all new nodes working under EXR tab category

This reverts commit 46010e1dd5.

.github/workflows/publish.yml

@@ -7,15 +7,19 @@ 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@main
+        uses: Comfy-Org/publish-node-action@v1
         with:
           ## Add your own personal access token to your Github Repository secrets and reference it here.
-          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

.gitignore

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

README.md

@@ -2,6 +2,9 @@
 
 Open source comfyui deployment platform, a `vercel` for generative workflow infra. (serverless hosted gpu with vertical intergation with comfyui)
 
+> [!NOTE]  
+> Im looking for creative hacker to join ComfyDeploy's core team! DM me on [twitter](https://x.com/BennyKokMusic)
+
 Join [Discord](https://discord.gg/EEYcQmdYZw) to chat more or visit [Comfy Deploy](https://comfydeploy.com/) to get started!
 
 Check out our latest [nextjs starter kit](https://github.com/BennyKok/comfyui-deploy-next-example) with Comfy Deploy
@@ -93,10 +96,6 @@ Major areas
 
 # Self Hosting with Vercel
 
-[![Video](https://img.mytsi.org/i/nFOG479.png)](https://www.youtube.com/watch?v=hWvsEY1cS2M)
-Tutorial Created by [Ross](https://github.com/rossman22590) and [Syn](https://github.com/mortlsyn)
-
-
 Build command
 
 ```

__init__.py

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

comfy-nodes/external_audio.py

@@ -0,0 +1,82 @@
+import io
+from folder_paths import get_annotated_filepath
+
+
+class ComfyUIDeployExternalAudio:
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("audio",)
+    FUNCTION = "load_audio"
+    CATEGORY = "🔗ComfyDeploy"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_audio"},
+                ),
+                "audio_file": ("STRING", {"default": ""}),
+            },
+            "optional": {
+                "default_value": ("AUDIO",),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+            },
+        }
+
+    @classmethod
+    def VALIDATE_INPUTS(s, audio_file, **kwargs):
+        return True
+
+    def load_audio(
+        self,
+        input_id,
+        audio_file,
+        default_value=None,
+        display_name=None,
+        description=None,
+    ):
+        try:
+            import torchaudio
+
+            if audio_file and audio_file != "":
+                if audio_file.startswith(("http://", "https://")):
+                    # Handle URL input
+                    try:
+                        import requests
+
+                        response = requests.get(audio_file)
+                        audio_data = io.BytesIO(response.content)
+                        waveform, sample_rate = torchaudio.load(audio_data)
+                    except Exception as e:
+                        print(f"Error loading audio from URL: {e}")
+                        return (default_value,)
+                else:
+                    # Handle local file
+                    try:
+                        audio_path = get_annotated_filepath(audio_file)
+                        waveform, sample_rate = torchaudio.load(audio_path)
+                    except Exception as e:
+                        print(f"Error loading local audio file: {e}")
+                        return (default_value,)
+
+                audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
+                return (audio,)
+            else:
+                return (default_value,)
+        except ImportError as e:
+            print(f"Error: torchaudio not installed or cannot be imported: {e}")
+            return (default_value,)
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalAudio": ComfyUIDeployExternalAudio}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalAudio": "External Audio (ComfyUI Deploy)"
+}

comfy-nodes/external_boolean.py

@@ -8,15 +8,26 @@ 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):
+    def run(self, input_id, default_value=None, display_name=None, description=None):
         print(f"Node '{input_id}' processing with switch set to {default_value}")
         return [default_value]
     

comfy-nodes/external_checkpoints.py

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

comfy-nodes/external_enum.py

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

comfy-nodes/external_exr_input.py

@@ -0,0 +1,93 @@
+import os
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+import cv2 as cv
+import numpy as np
+import torch
+from folder_paths import get_annotated_filepath
+
+def linear_to_srgb(np_array):
+    """Converts a linear RGB numpy array to sRGB."""
+    less = np_array <= 0.0031308
+    np_array[less] = np_array[less] * 12.92
+    np_array[~less] = np.power(np_array[~less], 1/2.4) * 1.055 - 0.055
+    return np_array
+
+class ExternalExrInput:
+    """
+    Node to load a single EXR image from a local file path.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "exr_file": ("STRING", {"default": "path/to/image.exr"}),
+                "tonemap": (["linear", "sRGB", "Reinhard"], {"default": "sRGB"}),
+            },
+            "optional": {
+                "default_image": ("IMAGE",),
+                "default_mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_NAMES = ("image", "mask",)
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy/EXR"
+
+    def run(self, exr_file, tonemap, default_image=None, default_mask=None):
+        image = None
+        try:
+            if exr_file and exr_file.strip() != "":
+                exr_path = get_annotated_filepath(exr_file)
+                if os.path.exists(exr_path):
+                    image = cv.imread(exr_path, cv.IMREAD_UNCHANGED).astype(np.float32)
+                else:
+                    print(f"Warning: File not found at {exr_path}")
+            
+            if image is None:
+                raise ValueError("Image could not be loaded.")
+
+            if len(image.shape) == 2: # Grayscale
+                image = np.repeat(image[..., np.newaxis], 3, axis=2)
+            
+            rgb = np.flip(image[:, :, :3], 2).copy() # BGR to RGB
+            
+            # Apply tonemapping
+            if tonemap == "sRGB":
+                rgb = linear_to_srgb(rgb)
+                rgb = np.clip(rgb, 0, 1)
+            elif tonemap == "Reinhard":
+                rgb = np.clip(rgb, 0, None)
+                rgb = rgb / (rgb + 1)
+                rgb = linear_to_srgb(rgb)
+                rgb = np.clip(rgb, 0, 1)
+            
+            rgb_tensor = torch.from_numpy(rgb).unsqueeze(0)
+            
+            # Handle alpha/mask
+            if image.shape[2] > 3:
+                mask = np.clip(image[:, :, 3], 0, 1)
+            else:
+                mask = np.ones_like(rgb[:, :, 0])
+            mask_tensor = torch.from_numpy(mask).unsqueeze(0)
+
+            return (rgb_tensor, mask_tensor)
+
+        except Exception as e:
+            print(f"Error loading EXR file '{exr_file}': {e}")
+            if default_image is not None and default_mask is not None:
+                print("Returning default image.")
+                return (default_image, default_mask)
+            
+            print("Warning: Error loading EXR and no default image. Returning a black image.")
+            blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            blank_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
+            return (blank_image, blank_mask)
+
+NODE_CLASS_MAPPINGS = {
+    "ExternalExrInput": ExternalExrInput
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ExternalExrInput": "External EXR Input (ComfyDeploy)"
+} 

comfy-nodes/external_exr_output.py

@@ -0,0 +1,73 @@
+import os
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+import cv2 as cv
+import torch
+import numpy as np
+import folder_paths
+
+def srgb_to_linear(np_array):
+    """Converts an sRGB numpy array to linear RGB."""
+    less = np_array <= 0.0404482362771082
+    np_array[less] = np_array[less] / 12.92
+    np_array[~less] = np.power((np_array[~less] + 0.055) / 1.055, 2.4)
+    return np_array
+
+class ExternalExrOutput:
+    """
+    Node to save a single image as an EXR file to a local path.
+    """
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "filepath": ("STRING", {"default": "/tmp/output.exr"}),
+                "tonemap": (["linear", "sRGB"], {"default": "linear"}),
+            },
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    CATEGORY = "🔗ComfyDeploy/EXR"
+
+    def run(self, images, filepath, tonemap):
+        if not filepath.endswith(".exr"):
+            raise ValueError("Filepath must end with '.exr'")
+
+        output_dir = os.path.dirname(filepath)
+        if not os.path.isabs(output_dir):
+            raise ValueError("Filepath must be an absolute path.")
+        
+        os.makedirs(output_dir, exist_ok=True)
+        
+        # We only process the first image in the batch
+        image_tensor = images[0]
+        
+        linear = image_tensor.cpu().numpy().astype(np.float32)
+        
+        # If the source is sRGB, convert to linear
+        if tonemap == "sRGB":
+            linear[...,:3] = srgb_to_linear(linear[...,:3])
+
+        # Convert RGB to BGR for OpenCV
+        bgr = np.flip(linear, 2).copy()
+        
+        # Save the image
+        cv.imwrite(filepath, bgr)
+        
+        print(f"Saved EXR file to: {filepath}")
+
+        return {"ui": {"images": [{"filename": os.path.basename(filepath), "subfolder": os.path.dirname(filepath), "type": self.type}]}}
+
+NODE_CLASS_MAPPINGS = {
+    "ExternalExrOutput": ExternalExrOutput
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ExternalExrOutput": "External EXR Output (ComfyDeploy)"
+} 

comfy-nodes/external_exr_sequence_input.py

@@ -0,0 +1,159 @@
+import os
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+import cv2 as cv
+import numpy as np
+import torch
+import re
+from folder_paths import get_annotated_filepath
+
+def linear_to_srgb(np_array):
+    """Converts a linear RGB numpy array to sRGB."""
+    less = np_array <= 0.0031308
+    np_array[less] = np_array[less] * 12.92
+    np_array[~less] = np.power(np_array[~less], 1/2.4) * 1.055 - 0.055
+    return np_array
+
+class ExternalExrSequenceInput:
+    """
+    Node to load a sequence of EXR images from a local filepath pattern, a directory,
+    or a single file within a sequence.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path_or_pattern": ("STRING", {"default": "path/to/frames_or_pattern"}),
+                "tonemap": (["linear", "sRGB", "Reinhard"], {"default": "sRGB"}),
+                "start_frame": ("INT", {"default": 1, "min": 1}),
+                "end_frame": ("INT", {"default": 50, "min": 1}),
+            },
+            "optional": {
+                "default_image": ("IMAGE",),
+                "default_mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_NAMES = ("image", "mask",)
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy/EXR"
+
+    def get_image_paths(self, path_input, start_frame, end_frame):
+        image_paths = []
+        
+        # Case 1: Input is a C-style pattern
+        if '%' in path_input:
+            print(f"Pattern detected: {path_input}")
+            for i in range(start_frame, end_frame + 1):
+                fpath = get_annotated_filepath(path_input % i)
+                if os.path.exists(fpath):
+                    image_paths.append(fpath)
+            return image_paths
+
+        annotated_path = get_annotated_filepath(path_input)
+        
+        # Case 2: Input is a directory
+        if os.path.isdir(annotated_path):
+            print(f"Directory detected: {annotated_path}")
+            files_in_dir = sorted(os.listdir(annotated_path))
+            for filename in files_in_dir:
+                if not filename.lower().endswith('.exr'):
+                    continue
+                
+                matches = re.findall(r'\d+', filename)
+                if not matches:
+                    continue
+                
+                frame_number = int(matches[-1])
+                if start_frame <= frame_number <= end_frame:
+                    image_paths.append(os.path.join(annotated_path, filename))
+            return image_paths
+            
+        # Case 3: Input is a single file from a sequence
+        if os.path.isfile(annotated_path):
+            print(f"Single file detected: {annotated_path}. Attempting to find sequence.")
