Merge branch 'main' into dev

custom route
chore(plugin): add log
2024-07-07 22:06:54 -07:00 · 2024-06-19 16:52:17 -07:00 · 2024-06-11 17:43:03 -07:00 · 2024-06-11 17:43:02 -07:00 · 2024-06-11 17:43:02 -07:00 · 2024-06-11 17:43:02 -07:00
59 changed files with 1002 additions and 16131 deletions
@@ -7,19 +7,15 @@ on:
    paths:
      - "pyproject.toml"
 permissions:
  issues: write
 jobs:
  publish-node:
    name: Publish Custom Node to registry
    runs-on: ubuntu-latest
    if: ${{ github.repository_owner == 'BennyKok' }}
    steps:
      - name: Check out code
        uses: actions/checkout@v4
      - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@v1
+        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 }}
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
@@ -1,3 +1,2 @@
 __pycache__
-.DS_Store
+.DS_Store
 file-hash-cache.json
@@ -2,13 +2,6 @@
 Open source comfyui deployment platform, a `vercel` for generative workflow infra. (serverless hosted gpu with vertical intergation with comfyui)
 Check out our latest lcoal demo -> https://github.com/comfy-deploy/comfyui-api-comfydeploy
 Full backend and frontend is here -> https://github.com/comfy-deploy/comfydeploy
 > [!NOTE]  
 > Im looking for creative hacker to join ComfyDeploy's core team! DM me on [twitter](https://x.com/BennyKokMusic)
 Join [Discord](https://discord.gg/EEYcQmdYZw) to chat more or visit [Comfy Deploy](https://comfydeploy.com/) to get started!
 Check out our latest [nextjs starter kit](https://github.com/BennyKok/comfyui-deploy-next-example) with Comfy Deploy
@@ -100,6 +93,10 @@ Major areas
 # Self Hosting with Vercel
 [![Video](https://img.mytsi.org/i/nFOG479.png)](https://www.youtube.com/watch?v=hWvsEY1cS2M)
 Tutorial Created by [Ross](https://github.com/rossman22590) and [Syn](https://github.com/mortlsyn)
 Build command
 ```
@@ -2,9 +2,8 @@
@author: BennyKok
@title: comfyui-deploy
@nickname: Comfy Deploy
-@description:
+@description: 
 """
 import os
 import sys
@@ -18,23 +17,19 @@ import requests
 import folder_paths
 from folder_paths import add_model_folder_path, get_filename_list, get_folder_paths
 from tqdm import tqdm
 import re
 from . import custom_routes
 # import routes
 ag_path = os.path.join(os.path.dirname(__file__))
 def get_python_files(path):
    return [f[:-3] for f in os.listdir(path) if f.endswith(".py")]
 def append_to_sys_path(path):
    if path not in sys.path:
        sys.path.append(path)
 paths = ["comfy-nodes"]
 files = []
@@ -46,45 +41,14 @@ for path in paths:
 NODE_CLASS_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS = {}
 def split_camel_case(name):
    # Split on underscores first, then split each part on camelCase
    parts = []
    for part in name.split("_"):
        # Find all camelCase boundaries
        words = re.findall("[A-Z][^A-Z]*", part)
        if not words:  # If no camelCase found, use the whole part
            words = [part]
        parts.extend(words)
    return parts
 # Import all the modules and append their mappings
 for file in files:
    module = importlib.import_module(file)
    # Check if the module has explicit mappings
    if hasattr(module, "NODE_CLASS_MAPPINGS"):
        NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
    if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS"):
        NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
    # Auto-discover classes with ComfyUI node attributes
    for name, obj in inspect.getmembers(module):
        # Check if it's a class and has the required ComfyUI node attributes
        if (
            inspect.isclass(obj)
            and hasattr(obj, "INPUT_TYPES")
            and hasattr(obj, "RETURN_TYPES")
        ):
            # Use the class name as the key if not already in mappings
            if name not in NODE_CLASS_MAPPINGS:
                NODE_CLASS_MAPPINGS[name] = obj
                # Create a display name by converting camelCase to Title Case with spaces
                words = split_camel_case(name.replace("ComfyUIDeploy", ""))
                display_name = " ".join(word.capitalize() for word in words)
                # print(display_name, name)
                NODE_DISPLAY_NAME_MAPPINGS[name] = display_name
 WEB_DIRECTORY = "web-plugin"
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
@@ -1,82 +0,0 @@
 import io
 from folder_paths import get_annotated_filepath
 class ComfyUIDeployExternalAudio:
    RETURN_TYPES = ("AUDIO",)
    RETURN_NAMES = ("audio",)
    FUNCTION = "load_audio"
    CATEGORY = "🔗ComfyDeploy"
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "input_id": (
                    "STRING",
                    {"multiline": False, "default": "input_audio"},
                ),
                "audio_file": ("STRING", {"default": ""}),
            },
            "optional": {
                "default_value": ("AUDIO",),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
            },
        }
    @classmethod
    def VALIDATE_INPUTS(s, audio_file, **kwargs):
        return True
    def load_audio(
        self,
        input_id,
        audio_file,
        default_value=None,
        display_name=None,
        description=None,
    ):
        try:
            import torchaudio
            if audio_file and audio_file != "":
                if audio_file.startswith(("http://", "https://")):
                    # Handle URL input
                    try:
                        import requests
                        response = requests.get(audio_file)
                        audio_data = io.BytesIO(response.content)
                        waveform, sample_rate = torchaudio.load(audio_data)
                    except Exception as e:
                        print(f"Error loading audio from URL: {e}")
                        return (default_value,)
                else:
                    # Handle local file
                    try:
                        audio_path = get_annotated_filepath(audio_file)
                        waveform, sample_rate = torchaudio.load(audio_path)
                    except Exception as e:
                        print(f"Error loading local audio file: {e}")
                        return (default_value,)
                audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
                return (audio,)
            else:
                return (default_value,)
        except ImportError as e:
            print(f"Error: torchaudio not installed or cannot be imported: {e}")
            return (default_value,)
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalAudio": ComfyUIDeployExternalAudio}
 NODE_DISPLAY_NAME_MAPPINGS = {
    "ComfyUIDeployExternalAudio": "External Audio (ComfyUI Deploy)"
 }
@@ -8,26 +8,15 @@ class ComfyUIDeployExternalBoolean:
                    {"multiline": False, "default": "input_bool"},
                ),
                "default_value": ("BOOLEAN", {"default": False})
            },
            "optional": {
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
    RETURN_TYPES = ("BOOLEAN",)
    RETURN_NAMES = ("bool_value",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    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]
@@ -5,12 +5,6 @@ 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):
@@ -23,26 +17,17 @@ class ComfyUIDeployExternalCheckpoint:
            },
            "optional": {
                "default_value": (folder_paths.get_filename_list("checkpoints"), ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
-    RETURN_TYPES = (WILDCARD,)
+    RETURN_TYPES = (folder_paths.get_filename_list("checkpoints"),)
    RETURN_NAMES = ("path",)
    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
+    CATEGORY = "deploy"
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
        import requests
        import os
        import uuid
@@ -1,46 +0,0 @@
 class AnyType(str):
    def __ne__(self, __value: object) -> bool:
        return False
 WILDCARD = AnyType("*")
 class ComfyUIDeployExternalEnum:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_id": (
                    "STRING",
                    {"multiline": False, "default": "input_enum"},
                ),
            },
            "optional": {
                "default_value": (
                    "STRING",
                    {"multiline": False, "default": "", "dynamic_enum": True},
                ),
                "options": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
    RETURN_TYPES = (WILDCARD,)
    RETURN_NAMES = ("text",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
    def run(self, input_id, options=None, default_value=None, display_name=None, description=None):
        return [default_value]
@@ -1,110 +0,0 @@
 import os
 import io
 import cv2 as cv
 import numpy as np
 import torch
 import requests
 from folder_paths import get_annotated_filepath
 class ComfyUIDeployExternalEXR:
    RETURN_TYPES = ("IMAGE", "MASK")
    RETURN_NAMES = ("image", "mask") 
    FUNCTION = "load_exr"
    CATEGORY = "🔗ComfyDeploy"
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "input_id": (
                    "STRING",
                    {"multiline": False, "default": "input_exr"},
                ),
                "exr_file": ("STRING", {"default": ""}),
                "tonemap": (["linear", "sRGB", "Reinhard"], {"default": "sRGB"}),
            },
            "optional": {
                "default_image": ("IMAGE",),
                "default_mask": ("MASK",),
                "display_name": (
                    "STRING", 
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
            }
        }
    @classmethod
    def VALIDATE_INPUTS(s, exr_file, **kwargs):
        return True
    def sRGBtoLinear(self, npArray):
        less = npArray <= 0.0404482362771082
        npArray[less] = npArray[less] / 12.92
        npArray[~less] = np.power((npArray[~less] + 0.055) / 1.055, 2.4)
    def linearToSRGB(self, npArray):
        less = npArray <= 0.0031308
        npArray[less] = npArray[less] * 12.92
        npArray[~less] = np.power(npArray[~less], 1/2.4) * 1.055 - 0.055
    def load_exr(self, input_id, exr_file, tonemap="sRGB", 
                 default_image=None, default_mask=None,
                 display_name=None, description=None):
        try:
            if exr_file and exr_file != "":
                if exr_file.startswith(('http://', 'https://')):
                    # Handle URL input
                    response = requests.get(exr_file)
                    # Write to temp buffer
                    buffer = io.BytesIO(response.content)
                    nparr = np.frombuffer(buffer.getvalue(), np.uint8)
                    image = cv.imdecode(nparr, cv.IMREAD_UNCHANGED).astype(np.float32)
                else:
                    # Handle local file
                    exr_path = get_annotated_filepath(exr_file)
                    image = cv.imread(exr_path, cv.IMREAD_UNCHANGED).astype(np.float32)
