Merge branch 'main' into dev

.github/workflows/publish.yml

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

.gitignore

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

README.md

@@ -2,9 +2,6 @@
 
 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
@@ -96,6 +93,10 @@ Major areas
 
 # Self Hosting with Vercel
 
+[![Video](https://img.mytsi.org/i/nFOG479.png)](https://www.youtube.com/watch?v=hWvsEY1cS2M)
+Tutorial Created by [Ross](https://github.com/rossman22590) and [Syn](https://github.com/mortlsyn)
+
+
 Build command
 
 ```

__init__.py

@@ -2,9 +2,8 @@
 @author: BennyKok
 @title: comfyui-deploy
 @nickname: Comfy Deploy
-@description:
+@description: 
 """
-
 import os
 import sys
 
@@ -18,23 +17,19 @@ import requests
 import folder_paths
 from folder_paths import add_model_folder_path, get_filename_list, get_folder_paths
 from tqdm import tqdm
-import re
 
 from . import custom_routes
 # import routes
 
 ag_path = os.path.join(os.path.dirname(__file__))
 
-
 def get_python_files(path):
     return [f[:-3] for f in os.listdir(path) if f.endswith(".py")]
 
-
 def append_to_sys_path(path):
     if path not in sys.path:
         sys.path.append(path)
 
-
 paths = ["comfy-nodes"]
 files = []
 
@@ -46,45 +41,14 @@ for path in paths:
 NODE_CLASS_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS = {}
 
-
-def split_camel_case(name):
-    # Split on underscores first, then split each part on camelCase
-    parts = []
-    for part in name.split("_"):
-        # Find all camelCase boundaries
-        words = re.findall("[A-Z][^A-Z]*", part)
-        if not words:  # If no camelCase found, use the whole part
-            words = [part]
-        parts.extend(words)
-    return parts
-
-
 # Import all the modules and append their mappings
 for file in files:
     module = importlib.import_module(file)
 
-    # Check if the module has explicit mappings
     if hasattr(module, "NODE_CLASS_MAPPINGS"):
         NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
     if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS"):
         NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
 
-    # Auto-discover classes with ComfyUI node attributes
-    for name, obj in inspect.getmembers(module):
-        # Check if it's a class and has the required ComfyUI node attributes
-        if (
-            inspect.isclass(obj)
-            and hasattr(obj, "INPUT_TYPES")
-            and hasattr(obj, "RETURN_TYPES")
-        ):
-            # Use the class name as the key if not already in mappings
-            if name not in NODE_CLASS_MAPPINGS:
-                NODE_CLASS_MAPPINGS[name] = obj
-                # Create a display name by converting camelCase to Title Case with spaces
-                words = split_camel_case(name.replace("ComfyUIDeploy", ""))
-                display_name = " ".join(word.capitalize() for word in words)
-                # print(display_name, name)
-                NODE_DISPLAY_NAME_MAPPINGS[name] = display_name
-
 WEB_DIRECTORY = "web-plugin"
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

comfy-nodes/external_audio.py

@@ -1,58 +0,0 @@
-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

@@ -8,26 +8,15 @@ class ComfyUIDeployExternalBoolean:
                     {"multiline": False, "default": "input_bool"},
                 ),
                 "default_value": ("BOOLEAN", {"default": False})
-            },
-            "optional": {
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
     RETURN_TYPES = ("BOOLEAN",)
     RETURN_NAMES = ("bool_value",)
-        
-    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
     
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    FUNCTION = "run"
+    
+    def run(self, input_id, default_value=None):
         print(f"Node '{input_id}' processing with switch set to {default_value}")
         return [default_value]
     

comfy-nodes/external_checkpoints.py

@@ -5,12 +5,6 @@ import torch
 import folder_paths
 from tqdm import tqdm
 
-class AnyType(str):
-    def __ne__(self, __value: object) -> bool:
-        return False
-        
-WILDCARD = AnyType("*")
-
 class ComfyUIDeployExternalCheckpoint:
     @classmethod
     def INPUT_TYPES(s):
@@ -23,26 +17,17 @@ class ComfyUIDeployExternalCheckpoint:
             },
             "optional": {
                 "default_value": (folder_paths.get_filename_list("checkpoints"), ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
-    RETURN_TYPES = (WILDCARD,)
+    RETURN_TYPES = (folder_paths.get_filename_list("checkpoints"),)
     RETURN_NAMES = ("path",)
-    
 
     FUNCTION = "run"
 
-    CATEGORY = "🔗ComfyDeploy"
+    CATEGORY = "deploy"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
         import requests
         import os
         import uuid

comfy-nodes/external_enum.py

@@ -1,46 +0,0 @@
-class AnyType(str):
-    def __ne__(self, __value: object) -> bool:
-        return False
-
-
-WILDCARD = AnyType("*")
-
-class ComfyUIDeployExternalEnum:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_id": (
-                    "STRING",
-                    {"multiline": False, "default": "input_enum"},
-                ),
-            },
-            "optional": {
-                "default_value": (
-                    "STRING",
-                    {"multiline": False, "default": "", "dynamic_enum": True},
-                ),
-                "options": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-            }
-        }
-
-    RETURN_TYPES = (WILDCARD,)
-    RETURN_NAMES = ("text",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "🔗ComfyDeploy"
-
-    def run(self, input_id, options=None, default_value=None, display_name=None, description=None):
-        return [default_value]

comfy-nodes/external_exr.py

@@ -1,110 +0,0 @@
-import os
-import io
-import cv2 as cv
-import numpy as np
-import torch
-import requests
-from folder_paths import get_annotated_filepath
-
-class ComfyUIDeployExternalEXR:
-    RETURN_TYPES = ("IMAGE", "MASK")
-    RETURN_NAMES = ("image", "mask") 
-    FUNCTION = "load_exr"
-    CATEGORY = "🔗ComfyDeploy"
-    
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "input_id": (
-                    "STRING",
-                    {"multiline": False, "default": "input_exr"},
-                ),
-                "exr_file": ("STRING", {"default": ""}),
-                "tonemap": (["linear", "sRGB", "Reinhard"], {"default": "sRGB"}),
-            },
-            "optional": {
-                "default_image": ("IMAGE",),
-                "default_mask": ("MASK",),
-                "display_name": (
-                    "STRING", 
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-            }
-        }
-
-    @classmethod
-    def VALIDATE_INPUTS(s, exr_file, **kwargs):
-        return True
-
-    def sRGBtoLinear(self, npArray):
-        less = npArray <= 0.0404482362771082
-        npArray[less] = npArray[less] / 12.92
-        npArray[~less] = np.power((npArray[~less] + 0.055) / 1.055, 2.4)
-
-    def linearToSRGB(self, npArray):
-        less = npArray <= 0.0031308
-        npArray[less] = npArray[less] * 12.92
-        npArray[~less] = np.power(npArray[~less], 1/2.4) * 1.055 - 0.055
-
-    def load_exr(self, input_id, exr_file, tonemap="sRGB", 
-                 default_image=None, default_mask=None,
-                 display_name=None, description=None):
-        try:
-            if exr_file and exr_file != "":
-                if exr_file.startswith(('http://', 'https://')):
-                    # Handle URL input
-                    response = requests.get(exr_file)
-                    # Write to temp buffer
-                    buffer = io.BytesIO(response.content)
-                    nparr = np.frombuffer(buffer.getvalue(), np.uint8)
-                    image = cv.imdecode(nparr, cv.IMREAD_UNCHANGED).astype(np.float32)
-                else:
-                    # Handle local file
-                    exr_path = get_annotated_filepath(exr_file)
-                    image = cv.imread(exr_path, cv.IMREAD_UNCHANGED).astype(np.float32)
-
-                if len(image.shape) == 2:
-                    image = np.repeat(image[..., np.newaxis], 3, axis=2)
-                
-                # Extract RGB and flip channels
-                rgb = np.flip(image[:,:,:3], 2).copy()
-                
-                # Apply tonemapping
-                if tonemap == "sRGB":
-                    self.linearToSRGB(rgb)
-                    rgb = np.clip(rgb, 0, 1)
-                elif tonemap == "Reinhard":
-                    rgb = np.clip(rgb, 0, None)
-                    rgb = rgb / (rgb + 1)
-                    self.linearToSRGB(rgb)
-                    rgb = np.clip(rgb, 0, 1)
-                
-                rgb = torch.unsqueeze(torch.from_numpy(rgb), 0)
-                
-                # Handle alpha/mask
-                mask = torch.zeros((1, image.shape[0], image.shape[1]), dtype=torch.float32)
-                if image.shape[2] > 3:
-                    mask[0] = torch.from_numpy(np.clip(image[:,:,3], 0, 1))
-
-                return (rgb, mask)
-            else:
-                # Return defaults if no file provided
-                return (default_image, default_mask)
-
-        except Exception as e:
-            print(f"Error loading EXR: {str(e)}")
-            # Return defaults on error
