqdm import tqdm
 
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+        
+WILDCARD = AnyType("*")
+
 class ComfyUIDeployExternalCheckpoint:
     @classmethod
     def INPUT_TYPES(s):
@@ -16,23 +22,23 @@ class ComfyUIDeployExternalCheckpoint:
                 ),
             },
             "optional": {
-                "default_checkpoint_name": (folder_paths.get_filename_list("checkpoints"), ),
+                "default_value": (folder_paths.get_filename_list("checkpoints"), ),
             }
         }
 
-    RETURN_TYPES = (folder_paths.get_filename_list("checkpoints"),)
+    RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
 
     FUNCTION = "run"
 
     CATEGORY = "deploy"
 
-    def run(self, input_id, default_checkpoint_name=None):
+    def run(self, input_id, default_value=None):
         import requests
         import os
         import uuid
 
-        if input_id and input_id.startswith('http'):
+        if default_value.startswith('http'):
             unique_filename = str(uuid.uuid4()) + ".safetensors"
             print(unique_filename)
             print(folder_paths.folder_names_and_paths["checkpoints"][0][0])
@@ -59,7 +65,7 @@ class ComfyUIDeployExternalCheckpoint:
                     out_file.write(chunk)
             return (unique_filename,)
         else:
-            return (default_checkpoints_name,)
+            return (default_value,)
 
 
 NODE_CLASS_MAPPINGS = {

comfy-nodes/external_image_batch.py

@@ -0,0 +1,85 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+import json
+import comfy
+
+class ComfyUIDeployExternalImageBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_images"},
+                ),
+                "images": (
+                    "STRING",
+                    {"multiline": False, "default": "[]"},
+                ),
+            },
+            "optional": {
+                "default_value": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "image"
+    
+    def run(self, input_id, images=None, default_value=None):
+        processed_images = []
+        try:
+            images_list = json.loads(images)  # Assuming images is a JSON array string
+            print(images_list)
+            for img_input in images_list:
+                if img_input.startswith('http'):
+                    import requests
+                    from io import BytesIO
+                    print("Fetching image from url: ", img_input)
+                    response = requests.get(img_input)
+                    image = Image.open(BytesIO(response.content))
+                elif img_input.startswith('data:image/png;base64,') or img_input.startswith('data:image/jpeg;base64,') or img_input.startswith('data:image/jpg;base64,'):
+                    import base64
+                    from io import BytesIO
+                    print("Decoding base64 image")
+                    base64_image = img_input[img_input.find(",")+1:]
+                    decoded_image = base64.b64decode(base64_image)
+                    image = Image.open(BytesIO(decoded_image))
+                else:
+                    raise ValueError("Invalid image url or base64 data provided.")
+
+                image = ImageOps.exif_transpose(image)
+                image = image.convert("RGB")
+                image = np.array(image).astype(np.float32) / 255.0
+                image_tensor = torch.from_numpy(image)[None,]
+                processed_images.append(image_tensor)
+        except Exception as e:
+            print(f"Error processing images: {e}")
+            pass
+            
+        if default_value is not None and len(images_list) == 0:
+            processed_images.append(default_value)  # Assuming default_value is a pre-processed image tensor
+
+        # Resize images if necessary and concatenate from MakeImageBatch in ImpactPack
+        if processed_images:
+            base_shape = processed_images[0].shape[1:]  # Get the shape of the first image for comparison
+            batch_tensor = processed_images[0]
+            for i in range(1, len(processed_images)):
+                if processed_images[i].shape[1:] != base_shape:
+                    # Resize to match the first image's dimensions
+                    processed_images[i] = comfy.utils.common_upscale(processed_images[i].movedim(-1, 1), base_shape[1], base_shape[0], "lanczos", "center").movedim(1, -1)
+
+                batch_tensor = torch.cat((batch_tensor, processed_images[i]), dim=0)
+            # Concatenate using torch.cat
+        else:
+            batch_tensor = None  # or handle the empty case as needed
+        return (batch_tensor, )
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalImageBatch": ComfyUIDeployExternalImageBatch}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalImageBatch": "External Image Batch (ComfyUI Deploy)"}

comfy-nodes/external_lora.py

@@ -5,6 +5,14 @@ import torch
 import folder_paths
 
 
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+WILDCARD = AnyType("*")
+
+
 class ComfyUIDeployExternalLora:
     @classmethod
     def INPUT_TYPES(s):
@@ -16,36 +24,56 @@ class ComfyUIDeployExternalLora:
                 ),
             },
             "optional": {
-                "default_lora_name": (folder_paths.get_filename_list("loras"), ),
+                "default_lora_name": (folder_paths.get_filename_list("loras"),),
-            }
+                "lora_save_name": ( # if `default_lora_name` is a link to download a file, we will attempt to save it with this name
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ), 
+            },
         }
 
-    RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
+    RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
 
     FUNCTION = "run"
 
     CATEGORY = "deploy"
 
-    def run(self, input_id, default_lora_name=None):
+    def run(self, input_id, default_lora_name=None, lora_save_name=None):
         import requests
         import os
         import uuid
 
-        if input_id and input_id.startswith('http'):
+        if default_lora_name.startswith("http"):
-            unique_filename = str(uuid.uuid4()) + ".safetensors"
+            if lora_save_name:
-            print(unique_filename)
+                existing_loras = folder_paths.get_filename_list("loras")
+                # Check if lora_save_name exists in the list
+                if lora_save_name in existing_loras:
+                    print(f"using lora: {lora_save_name}")
+                    return (lora_save_name,)
+            else:
+                lora_save_name = str(uuid.uuid4()) + ".safetensors"
+            print(lora_save_name)
             print(folder_paths.folder_names_and_paths["loras"][0][0])
-            destination_path = os.path.join(folder_paths.folder_names_and_paths["loras"][0][0], unique_filename)
+            destination_path = os.path.join(
+                folder_paths.folder_names_and_paths["loras"][0][0], lora_save_name
+            )
             print(destination_path)
             print("Downloading external lora - " + input_id + " to " + destination_path)
-            response = requests.get(input_id, headers={'User-Agent': 'Mozilla/5.0'}, allow_redirects=True)
+            response = requests.get(
-            with open(destination_path, 'wb') as out_file:
+                input_id,
+                headers={"User-Agent": "Mozilla/5.0"},
+                allow_redirects=True,
+            )
+            with open(destination_path, "wb") as out_file:
                 out_file.write(response.content)
-            return (unique_filename,)
+            return (lora_save_name,)
         else:
+            print(f"using lora: {default_lora_name}")
             return (default_lora_name,)
 
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalLora": ComfyUIDeployExternalLora}
-NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalLora": "External Lora (ComfyUI Deploy)"}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalLora": "External Lora (ComfyUI Deploy)"
+}

comfy-nodes/external_number.py

@@ -16,7 +16,7 @@ class ComfyUIDeployExternalNumber:
             "optional": {
                 "default_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "default": 0},
+                    {"multiline": True, "display": "number", "default": 0, "step": 0.01},
                 ),
             }
         }
@@ -29,9 +29,12 @@ class ComfyUIDeployExternalNumber:
     CATEGORY = "number"
 
     def run(self, input_id, default_value=None):
-        if not input_id or not input_id.strip().isdigit():
+        try:
+            float_value = float(input_id)
+            print("my number", float_value)
+            return [float_value]
+        except ValueError:
             return [default_value]
-        return [int(input_id)]
 
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalNumber": ComfyUIDeployExternalNumber}

comfy-nodes/external_number_int.py

@@ -29,7 +29,7 @@ class ComfyUIDeployExternalNumberInt:
     CATEGORY = "number"
 
     def run(self, input_id, default_value=None):
-        if not input_id or not input_id.strip().isdigit():
+        if not input_id or (isinstance(input_id, str) and not input_id.strip().isdigit()):
             return [default_value]
         return [int(input_id)]
 

comfy-nodes/external_number_slider.py

@@ -0,0 +1,48 @@
+class ComfyUIDeployExternalNumberSlider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_number_slider"},
+                ),
+            },
+            "optional": {
+                "default_value": (
+                    "FLOAT",
+                    {"multiline": True, "display": "number", "default": 0.5, "step": 0.01},
+                ),
+                "min_value": (
+                    "FLOAT",
+                    {"multiline": True, "display": "number", "default": 0, "step": 0.01},
+                ),
+                "max_value": (
+                    "FLOAT",
+                    {"multiline": True, "display": "number", "default": 1, "step": 0.01},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("value",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "number"
+
+    def run(self, input_id, default_value=None, min_value=0, max_value=1):
+        try:
+            float_value = float(input_id)
+            if min_value <= float_value <= max_value:
+                print("my number", float_value)
+                return [float_value]
+            else:
+                print("Number out of range. Returning default value:", default_value)
+                return [default_value]
+        except ValueError:
+            print("Invalid input. Returning default value:", default_value)
+            return [default_value]
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalNumberSlider": ComfyUIDeployExternalNumberSlider}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalNumberSlider": "External Number Slider (ComfyUI Deploy)"}

comfy-nodes/external_text_list.py

@@ -0,0 +1,43 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+import json
+
+class ComfyUIDeployExternalTextList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": 'input_text_list'},
+                ),
+                 "text": (
+                    "STRING",
+                    {"multiline": True, "default": "[]"},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("text",)
+
+    OUTPUT_IS_LIST = (True,)
+
+    FUNCTION = "run"
+
+    CATEGORY = "text"
+
+    def run(self, input_id, text=None):
+        text_list = []
+        try:
+            text_list = json.loads(text)  # Assuming text is a JSON array string
+        except Exception as e:
+            print(f"Error processing images: {e}")
+            pass
+        return [text_list]
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalTextList": ComfyUIDeployExternalTextList}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalTextList": "External Text List (ComfyUI Deploy)"}