+            base_dir = os.path.dirname(annotated_path)
+            filename = os.path.basename(annotated_path)
+
+            matches = list(re.finditer(r'(\d+)', filename))
+            if not matches: # It's a single file with no frame number
+                return [annotated_path]
+
+            last_match = matches[-1]
+            num_start_pos, num_end_pos = last_match.span()
+            prefix = filename[:num_start_pos]
+            suffix = filename[num_end_pos:]
+            padding = len(last_match.group(0))
+
+            for i in range(start_frame, end_frame + 1):
+                potential_filename = f"{prefix}{str(i).zfill(padding)}{suffix}"
+                potential_path = os.path.join(base_dir, potential_filename)
+                if os.path.exists(potential_path):
+                    image_paths.append(potential_path)
+            return image_paths
+
+        return [] # Return empty if no cases match
+
+    def run(self, path_or_pattern, tonemap, start_frame, end_frame, default_image=None, default_mask=None):
+        try:
+            image_paths = self.get_image_paths(path_or_pattern, start_frame, end_frame)
+            if not image_paths:
+                raise ValueError(f"No EXR files found for '{path_or_pattern}' between frames {start_frame}-{end_frame}.")
+
+            print(f"Found {len(image_paths)} EXR files to load.")
+            rgb_frames = []
+            mask_frames = []
+            
+            for path in image_paths:
+                image = cv.imread(path, cv.IMREAD_UNCHANGED)
+                if image is None:
+                    print(f"Warning: Could not read file {path}, skipping.")
+                    continue
+                
+                image = image.astype(np.float32)
+                if len(image.shape) == 2:
+                    image = np.repeat(image[..., np.newaxis], 3, axis=2)
+                
+                rgb = np.flip(image[:, :, :3], 2).copy()
+                
+                if tonemap == "sRGB":
+                    rgb = linear_to_srgb(rgb)
+                    rgb = np.clip(rgb, 0, 1)
+                elif tonemap == "Reinhard":
+                    rgb = np.clip(rgb, 0, None)
+                    rgb = rgb / (rgb + 1)
+                    rgb = linear_to_srgb(rgb)
+                    rgb = np.clip(rgb, 0, 1)
+                
+                rgb_frames.append(torch.from_numpy(rgb))
+                
+                if image.shape[2] > 3:
+                    mask = np.clip(image[:, :, 3], 0, 1)
+                else:
+                    mask = np.ones_like(rgb[:, :, 0])
+                mask_frames.append(torch.from_numpy(mask))
+
+            if not rgb_frames:
+                raise ValueError("No frames were loaded successfully.")
+
+            print(f"Successfully loaded {len(rgb_frames)} frames into a batch.")
+            return (torch.stack(rgb_frames, 0), torch.stack(mask_frames, 0))
+
+        except Exception as e:
+            print(f"Error loading EXR sequence: {e}")
+            if default_image is not None and default_mask is not None:
+                print("Returning default image.")
+                return (default_image, default_mask)
+
+            print("Warning: Error loading sequence and no default image. Returning a black image.")
+            blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            blank_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
+            return (blank_image, blank_mask)
+
+NODE_CLASS_MAPPINGS = {
+    "ExternalExrSequenceInput": ExternalExrSequenceInput
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ExternalExrSequenceInput": "External EXR Sequence Input (ComfyDeploy)"
+} 

comfy-nodes/external_exr_sequence_output.py

@@ -0,0 +1,88 @@
+import os
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+import cv2 as cv
+import torch
+import numpy as np
+import re
+
+def srgb_to_linear(np_array):
+    """Converts an sRGB numpy array to linear RGB."""
+    less = np_array <= 0.0404482362771082
+    np_array[less] = np_array[less] / 12.92
+    np_array[~less] = np.power((np_array[~less] + 0.055) / 1.055, 2.4)
+    return np_array
+
+class ExternalExrSequenceOutput:
+    """
+    Node to save a sequence of images as EXR files to a local directory.
+    It uses a filepath pattern like 'path/to/frame_%04d.exr' to save each frame.
+    """
+    def __init__(self):
+        self.type = "output"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "filepath_pattern": ("STRING", {"default": "/tmp/exr_sequence/frame_%04d.exr"}),
+                "tonemap": (["linear", "sRGB"], {"default": "linear"}),
+            },
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    CATEGORY = "🔗ComfyDeploy/EXR"
+
+    def run(self, images, filepath_pattern, tonemap):
+        # Basic validation for the filepath pattern
+        if not re.search(r'%0?\d+d', filepath_pattern):
+            raise ValueError("Filepath pattern must contain a C-style format specifier like '%04d'.")
+            
+        if not filepath_pattern.endswith(".exr"):
+            raise ValueError("Filepath pattern must end with '.exr'.")
+
+        output_dir = os.path.dirname(filepath_pattern)
+        if not os.path.isabs(output_dir):
+            raise ValueError("Filepath must be an absolute path.")
+            
+        os.makedirs(output_dir, exist_ok=True)
+        
+        # Convert tensor to numpy array
+        linear_images = images.cpu().numpy().astype(np.float32)
+        
+        # If the source is sRGB, convert to linear
+        if tonemap == "sRGB":
+            srgb_to_linear(linear_images[...,:3])
+        
+        # Convert RGB to BGR for OpenCV
+        bgr_images = np.flip(linear_images, 3).copy()
+
+        results = []
+        for i, bgr_image in enumerate(bgr_images):
+            frame_num = i + 1
+            try:
+                # Use the pattern to format the full file path
+                file_path = filepath_pattern % frame_num
+            except TypeError:
+                raise ValueError("Invalid format specifier in filepath_pattern. Use '%d', '%04d', etc.")
+
+            # Save the image
+            cv.imwrite(file_path, bgr_image)
+            
+            results.append({
+                "filename": os.path.basename(file_path),
+                "subfolder": os.path.dirname(file_path),
+                "type": self.type,
+            })
+        
+        return {"ui": {"images": results}}
+
+NODE_CLASS_MAPPINGS = {
+    "ExternalExrSequenceOutput": ExternalExrSequenceOutput
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ExternalExrSequenceOutput": "External EXR Sequence Output (ComfyDeploy)"
+} 

comfy-nodes/external_face_model.py

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

comfy-nodes/external_image.py

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

comfy-nodes/external_image_alpha.py

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

comfy-nodes/external_image_batch.py

@@ -21,24 +21,50 @@ 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 = "image"
+    CATEGORY = "🔗ComfyDeploy"
     
-    def run(self, input_id, images=None, default_value=None):
+    def process_image(self, image):
+        image = ImageOps.exif_transpose(image)
+        image = image.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image_tensor = torch.from_numpy(image)[None,]
+        return image_tensor
+    
+    def run(self, input_id, images=None, default_value=None, display_name=None, description=None):
+        import requests
+        import zipfile
+        import io
+        
         processed_images = []
         try:
             images_list = json.loads(images)  # Assuming images is a JSON array string
             print(images_list)
             for img_input in images_list:
-                if img_input.startswith('http'):
-                    import requests
+                if img_input.startswith('http') and img_input.endswith('.zip'):
+                    print("Fetching zip file from url: ", img_input)
+                    response = requests.get(img_input)
+                    zip_file = zipfile.ZipFile(io.BytesIO(response.content))
+                    for file_name in zip_file.namelist():
+                        if file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
+                            with zip_file.open(file_name) as file:
+                                image = Image.open(file)
+                                image = self.process_image(image)
+                                processed_images.append(image)
+                elif  img_input.startswith('http'):
                     from io import BytesIO
                     print("Fetching image from url: ", img_input)
                     response = requests.get(img_input)

comfy-nodes/external_lora.py

@@ -1,8 +1,12 @@
 import folder_paths
-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 ComfyUIDeployExternalLora:
@@ -17,40 +21,88 @@ class ComfyUIDeployExternalLora:
             },
             "optional": {
                 "default_lora_name": (folder_paths.get_filename_list("loras"),),
+                "lora_save_name": (  # if `default_lora_name` is a link to download a file, we will attempt to save it with this name
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+                "lora_url": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "bearer_token": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
             },
         }
 
-    RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
+    RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
-
     FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
 
-    CATEGORY = "deploy"
-
-    def run(self, input_id, default_lora_name=None):
+    def run(
+        self,
+        input_id,
+        default_lora_name=None,
+        lora_save_name=None,
+        display_name=None,
+        description=None,
+        lora_url=None,
+        bearer_token=None,
+    ):
         import requests
         import os
         import uuid
 
-        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,)
+        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
+                )
+                headers = {"User-Agent": "Mozilla/5.0"}
+                if bearer_token:
+                    headers["Authorization"] = f"Bearer {bearer_token}"
+                    print("using bearer token")
+                response = requests.get(
+                    lora_url,
+                    headers=headers,
+                    allow_redirects=True,
+                )
+                with open(destination_path, "wb") as out_file:
+                    out_file.write(response.content)
+                print(f"Ext Lora loading: {lora_url} to {lora_save_name}")
+                return (lora_save_name,)
+            else:
+                print(f"Ext Lora loading: {lora_url}")
+                return (lora_url,)
         else:
-            print(f"using lora: {default_lora_name}")
+            print(f"Ext Lora loading: {default_lora_name}")
             return (default_lora_name,)
 
 

comfy-nodes/external_number.py

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

comfy-nodes/external_number_int.py

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

comfy-nodes/external_number_slider.py

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

comfy-nodes/external_number_slider_int.py

@@ -0,0 +1,54 @@
+class ComfyUIDeployExternalNumberSliderInt:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_number_slider_int"},
+                ),
+            },
+            "optional": {
+                "default_value": (
+                    "INT",
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 1, "step": 1},
+                ),
+                "min_value": (
+                    "INT",
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0, "step": 1},
+                ),
+                "max_value": (
+                    "INT",
+                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 10, "step": 1},
+                ),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("value",)
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
+
+    def run(self, input_id, default_value=None, min_value=0, max_value=10, display_name=None, description=None):
+        try:
+            int_value = int(round(float(input_id)))
+            if min_value <= int_value <= max_value:
+                print("my integer", int_value)
+                return [int_value]
+            else:
+                print("Integer out of range. Returning default value:", default_value)
+                return [default_value]
+        except (ValueError, TypeError):
+            print("Invalid input. Returning default value:", default_value)
+            return [default_value]
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalNumberSliderInt": ComfyUIDeployExternalNumberSliderInt}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalNumberSliderInt": "External Number Slider Int (ComfyUI Deploy)"}

comfy-nodes/external_seed.py

@@ -0,0 +1,116 @@
+import random
+
+
+class ComfyUIDeployExternalSeed:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_seed"},
+                ),
+                "default_value": (
+                    "INT",
+                    {"default": -1},
+                ),
+                "min_value": (
+                    "INT",
+                    {"default": 1, "min": 1, "max": 999999999999999},
+                ),
+                "max_value": (
+                    "INT",
+                    {"default": 4294967295, "min": 1, "max": 999999999999999},
+                ),
+            },
+            "optional": {
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {
+                        "multiline": True,
+                        "default": 'For default value:\n"-1" (i.e. not in range): Randomize within the min and max value range. \nin range: Fixed, always the same value\n',
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("INT",)
+    RETURN_NAMES = ("seed",)
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy"
+
+    # Limits
+    _MAX_LIMIT = 999_999_999_999_999  # 15 digits
+
+    # Store cached seed when fixed flag is enabled
+    _cached_seed = None
+
+    @classmethod
+    def IS_CHANGED(
+        cls,
+        input_id,
+        min_value,
+        max_value,
+        default_value=None,
+        **kwargs,
+    ):
+        """Inform ComfyUI whether the node output should be considered changed.