-            return (default_image, default_mask)
-
-NODE_CLASS_MAPPINGS = {
-    "ComfyUIDeployExternalEXR": ComfyUIDeployExternalEXR
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ComfyUIDeployExternalEXR": "External EXR (ComfyUI Deploy)"
-}

comfy-nodes/external_face_model.py

@@ -1,106 +0,0 @@
-from PIL import Image, ImageOps
-import numpy as np
-import torch
-import folder_paths
-
-
-class AnyType(str):
-    def __ne__(self, __value: object) -> bool:
-        return False
-
-
-WILDCARD = AnyType("*")
-
-
-class ComfyUIDeployExternalFaceModel:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_id": (
-                    "STRING",
-                    {"multiline": False, "default": "input_reactor_face_model"},
-                ),
-            },
-            "optional": {
-                "default_face_model_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "face_model_save_name": (  # if `default_face_model_name` is a link to download a file, we will attempt to save it with this name
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-                "face_model_url": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-            },
-        }
-
-    RETURN_TYPES = (WILDCARD,)
-    RETURN_NAMES = ("path",)
-    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
-
-    def run(
-        self,
-        input_id,
-        default_face_model_name=None,
-        face_model_save_name=None,
-        display_name=None,
-        description=None,
-        face_model_url=None,
-    ):
-        import requests
-        import os
-        import uuid
-
-        if face_model_url and face_model_url.startswith("http"):
-            if face_model_save_name:
-                existing_face_models = folder_paths.get_filename_list("reactor/faces")
-                # Check if face_model_save_name exists in the list
-                if face_model_save_name in existing_face_models:
-                    print(f"using face model: {face_model_save_name}")
-                    return (face_model_save_name,)
-            else:
-                face_model_save_name = str(uuid.uuid4()) + ".safetensors"
-            print(face_model_save_name)
-            print(folder_paths.folder_names_and_paths["reactor/faces"][0][0])
-            destination_path = os.path.join(
-                folder_paths.folder_names_and_paths["reactor/faces"][0][0],
-                face_model_save_name,
-            )
-
-            print(destination_path)
-            print(
-                "Downloading external face model - "
-                + face_model_url
-                + " to "
-                + destination_path
-            )
-            response = requests.get(
-                face_model_url,
-                headers={"User-Agent": "Mozilla/5.0"},
-                allow_redirects=True,
-            )
-            with open(destination_path, "wb") as out_file:
-                out_file.write(response.content)
-            return (face_model_save_name,)
-        else:
-            print(f"using face model: {default_face_model_name}")
-            return (default_face_model_name,)
-
-
-NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalFaceModel": ComfyUIDeployExternalFaceModel}
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ComfyUIDeployExternalFaceModel": "External Face Model (ComfyUI Deploy)"
-}

comfy-nodes/external_image.py

@@ -15,61 +15,42 @@ class ComfyUIDeployExternalImage:
             },
             "optional": {
                 "default_value": ("IMAGE",),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "default_value_url": ("STRING", {"image_preview": True, "default": ""}),
             }
         }
 
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("image",)
-    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None, default_value_url=None):
+    FUNCTION = "run"
+
+    CATEGORY = "image"
+
+    def run(self, input_id, default_value=None):
         image = default_value
-        
-        # Try 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]
+        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]
 
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalImage": ComfyUIDeployExternalImage}

comfy-nodes/external_image_alpha.py

@@ -15,23 +15,17 @@ class ComfyUIDeployExternalImageAlpha:
             },
             "optional": {
                 "default_value": ("IMAGE",),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("image",)
-    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    FUNCTION = "run"
+
+    CATEGORY = "image"
+
+    def run(self, input_id, default_value=None):
         image = default_value
         try:
             if input_id.startswith('http'):

comfy-nodes/external_image_batch.py

@@ -21,50 +21,24 @@ class ComfyUIDeployExternalImageBatch:
             },
             "optional": {
                 "default_value": ("IMAGE",),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("image",)
+
     FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
+
+    CATEGORY = "image"
     
-    def process_image(self, image):
-        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
-        
+    def run(self, input_id, images=None, default_value=None):
         processed_images = []
         try:
             images_list = json.loads(images)  # Assuming images is a JSON array string
             print(images_list)
             for img_input in images_list:
-                if img_input.startswith('http') 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'):
+                if img_input.startswith('http'):
+                    import requests
                     from io import BytesIO
                     print("Fetching image from url: ", img_input)
                     response = requests.get(img_input)

comfy-nodes/external_lora.py

@@ -5,14 +5,6 @@ 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):
@@ -25,79 +17,40 @@ class ComfyUIDeployExternalLora:
             },
             "optional": {
                 "default_lora_name": (folder_paths.get_filename_list("loras"),),
-                "lora_save_name": (  # if `default_lora_name` is a link to download a file, we will attempt to save it with this name
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-                "lora_url": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
             },
         }
 
-    RETURN_TYPES = (WILDCARD,)
+    RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
     RETURN_NAMES = ("path",)
-    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
 
-    def run(
-        self,
-        input_id,
-        default_lora_name=None,
-        lora_save_name=None,
-        display_name=None,
-        description=None,
-        lora_url=None,
-    ):
+    FUNCTION = "run"
+
+    CATEGORY = "deploy"
+
+    def run(self, input_id, default_lora_name=None):
         import requests
         import os
         import uuid
 
-        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,)
+        if default_lora_name.startswith("http"):
+            unique_filename = str(uuid.uuid4()) + ".safetensors"
+            print(unique_filename)
+            print(folder_paths.folder_names_and_paths["loras"][0][0])
+            destination_path = os.path.join(
+                folder_paths.folder_names_and_paths["loras"][0][0], unique_filename
+            )
+            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,)
         else:
-            print(f"Ext Lora loading: {default_lora_name}")
+            print(f"using lora: {default_lora_name}")
             return (default_lora_name,)
 
 

comfy-nodes/external_number.py

@@ -16,25 +16,19 @@ class ComfyUIDeployExternalNumber:
             "optional": {
                 "default_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "default": 0, "min": -2147483647, "max": 2147483647, "step": 0.01},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
+                    {"multiline": True, "display": "number", "default": 0, "step": 0.01},
                 ),
             }
         }
 
     RETURN_TYPES = ("FLOAT",)
     RETURN_NAMES = ("value",)
-    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    FUNCTION = "run"
+
+    CATEGORY = "number"
+
+    def run(self, input_id, default_value=None):
         try:
             float_value = float(input_id)
             print("my number", float_value)

comfy-nodes/external_number_int.py

@@ -16,25 +16,19 @@ class ComfyUIDeployExternalNumberInt:
             "optional": {
                 "default_value": (
                     "INT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
+                    {"multiline": True, "display": "number", "default": 0},
                 ),
             }
         }
 
     RETURN_TYPES = ("INT",)
     RETURN_NAMES = ("value",)
-    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    FUNCTION = "run"
+
+    CATEGORY = "number"
+
+    def run(self, input_id, default_value=None):
         if not input_id or (isinstance(input_id, str) and not input_id.strip().isdigit()):
             return [default_value]
         return [int(input_id)]

comfy-nodes/external_number_slider.py

@@ -11,33 +11,27 @@ class ComfyUIDeployExternalNumberSlider:
             "optional": {
                 "default_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0.5, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 0.5, "step": 0.01},