comfy-nodes/external_vid.py

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

comfy-nodes/external_video.py

@@ -0,0 +1,855 @@
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
+# Intended to work with https://github.com/NicholasKao1029/ComfyUI-VideoHelperSuite/tree/main
+import os
+import itertools
+import numpy as np
+import torch
+from typing import Union
+from torch import Tensor
+import cv2
+import psutil
+
+from collections.abc import Mapping
+import folder_paths
+from comfy.utils import common_upscale
+
+### Utils
+import hashlib
+from typing import Iterable
+import shutil
+import subprocess
+import re
+import uuid
+
+import server
+from tqdm import tqdm
+
+BIGMIN = -(2**53 - 1)
+BIGMAX = 2**53 - 1
+
+DIMMAX = 8192
+
+
+def ffmpeg_suitability(path):
+    try:
+        version = subprocess.run(
+            [path, "-version"], check=True, capture_output=True
+        ).stdout.decode("utf-8")
+    except:
+        return 0
+    score = 0
+    # rough layout of the importance of various features
+    simple_criterion = [
+        ("libvpx", 20),
+        ("264", 10),
+        ("265", 3),
+        ("svtav1", 5),
+        ("libopus", 1),
+    ]
+    for criterion in simple_criterion:
+        if version.find(criterion[0]) >= 0:
+            score += criterion[1]
+    # obtain rough compile year from copyright information
+    copyright_index = version.find("2000-2")
+    if copyright_index >= 0:
+        copyright_year = version[copyright_index + 6 : copyright_index + 9]
+        if copyright_year.isnumeric():
+            score += int(copyright_year)
+    return score
+
+
+if "VHS_FORCE_FFMPEG_PATH" in os.environ:
+    ffmpeg_path = os.environ.get("VHS_FORCE_FFMPEG_PATH")
+else:
+    ffmpeg_paths = []
+    try:
+        from imageio_ffmpeg import get_ffmpeg_exe
+
+        imageio_ffmpeg_path = get_ffmpeg_exe()
+        ffmpeg_paths.append(imageio_ffmpeg_path)
+    except:
+        if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
+            raise
+    if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
+        ffmpeg_path = imageio_ffmpeg_path
+    else:
+        system_ffmpeg = shutil.which("ffmpeg")
+        if system_ffmpeg is not None:
+            ffmpeg_paths.append(system_ffmpeg)
+        if os.path.isfile("ffmpeg"):
+            ffmpeg_paths.append(os.path.abspath("ffmpeg"))
+        if os.path.isfile("ffmpeg.exe"):
+            ffmpeg_paths.append(os.path.abspath("ffmpeg.exe"))
+        if len(ffmpeg_paths) == 0:
+            ffmpeg_path = None
+        elif len(ffmpeg_paths) == 1:
+            # Evaluation of suitability isn't required, can take sole option
+            # to reduce startup time
+            ffmpeg_path = ffmpeg_paths[0]
+        else:
+            ffmpeg_path = max(ffmpeg_paths, key=ffmpeg_suitability)
+gifski_path = os.environ.get("VHS_GIFSKI", None)
+if gifski_path is None:
+    gifski_path = os.environ.get("JOV_GIFSKI", None)
+    if gifski_path is None:
+        gifski_path = shutil.which("gifski")
+
+
+def is_safe_path(path):
+    if "VHS_STRICT_PATHS" not in os.environ:
+        return True
+    basedir = os.path.abspath(".")
+    try:
+        common_path = os.path.commonpath([basedir, path])
+    except:
+        # Different drive on windows
+        return False
+    return common_path == basedir
+
+
+def get_sorted_dir_files_from_directory(
+    directory: str,
+    skip_first_images: int = 0,
+    select_every_nth: int = 1,
+    extensions: Iterable = None,
+):
+    directory = strip_path(directory)
+    dir_files = os.listdir(directory)
+    dir_files = sorted(dir_files)
+    dir_files = [os.path.join(directory, x) for x in dir_files]
+    dir_files = list(filter(lambda filepath: os.path.isfile(filepath), dir_files))
+    # filter by extension, if needed
+    if extensions is not None:
+        extensions = list(extensions)
+        new_dir_files = []
+        for filepath in dir_files:
+            ext = "." + filepath.split(".")[-1]
+            if ext.lower() in extensions:
+                new_dir_files.append(filepath)
+        dir_files = new_dir_files
+    # start at skip_first_images
+    dir_files = dir_files[skip_first_images:]
+    dir_files = dir_files[0::select_every_nth]
+    return dir_files
+
+
+# modified from https://stackoverflow.com/questions/22058048/hashing-a-file-in-python
+def calculate_file_hash(filename: str, hash_every_n: int = 1):
+    # Larger video files were taking >.5 seconds to hash even when cached,
+    # so instead the modified time from the filesystem is used as a hash
+    h = hashlib.sha256()
+    h.update(filename.encode())
+    h.update(str(os.path.getmtime(filename)).encode())
+    return h.hexdigest()
+
+
+prompt_queue = server.PromptServer.instance.prompt_queue
+
+
+def requeue_workflow_unchecked():
+    """Requeues the current workflow without checking for multiple requeues"""
+    currently_running = prompt_queue.currently_running
+    (_, _, prompt, extra_data, outputs_to_execute) = next(
+        iter(currently_running.values())
+    )
+
+    # Ensure batch_managers are marked stale
+    prompt = prompt.copy()
+    for uid in prompt:
+        if prompt[uid]["class_type"] == "VHS_BatchManager":
+            prompt[uid]["inputs"]["requeue"] = (
+                prompt[uid]["inputs"].get("requeue", 0) + 1
+            )
+
+    # execution.py has guards for concurrency, but server doesn't.
+    # TODO: Check that this won't be an issue
+    number = -server.PromptServer.instance.number
+    server.PromptServer.instance.number += 1
+    prompt_id = str(server.uuid.uuid4())
+    prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+
+
+requeue_guard = [None, 0, 0, {}]
+
+
+def requeue_workflow(requeue_required=(-1, True)):
+    assert len(prompt_queue.currently_running) == 1
+    global requeue_guard
+    (run_number, _, prompt, _, _) = next(iter(prompt_queue.currently_running.values()))
+    if requeue_guard[0] != run_number:
+        # Calculate a count of how many outputs are managed by a batch manager
+        managed_outputs = 0
+        for bm_uid in prompt:
+            if prompt[bm_uid]["class_type"] == "VHS_BatchManager":
+                for output_uid in prompt:
+                    if prompt[output_uid]["class_type"] in ["VHS_VideoCombine"]:
+                        for inp in prompt[output_uid]["inputs"].values():
+                            if inp == [bm_uid, 0]:
+                                managed_outputs += 1
+        requeue_guard = [run_number, 0, managed_outputs, {}]
+    requeue_guard[1] = requeue_guard[1] + 1
+    requeue_guard[3][requeue_required[0]] = requeue_required[1]
+    if requeue_guard[1] == requeue_guard[2] and max(requeue_guard[3].values()):
+        requeue_workflow_unchecked()
+
+
+def get_audio(file, start_time=0, duration=0):
+    args = [ffmpeg_path, "-i", file]
+    if start_time > 0:
+        args += ["-ss", str(start_time)]
+    if duration > 0:
+        args += ["-t", str(duration)]
+    try:
+        # TODO: scan for sample rate and maintain
+        res = subprocess.run(
+            args + ["-f", "f32le", "-"], capture_output=True, check=True
+        )
+        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
+        match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
+    except subprocess.CalledProcessError as e:
+        raise Exception(
+            f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
+        )
+    if match:
+        ar = int(match.group(1))
+        # NOTE: Just throwing an error for other channel types right now
+        # Will deal with issues if they come
+        ac = {"mono": 1, "stereo": 2}[match.group(2)]
+    else:
+        ar = 44100
+        ac = 2
+    audio = audio.reshape((-1, ac)).transpose(0, 1).unsqueeze(0)
+    return {"waveform": audio, "sample_rate": ar}
+
+
+class LazyAudioMap(Mapping):
+    def __init__(self, file, start_time, duration):
+        self.file = file
+        self.start_time = start_time
+        self.duration = duration
+        self._dict = None
+
+    def __getitem__(self, key):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return self._dict[key]
+
+    def __iter__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return iter(self._dict)
+
+    def __len__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return len(self._dict)
+
+
+def lazy_get_audio(file, start_time=0, duration=0):
+    return LazyAudioMap(file, start_time, duration)
+
+
+def lazy_eval(func):
+    class Cache:
+        def __init__(self, func):
+            self.res = None
+            self.func = func
+
+        def get(self):
+            if self.res is None:
+                self.res = self.func()
+            return self.res
+
+    cache = Cache(func)
+    return lambda: cache.get()
+
+
+def is_url(url):
+    return url.split("://")[0] in ["http", "https"]
+
+
+def validate_sequence(path):
+    # Check if path is a valid ffmpeg sequence that points to at least one file
+    (path, file) = os.path.split(path)
+    if not os.path.isdir(path):
+        return False
+    match = re.search("%0?\d+d", file)
+    if not match:
+        return False
+    seq = match.group()
+    if seq == "%d":
+        seq = "\\\\d+"
+    else:
+        seq = "\\\\d{%s}" % seq[1:-1]
+    file_matcher = re.compile(re.sub("%0?\d+d", seq, file))
+    for file in os.listdir(path):
+        if file_matcher.fullmatch(file):
+            return True
+    return False
+
+
+def strip_path(path):
+    # This leaves whitespace inside quotes and only a single "
+    # thus ' ""test"' -> '"test'
+    # consider path.strip(string.whitespace+"\"")
+    # or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1)
+    path = path.strip()
+    if path.startswith('"'):
+        path = path[1:]
+    if path.endswith('"'):
+        path = path[:-1]
+    return path
+
+
+def hash_path(path):
+    if path is None:
+        return "input"
+    if is_url(path):
+        return "url"
+    return calculate_file_hash(path.strip('"'))
+
+
+def validate_path(path, allow_none=False, allow_url=True):
+    if path is None:
+        return allow_none
+    if is_url(path):
+        # Probably not feasible to check if url resolves here
+        return True if allow_url else "URLs are unsupported for this path"
+    if not os.path.isfile(path.strip('"')):
+        return "Invalid file path: {}".format(path)
+    return True
+
+
+### Utils
+
+video_extensions = ["webm", "mp4", "mkv", "gif"]
+
+
+def is_gif(filename) -> bool:
+    file_parts = filename.split(".")
+    return len(file_parts) > 1 and file_parts[-1] == "gif"
+
+
+def target_size(
+    width, height, force_size, custom_width, custom_height
+) -> tuple[int, int]:
+    if force_size == "Custom":
+        return (custom_width, custom_height)
+    elif force_size == "Custom Height":
+        force_size = "?x" + str(custom_height)
+    elif force_size == "Custom Width":
+        force_size = str(custom_width) + "x?"
+
+    if force_size != "Disabled":
+        force_size = force_size.split("x")
+        if force_size[0] == "?":
+            width = (width * int(force_size[1])) // height
+            # Limit to a multple of 8 for latent conversion
+            width = int(width) + 4 & ~7
+            height = int(force_size[1])
+        elif force_size[1] == "?":
+            height = (height * int(force_size[0])) // width
+            height = int(height) + 4 & ~7
+            width = int(force_size[0])
+        else:
+            width = int(force_size[0])
+            height = int(force_size[1])
+    return (width, height)
+
+
+def validate_index(
+    index: int,
+    length: int = 0,
+    is_range: bool = False,
+    allow_negative=False,
+    allow_missing=False,
+) -> int:
+    # if part of range, do nothing
+    if is_range:
+        return index
+    # otherwise, validate index
+    # validate not out of range - only when latent_count is passed in
+    if length > 0 and index > length - 1 and not allow_missing:
+        raise IndexError(f"Index '{index}' out of range for {length} item(s).")
+    # if negative, validate not out of range
+    if index < 0:
+        if not allow_negative:
+            raise IndexError(f"Negative indeces not allowed, but was '{index}'.")
+        conv_index = length + index
+        if conv_index < 0 and not allow_missing:
+            raise IndexError(
+                f"Index '{index}', converted to '{conv_index}' out of range for {length} item(s)."
+            )
+        index = conv_index
+    return index
+
+
+def convert_to_index_int(
+    raw_index: str,
+    length: int = 0,
+    is_range: bool = False,
+    allow_negative=False,
+    allow_missing=False,
+) -> int:
+    try:
+        return validate_index(
+            int(raw_index),
+            length=length,
+            is_range=is_range,
+            allow_negative=allow_negative,
+            allow_missing=allow_missing,
+        )
+    except ValueError as e:
+        raise ValueError(f"Index '{raw_index}' must be an integer.", e)
+
+
+def convert_str_to_indexes(
+    indexes_str: str, length: int = 0, allow_missing=False
+) -> list[int]:
+    if not indexes_str:
+        return []
+    int_indexes = list(range(0, length))
+    allow_negative = length > 0
+    chosen_indexes = []
+    # parse string - allow positive ints, negative ints, and ranges separated by ':'
+    groups = indexes_str.split(",")
+    groups = [g.strip() for g in groups]
+    for g in groups:
+        # parse range of indeces (e.g. 2:16)
+        if ":" in g:
+            index_range = g.split(":", 2)
+            index_range = [r.strip() for r in index_range]
+
+            start_index = index_range[0]
+            if len(start_index) > 0:
+                start_index = convert_to_index_int(
+                    start_index,
+                    length=length,
+                    is_range=True,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            else:
+                start_index = 0
+            end_index = index_range[1]
+            if len(end_index) > 0:
+                end_index = convert_to_index_int(
+                    end_index,
+                    length=length,
+                    is_range=True,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            else:
+                end_index = length
+            # support step as well, to allow things like reversing, every-other, etc.
+            step = 1
+            if len(index_range) > 2:
+                step = index_range[2]
+                if len(step) > 0:
+                    step = convert_to_index_int(
+                        step,