                if len(image.shape) == 2:
                    image = np.repeat(image[..., np.newaxis], 3, axis=2)
                # Extract RGB and flip channels
                rgb = np.flip(image[:,:,:3], 2).copy()
                # Apply tonemapping
                if tonemap == "sRGB":
                    self.linearToSRGB(rgb)
                    rgb = np.clip(rgb, 0, 1)
                elif tonemap == "Reinhard":
                    rgb = np.clip(rgb, 0, None)
                    rgb = rgb / (rgb + 1)
                    self.linearToSRGB(rgb)
                    rgb = np.clip(rgb, 0, 1)
                rgb = torch.unsqueeze(torch.from_numpy(rgb), 0)
                # Handle alpha/mask
                mask = torch.zeros((1, image.shape[0], image.shape[1]), dtype=torch.float32)
                if image.shape[2] > 3:
                    mask[0] = torch.from_numpy(np.clip(image[:,:,3], 0, 1))
                return (rgb, mask)
            else:
                # Return defaults if no file provided
                return (default_image, default_mask)
        except Exception as e:
            print(f"Error loading EXR: {str(e)}")
            # Return defaults on error
            return (default_image, default_mask)
 NODE_CLASS_MAPPINGS = {
    "ComfyUIDeployExternalEXR": ComfyUIDeployExternalEXR
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
    "ComfyUIDeployExternalEXR": "External EXR (ComfyUI Deploy)"
 }
@@ -1,106 +0,0 @@
 from PIL import Image, ImageOps
 import numpy as np
 import torch
 import folder_paths
 class AnyType(str):
    def __ne__(self, __value: object) -> bool:
        return False
 WILDCARD = AnyType("*")
 class ComfyUIDeployExternalFaceModel:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_id": (
                    "STRING",
                    {"multiline": False, "default": "input_reactor_face_model"},
                ),
            },
            "optional": {
                "default_face_model_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "face_model_save_name": (  # if `default_face_model_name` is a link to download a file, we will attempt to save it with this name
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
                "face_model_url": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
            },
        }
    RETURN_TYPES = (WILDCARD,)
    RETURN_NAMES = ("path",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
    def run(
        self,
        input_id,
        default_face_model_name=None,
        face_model_save_name=None,
        display_name=None,
        description=None,
        face_model_url=None,
    ):
        import requests
        import os
        import uuid
        if face_model_url and face_model_url.startswith("http"):
            if face_model_save_name:
                existing_face_models = folder_paths.get_filename_list("reactor/faces")
                # Check if face_model_save_name exists in the list
                if face_model_save_name in existing_face_models:
                    print(f"using face model: {face_model_save_name}")
                    return (face_model_save_name,)
            else:
                face_model_save_name = str(uuid.uuid4()) + ".safetensors"
            print(face_model_save_name)
            print(folder_paths.folder_names_and_paths["reactor/faces"][0][0])
            destination_path = os.path.join(
                folder_paths.folder_names_and_paths["reactor/faces"][0][0],
                face_model_save_name,
            )
            print(destination_path)
            print(
                "Downloading external face model - "
                + face_model_url
                + " to "
                + destination_path
            )
            response = requests.get(
                face_model_url,
                headers={"User-Agent": "Mozilla/5.0"},
                allow_redirects=True,
            )
            with open(destination_path, "wb") as out_file:
                out_file.write(response.content)
            return (face_model_save_name,)
        else:
            print(f"using face model: {default_face_model_name}")
            return (default_face_model_name,)
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalFaceModel": ComfyUIDeployExternalFaceModel}
 NODE_DISPLAY_NAME_MAPPINGS = {
    "ComfyUIDeployExternalFaceModel": "External Face Model (ComfyUI Deploy)"
 }
@@ -1,137 +0,0 @@
 import folder_paths
 class AnyType(str):
    def __ne__(self, __value: object) -> bool:
        return False
 WILDCARD = AnyType("*")
 class ComfyUIDeployExternalFile:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_id": (
                    "STRING",
                    {"multiline": False, "default": "input_file"},
                ),
            },
            "optional": {
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
                "file_url": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
            },
        }
    RETURN_TYPES = (WILDCARD,)
    RETURN_NAMES = ("path",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
    def run(
        self,
        input_id,
        display_name=None,
        description=None,
        file_url=None,
    ):
        import requests
        import os
        import uuid
        from urllib.parse import urlparse
        if file_url:
            if file_url.startswith("http"):
                # Use cache directory for saving files
                cache_dir = folder_paths.get_temp_directory()
                if not os.path.exists(cache_dir):
                    os.makedirs(cache_dir)
                # Always generate random filename to avoid conflicts
                parsed_url = urlparse(file_url)
                original_filename = os.path.basename(parsed_url.path)
                # Extract file extension from original filename if available
                file_extension = ""
                if original_filename and "." in original_filename:
                    file_extension = os.path.splitext(original_filename)[1]
                else:
                    # Try to determine extension from content-type if no extension found
                    file_extension = ".bin"
                # Generate random filename with preserved extension
                filename = str(uuid.uuid4()) + file_extension
                destination_path = os.path.join(cache_dir, filename)
                print(f"Cache directory: {cache_dir}")
                print(f"Destination path: {destination_path}")
                print(
                    "Downloading external file - "
                    + file_url
                    + " to "
                    + destination_path
                )
                headers = {"User-Agent": "Mozilla/5.0"}
                try:
                    response = requests.get(
                        file_url,
                        headers=headers,
                        allow_redirects=True,
                        timeout=30,  # Add timeout to prevent hanging
                    )
                    response.raise_for_status()
                    with open(destination_path, "wb") as out_file:
                        out_file.write(response.content)
                    print(f"External file downloaded: {file_url} to {destination_path}")
                    return (destination_path,)
                except requests.exceptions.HTTPError as e:
                    error_msg = f"HTTP Error {e.response.status_code}: {e.response.reason} for URL: {file_url}"
                    print(f"⚠️ Download failed - {error_msg}")
                    if e.response.status_code == 404:
                        print(
                            "💡 This URL might have expired or the file may have been deleted"
                        )
                    # Return empty string instead of crashing
                    return ("",)
                except requests.exceptions.RequestException as e:
                    error_msg = (
                        f"Network error downloading file from {file_url}: {str(e)}"
                    )
                    print(f"⚠️ Download failed - {error_msg}")
                    return ("",)
                except Exception as e:
                    error_msg = (
                        f"Unexpected error downloading file from {file_url}: {str(e)}"
                    )
                    print(f"⚠️ Download failed - {error_msg}")
                    return ("",)
            else:
                print(f"External file loading: {file_url}")
                return (file_url,)
        else:
            print(f"No file URL provided")
            return ("",)
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalFile": ComfyUIDeployExternalFile}
 NODE_DISPLAY_NAME_MAPPINGS = {
    "ComfyUIDeployExternalFile": "External File (ComfyUI Deploy)"
 }
@@ -15,61 +15,42 @@ class ComfyUIDeployExternalImage:
            },
            "optional": {
                "default_value": ("IMAGE",),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "default_value_url": ("STRING", {"image_preview": True, "default": ""}),
            }
        }
    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
-    def run(self, input_id, default_value=None, display_name=None, description=None, default_value_url=None):
+    FUNCTION = "run"
    CATEGORY = "image"
    def run(self, input_id, default_value=None):
        image = default_value
-        
+        try:
-        # Try both input_id and default_value_url
+            if input_id.startswith('http'):
-        urls_to_try = [url for url in [input_id, default_value_url] if url]
+                import requests
-        
+                from io import BytesIO
-        print(default_value_url)
+                print("Fetching image from url: ", input_id)
-        
+                response = requests.get(input_id)
-        for url in urls_to_try:
+                image = Image.open(BytesIO(response.content))
-            try:
+            elif input_id.startswith('data:image/png;base64,') or input_id.startswith('data:image/jpeg;base64,') or input_id.startswith('data:image/jpg;base64,'):
-                if url.startswith('http'):
+                import base64
-                    import requests
+                from io import BytesIO
-                    from io import BytesIO
+                print("Decoding base64 image")
-                    print(f"Fetching image from url: {url}")
+                base64_image = input_id[input_id.find(",")+1:]
-                    response = requests.get(url)
+                decoded_image = base64.b64decode(base64_image)
-                    image = Image.open(BytesIO(response.content))
+                image = Image.open(BytesIO(decoded_image))
-                    break
+            else:
-                elif url.startswith(('data:image/png;base64,', 'data:image/jpeg;base64,', 'data:image/jpg;base64,')):
+                raise ValueError("Invalid image url provided.")