+
+        If default_value is within range (Fixed mode), we return the inputs tuple
+        so the cached result is reused until the user changes something.
+        For Randomize mode, we force re-execution each queue.
+        """
+        # Clamp values to allowed range for check
+        min_value = max(1, min_value)
+        max_value = min(cls._MAX_LIMIT, max_value)
+
+        # Fixed mode when default_value is within range
+        if (
+            default_value is not None
+            and default_value >= min_value
+            and default_value <= max_value
+        ):
+            return (input_id, default_value)
+
+        # For Randomize (default_value is -1 or out of range) we force re-execution
+        import random as _rnd
+
+        return _rnd.random()
+
+    def run(
+        self,
+        input_id,
+        min_value: int,
+        max_value: int,
+        display_name=None,
+        description=None,
+        default_value: int = -1,
+    ):
+        # Clamp values to allowed range
+        min_value = max(1, min_value)
+        max_value = min(self._MAX_LIMIT, max_value)
+
+        # Ensure limits are in correct order after clamping
+        if min_value > max_value:
+            min_value, max_value = max_value, min_value
+
+        # Fixed mode: default_value is within range
+        if default_value >= min_value and default_value <= max_value:
+            seed = int(default_value)
+            self._cached_seed = seed
+            return [seed]
+
+        # Randomize mode: default_value is -1 or out of range
+        seed = random.randint(min_value, max_value)
+        self._cached_seed = seed
+        return [seed]
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalSeed": ComfyUIDeployExternalSeed}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ComfyUIDeployExternalSeed": "External Seed (ComfyUI Deploy)"
+}

comfy-nodes/external_string_combine.py

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

comfy-nodes/external_text.py

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

comfy-nodes/external_text_any.py

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

comfy-nodes/external_vid.py

@@ -36,6 +36,7 @@ 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

@@ -1,10 +1,15 @@
-# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite and is meant to work with
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
+# Intended to work with https://github.com/NicholasKao1029/ComfyUI-VideoHelperSuite/tree/main
 import os
 import itertools
 import numpy as np
 import torch
+from typing import Union
+from torch import Tensor
 import cv2
+import psutil
 
+from collections.abc import Mapping
 import folder_paths
 from comfy.utils import common_upscale
 
@@ -90,13 +95,25 @@ 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 = directory.strip()
+    directory = strip_path(directory)
     dir_files = os.listdir(directory)
     dir_files = sorted(dir_files)
     dir_files = [os.path.join(directory, x) for x in dir_files]
@@ -177,18 +194,59 @@ def requeue_workflow(requeue_required=(-1, True)):
 
 
 def get_audio(file, start_time=0, duration=0):
-    args = [ffmpeg_path, "-v", "error", "-i", file]
+    args = [ffmpeg_path, "-i", file]
     if start_time > 0:
         args += ["-ss", str(start_time)]
     if duration > 0:
         args += ["-t", str(duration)]
     try:
+        # TODO: scan for sample rate and maintain
         res = subprocess.run(
-            args + ["-f", "wav", "-"], stdout=subprocess.PIPE, check=True
-        ).stdout
+            args + ["-f", "f32le", "-"], capture_output=True, check=True
+        )
+        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
+        match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
     except subprocess.CalledProcessError as e:
-        return False
-    return res
+        raise Exception(
+            f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
+        )
+    if match:
+        ar = int(match.group(1))
+        # NOTE: Just throwing an error for other channel types right now
+        # Will deal with issues if they come
+        ac = {"mono": 1, "stereo": 2}[match.group(2)]
+    else:
+        ar = 44100
+        ac = 2
+    audio = audio.reshape((-1, ac)).transpose(0, 1).unsqueeze(0)
+    return {"waveform": audio, "sample_rate": ar}
+
+
+class LazyAudioMap(Mapping):
+    def __init__(self, file, start_time, duration):
+        self.file = file
+        self.start_time = start_time
+        self.duration = duration
+        self._dict = None
+
+    def __getitem__(self, key):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return self._dict[key]
+
+    def __iter__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return iter(self._dict)
+
+    def __len__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return len(self._dict)
+
+
+def lazy_get_audio(file, start_time=0, duration=0):
+    return LazyAudioMap(file, start_time, duration)
 
 
 def lazy_eval(func):
@@ -230,6 +288,19 @@ 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"
@@ -286,6 +357,145 @@ 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,
@@ -295,9 +505,10 @@ def cv_frame_generator(
     meta_batch=None,
     unique_id=None,
 ):
-    video_cap = cv2.VideoCapture(video)
+    video_cap = cv2.VideoCapture(strip_path(video))
     if not video_cap.isOpened():
         raise ValueError(f"{video} could not be loaded with cv.")
+    pbar = None
 
     # extract video metadata
     fps = video_cap.get(cv2.CAP_PROP_FPS)
@@ -319,6 +530,8 @@ 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():
@@ -349,7 +562,8 @@ 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) / 255.0
+        frame = np.array(frame, dtype=np.float32)
+        torch.from_numpy(frame).div_(255)
         if prev_frame is not None:
             inp = yield prev_frame
             if inp is not None:
@@ -357,6 +571,8 @@ 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
@@ -367,6 +583,17 @@ 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,
@@ -378,6 +605,8 @@ 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(
@@ -401,30 +630,89 @@ 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]
         )
 
-    if meta_batch is not None:
-        gen = itertools.islice(gen, meta_batch.frames_per_batch)
+    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:
 
-    # 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))))
-    )
+            def rescale(frame):
+                s = torch.from_numpy(
+                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
+                )
+                s = s.movedim(-1, 1)
+                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+                return s.movedim(1, -1).numpy()
+
+            gen = itertools.chain.from_iterable(
+                map(rescale, batched(gen, frames_per_batch))
+            )
+    else:
+        new_size = width, height
+    if vae is not None:
+        gen = batched_vae_encode(gen, vae, frames_per_batch)
+        vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
+        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
+    else:
+        # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+        images = torch.from_numpy(
+            np.fromiter(gen, np.dtype((np.float32, (new_size[1], new_size[0], 3))))
+        )
+    if meta_batch is None and memory_limit is not None:
+        try:
+            next(original_gen)
+            raise RuntimeError(
+                f"Memory limit hit after loading {len(images)} frames. Stopping execution."