+                        length=length,
+                        is_range=True,
+                        allow_negative=True,
+                        allow_missing=True,
+                    )
+                else:
+                    step = 1
+            # if latents were passed in, base indeces on known latent count
+            if len(int_indexes) > 0:
+                chosen_indexes.extend(int_indexes[start_index:end_index][::step])
+            # otherwise, assume indeces are valid
+            else:
+                chosen_indexes.extend(list(range(start_index, end_index, step)))
+        # parse individual indeces
+        else:
+            chosen_indexes.append(
+                convert_to_index_int(
+                    g,
+                    length=length,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            )
+    return chosen_indexes
+
+
+def select_indexes(input_obj: Union[Tensor, list], idxs: list):
+    if type(input_obj) == Tensor:
+        return input_obj[idxs]
+    else:
+        return [input_obj[i] for i in idxs]
+
+
+def select_indexes_from_str(
+    input_obj: Union[Tensor, list], indexes: str, err_if_missing=True, err_if_empty=True
+):
+    real_idxs = convert_str_to_indexes(
+        indexes, len(input_obj), allow_missing=not err_if_missing
+    )
+    if err_if_empty and len(real_idxs) == 0:
+        raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
+    return select_indexes(input_obj, real_idxs)
+
+
+###
+
+
+def cv_frame_generator(
+    video,
+    force_rate,
+    frame_load_cap,
+    skip_first_frames,
+    select_every_nth,
+    meta_batch=None,
+    unique_id=None,
+):
+    video_cap = cv2.VideoCapture(strip_path(video))
+    if not video_cap.isOpened():
+        raise ValueError(f"{video} could not be loaded with cv.")
+    pbar = None
+
+    # extract video metadata
+    fps = video_cap.get(cv2.CAP_PROP_FPS)
+    width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(video_cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    duration = total_frames / fps
+
+    # set video_cap to look at start_index frame
+    total_frame_count = 0
+    total_frames_evaluated = -1
+    frames_added = 0
+    base_frame_time = 1 / fps
+    prev_frame = None
+
+    if force_rate == 0:
+        target_frame_time = base_frame_time
+    else:
+        target_frame_time = 1 / force_rate
+
+    yield (width, height, fps, duration, total_frames, target_frame_time)
+    if meta_batch is not None:
+        yield min(frame_load_cap, total_frames)
+
+    time_offset = target_frame_time - base_frame_time
+    while video_cap.isOpened():
+        if time_offset < target_frame_time:
+            is_returned = video_cap.grab()
+            # if didn't return frame, video has ended
+            if not is_returned:
+                break
+            time_offset += base_frame_time
+        if time_offset < target_frame_time:
+            continue
+        time_offset -= target_frame_time
+        # if not at start_index, skip doing anything with frame
+        total_frame_count += 1
+        if total_frame_count <= skip_first_frames:
+            continue
+        else:
+            total_frames_evaluated += 1
+
+        # if should not be selected, skip doing anything with frame
+        if total_frames_evaluated % select_every_nth != 0:
+            continue
+
+        # opencv loads images in BGR format (yuck), so need to convert to RGB for ComfyUI use
+        # follow up: can videos ever have an alpha channel?
+        # To my testing: No. opencv has no support for alpha
+        unused, frame = video_cap.retrieve()
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # convert frame to comfyui's expected format
+        # TODO: frame contains no exif information. Check if opencv2 has already applied
+        frame = np.array(frame, dtype=np.float32)
+        torch.from_numpy(frame).div_(255)
+        if prev_frame is not None:
+            inp = yield prev_frame
+            if inp is not None:
+                # ensure the finally block is called
+                return
+        prev_frame = frame
+        frames_added += 1
+        if pbar is not None:
+            pbar.update_absolute(frames_added, frame_load_cap)
+        # if cap exists and we've reached it, stop processing frames
+        if frame_load_cap > 0 and frames_added >= frame_load_cap:
+            break
+    if meta_batch is not None:
+        meta_batch.inputs.pop(unique_id)
+        meta_batch.has_closed_inputs = True
+    if prev_frame is not None:
+        yield prev_frame
+
+
+def batched(it, n):
+    while batch := tuple(itertools.islice(it, n)):
+        yield batch
+
+
+def batched_vae_encode(images, vae, frames_per_batch):
+    for batch in batched(images, frames_per_batch):
+        image_batch = torch.from_numpy(np.array(batch))
+        yield from vae.encode(image_batch).numpy()
+
+
+def load_video_cv(
+    video: str,
+    force_rate: int,
+    force_size: str,
+    custom_width: int,
+    custom_height: int,
+    frame_load_cap: int,
+    skip_first_frames: int,
+    select_every_nth: int,
+    meta_batch=None,
+    unique_id=None,
+    memory_limit_mb=None,
+    vae=None,
+):
+    if meta_batch is None or unique_id not in meta_batch.inputs:
+        gen = cv_frame_generator(
+            video,
+            force_rate,
+            frame_load_cap,
+            skip_first_frames,
+            select_every_nth,
+            meta_batch,
+            unique_id,
+        )
+        (width, height, fps, duration, total_frames, target_frame_time) = next(gen)
+
+        if meta_batch is not None:
+            meta_batch.inputs[unique_id] = (
+                gen,
+                width,
+                height,
+                fps,
+                duration,
+                total_frames,
+                target_frame_time,
+            )
+            meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
+
+    else:
+        (gen, width, height, fps, duration, total_frames, target_frame_time) = (
+            meta_batch.inputs[unique_id]
+        )
+
+    memory_limit = None
+    if memory_limit_mb is not None:
+        memory_limit *= 2**20
+    else:
+        # TODO: verify if garbage collection should be performed here.
+        # leaves ~128 MB unreserved for safety
+        try:
+            memory_limit = (
+                psutil.virtual_memory().available + psutil.swap_memory().free
+            ) - 2**27
+        except:
+            print(
+                "Failed to calculate available memory. Memory load limit has been disabled"
+            )
+    if memory_limit is not None:
+        if vae is not None:
+            # space required to load as f32, exist as latent with wiggle room, decode to f32
+            max_loadable_frames = int(
+                memory_limit // (width * height * 3 * (4 + 4 + 1 / 10))
+            )
+        else:
+            # TODO: use better estimate for when vae is not None
+            # Consider completely ignoring for load_latent case?
+            max_loadable_frames = int(memory_limit // (width * height * 3 * (0.1)))
+        if meta_batch is not None:
+            if meta_batch.frames_per_batch > max_loadable_frames:
+                raise RuntimeError(
+                    f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory"
+                )
+            gen = itertools.islice(gen, meta_batch.frames_per_batch)
+        else:
+            original_gen = gen
+            gen = itertools.islice(gen, max_loadable_frames)
+    downscale_ratio = getattr(vae, "downscale_ratio", 8)
+    frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
+    if force_size != "Disabled" or vae is not None:
+        new_size = target_size(
+            width, height, force_size, custom_width, custom_height, downscale_ratio
+        )
+        if new_size[0] != width or new_size[1] != height:
+
+            def rescale(frame):
+                s = torch.from_numpy(
+                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
+                )
+                s = s.movedim(-1, 1)
+                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+                return s.movedim(1, -1).numpy()
+
+            gen = itertools.chain.from_iterable(
+                map(rescale, batched(gen, frames_per_batch))
+            )
+    else:
+        new_size = width, height
+    if vae is not None:
+        gen = batched_vae_encode(gen, vae, frames_per_batch)
+        vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
+        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
+    else:
+        # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+        images = torch.from_numpy(
+            np.fromiter(gen, np.dtype((np.float32, (new_size[1], new_size[0], 3))))
+        )
+    if meta_batch is None and memory_limit is not None:
+        try:
+            next(original_gen)
+            raise RuntimeError(
+                f"Memory limit hit after loading {len(images)} frames. Stopping execution."
+            )
+        except StopIteration:
+            pass
+    if len(images) == 0:
+        raise RuntimeError("No frames generated")
+
+    # Setup lambda for lazy audio capture
+    audio = lazy_get_audio(
+        video,
+        skip_first_frames * target_frame_time,
+        frame_load_cap * target_frame_time * select_every_nth,
+    )
+    # Adjust target_frame_time for select_every_nth
+    target_frame_time *= select_every_nth
+    video_info = {
+        "source_fps": fps,
+        "source_frame_count": total_frames,
+        "source_duration": duration,
+        "source_width": width,
+        "source_height": height,
+        "loaded_fps": 1 / target_frame_time,
+        "loaded_frame_count": len(images),
+        "loaded_duration": len(images) * target_frame_time,
+        "loaded_width": new_size[0],
+        "loaded_height": new_size[1],
+    }
+    if vae is None:
+        return (images, len(images), audio, video_info, None)
+    else:
+        return (None, len(images), audio, video_info, {"samples": images})
+
+
+# modeled after Video upload node
+class ComfyUIDeployExternalVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = []
+        for f in os.listdir(input_dir):
+            if os.path.isfile(os.path.join(input_dir, f)):
+                file_parts = f.split(".")
+                if len(file_parts) > 1 and (file_parts[-1] in video_extensions):
+                    files.append(f)
+        return {"required": {
+                    "input_id": (
+                        "STRING",
+                        {"multiline": False, "default": "input_video"},
+                    ),
+                     "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
+                     "force_size": (["Disabled", "Custom Height", "Custom Width", "Custom", "256x?", "?x256", "256x256", "512x?", "?x512", "512x512"],),
+                     "custom_width": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
+                     "custom_height": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
+                     "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                     "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                     "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                     },
+                "optional": {
+                    "meta_batch": ("VHS_BatchManager",),
+                    "vae": ("VAE",),
+                    "default_value": (sorted(files),),
+                },
+                "hidden": {
+                    "unique_id": "UNIQUE_ID"
+                },
+                }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
+    RETURN_NAMES = (
+        "IMAGE",
+        "frame_count",
+        "audio",
+        "video_info",
+        "LATENT",
+    )
+
+    FUNCTION = "load_video"
+
+    def load_video(self, **kwargs):
+        input_id = kwargs.get("input_id")
+        force_rate = kwargs.get("force_rate")
+        force_size = kwargs.get("force_size", "Disabled")
+        custom_width = kwargs.get("custom_width")
+        custom_height = kwargs.get("custom_height")
+        frame_load_cap = kwargs.get("frame_load_cap")
+        skip_first_frames = kwargs.get("skip_first_frames")
+        select_every_nth = kwargs.get("select_every_nth")
+        meta_batch = kwargs.get("meta_batch")
+        unique_id = kwargs.get("unique_id")
+
+        video = kwargs.get("default_value")
+        video_path = folder_paths.get_annotated_filepath(video.strip('"'))
+
+        input_dir = folder_paths.get_input_directory()
+        if input_id.startswith("http"):
+            import requests
+
+            print("Fetching video from URL: ", input_id)
+            response = requests.get(input_id, stream=True)
+            file_size = int(response.headers.get("Content-Length", 0))
+            file_extension = input_id.split(".")[-1].split("?")[
+                0
+            ]  # Extract extension and handle URLs with parameters
+            if file_extension not in video_extensions:
+                file_extension = ".mp4"
+
+            unique_filename = str(uuid.uuid4()) + "." + file_extension
+            video_path = os.path.join(input_dir, unique_filename)
+            chunk_size = 1024  # 1 Kibibyte
+
+            num_bars = int(file_size / chunk_size)
+
+            with open(video_path, "wb") as out_file:
+                for chunk in tqdm(
+                    response.iter_content(chunk_size=chunk_size),
+                    total=num_bars,
+                    unit="KB",
+                    desc="Downloading",
+                    leave=True,
+                ):
+                    out_file.write(chunk)
+
+        print("video path: ", video_path)
+
+        return load_video_cv(
+            video=video_path,
+            force_rate=force_rate,
+            force_size=force_size,
+            custom_width=custom_width,
+            custom_height=custom_height,
+            frame_load_cap=frame_load_cap,
+            skip_first_frames=skip_first_frames,
+            select_every_nth=select_every_nth,
+            meta_batch=meta_batch,
+            unique_id=unique_id,
+        )
+
+    @classmethod
+    def IS_CHANGED(s, video, **kwargs):
+        image_path = folder_paths.get_annotated_filepath(video)
+        return calculate_file_hash(image_path)
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalVideo": ComfyUIDeployExternalVideo}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalVideo": "External Video (ComfyUI Deploy x VHS)"
+}