                 ),
                 "min_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 0, "step": 0.01},
                 ),
                 "max_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 1, "step": 0.01},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
+                    {"multiline": True, "display": "number", "default": 1, "step": 0.01},
                 ),
             }
         }
 
     RETURN_TYPES = ("FLOAT",)
     RETURN_NAMES = ("value",)
-    FUNCTION = "run"
-    CATEGORY = "🔗ComfyDeploy"
 
-    def run(self, input_id, default_value=None, min_value=0, max_value=1, display_name=None, description=None):
+    FUNCTION = "run"
+
+    CATEGORY = "number"
+
+    def run(self, input_id, default_value=None, min_value=0, max_value=1):
         try:
             float_value = float(input_id)
             if min_value <= float_value <= max_value:

comfy-nodes/external_string_combine.py

@@ -1,53 +0,0 @@
-import re
-
-
-class StringFunction:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "action": (["append", "replace"], {}),
-                "tidy_tags": (["yes", "no"], {}),
-            },
-            "optional": {
-                "text_a": ("STRING", {"multiline": True, "dynamicPrompts": False}),
-                "text_b": ("STRING", {"multiline": True, "dynamicPrompts": False}),
-                "text_c": ("STRING", {"multiline": True, "dynamicPrompts": False}),
-            },
-        }
-
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "exec"
-    CATEGORY = "🔗ComfyDeploy"
-    OUTPUT_NODE = True
-
-    def exec(self, action, tidy_tags, text_a="", text_b="", text_c=""):
-        tidy_tags = tidy_tags == "yes"
-        out = ""
-        if action == "append":
-            out = (", " if tidy_tags else "").join(
-                filter(None, [text_a, text_b, text_c])
-            )
-        else:
-            if text_c is None:
-                text_c = ""
-            if text_b.startswith("/") and text_b.endswith("/"):
-                regex = text_b[1:-1]
-                out = re.sub(regex, text_c, text_a)
-            else:
-                out = text_a.replace(text_b, text_c)
-        if tidy_tags:
-            out = re.sub(r"\s{2,}", " ", out)
-            out = out.replace(" ,", ",")
-            out = re.sub(r",{2,}", ",", out)
-            out = out.strip()
-        return {"ui": {"text": (out,)}, "result": (out,)}
-
-
-NODE_CLASS_MAPPINGS = {
-    "ComfyUIDeployStringCombine": StringFunction,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ComfyUIDeployStringCombine": "String Combine (ComfyUI Deploy)",
-}

comfy-nodes/external_text.py

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

comfy-nodes/external_text_any.py

@@ -1,46 +0,0 @@
-class AnyType(str):
-    def __ne__(self, __value: object) -> bool:
-        return False
-
-
-WILDCARD = AnyType("*")
-
-class ComfyUIDeployExternalTextAny:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_id": (
-                    "STRING",
-                    {"multiline": False, "default": "input_text"},
-                ),
-            },
-            "optional": {
-                "default_value": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-            }
-        }
-
-    RETURN_TYPES = (WILDCARD,)
-    RETURN_NAMES = ("text",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "🔗ComfyDeploy"
-
-    def run(self, input_id, default_value=None, display_name=None, description=None):
-        return [default_value]
-
-
-NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalTextAny": ComfyUIDeployExternalTextAny}
-NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalTextAny": "External Text Any (ComfyUI Deploy)"}

comfy-nodes/external_vid.py

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

comfy-nodes/external_video.py

@@ -1,15 +1,10 @@
-# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
-# Intended to work with https://github.com/NicholasKao1029/ComfyUI-VideoHelperSuite/tree/main
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite and is meant to work with
 import os
 import itertools
 import numpy as np
 import torch
-from typing import Union
-from torch import Tensor
 import cv2
-import psutil
 
-from collections.abc import Mapping
 import folder_paths
 from comfy.utils import common_upscale
 
@@ -95,25 +90,13 @@ if gifski_path is None:
         gifski_path = shutil.which("gifski")
 
 
-def is_safe_path(path):
-    if "VHS_STRICT_PATHS" not in os.environ:
-        return True
-    basedir = os.path.abspath(".")
-    try:
-        common_path = os.path.commonpath([basedir, path])
-    except:
-        # Different drive on windows
-        return False
-    return common_path == basedir
-
-
 def get_sorted_dir_files_from_directory(
     directory: str,
     skip_first_images: int = 0,
     select_every_nth: int = 1,
     extensions: Iterable = None,
 ):
-    directory = strip_path(directory)
+    directory = directory.strip()
     dir_files = os.listdir(directory)
     dir_files = sorted(dir_files)
     dir_files = [os.path.join(directory, x) for x in dir_files]
@@ -194,59 +177,18 @@ def requeue_workflow(requeue_required=(-1, True)):
 
 
 def get_audio(file, start_time=0, duration=0):
-    args = [ffmpeg_path, "-i", file]
+    args = [ffmpeg_path, "-v", "error", "-i", file]
     if start_time > 0:
         args += ["-ss", str(start_time)]
     if duration > 0:
         args += ["-t", str(duration)]
     try:
-        # TODO: scan for sample rate and maintain
         res = subprocess.run(
-            args + ["-f", "f32le", "-"], capture_output=True, check=True
-        )
-        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
-        match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
+            args + ["-f", "wav", "-"], stdout=subprocess.PIPE, check=True
+        ).stdout
     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)
+        return False
+    return res
 
 
 def lazy_eval(func):
@@ -288,19 +230,6 @@ def validate_sequence(path):
     return False
 
 
-def strip_path(path):
-    # This leaves whitespace inside quotes and only a single "
-    # thus ' ""test"' -> '"test'
-    # consider path.strip(string.whitespace+"\"")
-    # or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1)
-    path = path.strip()
-    if path.startswith('"'):
-        path = path[1:]
-    if path.endswith('"'):
-        path = path[:-1]
-    return path
-
-
 def hash_path(path):
     if path is None:
         return "input"
@@ -357,145 +286,6 @@ def target_size(
     return (width, height)
 
 
-def validate_index(
-    index: int,
-    length: int = 0,
-    is_range: bool = False,
-    allow_negative=False,
-    allow_missing=False,
-) -> int:
-    # if part of range, do nothing
-    if is_range:
-        return index
-    # otherwise, validate index
-    # validate not out of range - only when latent_count is passed in
-    if length > 0 and index > length - 1 and not allow_missing:
-        raise IndexError(f"Index '{index}' out of range for {length} item(s).")
-    # if negative, validate not out of range
-    if index < 0:
-        if not allow_negative:
-            raise IndexError(f"Negative indeces not allowed, but was '{index}'.")
-        conv_index = length + index
-        if conv_index < 0 and not allow_missing:
-            raise IndexError(
-                f"Index '{index}', converted to '{conv_index}' out of range for {length} item(s)."
-            )
-        index = conv_index
-    return index
-
-
-def convert_to_index_int(
-    raw_index: str,
-    length: int = 0,
-    is_range: bool = False,
-    allow_negative=False,
-    allow_missing=False,
-) -> int:
-    try:
-        return validate_index(
-            int(raw_index),
-            length=length,
-            is_range=is_range,
-            allow_negative=allow_negative,
-            allow_missing=allow_missing,
-        )
-    except ValueError as e:
-        raise ValueError(f"Index '{raw_index}' must be an integer.", e)
-
-
-def convert_str_to_indexes(
-    indexes_str: str, length: int = 0, allow_missing=False
-) -> list[int]:
-    if not indexes_str:
-        return []
-    int_indexes = list(range(0, length))
-    allow_negative = length > 0
-    chosen_indexes = []
-    # parse string - allow positive ints, negative ints, and ranges separated by ':'
-    groups = indexes_str.split(",")
-    groups = [g.strip() for g in groups]
-    for g in groups:
-        # parse range of indeces (e.g. 2:16)
-        if ":" in g:
-            index_range = g.split(":", 2)
-            index_range = [r.strip() for r in index_range]
-
-            start_index = index_range[0]
-            if len(start_index) > 0:
-                start_index = convert_to_index_int(
-                    start_index,
-                    length=length,
-                    is_range=True,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            else:
-                start_index = 0
-            end_index = index_range[1]
-            if len(end_index) > 0:
-                end_index = convert_to_index_int(
-                    end_index,
-                    length=length,
-                    is_range=True,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            else:
-                end_index = length
-            # support step as well, to allow things like reversing, every-other, etc.