+            )
+        except StopIteration:
+            pass
     if len(images) == 0:
         raise RuntimeError("No frames generated")
-    if force_size != "Disabled":
-        new_size = target_size(width, height, force_size, custom_width, custom_height)
-        if new_size[0] != width or new_size[1] != height:
-            s = images.movedim(-1, 1)
-            s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
-            images = s.movedim(1, -1)
 
     # Setup lambda for lazy audio capture
-    audio = lambda: get_audio(
+    audio = lazy_get_audio(
         video,
         skip_first_frames * target_frame_time,
         frame_load_cap * target_frame_time * select_every_nth,
@@ -440,13 +728,16 @@ def load_video_cv(
         "loaded_fps": 1 / target_frame_time,
         "loaded_frame_count": len(images),
         "loaded_duration": len(images) * target_frame_time,
-        "loaded_width": images.shape[2],
-        "loaded_height": images.shape[1],
+        "loaded_width": new_size[0],
+        "loaded_height": new_size[1],
     }
-
-    return (images, len(images), lazy_eval(audio), video_info)
+    if vae is None:
+        return (images, len(images), audio, video_info, None)
+    else:
+        return (None, len(images), audio, video_info, {"samples": images})
 
 
+# modeled after Video upload node
 class ComfyUIDeployExternalVideo:
     @classmethod
     def INPUT_TYPES(s):
@@ -501,27 +792,34 @@ class ComfyUIDeployExternalVideo:
             },
             "optional": {
                 "meta_batch": ("VHS_BatchManager",),
-                "default_value": (sorted(files),),
+                "vae": ("VAE",),
+                "default_video": (sorted(files),),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+                "default_value_url": ("STRING", {"image_preview": True, "default": ""}),
             },
             "hidden": {"unique_id": "UNIQUE_ID"},
         }
 
     CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
 
-    RETURN_TYPES = (
-        "IMAGE",
-        "INT",
-        "VHS_AUDIO",
-        "VHS_VIDEOINFO",
-    )
+    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
     RETURN_NAMES = (
         "IMAGE",
         "frame_count",
         "audio",
         "video_info",
+        "LATENT",
     )
 
     FUNCTION = "load_video"
+    CATEGORY = "🔗ComfyDeploy"
 
     def load_video(self, **kwargs):
         input_id = kwargs.get("input_id")
@@ -534,18 +832,21 @@ class ComfyUIDeployExternalVideo:
         select_every_nth = kwargs.get("select_every_nth")
         meta_batch = kwargs.get("meta_batch")
         unique_id = kwargs.get("unique_id")
-
-        video = kwargs.get("default_value")
-        video_path = folder_paths.get_annotated_filepath(video.strip('"'))
+        default_value_url = kwargs.get("default_value_url")
 
         input_dir = folder_paths.get_input_directory()
-        if input_id.startswith("http"):
+        if input_id.startswith("http") or (
+            default_value_url and default_value_url.startswith("http")
+        ):
             import requests
 
-            print("Fetching video from URL: ", input_id)
-            response = requests.get(input_id, stream=True)
+            # Use input_id if it's a URL, otherwise use default_value_url
+            url = input_id if input_id.startswith("http") else default_value_url
+
+            print("Fetching video from URL: ", url)
+            response = requests.get(url, stream=True)
             file_size = int(response.headers.get("Content-Length", 0))
-            file_extension = input_id.split(".")[-1].split("?")[
+            file_extension = url.split(".")[-1].split("?")[
                 0
             ]  # Extract extension and handle URLs with parameters
             if file_extension not in video_extensions:
@@ -566,8 +867,11 @@ class ComfyUIDeployExternalVideo:
                     leave=True,
                 ):
                     out_file.write(chunk)
-
-        print("video path: ", video_path)
+        else:
+            video = kwargs.get("default_video", None)
+            if video is None:
+                raise "No default video given and no external video provided"
+            video_path = folder_paths.get_annotated_filepath(video.strip('"'))
 
         return load_video_cv(
             video=video_path,

comfy-nodes/http_exr_input.py

@@ -0,0 +1,111 @@
+import os
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+import cv2 as cv
+import numpy as np
+import torch
+import requests
+
+def linear_to_srgb(np_array):
+    """Converts a linear RGB numpy array to sRGB."""
+    less = np_array <= 0.0031308
+    np_array[less] = np_array[less] * 12.92
+    np_array[~less] = np.power(np_array[~less], 1/2.4) * 1.055 - 0.055
+    return np_array
+
+class HttpExrInput:
+    """
+    Node to load a single EXR image from a URL, with optional tonemapping.
+    This node is designed to be used in a ComfyDeploy environment where input files are provided via signed URLs.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "get_signed_url": ("STRING", {"multiline": True, "default": ""}),
+                "tonemap": (["linear", "sRGB", "Reinhard"], {"default": "sRGB"}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "default_image": ("IMAGE",),
+                "default_mask": ("MASK",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_NAMES = ("image", "mask",)
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy/EXR"
+
+    def load_exr_from_data(self, exr_data):
+        try:
+            nparr = np.frombuffer(exr_data, np.uint8)
+            # Use cv.IMREAD_UNCHANGED to keep all channels (e.g., alpha)
+            image = cv.imdecode(nparr, cv.IMREAD_UNCHANGED)
+            if image is None:
+                raise ValueError("Failed to decode EXR data.")
+            return image.astype(np.float32)
+        except Exception as e:
+            print(f"Error decoding EXR data: {e}")
+            return None
+
+    def run(self, get_signed_url, tonemap, seed, default_image=None, default_mask=None):
+        if not get_signed_url or get_signed_url.strip() == "":
+            print("Warning: No input URL provided. Returning default image if available.")
+            if default_image is not None and default_mask is not None:
+                return (default_image, default_mask)
+            
+            print("Warning: No input URL and no default image. Returning a black image.")
+            blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            blank_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
+            return (blank_image, blank_mask)
+
+        image = None
+        try:
+            print(f"Fetching EXR from URL: {get_signed_url}")
+            response = requests.get(get_signed_url)
+            response.raise_for_status()
+            image = self.load_exr_from_data(response.content)
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching EXR from URL {get_signed_url}: {e}")
+
+        if image is None:
+            print("Warning: Could not load or decode EXR image. Returning default image if available.")
+            if default_image is not None and default_mask is not None:
+                return (default_image, default_mask)
+
+            print("Warning: Failed to load EXR and no default image. Returning a black image.")
+            blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            blank_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
+            return (blank_image, blank_mask)
+                    
+        # BGR to RGB conversion and channel handling
+        if len(image.shape) == 2: # Grayscale
+            image = np.repeat(image[..., np.newaxis], 3, axis=2)
+        
+        rgb = np.flip(image[:, :, :3], 2).copy() # OpenCV loads as BGR, convert to RGB
+        
+        # Tonemapping
+        if tonemap == "sRGB":
+            rgb = linear_to_srgb(rgb)
+            rgb = np.clip(rgb, 0, 1)
+        elif tonemap == "Reinhard":
+            rgb = np.clip(rgb, 0, None) # Ensure no negative values
+            rgb = rgb / (rgb + 1)
+            rgb = linear_to_srgb(rgb)
+            rgb = np.clip(rgb, 0, 1)
+        
+        # Handle alpha channel if it exists
+        if image.shape[2] > 3:
+            mask = np.clip(image[:, :, 3], 0, 1)
+        else:
+            mask = np.ones_like(rgb[:, :, 0]) # Create a full white mask if no alpha
+
+        return (torch.from_numpy(rgb).unsqueeze(0), torch.from_numpy(mask).unsqueeze(0),)
+
+NODE_CLASS_MAPPINGS = {
+    "HttpExrInput": HttpExrInput
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "HttpExrInput": "HTTP EXR Input (ComfyDeploy)"
+} 

comfy-nodes/http_exr_output.py

@@ -0,0 +1,80 @@
+import os
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+import cv2 as cv
+import torch
+import numpy as np
+import requests
+
+def srgb_to_linear(np_array):
+    """Converts an sRGB numpy array to linear RGB."""
+    less = np_array <= 0.0404482362771082
+    np_array[less] = np_array[less] / 12.92
+    np_array[~less] = np.power((np_array[~less] + 0.055) / 1.055, 2.4)
+    return np_array
+
+class HttpExrOutput:
+    """
+    Node to save a single EXR image to a pre-signed URL.
+    This node is designed for ComfyDeploy to upload the generated EXR file.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "put_signed_url": ("STRING", {"multiline": True, "default": ""}),
+                "tonemap": (["linear", "sRGB"], {"default": "linear"}),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    CATEGORY = "ComfyDeploy/EXR"
+
+    def run(self, images, put_signed_url, tonemap, prompt=None, extra_pnginfo=None):
+        if not put_signed_url or put_signed_url.strip() == "":
+            print("Warning: No put_signed_url provided. Nothing will be uploaded.")
+            return {"ui": {"images": []}}
+        
+        # We process only the first image of the batch
+        image_tensor = images[0]
+        
+        # Convert tensor to numpy array, assuming it's in range [0, 1]
+        linear = image_tensor.cpu().numpy().astype(np.float32)
+        
+        # If the source is sRGB, convert to linear
+        if tonemap == "sRGB":
+            linear[...,:3] = srgb_to_linear(linear[...,:3])
+
+        # Convert RGB to BGR for OpenCV
+        bgr = np.flip(linear, 2).copy()
+        
+        results = []
+        try:
+            # Encode the image to the EXR format in memory
+            is_success, buffer = cv.imencode(".exr", bgr)
+            if not is_success:
+                raise Exception("Failed to encode image to EXR format.")
+            
+            # Upload the image data to the pre-signed URL
+            response = requests.put(put_signed_url, data=buffer.tobytes(), headers={'Content-Type': 'image/x-exr'})
+            response.raise_for_status()
+            
+            print(f"Successfully uploaded EXR to: {put_signed_url}")
+            # The UI can optionally display a link or confirmation
+            results.append({"url": put_signed_url, "output_id": "output_http_exr"})
+            
+        except Exception as e:
+            print(f"Error uploading EXR to signed URL: {e}")
+
+        return {"ui": {"images": results}}
+
+NODE_CLASS_MAPPINGS = {
+    "HttpExrOutput": HttpExrOutput
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "HttpExrOutput": "HTTP EXR Output (ComfyDeploy)"
+} 

comfy-nodes/http_exr_sequence_input.py

@@ -0,0 +1,126 @@
+import os
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+import cv2 as cv
+import numpy as np
+import torch
+import requests
+import json
+
+def linear_to_srgb(np_array):
+    """Converts a linear RGB numpy array to sRGB."""
+    less = np_array <= 0.0031308
+    np_array[less] = np_array[less] * 12.92
+    np_array[~less] = np.power(np_array[~less], 1/2.4) * 1.055 - 0.055
+    return np_array
+
+class HttpExrSequenceInput:
+    """
+    Node to load a sequence of EXR images from a list of URLs provided as a JSON string.
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "urls_json": ("STRING", {"multiline": True, "default": "[]"}),
+                "tonemap": (["linear", "sRGB", "Reinhard"], {"default": "sRGB"}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "default_image": ("IMAGE",),
+                "default_mask": ("MASK",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_NAMES = ("image", "mask",)
+    FUNCTION = "run"
+    CATEGORY = "🔗ComfyDeploy/EXR"
+
+    def load_exr_from_data(self, exr_data):
+        try:
+            nparr = np.frombuffer(exr_data, np.uint8)
+            image = cv.imdecode(nparr, cv.IMREAD_UNCHANGED)
+            if image is None:
+                raise ValueError("Failed to decode EXR data.")