comfy-nodes/input_websocket_image.py

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

comfy-nodes/output_websocket_image.py

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

globals.py

@@ -0,0 +1,116 @@
+import struct
+from enum import Enum
+import aiohttp
+from typing import List, Union, Any, Optional
+from PIL import Image, ImageOps
+from io import BytesIO
+from pydantic import BaseModel as PydanticBaseModel
+
+class BaseModel(PydanticBaseModel):
+    class Config:
+        arbitrary_types_allowed = True
+        
+class Status(Enum):
+    NOT_STARTED = "not-started"
+    RUNNING = "running"
+    SUCCESS = "success"
+    FAILED = "failed"
+    UPLOADING = "uploading"
+
+class StreamingPrompt(BaseModel):
+    workflow_api: Any
+    auth_token: str
+    inputs: dict[str, Union[str, bytes, Image.Image]]
+    running_prompt_ids: set[str] = set()
+    status_endpoint: Optional[str]
+    file_upload_endpoint: Optional[str]
+    
+class SimplePrompt(BaseModel):
+    status_endpoint: Optional[str]
+    file_upload_endpoint: Optional[str]
+    
+    workflow_api: dict
+    status: Status = Status.NOT_STARTED
+    progress: set = set()
+    last_updated_node: Optional[str] = None,
+    uploading_nodes: set = set()
+    done: bool = False
+    is_realtime: bool = False,
+    start_time: Optional[float] = None,
+
+sockets = dict()
+prompt_metadata: dict[str, SimplePrompt] = {}
+streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
+
+class BinaryEventTypes:
+    PREVIEW_IMAGE = 1
+    UNENCODED_PREVIEW_IMAGE = 2
+    
+max_output_id_length = 24
+
+async def send_image(image_data, sid=None, output_id:str = None):
+    max_length = max_output_id_length
+    output_id = output_id[:max_length]
+    padded_output_id = output_id.ljust(max_length, '\x00')
+    encoded_output_id = padded_output_id.encode('ascii', 'replace')
+    
+    image_type = image_data[0]
+    image = image_data[1]
+    max_size = image_data[2]
+    quality = image_data[3]
+    if max_size is not None:
+        if hasattr(Image, 'Resampling'):
+            resampling = Image.Resampling.BILINEAR
+        else:
+            resampling = Image.ANTIALIAS
+
+        image = ImageOps.contain(image, (max_size, max_size), resampling)
+    type_num = 1
+    if image_type == "JPEG":
+        type_num = 1
+    elif image_type == "PNG":
+        type_num = 2
+    elif image_type == "WEBP":
+        type_num = 3
+
+    bytesIO = BytesIO()
+    header = struct.pack(">I", type_num)
+    # 4 bytes for the type
+    bytesIO.write(header)
+    # 10 bytes for the output_id
+    position_before = bytesIO.tell()
+    bytesIO.write(encoded_output_id)
+    position_after = bytesIO.tell()
+    bytes_written = position_after - position_before
+    print(f"Bytes written: {bytes_written}")
+    
+    image.save(bytesIO, format=image_type, quality=quality, compress_level=1)
+    preview_bytes = bytesIO.getvalue()
+    await send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
+        
+async def send_socket_catch_exception(function, message):
+    try:
+        await function(message)
+    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
+        print("send error:", err)
+
+def encode_bytes(event, data):
+    if not isinstance(event, int):
+        raise RuntimeError(f"Binary event types must be integers, got {event}")
+
+    packed = struct.pack(">I", event)
+    message = bytearray(packed)
+    message.extend(data)
+    return message
+
+async def send_bytes(event, data, sid=None):
+    message = encode_bytes(event, data)
+    
+    print("sending image to ", event, sid)
+
+    if sid is None:
+        _sockets = list(sockets.values())
+        for ws in _sockets:
+            await send_socket_catch_exception(ws.send_bytes, message)
+    elif sid in sockets:
+        await send_socket_catch_exception(sockets[sid].send_bytes, message)

