Use parent folder name as subfolder for model file uploads

This node was created just for an experiment in the previous year, but never was a core feature.

I'm removing it because looks that is braking the import in local machines because i'm seeing this error locally:

```
  File "F:\ComfyUI\custom_nodes\comfyui-deploy\comfy-nodes\model_list.py", line 35, in <module>
    allModels = fetch_files("./models")
                ^^^^^^^^^^^^^^^^^^^^^^^
  File "F:\ComfyUI\custom_nodes\comfyui-deploy\comfy-nodes\model_list.py", line 23, in fetch_files
    fs.extend(fetch_files(f"{dirpath}/{dirname}"))
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "F:\ComfyUI\custom_nodes\comfyui-deploy\comfy-nodes\model_list.py", line 23, in fetch_files
    fs.extend(fetch_files(f"{dirpath}/{dirname}"))
TypeError: 'NoneType' object is not iterable

Cannot import F:\ComfyUI\custom_nodes\comfyui-deploy module for custom nodes: 'NoneType' object is not iterable
```


I'm not sure why, previously was working but was a long time ago and this node was just an experiment, so i'm deleting it

.github/workflows/publish.yml

@@ -0,0 +1,25 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    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
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.gitignore

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

README.md

@@ -2,6 +2,9 @@
 
 Open source comfyui deployment platform, a `vercel` for generative workflow infra. (serverless hosted gpu with vertical intergation with comfyui)
 
+> [!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
@@ -93,10 +96,6 @@ Major areas
 
 # Self Hosting with Vercel
 
-[![Video](https://img.mytsi.org/i/nFOG479.png)](https://www.youtube.com/watch?v=hWvsEY1cS2M)
-Tutorial Created by [Ross](https://github.com/rossman22590) and [Syn](https://github.com/mortlsyn)
-
-
 Build command
 
 ```

__init__.py

@@ -2,8 +2,9 @@
 @author: BennyKok
 @title: comfyui-deploy
 @nickname: Comfy Deploy
-@description: 
+@description:
 """
+
 import os
 import sys
 
@@ -17,19 +18,23 @@ 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 = []
 
@@ -41,14 +46,45 @@ 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"]

comfy-nodes/external_audio.py

@@ -0,0 +1,58 @@
+import os
+import io
+import torchaudio
+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):
+        if audio_file and audio_file != "":
+            if audio_file.startswith(('http://', 'https://')):
+                # Handle URL input
+                import requests
+                response = requests.get(audio_file)
+                audio_data = io.BytesIO(response.content)
+                waveform, sample_rate = torchaudio.load(audio_data)
+            else:
+                # Handle local file
+                audio_path = get_annotated_filepath(audio_file)
+                waveform, sample_rate = torchaudio.load(audio_path)
+            
+            audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
+            return (audio,)
+        else:
+            return (default_value,)
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalAudio": ComfyUIDeployExternalAudio}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalAudio": "External Audio (ComfyUI Deploy)"}

comfy-nodes/external_boolean.py

@@ -0,0 +1,36 @@
+class ComfyUIDeployExternalBoolean:
+    @classmethod    
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_bool"},
+                ),
+                "default_value": ("BOOLEAN", {"default": False})
+            },
+            "optional": {
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("bool_value",)
+        
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
+    
+    def run(self, input_id, default_value=None, display_name=None, description=None):
+        print(f"Node '{input_id}' processing with switch set to {default_value}")
+        return [default_value]
+    
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalBoolean": ComfyUIDeployExternalBoolean}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalBoolean": "External Boolean (ComfyUI Deploy)"}

comfy-nodes/external_checkpoints.py

@@ -5,6 +5,12 @@ import torch
 import folder_paths
 from tqdm import tqdm
 
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+        
+WILDCARD = AnyType("*")
+
 class ComfyUIDeployExternalCheckpoint:
     @classmethod
     def INPUT_TYPES(s):
@@ -16,23 +22,32 @@ class ComfyUIDeployExternalCheckpoint:
                 ),
             },
             "optional": {
-                "default_checkpoint_name": (folder_paths.get_filename_list("checkpoints"), ),
+                "default_value": (folder_paths.get_filename_list("checkpoints"), ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
             }
         }
 
-    RETURN_TYPES = (folder_paths.get_filename_list("checkpoints"),)
+    RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
+    
 
     FUNCTION = "run"
 
-    CATEGORY = "deploy"
+    CATEGORY = "🔗ComfyDeploy"
 
-    def run(self, input_id, default_checkpoint_name=None):
+    def run(self, input_id, default_value=None, display_name=None, description=None):
         import requests
         import os
         import uuid
 
-        if input_id and input_id.startswith('http'):
+        if default_value.startswith('http'):
             unique_filename = str(uuid.uuid4()) + ".safetensors"
             print(unique_filename)
             print(folder_paths.folder_names_and_paths["checkpoints"][0][0])
@@ -59,7 +74,7 @@ class ComfyUIDeployExternalCheckpoint:
                     out_file.write(chunk)
             return (unique_filename,)
         else:
-            return (default_checkpoints_name,)
+            return (default_value,)
 
 
 NODE_CLASS_MAPPINGS = {

comfy-nodes/external_enum.py

@@ -0,0 +1,46 @@
+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]

comfy-nodes/external_exr.py

@@ -0,0 +1,110 @@
+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)"
+}

comfy-nodes/external_face_model.py

@@ -0,0 +1,106 @@
+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)"
+}

comfy-nodes/external_image.py

@@ -15,42 +15,61 @@ 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"
 
-    CATEGORY = "image"
-
-    def run(self, input_id, default_value=None):
+    def run(self, input_id, default_value=None, display_name=None, description=None, default_value_url=None):
         image = default_value
-        try:
-            if input_id.startswith('http'):
-                import requests
-                from io import BytesIO
-                print("Fetching image from url: ", input_id)
-                response = requests.get(input_id)
-                image = Image.open(BytesIO(response.content))
-            elif input_id.startswith('data:image/png;base64,') or input_id.startswith('data:image/jpeg;base64,') or input_id.startswith('data:image/jpg;base64,'):
-                import base64
-                from io import BytesIO
-                print("Decoding base64 image")
-                base64_image = input_id[input_id.find(",")+1:]
-                decoded_image = base64.b64decode(base64_image)
-                image = Image.open(BytesIO(decoded_image))
-            else:
-                raise ValueError("Invalid image url provided.")
-
-            image = ImageOps.exif_transpose(image)
-            image = image.convert("RGB")
-            image = np.array(image).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[None,]
-            return [image]
-        except:
-            return [image]
+        
+        # Try both input_id and default_value_url
+        urls_to_try = [url for url in [input_id, default_value_url] if url]
+        
+        print(default_value_url)
+        
+        for url in urls_to_try:
+            try:
+                if url.startswith('http'):
+                    import requests
+                    from io import BytesIO
+                    print(f"Fetching image from url: {url}")
+                    response = requests.get(url)
+                    image = Image.open(BytesIO(response.content))
+                    break
+                elif url.startswith(('data:image/png;base64,', 'data:image/jpeg;base64,', 'data:image/jpg;base64,')):
+                    import base64
+                    from io import BytesIO
+                    print("Decoding base64 image")
+                    base64_image = url[url.find(",")+1:]
+                    decoded_image = base64.b64decode(base64_image)
+                    image = Image.open(BytesIO(decoded_image))
+                    break
+            except:
+                continue
+        
+        if image is not None:
+            try:
+                image = ImageOps.exif_transpose(image)
+                image = image.convert("RGB")
+                image = np.array(image).astype(np.float32) / 255.0
+                image = torch.from_numpy(image)[None,]
+            except:
+                pass
+                
+        return [image]
 
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalImage": ComfyUIDeployExternalImage}

comfy-nodes/external_image_alpha.py

@@ -15,17 +15,23 @@ 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"
 
-    CATEGORY = "image"
-
-    def run(self, input_id, default_value=None):
+    def run(self, input_id, default_value=None, display_name=None, description=None):
         image = default_value
         try:
             if input_id.startswith('http'):

comfy-nodes/external_image_batch.py

@@ -0,0 +1,111 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+import json
+import comfy
+
+class ComfyUIDeployExternalImageBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_images"},
+                ),
+                "images": (
+                    "STRING",
+                    {"multiline": False, "default": "[]"},
+                ),
+            },
+            "optional": {
+                "default_value": ("IMAGE",),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
+    
+    def process_image(self, image):
+        image = ImageOps.exif_transpose(image)
+        image = image.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image_tensor = torch.from_numpy(image)[None,]
+        return image_tensor
+    
+    def run(self, input_id, images=None, default_value=None, display_name=None, description=None):
+        import requests
+        import zipfile
+        import io
+        
+        processed_images = []
+        try:
+            images_list = json.loads(images)  # Assuming images is a JSON array string
+            print(images_list)
+            for img_input in images_list:
+                if img_input.startswith('http') and img_input.endswith('.zip'):
+                    print("Fetching zip file from url: ", img_input)
+                    response = requests.get(img_input)
+                    zip_file = zipfile.ZipFile(io.BytesIO(response.content))
+                    for file_name in zip_file.namelist():
+                        if file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
+                            with zip_file.open(file_name) as file:
+                                image = Image.open(file)
+                                image = self.process_image(image)
+                                processed_images.append(image)
+                elif  img_input.startswith('http'):
+                    from io import BytesIO
+                    print("Fetching image from url: ", img_input)
+                    response = requests.get(img_input)
+                    image = Image.open(BytesIO(response.content))
+                elif img_input.startswith('data:image/png;base64,') or img_input.startswith('data:image/jpeg;base64,') or img_input.startswith('data:image/jpg;base64,'):
+                    import base64
+                    from io import BytesIO
+                    print("Decoding base64 image")
+                    base64_image = img_input[img_input.find(",")+1:]
+                    decoded_image = base64.b64decode(base64_image)
+                    image = Image.open(BytesIO(decoded_image))
+                else:
+                    raise ValueError("Invalid image url or base64 data provided.")