+            return image.astype(np.float32)
+        except Exception as e:
+            print(f"Error decoding EXR data: {e}")
+            return None
+
+    def run(self, urls_json, tonemap, seed, default_image=None, default_mask=None):
+        try:
+            urls = json.loads(urls_json)
+            if not isinstance(urls, list) or not all(isinstance(u, str) for u in urls):
+                raise ValueError("urls_json must be a JSON array of URL strings.")
+        except (json.JSONDecodeError, ValueError) as e:
+            print(f"Error parsing urls_json: {e}. Using default image if available.")
+            urls = []
+
+        if not urls:
+            if default_image is not None and default_mask is not None:
+                return (default_image, default_mask)
+            
+            print("Warning: No valid URLs and no default image. Returning a black image.")
+            blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            blank_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
+            return (blank_image, blank_mask)
+
+        rgb_frames = []
+        mask_frames = []
+
+        for url in urls:
+            image = None
+            try:
+                print(f"Fetching EXR from URL: {url}")
+                response = requests.get(url)
+                response.raise_for_status()
+                image = self.load_exr_from_data(response.content)
+            except requests.exceptions.RequestException as e:
+                print(f"Error fetching EXR from URL {url}: {e}")
+            
+            if image is None:
+                print(f"Warning: Could not decode EXR from {url}. Skipping frame.")
+                continue
+                
+            if len(image.shape) == 2: # Grayscale
+                image = np.repeat(image[..., np.newaxis], 3, axis=2)
+            
+            rgb = np.flip(image[:, :, :3], 2).copy() # BGR to RGB
+            
+            if tonemap == "sRGB":
+                rgb = linear_to_srgb(rgb)
+                rgb = np.clip(rgb, 0, 1)
+            elif tonemap == "Reinhard":
+                rgb = np.clip(rgb, 0, None)
+                rgb = rgb / (rgb + 1)
+                rgb = linear_to_srgb(rgb)
+                rgb = np.clip(rgb, 0, 1)
+            
+            rgb_frames.append(torch.from_numpy(rgb))
+
+            if image.shape[2] > 3:
+                mask = np.clip(image[:, :, 3], 0, 1)
+            else:
+                mask = np.ones_like(rgb[:, :, 0])
+            mask_frames.append(torch.from_numpy(mask))
+
+        if not rgb_frames:
+            print("Could not load any frames. Returning default image if available.")
+            if default_image is not None and default_mask is not None:
+                return (default_image, default_mask)
+            
+            print("Warning: Failed to load any frames and no default image. Returning a black image.")
+            blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
+            blank_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
+            return (blank_image, blank_mask)
+
+        print(f"Loaded {len(rgb_frames)} frames successfully.")
+        return (torch.stack(rgb_frames, 0), torch.stack(mask_frames, 0))
+
+NODE_CLASS_MAPPINGS = {
+    "HttpExrSequenceInput": HttpExrSequenceInput
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "HttpExrSequenceInput": "HTTP EXR Sequence Input (ComfyDeploy)"
+} 

comfy-nodes/http_exr_sequence_output.py

@@ -0,0 +1,91 @@
+import os
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+import cv2 as cv
+import torch
+import numpy as np
+import requests
+import json
+
+def srgb_to_linear(np_array):
+    """Converts an sRGB numpy array to linear RGB."""
+    less = np_array <= 0.0404482362771082
+    np_array[less] = np_array[less] / 12.92
+    np_array[~less] = np.power((np_array[~less] + 0.055) / 1.055, 2.4)
+    return np_array
+
+class HttpExrSequenceOutput:
+    """
+    Node to save a sequence of images as EXR files to a list of pre-signed URLs.
+    """
+    def __init__(self):
+        self.type = "output"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "upload_urls_json": ("STRING", {"multiline": True, "default": "[]"}),
+                "tonemap": (["linear", "sRGB"], {"default": "linear"}),
+            },
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "run"
+    OUTPUT_NODE = True
+    CATEGORY = "🔗ComfyDeploy/EXR"
+
+    def run(self, images, upload_urls_json, tonemap):
+        try:
+            upload_urls = json.loads(upload_urls_json)
+            if not isinstance(upload_urls, list) or not all(isinstance(u, str) for u in upload_urls):
+                raise ValueError("upload_urls_json must be a JSON array of URL strings.")
+        except (json.JSONDecodeError, ValueError) as e:
+            print(f"Error parsing upload_urls_json: {e}. Aborting upload.")
+            return {"ui": {"images": []}}
+
+        if not upload_urls:
+            print("Warning: No upload URLs provided. Nothing will be uploaded.")
+            return {"ui": {"images": []}}
+        
+        if len(images) != len(upload_urls):
+            print(f"Warning: Mismatch between number of images ({len(images)}) and upload URLs ({len(upload_urls)}). Aborting upload.")
+            return {"ui": {"images": []}}
+
+        # Convert tensor to numpy array
+        linear_images = images.cpu().numpy().astype(np.float32)
+        
+        # If the source is sRGB, convert all images to linear
+        if tonemap == "sRGB":
+            srgb_to_linear(linear_images[...,:3])
+        
+        # Convert RGB to BGR for OpenCV
+        bgr_images = np.flip(linear_images, 3).copy()
+
+        results = []
+        for i, (bgr_image, url) in enumerate(zip(bgr_images, upload_urls)):
+            try:
+                # Encode the image to the EXR format in memory
+                is_success, buffer = cv.imencode(".exr", bgr_image)
+                if not is_success:
+                    raise Exception("Failed to encode image to EXR format.")
+                
+                # Upload the image data to the pre-signed URL
+                response = requests.put(url, data=buffer.tobytes(), headers={'Content-Type': 'image/x-exr'})
+                response.raise_for_status()
+                
+                print(f"Successfully uploaded frame {i+1} to: {url}")
+                results.append({"url": url})
+                
+            except Exception as e:
+                print(f"Error uploading frame {i+1} to {url}: {e}")
+
+        return {"ui": {"images": results}}
+
+NODE_CLASS_MAPPINGS = {
+    "HttpExrSequenceOutput": HttpExrSequenceOutput
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "HttpExrSequenceOutput": "HTTP EXR Sequence Output (ComfyDeploy)"
+} 

comfy-nodes/input_websocket_image.py

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

comfy-nodes/output_image.py

@@ -0,0 +1,101 @@
+import os
+import json
+import numpy as np
+from PIL import Image
+from PIL.PngImagePlugin import PngInfo
+import folder_paths
+
+
+class ComfyDeployOutputImage:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "The images to save."}),
+                "filename_prefix": (
+                    "STRING",
+                    {
+                        "default": "ComfyUI",
+                        "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
+                    },
+                ),
+                "file_type": (["png", "jpg", "webp"], {"default": "webp"}),
+                "quality": ("INT", {"default": 80, "min": 1, "max": 100, "step": 1}),
+            },
+            "optional": {
+                "output_id": (
+                    "STRING",
+                    {"multiline": False, "default": "output_images"},
+                ),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "run"
+
+    OUTPUT_NODE = True
+    CATEGORY = "🔗ComfyDeploy"
+    DESCRIPTION = "Saves the input images to your ComfyUI output directory."
+
+    def run(
+        self,
+        images,
+        filename_prefix="ComfyUI",
+        file_type="png",
+        quality=80,
+        output_id="output_images",
+        prompt=None,
+        extra_pnginfo=None,
+    ):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = (
+            folder_paths.get_save_image_path(
+                filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
+            )
+        )
+        results = list()
+        for batch_number, image in enumerate(images):
+            i = 255.0 * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            metadata = PngInfo()
+            if prompt is not None:
+                metadata.add_text("prompt", json.dumps(prompt))
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.{file_type}"
+            file_path = os.path.join(full_output_folder, file)
+
+            if file_type == "png":
+                img.save(
+                    file_path, pnginfo=metadata, compress_level=self.compress_level
+                )
+            elif file_type == "jpg":
+                img.save(file_path, quality=quality, optimize=True)
+            elif file_type == "webp":
+                img.save(file_path, quality=quality)
+
+            results.append(
+                {
+                    "filename": file,
+                    "subfolder": subfolder,
+                    "type": self.type,
+                    "output_id": output_id,
+                }
+            )
+            counter += 1
+
+        return {"ui": {"images": results}}
+
+
+NODE_CLASS_MAPPINGS = {"ComfyDeployOutputImage": ComfyDeployOutputImage}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployOutputImage": "Image Output (ComfyDeploy)"}

comfy-nodes/output_text.py

@@ -0,0 +1,99 @@
+import os
+import json
+import folder_paths
+
+
+class ComfyDeployOutputText:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "text": (
+                    "STRING",
+                    {
+                        "multiline": True,
+                        "forceInput": True,
+                        "tooltip": "The text to save.",
+                    },
+                ),
+                "filename_prefix": (
+                    "STRING",
+                    {
+                        "default": "ComfyUI",
+                        "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% to include values from nodes.",
+                    },
+                ),
+                "file_type": (["txt", "json", "md"], {"default": "txt"}),
+            },
+            "optional": {
+                "output_id": (
+                    "STRING",
+                    {"multiline": False, "default": "output_text"},
+                ),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "run"
+
+    OUTPUT_NODE = True
+    CATEGORY = "🔗ComfyDeploy"
+    DESCRIPTION = "Saves the input text to your ComfyUI output directory."