-            step = 1
-            if len(index_range) > 2:
-                step = index_range[2]
-                if len(step) > 0:
-                    step = convert_to_index_int(
-                        step,
-                        length=length,
-                        is_range=True,
-                        allow_negative=True,
-                        allow_missing=True,
-                    )
-                else:
-                    step = 1
-            # if latents were passed in, base indeces on known latent count
-            if len(int_indexes) > 0:
-                chosen_indexes.extend(int_indexes[start_index:end_index][::step])
-            # otherwise, assume indeces are valid
-            else:
-                chosen_indexes.extend(list(range(start_index, end_index, step)))
-        # parse individual indeces
-        else:
-            chosen_indexes.append(
-                convert_to_index_int(
-                    g,
-                    length=length,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            )
-    return chosen_indexes
-
-
-def select_indexes(input_obj: Union[Tensor, list], idxs: list):
-    if type(input_obj) == Tensor:
-        return input_obj[idxs]
-    else:
-        return [input_obj[i] for i in idxs]
-
-
-def select_indexes_from_str(
-    input_obj: Union[Tensor, list], indexes: str, err_if_missing=True, err_if_empty=True
-):
-    real_idxs = convert_str_to_indexes(
-        indexes, len(input_obj), allow_missing=not err_if_missing
-    )
-    if err_if_empty and len(real_idxs) == 0:
-        raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
-    return select_indexes(input_obj, real_idxs)
-
-
-###
-
-
 def cv_frame_generator(
     video,
     force_rate,
@@ -505,10 +295,9 @@ def cv_frame_generator(
     meta_batch=None,
     unique_id=None,
 ):
-    video_cap = cv2.VideoCapture(strip_path(video))
+    video_cap = cv2.VideoCapture(video)
     if not video_cap.isOpened():
         raise ValueError(f"{video} could not be loaded with cv.")
-    pbar = None
 
     # extract video metadata
     fps = video_cap.get(cv2.CAP_PROP_FPS)
@@ -530,8 +319,6 @@ def cv_frame_generator(
         target_frame_time = 1 / force_rate
 
     yield (width, height, fps, duration, total_frames, target_frame_time)
-    if meta_batch is not None:
-        yield min(frame_load_cap, total_frames)
 
     time_offset = target_frame_time - base_frame_time
     while video_cap.isOpened():
@@ -562,8 +349,7 @@ def cv_frame_generator(
         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         # convert frame to comfyui's expected format
         # TODO: frame contains no exif information. Check if opencv2 has already applied
-        frame = np.array(frame, dtype=np.float32)
-        torch.from_numpy(frame).div_(255)
+        frame = np.array(frame, dtype=np.float32) / 255.0
         if prev_frame is not None:
             inp = yield prev_frame
             if inp is not None:
@@ -571,8 +357,6 @@ def cv_frame_generator(
                 return
         prev_frame = frame
         frames_added += 1
-        if pbar is not None:
-            pbar.update_absolute(frames_added, frame_load_cap)
         # if cap exists and we've reached it, stop processing frames
         if frame_load_cap > 0 and frames_added >= frame_load_cap:
             break
@@ -583,17 +367,6 @@ def cv_frame_generator(
         yield prev_frame
 
 
-def batched(it, n):
-    while batch := tuple(itertools.islice(it, n)):
-        yield batch
-
-
-def batched_vae_encode(images, vae, frames_per_batch):
-    for batch in batched(images, frames_per_batch):
-        image_batch = torch.from_numpy(np.array(batch))
-        yield from vae.encode(image_batch).numpy()
-
-
 def load_video_cv(
     video: str,
     force_rate: int,
@@ -605,8 +378,6 @@ def load_video_cv(
     select_every_nth: int,
     meta_batch=None,
     unique_id=None,
-    memory_limit_mb=None,
-    vae=None,
 ):
     if meta_batch is None or unique_id not in meta_batch.inputs:
         gen = cv_frame_generator(
@@ -630,89 +401,30 @@ def load_video_cv(
                 total_frames,
                 target_frame_time,
             )
-            meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
 
     else:
         (gen, width, height, fps, duration, total_frames, target_frame_time) = (
             meta_batch.inputs[unique_id]
         )
 
-    memory_limit = None
-    if 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:
+    if meta_batch is not None:
+        gen = itertools.islice(gen, meta_batch.frames_per_batch)
 
-            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
+    # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+    images = torch.from_numpy(
+        np.fromiter(gen, np.dtype((np.float32, (height, width, 3))))
+    )
     if len(images) == 0:
         raise RuntimeError("No frames generated")
+    if force_size != "Disabled":
+        new_size = target_size(width, height, force_size, custom_width, custom_height)
+        if new_size[0] != width or new_size[1] != height:
+            s = images.movedim(-1, 1)
+            s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+            images = s.movedim(1, -1)
 
     # Setup lambda for lazy audio capture
-    audio = lazy_get_audio(
+    audio = lambda: get_audio(
         video,
         skip_first_frames * target_frame_time,
         frame_load_cap * target_frame_time * select_every_nth,
@@ -728,16 +440,13 @@ def load_video_cv(
         "loaded_fps": 1 / target_frame_time,
         "loaded_frame_count": len(images),
         "loaded_duration": len(images) * target_frame_time,
-        "loaded_width": new_size[0],
-        "loaded_height": new_size[1],
+        "loaded_width": images.shape[2],
+        "loaded_height": images.shape[1],
     }
-    if vae is None:
-        return (images, len(images), audio, video_info, None)
-    else:
-        return (None, len(images), audio, video_info, {"samples": images})
+
+    return (images, len(images), lazy_eval(audio), video_info)
 
 
-# modeled after Video upload node
 class ComfyUIDeployExternalVideo:
     @classmethod
     def INPUT_TYPES(s):
@@ -748,50 +457,71 @@ class ComfyUIDeployExternalVideo:
                 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"
-                },
-                }
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_video"},
+                ),
+                "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
+                "force_size": (
+                    [
+                        "Disabled",
+                        "Custom Height",
+                        "Custom Width",
+                        "Custom",
+                        "256x?",
+                        "?x256",
+                        "256x256",
+                        "512x?",
+                        "?x512",
+                        "512x512",
+                    ],
+                ),
+                "custom_width": (
+                    "INT",
+                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
+                ),
+                "custom_height": (
+                    "INT",
+                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
+                ),
+                "frame_load_cap": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
+                ),
+                "skip_first_frames": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
+                ),
+                "select_every_nth": (
+                    "INT",
+                    {"default": 1, "min": 1, "max": BIGMAX, "step": 1},
+                ),
+            },
+            "optional": {
+                "meta_batch": ("VHS_BatchManager",),
+                "default_value": (sorted(files),),
+            },
+            "hidden": {"unique_id": "UNIQUE_ID"},
+        }
 
     CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
 
-    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
+    RETURN_TYPES = (
+        "IMAGE",
+        "INT",
+        "VHS_AUDIO",
+        "VHS_VIDEOINFO",
+    )
     RETURN_NAMES = (
         "IMAGE",
         "frame_count",
         "audio",
         "video_info",
-        "LATENT",
     )
 
     FUNCTION = "load_video"
-    CATEGORY = "🔗ComfyDeploy"
 
     def load_video(self, **kwargs):
         input_id = kwargs.get("input_id")
@@ -805,6 +535,8 @@ class ComfyUIDeployExternalVideo:
         meta_batch = kwargs.get("meta_batch")
         unique_id = kwargs.get("unique_id")
 
+        video = kwargs.get("default_value")
+        video_path = folder_paths.get_annotated_filepath(video.strip('"'))
 
         input_dir = folder_paths.get_input_directory()
         if input_id.startswith("http"):
@@ -834,11 +566,8 @@ class ComfyUIDeployExternalVideo:
                     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('"'))
+
+        print("video path: ", video_path)
 
         return load_video_cv(
             video=video_path,

comfy-nodes/input_websocket_image.py

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

comfy-nodes/output_image.py

@@ -1,101 +0,0 @@
-import os