+
+                image = ImageOps.exif_transpose(image)
+                image = image.convert("RGB")
+                image = np.array(image).astype(np.float32) / 255.0
+                image_tensor = torch.from_numpy(image)[None,]
+                processed_images.append(image_tensor)
+        except Exception as e:
+            print(f"Error processing images: {e}")
+            pass
+            
+        if default_value is not None and len(images_list) == 0:
+            processed_images.append(default_value)  # Assuming default_value is a pre-processed image tensor
+
+        # Resize images if necessary and concatenate from MakeImageBatch in ImpactPack
+        if processed_images:
+            base_shape = processed_images[0].shape[1:]  # Get the shape of the first image for comparison
+            batch_tensor = processed_images[0]
+            for i in range(1, len(processed_images)):
+                if processed_images[i].shape[1:] != base_shape:
+                    # Resize to match the first image's dimensions
+                    processed_images[i] = comfy.utils.common_upscale(processed_images[i].movedim(-1, 1), base_shape[1], base_shape[0], "lanczos", "center").movedim(1, -1)
+
+                batch_tensor = torch.cat((batch_tensor, processed_images[i]), dim=0)
+            # Concatenate using torch.cat
+        else:
+            batch_tensor = None  # or handle the empty case as needed
+        return (batch_tensor, )
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalImageBatch": ComfyUIDeployExternalImageBatch}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalImageBatch": "External Image Batch (ComfyUI Deploy)"}

comfy-nodes/external_lora.py

@@ -5,6 +5,14 @@ import torch
 import folder_paths
 
 
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+WILDCARD = AnyType("*")
+
+
 class ComfyUIDeployExternalLora:
     @classmethod
     def INPUT_TYPES(s):
@@ -16,36 +24,84 @@ class ComfyUIDeployExternalLora:
                 ),
             },
             "optional": {
-                "default_lora_name": (folder_paths.get_filename_list("loras"), ),
-            }
+                "default_lora_name": (folder_paths.get_filename_list("loras"),),
+                "lora_save_name": (  # if `default_lora_name` is a link to download a file, we will attempt to save it with this name
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+                "lora_url": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+            },
         }
 
-    RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
+    RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
-
     FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
 
-    CATEGORY = "deploy"
-
-    def run(self, input_id, default_lora_name=None):
+    def run(
+        self,
+        input_id,
+        default_lora_name=None,
+        lora_save_name=None,
+        display_name=None,
+        description=None,
+        lora_url=None,
+    ):
         import requests
         import os
         import uuid
 
-        if input_id and input_id.startswith('http'):
-            unique_filename = str(uuid.uuid4()) + ".safetensors"
-            print(unique_filename)
-            print(folder_paths.folder_names_and_paths["loras"][0][0])
-            destination_path = os.path.join(folder_paths.folder_names_and_paths["loras"][0][0], unique_filename)
-            print(destination_path)
-            print("Downloading external lora - " + input_id + " to " + destination_path)
-            response = requests.get(input_id, headers={'User-Agent': 'Mozilla/5.0'}, allow_redirects=True)
-            with open(destination_path, 'wb') as out_file:
-                out_file.write(response.content)
-            return (unique_filename,)
+        if lora_url:
+            if lora_url.startswith("http"):
+                if lora_save_name:
+                    existing_loras = folder_paths.get_filename_list("loras")
+                    # Check if lora_save_name exists in the list
+                    if lora_save_name in existing_loras:
+                        print(f"using lora: {lora_save_name}")
+                        return (lora_save_name,)
+                else:
+                    lora_save_name = str(uuid.uuid4()) + ".safetensors"
+                print(lora_save_name)
+                print(folder_paths.folder_names_and_paths["loras"][0][0])
+                destination_path = os.path.join(
+                    folder_paths.folder_names_and_paths["loras"][0][0], lora_save_name
+                )
+                print(destination_path)
+                print(
+                    "Downloading external lora - "
+                    + lora_url
+                    + " to "
+                    + destination_path
+                )
+                response = requests.get(
+                    lora_url,
+                    headers={"User-Agent": "Mozilla/5.0"},
+                    allow_redirects=True,
+                )
+                with open(destination_path, "wb") as out_file:
+                    out_file.write(response.content)
+                print(f"Ext Lora loading: {lora_url} to {lora_save_name}")
+                return (lora_save_name,)
+            else:
+                print(f"Ext Lora loading: {lora_url}")
+                return (lora_url,)
         else:
+            print(f"Ext Lora loading: {default_lora_name}")
             return (default_lora_name,)
-        
+
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalLora": ComfyUIDeployExternalLora}
-NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalLora": "External Lora (ComfyUI Deploy)"}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalLora": "External Lora (ComfyUI Deploy)"
+}

comfy-nodes/external_number.py

@@ -16,22 +16,31 @@ class ComfyUIDeployExternalNumber:
             "optional": {
                 "default_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "default": 0},
+                    {"multiline": True, "display": "number", "default": 0, "min": -2147483647, "max": 2147483647, "step": 0.01},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
                 ),
             }
         }
 
     RETURN_TYPES = ("FLOAT",)
     RETURN_NAMES = ("value",)
-
     FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
 
-    CATEGORY = "number"
-
-    def run(self, input_id, default_value=None):
-        if not input_id or not input_id.strip().isdigit():
+    def run(self, input_id, default_value=None, display_name=None, description=None):
+        try:
+            float_value = float(input_id)
+            print("my number", float_value)
+            return [float_value]
+        except ValueError:
             return [default_value]
-        return [int(input_id)]
 
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalNumber": ComfyUIDeployExternalNumber}

comfy-nodes/external_number_int.py

@@ -16,20 +16,26 @@ class ComfyUIDeployExternalNumberInt:
             "optional": {
                 "default_value": (
                     "INT",
-                    {"multiline": True, "display": "number", "default": 0},
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
                 ),
             }
         }
 
     RETURN_TYPES = ("INT",)
     RETURN_NAMES = ("value",)
-
     FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
 
-    CATEGORY = "number"
-
-    def run(self, input_id, default_value=None):
-        if not input_id or not input_id.strip().isdigit():
+    def run(self, input_id, default_value=None, display_name=None, description=None):
+        if not input_id or (isinstance(input_id, str) and not input_id.strip().isdigit()):
             return [default_value]
         return [int(input_id)]
 

comfy-nodes/external_number_slider.py

@@ -0,0 +1,54 @@
+class ComfyUIDeployExternalNumberSlider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_number_slider"},
+                ),
+            },
+            "optional": {
+                "default_value": (
+                    "FLOAT",
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0.5, "step": 0.01},
+                ),
+                "min_value": (
+                    "FLOAT",
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0, "step": 0.01},
+                ),
+                "max_value": (
+                    "FLOAT",
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 1, "step": 0.01},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("value",)
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
+
+    def run(self, input_id, default_value=None, min_value=0, max_value=1, display_name=None, description=None):
+        try:
+            float_value = float(input_id)
+            if min_value <= float_value <= max_value:
+                print("my number", float_value)
+                return [float_value]
+            else:
+                print("Number out of range. Returning default value:", default_value)
+                return [default_value]
+        except ValueError:
+            print("Invalid input. Returning default value:", default_value)
+            return [default_value]
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalNumberSlider": ComfyUIDeployExternalNumberSlider}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalNumberSlider": "External Number Slider (ComfyUI Deploy)"}

comfy-nodes/external_string_combine.py

@@ -0,0 +1,53 @@
+import re
+
+
+class StringFunction:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "action": (["append", "replace"], {}),
+                "tidy_tags": (["yes", "no"], {}),
+            },
+            "optional": {
+                "text_a": ("STRING", {"multiline": True, "dynamicPrompts": False}),
+                "text_b": ("STRING", {"multiline": True, "dynamicPrompts": False}),
+                "text_c": ("STRING", {"multiline": True, "dynamicPrompts": False}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "exec"
+    CATEGORY = "🔗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)",
+}

comfy-nodes/external_text.py

@@ -18,6 +18,14 @@ class ComfyUIDeployExternalText:
                     "STRING",
                     {"multiline": True, "default": ""},
                 ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
             }
         }
 
@@ -26,9 +34,9 @@ class ComfyUIDeployExternalText:
 
     FUNCTION = "run"
 
-    CATEGORY = "text"
+    CATEGORY = "🔗ComfyDeploy"
 
-    def run(self, input_id, default_value=None):
+    def run(self, input_id, default_value=None, display_name=None, description=None):
         return [default_value]
 
 

comfy-nodes/external_text_any.py

@@ -0,0 +1,46 @@
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+WILDCARD = AnyType("*")
+
+class ComfyUIDeployExternalTextAny:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_text"},
+                ),
+            },
+            "optional": {
+                "default_value": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+            }
+        }
+
+    RETURN_TYPES = (WILDCARD,)
+    RETURN_NAMES = ("text",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "🔗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)"}

comfy-nodes/external_vid.py

@@ -0,0 +1,79 @@
+import os
+import folder_paths
+import uuid
+
+from tqdm import tqdm
+
+video_extensions = ["webm", "mp4", "mkv", "gif"]
+
+
+class ComfyUIDeployExternalVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = []
+        for f in os.listdir(input_dir):
+            if os.path.isfile(os.path.join(input_dir, f)):
+                file_parts = f.split(".")