+
+    def run(
+        self,
+        text,
+        filename_prefix="ComfyUI",
+        file_type="txt",
+        output_id="output_text",
+        prompt=None,
+        extra_pnginfo=None,
+    ):
+        filename_prefix += self.prefix_append
+        # For text, we don't need dimensions, so pass 0, 0
+        full_output_folder, filename, counter, subfolder, filename_prefix = (
+            folder_paths.get_save_image_path(filename_prefix, self.output_dir, 0, 0)
+        )
+
+        results = list()
+
+        # Create file path
+        file = f"{filename}_{counter:05}_.{file_type}"
+        file_path = os.path.join(full_output_folder, file)
+
+        # Save the text based on file type
+        if file_type == "json":
+            try:
+                # Try to save as JSON if the text is valid JSON
+                json_data = json.loads(text) if isinstance(text, str) else text
+                with open(file_path, "w", encoding="utf-8") as f:
+                    json.dump(json_data, f, indent=2)
+            except json.JSONDecodeError:
+                # Fall back to saving as plain text if not valid JSON
+                with open(file_path, "w", encoding="utf-8") as f:
+                    f.write(text)
+        else:
+            # Save as plain text for txt and md
+            with open(file_path, "w", encoding="utf-8") as f:
+                f.write(text)
+
+        results.append(
+            {
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type,
+                "output_id": output_id,
+            }
+        )
+
+        return {"ui": {"text_file": results}}
+
+
+NODE_CLASS_MAPPINGS = {"ComfyDeployOutputText": ComfyDeployOutputText}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployOutputText": "Text Output (ComfyDeploy)"}

comfy-nodes/output_websocket_image.py

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

example_workflows/lora-model.json

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

example_workflows/masks.json

@@ -0,0 +1,873 @@
+{
+  "id": "351f402b-62f2-4f62-8a5e-0b9d3510e8f9",
+  "revision": 0,
+  "last_node_id": 29,
+  "last_link_id": 28,
+  "nodes": [
+    {
+      "id": 11,
+      "type": "JoinImageWithAlpha",
+      "pos": [
+        814.478271484375,
+        419.3052062988281
+      ],
+      "size": [
+        264.5999755859375,
+        46
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 10
+        },
+        {
+          "name": "alpha",
+          "type": "MASK",
+          "link": 12
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            11
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "JoinImageWithAlpha"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 15,
+      "type": "PreviewImage",
+      "pos": [
+        1950,
+        640
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 18,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 16
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 10,
+      "type": "LoadImage",
+      "pos": [
+        467.8168640136719,
+        422.453857421875
+      ],
+      "size": [
+        315,
+        314
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            10
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            12
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "Bob-Minion-Background-PNG-Image.png",
+        "image",
+        ""
+      ]
+    },
+    {
+      "id": 18,
+      "type": "Note",
+      "pos": [
+        460.8001708984375,
+        263.64251708984375
+      ],
+      "size": [
+        379.4292297363281,
+        88
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Option 1: CREATE THE MASK FROM THE ALPHA CHANNEL (Useful for example to generate the background of an image)\n\nMake sure that you are using \"External Image Alpha\". \n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 21,
+      "type": "LoadImage",
+      "pos": [
+        469.57025146484375,
+        1506.3018798828125
+      ],
+      "size": [
+        315,
+        314
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            18
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "ComfyUI_temp_otmos_00005_.png",
+        "image",
+        ""
+      ]
+    },
+    {
+      "id": 25,
+      "type": "MaskToImage",
+      "pos": [
+        1283.1668701171875,
+        1579.603271484375
+      ],
+      "size": [
+        176.39999389648438,
+        26
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 28
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            21
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "MaskToImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 13,
+      "type": "SplitImageWithAlpha",
+      "pos": [
+        1602.5518798828125,
+        417.59869384765625
+      ],
+      "size": [
+        277.20001220703125,
+        46
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 13
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            14,
+            25
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            15,
+            26
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "SplitImageWithAlpha"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 29,
+      "type": "VAEEncodeForInpaint",
+      "pos": [
+        2220.89453125,
+        401.0885009765625
+      ],
+      "size": [
+        340.20001220703125,
+        98
+      ],
+      "flags": {},
+      "order": 16,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "pixels",
+          "type": "IMAGE",
+          "link": 25
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 26
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "VAEEncodeForInpaint"
+      },
+      "widgets_values": [
+        6
+      ]
+    },
+    {
+      "id": 14,
+      "type": "PreviewImage",
+      "pos": [
+        1950,
+        350
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 14
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 17,
+      "type": "MaskToImage",
+      "pos": [
+        1752.33203125,
+        572.061767578125
+      ],
+      "size": [
+        176.39999389648438,
+        26
+      ],
+      "flags": {},
+      "order": 15,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 15
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            16
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "MaskToImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 27,
+      "type": "PreviewImage",
+      "pos": [
+        1329.2960205078125,
+        1132.759765625
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 22
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 20,
+      "type": "LoadImage",
+      "pos": [
+        468.1963806152344,
+        1128.9498291015625
+      ],
+      "size": [
+        315,
+        314
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            17
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": []
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "clipspace/clipspace-mask-1126817.300000012.png [input]",
+        "image",
+        ""
+      ]
+    },
+    {
+      "id": 24,
+      "type": "ImageToMask",
+      "pos": [
+        1255.560302734375,
+        1468.347900390625
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 19
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            24,
+            28
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "ImageToMask"
+      },
+      "widgets_values": [
+        "red"
+      ]
+    },
+    {
+      "id": 26,
+      "type": "PreviewImage",
+      "pos": [
+        1488.1925048828125,
+        1579.146728515625
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 17,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 21
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 28,
+      "type": "VAEEncodeForInpaint",
+      "pos": [
+        1637.603271484375,
+        1182.81298828125
+      ],
+      "size": [
+        340.20001220703125,
+        98
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "pixels",
+          "type": "IMAGE",
+          "link": 23
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 24
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.27",
+        "Node name for S&R": "VAEEncodeForInpaint"
+      },
+      "widgets_values": [
+        6
+      ]
+    },
+    {
+      "id": 19,
+      "type": "Note",
+      "pos": [
+        467.4615173339844,
+        985.1439819335938
+      ],
+      "size": [
+        379.4292297363281,
+        88
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "Option 2: UPLOAD THE MASK AS OTHER IMAGE (useful if you require the image that is below the mask to do inpainting, in this case to generate new glasses)"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 22,
+      "type": "ComfyUIDeployExternalImage",
+      "pos": [
+        910.8199462890625,
+        1132.306396484375
+      ],
+      "size": [
+        390.5999755859375,
+        154
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "default_value",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": 17
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            22,
+            23
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfyui-deploy",
+        "ver": "cd3a2ff5471828f9c840e746551592e882c05aa4",
+        "Node name for S&R": "ComfyUIDeployExternalImage"
+      },
+      "widgets_values": [
+        "input_image",
+        "",
+        "",
+        "",
+        ""
+      ]
+    },
+    {
+      "id": 23,
+      "type": "ComfyUIDeployExternalImage",
+      "pos": [
+        843.6348876953125,
+        1467.8876953125
+      ],
+      "size": [
+        390.5999755859375,
+        154
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "default_value",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": 18
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            19
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfyui-deploy",
+        "ver": "cd3a2ff5471828f9c840e746551592e882c05aa4",
+        "Node name for S&R": "ComfyUIDeployExternalImage"
+      },
+      "widgets_values": [
+        "input_image_mask",
+        "",
+        "",
+        "",
+        ""
+      ]
+    },
+    {
+      "id": 12,
+      "type": "ComfyUIDeployExternalImageAlpha",
+      "pos": [
+        1107.891357421875,
+        418.2679138183594
+      ],
+      "size": [
+        466.1999816894531,
+        200
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "default_value",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": 11
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            13
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfyui-deploy",
+        "ver": "cd3a2ff5471828f9c840e746551592e882c05aa4",
+        "Node name for S&R": "ComfyUIDeployExternalImageAlpha"
+      },
+      "widgets_values": [
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+}

examples/flux-dev.json

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+        { "link": 55, "name": "negative", "type": "CONDITIONING" },
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+      "order": 9,
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+      "widgets_values": [
+        "🛈 [Learn more about this workflow](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-dev-1)"
+      ]
+    },
+    {
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+      "pos": [825, 510],
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+      "pos": [48, 192],
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+          "type": "CONDITIONING",
+          "links": [55],
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+          "type": "LATENT",
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+}

examples/flux-schnell.json

@@ -0,0 +1,423 @@
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+      "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 }
+      ],
+      "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": [],
+      "outputs": [
+        { "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
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 45,
+  "last_node_id": 30
+}

examples/hunyuan-video.json

@@ -0,0 +1,482 @@
+{
+  "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

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

examples/stable-diffusion-v35-large.json

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

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

@@ -0,0 +1,452 @@
+{
+  "extra": {
+    "ds": {
+      "scale": 0.6934334949441662,
+      "offset": [276.7941771713029, -7.923387380923855]
+    },
+    "node_versions": {
+      "comfy-core": "0.3.18",
+      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
+    }
+  },
+  "links": [
+    [35, 3, 0, 8, 0, "LATENT"],
+    [56, 8, 0, 28, 0, "IMAGE"],
+    [74, 38, 0, 6, 0, "CLIP"],
+    [75, 38, 0, 7, 0, "CLIP"],
+    [76, 39, 0, 8, 1, "VAE"],
+    [93, 8, 0, 47, 0, "IMAGE"],
+    [94, 49, 0, 51, 0, "CLIP_VISION"],
+    [97, 6, 0, 50, 0, "CONDITIONING"],
+    [98, 7, 0, 50, 1, "CONDITIONING"],
+    [99, 39, 0, 50, 2, "VAE"],
+    [101, 50, 0, 3, 1, "CONDITIONING"],
+    [102, 50, 1, 3, 2, "CONDITIONING"],
+    [103, 50, 2, 3, 3, "LATENT"],
+    [107, 51, 0, 50, 3, "CLIP_VISION_OUTPUT"],
+    [110, 37, 0, 54, 0, "MODEL"],
+    [111, 54, 0, 3, 0, "MODEL"],
+    [112, 55, 0, 6, 1, "STRING"],
+    [113, 56, 0, 7, 1, "STRING"],
+    [115, 57, 0, 50, 4, "IMAGE"],
+    [116, 57, 0, 51, 1, "IMAGE"],
+    [117, 58, 0, 50, 5, "INT"],
+    [118, 59, 0, 50, 6, "INT"]
+  ],
+  "nodes": [
+    {
+      "id": 8,
+      "pos": [1210, 190],
+      "mode": 0,
+      "size": [210, 46],
+      "type": "VAEDecode",
+      "flags": {},
+      "order": 15,
+      "inputs": [
+        { "link": 35, "name": "samples", "type": "LATENT" },
+        { "link": 76, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [56, 93], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
+    },
+    {
+      "id": 39,
