Merge branch 'main' into dev

* fix(image upload): skip when using the CD_BYPASS_UPLOAD env var

* Revert "fix(image upload): skip when using the CD_BYPASS_UPLOAD env var"

This reverts commit 384eda63e6.

* fix(upload outputs): skip images/gifs/files/mesh when env var is true

The env var is `CD_BYPASS_UPLOAD`.
When that variables is `True`, we don't upload the media to our comfy
deploy s3 bucket.

There are 2 steps.
1. save the file into our s3 bucket
2. save the saving into our database.

When `CD_BYPASS_UPLOAD` is True:
1. Skip the save file into our s3 bucket
2. Skip the save into our database

Previously we were skipping the step 1, but not the step 2. So that is
the reason of why we keep seeing the comfy deploy URL when fetching the
run details:

```
outputs: [
  {
    data:{
      gifs: [
        {
          url: "https://comfy-deploy-output.s3.amazonaws.com/video.mp4"
        }
      ],
      text: [
        "A text that you displayed with show text node"
      ]
    }
  }
]
```

With the new changes we don't save that into our database, and fetching
the details of a run will look like this:
```
outputs: [
  {
    data:{
      text: [
        "A text that you displayed with show text node"
      ]
    }
  }
]
```

.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 }}

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_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

@@ -16,8 +16,8 @@ class ComfyUIDeployExternalLora:
                 ),
             },
             "optional": {
-                "default_lora_name": (folder_paths.get_filename_list("loras"), ),
-            }
+                "default_lora_name": (folder_paths.get_filename_list("loras"),),
+            },
         }
 
     RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
@@ -32,20 +32,29 @@ class ComfyUIDeployExternalLora:
         import os
         import uuid
 
-        if input_id and input_id.startswith('http'):
+        if default_lora_name.startswith("http"):
             unique_filename = str(uuid.uuid4()) + ".safetensors"
             print(unique_filename)
             print(folder_paths.folder_names_and_paths["loras"][0][0])
-            destination_path = os.path.join(folder_paths.folder_names_and_paths["loras"][0][0], unique_filename)
+            destination_path = os.path.join(
+                folder_paths.folder_names_and_paths["loras"][0][0], unique_filename
+            )
             print(destination_path)
             print("Downloading external lora - " + input_id + " to " + destination_path)
-            response = requests.get(input_id, headers={'User-Agent': 'Mozilla/5.0'}, allow_redirects=True)
-            with open(destination_path, 'wb') as out_file:
+            response = requests.get(
+                input_id,
+                headers={"User-Agent": "Mozilla/5.0"},
+                allow_redirects=True,
+            )
+            with open(destination_path, "wb") as out_file:
                 out_file.write(response.content)
             return (unique_filename,)
         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_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_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 +1,594 @@
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite and is meant to work with
+import os
+import itertools
+import numpy as np
+import torch
+import cv2
+
+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 get_sorted_dir_files_from_directory(
+    directory: str,
+    skip_first_images: int = 0,
+    select_every_nth: int = 1,
+    extensions: Iterable = None,
+):
+    directory = directory.strip()
+    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, "-v", "error", "-i", file]
+    if start_time > 0:
+        args += ["-ss", str(start_time)]
+    if duration > 0:
+        args += ["-t", str(duration)]
+    try:
+        res = subprocess.run(
+            args + ["-f", "wav", "-"], stdout=subprocess.PIPE, check=True
+        ).stdout
+    except subprocess.CalledProcessError as e:
+        return False
+    return res
+
+
+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 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 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(video)
+    if not video_cap.isOpened():
+        raise ValueError(f"{video} could not be loaded with cv.")
+
+    # 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)
+
+    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) / 255.0
+        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 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 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,
+):
+    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,
+            )
+
+    else:
+        (gen, width, height, fps, duration, total_frames, target_frame_time) = (
+            meta_batch.inputs[unique_id]
+        )
+
+    if meta_batch is not None:
+        gen = itertools.islice(gen, meta_batch.frames_per_batch)
+
+    # 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, (height, width, 3))))
+    )
+    if len(images) == 0:
+        raise RuntimeError("No frames generated")
+    if force_size != "Disabled":
+        new_size = target_size(width, height, force_size, custom_width, custom_height)
+        if new_size[0] != width or new_size[1] != height:
+            s = images.movedim(-1, 1)
+            s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+            images = s.movedim(1, -1)
+
+    # Setup lambda for lazy audio capture
+    audio = lambda: 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": images.shape[2],
+        "loaded_height": images.shape[1],
+    }
+
+    return (images, len(images), lazy_eval(audio), video_info)
+
+
+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",),
+                "default_value": (sorted(files),),
+            },
+            "hidden": {"unique_id": "UNIQUE_ID"},
+        }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = (
+        "IMAGE",
+        "INT",
+        "VHS_AUDIO",
+        "VHS_VIDEOINFO",
+    )
+    RETURN_NAMES = (
+        "IMAGE",
+        "frame_count",
+        "audio",
+        "video_info",
+    )
+
+    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)"
+}

globals.py

@@ -1,26 +1,45 @@
 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: str
-    file_upload_endpoint: str
+    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:

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__))
@@ -66,4 +69,7 @@ try:
     current_git_commit = subprocess.check_output(['git', 'rev-parse', 'HEAD']).decode('utf-8').strip()
     print(f"** Comfy Deploy Revision: {current_git_commit}")
 except Exception as e:
-    print(f"** Comfy Deploy failed to get current git commit: {str(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

@@ -1,2 +1,5 @@
 aiofiles
-pydantic
+pydantic
+opencv-python
+imageio-ffmpeg
+logfire

web-plugin/index.js

@@ -1,10 +1,90 @@
 import { app } from "./app.js";
 import { api } from "./api.js";
 import { ComfyWidgets, LGraphNode } from "./widgets.js";
-import { generateDependencyGraph } from "https://esm.sh/[email protected]2";
+import { generateDependencyGraph } from "https://esm.sh/[email protected]5";
 
 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} */
 const ext = {
@@ -18,6 +98,34 @@ 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;
@@ -152,9 +260,37 @@ const ext = {
   async setup() {
     // const graphCanvas = document.getElementById("graph-canvas");
 
-    window.addEventListener("message", (event) => {
-      if (!event.data.flow || Object.entries(event.data.flow).length <= 0)
-        return;
+    window.addEventListener("message", async (event) => {
+      try {
+        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) {
+            app.loadGraphData(comfyUIWorkflow);
+          }
+        } else if (message.type === "deploy") {
+          // deployWorkflow();
+          const prompt = await app.graphToPrompt();
+          sendEventToCD("cd_plugin_onDeployChanges", prompt);
+        } else if (message.type === "queue_prompt") {
+          const prompt = await app.graphToPrompt();
+          sendEventToCD("cd_plugin_onQueuePrompt", prompt);
+        } else if (message.type === "event") {
+          dispatchAPIEventData(message.data);
+        }
+        // else if (message.type === "refresh") {
+        //   sendEventToCD("cd_plugin_onRefresh");
+        // }
+      } catch (error) {
+        // console.error("Error processing message:", error);
+      }
+
+      // if (!event.data.flow || Object.entries(event.data.flow).length <= 0)
+      //   return;
       //   updateBlendshapesPrompts(event.data.flow);
     });
 
@@ -167,6 +303,18 @@ 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");
   },
 };
 
@@ -267,294 +415,296 @@ function createDynamicUIHtml(data) {
   return html;
 }
 
-function addButton() {
-  const menu = document.querySelector(".comfy-menu");
+async function deployWorkflow() {
+  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;
+  /** @type {LGraph} */
+  const graph = app.graph;
 
-    let { endpoint, apiKey, displayName } = getData();
+  let { endpoint, apiKey, displayName } = getData();
 
-    if (!endpoint || !apiKey || apiKey === "" || endpoint === "") {
-      configDialog.show();
-      return;
-    }
+  if (!endpoint || !apiKey || apiKey === "" || endpoint === "") {
+    configDialog.show();
+    return;
+  }
 
-    let deployMeta = graph.findNodesByType("ComfyDeploy");
+  let deployMeta = graph.findNodesByType("ComfyDeploy");
 
-    if (deployMeta.length == 0) {
-      const text = await inputDialog.input(
-        "Create your deployment",
-        "Workflow name",
-      );
-      if (!text) return;
-      console.log(text);
-      app.graph.beforeChange();
-      var node = LiteGraph.createNode("ComfyDeploy");
-      node.configure({
-        widgets_values: [text],
-      });
-      node.pos = [0, 0];
-      app.graph.add(node);
-      app.graph.afterChange();
-      deployMeta = [node];
-    }
-
-    const deployMetaNode = deployMeta[0];
-
-    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 (deployMeta.length == 0) {
+    const text = await inputDialog.input(
+      "Create your deployment",
+      "Workflow name",
     );
-    if (!ok) return;
+    if (!text) return;
+    console.log(text);
+    app.graph.beforeChange();
+    var node = LiteGraph.createNode("ComfyDeploy");
+    node.configure({
+      widgets_values: [text],
+    });
+    node.pos = [0, 0];
+    app.graph.add(node);
+    app.graph.afterChange();
+    deployMeta = [node];
+  }
 
-    const includeDeps = document.getElementById("include-deps").checked;
-    const reuseHash = document.getElementById("reuse-hash").checked;
+  const deployMetaNode = deployMeta[0];
 