prestartup_script.py

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

pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "comfyui-deploy"
+description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
+version = "1.0.0"
+license = "LICENSE"
+dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
+
+[project.urls]
+Repository = "https://github.com/BennyKok/comfyui-deploy"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "comfydeploy"
+DisplayName = "comfyui-deploy"
+Icon = ""

requirements.txt

@@ -0,0 +1,5 @@
+aiofiles
+pydantic
+opencv-python
+imageio-ffmpeg
+# logfire

web-plugin/index.js

@@ -1,7 +1,95 @@
 import { app } from "./app.js";
 import { api } from "./api.js";
 import { ComfyWidgets, LGraphNode } from "./widgets.js";
-import { generateDependencyGraph } from "https://esm.sh/[email protected].19";
+import { generateDependencyGraph } from "https://esm.sh/[email protected].25";
+
+const loadingIcon = `<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 24 24"><g fill="none" stroke="#888888" stroke-linecap="round" stroke-width="2"><path stroke-dasharray="60" stroke-dashoffset="60" stroke-opacity=".3" d="M12 3C16.9706 3 21 7.02944 21 12C21 16.9706 16.9706 21 12 21C7.02944 21 3 16.9706 3 12C3 7.02944 7.02944 3 12 3Z"><animate fill="freeze" attributeName="stroke-dashoffset" dur="1.3s" values="60;0"/></path><path stroke-dasharray="15" stroke-dashoffset="15" d="M12 3C16.9706 3 21 7.02944 21 12"><animate fill="freeze" attributeName="stroke-dashoffset" dur="0.3s" values="15;0"/><animateTransform attributeName="transform" dur="1.5s" repeatCount="indefinite" type="rotate" values="0 12 12;360 12 12"/></path></g></svg>`;
+
+function sendEventToCD(event, data) {
+    const message = {
+        type: event,
+        data: data,
+    };
+    window.parent.postMessage(JSON.stringify(message), "*");
+}
+
+function dispatchAPIEventData(data) {
+    const msg = JSON.parse(data);
+
+    // Custom parse error
+    if (msg.error) {
+        let message = msg.error.message;
+        if (msg.error.details) message += ": " + msg.error.details;
+        for (const [nodeID, nodeError] of Object.entries(msg.node_errors)) {
+            message += "\n" + nodeError.class_type + ":";
+            for (const errorReason of nodeError.errors) {
+                message +=
+                    "\n    - " +
+                    errorReason.message +
+                    ": " +
+                    errorReason.details;
+            }
+        }
+
+        app.ui.dialog.show(message);
+        if (msg.node_errors) {
+            app.lastNodeErrors = msg.node_errors;
+            app.canvas.draw(true, true);
+        }
+    }
+
+    switch (msg.event) {
+        case "error":
+            break;
+        case "status":
+            if (msg.data.sid) {
+                // this.clientId = msg.data.sid;
+                // window.name = this.clientId; // use window name so it isnt reused when duplicating tabs
+                // sessionStorage.setItem("clientId", this.clientId); // store in session storage so duplicate tab can load correct workflow
+            }
+            api.dispatchEvent(
+                new CustomEvent("status", { detail: msg.data.status })
+            );
+            break;
+        case "progress":
+            api.dispatchEvent(
+                new CustomEvent("progress", { detail: msg.data })
+            );
+            break;
+        case "executing":
+            api.dispatchEvent(
+                new CustomEvent("executing", { detail: msg.data.node })
+            );
+            break;
+        case "executed":
+            api.dispatchEvent(
+                new CustomEvent("executed", { detail: msg.data })
+            );
+            break;
+        case "execution_start":
+            api.dispatchEvent(
+                new CustomEvent("execution_start", { detail: msg.data })
+            );
+            break;
+        case "execution_error":
+            api.dispatchEvent(
+                new CustomEvent("execution_error", { detail: msg.data })
+            );
+            break;
+        case "execution_cached":
+            api.dispatchEvent(
+                new CustomEvent("execution_cached", { detail: msg.data })
+            );
+            break;
+        default:
+            api.dispatchEvent(new CustomEvent(msg.type, { detail: msg.data }));
+        // default:
+        // if (this.#registered.has(msg.type)) {
+        // } else {
+        // throw new Error(`Unknown message type ${msg.type}`);
+        // }
+    }
+}
 
 /** @typedef {import('../../../web/types/comfy.js').ComfyExtension} ComfyExtension*/
 /** @type {ComfyExtension} */
@@ -16,6 +104,34 @@ const ext = {
         const auth_token = queryParams.get("auth_token");
         const org_display = queryParams.get("org_display");
         const origin = queryParams.get("origin");
+        const workspace_mode = queryParams.get("workspace_mode");
+
+        if (workspace_mode) {
+            document.querySelector(".comfy-menu").style.display = "none";
+
+            sendEventToCD("cd_plugin_onInit");
+
+            app.queuePrompt = ((originalFunction) => async () => {
+                // const prompt = await app.graphToPrompt();
+                sendEventToCD("cd_plugin_onQueuePromptTrigger");
+            })(app.queuePrompt);
+
+            // // Intercept the onkeydown event
+            // window.addEventListener(
+            //   "keydown",
+            //   (event) => {
+            //     // Check for specific keys if necessary
+            //     console.log("hi");
+            //     if ((event.metaKey || event.ctrlKey) && event.key === "Enter") {
+            //       event.preventDefault();
+            //       event.stopImmediatePropagation();
+            //       event.stopPropagation();
+            //       sendEventToCD("cd_plugin_onQueuePrompt", prompt);
+            //     }
+            //   },
+            //   true,
+            // );
+        }
 
         const data = getData();
         let endpoint = data.endpoint;
@@ -36,11 +152,13 @@ const ext = {
         }
 
         if (!workflow_version_id) {
-      console.error("No workflow_version_id provided in query parameters.");
+            console.error(
+                "No workflow_version_id provided in query parameters."
+            );
         } else {
             loadingDialog.showLoading(
                 "Loading workflow from " + org_display,
-        "Please wait...",
+                "Please wait..."
             );
             fetch(endpoint + "/api/workflow-version/" + workflow_version_id, {
                 method: "GET",
@@ -53,12 +171,15 @@ const ext = {
                     const data = await res.json();
                     const { workflow, workflow_id, error } = data;
                     if (error) {
-            infoDialog.showMessage("Unable to load this workflow", error);
+                        infoDialog.showMessage(
+                            "Unable to load this workflow",
+                            error
+                        );
                         return;
                     }
 
-          // Adding a delay to wait for the intial graph to load
+                    // // Adding a delay to wait for the intial graph to load
-          await new Promise((resolve) => setTimeout(resolve, 2000));
+                    // await new Promise((resolve) => setTimeout(resolve, 2000));
 
                     workflow?.nodes.forEach((x) => {
                         if (x?.type === "ComfyDeploy") {
@@ -76,7 +197,7 @@ const ext = {
                     window.history.replaceState(
                         {},
                         document.title,
-            window.location.pathname,
+                        window.location.pathname
                     );
                 });
         }
@@ -99,22 +220,37 @@ const ext = {
                 ComfyWidgets.STRING(
                     this,
                     "workflow_name",
-          ["", { default: this.properties.workflow_name, multiline: false }],
+                    [
-          app,
+                        "",
+                        {
+                            default: this.properties.workflow_name,
+                            multiline: false,
+                        },
+                    ],
+                    app
                 );
 
                 ComfyWidgets.STRING(
                     this,
                     "workflow_id",
-          ["", { default: this.properties.workflow_id, multiline: false }],
+                    [
-          app,
+                        "",
+                        {
+                            default: this.properties.workflow_id,
+                            multiline: false,
+                        },
+                    ],
+                    app
                 );
 