+                if len(file_parts) > 1 and (file_parts[-1] in video_extensions):
+                    files.append(f)
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_video"},
+                ),
+            },
+            "optional": {
+                "meta_batch": ("VHS_BatchManager",),
+                "default_value": (sorted(files),),
+            },
+        }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("video")
+
+    FUNCTION = "load_video"
+    CATEGORY = "🔗ComfyDeploy"
+
+    def load_video(self, input_id, default_value):
+        input_dir = folder_paths.get_input_directory()
+        if input_id.startswith("http"):
+            import requests
+
+            print("Fetching video from URL: ", input_id)
+            response = requests.get(input_id, stream=True)
+            file_size = int(response.headers.get("Content-Length", 0))
+            file_extension = input_id.split(".")[-1].split("?")[
+                0
+            ]  # Extract extension and handle URLs with parameters
+            if file_extension not in video_extensions:
+                file_extension = ".mp4"
+
+            unique_filename = str(uuid.uuid4()) + "." + file_extension
+            video_path = os.path.join(input_dir, unique_filename)
+            chunk_size = 1024  # 1 Kibibyte
+
+            num_bars = int(file_size / chunk_size)
+
+            with open(video_path, "wb") as out_file:
+                for chunk in tqdm(
+                    response.iter_content(chunk_size=chunk_size),
+                    total=num_bars,
+                    unit="KB",
+                    desc="Downloading",
+                    leave=True,
+                ):
+                    out_file.write(chunk)
+        else:
+            video_path = os.path.abspath(os.path.join(input_dir, default_value))
+
+        return (video_path,)
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalVid": ComfyUIDeployExternalVideo}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalVid": "External Video (ComfyUI Deploy) path"
+}

comfy-nodes/external_video.py

@@ -0,0 +1,865 @@
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
+# Intended to work with https://github.com/NicholasKao1029/ComfyUI-VideoHelperSuite/tree/main
+import os
+import itertools
+import numpy as np
+import torch
+from typing import Union
+from torch import Tensor
+import cv2
+import psutil
+
+from collections.abc import Mapping
+import folder_paths
+from comfy.utils import common_upscale
+
+### Utils
+import hashlib
+from typing import Iterable
+import shutil
+import subprocess
+import re
+import uuid
+
+import server
+from tqdm import tqdm
+
+BIGMIN = -(2**53 - 1)
+BIGMAX = 2**53 - 1
+
+DIMMAX = 8192
+
+
+def ffmpeg_suitability(path):
+    try:
+        version = subprocess.run(
+            [path, "-version"], check=True, capture_output=True
+        ).stdout.decode("utf-8")
+    except:
+        return 0
+    score = 0
+    # rough layout of the importance of various features
+    simple_criterion = [
+        ("libvpx", 20),
+        ("264", 10),
+        ("265", 3),
+        ("svtav1", 5),
+        ("libopus", 1),
+    ]
+    for criterion in simple_criterion:
+        if version.find(criterion[0]) >= 0:
+            score += criterion[1]
+    # obtain rough compile year from copyright information
+    copyright_index = version.find("2000-2")
+    if copyright_index >= 0:
+        copyright_year = version[copyright_index + 6 : copyright_index + 9]
+        if copyright_year.isnumeric():
+            score += int(copyright_year)
+    return score
+
+
+if "VHS_FORCE_FFMPEG_PATH" in os.environ:
+    ffmpeg_path = os.environ.get("VHS_FORCE_FFMPEG_PATH")
+else:
+    ffmpeg_paths = []
+    try:
+        from imageio_ffmpeg import get_ffmpeg_exe
+
+        imageio_ffmpeg_path = get_ffmpeg_exe()
+        ffmpeg_paths.append(imageio_ffmpeg_path)
+    except:
+        if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
+            raise
+    if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
+        ffmpeg_path = imageio_ffmpeg_path
+    else:
+        system_ffmpeg = shutil.which("ffmpeg")
+        if system_ffmpeg is not None:
+            ffmpeg_paths.append(system_ffmpeg)
+        if os.path.isfile("ffmpeg"):
+            ffmpeg_paths.append(os.path.abspath("ffmpeg"))
+        if os.path.isfile("ffmpeg.exe"):
+            ffmpeg_paths.append(os.path.abspath("ffmpeg.exe"))
+        if len(ffmpeg_paths) == 0:
+            ffmpeg_path = None
+        elif len(ffmpeg_paths) == 1:
+            # Evaluation of suitability isn't required, can take sole option
+            # to reduce startup time
+            ffmpeg_path = ffmpeg_paths[0]
+        else:
+            ffmpeg_path = max(ffmpeg_paths, key=ffmpeg_suitability)
+gifski_path = os.environ.get("VHS_GIFSKI", None)
+if gifski_path is None:
+    gifski_path = os.environ.get("JOV_GIFSKI", None)
+    if gifski_path is None:
+        gifski_path = shutil.which("gifski")
+
+
+def is_safe_path(path):
+    if "VHS_STRICT_PATHS" not in os.environ:
+        return True
+    basedir = os.path.abspath(".")
+    try:
+        common_path = os.path.commonpath([basedir, path])
+    except:
+        # Different drive on windows
+        return False
+    return common_path == basedir
+
+
+def get_sorted_dir_files_from_directory(
+    directory: str,
+    skip_first_images: int = 0,
+    select_every_nth: int = 1,
+    extensions: Iterable = None,
+):
+    directory = strip_path(directory)
+    dir_files = os.listdir(directory)
+    dir_files = sorted(dir_files)
+    dir_files = [os.path.join(directory, x) for x in dir_files]
+    dir_files = list(filter(lambda filepath: os.path.isfile(filepath), dir_files))
+    # filter by extension, if needed
+    if extensions is not None:
+        extensions = list(extensions)
+        new_dir_files = []
+        for filepath in dir_files:
+            ext = "." + filepath.split(".")[-1]
+            if ext.lower() in extensions:
+                new_dir_files.append(filepath)
+        dir_files = new_dir_files
+    # start at skip_first_images
+    dir_files = dir_files[skip_first_images:]
+    dir_files = dir_files[0::select_every_nth]
+    return dir_files
+
+
+# modified from https://stackoverflow.com/questions/22058048/hashing-a-file-in-python
+def calculate_file_hash(filename: str, hash_every_n: int = 1):
+    # Larger video files were taking >.5 seconds to hash even when cached,
+    # so instead the modified time from the filesystem is used as a hash
+    h = hashlib.sha256()
+    h.update(filename.encode())
+    h.update(str(os.path.getmtime(filename)).encode())
+    return h.hexdigest()
+
+
+prompt_queue = server.PromptServer.instance.prompt_queue
+
+
+def requeue_workflow_unchecked():
+    """Requeues the current workflow without checking for multiple requeues"""
+    currently_running = prompt_queue.currently_running
+    (_, _, prompt, extra_data, outputs_to_execute) = next(
+        iter(currently_running.values())
+    )
+
+    # Ensure batch_managers are marked stale
+    prompt = prompt.copy()
+    for uid in prompt:
+        if prompt[uid]["class_type"] == "VHS_BatchManager":
+            prompt[uid]["inputs"]["requeue"] = (
+                prompt[uid]["inputs"].get("requeue", 0) + 1
+            )
+
+    # execution.py has guards for concurrency, but server doesn't.