-    if (endpoint.endsWith("/")) {
-      endpoint = endpoint.slice(0, -1);
-    }
-    loadingDialog.showLoading("Generating snapshot");
+  const workflow_name = deployMetaNode.widgets[0].value;
+  const workflow_id = deployMetaNode.widgets[1].value;
 
-    const snapshot = await fetch("/snapshot/get_current").then((x) => x.json());
-    // console.log(snapshot);
-    loadingDialog.close();
+  const ok = await confirmDialog.confirm(
+    `Confirm deployment`,
+    `
+    <div>
 
-    if (!snapshot) {
-      showError(
-        "Error when deploying",
-        "Unable to generate snapshot, please install ComfyUI Manager",
-      );
-      return;
-    }
+    A new version of <button style="font-size: 18px;">${workflow_name}</button> will be deployed, do you confirm? 
+    <br><br>
 
-    const title = deploy.querySelector("#button-title");
+    <button style="font-size: 18px;">${displayName}</button>
+    <br>
+    <button style="font-size: 18px;">${endpoint}</button>
 
-    const prompt = await app.graphToPrompt();
-    let deps = undefined;
+    <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;
 
-    if (includeDeps) {
-      loadingDialog.showLoading("Fetching existing version");
+  const includeDeps = document.getElementById("include-deps").checked;
+  const reuseHash = document.getElementById("reuse-hash").checked;
 
-      const existing_workflow = await fetch(
-        endpoint + "/api/workflow/" + workflow_id,
-        {
-          method: "GET",
-          headers: {
-            "Content-Type": "application/json",
-            Authorization: "Bearer " + apiKey,
-          },
-        },
-      )
-        .then((x) => x.json())
-        .catch(() => {
-          return {};
-        });
+  if (endpoint.endsWith("/")) {
+    endpoint = endpoint.slice(0, -1);
+  }
+  loadingDialog.showLoading("Generating snapshot");
 
-      loadingDialog.close();
+  const snapshot = await fetch("/snapshot/get_current").then((x) => x.json());
+  // console.log(snapshot);
+  loadingDialog.close();
 
-      loadingDialog.showLoading("Generating dependency graph");
-      deps = await generateDependencyGraph({
-        workflow_api: prompt.output,
-        snapshot: snapshot,
-        computeFileHash: async (file) => {
-          console.log(existing_workflow?.dependencies?.models);
+  if (!snapshot) {
+    showError(
+      "Error when deploying",
+      "Unable to generate snapshot, please install ComfyUI Manager",
+    );
+    return;
+  }
 
-          // 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 title = deploy.querySelector("#button-title");
 
-            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,
-            )}`,
-          ).then((x) => x.json());
-          loadingDialog.showLoading("Generating hash", file);
-          console.log(hash);
-          return hash.file_hash;
-        },
-        handleFileUpload: async (file, hash, prevhash) => {
-          console.log("Uploading ", file);
-          loadingDialog.showLoading("Uploading file", file);
-          try {
-            const { download_url } = await fetch(
-              `/comfyui-deploy/upload-file`,
-              {
-                method: "POST",
-                body: JSON.stringify({
-                  file_path: file,
-                  token: apiKey,
-                  url: endpoint + "/api/upload-url",
-                }),
-              },
-            )
-              .then((x) => x.json())
-              .catch(() => {
-                loadingDialog.close();
-                confirmDialog.confirm("Error", "Unable to upload file " + file);
-              });
-            loadingDialog.showLoading("Uploaded file", file);
-            console.log(download_url);
-            return download_url;
-          } catch (error) {
-            return undefined;
-          }
-        },
-        existingDependencies: existing_workflow.dependencies,
-      });
+  const prompt = await app.graphToPrompt();
+  let deps = undefined;
 
-      // 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",
-        };
+  if (includeDeps) {
+    loadingDialog.showLoading("Fetching existing version");
 
-      loadingDialog.close();
-
-      const depsOk = await confirmDialog.confirm(
-        "Check dependencies",
-        // JSON.stringify(deps, null, 2),
-        `
-        <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;
-
-      console.log(deps);
-    }
-
-    loadingDialog.showLoading("Deploying...");
-
-    title.innerText = "Deploying...";
-    title.style.color = "orange";
-
-    // console.log(prompt);
-
-    // TODO trim the ending / from endpoint is there is
-    if (endpoint.endsWith("/")) {
-      endpoint = endpoint.slice(0, -1);
-    }
-
-    // console.log(prompt.workflow);
-
-    const apiRoute = endpoint + "/api/workflow";
-    // const userId = apiKey
-    try {
-      const body = {
-        workflow_name,
-        workflow_id,
-        workflow: prompt.workflow,
-        workflow_api: prompt.output,
-        snapshot: snapshot,
-        dependencies: deps,
-      };
-      console.log(body);
-      let data = await fetch(apiRoute, {
-        method: "POST",
-        body: JSON.stringify(body),
+    const existing_workflow = await fetch(
+      endpoint + "/api/workflow/" + workflow_id,
+      {
+        method: "GET",
         headers: {
           "Content-Type": "application/json",
           Authorization: "Bearer " + apiKey,
         },
+      },
+    )
+      .then((x) => x.json())
+      .catch(() => {
+        return {};
       });
 