-import json
-import numpy as np
-from PIL import Image
-from PIL.PngImagePlugin import PngInfo
-import folder_paths
-
-
-class ComfyDeployOutputImage:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
-        self.compress_level = 4
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE", {"tooltip": "The images to save."}),
-                "filename_prefix": (
-                    "STRING",
-                    {
-                        "default": "ComfyUI",
-                        "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
-                    },
-                ),
-                "file_type": (["png", "jpg", "webp"], {"default": "webp"}),
-                "quality": ("INT", {"default": 80, "min": 1, "max": 100, "step": 1}),
-            },
-            "optional": {
-                "output_id": (
-                    "STRING",
-                    {"multiline": False, "default": "output_images"},
-                ),
-            },
-            "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

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

example_workflows/lora-model.json

@@ -1,152 +0,0 @@
-{
-  "id": "ed93ac94-4f26-4ed3-a57b-73cd8f4d3494",
-  "revision": 0,
-  "last_node_id": 5,
-  "last_link_id": 1,
-  "nodes": [
-    {
-      "id": 2,
-      "type": "LoraLoader",
-      "pos": [
-        736.646728515625,
-        628.3823852539062
-      ],
-      "size": [
-        315,
-        126
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
-          "type": "MODEL",
-          "link": null
-        },
-        {
-          "name": "clip",
-          "type": "CLIP",
-          "link": null
-        },
-        {
-          "name": "lora_name",
-          "type": "COMBO",
-          "widget": {
-            "name": "lora_name"
-          },
-          "link": 1
-        }
-      ],
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "links": null
-        },
-        {
-          "name": "CLIP",
-          "type": "CLIP",
-          "links": null
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.27",
-        "Node name for S&R": "LoraLoader"
-      },
-      "widgets_values": [
-        "1-292.safetensors",
-        1,
-        1
-      ]
-    },
-    {
-      "id": 1,
-      "type": "ComfyUIDeployExternalLora",
-      "pos": [
-        299.6898498535156,
-        624.7929077148438
-      ],
-      "size": [
-        400,
-        208
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "path",
-          "type": "*",
-          "links": [
-            1
-          ]
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfyui-deploy",
-        "ver": "cd3a2ff5471828f9c840e746551592e882c05aa4",
-        "Node name for S&R": "ComfyUIDeployExternalLora"
-      },
-      "widgets_values": [
-        "input_lora",
-        "HyperSD\\FLUX.1\\Hyper-FLUX.1-dev-16steps-lora.safetensors",
-        "",
-        "",
-        "",
-        ""
-      ]
-    },
-    {
-      "id": 5,
-      "type": "Note",
-      "pos": [
-        302.09033203125,
-        401.2951965332031
-      ],
-      "size": [
-        479.4894104003906,
-        161.61924743652344
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [],
-      "properties": {},
-      "widgets_values": [
-        "\"External Lora\" node will let you to use different loras from the Comfy Deploy UI or even via API.\n\n- lora_url:\n  url that will be used to download your LoRA model in execution time\n\n- lora_save_name:\n  when we download your model, this will be saved in your private storage, \n  give it a good name :D"
-      ],
-      "color": "#432",
-      "bgcolor": "#653"
-    }
-  ],
-  "links": [
-    [
-      1,
-      1,
-      0,
-      2,
-      2,
-      "COMBO"
-    ]
-  ],
-  "groups": [],
-  "config": {},
-  "extra": {
-    "ds": {
-      "scale": 1.167184107045006,
-      "offset": [
-        298.431389807788,
-        -207.58877445762934
-      ]
-    },
-    "VHS_latentpreview": false,
-    "VHS_latentpreviewrate": 0,
-    "VHS_MetadataImage": true,
-    "VHS_KeepIntermediate": true
-  },
-  "version": 0.4
-}

example_workflows/masks.json

@@ -1,873 +0,0 @@
-{
-  "id": "351f402b-62f2-4f62-8a5e-0b9d3510e8f9",
-  "revision": 0,
-  "last_node_id": 29,
-  "last_link_id": 28,
-  "nodes": [
-    {
-      "id": 11,
-      "type": "JoinImageWithAlpha",
-      "pos": [
-        814.478271484375,
-        419.3052062988281
-      ],
-      "size": [
-        264.5999755859375,
-        46
-      ],
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 10
-        },
-        {
-          "name": "alpha",
-          "type": "MASK",
-          "link": 12
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            11
-          ]
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.27",
-        "Node name for S&R": "JoinImageWithAlpha"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 15,
-      "type": "PreviewImage",
-      "pos": [
-        1950,
-        640
-      ],
-      "size": [
-        210,
-        246
-      ],
-      "flags": {},
-      "order": 18,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 16
-        }
-      ],
-      "outputs": [],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.27",
-        "Node name for S&R": "PreviewImage"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 10,
-      "type": "LoadImage",
-      "pos": [
-        467.8168640136719,
-        422.453857421875
-      ],
-      "size": [
-        315,
-        314
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            10
-          ]
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": [
-            12
-          ]
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.27",
-        "Node name for S&R": "LoadImage"
-      },
-      "widgets_values": [
-        "Bob-Minion-Background-PNG-Image.png",
-        "image",
-        ""
-      ]
-    },
-    {
-      "id": 18,
-      "type": "Note",
-      "pos": [
-        460.8001708984375,
-        263.64251708984375
-      ],
-      "size": [
-        379.4292297363281,
-        88
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [],
-      "properties": {},
-      "widgets_values": [
-        "Option 1: CREATE THE MASK FROM THE ALPHA CHANNEL (Useful for example to generate the background of an image)\n\nMake sure that you are using \"External Image Alpha\". \n"
-      ],
-      "color": "#432",
-      "bgcolor": "#653"
-    },
-    {
-      "id": 21,
-      "type": "LoadImage",
-      "pos": [
-        469.57025146484375,
-        1506.3018798828125
-      ],
-      "size": [
-        315,
-        314
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            18
-          ]
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": null
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.27",
-        "Node name for S&R": "LoadImage"
-      },
-      "widgets_values": [
-        "ComfyUI_temp_otmos_00005_.png",
-        "image",
-        ""
-      ]
-    },
-    {
-      "id": 25,
-      "type": "MaskToImage",
-      "pos": [
-        1283.1668701171875,
-        1579.603271484375
-      ],
-      "size": [
-        176.39999389648438,
-        26
-      ],
-      "flags": {},
-      "order": 13,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "mask",
-          "type": "MASK",
-          "link": 28
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            21
-          ]
-        }
-      ],
-      "properties": {
-        "cnr_id": "comfy-core",
-        "ver": "0.3.27",
-        "Node name for S&R": "MaskToImage"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 13,
-      "type": "SplitImageWithAlpha",
-      "pos": [
-        1602.5518798828125,
-        417.59869384765625
-      ],
-      "size": [
-        277.20001220703125,
-        46
-      ],
-      "flags": {},
-      "order": 11,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 13
-        }
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examples/flux-dev.json

@@ -1,359 +0,0 @@
-{
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-    [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"]
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-      "inputs": [
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-          "links": [57],
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-        "🛈 [Learn more about this workflow](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-dev-1)"
-      ]
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-      "pos": [48, 192],
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-          "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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-      "pos": [-497.8695068359375, 864.68994140625],
-      "mode": 0,
-      "size": [453.5999755859375, 200],
-      "type": "ComfyUIDeployExternalNumberInt",
-      "flags": {},
-      "order": 5,
-      "inputs": [],
-      "outputs": [
-        { "name": "value", "type": "INT", "links": [59], "slot_index": 0 }
-      ],
-      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
-      "widgets_values": ["height", 1024, "Height", "The height of the image."]