+    # TODO: Check that this won't be an issue
+    number = -server.PromptServer.instance.number
+    server.PromptServer.instance.number += 1
+    prompt_id = str(server.uuid.uuid4())
+    prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
+
+
+requeue_guard = [None, 0, 0, {}]
+
+
+def requeue_workflow(requeue_required=(-1, True)):
+    assert len(prompt_queue.currently_running) == 1
+    global requeue_guard
+    (run_number, _, prompt, _, _) = next(iter(prompt_queue.currently_running.values()))
+    if requeue_guard[0] != run_number:
+        # Calculate a count of how many outputs are managed by a batch manager
+        managed_outputs = 0
+        for bm_uid in prompt:
+            if prompt[bm_uid]["class_type"] == "VHS_BatchManager":
+                for output_uid in prompt:
+                    if prompt[output_uid]["class_type"] in ["VHS_VideoCombine"]:
+                        for inp in prompt[output_uid]["inputs"].values():
+                            if inp == [bm_uid, 0]:
+                                managed_outputs += 1
+        requeue_guard = [run_number, 0, managed_outputs, {}]
+    requeue_guard[1] = requeue_guard[1] + 1
+    requeue_guard[3][requeue_required[0]] = requeue_required[1]
+    if requeue_guard[1] == requeue_guard[2] and max(requeue_guard[3].values()):
+        requeue_workflow_unchecked()
+
+
+def get_audio(file, start_time=0, duration=0):
+    args = [ffmpeg_path, "-i", file]
+    if start_time > 0:
+        args += ["-ss", str(start_time)]
+    if duration > 0:
+        args += ["-t", str(duration)]
+    try:
+        # TODO: scan for sample rate and maintain
+        res = subprocess.run(
+            args + ["-f", "f32le", "-"], capture_output=True, check=True
+        )
+        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
+        match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
+    except subprocess.CalledProcessError as e:
+        raise Exception(
+            f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
+        )
+    if match:
+        ar = int(match.group(1))
+        # NOTE: Just throwing an error for other channel types right now
+        # Will deal with issues if they come
+        ac = {"mono": 1, "stereo": 2}[match.group(2)]
+    else:
+        ar = 44100
+        ac = 2
+    audio = audio.reshape((-1, ac)).transpose(0, 1).unsqueeze(0)
+    return {"waveform": audio, "sample_rate": ar}
+
+
+class LazyAudioMap(Mapping):
+    def __init__(self, file, start_time, duration):
+        self.file = file
+        self.start_time = start_time
+        self.duration = duration
+        self._dict = None
+
+    def __getitem__(self, key):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return self._dict[key]
+
+    def __iter__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return iter(self._dict)
+
+    def __len__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return len(self._dict)
+
+
+def lazy_get_audio(file, start_time=0, duration=0):
+    return LazyAudioMap(file, start_time, duration)
+
+
+def lazy_eval(func):
+    class Cache:
+        def __init__(self, func):
+            self.res = None
+            self.func = func
+
+        def get(self):
+            if self.res is None:
+                self.res = self.func()
+            return self.res
+
+    cache = Cache(func)
+    return lambda: cache.get()
+
+
+def is_url(url):
+    return url.split("://")[0] in ["http", "https"]
+
+
+def validate_sequence(path):
+    # Check if path is a valid ffmpeg sequence that points to at least one file
+    (path, file) = os.path.split(path)
+    if not os.path.isdir(path):
+        return False
+    match = re.search("%0?\d+d", file)
+    if not match:
+        return False
+    seq = match.group()
+    if seq == "%d":
+        seq = "\\\\d+"
+    else:
+        seq = "\\\\d{%s}" % seq[1:-1]
+    file_matcher = re.compile(re.sub("%0?\d+d", seq, file))
+    for file in os.listdir(path):
+        if file_matcher.fullmatch(file):
+            return True
+    return False
+
+
+def strip_path(path):
+    # This leaves whitespace inside quotes and only a single "
+    # thus ' ""test"' -> '"test'
+    # consider path.strip(string.whitespace+"\"")
+    # or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1)
+    path = path.strip()
+    if path.startswith('"'):
+        path = path[1:]
+    if path.endswith('"'):
+        path = path[:-1]
+    return path
+
+
+def hash_path(path):
+    if path is None:
+        return "input"
+    if is_url(path):
+        return "url"
+    return calculate_file_hash(path.strip('"'))
+
+
+def validate_path(path, allow_none=False, allow_url=True):
+    if path is None:
+        return allow_none
+    if is_url(path):
+        # Probably not feasible to check if url resolves here
+        return True if allow_url else "URLs are unsupported for this path"
+    if not os.path.isfile(path.strip('"')):
+        return "Invalid file path: {}".format(path)
+    return True
+
+
+### Utils
+
+video_extensions = ["webm", "mp4", "mkv", "gif"]
+
+
+def is_gif(filename) -> bool:
+    file_parts = filename.split(".")
+    return len(file_parts) > 1 and file_parts[-1] == "gif"
+
+
+def target_size(
+    width, height, force_size, custom_width, custom_height
+) -> tuple[int, int]:
+    if force_size == "Custom":
+        return (custom_width, custom_height)
+    elif force_size == "Custom Height":
+        force_size = "?x" + str(custom_height)
+    elif force_size == "Custom Width":
+        force_size = str(custom_width) + "x?"
+
+    if force_size != "Disabled":
+        force_size = force_size.split("x")
+        if force_size[0] == "?":
+            width = (width * int(force_size[1])) // height
+            # Limit to a multple of 8 for latent conversion
+            width = int(width) + 4 & ~7
+            height = int(force_size[1])
+        elif force_size[1] == "?":
+            height = (height * int(force_size[0])) // width
+            height = int(height) + 4 & ~7
+            width = int(force_size[0])
+        else:
+            width = int(force_size[0])
+            height = int(force_size[1])
+    return (width, height)
+
+
+def validate_index(
+    index: int,
+    length: int = 0,
+    is_range: bool = False,
+    allow_negative=False,
+    allow_missing=False,
+) -> int:
+    # if part of range, do nothing
+    if is_range:
+        return index
+    # otherwise, validate index
+    # validate not out of range - only when latent_count is passed in
+    if length > 0 and index > length - 1 and not allow_missing:
+        raise IndexError(f"Index '{index}' out of range for {length} item(s).")
+    # if negative, validate not out of range
+    if index < 0:
+        if not allow_negative:
+            raise IndexError(f"Negative indeces not allowed, but was '{index}'.")
+        conv_index = length + index
+        if conv_index < 0 and not allow_missing:
+            raise IndexError(
+                f"Index '{index}', converted to '{conv_index}' out of range for {length} item(s)."
+            )
+        index = conv_index
+    return index
+
+
+def convert_to_index_int(
+    raw_index: str,
+    length: int = 0,
+    is_range: bool = False,
+    allow_negative=False,
+    allow_missing=False,
+) -> int:
+    try:
+        return validate_index(
+            int(raw_index),
+            length=length,
+            is_range=is_range,
+            allow_negative=allow_negative,
+            allow_missing=allow_missing,
+        )
+    except ValueError as e:
+        raise ValueError(f"Index '{raw_index}' must be an integer.", e)
+
+
+def convert_str_to_indexes(
+    indexes_str: str, length: int = 0, allow_missing=False
+) -> list[int]:
+    if not indexes_str:
+        return []
+    int_indexes = list(range(0, length))
+    allow_negative = length > 0
+    chosen_indexes = []
+    # parse string - allow positive ints, negative ints, and ranges separated by ':'
+    groups = indexes_str.split(",")
+    groups = [g.strip() for g in groups]
+    for g in groups:
+        # parse range of indeces (e.g. 2:16)
+        if ":" in g:
+            index_range = g.split(":", 2)
+            index_range = [r.strip() for r in index_range]
+
+            start_index = index_range[0]
+            if len(start_index) > 0:
+                start_index = convert_to_index_int(
+                    start_index,
+                    length=length,
+                    is_range=True,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            else:
+                start_index = 0
+            end_index = index_range[1]
+            if len(end_index) > 0:
+                end_index = convert_to_index_int(
+                    end_index,
+                    length=length,
+                    is_range=True,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            else:
+                end_index = length
+            # support step as well, to allow things like reversing, every-other, etc.
+            step = 1
+            if len(index_range) > 2:
+                step = index_range[2]
+                if len(step) > 0:
+                    step = convert_to_index_int(
+                        step,
+                        length=length,
+                        is_range=True,
+                        allow_negative=True,
+                        allow_missing=True,
+                    )
+                else:
+                    step = 1
+            # if latents were passed in, base indeces on known latent count
+            if len(int_indexes) > 0:
+                chosen_indexes.extend(int_indexes[start_index:end_index][::step])
+            # otherwise, assume indeces are valid
+            else:
+                chosen_indexes.extend(list(range(start_index, end_index, step)))
+        # parse individual indeces
+        else:
+            chosen_indexes.append(
+                convert_to_index_int(
+                    g,
+                    length=length,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            )
+    return chosen_indexes
+
+
+def select_indexes(input_obj: Union[Tensor, list], idxs: list):
+    if type(input_obj) == Tensor:
+        return input_obj[idxs]
+    else:
+        return [input_obj[i] for i in idxs]
+
+
+def select_indexes_from_str(
+    input_obj: Union[Tensor, list], indexes: str, err_if_missing=True, err_if_empty=True
+):
+    real_idxs = convert_str_to_indexes(
+        indexes, len(input_obj), allow_missing=not err_if_missing
+    )
+    if err_if_empty and len(real_idxs) == 0:
+        raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
+    return select_indexes(input_obj, real_idxs)
+
+
+###
+
+
+def cv_frame_generator(
+    video,
+    force_rate,
+    frame_load_cap,
+    skip_first_frames,
+    select_every_nth,
+    meta_batch=None,
+    unique_id=None,
+):
+    video_cap = cv2.VideoCapture(strip_path(video))
+    if not video_cap.isOpened():
+        raise ValueError(f"{video} could not be loaded with cv.")
+    pbar = None
+
+    # extract video metadata
+    fps = video_cap.get(cv2.CAP_PROP_FPS)
+    width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(video_cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    duration = total_frames / fps
+
+    # set video_cap to look at start_index frame
+    total_frame_count = 0
+    total_frames_evaluated = -1
+    frames_added = 0
+    base_frame_time = 1 / fps
+    prev_frame = None
+
+    if force_rate == 0:
+        target_frame_time = base_frame_time
+    else:
+        target_frame_time = 1 / force_rate
+
+    yield (width, height, fps, duration, total_frames, target_frame_time)
+    if meta_batch is not None:
+        yield min(frame_load_cap, total_frames)
+
+    time_offset = target_frame_time - base_frame_time
+    while video_cap.isOpened():
+        if time_offset < target_frame_time:
+            is_returned = video_cap.grab()
+            # if didn't return frame, video has ended
+            if not is_returned:
+                break
+            time_offset += base_frame_time
+        if time_offset < target_frame_time:
+            continue
+        time_offset -= target_frame_time
+        # if not at start_index, skip doing anything with frame
+        total_frame_count += 1
+        if total_frame_count <= skip_first_frames:
+            continue
+        else:
+            total_frames_evaluated += 1
+
+        # if should not be selected, skip doing anything with frame
+        if total_frames_evaluated % select_every_nth != 0:
+            continue
+
+        # opencv loads images in BGR format (yuck), so need to convert to RGB for ComfyUI use
+        # follow up: can videos ever have an alpha channel?