-      console.log(data);
+    loadingDialog.close();
 
-      if (data.status !== 200) {
-        throw new Error(await data.text());
-      } else {
-        data = await data.json();
-      }
+    loadingDialog.showLoading("Generating dependency graph");
+    deps = await generateDependencyGraph({
+      workflow_api: prompt.output,
+      snapshot: snapshot,
+      computeFileHash: async (file) => {
+        console.log(existing_workflow?.dependencies?.models);
 
-      loadingDialog.close();
+        // 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);
 
-      title.textContent = "Done";
-      title.style.color = "green";
+          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)}`,
+        ).then((x) => x.json());
+        loadingDialog.showLoading("Generating hash", file);
+        console.log(hash);
+        return hash.file_hash;
+      },
+      handleFileUpload: async (file, hash, prevhash) => {
+        console.log("Uploading ", file);
+        loadingDialog.showLoading("Uploading file", file);
+        try {
+          const { download_url } = await fetch(`/comfyui-deploy/upload-file`, {
+            method: "POST",
+            body: JSON.stringify({
+              file_path: file,
+              token: apiKey,
+              url: endpoint + "/api/upload-url",
+            }),
+          })
+            .then((x) => x.json())
+            .catch(() => {
+              loadingDialog.close();
+              confirmDialog.confirm("Error", "Unable to upload file " + file);
+            });
+          loadingDialog.showLoading("Uploaded file", file);
+          console.log(download_url);
+          return download_url;
+        } catch (error) {
+          return undefined;
+        }
+      },
+      existingDependencies: existing_workflow.dependencies,
+    });
 
-      deployMetaNode.widgets[1].value = data.workflow_id;
-      deployMetaNode.widgets[2].value = data.version;
-      graph.change();
+    // 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",
+      };
 
-      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/>`,
-      );
+    loadingDialog.close();
 
-      setTimeout(() => {
-        title.textContent = "Deploy";
-        title.style.color = "white";
-      }, 1000);
-    } catch (e) {
-      loadingDialog.close();
-      app.ui.dialog.show(e);
-      console.error(e);
-      title.textContent = "Error";
-      title.style.color = "red";
-      setTimeout(() => {
-        title.textContent = "Deploy";
-        title.style.color = "white";
-      }, 1000);
+    const depsOk = await confirmDialog.confirm(
+      "Check dependencies",
+      // JSON.stringify(deps, null, 2),
+      `
+      <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;
+
+    console.log(deps);
+  }
+
+  loadingDialog.showLoading("Deploying...");
+
+  title.innerText = "Deploying...";
+  title.style.color = "orange";
+
+  // console.log(prompt);
+
+  // TODO trim the ending / from endpoint is there is
+  if (endpoint.endsWith("/")) {
+    endpoint = endpoint.slice(0, -1);
+  }
+
+  // console.log(prompt.workflow);
+
+  const apiRoute = endpoint + "/api/workflow";
+  // const userId = apiKey
+  try {
+    const body = {
+      workflow_name,
+      workflow_id,
+      workflow: prompt.workflow,
+      workflow_api: prompt.output,
+      snapshot: snapshot,
+      dependencies: deps,
+    };
+    console.log(body);
+    let data = await fetch(apiRoute, {
+      method: "POST",
+      body: JSON.stringify(body),
+      headers: {
+        "Content-Type": "application/json",
+        Authorization: "Bearer " + apiKey,
+      },
+    });
+
+    console.log(data);
+
+    if (data.status !== 200) {
+      throw new Error(await data.text());
+    } else {
+      data = await data.json();
     }
+
+    loadingDialog.close();
+
+    title.textContent = "Done";
+    title.style.color = "green";
+
+    deployMetaNode.widgets[1].value = data.workflow_id;
+    deployMetaNode.widgets[2].value = data.version;
+    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/>`,
+    );
+
+    setTimeout(() => {
+      title.textContent = "Deploy";
+      title.style.color = "white";
+    }, 1000);
+  } catch (e) {
+    loadingDialog.close();
+    app.ui.dialog.show(e);
+    console.error(e);
+    title.textContent = "Error";
+    title.style.color = "red";
+    setTimeout(() => {
+      title.textContent = "Deploy";
+      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");