+      "pos": [866.3932495117188, 499.18597412109375],
+      "mode": 0,
+      "size": [306.36004638671875, 58],
+      "type": "VAELoader",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "outputs": [
+        { "name": "VAE", "type": "VAE", "links": [76, 99], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAELoader" },
+      "widgets_values": ["wan_2.1_vae.safetensors"]
+    },
+    {
+      "id": 28,
+      "pos": [1460, 190],
+      "mode": 0,
+      "size": [870.8511352539062, 643.7430419921875],
+      "type": "SaveAnimatedWEBP",
+      "flags": {},
+      "order": 16,
+      "inputs": [{ "link": 56, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI", 16, false, 90, "default", ""]
+    },
+    {
+      "id": 47,
+      "pos": [2367.213134765625, 193.6114959716797],
+      "mode": 4,
+      "size": [315, 130],
+      "type": "SaveWEBM",
+      "flags": {},
+      "order": 17,
+      "inputs": [{ "link": 93, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": { "Node name for S&R": "SaveWEBM" },
+      "widgets_values": ["ComfyUI", "vp9", 24, 32]
+    },
+    {
+      "id": 50,
+      "pos": [673.0507202148438, 627.272705078125],
+      "mode": 0,
+      "size": [342.5999755859375, 250],
+      "type": "WanImageToVideo",
+      "flags": {},
+      "order": 13,
+      "inputs": [
+        { "link": 97, "name": "positive", "type": "CONDITIONING" },
+        { "link": 98, "name": "negative", "type": "CONDITIONING" },
+        { "link": 99, "name": "vae", "type": "VAE" },
+        {
+          "link": 107,
+          "name": "clip_vision_output",
+          "type": "CLIP_VISION_OUTPUT",
+          "shape": 7
+        },
+        { "link": 115, "name": "start_image", "type": "IMAGE", "shape": 7 },
+        {
+          "pos": [10, 116],
+          "link": 117,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        },
+        {
+          "pos": [10, 140],
+          "link": 118,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [101],
+          "slot_index": 0
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [102],
+          "slot_index": 1
+        },
+        { "name": "latent", "type": "LATENT", "links": [103], "slot_index": 2 }
+      ],
+      "properties": { "Node name for S&R": "WanImageToVideo" },
+      "widgets_values": [512, 512, 33, 1]
+    },
+    {
+      "id": 3,
+      "pos": [863, 187],
+      "mode": 0,
+      "size": [315, 262],
+      "type": "KSampler",
+      "flags": {},
+      "order": 14,
+      "inputs": [
+        { "link": 111, "name": "model", "type": "MODEL" },
+        { "link": 101, "name": "positive", "type": "CONDITIONING" },
+        { "link": 102, "name": "negative", "type": "CONDITIONING" },
+        { "link": 103, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [35], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
+      "widgets_values": [
+        55104841546759,
+        "randomize",
+        20,
+        6,
+        "uni_pc",
+        "simple",
+        1
+      ]
+    },
+    {
+      "id": 51,
+      "pos": [360, 640],
+      "mode": 0,
+      "size": [253.60000610351562, 78],
+      "type": "CLIPVisionEncode",
+      "flags": {},
+      "order": 12,
+      "inputs": [
+        { "link": 94, "name": "clip_vision", "type": "CLIP_VISION" },
+        { "link": 116, "name": "image", "type": "IMAGE" }
+      ],
+      "outputs": [
+        {
+          "name": "CLIP_VISION_OUTPUT",
+          "type": "CLIP_VISION_OUTPUT",
+          "links": [107],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionEncode" },
+      "widgets_values": ["none"]
+    },
+    {
+      "id": 54,
+      "pos": [510, 70],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "ModelSamplingSD3",
+      "flags": {},
+      "order": 9,
+      "inputs": [{ "link": 110, "name": "model", "type": "MODEL" }],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [111], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ModelSamplingSD3" },
+      "widgets_values": [8]
+    },
+    {
+      "id": 6,
+      "pos": [415, 186],
+      "mode": 0,
+      "size": [422.84503173828125, 164.31304931640625],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 10,
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 74, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 112,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [97],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around"
+      ]
+    },
+    {
+      "id": 55,
+      "pos": [-672.6411743164062, 140.167724609375],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [112] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "positive_prompt",
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around",
+        "Prompt",
+        "The text prompt to guide video generation."
+      ]
+    },
+    {
+      "id": 57,
+      "pos": [-664.3828125, 405.5113525390625],
+      "mode": 0,
+      "size": [390.5999755859375, 366],
+      "type": "ComfyUIDeployExternalImage",
+      "flags": {},
+      "order": 2,
+      "inputs": [
+        { "link": null, "name": "default_value", "type": "IMAGE", "shape": 7 }
+      ],
+      "outputs": [{ "name": "image", "type": "IMAGE", "links": [115, 116] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalImage" },
+      "widgets_values": [
+        "image_url",
+        "Image Url",
+        "URL of the input image.",
+        "https://comfy-deploy-output.s3.us-east-2.amazonaws.com/assets/img_GZMJYXDnLbYjWybu.png",
+        ""
+      ]
+    },
+    {
+      "id": 56,
+      "pos": [-674.42333984375, 953.0498046875],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [113] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "negative_prompt",
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+        "Negative Prompt",
+        "The negative prompt to use. Use it to address details that you don't want in the video. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
+      ]
+    },
+    {
+      "id": 58,
+      "pos": [-668.314453125, 1208.204833984375],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [117], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 512, "Width", "The width of the video. "]
+    },
+    {
+      "id": 59,
+      "pos": [-665.8831787109375, 1461.255126953125],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 5,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [118], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 512, "Height", "The Height of the video."]
+    },
+    {
+      "id": 37,
+      "pos": [-3.3700287342071533, 81.69654846191406],
+      "mode": 0,
+      "size": [346.7470703125, 82],
+      "type": "UNETLoader",
+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [110], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["wan2.1_i2v_480p_14B_bf16.safetensors", "default"]
+    },
+    {
+      "id": 38,
+      "pos": [-3.7316977977752686, 230.76495361328125],
+      "mode": 0,
+      "size": [390, 98],
+      "type": "CLIPLoader",
+      "flags": {},
+      "order": 7,
+      "inputs": [],
+      "outputs": [
+        { "name": "CLIP", "type": "CLIP", "links": [74, 75], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "CLIPLoader" },
+      "widgets_values": [
+        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "wan",
+        "default"
+      ]
+    },
+    {
+      "id": 49,
+      "pos": [16.57183265686035, 640.22314453125],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "CLIPVisionLoader",
+      "flags": {},
+      "order": 8,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP_VISION",
+          "type": "CLIP_VISION",
+          "links": [94],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionLoader" },
+      "widgets_values": ["clip_vision_h.safetensors"]
+    },
+    {
+      "id": 7,
+      "pos": [414.1811828613281, 393.8824768066406],
+      "mode": 0,
+      "size": [425.27801513671875, 180.6060791015625],
+      "type": "CLIPTextEncode",
+      "color": "#322",
+      "flags": {},
+      "order": 11,
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "inputs": [
+        { "link": 75, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 113,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [98],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+      ]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Input",
+      "bounding": [
+        -708.2940063476562, 47.549102783203125, 617.7969360351562,
+        761.303955078125
+      ],
+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#b06634",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
+        -703.6196899414062, 861.310791015625, 625.998779296875,
+        845.0675048828125
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 118,
+  "last_node_id": 59
+}

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

@@ -0,0 +1,452 @@
+{
+  "extra": {
+    "ds": {
+      "scale": 0.6303940863128795,
+      "offset": [-209.31226203277674, -32.62073943929215]
+    },
+    "node_versions": {
+      "comfy-core": "0.3.18",
+      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
+    }
+  },
+  "links": [
+    [1, 6, 0, 1, 0, "LATENT"],
+    [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"]
+  ],
+  "nodes": [
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+      "flags": {},
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+      "inputs": [
+        { "link": 1, "name": "samples", "type": "LATENT" },
+        { "link": 2, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [3, 4], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
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+    {
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+      "pos": [1480.382080078125, 1011.8104248046875],
+      "mode": 0,
+      "size": [306.36004638671875, 58],
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+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "outputs": [
+        { "name": "VAE", "type": "VAE", "links": [2, 7], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAELoader" },
+      "widgets_values": ["wan_2.1_vae.safetensors"]
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+      "outputs": [],
+      "properties": { "Node name for S&R": "SaveWEBM" },
+      "widgets_values": ["ComfyUI", "vp9", 24, 32]
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+        { "link": 6, "name": "negative", "type": "CONDITIONING" },
+        { "link": 7, "name": "vae", "type": "VAE" },
+        {
+          "link": 8,
+          "name": "clip_vision_output",
+          "type": "CLIP_VISION_OUTPUT",
+          "shape": 7
+        },
+        { "link": 9, "name": "start_image", "type": "IMAGE", "shape": 7 },
+        {
+          "pos": [10, 116],
+          "link": 10,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        },
+        {
+          "pos": [10, 140],
+          "link": 11,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "links": [13],
+          "slot_index": 0
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "links": [14],
+          "slot_index": 1
+        },
+        { "name": "latent", "type": "LATENT", "links": [15], "slot_index": 2 }
+      ],
+      "properties": { "Node name for S&R": "WanImageToVideo" },
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+    },
+    {
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+        { "link": 12, "name": "model", "type": "MODEL" },
+        { "link": 13, "name": "positive", "type": "CONDITIONING" },
+        { "link": 14, "name": "negative", "type": "CONDITIONING" },
+        { "link": 15, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [1], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
+      "widgets_values": [
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+        "randomize",
+        20,
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+      "size": [253.60000610351562, 78],
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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
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionEncode" },
+      "widgets_values": ["none"]
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+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [12], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ModelSamplingSD3" },
+      "widgets_values": [8]
+    },
+    {
+      "id": 9,
+      "pos": [1028.98876953125, 698.6244506835938],
+      "mode": 0,
+      "size": [422.84503173828125, 164.31304931640625],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 12,
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 19, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 20,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [5],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit turning around"
+      ]
+    },
+    {
+      "id": 11,
+      "pos": [-50.3940315246582, 918.1358032226562],
+      "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",
+        ""
+      ]
+    },
+    {
+      "id": 12,
+      "pos": [-60.4345588684082, 1465.6741943359375],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 2,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [22] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "negative_prompt",
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+        "Negative Prompt",
+        "The negative prompt to use. Use it to address details that you don't want in the video. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
+      ]
+    },
+    {
+      "id": 13,
+      "pos": [-54.3256721496582, 1720.8292236328125],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [10], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 512, "Width", "The width of the video. "]
+    },
+    {
+      "id": 14,
+      "pos": [-51.8943977355957, 1973.8795166015625],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [11], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 512, "Height", "The Height of the video."]