-    }
-  ],
-  "config": {},
-  "groups": [
-    {
-      "id": 1,
-      "color": "#3f789e",
-      "flags": {},
-      "title": "Input",
-      "bounding": [
-        -508.010986328125, 186.76593017578125, 453.23040771484375,
-        334.3218994140625
-      ],
-      "font_size": 24
-    },
-    {
-      "id": 2,
-      "color": "#A88",
-      "flags": {},
-      "title": "Additional",
-      "bounding": [
-        -517.1227416992188, 537.53759765625, 644.4954223632812,
-        599.2609252929688
-      ],
-      "font_size": 24
-    }
-  ],
-  "version": 0.4,
-  "last_link_id": 60,
-  "last_node_id": 40
-}

examples/flux-schnell.json

@@ -1,423 +0,0 @@
-{
-  "extra": {
-    "ds": {
-      "scale": 0.6830134553650709,
-      "offset": [750.2474149300585, 120.48924647647146]
-    },
-    "node_versions": {
-      "comfy-core": "0.3.19",
-      "comfyui-deploy": "171a227856bd5f31e97828d89f83f3741004d05e"
-    }
-  },
-  "links": [
-    [9, 8, 0, 9, 0, "IMAGE"],
-    [10, 11, 0, 6, 0, "CLIP"],
-    [12, 10, 0, 8, 1, "VAE"],
-    [19, 16, 0, 13, 2, "SAMPLER"],
-    [20, 17, 0, 13, 3, "SIGMAS"],
-    [23, 5, 0, 13, 4, "LATENT"],
-    [24, 13, 0, 8, 0, "LATENT"],
-    [30, 22, 0, 13, 1, "GUIDER"],
-    [37, 25, 0, 13, 0, "NOISE"],
-    [38, 12, 0, 17, 0, "MODEL"],
-    [39, 12, 0, 22, 0, "MODEL"],
-    [40, 6, 0, 22, 1, "CONDITIONING"],
-    [41, 28, 0, 6, 1, "STRING"],
-    [44, 29, 0, 5, 0, "INT"],
-    [45, 30, 0, 5, 1, "INT"]
-  ],
-  "nodes": [
-    {
-      "id": 13,
-      "pos": [842, 215],
-      "mode": 0,
-      "size": [355.20001220703125, 106],
-      "type": "SamplerCustomAdvanced",
-      "flags": {},
-      "order": 14,
-      "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": 23,
-          "name": "latent_image",
-          "type": "LATENT",
-          "slot_index": 4
-        }
-      ],
-      "outputs": [
-        {
-          "name": "output",
-          "type": "LATENT",
-          "links": [24],
-          "shape": 3,
-          "slot_index": 0
-        },
-        {
-          "name": "denoised_output",
-          "type": "LATENT",
-          "links": null,
-          "shape": 3
-        }
-      ],
-      "properties": { "Node name for S&R": "SamplerCustomAdvanced" },
-      "widgets_values": []
-    },
-    {
-      "id": 8,
-      "pos": [1248, 192],
-      "mode": 0,
-      "size": [210, 46],
-      "type": "VAEDecode",
-      "flags": {},
-      "order": 15,
-      "inputs": [
-        { "link": 24, "name": "samples", "type": "LATENT" },
-        { "link": 12, "name": "vae", "type": "VAE" }
-      ],
-      "outputs": [
-        { "name": "IMAGE", "type": "IMAGE", "links": [9], "slot_index": 0 }
-      ],
-      "properties": { "Node name for S&R": "VAEDecode" },
-      "widgets_values": []
-    },
-    {
-      "id": 22,
-      "pos": [559, 125],
-      "mode": 0,
-      "size": [241.79998779296875, 46],
-      "type": "BasicGuider",
-      "flags": {},
-      "order": 13,
-      "inputs": [
-        { "link": 39, "name": "model", "type": "MODEL", "slot_index": 0 },
-        {
-          "link": 40,
-          "name": "conditioning",
-          "type": "CONDITIONING",
-          "slot_index": 1
-        }
-      ],
-      "outputs": [
-        {
-          "name": "GUIDER",
-          "type": "GUIDER",
-          "links": [30],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": { "Node name for S&R": "BasicGuider" },
-      "widgets_values": []
-    },
-    {
-      "id": 16,
-      "pos": [480, 720],
-      "mode": 0,
-      "size": [315, 58],
-      "type": "KSamplerSelect",
-      "flags": {},
-      "order": 0,
-      "inputs": [],
-      "outputs": [
-        { "name": "SAMPLER", "type": "SAMPLER", "links": [19], "shape": 3 }
-      ],
-      "properties": { "Node name for S&R": "KSamplerSelect" },
-      "widgets_values": ["euler"]
-    },
-    {
-      "id": 17,
-      "pos": [480, 816],
-      "mode": 0,
-      "size": [315, 106],
-      "type": "BasicScheduler",
-      "flags": {},
-      "order": 10,
-      "inputs": [
-        { "link": 38, "name": "model", "type": "MODEL", "slot_index": 0 }
-      ],
-      "outputs": [
-        { "name": "SIGMAS", "type": "SIGMAS", "links": [20], "shape": 3 }
-      ],
-      "properties": { "Node name for S&R": "BasicScheduler" },
-      "widgets_values": ["simple", 4, 1]
-    },
-    {
-      "id": 27,
-      "pos": [480, 960],
-      "mode": 0,
-      "size": [311.3529052734375, 131.16229248046875],
-      "type": "Note",
-      "color": "#432",
-      "flags": {},
-      "order": 1,
-      "inputs": [],
-      "bgcolor": "#653",
-      "outputs": [],
-      "properties": { "text": "" },
-      "widgets_values": [
-        "The schnell model is a distilled model that can generate a good image with only 4 steps."
-      ]
-    },
-    {
-      "id": 9,
-      "pos": [1488, 192],
-      "mode": 0,
-      "size": [985.3012084960938, 1060.3828125],
-      "type": "SaveImage",
-      "flags": {},
-      "order": 16,
-      "inputs": [{ "link": 9, "name": "images", "type": "IMAGE" }],
-      "outputs": [],
-      "properties": {},
-      "widgets_values": ["ComfyUI"]
-    },
-    {
-      "id": 25,
-      "pos": [480, 587.7890625],
-      "mode": 0,
-      "size": [315, 82],
-      "type": "RandomNoise",
-      "color": "#2a363b",
-      "flags": {},
-      "order": 2,
-      "inputs": [],
-      "bgcolor": "#3f5159",
-      "outputs": [
-        { "name": "NOISE", "type": "NOISE", "links": [37], "shape": 3 }
-      ],
-      "properties": { "Node name for S&R": "RandomNoise" },
-      "widgets_values": [393845863554716, "randomize"]
-    },
-    {
-      "id": 5,
-      "pos": [471.7746276855469, 423.91571044921875],
-      "mode": 0,
-      "size": [315, 126],
-      "type": "EmptyLatentImage",
-      "color": "#323",
-      "flags": {},
-      "order": 12,
-      "inputs": [
-        {
-          "pos": [10, 36],
-          "link": 44,
-          "name": "width",
-          "type": "INT",
-          "widget": { "name": "width" }
-        },
-        {
-          "pos": [10, 60],
-          "link": 45,
-          "name": "height",
-          "type": "INT",
-          "widget": { "name": "height" }
-        }
-      ],
-      "bgcolor": "#535",
-      "outputs": [
-        { "name": "LATENT", "type": "LATENT", "links": [23], "slot_index": 0 }
-      ],
-      "properties": { "Node name for S&R": "EmptyLatentImage" },
-      "widgets_values": [1024, 1024, 1]
-    },
-    {
-      "id": 26,
-      "pos": [41.60395812988281, 517.059326171875],
-      "mode": 0,
-      "size": [336, 288],
-      "type": "Note",
-      "color": "#432",
-      "flags": {},
-      "order": 3,
-      "inputs": [],
-      "bgcolor": "#653",
-      "outputs": [],
-      "properties": { "text": "" },
-      "widgets_values": [
-        "If you get an error in any of the nodes above make sure the files are in the correct directories.\n\nSee the top of the examples page for the links : https://comfyanonymous.github.io/ComfyUI_examples/flux/\n\nflux1-schnell.safetensors goes in: ComfyUI/models/unet/\n\nt5xxl_fp16.safetensors and clip_l.safetensors go in: ComfyUI/models/clip/\n\nae.safetensors goes in: ComfyUI/models/vae/\n\n\nTip: You can set the weight_dtype above to one of the fp8 types if you have memory issues."
-      ]
-    },
-    {
-      "id": 11,
-      "pos": [45.584651947021484, 229.13525390625],
-      "mode": 0,
-      "size": [315, 122],
-      "type": "DualCLIPLoader",
-      "flags": {},
-      "order": 4,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "CLIP",
-          "type": "CLIP",
-          "links": [10],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": { "Node name for S&R": "DualCLIPLoader" },
-      "widgets_values": [
-        "t5xxl_fp16.safetensors",
-        "clip_l.safetensors",
-        "flux",
-        "default"
-      ]
-    },
-    {
-      "id": 12,
-      "pos": [45.92215347290039, 95.6536865234375],
-      "mode": 0,
-      "size": [315, 82],
-      "type": "UNETLoader",
-      "color": "#223",
-      "flags": { "pinned": true },
-      "order": 5,
-      "inputs": [],
-      "bgcolor": "#335",
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "links": [38, 39],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": { "Node name for S&R": "UNETLoader" },
-      "widgets_values": ["flux1-schnell.safetensors", "default"]
-    },
-    {
-      "id": 6,
-      "pos": [385.87054443359375, 216.75625610351562],
-      "mode": 0,
-      "size": [422.84503173828125, 164.31304931640625],
-      "type": "CLIPTextEncode",
-      "color": "#232",
-      "flags": {},
-      "order": 11,
-      "inputs": [
-        { "link": 10, "name": "clip", "type": "CLIP" },
-        {
-          "pos": [10, 36],
-          "link": 41,
-          "name": "text",
-          "type": "STRING",
-          "widget": { "name": "text" }
-        }
-      ],
-      "bgcolor": "#353",
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [40],
-          "slot_index": 0
-        }
-      ],
-      "properties": { "Node name for S&R": "CLIPTextEncode" },
-      "widgets_values": [
-        "a bottle with a beautiful rainbow galaxy inside it on top of a wooden table in the middle of a modern kitchen beside a plate of vegetables and mushrooms and a wine glasse that contains a planet earth with a plate with a half eaten apple pie on it"
-      ]
-    },
-    {
-      "id": 10,
-      "pos": [42.45906066894531, 405.2187805175781],
-      "mode": 0,
-      "size": [315, 58],
-      "type": "VAELoader",
-      "flags": {},
-      "order": 6,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "VAE",
-          "type": "VAE",
-          "links": [12],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": { "Node name for S&R": "VAELoader" },
-      "widgets_values": ["ae.sft"]
-    },
-    {
-      "id": 28,
-      "pos": [-620.3922119140625, 199.8013916015625],
-      "mode": 0,
-      "size": [400, 200],
-      "type": "ComfyUIDeployExternalText",
-      "flags": {},
-      "order": 7,
-      "inputs": [],
-      "outputs": [{ "name": "text", "type": "STRING", "links": [41] }],
-      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
-      "widgets_values": [
-        "prompt",
-        "a bottle with a beautiful rainbow galaxy inside it on top of a wooden table in the middle of a modern kitchen beside a plate of vegetables and mushrooms and a wine glasse that contains a planet earth with a plate with a half eaten apple pie on it",
-        "Prompt",
-        "The prompt to generate an image from."