+        # To my testing: No. opencv has no support for alpha
+        unused, frame = video_cap.retrieve()
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        # convert frame to comfyui's expected format
+        # TODO: frame contains no exif information. Check if opencv2 has already applied
+        frame = np.array(frame, dtype=np.float32)
+        torch.from_numpy(frame).div_(255)
+        if prev_frame is not None:
+            inp = yield prev_frame
+            if inp is not None:
+                # ensure the finally block is called
+                return
+        prev_frame = frame
+        frames_added += 1
+        if pbar is not None:
+            pbar.update_absolute(frames_added, frame_load_cap)
+        # if cap exists and we've reached it, stop processing frames
+        if frame_load_cap > 0 and frames_added >= frame_load_cap:
+            break
+    if meta_batch is not None:
+        meta_batch.inputs.pop(unique_id)
+        meta_batch.has_closed_inputs = True
+    if prev_frame is not None:
+        yield prev_frame
+
+
+def batched(it, n):
+    while batch := tuple(itertools.islice(it, n)):
+        yield batch
+
+
+def batched_vae_encode(images, vae, frames_per_batch):
+    for batch in batched(images, frames_per_batch):
+        image_batch = torch.from_numpy(np.array(batch))
+        yield from vae.encode(image_batch).numpy()
+
+
+def load_video_cv(
+    video: str,
+    force_rate: int,
+    force_size: str,
+    custom_width: int,
+    custom_height: int,
+    frame_load_cap: int,
+    skip_first_frames: int,
+    select_every_nth: int,
+    meta_batch=None,
+    unique_id=None,
+    memory_limit_mb=None,
+    vae=None,
+):
+    if meta_batch is None or unique_id not in meta_batch.inputs:
+        gen = cv_frame_generator(
+            video,
+            force_rate,
+            frame_load_cap,
+            skip_first_frames,
+            select_every_nth,
+            meta_batch,
+            unique_id,
+        )
+        (width, height, fps, duration, total_frames, target_frame_time) = next(gen)
+
+        if meta_batch is not None:
+            meta_batch.inputs[unique_id] = (
+                gen,
+                width,
+                height,
+                fps,
+                duration,
+                total_frames,
+                target_frame_time,
+            )
+            meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
+
+    else:
+        (gen, width, height, fps, duration, total_frames, target_frame_time) = (
+            meta_batch.inputs[unique_id]
+        )
+
+    memory_limit = None
+    if memory_limit_mb is not None:
+        memory_limit *= 2**20
+    else:
+        # TODO: verify if garbage collection should be performed here.
+        # leaves ~128 MB unreserved for safety
+        try:
+            memory_limit = (
+                psutil.virtual_memory().available + psutil.swap_memory().free
+            ) - 2**27
+        except:
+            print(
+                "Failed to calculate available memory. Memory load limit has been disabled"
+            )
+    if memory_limit is not None:
+        if vae is not None:
+            # space required to load as f32, exist as latent with wiggle room, decode to f32
+            max_loadable_frames = int(
+                memory_limit // (width * height * 3 * (4 + 4 + 1 / 10))
+            )
+        else:
+            # TODO: use better estimate for when vae is not None
+            # Consider completely ignoring for load_latent case?
+            max_loadable_frames = int(memory_limit // (width * height * 3 * (0.1)))
+        if meta_batch is not None:
+            if meta_batch.frames_per_batch > max_loadable_frames:
+                raise RuntimeError(
+                    f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory"
+                )
+            gen = itertools.islice(gen, meta_batch.frames_per_batch)
+        else:
+            original_gen = gen
+            gen = itertools.islice(gen, max_loadable_frames)
+    downscale_ratio = getattr(vae, "downscale_ratio", 8)
+    frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
+    if force_size != "Disabled" or vae is not None:
+        new_size = target_size(
+            width, height, force_size, custom_width, custom_height, downscale_ratio
+        )
+        if new_size[0] != width or new_size[1] != height:
+
+            def rescale(frame):
+                s = torch.from_numpy(
+                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
+                )
+                s = s.movedim(-1, 1)
+                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+                return s.movedim(1, -1).numpy()
+
+            gen = itertools.chain.from_iterable(
+                map(rescale, batched(gen, frames_per_batch))
+            )
+    else:
+        new_size = width, height
+    if vae is not None:
+        gen = batched_vae_encode(gen, vae, frames_per_batch)
+        vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
+        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
+    else:
+        # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+        images = torch.from_numpy(
+            np.fromiter(gen, np.dtype((np.float32, (new_size[1], new_size[0], 3))))
+        )
+    if meta_batch is None and memory_limit is not None:
+        try:
+            next(original_gen)
+            raise RuntimeError(
+                f"Memory limit hit after loading {len(images)} frames. Stopping execution."
+            )
+        except StopIteration:
+            pass
+    if len(images) == 0:
+        raise RuntimeError("No frames generated")
+
+    # Setup lambda for lazy audio capture
+    audio = lazy_get_audio(
+        video,
+        skip_first_frames * target_frame_time,
+        frame_load_cap * target_frame_time * select_every_nth,
+    )
+    # Adjust target_frame_time for select_every_nth
+    target_frame_time *= select_every_nth
+    video_info = {
+        "source_fps": fps,
+        "source_frame_count": total_frames,
+        "source_duration": duration,
+        "source_width": width,
+        "source_height": height,
+        "loaded_fps": 1 / target_frame_time,
+        "loaded_frame_count": len(images),
+        "loaded_duration": len(images) * target_frame_time,
+        "loaded_width": new_size[0],
+        "loaded_height": new_size[1],
+    }
+    if vae is None:
+        return (images, len(images), audio, video_info, None)
+    else:
+        return (None, len(images), audio, video_info, {"samples": images})
+
+
+# modeled after Video upload node
+class ComfyUIDeployExternalVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = []
+        for f in os.listdir(input_dir):
+            if os.path.isfile(os.path.join(input_dir, f)):
+                file_parts = f.split(".")
+                if len(file_parts) > 1 and (file_parts[-1] in video_extensions):
+                    files.append(f)
+        return {"required": {
+                    "input_id": (
+                        "STRING",
+                        {"multiline": False, "default": "input_video"},
+                    ),
+                     "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
+                     "force_size": (["Disabled", "Custom Height", "Custom Width", "Custom", "256x?", "?x256", "256x256", "512x?", "?x512", "512x512"],),
+                     "custom_width": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
+                     "custom_height": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
+                     "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                     "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                     "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                     },
+                "optional": {
+                    "meta_batch": ("VHS_BatchManager",),
+                    "vae": ("VAE",),
+                    "default_video": (sorted(files),),
+                    "display_name": (
+                        "STRING",
+                        {"multiline": False, "default": ""},
+                    ),
+                    "description": (
+                        "STRING",
+                        {"multiline": True, "default": ""},
+                    ),
+                },
+                "hidden": {
+                    "unique_id": "UNIQUE_ID"
+                },
+                }
+
+    CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
+
+    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
+    RETURN_NAMES = (
+        "IMAGE",
+        "frame_count",
+        "audio",
+        "video_info",
+        "LATENT",
+    )
+
+    FUNCTION = "load_video"
+    CATEGORY = "🔗ComfyDeploy"
+
+    def load_video(self, **kwargs):
+        input_id = kwargs.get("input_id")
+        force_rate = kwargs.get("force_rate")
+        force_size = kwargs.get("force_size", "Disabled")
+        custom_width = kwargs.get("custom_width")
+        custom_height = kwargs.get("custom_height")
+        frame_load_cap = kwargs.get("frame_load_cap")
+        skip_first_frames = kwargs.get("skip_first_frames")
+        select_every_nth = kwargs.get("select_every_nth")
+        meta_batch = kwargs.get("meta_batch")
+        unique_id = kwargs.get("unique_id")
+
+
+        input_dir = folder_paths.get_input_directory()
+        if input_id.startswith("http"):
+            import requests
+
+            print("Fetching video from URL: ", input_id)
+            response = requests.get(input_id, stream=True)
+            file_size = int(response.headers.get("Content-Length", 0))
+            file_extension = input_id.split(".")[-1].split("?")[
+                0
+            ]  # Extract extension and handle URLs with parameters
+            if file_extension not in video_extensions:
+                file_extension = ".mp4"
+
+            unique_filename = str(uuid.uuid4()) + "." + file_extension
+            video_path = os.path.join(input_dir, unique_filename)
+            chunk_size = 1024  # 1 Kibibyte
+
+            num_bars = int(file_size / chunk_size)
+
+            with open(video_path, "wb") as out_file:
+                for chunk in tqdm(
+                    response.iter_content(chunk_size=chunk_size),
+                    total=num_bars,
+                    unit="KB",
+                    desc="Downloading",
+                    leave=True,
+                ):
+                    out_file.write(chunk)
+        else:
+            video = kwargs.get("default_video", None)
+            if video is None:
+                raise "No default video given and no external video provided"
+            video_path = folder_paths.get_annotated_filepath(video.strip('"'))
+
+        return load_video_cv(
+            video=video_path,
+            force_rate=force_rate,
+            force_size=force_size,
+            custom_width=custom_width,
+            custom_height=custom_height,
+            frame_load_cap=frame_load_cap,
+            skip_first_frames=skip_first_frames,
+            select_every_nth=select_every_nth,
+            meta_batch=meta_batch,
+            unique_id=unique_id,
+        )
+
+    @classmethod
+    def IS_CHANGED(s, video, **kwargs):
+        image_path = folder_paths.get_annotated_filepath(video)
+        return calculate_file_hash(image_path)
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalVideo": ComfyUIDeployExternalVideo}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalVideo": "External Video (ComfyUI Deploy x VHS)"
+}

comfy-nodes/input_websocket_image.py