-                    import base64
+
-                    from io import BytesIO
+            image = ImageOps.exif_transpose(image)
-                    print("Decoding base64 image")
+            image = image.convert("RGB")
-                    base64_image = url[url.find(",")+1:]
+            image = np.array(image).astype(np.float32) / 255.0
-                    decoded_image = base64.b64decode(base64_image)
+            image = torch.from_numpy(image)[None,]
-                    image = Image.open(BytesIO(decoded_image))
+            return [image]
-                    break
+        except:
-            except:
+            return [image]
                continue
        if image is not None:
            try:
                image = ImageOps.exif_transpose(image)
                image = image.convert("RGB")
                image = np.array(image).astype(np.float32) / 255.0
                image = torch.from_numpy(image)[None,]
            except:
                pass
        return [image]
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalImage": ComfyUIDeployExternalImage}
@@ -15,23 +15,17 @@ class ComfyUIDeployExternalImageAlpha:
            },
            "optional": {
                "default_value": ("IMAGE",),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    FUNCTION = "run"
    CATEGORY = "image"
    def run(self, input_id, default_value=None):
        image = default_value
        try:
            if input_id.startswith('http'):
@@ -21,50 +21,24 @@ class ComfyUIDeployExternalImageBatch:
            },
            "optional": {
                "default_value": ("IMAGE",),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
+
    CATEGORY = "image"
-    def process_image(self, image):
+    def run(self, input_id, images=None, default_value=None):
        image = ImageOps.exif_transpose(image)
        image = image.convert("RGB")
        image = np.array(image).astype(np.float32) / 255.0
        image_tensor = torch.from_numpy(image)[None,]
        return image_tensor
    def run(self, input_id, images=None, default_value=None, display_name=None, description=None):
        import requests
        import zipfile
        import io
        processed_images = []
        try:
            images_list = json.loads(images)  # Assuming images is a JSON array string
            print(images_list)
            for img_input in images_list:
-                if img_input.startswith('http') and img_input.endswith('.zip'):
+                if img_input.startswith('http'):
-                    print("Fetching zip file from url: ", img_input)
+                    import requests
                    response = requests.get(img_input)
                    zip_file = zipfile.ZipFile(io.BytesIO(response.content))
                    for file_name in zip_file.namelist():
                        if file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
                            with zip_file.open(file_name) as file:
                                image = Image.open(file)
                                image = self.process_image(image)
                                processed_images.append(image)
                elif  img_input.startswith('http'):
                    from io import BytesIO
                    print("Fetching image from url: ", img_input)
                    response = requests.get(img_input)
@@ -1,12 +1,8 @@
 import folder_paths
-
+from PIL import Image, ImageOps
-
+import numpy as np
-class AnyType(str):
+import torch
-    def __ne__(self, __value: object) -> bool:
+import folder_paths
        return False
 WILDCARD = AnyType("*")
 class ComfyUIDeployExternalLora:
@@ -21,88 +17,40 @@ class ComfyUIDeployExternalLora:
            },
            "optional": {
                "default_lora_name": (folder_paths.get_filename_list("loras"),),
                "lora_save_name": (  # if `default_lora_name` is a link to download a file, we will attempt to save it with this name
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
                "lora_url": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "bearer_token": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
            },
        }
-    RETURN_TYPES = (WILDCARD,)
+    RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
    RETURN_NAMES = ("path",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
-    def run(
+    FUNCTION = "run"
-        self,
+
-        input_id,
+    CATEGORY = "deploy"
-        default_lora_name=None,
+
-        lora_save_name=None,
+    def run(self, input_id, default_lora_name=None):
        display_name=None,
        description=None,
        lora_url=None,
        bearer_token=None,
    ):
        import requests
        import os
        import uuid
-        if lora_url:
+        if default_lora_name.startswith("http"):
-            if lora_url.startswith("http"):
+            unique_filename = str(uuid.uuid4()) + ".safetensors"
-                if lora_save_name:
+            print(unique_filename)
-                    existing_loras = folder_paths.get_filename_list("loras")
+            print(folder_paths.folder_names_and_paths["loras"][0][0])
-                    # Check if lora_save_name exists in the list
+            destination_path = os.path.join(
-                    if lora_save_name in existing_loras:
+                folder_paths.folder_names_and_paths["loras"][0][0], unique_filename
-                        print(f"using lora: {lora_save_name}")
+            )
-                        return (lora_save_name,)
+            print(destination_path)
-                else:
+            print("Downloading external lora - " + input_id + " to " + destination_path)
-                    lora_save_name = str(uuid.uuid4()) + ".safetensors"
+            response = requests.get(
-                print(lora_save_name)
+                input_id,
-                print(folder_paths.folder_names_and_paths["loras"][0][0])
+                headers={"User-Agent": "Mozilla/5.0"},
-                destination_path = os.path.join(
+                allow_redirects=True,
-                    folder_paths.folder_names_and_paths["loras"][0][0], lora_save_name
+            )
-                )
+            with open(destination_path, "wb") as out_file:
-                print(destination_path)
+                out_file.write(response.content)
-                print(
+            return (unique_filename,)
                    "Downloading external lora - "
                    + lora_url
                    + " to "
                    + destination_path
                )
                headers = {"User-Agent": "Mozilla/5.0"}
                if bearer_token:
                    headers["Authorization"] = f"Bearer {bearer_token}"
                    print("using bearer token")
                response = requests.get(
                    lora_url,
                    headers=headers,
                    allow_redirects=True,
                )
                with open(destination_path, "wb") as out_file:
                    out_file.write(response.content)
                print(f"Ext Lora loading: {lora_url} to {lora_save_name}")
                return (lora_save_name,)
            else:
                print(f"Ext Lora loading: {lora_url}")
                return (lora_url,)
        else:
-            print(f"Ext Lora loading: {default_lora_name}")
+            print(f"using lora: {default_lora_name}")
            return (default_lora_name,)
@@ -16,25 +16,19 @@ class ComfyUIDeployExternalNumber:
            "optional": {
                "default_value": (
                    "FLOAT",
-                    {"multiline": True, "display": "number", "default": 0, "min": -2147483647, "max": 2147483647, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 0, "step": 0.01},
                ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
    RETURN_TYPES = ("FLOAT",)
    RETURN_NAMES = ("value",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    FUNCTION = "run"
    CATEGORY = "number"
    def run(self, input_id, default_value=None):
        try:
            float_value = float(input_id)
            print("my number", float_value)
@@ -16,25 +16,19 @@ class ComfyUIDeployExternalNumberInt:
            "optional": {
                "default_value": (
                    "INT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0},
+                    {"multiline": True, "display": "number", "default": 0},
                ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
    RETURN_TYPES = ("INT",)
    RETURN_NAMES = ("value",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    FUNCTION = "run"
    CATEGORY = "number"
    def run(self, input_id, default_value=None):
        if not input_id or (isinstance(input_id, str) and not input_id.strip().isdigit()):
            return [default_value]
        return [int(input_id)]
@@ -11,33 +11,27 @@ class ComfyUIDeployExternalNumberSlider:
            "optional": {
                "default_value": (
                    "FLOAT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0.5, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 0.5, "step": 0.01},
                ),
                "min_value": (
                    "FLOAT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 0, "step": 0.01},
                ),
                "max_value": (
                    "FLOAT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 1, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 1, "step": 0.01},
                ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
    RETURN_TYPES = ("FLOAT",)
    RETURN_NAMES = ("value",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
-    def run(self, input_id, default_value=None, min_value=0, max_value=1, display_name=None, description=None):
+    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:
@@ -1,54 +0,0 @@
 class ComfyUIDeployExternalNumberSliderInt:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_id": (
                    "STRING",
                    {"multiline": False, "default": "input_number_slider_int"},
                ),
            },
            "optional": {
                "default_value": (
                    "INT",
                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 1, "step": 1},
                ),
                "min_value": (
                    "INT",
                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0, "step": 1},
                ),
                "max_value": (
                    "INT",
                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 10, "step": 1},
                ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
    RETURN_TYPES = ("INT",)
    RETURN_NAMES = ("value",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
    def run(self, input_id, default_value=None, min_value=0, max_value=10, display_name=None, description=None):
        try:
            int_value = int(round(float(input_id)))
            if min_value <= int_value <= max_value:
                print("my integer", int_value)
                return [int_value]
            else:
                print("Integer out of range. Returning default value:", default_value)
                return [default_value]
        except (ValueError, TypeError):
            print("Invalid input. Returning default value:", default_value)
            return [default_value]
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalNumberSliderInt": ComfyUIDeployExternalNumberSliderInt}
 NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalNumberSliderInt": "External Number Slider Int (ComfyUI Deploy)"}
@@ -1,116 +0,0 @@
 import random
 class ComfyUIDeployExternalSeed:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_id": (
                    "STRING",
                    {"multiline": False, "default": "input_seed"},
                ),
                "default_value": (
                    "INT",
                    {"default": -1},
                ),
                "min_value": (
                    "INT",
                    {"default": 1, "min": 1, "max": 999999999999999},
                ),
                "max_value": (
                    "INT",
                    {"default": 4294967295, "min": 1, "max": 999999999999999},
                ),
            },
            "optional": {
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {
                        "multiline": True,
                        "default": 'For default value:\n"-1" (i.e. not in range): Randomize within the min and max value range. \nin range: Fixed, always the same value\n',
                    },
                ),
            },
        }
    RETURN_TYPES = ("INT",)
    RETURN_NAMES = ("seed",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
    # Limits
    _MAX_LIMIT = 999_999_999_999_999  # 15 digits
    # Store cached seed when fixed flag is enabled
    _cached_seed = None
    @classmethod
    def IS_CHANGED(
        cls,
        input_id,
        min_value,
        max_value,
        default_value=None,
        **kwargs,
    ):
        """Inform ComfyUI whether the node output should be considered changed.
        If default_value is within range (Fixed mode), we return the inputs tuple
        so the cached result is reused until the user changes something.
        For Randomize mode, we force re-execution each queue.
        """
        # Clamp values to allowed range for check
        min_value = max(1, min_value)
        max_value = min(cls._MAX_LIMIT, max_value)
        # Fixed mode when default_value is within range
        if (
            default_value is not None
            and default_value >= min_value
            and default_value <= max_value
        ):
            return (input_id, default_value)
        # For Randomize (default_value is -1 or out of range) we force re-execution
        import random as _rnd
        return _rnd.random()
    def run(
        self,
        input_id,
        min_value: int,
        max_value: int,
        display_name=None,
        description=None,
        default_value: int = -1,
    ):
        # Clamp values to allowed range
        min_value = max(1, min_value)