-      ]
-    },
-    {
-      "id": 29,
-      "pos": [-677.4290161132812, 597.3389282226562],
-      "mode": 0,
-      "size": [453.5999755859375, 200],
-      "type": "ComfyUIDeployExternalNumberInt",
-      "flags": {},
-      "order": 8,
-      "inputs": [],
-      "outputs": [
-        { "name": "value", "type": "INT", "links": [44], "slot_index": 0 }
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-      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
-      "widgets_values": ["width", 1024, "Width", "The width of the image."]
-    },
-    {
-      "id": 30,
-      "pos": [-670.0703735351562, 848.3707885742188],
-      "mode": 0,
-      "size": [453.5999755859375, 200],
-      "type": "ComfyUIDeployExternalNumberInt",
-      "flags": {},
-      "order": 9,
-      "inputs": [],
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-        { "name": "value", "type": "INT", "links": [45], "slot_index": 0 }
-      ],
-      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
-      "widgets_values": ["height", 1024, "Height", "The height of the image."]
-    }
-  ],
-  "config": {},
-  "groups": [
-    {
-      "id": 1,
-      "color": "#3f789e",
-      "flags": {},
-      "title": "Input",
-      "bounding": [
-        -652.184326171875, 99.11231994628906, 565.7923583984375,
-        362.8800354003906
-      ],
-      "font_size": 24
-    },
-    {
-      "id": 2,
-      "color": "#A88",
-      "flags": {},
-      "title": "Additional",
-      "bounding": [
-        -720.3274536132812, 508.1206970214844, 703.0422973632812,
-        576.5584716796875
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-      "font_size": 24
-    }
-  ],
-  "version": 0.4,
-  "last_link_id": 45,
-  "last_node_id": 30
-}

examples/hunyuan-video.json

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

examples/stable-diffusion-v15.json

@@ -1,240 +0,0 @@
-{
-  "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

@@ -1,409 +0,0 @@
-{
-  "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],
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-        { "name": "CLIP", "type": "CLIP", "links": [], "slot_index": 1 },
-        { "name": "VAE", "type": "VAE", "links": [53], "slot_index": 2 }
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-      "mode": 0,
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-      "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" }
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-      "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",
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-      "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": [],
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-          "name": "CLIP",
-          "type": "CLIP",
-          "links": [103, 104],
-          "shape": 3,
-          "slot_index": 0
-        }
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-      "mode": 0,
-      "size": [315, 106],
-      "type": "DualCLIPLoader",
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-          "type": "CLIP",
-          "links": [],
-          "shape": 3,
-          "slot_index": 0
-        }
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-        {
-          "link": 106,
-          "name": "text",
-          "type": "STRING",
-          "widget": { "name": "text" }
-        }
-      ],
-      "bgcolor": "#533",
-      "outputs": [
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-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [80],
-          "shape": 3,
-          "slot_index": 0
-        }
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-      "properties": { "Node name for S&R": "CLIPTextEncode" },
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-          "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"
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-      "mode": 0,
-      "size": [453.5999755859375, 200],
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-      "flags": {},
-      "order": 6,
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-      "outputs": [
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-      ],
-      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
-      "widgets_values": ["height", 1024, "Height", "The height of the image. "]
-    },
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-      "mode": 0,
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-      "order": 7,
-      "inputs": [],
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-      ],
-      "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],
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-      "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",
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-        824.6746215820312
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-      "flags": {},
-      "title": "Input",
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-  "version": 0.4,
-  "last_link_id": 108,
-  "last_node_id": 57
-}

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

@@ -1,452 +0,0 @@
-{
-  "extra": {
-    "ds": {
-      "scale": 0.6934334949441662,
-      "offset": [276.7941771713029, -7.923387380923855]
-    },
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-      "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": []
-    },
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-      "pos": [866.3932495117188, 499.18597412109375],
-      "mode": 0,
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-      "type": "VAELoader",
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-      "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"]
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-    {
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-      "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,
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-      "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,
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-      "type": "WanImageToVideo",
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-      "order": 13,
-      "inputs": [
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-        { "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]
-    },
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-        { "link": 101, "name": "positive", "type": "CONDITIONING" },
-        { "link": 102, "name": "negative", "type": "CONDITIONING" },
-        { "link": 103, "name": "latent_image", "type": "LATENT" }
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-      ],
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-      "type": "CLIPVisionEncode",
-      "flags": {},
-      "order": 12,
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-        { "link": 94, "name": "clip_vision", "type": "CLIP_VISION" },
-        { "link": 116, "name": "image", "type": "IMAGE" }
-      ],
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-        {
-          "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" }],
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-      ],
-      "properties": { "Node name for S&R": "ModelSamplingSD3" },
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-      "order": 10,
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-      "inputs": [
-        { "link": 74, "name": "clip", "type": "CLIP" },
-        {
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-          "name": "text",
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-          "widget": { "name": "text" }
-        }
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-      "bgcolor": "#353",
-      "outputs": [
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-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [97],
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-      "properties": { "Node name for S&R": "CLIPTextEncode" },
-      "widgets_values": [
-        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around"
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-    {
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-      "inputs": [],
-      "outputs": [{ "name": "text", "type": "STRING", "links": [112] }],
-      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
-      "widgets_values": [
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-        "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,
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-      "inputs": [
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-      ],
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-      "properties": { "Node name for S&R": "ComfyUIDeployExternalImage" },
-      "widgets_values": [
-        "image_url",
-        "Image Url",
-        "URL of the input image.",
-        "https://comfy-deploy-output.s3.us-east-2.amazonaws.com/assets/img_GZMJYXDnLbYjWybu.png",
-        ""
-      ]
-    },
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-      "inputs": [],
-      "outputs": [{ "name": "text", "type": "STRING", "links": [113] }],
-      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
-      "widgets_values": [
-        "negative_prompt",
-        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
-        "Negative Prompt",
-        "The negative prompt to use. Use it to address details that you don't want in the video. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
-      ]
-    },
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-      "widgets_values": ["width", 512, "Width", "The width of the video. "]
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-      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
-      "widgets_values": ["height", 512, "Height", "The Height of the video."]