@@ -0,0 +1,67 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+from server import PromptServer, BinaryEventTypes
+import asyncio
+
+from globals import streaming_prompt_metadata, max_output_id_length
+
+class ComfyDeployWebscoketImageInput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_id"},
+                ),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "default_value": ("IMAGE", ),
+                "client_id": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+            }
+        }
+
+    OUTPUT_NODE = True
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("images",)
+
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
+
+    @classmethod
+    def VALIDATE_INPUTS(s, input_id):
+        try:
+            if len(input_id.encode('ascii')) > max_output_id_length:
+                raise ValueError(f"input_id size is greater than {max_output_id_length} bytes")
+        except UnicodeEncodeError:
+            raise ValueError("input_id is not ASCII encodable")
+
+        return True
+
+    def run(self, input_id, seed, default_value=None ,client_id=None):
+        # print(streaming_prompt_metadata[client_id].inputs)
+        if client_id in streaming_prompt_metadata and input_id in streaming_prompt_metadata[client_id].inputs:
+            if isinstance(streaming_prompt_metadata[client_id].inputs[input_id], Image.Image):
+                print("Returning image from websocket input")
+                
+                image = streaming_prompt_metadata[client_id].inputs[input_id]
+                
+                image = ImageOps.exif_transpose(image)
+                image = image.convert("RGB")
+                image = np.array(image).astype(np.float32) / 255.0
+                image = torch.from_numpy(image)[None,]
+                
+                return [image]
+
+        print("Returning default value")
+        return [default_value]
+
+NODE_CLASS_MAPPINGS = {"ComfyDeployWebscoketImageInput": ComfyDeployWebscoketImageInput}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployWebscoketImageInput": "Image Websocket Input (ComfyDeploy)"}

comfy-nodes/output_image.py

@@ -0,0 +1,101 @@
+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"},
+                ),
+            },
+            "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,
+    ):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = (
+            folder_paths.get_save_image_path(
+                filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
+            )
+        )
+        results = list()
+        for batch_number, image in enumerate(images):
+            i = 255.0 * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            metadata = PngInfo()
+            if prompt is not None:
+                metadata.add_text("prompt", json.dumps(prompt))
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.{file_type}"
+            file_path = os.path.join(full_output_folder, file)
+
+            if file_type == "png":
+                img.save(
+                    file_path, pnginfo=metadata, compress_level=self.compress_level
+                )
+            elif file_type == "jpg":
+                img.save(file_path, quality=quality, optimize=True)
+            elif file_type == "webp":
+                img.save(file_path, quality=quality)
+
+            results.append(
+                {
+                    "filename": file,
+                    "subfolder": subfolder,
+                    "type": self.type,
+                    "output_id": output_id,
+                }
+            )
+            counter += 1
+
+        return {"ui": {"images": results}}
+
+
+NODE_CLASS_MAPPINGS = {"ComfyDeployOutputImage": ComfyDeployOutputImage}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployOutputImage": "Image Output (ComfyDeploy)"}

comfy-nodes/output_websocket_image.py

@@ -0,0 +1,69 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+from server import PromptServer, BinaryEventTypes
+import asyncio
+
+from globals import send_image, max_output_id_length
+
+class ComfyDeployWebscoketImageOutput:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "output_id": (
+                    "STRING",
+                    {"multiline": False, "default": "output_id"},
+                ),
+                "images": ("IMAGE", ),
+                "file_type": (["WEBP", "PNG", "JPEG"], ),
+                "quality": ("INT", {"default": 80, "min": 1, "max": 100, "step": 1}),
+            },
+            "optional": {
+                    "client_id": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+            }
+            # "hidden": {"client_id": "CLIENT_ID"},
+        }
+
+    OUTPUT_NODE = True
+
+    RETURN_TYPES = ()
+    RETURN_NAMES = ("text",)
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
+    
+    @classmethod
+    def VALIDATE_INPUTS(s, output_id):
+        try:
+            if len(output_id.encode('ascii')) > max_output_id_length:
+                raise ValueError(f"output_id size is greater than {max_output_id_length} bytes")
+        except UnicodeEncodeError:
+            raise ValueError("output_id is not ASCII encodable")
+
+        return True
+
+    def run(self, output_id, images, file_type, quality, client_id):
+        prompt_server = PromptServer.instance
+        loop = prompt_server.loop
+        
+        def schedule_coroutine_blocking(target, *args):
+            future = asyncio.run_coroutine_threadsafe(target(*args), loop)
+            return future.result()  # This makes the call blocking
+        
+        for tensor in images:
+            array = 255.0 * tensor.cpu().numpy()
+            image = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8))
+
+            schedule_coroutine_blocking(send_image, [file_type, image, None, quality], client_id, output_id)
+            print("Image sent")
+
+        return {"ui": {}}
+        
+
+
+NODE_CLASS_MAPPINGS = {"ComfyDeployWebscoketImageOutput": ComfyDeployWebscoketImageOutput}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployWebscoketImageOutput": "Image Websocket Output (ComfyDeploy)"}

example_workflows/lora-model.json

@@ -0,0 +1,152 @@
+{
+  "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
+}

example_workflows/masks.json

@@ -0,0 +1,873 @@
+{
+  "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
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+      "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,
+      11,
+      0,
+      "IMAGE"
+    ],
+    [
+      11,
+      11,
+      0,
+      12,
+      0,
+      "IMAGE"
+    ],
+    [
+      12,
+      10,
+      1,
+      11,
+      1,
+      "MASK"
+    ],
+    [
+      13,
+      12,
+      0,
+      13,
+      0,
+      "IMAGE"
+    ],
+    [
+      14,
+      13,
+      0,
+      14,
+      0,
+      "IMAGE"
+    ],
+    [
+      15,
+      13,
+      1,
+      17,
+      0,
+      "MASK"
+    ],
+    [
+      16,
+      17,
+      0,
+      15,
+      0,
+      "IMAGE"
+    ],
+    [
+      17,
+      20,
+      0,
+      22,
+      0,
+      "IMAGE"
+    ],
+    [
+      18,
+      21,
+      0,
+      23,
+      0,
+      "IMAGE"
+    ],
+    [
+      19,
+      23,
+      0,
+      24,
+      0,
+      "IMAGE"
+    ],
+    [
+      21,
+      25,
+      0,
+      26,
+      0,
+      "IMAGE"
+    ],
+    [
+      22,
+      22,
+      0,
+      27,
+      0,
+      "IMAGE"
+    ],
+    [
+      23,
+      22,
+      0,
+      28,
+      0,
+      "IMAGE"
+    ],
+    [
+      24,
+      24,
+      0,
+      28,
+      2,
+      "MASK"
+    ],
+    [
+      25,
+      13,
+      0,
+      29,
+      0,
+      "IMAGE"
+    ],
+    [
+      26,
+      13,
+      1,
+      29,
+      2,
+      "MASK"
+    ],
+    [
+      28,
+      24,
+      0,
+      25,
+      0,
+      "MASK"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.9646149645000013,
+      "offset": [
+        48.66905973637718,
+        -817.5683540167485
+      ]
+    },
+    "VHS_latentpreview": false,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

examples/flux-dev.json

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

examples/flux-schnell.json

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+          "name": "latent_image",
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+      "widgets_values": [
+        "The schnell model is a distilled model that can generate a good image with only 4 steps."
+      ]
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+      "pos": [1488, 192],
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+      "widgets_values": ["ComfyUI"]
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+      "color": "#2a363b",
+      "flags": {},
+      "order": 2,
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+      "bgcolor": "#3f5159",
+      "outputs": [
+        { "name": "NOISE", "type": "NOISE", "links": [37], "shape": 3 }
+      ],
+      "properties": { "Node name for S&R": "RandomNoise" },
+      "widgets_values": [393845863554716, "randomize"]
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+      ],
+      "bgcolor": "#535",
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [23], "slot_index": 0 }
+      ],
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+      "order": 3,
+      "inputs": [],
+      "bgcolor": "#653",
+      "outputs": [],
+      "properties": { "text": "" },
+      "widgets_values": [
+        "If you get an error in any of the nodes above make sure the files are in the correct directories.\n\nSee the top of the examples page for the links : https://comfyanonymous.github.io/ComfyUI_examples/flux/\n\nflux1-schnell.safetensors goes in: ComfyUI/models/unet/\n\nt5xxl_fp16.safetensors and clip_l.safetensors go in: ComfyUI/models/clip/\n\nae.safetensors goes in: ComfyUI/models/vae/\n\n\nTip: You can set the weight_dtype above to one of the fp8 types if you have memory issues."
+      ]
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+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [10],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "DualCLIPLoader" },
+      "widgets_values": [
+        "t5xxl_fp16.safetensors",
+        "clip_l.safetensors",
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+        "default"
+      ]
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+      "pos": [45.92215347290039, 95.6536865234375],
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+      "size": [315, 82],
+      "type": "UNETLoader",
+      "color": "#223",
+      "flags": { "pinned": true },
+      "order": 5,
+      "inputs": [],
+      "bgcolor": "#335",
+      "outputs": [
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+          "type": "MODEL",
+          "links": [38, 39],
+          "shape": 3,
+          "slot_index": 0
+        }
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+      "properties": { "Node name for S&R": "UNETLoader" },
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+      "outputs": [{ "name": "text", "type": "STRING", "links": [41] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "prompt",
+        "a bottle with a beautiful rainbow galaxy inside it on top of a wooden table in the middle of a modern kitchen beside a plate of vegetables and mushrooms and a wine glasse that contains a planet earth with a plate with a half eaten apple pie on it",
+        "Prompt",
+        "The prompt to generate an image from."
+      ]
+    },
+    {
+      "id": 29,
+      "pos": [-677.4290161132812, 597.3389282226562],
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+      "widgets_values": ["height", 1024, "Height", "The height of the image."]