        max_value = min(self._MAX_LIMIT, max_value)
        # Ensure limits are in correct order after clamping
        if min_value > max_value:
            min_value, max_value = max_value, min_value
        # Fixed mode: default_value is within range
        if default_value >= min_value and default_value <= max_value:
            seed = int(default_value)
            self._cached_seed = seed
            return [seed]
        # Randomize mode: default_value is -1 or out of range
        seed = random.randint(min_value, max_value)
        self._cached_seed = seed
        return [seed]
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalSeed": ComfyUIDeployExternalSeed}
 NODE_DISPLAY_NAME_MAPPINGS = {
    "ComfyUIDeployExternalSeed": "External Seed (ComfyUI Deploy)"
 }
@@ -1,53 +0,0 @@
 import re
 class StringFunction:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "action": (["append", "replace"], {}),
                "tidy_tags": (["yes", "no"], {}),
            },
            "optional": {
                "text_a": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                "text_b": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                "text_c": ("STRING", {"multiline": True, "dynamicPrompts": False}),
            },
        }
    RETURN_TYPES = ("STRING",)
    FUNCTION = "exec"
    CATEGORY = "🔗ComfyDeploy"
    OUTPUT_NODE = True
    def exec(self, action, tidy_tags, text_a="", text_b="", text_c=""):
        tidy_tags = tidy_tags == "yes"
        out = ""
        if action == "append":
            out = (", " if tidy_tags else "").join(
                filter(None, [text_a, text_b, text_c])
            )
        else:
            if text_c is None:
                text_c = ""
            if text_b.startswith("/") and text_b.endswith("/"):
                regex = text_b[1:-1]
                out = re.sub(regex, text_c, text_a)
            else:
                out = text_a.replace(text_b, text_c)
        if tidy_tags:
            out = re.sub(r"\s{2,}", " ", out)
            out = out.replace(" ,", ",")
            out = re.sub(r",{2,}", ",", out)
            out = out.strip()
        return {"ui": {"text": (out,)}, "result": (out,)}
 NODE_CLASS_MAPPINGS = {
    "ComfyUIDeployStringCombine": StringFunction,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
    "ComfyUIDeployStringCombine": "String Combine (ComfyUI Deploy)",
 }
@@ -18,14 +18,6 @@ class ComfyUIDeployExternalText:
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
@@ -34,9 +26,9 @@ class ComfyUIDeployExternalText:
    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
+    CATEGORY = "text"
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
        return [default_value]
@@ -1,46 +0,0 @@
 class AnyType(str):
    def __ne__(self, __value: object) -> bool:
        return False
 WILDCARD = AnyType("*")
 class ComfyUIDeployExternalTextAny:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_id": (
                    "STRING",
                    {"multiline": False, "default": "input_text"},
                ),
            },
            "optional": {
                "default_value": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
            }
        }
    RETURN_TYPES = (WILDCARD,)
    RETURN_NAMES = ("text",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
    def run(self, input_id, default_value=None, display_name=None, description=None):
        return [default_value]
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalTextAny": ComfyUIDeployExternalTextAny}
 NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalTextAny": "External Text Any (ComfyUI Deploy)"}
@@ -36,7 +36,6 @@ class ComfyUIDeployExternalVideo:
    RETURN_NAMES = ("video")
    FUNCTION = "load_video"
    CATEGORY = "🔗ComfyDeploy"
    def load_video(self, input_id, default_value):
        input_dir = folder_paths.get_input_directory()
@@ -1,15 +1,10 @@
-# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite and is meant to work with
 # 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
@@ -95,25 +90,13 @@ 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)
+    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]
@@ -194,59 +177,18 @@ def requeue_workflow(requeue_required=(-1, True)):
 def get_audio(file, start_time=0, duration=0):
-    args = [ffmpeg_path, "-i", file]
+    args = [ffmpeg_path, "-v", "error", "-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
+            args + ["-f", "wav", "-"], stdout=subprocess.PIPE, check=True
-        )
+        ).stdout
        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(
+        return False
-            f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
+    return res
        )
    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):
@@ -288,19 +230,6 @@ def validate_sequence(path):
    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"
@@ -357,145 +286,6 @@ def target_size(
    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,
@@ -505,10 +295,9 @@ def cv_frame_generator(
    meta_batch=None,
    unique_id=None,
 ):
-    video_cap = cv2.VideoCapture(strip_path(video))
+    video_cap = cv2.VideoCapture(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)
@@ -530,8 +319,6 @@ def cv_frame_generator(
        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():
@@ -562,8 +349,7 @@ def cv_frame_generator(
        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)
+        frame = np.array(frame, dtype=np.float32) / 255.0
        torch.from_numpy(frame).div_(255)
        if prev_frame is not None:
            inp = yield prev_frame
            if inp is not None:
@@ -571,8 +357,6 @@ def cv_frame_generator(
                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
@@ -583,17 +367,6 @@ def cv_frame_generator(
        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,
@@ -605,8 +378,6 @@ def load_video_cv(
    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(
@@ -630,89 +401,30 @@ def load_video_cv(
                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 meta_batch is not None:
-    if memory_limit_mb is not None:
+        gen = itertools.islice(gen, meta_batch.frames_per_batch)
        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):
+    # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
-                s = torch.from_numpy(
+    images = torch.from_numpy(
-                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
+        np.fromiter(gen, 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")
    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 = lazy_get_audio(
+    audio = lambda: get_audio(
        video,
        skip_first_frames * target_frame_time,
        frame_load_cap * target_frame_time * select_every_nth,
@@ -728,16 +440,13 @@ def load_video_cv(
        "loaded_fps": 1 / target_frame_time,
        "loaded_frame_count": len(images),
        "loaded_duration": len(images) * target_frame_time,
-        "loaded_width": new_size[0],
+        "loaded_width": images.shape[2],
-        "loaded_height": new_size[1],
+        "loaded_height": images.shape[1],
    }
-    if vae is None:
+
-        return (images, len(images), audio, video_info, None)
+    return (images, len(images), lazy_eval(audio), video_info)
    else:
        return (None, len(images), audio, video_info, {"samples": images})
 # modeled after Video upload node
 class ComfyUIDeployExternalVideo:
    @classmethod
    def INPUT_TYPES(s):
@@ -792,34 +501,27 @@ class ComfyUIDeployExternalVideo:
            },
            "optional": {
                "meta_batch": ("VHS_BatchManager",),
-                "vae": ("VAE",),
+                "default_value": (sorted(files),),
                "default_video": (sorted(files),),
                "display_name": (
                    "STRING",
                    {"multiline": False, "default": ""},
                ),
                "description": (
                    "STRING",
                    {"multiline": True, "default": ""},
                ),
                "default_value_url": ("STRING", {"image_preview": True, "default": ""}),
            },
            "hidden": {"unique_id": "UNIQUE_ID"},
        }
    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
-    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
+    RETURN_TYPES = (
        "IMAGE",
        "INT",
        "VHS_AUDIO",
        "VHS_VIDEOINFO",
    )
    RETURN_NAMES = (
        "IMAGE",
        "frame_count",
        "audio",
        "video_info",
        "LATENT",
    )
    FUNCTION = "load_video"
    CATEGORY = "🔗ComfyDeploy"
    def load_video(self, **kwargs):
        input_id = kwargs.get("input_id")
@@ -832,21 +534,18 @@ class ComfyUIDeployExternalVideo:
        select_every_nth = kwargs.get("select_every_nth")
        meta_batch = kwargs.get("meta_batch")
        unique_id = kwargs.get("unique_id")
-        default_value_url = kwargs.get("default_value_url")
+
        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") or (
+        if input_id.startswith("http"):
            default_value_url and default_value_url.startswith("http")
        ):
            import requests
-            # Use input_id if it's a URL, otherwise use default_value_url
+            print("Fetching video from URL: ", input_id)
-            url = input_id if input_id.startswith("http") else default_value_url
+            response = requests.get(input_id, stream=True)
            print("Fetching video from URL: ", url)
            response = requests.get(url, stream=True)
            file_size = int(response.headers.get("Content-Length", 0))
-            file_extension = url.split(".")[-1].split("?")[
+            file_extension = input_id.split(".")[-1].split("?")[
                0
            ]  # Extract extension and handle URLs with parameters
            if file_extension not in video_extensions:
@@ -867,11 +566,8 @@ class ComfyUIDeployExternalVideo:
                    leave=True,
                ):
                    out_file.write(chunk)
-        else:
+
-            video = kwargs.get("default_video", None)
+        print("video path: ", video_path)
            if video is None:
                raise "No default video given and no external video provided"
            video_path = folder_paths.get_annotated_filepath(video.strip('"'))
        return load_video_cv(
            video=video_path,
@@ -33,7 +33,6 @@ class ComfyDeployWebscoketImageInput:
    RETURN_NAMES = ("images",)
    FUNCTION = "run"
    CATEGORY = "🔗ComfyDeploy"
    @classmethod
    def VALIDATE_INPUTS(s, input_id):
@@ -1,78 +0,0 @@
 # In file: comfyui-deploy/comfy-nodes/output_exr.py
 import os
 import numpy as np
 import folder_paths
 # Try to set up OpenCV for EXR writing.
 try:
    os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
    import cv2
    OPENCV_AVAILABLE = True
 except ImportError:
    print("Warning: OpenCV not found for ComfyDeployOutputEXR. Please add opencv-python-headless to requirements.txt")
    OPENCV_AVAILABLE = False
 # ALIGNED: Renamed class to match project conventions
 class ComfyDeployOutputEXR:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()
        self.type = "output"
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images": ("IMAGE", ),
                "filename_prefix": ("STRING", {"default": "ComfyDeploy_EXR"})
            },
            # ADDED: Optional output_id for consistency with other ComfyDeploy nodes
            "optional": {
                "output_id": ("STRING", {"multiline": False, "default": "output_exr"}),
            },
        }
    RETURN_TYPES = ()
    # ALIGNED: Changed function name to 'run'
    FUNCTION = "run"
    OUTPUT_NODE = True
    # ALIGNED: Matched the category name
    CATEGORY = "🔗ComfyDeploy"
    DESCRIPTION = "Saves the input images as EXR (HDR) files."
    def run(self, images, filename_prefix="ComfyDeploy_EXR", output_id="output_exr"):
        if not OPENCV_AVAILABLE:
            raise ImportError("OpenCV is required to save EXR files. Please ensure opencv-python-headless is in requirements.txt.")
        full_output_folder, filename, counter, subfolder, filename_prefix = (
            folder_paths.get_save_image_path(
                filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
            )
        )
        results = list()
        for image in images:
            image_np = image.cpu().numpy()
            if image_np.dtype != np.float32:
                 image_np = image_np.astype(np.float32)
            file = f"{filename}_{counter:05}.exr"
            file_path = os.path.join(full_output_folder, file)
            image_np_bgr = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
            cv2.imwrite(file_path, image_np_bgr)
            results.append({
                "filename": file,
                "subfolder": subfolder,
                "type": self.type,
                "output_id": output_id, # ADDED
            })
            counter += 1
        return {"ui": {"images": results}}
 # ALIGNED: Mappings are defined at the bottom of the node file in this project
 NODE_CLASS_MAPPINGS = {"ComfyDeployOutputEXR": ComfyDeployOutputEXR}
 NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployOutputEXR": "EXR Output (ComfyDeploy)"}
@@ -1,168 +0,0 @@
 import folder_paths
 import os
 import shutil
 import uuid
 class AnyType(str):
    def __ne__(self, __value: object) -> bool:
        return False
 WILDCARD = AnyType("*")
 class ComfyDeployOutputFile:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()
        self.type = "output"
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "file_path": (
                    "STRING",
                    {
                        "forceInput": True,
                        "tooltip": "Path to the file to output and upload.",
                    },
                ),
            },
            "optional": {
                "output_id": (
                    "STRING",
                    {"multiline": False, "default": "output_file"},
                ),
            },
        }
    RETURN_TYPES = ()
    FUNCTION = "run"
    OUTPUT_NODE = True
    CATEGORY = "🔗ComfyDeploy"
    DESCRIPTION = "Outputs any file by path for upload to ComfyDeploy."