                 ComfyWidgets.STRING(
                     this,
                     "version",
-          ["", { default: this.properties.version, multiline: false }],
+                    [
-          app,
+                        "",
+                        { default: this.properties.version, multiline: false },
+                    ],
+                    app
                 );
 
                 // this.widgets.forEach((w) => {
@@ -141,7 +277,7 @@ const ext = {
                 title_mode: LiteGraph.NORMAL_TITLE,
                 title: "Comfy Deploy",
                 collapsable: true,
-      }),
+            })
         );
 
         ComfyDeploy.category = "deploy";
@@ -150,10 +286,129 @@ const ext = {
     async setup() {
         // const graphCanvas = document.getElementById("graph-canvas");
 
-    window.addEventListener("message", (event) => {
+        window.addEventListener("message", async (event) => {
-      if (!event.data.flow || Object.entries(event.data.flow).length <= 0)
+            //   console.log("message", event);
-        return;
+            try {
-      //   updateBlendshapesPrompts(event.data.flow);
+                const message = JSON.parse(event.data);
+                if (message.type === "graph_load") {
+                    const comfyUIWorkflow = message.data;
+                    // console.log("recieved: ", comfyUIWorkflow);
+                    // Assuming there's a method to load the workflow data into the ComfyUI
+                    // This part of the code would depend on how the ComfyUI expects to receive and process the workflow data
+                    // For demonstration, let's assume there's a loadWorkflow method in the ComfyUI API
+                    if (comfyUIWorkflow && app && app.loadGraphData) {
+                        console.log("loadGraphData");
+                        app.loadGraphData(comfyUIWorkflow);
+                    }
+                } else if (message.type === "deploy") {
+                    // deployWorkflow();
+                    const prompt = await app.graphToPrompt();
+                    // api.handlePromptGenerated(prompt);
+                    sendEventToCD("cd_plugin_onDeployChanges", prompt);
+                } else if (message.type === "queue_prompt") {
+                    const prompt = await app.graphToPrompt();
+                    if (typeof api.handlePromptGenerated === "function") {
+                        api.handlePromptGenerated(prompt);
+                    } else {
+                        console.warn(
+                            "api.handlePromptGenerated is not a function"
+                        );
+                    }
+                    sendEventToCD("cd_plugin_onQueuePrompt", prompt);
+                } else if (message.type === "get_prompt") {
+                    const prompt = await app.graphToPrompt();
+                    sendEventToCD("cd_plugin_onGetPrompt", prompt);
+                } else if (message.type === "event") {
+                    dispatchAPIEventData(message.data);
+                } else if (message.type === "add_node") {
+                    console.log("add node", message.data);
+                    app.graph.beforeChange();
+                    var node = LiteGraph.createNode(message.data.type);
+                    node.configure({
+                        widgets_values: message.data.widgets_values,
+                    });
+
+                    console.log("node", node);
+
+                    const graphMouse = app.canvas.graph_mouse;
+
+                    node.pos = [graphMouse[0], graphMouse[1]];
+
+                    app.graph.add(node);
+                    app.graph.afterChange();
+                } else if (message.type === "zoom_to_node") {
+                    const nodeId = message.data.nodeId;
+                    const position = message.data.position;
+
+                    const node = app.graph.getNodeById(nodeId);
+                    if (!node) return;
+
+                    const canvas = app.canvas;
+                    const targetScale = 1;
+                    const targetOffsetX =
+                        canvas.canvas.width / 4 -
+                        position[0] -
+                        node.size[0] / 2;
+                    const targetOffsetY =
+                        canvas.canvas.height / 4 -
+                        position[1] -
+                        node.size[1] / 2;
+
+                    const startScale = canvas.ds.scale;
+                    const startOffsetX = canvas.ds.offset[0];
+                    const startOffsetY = canvas.ds.offset[1];
+
+                    const duration = 400; // Animation duration in milliseconds
+                    const startTime = Date.now();
+
+                    function easeOutCubic(t) {
+                        return 1 - Math.pow(1 - t, 3);
+                    }
+
+                    function lerp(start, end, t) {
+                        return start * (1 - t) + end * t;
+                    }
+
+                    function animate() {
+                        const currentTime = Date.now();
+                        const elapsedTime = currentTime - startTime;
+                        const t = Math.min(elapsedTime / duration, 1);
+
+                        const easedT = easeOutCubic(t);
+
+                        const currentScale = lerp(
+                            startScale,
+                            targetScale,
+                            easedT
+                        );
+                        const currentOffsetX = lerp(
+                            startOffsetX,
+                            targetOffsetX,
+                            easedT
+                        );
+                        const currentOffsetY = lerp(
+                            startOffsetY,
+                            targetOffsetY,
+                            easedT
+                        );
+
+                        canvas.setZoom(currentScale);
+                        canvas.ds.offset = [currentOffsetX, currentOffsetY];
+                        canvas.draw(true, true);
+
+                        if (t < 1) {
+                            requestAnimationFrame(animate);
+                        }
+                    }
+
+                    animate();
+                }
+                // else if (message.type === "refresh") {
+                //   sendEventToCD("cd_plugin_onRefresh");
+                // }
+            } catch (error) {
+                // console.error("Error processing message:", error);
+            }
         });
 
         api.addEventListener("executed", (evt) => {
@@ -165,6 +420,18 @@ const ext = {
 
             //   }
         });
+
+        app.graph.onAfterChange = ((originalFunction) =>
+            async function () {
+                const prompt = await app.graphToPrompt();
+                sendEventToCD("cd_plugin_onAfterChange", prompt);
+
+                if (typeof originalFunction === "function") {
+                    originalFunction.apply(this, arguments);
+                }
+            })(app.graph.onAfterChange);
+
+        sendEventToCD("cd_plugin_setup");
     },
 };
 
@@ -175,7 +442,7 @@ const ext = {
 
 function showError(title, message) {
     infoDialog.show(
-    `<h3 style="margin: 0px; color: red;">${title}</h3><br><span>${message}</span> `,
+        `<h3 style="margin: 0px; color: red;">${title}</h3><br><span>${message}</span> `
     );
 }
 
@@ -265,14 +532,9 @@ function createDynamicUIHtml(data) {
     return html;
 }
 
-function addButton() {
+async function deployWorkflow() {
-  const menu = document.querySelector(".comfy-menu");
+    const deploy = document.getElementById("deploy-button");
 
-  const deploy = document.createElement("button");
-  deploy.style.position = "relative";
-  deploy.style.display = "block";
-  deploy.innerHTML = "<div id='button-title'>Deploy</div>";
-  deploy.onclick = async () => {
     /** @type {LGraph} */
     const graph = app.graph;
 
@@ -283,43 +545,12 @@ function addButton() {
         return;
     }
 
-    const ok = await confirmDialog.confirm(
-      "Confirm deployment -> " +
-        displayName +
-        "<br><br><small><div style='font-weight: normal;'>" +
-        endpoint +
-        "<div><br>",
-      `A new version will be deployed, are you confirm? <br><br><input id="include-deps" type="checkbox" checked>Include dependence</input>`,
-    );
-    if (!ok) return;
-
-    const includeDeps = document.getElementById("include-deps").checked;
-
-    if (endpoint.endsWith("/")) {
-      endpoint = endpoint.slice(0, -1);
-    }
-    loadingDialog.showLoading("Generating snapshot", "Please wait...");
-
-    const snapshot = await fetch("/snapshot/get_current").then((x) => x.json());
-    // console.log(snapshot);
-    loadingDialog.close();
-
-    if (!snapshot) {
-      showError(
-        "Error when deploying",
-        "Unable to generate snapshot, please install ComfyUI Manager",
-      );
-      return;
-    }
-
-    const title = deploy.querySelector("#button-title");
-
     let deployMeta = graph.findNodesByType("ComfyDeploy");
 
     if (deployMeta.length == 0) {
         const text = await inputDialog.input(
             "Create your deployment",
-        "Workflow name",
+            "Workflow name"
         );
         if (!text) return;
         console.log(text);
@@ -339,11 +570,58 @@ function addButton() {
     const workflow_name = deployMetaNode.widgets[0].value;
     const workflow_id = deployMetaNode.widgets[1].value;
 
+    const ok = await confirmDialog.confirm(
+        `Confirm deployment`,
+        `
+    <div>
+
+    A new version of <button style="font-size: 18px;">${workflow_name}</button> will be deployed, do you confirm? 
+    <br><br>
+
+    <button style="font-size: 18px;">${displayName}</button>
+    <br>
+    <button style="font-size: 18px;">${endpoint}</button>
+
+    <br><br>
+    <label>
+    <input id="include-deps" type="checkbox" checked>Include dependency</input>
+    </label>
+    <br>
+    <label>
+    <input id="reuse-hash" type="checkbox" checked>Reuse hash from last version</input>
+    </label>
+    </div>
+    `
+    );
+    if (!ok) return;
+
+    const includeDeps = document.getElementById("include-deps").checked;
+    const reuseHash = document.getElementById("reuse-hash").checked;
+
+    if (endpoint.endsWith("/")) {
+        endpoint = endpoint.slice(0, -1);
+    }
+    loadingDialog.showLoading("Generating snapshot");
+
+    const snapshot = await fetch("/snapshot/get_current").then((x) => x.json());
+    // console.log(snapshot);
+    loadingDialog.close();
+
+    if (!snapshot) {
+        showError(
+            "Error when deploying",
+            "Unable to generate snapshot, please install ComfyUI Manager"
+        );
+        return;
+    }
+
+    const title = deploy.querySelector("#button-title");
+
     const prompt = await app.graphToPrompt();
     let deps = undefined;
 
     if (includeDeps) {
-      loadingDialog.showLoading("Fetching existing version", "Please wait...");
+        loadingDialog.showLoading("Fetching existing version");
 
         const existing_workflow = await fetch(
             endpoint + "/api/workflow/" + workflow_id,
@@ -353,7 +631,7 @@ function addButton() {
                     "Content-Type": "application/json",
                     Authorization: "Bearer " + apiKey,
                 },
-        },
+            }
         )
             .then((x) => x.json())
             .catch(() => {
@@ -362,20 +640,41 @@ function addButton() {
 
         loadingDialog.close();
 