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+}

examples/hunyuan-video.json

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

examples/stable-diffusion-v15.json

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

examples/stable-diffusion-v35-large.json

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

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

@@ -0,0 +1,452 @@
+{
+  "extra": {
+    "ds": {
+      "scale": 0.6934334949441662,
+      "offset": [276.7941771713029, -7.923387380923855]
+    },
+    "node_versions": {
+      "comfy-core": "0.3.18",
+      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
+    }
+  },
+  "links": [
+    [35, 3, 0, 8, 0, "LATENT"],
+    [56, 8, 0, 28, 0, "IMAGE"],
+    [74, 38, 0, 6, 0, "CLIP"],
+    [75, 38, 0, 7, 0, "CLIP"],
+    [76, 39, 0, 8, 1, "VAE"],
+    [93, 8, 0, 47, 0, "IMAGE"],
+    [94, 49, 0, 51, 0, "CLIP_VISION"],
+    [97, 6, 0, 50, 0, "CONDITIONING"],
+    [98, 7, 0, 50, 1, "CONDITIONING"],
+    [99, 39, 0, 50, 2, "VAE"],
+    [101, 50, 0, 3, 1, "CONDITIONING"],
+    [102, 50, 1, 3, 2, "CONDITIONING"],
+    [103, 50, 2, 3, 3, "LATENT"],
+    [107, 51, 0, 50, 3, "CLIP_VISION_OUTPUT"],
+    [110, 37, 0, 54, 0, "MODEL"],
+    [111, 54, 0, 3, 0, "MODEL"],
+    [112, 55, 0, 6, 1, "STRING"],
+    [113, 56, 0, 7, 1, "STRING"],
+    [115, 57, 0, 50, 4, "IMAGE"],
+    [116, 57, 0, 51, 1, "IMAGE"],
+    [117, 58, 0, 50, 5, "INT"],
+    [118, 59, 0, 50, 6, "INT"]
+  ],
+  "nodes": [
+    {
+      "id": 8,
+      "pos": [1210, 190],
+      "mode": 0,
+      "size": [210, 46],
+      "type": "VAEDecode",
+      "flags": {},
+      "order": 15,
+      "inputs": [
+        { "link": 35, "name": "samples", "type": "LATENT" },
+        { "link": 76, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [56, 93], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
+    },
+    {
+      "id": 39,
+      "pos": [866.3932495117188, 499.18597412109375],
+      "mode": 0,
+      "size": [306.36004638671875, 58],
+      "type": "VAELoader",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "outputs": [
+        { "name": "VAE", "type": "VAE", "links": [76, 99], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAELoader" },
+      "widgets_values": ["wan_2.1_vae.safetensors"]
+    },
+    {
+      "id": 28,
+      "pos": [1460, 190],
+      "mode": 0,
+      "size": [870.8511352539062, 643.7430419921875],
+      "type": "SaveAnimatedWEBP",
+      "flags": {},
+      "order": 16,
+      "inputs": [{ "link": 56, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI", 16, false, 90, "default", ""]
+    },
+    {
+      "id": 47,
+      "pos": [2367.213134765625, 193.6114959716797],
+      "mode": 4,
+      "size": [315, 130],
+      "type": "SaveWEBM",
+      "flags": {},
+      "order": 17,
+      "inputs": [{ "link": 93, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": { "Node name for S&R": "SaveWEBM" },
+      "widgets_values": ["ComfyUI", "vp9", 24, 32]
+    },
+    {
+      "id": 50,
+      "pos": [673.0507202148438, 627.272705078125],
+      "mode": 0,
+      "size": [342.5999755859375, 250],
+      "type": "WanImageToVideo",
+      "flags": {},
+      "order": 13,
+      "inputs": [
+        { "link": 97, "name": "positive", "type": "CONDITIONING" },
+        { "link": 98, "name": "negative", "type": "CONDITIONING" },
+        { "link": 99, "name": "vae", "type": "VAE" },
+        {
+          "link": 107,
+          "name": "clip_vision_output",
+          "type": "CLIP_VISION_OUTPUT",
+          "shape": 7
+        },
+        { "link": 115, "name": "start_image", "type": "IMAGE", "shape": 7 },
+        {
+          "pos": [10, 116],
+          "link": 117,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        },
+        {
+          "pos": [10, 140],
+          "link": 118,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [101],
+          "slot_index": 0
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [102],
+          "slot_index": 1
+        },
+        { "name": "latent", "type": "LATENT", "links": [103], "slot_index": 2 }
+      ],
+      "properties": { "Node name for S&R": "WanImageToVideo" },
+      "widgets_values": [512, 512, 33, 1]
+    },
+    {
+      "id": 3,
+      "pos": [863, 187],
+      "mode": 0,
+      "size": [315, 262],
+      "type": "KSampler",
+      "flags": {},
+      "order": 14,
+      "inputs": [
+        { "link": 111, "name": "model", "type": "MODEL" },
+        { "link": 101, "name": "positive", "type": "CONDITIONING" },
+        { "link": 102, "name": "negative", "type": "CONDITIONING" },
+        { "link": 103, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [35], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
+      "widgets_values": [
+        55104841546759,
+        "randomize",
+        20,
+        6,
+        "uni_pc",
+        "simple",
+        1
+      ]
+    },
+    {
+      "id": 51,
+      "pos": [360, 640],
+      "mode": 0,
+      "size": [253.60000610351562, 78],
+      "type": "CLIPVisionEncode",
+      "flags": {},
+      "order": 12,
+      "inputs": [
+        { "link": 94, "name": "clip_vision", "type": "CLIP_VISION" },
+        { "link": 116, "name": "image", "type": "IMAGE" }
+      ],
+      "outputs": [
+        {
+          "name": "CLIP_VISION_OUTPUT",
+          "type": "CLIP_VISION_OUTPUT",
+          "links": [107],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionEncode" },
+      "widgets_values": ["none"]
+    },
+    {
+      "id": 54,
+      "pos": [510, 70],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "ModelSamplingSD3",
+      "flags": {},
+      "order": 9,
+      "inputs": [{ "link": 110, "name": "model", "type": "MODEL" }],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [111], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ModelSamplingSD3" },
+      "widgets_values": [8]
+    },
+    {
+      "id": 6,
+      "pos": [415, 186],
+      "mode": 0,
+      "size": [422.84503173828125, 164.31304931640625],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 10,
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 74, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 112,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [97],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around"
+      ]
+    },
+    {
+      "id": 55,
+      "pos": [-672.6411743164062, 140.167724609375],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [112] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "positive_prompt",
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around",
+        "Prompt",
+        "The text prompt to guide video generation."
+      ]
+    },
+    {
+      "id": 57,
+      "pos": [-664.3828125, 405.5113525390625],
+      "mode": 0,
+      "size": [390.5999755859375, 366],
+      "type": "ComfyUIDeployExternalImage",
+      "flags": {},
+      "order": 2,
+      "inputs": [
+        { "link": null, "name": "default_value", "type": "IMAGE", "shape": 7 }
+      ],
+      "outputs": [{ "name": "image", "type": "IMAGE", "links": [115, 116] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalImage" },
+      "widgets_values": [
+        "image_url",
+        "Image Url",
+        "URL of the input image.",
+        "https://comfy-deploy-output.s3.us-east-2.amazonaws.com/assets/img_GZMJYXDnLbYjWybu.png",
+        ""
+      ]
+    },
+    {
+      "id": 56,
+      "pos": [-674.42333984375, 953.0498046875],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [113] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "negative_prompt",
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+        "Negative Prompt",
+        "The negative prompt to use. Use it to address details that you don't want in the video. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
+      ]
+    },
+    {
+      "id": 58,
+      "pos": [-668.314453125, 1208.204833984375],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [117], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 512, "Width", "The width of the video. "]
+    },
+    {
+      "id": 59,
+      "pos": [-665.8831787109375, 1461.255126953125],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 5,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [118], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 512, "Height", "The Height of the video."]
+    },
+    {
+      "id": 37,
+      "pos": [-3.3700287342071533, 81.69654846191406],
+      "mode": 0,
+      "size": [346.7470703125, 82],
+      "type": "UNETLoader",
+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [110], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["wan2.1_i2v_480p_14B_bf16.safetensors", "default"]
+    },
+    {
+      "id": 38,
+      "pos": [-3.7316977977752686, 230.76495361328125],
+      "mode": 0,
+      "size": [390, 98],
+      "type": "CLIPLoader",
+      "flags": {},
+      "order": 7,
+      "inputs": [],
+      "outputs": [
+        { "name": "CLIP", "type": "CLIP", "links": [74, 75], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "CLIPLoader" },
+      "widgets_values": [
+        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "wan",
+        "default"
+      ]
+    },
+    {
+      "id": 49,
+      "pos": [16.57183265686035, 640.22314453125],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "CLIPVisionLoader",
+      "flags": {},
+      "order": 8,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP_VISION",
+          "type": "CLIP_VISION",
+          "links": [94],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionLoader" },
+      "widgets_values": ["clip_vision_h.safetensors"]
+    },
+    {
+      "id": 7,
+      "pos": [414.1811828613281, 393.8824768066406],
+      "mode": 0,
+      "size": [425.27801513671875, 180.6060791015625],
+      "type": "CLIPTextEncode",
+      "color": "#322",
+      "flags": {},
+      "order": 11,
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "inputs": [
+        { "link": 75, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 113,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [98],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "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"
+      ]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
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+  ],
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+  "last_link_id": 118,
+  "last_node_id": 59
+}

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

@@ -0,0 +1,452 @@
+{
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+          "link": 11,
+          "name": "height",
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+          "widget": { "name": "height" }
+        }
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+          "type": "CONDITIONING",
+          "links": [13],
+          "slot_index": 0
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+          "slot_index": 1
+        },
+        { "name": "latent", "type": "LATENT", "links": [15], "slot_index": 2 }
+      ],
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+        { "link": 14, "name": "negative", "type": "CONDITIONING" },
+        { "link": 15, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [1], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
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+        { "link": 17, "name": "image", "type": "IMAGE" }
+      ],
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+        {
+          "name": "CLIP_VISION_OUTPUT",
+          "type": "CLIP_VISION_OUTPUT",
+          "links": [8],
+          "slot_index": 0
+        }
+      ],
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+      "widgets_values": ["none"]
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+        { "name": "MODEL", "type": "MODEL", "links": [12], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ModelSamplingSD3" },
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+      "color": "#232",
+      "flags": {},
+      "order": 12,
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 19, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 20,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [5],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around"
+      ]
+    },
+    {
+      "id": 11,
+      "pos": [-50.3940315246582, 918.1358032226562],
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+      "size": [390.5999755859375, 366],
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+      "flags": {},
+      "order": 1,
+      "inputs": [
+        { "link": null, "name": "default_value", "type": "IMAGE", "shape": 7 }
+      ],
+      "outputs": [{ "name": "image", "type": "IMAGE", "links": [9, 17] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalImage" },
+      "widgets_values": [
+        "image_url",
+        "Image Url",
+        "URL of the input image.",
+        "https://comfy-deploy-output.s3.us-east-2.amazonaws.com/assets/img_GZMJYXDnLbYjWybu.png",
+        ""
+      ]
+    },
+    {
+      "id": 12,
+      "pos": [-60.4345588684082, 1465.6741943359375],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 2,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [22] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "negative_prompt",
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+        "Negative Prompt",
+        "The negative prompt to use. Use it to address details that you don't want in the video. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
+      ]
+    },
+    {
+      "id": 13,
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+      "outputs": [
+        { "name": "value", "type": "INT", "links": [10], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 512, "Width", "The width of the video. "]
+    },
+    {
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+      "pos": [-51.8943977355957, 1973.8795166015625],
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+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [11], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 512, "Height", "The Height of the video."]