    def run(self, file_path, output_id="output_file"):
        if not file_path or not os.path.exists(file_path):
            print(f"⚠️ File not found: {file_path}")
            return {"ui": {"files": []}}
        # Security checks - ensure file is within safe ComfyUI paths
        try:
            # Get absolute paths for comparison
            file_abs_path = os.path.abspath(file_path)
            base_path = folder_paths.base_path
            temp_dir = folder_paths.get_temp_directory()
            # Check if file is within ComfyUI base path or temp directory
            if not (
                file_abs_path.startswith(os.path.abspath(base_path))
                or file_abs_path.startswith(os.path.abspath(temp_dir))
            ):
                print(f"⚠️ Security: File outside allowed ComfyUI paths: {file_path}")
                return {"ui": {"files": []}}
            # Check for path traversal attempts (but allow absolute paths within ComfyUI)
            if ".." in file_path:
                print(f"⚠️ Security: Path traversal attempt detected: {file_path}")
                return {"ui": {"files": []}}
        except Exception as e:
            print(f"⚠️ Security check failed: {str(e)}")
            return {"ui": {"files": []}}
        # Get the original filename and extension
        original_filename = os.path.basename(file_path)
        file_extension = os.path.splitext(original_filename)[1]
        # Additional filename security check
        if ".." in original_filename:
            print(f"⚠️ Security: Insecure filename: {original_filename}")
            return {"ui": {"files": []}}
        results = []
        # Check if file is in output folder, if not, symlink it there
        try:
            if file_path.startswith(self.output_dir):
                # File is already in output directory - use as is
                relative_path = os.path.relpath(file_path, self.output_dir)
                path_parts = relative_path.split(os.sep)
                if len(path_parts) > 1:
                    subfolder = os.sep.join(path_parts[:-1])
                else:
                    subfolder = ""
                filename = path_parts[-1]
                file_type = self.type
            else:
                # File is not in output folder - symlink it to output/temp
                print(
                    f"File is not in output folder, symlinking to output/temp: {file_path}"
                )
                output_temp_dir = os.path.join(self.output_dir, "temp")
                if not os.path.exists(output_temp_dir):
                    os.makedirs(output_temp_dir)
                # Use the existing filename but with UUID prefix to avoid conflicts
                file_ext = os.path.splitext(original_filename)[1]
                temp_filename = f"{uuid.uuid4()}{file_ext}"
                temp_path = os.path.join(output_temp_dir, temp_filename)
                # Create symlink to file in output/temp directory where upload system expects it
                try:
                    # Remove existing symlink if it exists
                    if os.path.exists(temp_path):
                        os.remove(temp_path)
                    os.symlink(file_path, temp_path)
                    print(f"File symlinked to output/temp: {temp_path} -> {file_path}")
                except OSError as e:
                    # Fall back to copying if symlink fails
                    print(f"Symlink failed ({e}), falling back to copy")
                    shutil.copy2(file_path, temp_path)
                    print(f"File copied to output/temp: {temp_path}")
                # Use output/temp directory structure for upload
                subfolder = "temp"
                filename = temp_filename
                file_type = self.type
            results.append(
                {
                    "filename": filename,
                    "subfolder": subfolder,
                    "type": file_type,
                    "output_id": output_id,
                }
            )
        except Exception as e:
            print(f"⚠️ Error processing file path: {str(e)}")
            return {"ui": {"files": []}}
        # Determine the appropriate UI key based on file type
        file_ext = file_extension.lower()
        if file_ext in [".png", ".jpg", ".jpeg", ".webp", ".gif", ".bmp", ".tiff"]:
            ui_key = "images"
        elif file_ext in [".mp3", ".wav", ".flac", ".aac", ".ogg"]:
            ui_key = "audio"
        elif file_ext in [".txt", ".json", ".md", ".csv"]:
            ui_key = "text_file"
        elif file_ext in [".exr", ".hdr"]:
            ui_key = "images"  # EXR files are still images
        elif file_ext in [".zip", ".psb", ".psd"]:
            ui_key = "files"  # Archives and Photoshop project files
        else:
            ui_key = "files"  # Generic files
        return {"ui": {ui_key: results}}
 NODE_CLASS_MAPPINGS = {
    "ComfyDeployOutputFile": ComfyDeployOutputFile,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
    "ComfyDeployOutputFile": "File Output (ComfyDeploy)",
 }
@@ -1,104 +0,0 @@
 import os
 import json
 import numpy as np
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
 import folder_paths
 class ComfyDeployOutputImage:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()
        self.type = "output"
        self.prefix_append = ""
        self.compress_level = 4
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images": ("IMAGE", {"tooltip": "The images to save."}),
                "filename_prefix": (
                    "STRING",
                    {
                        "default": "ComfyUI",
                        "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
                    },
                ),
                "file_type": (["png", "jpg", "webp"], {"default": "webp"}),
                "quality": ("INT", {"default": 80, "min": 1, "max": 100, "step": 1}),
            },
            "optional": {
                "output_id": (
                    "STRING",
                    {"multiline": False, "default": "output_images"},
                ),
                "remove_metadata": ("BOOLEAN", {"default": True}),
            },
            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
        }
    RETURN_TYPES = ()
    FUNCTION = "run"
    OUTPUT_NODE = True
    CATEGORY = "🔗ComfyDeploy"
    DESCRIPTION = "Saves the input images to your ComfyUI output directory."
    def run(
        self,
        images,
        filename_prefix="ComfyUI",
        file_type="png",
        quality=80,
        output_id="output_images",
        prompt=None,
        extra_pnginfo=None,
        remove_metadata=True,
    ):
        filename_prefix += self.prefix_append
        full_output_folder, filename, counter, subfolder, filename_prefix = (
            folder_paths.get_save_image_path(
                filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
            )
        )
        results = list()
        for batch_number, image in enumerate(images):
            i = 255.0 * image.cpu().numpy()
            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
            metadata = PngInfo()
            if not remove_metadata:
                if prompt is not None:
                    metadata.add_text("prompt", json.dumps(prompt))
                if extra_pnginfo is not None:
                    for x in extra_pnginfo:
                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
            file = f"{filename_with_batch_num}_{counter:05}_.{file_type}"
            file_path = os.path.join(full_output_folder, file)
            if file_type == "png":
                img.save(
                    file_path, pnginfo=metadata, compress_level=self.compress_level
                )
            elif file_type == "jpg":
                img.save(file_path, quality=quality, optimize=True)
            elif file_type == "webp":
                img.save(file_path, quality=quality)
            results.append(
                {
                    "filename": file,
                    "subfolder": subfolder,
                    "type": self.type,
                    "output_id": output_id,
                }
            )
            counter += 1
        return {"ui": {"images": results}}
 NODE_CLASS_MAPPINGS = {"ComfyDeployOutputImage": ComfyDeployOutputImage}
 NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployOutputImage": "Image Output (ComfyDeploy)"}
@@ -1,99 +0,0 @@
 import os
 import json
 import folder_paths
 class ComfyDeployOutputText:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()
        self.type = "output"
        self.prefix_append = ""
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "text": (
                    "STRING",
                    {
                        "multiline": True,
                        "forceInput": True,
                        "tooltip": "The text to save.",
                    },
                ),
                "filename_prefix": (
                    "STRING",
                    {
                        "default": "ComfyUI",
                        "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% to include values from nodes.",
                    },
                ),
                "file_type": (["txt", "json", "md"], {"default": "txt"}),
            },
            "optional": {
                "output_id": (
                    "STRING",
                    {"multiline": False, "default": "output_text"},
                ),
            },
            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
        }
    RETURN_TYPES = ()
    FUNCTION = "run"
    OUTPUT_NODE = True
    CATEGORY = "🔗ComfyDeploy"
    DESCRIPTION = "Saves the input text to your ComfyUI output directory."
    def run(
        self,
        text,
        filename_prefix="ComfyUI",
        file_type="txt",
        output_id="output_text",
        prompt=None,
        extra_pnginfo=None,
    ):
        filename_prefix += self.prefix_append
        # For text, we don't need dimensions, so pass 0, 0
        full_output_folder, filename, counter, subfolder, filename_prefix = (
            folder_paths.get_save_image_path(filename_prefix, self.output_dir, 0, 0)
        )
        results = list()
        # Create file path
        file = f"{filename}_{counter:05}_.{file_type}"
        file_path = os.path.join(full_output_folder, file)
        # Save the text based on file type
        if file_type == "json":
            try:
                # Try to save as JSON if the text is valid JSON
                json_data = json.loads(text) if isinstance(text, str) else text
                with open(file_path, "w", encoding="utf-8") as f:
                    json.dump(json_data, f, indent=2)
            except json.JSONDecodeError:
                # Fall back to saving as plain text if not valid JSON
                with open(file_path, "w", encoding="utf-8") as f:
                    f.write(text)
        else:
            # Save as plain text for txt and md
            with open(file_path, "w", encoding="utf-8") as f:
                f.write(text)
        results.append(
            {
                "filename": file,
                "subfolder": subfolder,
                "type": self.type,
                "output_id": output_id,
            }
        )
        return {"ui": {"text_file": results}}
 NODE_CLASS_MAPPINGS = {"ComfyDeployOutputText": ComfyDeployOutputText}
 NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployOutputText": "Text Output (ComfyDeploy)"}
@@ -33,8 +33,10 @@ class ComfyDeployWebscoketImageOutput:
    RETURN_TYPES = ()
    RETURN_NAMES = ("text",)
    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
+
    CATEGORY = "output"
    @classmethod
    def VALIDATE_INPUTS(s, output_id):
@@ -1,152 +0,0 @@
 {
  "id": "ed93ac94-4f26-4ed3-a57b-73cd8f4d3494",
  "revision": 0,
  "last_node_id": 5,
  "last_link_id": 1,
  "nodes": [
    {
      "id": 2,
      "type": "LoraLoader",
      "pos": [
        736.646728515625,
        628.3823852539062
      ],
      "size": [
        315,
        126
      ],
      "flags": {},
      "order": 2,
      "mode": 0,
      "inputs": [
        {
          "name": "model",
          "type": "MODEL",
          "link": null
        },
        {
          "name": "clip",
          "type": "CLIP",
          "link": null
        },
        {
          "name": "lora_name",
          "type": "COMBO",
          "widget": {
            "name": "lora_name"
          },
          "link": 1
        }
      ],
      "outputs": [
        {
          "name": "MODEL",
          "type": "MODEL",
          "links": null
        },
        {
          "name": "CLIP",
          "type": "CLIP",
          "links": null
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "LoraLoader"
      },
      "widgets_values": [
        "1-292.safetensors",
        1,
        1
      ]
    },
    {
      "id": 1,
      "type": "ComfyUIDeployExternalLora",
      "pos": [
        299.6898498535156,
        624.7929077148438
      ],
      "size": [
        400,
        208
      ],
      "flags": {},
      "order": 0,
      "mode": 0,
      "inputs": [],
      "outputs": [
        {
          "name": "path",
          "type": "*",
          "links": [
            1
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfyui-deploy",
        "ver": "cd3a2ff5471828f9c840e746551592e882c05aa4",
        "Node name for S&R": "ComfyUIDeployExternalLora"
      },
      "widgets_values": [
        "input_lora",
        "HyperSD\\FLUX.1\\Hyper-FLUX.1-dev-16steps-lora.safetensors",
        "",
        "",
        "",
        ""
      ]
    },
    {
      "id": 5,
      "type": "Note",
      "pos": [
        302.09033203125,
        401.2951965332031
      ],
      "size": [
        479.4894104003906,
        161.61924743652344
      ],
      "flags": {},
      "order": 1,
      "mode": 0,
      "inputs": [],
      "outputs": [],
      "properties": {},
      "widgets_values": [
        "\"External Lora\" node will let you to use different loras from the Comfy Deploy UI or even via API.\n\n- lora_url:\n  url that will be used to download your LoRA model in execution time\n\n- lora_save_name:\n  when we download your model, this will be saved in your private storage, \n  give it a good name :D"
      ],
      "color": "#432",
      "bgcolor": "#653"
    }
  ],
  "links": [
    [
      1,
      1,
      0,
      2,
      2,
      "COMBO"
    ]
  ],
  "groups": [],
  "config": {},
  "extra": {
    "ds": {
      "scale": 1.167184107045006,
      "offset": [
        298.431389807788,
        -207.58877445762934
      ]
    },
    "VHS_latentpreview": false,
    "VHS_latentpreviewrate": 0,
    "VHS_MetadataImage": true,
    "VHS_KeepIntermediate": true
  },
  "version": 0.4
 }
@@ -1,873 +0,0 @@
 {
  "id": "351f402b-62f2-4f62-8a5e-0b9d3510e8f9",
  "revision": 0,
  "last_node_id": 29,
  "last_link_id": 28,
  "nodes": [
    {
      "id": 11,
      "type": "JoinImageWithAlpha",
      "pos": [
        814.478271484375,
        419.3052062988281
      ],
      "size": [
        264.5999755859375,
        46
      ],
      "flags": {},
      "order": 5,
      "mode": 0,
      "inputs": [
        {
          "name": "image",
          "type": "IMAGE",
          "link": 10
        },
        {
          "name": "alpha",
          "type": "MASK",
          "link": 12
        }
      ],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            11
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "JoinImageWithAlpha"
      },
      "widgets_values": []
    },
    {
      "id": 15,
      "type": "PreviewImage",
      "pos": [
        1950,
        640
      ],
      "size": [
        210,
        246
      ],
      "flags": {},
      "order": 18,
      "mode": 0,
      "inputs": [
        {
          "name": "images",
          "type": "IMAGE",
          "link": 16
        }
      ],
      "outputs": [],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "PreviewImage"
      },
      "widgets_values": []
    },
    {
      "id": 10,
      "type": "LoadImage",
      "pos": [
        467.8168640136719,
        422.453857421875
      ],
      "size": [
        315,
        314
      ],
      "flags": {},
      "order": 0,
      "mode": 0,
      "inputs": [],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            10
          ]
        },
        {
          "name": "MASK",
          "type": "MASK",
          "links": [
            12
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "LoadImage"
      },
      "widgets_values": [
        "Bob-Minion-Background-PNG-Image.png",
        "image",
        ""
      ]
    },
    {
      "id": 18,
      "type": "Note",
      "pos": [
        460.8001708984375,
        263.64251708984375
      ],
      "size": [
        379.4292297363281,
        88
      ],
      "flags": {},
      "order": 1,
      "mode": 0,
      "inputs": [],
      "outputs": [],
      "properties": {},
      "widgets_values": [
        "Option 1: CREATE THE MASK FROM THE ALPHA CHANNEL (Useful for example to generate the background of an image)\n\nMake sure that you are using \"External Image Alpha\". \n"
      ],
      "color": "#432",
      "bgcolor": "#653"
    },
    {
      "id": 21,
      "type": "LoadImage",
      "pos": [
        469.57025146484375,
        1506.3018798828125
      ],
      "size": [
        315,
        314
      ],
      "flags": {},
      "order": 2,
      "mode": 0,
      "inputs": [],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            18
          ]
        },
        {
          "name": "MASK",
          "type": "MASK",
          "links": null
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "LoadImage"
      },
      "widgets_values": [
        "ComfyUI_temp_otmos_00005_.png",
        "image",
        ""
      ]
    },
    {
      "id": 25,
      "type": "MaskToImage",
      "pos": [
        1283.1668701171875,
        1579.603271484375
      ],
      "size": [
        176.39999389648438,
        26
      ],
      "flags": {},
      "order": 13,
      "mode": 0,
      "inputs": [
        {
          "name": "mask",
          "type": "MASK",
          "link": 28
        }
      ],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            21
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "MaskToImage"
      },
      "widgets_values": []
    },
    {
      "id": 13,
      "type": "SplitImageWithAlpha",
      "pos": [
        1602.5518798828125,
        417.59869384765625
      ],
      "size": [
        277.20001220703125,
        46
      ],
      "flags": {},
      "order": 11,
      "mode": 0,
      "inputs": [
        {
          "name": "image",
          "type": "IMAGE",
          "link": 13
        }
      ],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            14,
            25
          ]
        },
        {
          "name": "MASK",
          "type": "MASK",
          "links": [
            15,
            26
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "SplitImageWithAlpha"
      },
      "widgets_values": []
    },
    {
      "id": 29,
      "type": "VAEEncodeForInpaint",
      "pos": [
        2220.89453125,
        401.0885009765625
      ],
      "size": [
        340.20001220703125,
        98
      ],
      "flags": {},
      "order": 16,
      "mode": 0,
      "inputs": [
        {
          "name": "pixels",
          "type": "IMAGE",
          "link": 25
        },
        {
          "name": "vae",
          "type": "VAE",
          "link": null
        },
        {
          "name": "mask",
          "type": "MASK",
          "link": 26
        }
      ],
      "outputs": [
        {
          "name": "LATENT",
          "type": "LATENT",
          "links": null
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "VAEEncodeForInpaint"
      },
      "widgets_values": [
        6
      ]
    },
    {
      "id": 14,
      "type": "PreviewImage",
      "pos": [
        1950,
        350
      ],
      "size": [
        210,
        246
      ],
      "flags": {},
      "order": 14,
      "mode": 0,
      "inputs": [
        {
          "name": "images",
          "type": "IMAGE",
          "link": 14
        }
      ],
      "outputs": [],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "PreviewImage"
      },
      "widgets_values": []
    },
    {
      "id": 17,
      "type": "MaskToImage",
      "pos": [
        1752.33203125,
        572.061767578125
      ],
      "size": [
        176.39999389648438,
        26
      ],
      "flags": {},
      "order": 15,
      "mode": 0,
      "inputs": [
        {
          "name": "mask",
          "type": "MASK",
          "link": 15
        }
      ],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            16
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "MaskToImage"
      },
      "widgets_values": []
    },
    {
      "id": 27,
      "type": "PreviewImage",
      "pos": [
        1329.2960205078125,
        1132.759765625
      ],
      "size": [
        210,
        246
      ],
      "flags": {},
      "order": 10,
      "mode": 0,
      "inputs": [
        {
          "name": "images",
          "type": "IMAGE",
          "link": 22
        }
      ],
      "outputs": [],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "PreviewImage"
      },
      "widgets_values": []
    },
    {
      "id": 20,
      "type": "LoadImage",
      "pos": [
        468.1963806152344,
        1128.9498291015625
      ],
      "size": [
        315,
        314
      ],
      "flags": {},
      "order": 3,
      "mode": 0,
      "inputs": [],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "links": [
            17
          ]
        },
        {
          "name": "MASK",
          "type": "MASK",
          "links": []
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "LoadImage"
      },
      "widgets_values": [
        "clipspace/clipspace-mask-1126817.300000012.png [input]",
        "image",
        ""
      ]
    },
    {
      "id": 24,
      "type": "ImageToMask",
      "pos": [
        1255.560302734375,
        1468.347900390625
      ],
      "size": [
        315,
        58
      ],
      "flags": {},
      "order": 9,
      "mode": 0,
      "inputs": [
        {
          "name": "image",
          "type": "IMAGE",
          "link": 19
        }
      ],
      "outputs": [
        {
          "name": "MASK",
          "type": "MASK",
          "links": [
            24,
            28
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "ImageToMask"
      },
      "widgets_values": [
        "red"
      ]
    },
    {
      "id": 26,
      "type": "PreviewImage",
      "pos": [
        1488.1925048828125,
        1579.146728515625
      ],
      "size": [
        210,
        246
      ],
      "flags": {},
      "order": 17,
      "mode": 0,
      "inputs": [
        {