-      loadingDialog.showLoading(
+        loadingDialog.showLoading("Generating dependency graph");
-        "Generating dependency graph",
-        "Please wait...",
-      );
         deps = await generateDependencyGraph({
             workflow_api: prompt.output,
             snapshot: snapshot,
             computeFileHash: async (file) => {
+                console.log(existing_workflow?.dependencies?.models);
+
+                // Match previous hash for models
+                if (reuseHash && existing_workflow?.dependencies?.models) {
+                    const previousModelHash = Object.entries(
+                        existing_workflow?.dependencies?.models
+                    ).flatMap(([key, value]) => {
+                        return Object.values(value).map((x) => ({
+                            ...x,
+                            name: "models/" + key + "/" + x.name,
+                        }));
+                    });
+                    console.log(previousModelHash);
+
+                    const match = previousModelHash.find((x) => {
+                        console.log(file, x.name);
+                        return file == x.name;
+                    });
+                    console.log(match);
+                    if (match && match.hash) {
+                        console.log("cached hash used");
+                        return match.hash;
+                    }
+                }
                 console.log(file);
                 loadingDialog.showLoading("Generating hash", file);
                 const hash = await fetch(
                     `/comfyui-deploy/get-file-hash?file_path=${encodeURIComponent(
-              file,
+                        file
-            )}`,
+                    )}`
                 ).then((x) => x.json());
                 loadingDialog.showLoading("Generating hash", file);
                 console.log(hash);
@@ -394,12 +693,15 @@ function addButton() {
                                 token: apiKey,
                                 url: endpoint + "/api/upload-url",
                             }),
-              },
+                        }
                     )
                         .then((x) => x.json())
                         .catch(() => {
                             loadingDialog.close();
-                confirmDialog.confirm("Error", "Unable to upload file " + file);
+                            confirmDialog.confirm(
+                                "Error",
+                                "Unable to upload file " + file
+                            );
                         });
                     loadingDialog.showLoading("Uploaded file", file);
                     console.log(download_url);
@@ -411,12 +713,34 @@ function addButton() {
             existingDependencies: existing_workflow.dependencies,
         });
 
+        // Need to find a way to include this if this is not included in comfyui-json level
+        if (
+            !deps.custom_nodes["https://github.com/BennyKok/comfyui-deploy"] &&
+            !deps.custom_nodes["https://github.com/BennyKok/comfyui-deploy.git"]
+        )
+            deps.custom_nodes["https://github.com/BennyKok/comfyui-deploy"] = {
+                url: "https://github.com/BennyKok/comfyui-deploy",
+                install_type: "git-clone",
+                hash:
+                    snapshot?.git_custom_nodes?.[
+                        "https://github.com/BennyKok/comfyui-deploy"
+                    ]?.hash ?? "HEAD",
+                name: "ComfyUI Deploy",
+            };
+
         loadingDialog.close();
 
         const depsOk = await confirmDialog.confirm(
             "Check dependencies",
             // JSON.stringify(deps, null, 2),
-        createDynamicUIHtml(deps),
+            `
+      <div style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%);">${loadingIcon}</div>
+      <iframe 
+      style="z-index: 10; min-width: 600px; max-width: 1024px; min-height: 600px; border: none; background-color: transparent;"
+      src="https://www.comfydeploy.com/dependency-graph?deps=${encodeURIComponent(
+          JSON.stringify(deps)
+      )}" />`
+            // createDynamicUIHtml(deps),
         );
         if (!depsOk) return;
 
@@ -476,7 +800,7 @@ function addButton() {
         graph.change();
 
         infoDialog.show(
-        `<span style="color:green;">Deployed successfully!</span>  <a style="color:white;" target="_blank" href=${endpoint}/workflows/${data.workflow_id}>-> View here</a> <br/> <br/> Workflow ID: ${data.workflow_id} <br/> Workflow Name: ${workflow_name} <br/> Workflow Version: ${data.version} <br/>`,
+            `<span style="color:green;">Deployed successfully!</span>  <a style="color:white;" target="_blank" href=${endpoint}/workflows/${data.workflow_id}>-> View here</a> <br/> <br/> Workflow ID: ${data.workflow_id} <br/> Workflow Name: ${workflow_name} <br/> Workflow Version: ${data.version} <br/>`
         );
 
         setTimeout(() => {
@@ -494,6 +818,18 @@ function addButton() {
             title.style.color = "white";
         }, 1000);
     }
+}
+
+function addButton() {
+    const menu = document.querySelector(".comfy-menu");
+
+    const deploy = document.createElement("button");
+    deploy.id = "deploy-button";
+    deploy.style.position = "relative";
+    deploy.style.display = "block";
+    deploy.innerHTML = "<div id='button-title'>Deploy</div>";
+    deploy.onclick = async () => {
+        await deployWorkflow();
     };
 
     const config = document.createElement("img");
@@ -570,12 +906,10 @@ export class InfoDialog extends ComfyDialog {
       `);
     }
 
-  loadingIcon = `<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 24 24"><g fill="none" stroke="#888888" stroke-linecap="round" stroke-width="2"><path stroke-dasharray="60" stroke-dashoffset="60" stroke-opacity=".3" d="M12 3C16.9706 3 21 7.02944 21 12C21 16.9706 16.9706 21 12 21C7.02944 21 3 16.9706 3 12C3 7.02944 7.02944 3 12 3Z"><animate fill="freeze" attributeName="stroke-dashoffset" dur="1.3s" values="60;0"/></path><path stroke-dasharray="15" stroke-dashoffset="15" d="M12 3C16.9706 3 21 7.02944 21 12"><animate fill="freeze" attributeName="stroke-dashoffset" dur="0.3s" values="15;0"/><animateTransform attributeName="transform" dur="1.5s" repeatCount="indefinite" type="rotate" values="0 12 12;360 12 12"/></path></g></svg>`;
-
     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;">${title} ${this.loadingIcon}</h3>
+        <h3 style="margin: 0px; display: flex; align-items: center; justify-content: center;">${title} ${loadingIcon}</h3>
         <label>
           ${message}
         </label>
@@ -620,7 +954,11 @@ export class LoadingDialog extends ComfyDialog {
         <h3 style="margin: 0px; display: flex; align-items: center; justify-content: center; gap: 12px;">${title} ${
             this.loadingIcon
         }</h3>
-          ${message ? `<label>${message}</label>` : ""}
+          ${
+              message
+                  ? `<label style="max-width: 100%; white-space: pre-wrap; word-wrap: break-word;">${message}</label>`
+                  : ""
+          }
         </div>
       `);
     }
@@ -659,17 +997,22 @@ export class InputDialog extends InfoDialog {
                         type: "button",
                         textContent: "Save",
                         onclick: () => {
-              const input = this.textElement.querySelector("#input").value;
+                            const input =
+                                this.textElement.querySelector("#input").value;
                             if (input.trim() === "") {
-                showError("Input validation", "Input cannot be empty");
+                                showError(
+                                    "Input validation",
+                                    "Input cannot be empty"
+                                );
                             } else {
                                 this.callback?.(input);
                                 this.close();
-                this.textElement.querySelector("#input").value = "";
+                                this.textElement.querySelector("#input").value =
+                                    "";
                             }
                         },
                     }),
-        ],
+                ]
             ),
         ];
     }
@@ -730,7 +1073,7 @@ export class ConfirmDialog extends InfoDialog {
                             this.close();
                         },
                     }),
-        ],
+                ]
             ),
         ];
     }
@@ -739,11 +1082,9 @@ export class ConfirmDialog extends InfoDialog {
         return new Promise((resolve, reject) => {
             this.callback = resolve;
             this.show(`
-      <div style="width: 100%; max-width: 600px; display: flex; gap: 18px; flex-direction: column; overflow: unset">
+      <div style="width: 100%; max-width: 600px; display: flex; gap: 18px; flex-direction: column; overflow: unset; position: relative;">
         <h3 style="margin: 0px;">${title}</h3>
-        <label>
         ${message}
-        </label>
         </div>
       `);
         });
@@ -789,7 +1130,7 @@ function getData(environment) {
 function saveData(data) {
     localStorage.setItem(
         "comfy_deploy_env_data_" + data.environment,
-    JSON.stringify(data),
+        JSON.stringify(data)
     );
 }
 