+    },
+    {
+      "id": 16,
+      "pos": [610.257080078125, 743.389404296875],
+      "mode": 0,
+      "size": [390, 98],
+      "type": "CLIPLoader",
+      "flags": {},
+      "order": 5,
+      "inputs": [],
+      "outputs": [
+        { "name": "CLIP", "type": "CLIP", "links": [19, 21], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "CLIPLoader" },
+      "widgets_values": [
+        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "wan",
+        "default"
+      ]
+    },
+    {
+      "id": 17,
+      "pos": [630.5606079101562, 1152.8475341796875],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "CLIPVisionLoader",
+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP_VISION",
+          "type": "CLIP_VISION",
+          "links": [16],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionLoader" },
+      "widgets_values": ["clip_vision_h.safetensors"]
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+    {
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+      "pos": [1028.169921875, 906.5068969726562],
+      "mode": 0,
+      "size": [425.27801513671875, 180.6060791015625],
+      "type": "CLIPTextEncode",
+      "color": "#322",
+      "flags": {},
+      "order": 9,
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "inputs": [
+        { "link": 21, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 22,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [6],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+      ]
+    },
+    {
+      "id": 15,
+      "pos": [610.6187744140625, 594.3209838867188],
+      "mode": 0,
+      "size": [346.7470703125, 82],
+      "type": "UNETLoader",
+      "flags": {},
+      "order": 7,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [18], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["wan2.1_i2v_720p_14B_bf16.safetensors", "default"]
+    },
+    {
+      "id": 10,
+      "pos": [-60.696964263916016, 649.5515747070312],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 8,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [20] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "positive_prompt",
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit running towards front happily",
+        "Prompt",
+        "The text prompt to guide video generation."
+      ]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Input",
+      "bounding": [
+        -94.30522155761719, 560.1735229492188, 617.7969360351562,
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+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#b06634",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
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+      "font_size": 24
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+  ],
+  "version": 0.4,
+  "last_link_id": 22,
+  "last_node_id": 18
+}

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

@@ -0,0 +1,359 @@
+{
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+      "flags": {},
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+      "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],
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+      ],
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+      "widgets_values": ["ComfyUI", "vp9", 24, 32]
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+        { "link": 52, "name": "negative", "type": "CONDITIONING" },
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+      "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
+}

globals.py

@@ -0,0 +1,136 @@
+import struct
+from enum import Enum
+import aiohttp
+from typing import List, Union, Any, Optional
+from PIL import Image, ImageOps
+from io import BytesIO
+from pydantic import BaseModel as PydanticBaseModel
+
+
+class BaseModel(PydanticBaseModel):
+    class Config:
+        arbitrary_types_allowed = True
+
+
+class Status(Enum):
+    NOT_STARTED = "not-started"
+    RUNNING = "running"
+    SUCCESS = "success"
+    FAILED = "failed"
+    UPLOADING = "uploading"
+
+
+class StreamingPrompt(BaseModel):
+    workflow_api: Any
+    auth_token: str
+    inputs: dict[str, Union[str, bytes, Image.Image]]
+    running_prompt_ids: set[str] = set()
+    status_endpoint: Optional[str]
+    file_upload_endpoint: Optional[str]
+    workflow: Any
+    gpu_event_id: Optional[str] = None
+
+
+class SimplePrompt(BaseModel):
+    status_endpoint: Optional[str]
+    file_upload_endpoint: Optional[str]
+
+    token: Optional[str]
+
+    workflow_api: dict
+    status: Status = Status.NOT_STARTED
+    progress: set = set()
+    last_updated_node: Optional[str] = None
+    uploading_nodes: set = set()
+    done: bool = False
+    is_realtime: bool = False
+    start_time: Optional[float] = None
+    gpu_event_id: Optional[str] = None
+
+
+sockets = dict()
+prompt_metadata: dict[str, SimplePrompt] = {}
+streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
+
+
+class BinaryEventTypes:
+    PREVIEW_IMAGE = 1
+    UNENCODED_PREVIEW_IMAGE = 2
+
+
+max_output_id_length = 24
+
+
+async def send_image(image_data, sid=None, output_id: str = None):
+    max_length = max_output_id_length
+    output_id = output_id[:max_length]
+    padded_output_id = output_id.ljust(max_length, "\x00")
+    encoded_output_id = padded_output_id.encode("ascii", "replace")
+
+    image_type = image_data[0]
+    image = image_data[1]
+    max_size = image_data[2]
+    quality = image_data[3]
+    if max_size is not None:
+        if hasattr(Image, "Resampling"):
+            resampling = Image.Resampling.BILINEAR
+        else:
+            resampling = Image.ANTIALIAS
+
+        image = ImageOps.contain(image, (max_size, max_size), resampling)
+    type_num = 1
+    if image_type == "JPEG":
+        type_num = 1
+    elif image_type == "PNG":
+        type_num = 2
+    elif image_type == "WEBP":
+        type_num = 3
+
+    bytesIO = BytesIO()
+    header = struct.pack(">I", type_num)
+    # 4 bytes for the type
+    bytesIO.write(header)
+    # 10 bytes for the output_id
+    position_before = bytesIO.tell()
+    bytesIO.write(encoded_output_id)
+    position_after = bytesIO.tell()
+    bytes_written = position_after - position_before
+    print(f"Bytes written: {bytes_written}")
+
+    image.save(bytesIO, format=image_type, quality=quality, compress_level=1)
+    preview_bytes = bytesIO.getvalue()
+    await send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
+
+
+async def send_socket_catch_exception(function, message):
+    try:
+        await function(message)
+    except (
+        aiohttp.ClientError,
+        aiohttp.ClientPayloadError,
+        ConnectionResetError,
+    ) as err:
+        print("send error:", err)
+
+
+def encode_bytes(event, data):
+    if not isinstance(event, int):
+        raise RuntimeError(f"Binary event types must be integers, got {event}")
+
+    packed = struct.pack(">I", event)
+    message = bytearray(packed)
+    message.extend(data)
+    return message
+
+
+async def send_bytes(event, data, sid=None):
+    message = encode_bytes(event, data)
+
+    print("sending image to ", event, sid)
+
+    if sid is None:
+        _sockets = list(sockets.values())
+        for ws in _sockets:
+            await send_socket_catch_exception(ws.send_bytes, message)
+    elif sid in sockets:
+        await send_socket_catch_exception(sockets[sid].send_bytes, message)

prestartup_script.py

@@ -58,6 +58,9 @@ if cd_enable_log:
    print("** Comfy Deploy logging enabled")
    setup() 
 
+
+# Store the original working directory
+original_cwd = os.getcwd()
 try:
     # Get the absolute path of the script's directory
     script_dir = os.path.dirname(os.path.abspath(__file__))
@@ -66,4 +69,7 @@ try:
     current_git_commit = subprocess.check_output(['git', 'rev-parse', 'HEAD']).decode('utf-8').strip()
     print(f"** Comfy Deploy Revision: {current_git_commit}")
 except Exception as e:
-    print(f"** Comfy Deploy failed to get current git commit: {str(e)}")
+    print(f"** Comfy Deploy failed to get current git commit: {str(e)}")
+finally:
+    # Change back to the original directory
+    os.chdir(original_cwd)

pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "comfyui-deploy"
+description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
+version = "2.1.0"
+license = { file = "LICENSE" }
+dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg", "tabulate", "brotli"]
+
+[project.urls]
+Repository = "https://github.com/BennyKok/comfyui-deploy"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "comfydeploy"
+DisplayName = "comfyui-deploy"
+Icon = ""

requirements.txt

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

web-plugin/api.js

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

web-plugin/app.js

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

web-plugin/widgets.js

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

web/package.json

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

web/src/components/customInputNodes.tsx

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

web/src/routes/registerCreateRunRoute.ts

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

web/src/server/createRun.ts

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