          "name": "images",
          "type": "IMAGE",
          "link": 21
        }
      ],
      "outputs": [],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "PreviewImage"
      },
      "widgets_values": []
    },
    {
      "id": 28,
      "type": "VAEEncodeForInpaint",
      "pos": [
        1637.603271484375,
        1182.81298828125
      ],
      "size": [
        340.20001220703125,
        98
      ],
      "flags": {},
      "order": 12,
      "mode": 0,
      "inputs": [
        {
          "name": "pixels",
          "type": "IMAGE",
          "link": 23
        },
        {
          "name": "vae",
          "type": "VAE",
          "link": null
        },
        {
          "name": "mask",
          "type": "MASK",
          "link": 24
        }
      ],
      "outputs": [
        {
          "name": "LATENT",
          "type": "LATENT",
          "links": null
        }
      ],
      "properties": {
        "cnr_id": "comfy-core",
        "ver": "0.3.27",
        "Node name for S&R": "VAEEncodeForInpaint"
      },
      "widgets_values": [
        6
      ]
    },
    {
      "id": 19,
      "type": "Note",
      "pos": [
        467.4615173339844,
        985.1439819335938
      ],
      "size": [
        379.4292297363281,
        88
      ],
      "flags": {},
      "order": 4,
      "mode": 0,
      "inputs": [],
      "outputs": [],
      "properties": {},
      "widgets_values": [
        "Option 2: UPLOAD THE MASK AS OTHER IMAGE (useful if you require the image that is below the mask to do inpainting, in this case to generate new glasses)"
      ],
      "color": "#432",
      "bgcolor": "#653"
    },
    {
      "id": 22,
      "type": "ComfyUIDeployExternalImage",
      "pos": [
        910.8199462890625,
        1132.306396484375
      ],
      "size": [
        390.5999755859375,
        154
      ],
      "flags": {},
      "order": 7,
      "mode": 0,
      "inputs": [
        {
          "name": "default_value",
          "shape": 7,
          "type": "IMAGE",
          "link": 17
        }
      ],
      "outputs": [
        {
          "name": "image",
          "type": "IMAGE",
          "links": [
            22,
            23
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfyui-deploy",
        "ver": "cd3a2ff5471828f9c840e746551592e882c05aa4",
        "Node name for S&R": "ComfyUIDeployExternalImage"
      },
      "widgets_values": [
        "input_image",
        "",
        "",
        "",
        ""
      ]
    },
    {
      "id": 23,
      "type": "ComfyUIDeployExternalImage",
      "pos": [
        843.6348876953125,
        1467.8876953125
      ],
      "size": [
        390.5999755859375,
        154
      ],
      "flags": {},
      "order": 6,
      "mode": 0,
      "inputs": [
        {
          "name": "default_value",
          "shape": 7,
          "type": "IMAGE",
          "link": 18
        }
      ],
      "outputs": [
        {
          "name": "image",
          "type": "IMAGE",
          "links": [
            19
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfyui-deploy",
        "ver": "cd3a2ff5471828f9c840e746551592e882c05aa4",
        "Node name for S&R": "ComfyUIDeployExternalImage"
      },
      "widgets_values": [
        "input_image_mask",
        "",
        "",
        "",
        ""
      ]
    },
    {
      "id": 12,
      "type": "ComfyUIDeployExternalImageAlpha",
      "pos": [
        1107.891357421875,
        418.2679138183594
      ],
      "size": [
        466.1999816894531,
        200
      ],
      "flags": {},
      "order": 8,
      "mode": 0,
      "inputs": [
        {
          "name": "default_value",
          "shape": 7,
          "type": "IMAGE",
          "link": 11
        }
      ],
      "outputs": [
        {
          "name": "image",
          "type": "IMAGE",
          "links": [
            13
          ]
        }
      ],
      "properties": {
        "cnr_id": "comfyui-deploy",
        "ver": "cd3a2ff5471828f9c840e746551592e882c05aa4",
        "Node name for S&R": "ComfyUIDeployExternalImageAlpha"
      },
      "widgets_values": [
        "input_image_alpha",
        "",
        ""
      ]
    }
  ],
  "links": [
    [
      10,
      10,
      0,
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@@ -1,409 +0,0 @@
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  "last_link_id": 22,
  "last_node_id": 18
 }
@@ -1,359 +0,0 @@
 {
  "extra": {
    "ds": {
      "scale": 0.8390545288824369,
      "offset": [814.3725295729478, -347.90757575249455]
    },
    "node_versions": {
      "comfy-core": "0.3.18",
      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
    }
  },
  "links": [
    [35, 3, 0, 8, 0, "LATENT"],
    [46, 6, 0, 3, 1, "CONDITIONING"],
    [52, 7, 0, 3, 2, "CONDITIONING"],
    [56, 8, 0, 28, 0, "IMAGE"],
    [74, 38, 0, 6, 0, "CLIP"],
    [75, 38, 0, 7, 0, "CLIP"],
    [76, 39, 0, 8, 1, "VAE"],
    [91, 40, 0, 3, 3, "LATENT"],
    [93, 8, 0, 47, 0, "IMAGE"],
    [94, 37, 0, 48, 0, "MODEL"],
    [95, 48, 0, 3, 0, "MODEL"],
    [96, 49, 0, 6, 1, "STRING"],
    [97, 50, 0, 7, 1, "STRING"],
    [99, 52, 0, 40, 1, "INT"],
    [100, 51, 0, 40, 0, "INT"]
  ],
  "nodes": [
    {
      "id": 8,
      "pos": [1210, 190],
      "mode": 0,
      "size": [210, 46],
      "type": "VAEDecode",
      "flags": {},
      "order": 12,
      "inputs": [
        { "link": 35, "name": "samples", "type": "LATENT" },
        { "link": 76, "name": "vae", "type": "VAE" }
      ],
      "outputs": [
        { "name": "IMAGE", "type": "IMAGE", "links": [56, 93], "slot_index": 0 }
      ],
      "properties": { "Node name for S&R": "VAEDecode" },
      "widgets_values": []
    },
    {
      "id": 39,
      "pos": [866.3932495117188, 499.18597412109375],
      "mode": 0,
      "size": [306.36004638671875, 58],
      "type": "VAELoader",
      "flags": {},
      "order": 0,
      "inputs": [],
      "outputs": [
        { "name": "VAE", "type": "VAE", "links": [76], "slot_index": 0 }
      ],
      "properties": { "Node name for S&R": "VAELoader" },
      "widgets_values": ["wan_2.1_vae.safetensors"]
    },
    {
      "id": 47,
      "pos": [2367.213134765625, 193.6114959716797],
      "mode": 4,
      "size": [315, 130],
      "type": "SaveWEBM",
      "flags": {},
      "order": 14,
      "inputs": [{ "link": 93, "name": "images", "type": "IMAGE" }],
      "outputs": [],
      "properties": { "Node name for S&R": "SaveWEBM" },
      "widgets_values": ["ComfyUI", "vp9", 24, 32]
    },
    {
      "id": 3,
      "pos": [863, 187],
      "mode": 0,
      "size": [315, 262],
      "type": "KSampler",
      "flags": {},
      "order": 11,
      "inputs": [
        { "link": 95, "name": "model", "type": "MODEL" },
        { "link": 46, "name": "positive", "type": "CONDITIONING" },
        { "link": 52, "name": "negative", "type": "CONDITIONING" },
        { "link": 91, "name": "latent_image", "type": "LATENT" }
      ],
      "outputs": [
        { "name": "LATENT", "type": "LATENT", "links": [35], "slot_index": 0 }
      ],
      "properties": { "Node name for S&R": "KSampler" },
      "widgets_values": [
        577746309562741,
        "randomize",
        30,
        6,
        "uni_pc",
        "simple",
        1
      ]
    },
    {
      "id": 48,
      "pos": [440, 50],
      "mode": 0,
      "size": [210, 58],
      "type": "ModelSamplingSD3",
      "flags": {},
      "order": 7,
      "inputs": [{ "link": 94, "name": "model", "type": "MODEL" }],
      "outputs": [
        { "name": "MODEL", "type": "MODEL", "links": [95], "slot_index": 0 }
      ],
      "properties": { "Node name for S&R": "ModelSamplingSD3" },
      "widgets_values": [8]
    },
    {
      "id": 37,
      "pos": [20, 40],
      "mode": 0,
      "size": [346.7470703125, 82],
      "type": "UNETLoader",
      "flags": {},
      "order": 1,
      "inputs": [],
      "outputs": [
        { "name": "MODEL", "type": "MODEL", "links": [94], "slot_index": 0 }
      ],
      "properties": { "Node name for S&R": "UNETLoader" },
      "widgets_values": ["wan2.1_t2v_1.3B_fp16.safetensors", "default"]
    },
    {
      "id": 6,
      "pos": [415, 186],
      "mode": 0,
      "size": [422.84503173828125, 164.31304931640625],
      "type": "CLIPTextEncode",
      "color": "#232",
      "flags": {},
      "order": 8,
      "title": "CLIP Text Encode (Positive Prompt)",
      "inputs": [
        { "link": 74, "name": "clip", "type": "CLIP" },
        {
          "pos": [10, 36],
          "link": 96,
          "name": "text",
          "type": "STRING",
          "widget": { "name": "text" }
        }
      ],
      "bgcolor": "#353",
      "outputs": [
        {
          "name": "CONDITIONING",
          "type": "CONDITIONING",
          "links": [46],
          "slot_index": 0
        }
      ],
      "properties": { "Node name for S&R": "CLIPTextEncode" },
      "widgets_values": [
        "a fox moving quickly in a beautiful winter scenery nature trees mountains daytime tracking camera"
      ]
    },
    {
      "id": 7,
      "pos": [413, 389],
      "mode": 0,
      "size": [425.27801513671875, 180.6060791015625],
      "type": "CLIPTextEncode",
      "color": "#322",
      "flags": {},
      "order": 9,
      "title": "CLIP Text Encode (Negative Prompt)",
      "inputs": [
        { "link": 75, "name": "clip", "type": "CLIP" },
        {
          "pos": [10, 36],
          "link": 97,
          "name": "text",
          "type": "STRING",
          "widget": { "name": "text" }
        }
      ],
      "bgcolor": "#533",
      "outputs": [
        {
          "name": "CONDITIONING",
          "type": "CONDITIONING",
          "links": [52],
          "slot_index": 0
        }
      ],
      "properties": { "Node name for S&R": "CLIPTextEncode" },
      "widgets_values": [
        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
      ]
    },
    {
      "id": 38,
      "pos": [-10.047812461853027, 187.37384033203125],
      "mode": 0,
      "size": [390, 98],
      "type": "CLIPLoader",
      "flags": {},
      "order": 2,
      "inputs": [],
      "outputs": [
        { "name": "CLIP", "type": "CLIP", "links": [74, 75], "slot_index": 0 }
      ],
      "properties": { "Node name for S&R": "CLIPLoader" },
      "widgets_values": [
        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
        "wan",
        "default"
      ]
    },
    {
      "id": 49,
      "pos": [-535.2967529296875, 342.3277587890625],
      "mode": 0,
      "size": [400, 200],
      "type": "ComfyUIDeployExternalText",
      "flags": {},
      "order": 3,
      "inputs": [],
      "outputs": [{ "name": "text", "type": "STRING", "links": [96] }],
      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
      "widgets_values": [
        "positive_prompt",
        "a fox moving quickly in a beautiful winter scenery nature trees mountains daytime tracking camera",
        "Prompt",
        "The text prompt to guide video generation. "
      ]
    },
    {
      "id": 50,
      "pos": [-526.2716064453125, 703.8343505859375],
      "mode": 0,
      "size": [400, 200],
      "type": "ComfyUIDeployExternalText",
      "flags": {},
      "order": 4,
      "inputs": [],
      "outputs": [{ "name": "text", "type": "STRING", "links": [97] }],
      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
      "widgets_values": [
        "negative_prompt",
        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
        "Negative Prompt",