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-      ],
-      "properties": { "Node name for S&R": "UNETLoader" },
-      "widgets_values": ["wan2.1_i2v_480p_14B_bf16.safetensors", "default"]
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-        { "name": "CLIP", "type": "CLIP", "links": [74, 75], "slot_index": 0 }
-      ],
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-        "wan",
-        "default"
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-    {
-      "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"]
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-      "id": 7,
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-      "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"
-      ]
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-  ],
-  "config": {},
-  "groups": [
-    {
-      "id": 1,
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-      "flags": {},
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-        -708.2940063476562, 47.549102783203125, 617.7969360351562,
-        761.303955078125
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-      "font_size": 24
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-    {
-      "id": 2,
-      "color": "#b06634",
-      "flags": {},
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-        -703.6196899414062, 861.310791015625, 625.998779296875,
-        845.0675048828125
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-  "version": 0.4,
-  "last_link_id": 118,
-  "last_node_id": 59
-}

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

@@ -1,452 +0,0 @@
-{
-  "extra": {
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-      "offset": [-209.31226203277674, -32.62073943929215]
-    },
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-      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
-    }
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-  "links": [
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-    [2, 2, 0, 1, 1, "VAE"],
-    [3, 1, 0, 3, 0, "IMAGE"],
-    [4, 1, 0, 4, 0, "IMAGE"],
-    [5, 9, 0, 5, 0, "CONDITIONING"],
-    [6, 18, 0, 5, 1, "CONDITIONING"],
-    [7, 2, 0, 5, 2, "VAE"],
-    [8, 7, 0, 5, 3, "CLIP_VISION_OUTPUT"],
-    [9, 11, 0, 5, 4, "IMAGE"],
-    [10, 13, 0, 5, 5, "INT"],
-    [11, 14, 0, 5, 6, "INT"],
-    [12, 8, 0, 6, 0, "MODEL"],
-    [13, 5, 0, 6, 1, "CONDITIONING"],
-    [14, 5, 1, 6, 2, "CONDITIONING"],
-    [15, 5, 2, 6, 3, "LATENT"],
-    [16, 17, 0, 7, 0, "CLIP_VISION"],
-    [17, 11, 0, 7, 1, "IMAGE"],
-    [18, 15, 0, 8, 0, "MODEL"],
-    [19, 16, 0, 9, 0, "CLIP"],
-    [20, 10, 0, 9, 1, "STRING"],
-    [21, 16, 0, 18, 0, "CLIP"],
-    [22, 12, 0, 18, 1, "STRING"]
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-        { "link": 1, "name": "samples", "type": "LATENT" },
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-      "widgets_values": ["wan_2.1_vae.safetensors"]
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-        { "link": 14, "name": "negative", "type": "CONDITIONING" },
-        { "link": 15, "name": "latent_image", "type": "LATENT" }
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-      "outputs": [
-        { "name": "LATENT", "type": "LATENT", "links": [1], "slot_index": 0 }
-      ],
-      "properties": { "Node name for S&R": "KSampler" },
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-        { "link": 16, "name": "clip_vision", "type": "CLIP_VISION" },
-        { "link": 17, "name": "image", "type": "IMAGE" }
-      ],
-      "outputs": [
-        {
-          "name": "CLIP_VISION_OUTPUT",
-          "type": "CLIP_VISION_OUTPUT",
-          "links": [8],
-          "slot_index": 0
-        }
-      ],
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-      "widgets_values": ["none"]
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-      "type": "ModelSamplingSD3",
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-      "outputs": [
-        { "name": "MODEL", "type": "MODEL", "links": [12], "slot_index": 0 }
-      ],
-      "properties": { "Node name for S&R": "ModelSamplingSD3" },
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-    {
-      "id": 9,
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-      "type": "CLIPTextEncode",
-      "color": "#232",
-      "flags": {},
-      "order": 12,
-      "title": "CLIP Text Encode (Positive Prompt)",
-      "inputs": [
-        { "link": 19, "name": "clip", "type": "CLIP" },
-        {
-          "pos": [10, 36],
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-          "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],
-      "mode": 0,
-      "size": [390.5999755859375, 366],
-      "type": "ComfyUIDeployExternalImage",
-      "flags": {},
-      "order": 1,
-      "inputs": [
-        { "link": null, "name": "default_value", "type": "IMAGE", "shape": 7 }
-      ],
-      "outputs": [{ "name": "image", "type": "IMAGE", "links": [9, 17] }],
-      "properties": { "Node name for S&R": "ComfyUIDeployExternalImage" },
-      "widgets_values": [
-        "image_url",
-        "Image Url",
-        "URL of the input image.",
-        "https://comfy-deploy-output.s3.us-east-2.amazonaws.com/assets/img_GZMJYXDnLbYjWybu.png",
-        ""
-      ]
-    },
-    {
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-      "mode": 0,
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-      "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). "
-      ]
-    },
-    {
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-      ],
-      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
-      "widgets_values": ["height", 512, "Height", "The Height of the video."]
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-    {
-      "id": 16,
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-      "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"
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-      "type": "CLIPVisionLoader",
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-      "inputs": [],
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-        {
-          "name": "CLIP_VISION",
-          "type": "CLIP_VISION",
-          "links": [16],
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-      "flags": {},
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-      "title": "CLIP Text Encode (Negative Prompt)",
-      "inputs": [
-        { "link": 21, "name": "clip", "type": "CLIP" },
-        {
-          "pos": [10, 36],
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-          "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"
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-      "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"]
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-    {
-      "id": 10,
-      "pos": [-60.696964263916016, 649.5515747070312],
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-      "type": "ComfyUIDeployExternalText",
-      "flags": {},
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-      "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": [
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-      "font_size": 24
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-    {
-      "id": 2,
-      "color": "#b06634",
-      "flags": {},
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-}

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

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

globals.py

@@ -6,12 +6,10 @@ 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"
@@ -19,7 +17,6 @@ class Status(Enum):
     FAILED = "failed"
     UPLOADING = "uploading"
 
-
 class StreamingPrompt(BaseModel):
     workflow_api: Any
     auth_token: str
@@ -27,52 +24,42 @@ class StreamingPrompt(BaseModel):
     running_prompt_ids: set[str] = set()
     status_endpoint: Optional[str]
     file_upload_endpoint: Optional[str]
-    workflow: Any
-    gpu_event_id: Optional[str] = None
-
-
+    
 class SimplePrompt(BaseModel):
     status_endpoint: Optional[str]
     file_upload_endpoint: Optional[str]
-
-    token: Optional[str]
-
+    
     workflow_api: dict
     status: Status = Status.NOT_STARTED
     progress: set = set()
-    last_updated_node: Optional[str] = None
+    last_updated_node: Optional[str] = None,
     uploading_nodes: set = set()
     done: bool = False
-    is_realtime: bool = False
-    start_time: Optional[float] = None
-    gpu_event_id: Optional[str] = None
-
+    is_realtime: bool = False,
+    start_time: Optional[float] = None,
 
 sockets = dict()
 prompt_metadata: dict[str, SimplePrompt] = {}
 streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
 
-
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
     UNENCODED_PREVIEW_IMAGE = 2
-
-
+    
 max_output_id_length = 24
 
-
-async def send_image(image_data, sid=None, output_id: str = None):
+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")
-
+    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"):
+        if hasattr(Image, 'Resampling'):
             resampling = Image.Resampling.BILINEAR
         else:
             resampling = Image.ANTIALIAS
@@ -96,23 +83,17 @@ async def send_image(image_data, sid=None, output_id: str = None):
     position_after = bytesIO.tell()
     bytes_written = position_after - position_before
     print(f"Bytes written: {bytes_written}")
-
+    
     image.save(bytesIO, format=image_type, quality=quality, compress_level=1)
     preview_bytes = bytesIO.getvalue()
     await send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
-
-
+        
 async def send_socket_catch_exception(function, message):
     try:
         await function(message)
-    except (
-        aiohttp.ClientError,
-        aiohttp.ClientPayloadError,
-        ConnectionResetError,
-    ) as err:
+    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}")
@@ -122,10 +103,9 @@ def encode_bytes(event, data):
     message.extend(data)
     return message
 
-
 async def send_bytes(event, data, sid=None):
     message = encode_bytes(event, data)
-
+    
     print("sending image to ", event, sid)
 
     if sid is None:
@@ -133,4 +113,4 @@ async def send_bytes(event, data, sid=None):
         for ws in _sockets:
             await send_socket_catch_exception(ws.send_bytes, message)
     elif sid in sockets:
-        await send_socket_catch_exception(sockets[sid].send_bytes, message)
+        await send_socket_catch_exception(sockets[sid].send_bytes, message)

pyproject.toml

@@ -1,9 +1,9 @@
 [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"]
+version = "1.0.0"
+license = "LICENSE"
+dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
 
 [project.urls]
 Repository = "https://github.com/BennyKok/comfyui-deploy"

requirements.txt

@@ -2,6 +2,4 @@ aiofiles
 pydantic
 opencv-python
 imageio-ffmpeg
-brotli
-tabulate
-# logfire
+logfire

web-plugin/api.js

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

web-plugin/app.js

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

web-plugin/widgets.js

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

web/package.json

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

web/src/components/customInputNodes.tsx

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

web/src/routes/registerCreateRunRoute.ts

@@ -51,9 +51,7 @@ const createRunRoute = createRoute({
 export const registerCreateRunRoute = (app: App) => {
   app.openapi(createRunRoute, async (c) => {
     const data = c.req.valid("json");
-    const proto = c.req.headers.get('x-forwarded-proto')  || "http";
-    const host = c.req.headers.get('x-forwarded-host') || c.req.headers.get('host');
-    const origin = `${proto}://${host}` || new URL(c.req.url).origin;
+    const origin = 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_raw: workflow_api,
+      workflow_api: workflow_api,
       status_endpoint: `${origin}/api/update-run`,
       file_upload_endpoint: `${origin}/api/file-upload`,
     };