@@ -804,7 +1145,9 @@ export class ConfigDialog extends ComfyDialog {
         this.element.style.paddingBottom = "20px";
 
         this.container = document.createElement("div");
-    this.element.querySelector(".comfy-modal-content").prepend(this.container);
+        this.element
+            .querySelector(".comfy-modal-content")
+            .prepend(this.container);
     }
 
     createButtons() {
@@ -819,7 +1162,10 @@ export class ConfigDialog extends ComfyDialog {
                         justifyContent: "flex-end",
                         width: "100%",
                     },
-          onclick: () => this.save(),
+                    onclick: () => {
+                        this.save();
+                        this.close();
+                    },
                 },
                 [
                     $el("button", {
@@ -830,9 +1176,12 @@ export class ConfigDialog extends ComfyDialog {
                     $el("button", {
                         type: "button",
                         textContent: "Save",
-            onclick: () => this.save(),
+                        onclick: () => {
+                            this.save();
+                            this.close();
+                        },
                     }),
-        ],
+                ]
             ),
         ];
     }
@@ -844,20 +1193,27 @@ export class ConfigDialog extends ComfyDialog {
     }
 
     save(api_key, displayName) {
-    if (!displayName) displayName = getData().displayName;
+        const deployOption =
-
+            this.container.querySelector("#deployOption").value;
-    const deployOption = this.container.querySelector("#deployOption").value;
         localStorage.setItem("comfy_deploy_env", deployOption);
 
         const endpoint = this.container.querySelector("#endpoint").value;
         const apiKey = api_key ?? this.container.querySelector("#apiKey").value;
+
+        if (!displayName) {
+            if (apiKey != getData().apiKey) {
+                displayName = "Custom";
+            } else {
+                displayName = getData().displayName;
+            }
+        }
+
         saveData({
             endpoint,
             apiKey,
             displayName,
             environment: deployOption,
         });
-    this.close();
     }
 
     show() {
@@ -870,8 +1226,12 @@ export class ConfigDialog extends ComfyDialog {
     <h3 style="margin: 0px;">Comfy Deploy Config</h3>
     <label style="color: white; width: 100%;">
       <select id="deployOption" style="margin: 8px 0px; width: 100%; height:30px; box-sizing: border-box;" >
-        <option value="cloud" ${data.environment === "cloud" ? "selected" : ""}>Cloud</option>
+        <option value="cloud" ${
-        <option value="local" ${data.environment === "local" ? "selected" : ""}>Local</option>
+            data.environment === "cloud" ? "selected" : ""
+        }>Cloud</option>
+        <option value="local" ${
+            data.environment === "local" ? "selected" : ""
+        }>Local</option>
       </select>
     </label>
       <label style="color: white; width: 100%;">
@@ -880,22 +1240,29 @@ export class ConfigDialog extends ComfyDialog {
             data.endpoint
         }">
       </label>
-      <label style="color: white;">
+      <div style="color: white;">
-        API Key: ${data.displayName ?? ""}
+        API Key: User / Org <button style="font-size: 18px;">${
+            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>
-      </label>
+      </div>
       </div>
     `;
 
         const button = this.container.querySelector("#loginButton");
         button.onclick = () => {
+            this.save();
+            const data = getData();
+
             const uuid =
                 Math.random().toString(36).substring(2, 15) +
                 Math.random().toString(36).substring(2, 15);
@@ -905,24 +1272,28 @@ export class ConfigDialog extends ComfyDialog {
                 clearInterval(poll);
                 infoDialog.showMessage(
                     "Timeout",
-          "Wait too long for the response, please try re-login",
+                    "Wait too long for the response, please try re-login"
                 );
             }, 30000); // Stop polling after 30 seconds
 
             this.poll = setInterval(() => {
                 fetch(data.endpoint + "/api/auth-response/" + uuid)
                     .then((response) => response.json())
-          .then((json) => {
+                    .then(async (json) => {
                         if (json.api_key) {
                             this.save(json.api_key, json.name);
-              this.container.querySelector("#apiKey").value = json.api_key;
+                            this.close();
-              infoDialog.show();
+                            this.container.querySelector("#apiKey").value =
+                                json.api_key;
+                            // infoDialog.show();
                             clearInterval(this.poll);
                             clearTimeout(this.timeout);
-              infoDialog.showMessage(
+                            // Refresh dialog
+                            const a = await confirmDialog.confirm(
                                 "Authenticated",
-                "You will be able to upload workflow to " + json.name,
+                                `<div>You will be able to upload workflow to <button style="font-size: 18px; width: fit;">${json.name}</button></div>`
                             );
+                            configDialog.show();
                         }
                     })
                     .catch((error) => {

web/package.json

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

web/src/app/(app)/globals.css

@@ -80,7 +80,9 @@
   /* @apply rounded-lg p-2 overflow-x-scroll */
   @apply  p-2 max-w-full overflow-auto w-full
 }
- 
+.vsc-controller{
+  position: absolute;
+}
 @layer base {
   * {
     @apply border-border;

web/src/components/NavbarMenu.tsx

@@ -5,7 +5,6 @@ import { Tabs, TabsList, TabsTrigger } from "@/components/ui/tabs";
 import { cn } from "@/lib/utils";
 import Link from "next/link";
 import { usePathname } from "next/navigation";
-import { useRouter } from "next/navigation";
 import { useEffect, useState } from "react";
 import { useMediaQuery } from "usehooks-ts";
 
@@ -25,8 +24,6 @@ export function NavbarMenu({
   const pathnames = usePathname();
   const pathname = `/${pathnames.split("/")[1]}`;
 
-  const router = useRouter();
-
   const pages = [
     {
       name: "Workflows",
@@ -59,11 +56,8 @@ export function NavbarMenu({
               <TabsTrigger
                 key={page.name}
                 value={page.path}
-                onClick={() => {
-                  router.push(page.path);
-                }}
               >
-                {page.name}
+                <Link href={page.path}>{page.name}</Link>
               </TabsTrigger>
             ))}
           </TabsList>

web/src/components/VersionSelect.tsx

@@ -60,6 +60,7 @@ import { callServerPromise } from "./callServerPromise";
 import fetcher from "./fetcher";
 import { ButtonAction } from "@/components/ButtonActionLoader";
 import { editWorkflowOnMachine } from "@/server/editWorkflowOnMachine";
+import { VisualizeImagesGrid } from "@/components/VisualizeImagesGrid";
 
 export function VersionSelect({
   workflow,
@@ -169,19 +170,21 @@ export function useSelectedMachine(
 }
 
 type PublicRunStore = {
-  image: string;
+  image: {
+    url: string;
+  }[] | null;
   loading: boolean;
   runId: string;
   status: string;
 
-  setImage: (image: string) => void;
+  setImage: (image: { url: string; }[]) => void;
   setLoading: (loading: boolean) => void;
   setRunId: (runId: string) => void;
   setStatus: (status: string) => void;
 };
 
 export const publicRunStore = create<PublicRunStore>((set) => ({
-  image: "",
+  image: null,
   loading: false,
   runId: "",
   status: "",
@@ -205,7 +208,10 @@ export function PublicRunOutputs(props: {
         console.log(res?.status);
         if (res) setStatus(res.status);
         if (res && res.status === "success") {
-          setImage(res.outputs[0]?.data.images[0].url);
+          const imageURLs = res.outputs[0]?.data.images.map((item: { url: string; }) => {
+            return { url: item.url };
+          });
+          setImage(imageURLs);
           setLoading(false);
           clearInterval(interval);
         }
@@ -214,30 +220,25 @@ export function PublicRunOutputs(props: {
     return () => clearInterval(interval);
   }, [runId]);
 
+  if (loading) {
     return (
-    <div className="border border-gray-200 w-full square h-[400px] rounded-lg relative">
+      <div className="border border-gray-200 w-full h-[400px] square rounded-lg relative p-4 ">
-      {!loading && !image && props.preview && props.preview.length > 0 && (
-        <>
-          <img
-            className="w-full h-full object-contain"
-            src={props.preview[0]?.url}
-            alt="Generated image"
-          />
-        </>
-      )}
-      {!loading && image && (
-        <img
-          className="w-full h-full object-contain"
-          src={image}
-          alt="Generated image"
-        />
-      )}
-      {loading && (
         <div className="absolute top-0 left-0 w-full h-full flex items-center justify-center gap-2">
           {status} <LoadingIcon />
         </div>
+        <Skeleton className="w-full h-full" />
+      </div>
+    );
+  }
+
+  return (
+    <div className="border border-gray-200 w-full min-h-[400px] square rounded-lg relative p-4 ">
+      {!image && props.preview && props.preview.length > 0 &&
+        <VisualizeImagesGrid images={props.preview} />
+      }
+      {image && (
+        <VisualizeImagesGrid images={image} />
       )}
-      {loading && <Skeleton className="w-full h-full" />}
     </div>
   );
 }

web/src/components/VisualizeImagesGrid.tsx

@@ -0,0 +1,42 @@
+type imagesType = {
+  url: string;
+  width?: number;
+  height?: number;
+};
+type VisualizeImagesGridProps = {
+  images: imagesType[];
+  layout?: 'justify-between' | 'justify-center' | 'justify-start' | 'justify-end';
+};
+export function VisualizeImagesGrid({ images, layout }: VisualizeImagesGridProps) {
+
+  return (
+    <div className={`flex gap-4 flex-wrap ${layout || 'justify-center'}`}>
+      <>
+        {images && images.length > 0 &&
+          images.map(item => {
+            if (!item) {
+              return;
+            }
+            if (item?.url.endsWith(".mp4") || item?.url.endsWith(".webm")) {
+              return (
+                <video key={item?.url} controls autoPlay className="rounded-xl" style={{ maxHeight: item.height || 370, maxWidth: item.width || "auto" }}>
+                  <source src={item?.url} type="video/mp4" />
+                  <source src={item?.url} type="video/webm" />
+                  Your browser does not support the video tag.
+                </video>
+              );
+            }
+            return <img
+              key={item?.url}
+              className="object-contain overflow-hidden rounded-xl"
+              src={item?.url}
+              alt="Generated image"
+              style={{ maxHeight: item.height || 370, maxWidth: item.width || "auto" }}
+            />;
+          })
+        }
+      </>
+    </div>
+  );
+
+}

web/src/routes/registerCreateRunRoute.ts

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

web/src/server/createRun.ts

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