        "The negative prompt to use. Use it to address details that you don't want in the image. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
      ]
    },
    {
      "id": 40,
      "pos": [516.926513671875, 619.59716796875],
      "mode": 0,
      "size": [315, 150],
      "type": "EmptyHunyuanLatentVideo",
      "flags": {},
      "order": 10,
      "inputs": [
        {
          "pos": [10, 36],
          "link": 100,
          "name": "width",
          "type": "INT",
          "widget": { "name": "width" }
        },
        {
          "pos": [10, 60],
          "link": 99,
          "name": "height",
          "type": "INT",
          "widget": { "name": "height" }
        }
      ],
      "outputs": [
        { "name": "LATENT", "type": "LATENT", "links": [91], "slot_index": 0 }
      ],
      "properties": { "Node name for S&R": "EmptyHunyuanLatentVideo" },
      "widgets_values": [832, 480, 33, 1]
    },
    {
      "id": 28,
      "pos": [1460, 190],
      "mode": 0,
      "size": [870.8511352539062, 643.7430419921875],
      "type": "SaveAnimatedWEBP",
      "flags": {},
      "order": 13,
      "inputs": [{ "link": 56, "name": "images", "type": "IMAGE" }],
      "outputs": [],
      "properties": {},
      "widgets_values": ["ComfyUI", 16, false, 90, "default"]
    },
    {
      "id": 51,
      "pos": [-522.7415161132812, 959.3386840820312],
      "mode": 0,
      "size": [453.5999755859375, 200],
      "type": "ComfyUIDeployExternalNumberInt",
      "flags": {},
      "order": 5,
      "inputs": [],
      "outputs": [
        { "name": "value", "type": "INT", "links": [100], "slot_index": 0 }
      ],
      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
      "widgets_values": ["width", 832, "Width", "The Width of the Video. "]
    },
    {
      "id": 52,
      "pos": [-518.9917602539062, 1207.9444580078125],
      "mode": 0,
      "size": [453.5999755859375, 200],
      "type": "ComfyUIDeployExternalNumberInt",
      "flags": {},
      "order": 6,
      "inputs": [],
      "outputs": [
        { "name": "value", "type": "INT", "links": [99], "slot_index": 0 }
      ],
      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
      "widgets_values": ["height", 480, "Height", "The Height of the Video. "]
    }
  ],
  "config": {},
  "groups": [
    {
      "id": 1,
      "color": "#3f789e",
      "flags": {},
      "title": "Inputs",
      "bounding": [
        -560.9110717773438, 255.1485595703125, 500.94989013671875,
        333.4786682128906
      ],
      "font_size": 24
    },
    {
      "id": 2,
      "color": "#b06634",
      "flags": {},
      "title": "Additional",
      "bounding": [
        -556.5305786132812, 619.87548828125, 761.2673950195312,
        811.6837768554688
      ],
      "font_size": 24
    }
  ],
  "version": 0.4,
  "last_link_id": 100,
  "last_node_id": 52
 }
@@ -6,20 +6,16 @@ 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"
    CANCELLED = "cancelled"
 class StreamingPrompt(BaseModel):
    workflow_api: Any
@@ -28,52 +24,42 @@ class StreamingPrompt(BaseModel):
    running_prompt_ids: set[str] = set()
    status_endpoint: Optional[str]
    file_upload_endpoint: Optional[str]
-    workflow: Any
+    
    gpu_event_id: Optional[str] = None
 class SimplePrompt(BaseModel):
    status_endpoint: Optional[str]
    file_upload_endpoint: Optional[str]
-
+    
    token: Optional[str]
    workflow_api: dict
    status: Status = Status.NOT_STARTED
    progress: set = set()
-    last_updated_node: Optional[str] = None
+    last_updated_node: Optional[str] = None,
    uploading_nodes: set = set()
    done: bool = False
-    is_realtime: bool = False
+    is_realtime: bool = False,
-    start_time: Optional[float] = None
+    start_time: Optional[float] = None,
    gpu_event_id: Optional[str] = None
 sockets = dict()
 prompt_metadata: dict[str, SimplePrompt] = {}
 streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
 class BinaryEventTypes:
    PREVIEW_IMAGE = 1
    UNENCODED_PREVIEW_IMAGE = 2
-
+    
 max_output_id_length = 24
-
+async def send_image(image_data, sid=None, output_id:str = None):
 async def send_image(image_data, sid=None, output_id: str = None):
    max_length = max_output_id_length
    output_id = output_id[:max_length]
-    padded_output_id = output_id.ljust(max_length, "\x00")
+    padded_output_id = output_id.ljust(max_length, '\x00')
-    encoded_output_id = padded_output_id.encode("ascii", "replace")
+    encoded_output_id = padded_output_id.encode('ascii', 'replace')
-
+    
    image_type = image_data[0]
    image = image_data[1]
    max_size = image_data[2]
    quality = image_data[3]
    if max_size is not None:
-        if hasattr(Image, "Resampling"):
+        if hasattr(Image, 'Resampling'):
            resampling = Image.Resampling.BILINEAR
        else:
            resampling = Image.ANTIALIAS
@@ -97,23 +83,17 @@ async def send_image(image_data, sid=None, output_id: str = None):
    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 (
+    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
        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}")
@@ -123,10 +103,9 @@ def encode_bytes(event, data):
    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:
@@ -134,4 +113,4 @@ async def send_bytes(event, data, sid=None):
        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)
+        await send_socket_catch_exception(sockets[sid].send_bytes, message)
@@ -1,9 +1,9 @@
 [project]
 name = "comfyui-deploy"
 description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
-version = "2.3.7"
+version = "1.0.0"
-license = { file = "LICENSE" }
+license = "LICENSE"
-dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg", "tabulate", "brotli"]
+dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
 [project.urls]
 Repository = "https://github.com/BennyKok/comfyui-deploy"
@@ -2,6 +2,4 @@ aiofiles
 pydantic
 opencv-python
 imageio-ffmpeg
-brotli
+logfire
 tabulate
 # logfire
@@ -0,0 +1,4 @@
 /** @typedef {import('../../../web/scripts/api.js').api} API*/
 import { api as _api } from '../../scripts/api.js';
 /** @type {API} */
 export const api = _api;
@@ -0,0 +1,4 @@
 /** @typedef {import('../../../web/scripts/app.js').ComfyApp} ComfyApp*/
 import { app as _app } from '../../scripts/app.js';
 /** @type {ComfyApp} */
 export const app = _app;
@@ -1,68 +0,0 @@
 // Snapshot Utilities
 // Centralized snapshot fetching with ComfyUI version fallback
 /**
 * Fetches the current snapshot with ComfyUI version fallback
 * If the snapshot response has null comfyui field, it will fetch the latest ComfyUI version
 * and update the snapshot with the comfyui_hash
 *
 * @param {Function} getDataFn - Function that returns { apiKey, apiUrl } for ComfyUI version API calls
 * @returns {Promise<Object>} - The snapshot data with comfyui field populated
 */
 export async function fetchSnapshot(getDataFn = null) {
  try {
    // Fetch the current snapshot
    const response = await fetch("/snapshot/get_current");
    if (!response.ok) {
      throw new Error(`Snapshot fetch failed: ${response.status}`);
    }
    const snapshot = await response.json();
    // Check if comfyui field is null and we have getDataFn for fallback
    if (snapshot.comfyui === null && getDataFn) {
      console.log(
        "ComfyUI version is null in snapshot, fetching latest version..."
      );
      try {
        const data = getDataFn();
        if (data && data.apiKey) {
          const comfyuiVersionResponse = await fetch(
            `/comfyui-deploy/comfyui-version?api_url=${encodeURIComponent(
              data.apiUrl || "https://api.comfydeploy.com"
            )}`,
            {
              headers: {
                Authorization: `Bearer ${data.apiKey}`,
              },
            }
          );
          if (comfyuiVersionResponse.ok) {
            const versionData = await comfyuiVersionResponse.json();
            if (versionData.comfyui_hash) {
              console.log(
                `Using ComfyUI hash from API: ${versionData.comfyui_hash}`
              );
              snapshot.comfyui = versionData.comfyui_hash;
            }
          } else {
            console.warn(
              "Failed to fetch ComfyUI version from API:",
              comfyuiVersionResponse.status
            );
          }
        }
      } catch (error) {
        console.warn("Error fetching ComfyUI version fallback:", error);
        // Continue with original snapshot even if fallback fails
      }
    }
    return snapshot;
  } catch (error) {
    console.error("Error fetching snapshot:", error);
    throw error;
  }
 }
@@ -0,0 +1,18 @@
 // /** @typedef {import('../../../web/scripts/api.js').api} API*/
 // import { api as _api } from "../../scripts/api.js";
 // /** @type {API} */
 // export const api = _api;
 /** @typedef {typeof import('../../../web/scripts/widgets.js').ComfyWidgets} Widgets*/
 import { ComfyWidgets as _ComfyWidgets } from "../../scripts/widgets.js";
 /**
 * @type {Widgets}
 */
 export const ComfyWidgets = _ComfyWidgets;
 // import { LGraphNode as _LGraphNode } from "../../types/litegraph.js";
 /** @typedef {typeof import('../../../web/types/litegraph.js').LGraphNode} LGraphNode*/
 /** @type {LGraphNode}*/
 export const LGraphNode = LiteGraph.LGraphNode;
@@ -74,7 +74,7 @@
    "mitata": "^0.1.6",
    "ms": "^2.1.3",
    "nanoid": "^5.0.4",
-    "next": "14.2",
+    "next": "14.1",
    "next-plausible": "^3.12.0",
    "next-themes": "^0.2.1",
    "next-usequerystate": "^1.13.2",
@@ -6,5 +6,4 @@ export const customInputNodes: Record<string, string> = {
  ComfyUIDeployExternalNumberInt: "integer",
  ComfyUIDeployExternalLora: "string - (public lora download url)",
  ComfyUIDeployExternalCheckpoint: "string - (public checkpoints download url)",
  ComfyUIDeployExternalFaceModel: "string - (public face model download url)",
 };
@@ -51,9 +51,7 @@ const createRunRoute = createRoute({
 export const registerCreateRunRoute = (app: App) => {
  app.openapi(createRunRoute, async (c) => {
    const data = c.req.valid("json");
-    const proto = c.req.headers.get('x-forwarded-proto')  || "http";
+    const origin = new URL(c.req.url).origin;
    const host = c.req.headers.get('x-forwarded-host') || c.req.headers.get('host');
    const origin = `${proto}://${host}` || new URL(c.req.url).origin;
    const apiKeyTokenData = c.get("apiKeyTokenData")!;
    const { deployment_id, inputs } = data;
@@ -102,7 +102,7 @@ export const createRun = withServerPromise(
    let prompt_id: string | undefined = undefined;
    const shareData = {
-      workflow_api_raw: workflow_api,
+      workflow_api: workflow_api,
      status_endpoint: `${origin}/api/update-run`,
      file_upload_endpoint: `${origin}/api/file-upload`,
    };
Author	SHA1	Message	Date
BennyKok	8c8f2abc16	Merge branch 'main' into dev	2024-07-07 22:06:54 -07:00
nick	c4d1b09a24	custom route	2024-06-19 16:52:17 -07:00
bennykok	c70e08a706	chore(plugin): add log	2024-06-11 17:43:03 -07:00
bennykok	daf1669e70	fix: node_error proxy	2024-06-11 17:43:02 -07:00
bennykok	62df715655	fix: prompt error	2024-06-11 17:43:02 -07:00
bennykok	04fd08d5ba	fix: streaming event format	2024-06-11 17:43:02 -07:00
bennykok	4a8ef7c77c	fix(plugin): event	2024-06-11 17:43:02 -07:00
bennykok	5b8dac37fb	feat(plugin): add dispatchAPIEventData	2024-06-11 17:43:02 -07:00
bennykok	875f7f24d1	fix: run issues	2024-06-11 17:43:02 -07:00
bennykok	af0fac7afc	feat: add streaming endpoint	2024-06-11 17:43:02 -07:00