+    },
+    {
+      "id": 16,
+      "pos": [610.257080078125, 743.389404296875],
+      "mode": 0,
+      "size": [390, 98],
+      "type": "CLIPLoader",
+      "flags": {},
+      "order": 5,
+      "inputs": [],
+      "outputs": [
+        { "name": "CLIP", "type": "CLIP", "links": [19, 21], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "CLIPLoader" },
+      "widgets_values": [
+        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "wan",
+        "default"
+      ]
+    },
+    {
+      "id": 17,
+      "pos": [630.5606079101562, 1152.8475341796875],
+      "mode": 0,
+      "size": [315, 58],
+      "type": "CLIPVisionLoader",
+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "CLIP_VISION",
+          "type": "CLIP_VISION",
+          "links": [16],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPVisionLoader" },
+      "widgets_values": ["clip_vision_h.safetensors"]
+    },
+    {
+      "id": 18,
+      "pos": [1028.169921875, 906.5068969726562],
+      "mode": 0,
+      "size": [425.27801513671875, 180.6060791015625],
+      "type": "CLIPTextEncode",
+      "color": "#322",
+      "flags": {},
+      "order": 9,
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "inputs": [
+        { "link": 21, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 22,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [6],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+      ]
+    },
+    {
+      "id": 15,
+      "pos": [610.6187744140625, 594.3209838867188],
+      "mode": 0,
+      "size": [346.7470703125, 82],
+      "type": "UNETLoader",
+      "flags": {},
+      "order": 7,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [18], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["wan2.1_i2v_720p_14B_bf16.safetensors", "default"]
+    },
+    {
+      "id": 10,
+      "pos": [-60.696964263916016, 649.5515747070312],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 8,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [20] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "positive_prompt",
+        "a cute anime girl with massive fennec ears and a big fluffy tail wearing a maid outfit running towards front happily",
+        "Prompt",
+        "The text prompt to guide video generation."
+      ]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Input",
+      "bounding": [
+        -94.30522155761719, 560.1735229492188, 617.7969360351562,
+        761.303955078125
+      ],
+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#b06634",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
+        -89.63090515136719, 1373.9351806640625, 625.998779296875,
+        845.0675048828125
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 22,
+  "last_node_id": 18
+}

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

@@ -0,0 +1,359 @@
+{
+  "extra": {
+    "ds": {
+      "scale": 0.8390545288824369,
+      "offset": [814.3725295729478, -347.90757575249455]
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+      "comfyui-deploy": "b3df94d1affcf7ce05ee7eeda99989194bcd9159"
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+    [46, 6, 0, 3, 1, "CONDITIONING"],
+    [52, 7, 0, 3, 2, "CONDITIONING"],
+    [56, 8, 0, 28, 0, "IMAGE"],
+    [74, 38, 0, 6, 0, "CLIP"],
+    [75, 38, 0, 7, 0, "CLIP"],
+    [76, 39, 0, 8, 1, "VAE"],
+    [91, 40, 0, 3, 3, "LATENT"],
+    [93, 8, 0, 47, 0, "IMAGE"],
+    [94, 37, 0, 48, 0, "MODEL"],
+    [95, 48, 0, 3, 0, "MODEL"],
+    [96, 49, 0, 6, 1, "STRING"],
+    [97, 50, 0, 7, 1, "STRING"],
+    [99, 52, 0, 40, 1, "INT"],
+    [100, 51, 0, 40, 0, "INT"]
+  ],
+  "nodes": [
+    {
+      "id": 8,
+      "pos": [1210, 190],
+      "mode": 0,
+      "size": [210, 46],
+      "type": "VAEDecode",
+      "flags": {},
+      "order": 12,
+      "inputs": [
+        { "link": 35, "name": "samples", "type": "LATENT" },
+        { "link": 76, "name": "vae", "type": "VAE" }
+      ],
+      "outputs": [
+        { "name": "IMAGE", "type": "IMAGE", "links": [56, 93], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAEDecode" },
+      "widgets_values": []
+    },
+    {
+      "id": 39,
+      "pos": [866.3932495117188, 499.18597412109375],
+      "mode": 0,
+      "size": [306.36004638671875, 58],
+      "type": "VAELoader",
+      "flags": {},
+      "order": 0,
+      "inputs": [],
+      "outputs": [
+        { "name": "VAE", "type": "VAE", "links": [76], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "VAELoader" },
+      "widgets_values": ["wan_2.1_vae.safetensors"]
+    },
+    {
+      "id": 47,
+      "pos": [2367.213134765625, 193.6114959716797],
+      "mode": 4,
+      "size": [315, 130],
+      "type": "SaveWEBM",
+      "flags": {},
+      "order": 14,
+      "inputs": [{ "link": 93, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": { "Node name for S&R": "SaveWEBM" },
+      "widgets_values": ["ComfyUI", "vp9", 24, 32]
+    },
+    {
+      "id": 3,
+      "pos": [863, 187],
+      "mode": 0,
+      "size": [315, 262],
+      "type": "KSampler",
+      "flags": {},
+      "order": 11,
+      "inputs": [
+        { "link": 95, "name": "model", "type": "MODEL" },
+        { "link": 46, "name": "positive", "type": "CONDITIONING" },
+        { "link": 52, "name": "negative", "type": "CONDITIONING" },
+        { "link": 91, "name": "latent_image", "type": "LATENT" }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [35], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "KSampler" },
+      "widgets_values": [
+        577746309562741,
+        "randomize",
+        30,
+        6,
+        "uni_pc",
+        "simple",
+        1
+      ]
+    },
+    {
+      "id": 48,
+      "pos": [440, 50],
+      "mode": 0,
+      "size": [210, 58],
+      "type": "ModelSamplingSD3",
+      "flags": {},
+      "order": 7,
+      "inputs": [{ "link": 94, "name": "model", "type": "MODEL" }],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [95], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ModelSamplingSD3" },
+      "widgets_values": [8]
+    },
+    {
+      "id": 37,
+      "pos": [20, 40],
+      "mode": 0,
+      "size": [346.7470703125, 82],
+      "type": "UNETLoader",
+      "flags": {},
+      "order": 1,
+      "inputs": [],
+      "outputs": [
+        { "name": "MODEL", "type": "MODEL", "links": [94], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "UNETLoader" },
+      "widgets_values": ["wan2.1_t2v_1.3B_fp16.safetensors", "default"]
+    },
+    {
+      "id": 6,
+      "pos": [415, 186],
+      "mode": 0,
+      "size": [422.84503173828125, 164.31304931640625],
+      "type": "CLIPTextEncode",
+      "color": "#232",
+      "flags": {},
+      "order": 8,
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "inputs": [
+        { "link": 74, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 96,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#353",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [46],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "a fox moving quickly in a beautiful winter scenery nature trees mountains daytime tracking camera"
+      ]
+    },
+    {
+      "id": 7,
+      "pos": [413, 389],
+      "mode": 0,
+      "size": [425.27801513671875, 180.6060791015625],
+      "type": "CLIPTextEncode",
+      "color": "#322",
+      "flags": {},
+      "order": 9,
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "inputs": [
+        { "link": 75, "name": "clip", "type": "CLIP" },
+        {
+          "pos": [10, 36],
+          "link": 97,
+          "name": "text",
+          "type": "STRING",
+          "widget": { "name": "text" }
+        }
+      ],
+      "bgcolor": "#533",
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [52],
+          "slot_index": 0
+        }
+      ],
+      "properties": { "Node name for S&R": "CLIPTextEncode" },
+      "widgets_values": [
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+      ]
+    },
+    {
+      "id": 38,
+      "pos": [-10.047812461853027, 187.37384033203125],
+      "mode": 0,
+      "size": [390, 98],
+      "type": "CLIPLoader",
+      "flags": {},
+      "order": 2,
+      "inputs": [],
+      "outputs": [
+        { "name": "CLIP", "type": "CLIP", "links": [74, 75], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "CLIPLoader" },
+      "widgets_values": [
+        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "wan",
+        "default"
+      ]
+    },
+    {
+      "id": 49,
+      "pos": [-535.2967529296875, 342.3277587890625],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 3,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [96] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "positive_prompt",
+        "a fox moving quickly in a beautiful winter scenery nature trees mountains daytime tracking camera",
+        "Prompt",
+        "The text prompt to guide video generation. "
+      ]
+    },
+    {
+      "id": 50,
+      "pos": [-526.2716064453125, 703.8343505859375],
+      "mode": 0,
+      "size": [400, 200],
+      "type": "ComfyUIDeployExternalText",
+      "flags": {},
+      "order": 4,
+      "inputs": [],
+      "outputs": [{ "name": "text", "type": "STRING", "links": [97] }],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalText" },
+      "widgets_values": [
+        "negative_prompt",
+        "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+        "Negative Prompt",
+        "The negative prompt to use. Use it to address details that you don't want in the image. This could be colors, objects, scenery and even the small details (e.g. moustache, blurry, low resolution). "
+      ]
+    },
+    {
+      "id": 40,
+      "pos": [516.926513671875, 619.59716796875],
+      "mode": 0,
+      "size": [315, 150],
+      "type": "EmptyHunyuanLatentVideo",
+      "flags": {},
+      "order": 10,
+      "inputs": [
+        {
+          "pos": [10, 36],
+          "link": 100,
+          "name": "width",
+          "type": "INT",
+          "widget": { "name": "width" }
+        },
+        {
+          "pos": [10, 60],
+          "link": 99,
+          "name": "height",
+          "type": "INT",
+          "widget": { "name": "height" }
+        }
+      ],
+      "outputs": [
+        { "name": "LATENT", "type": "LATENT", "links": [91], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "EmptyHunyuanLatentVideo" },
+      "widgets_values": [832, 480, 33, 1]
+    },
+    {
+      "id": 28,
+      "pos": [1460, 190],
+      "mode": 0,
+      "size": [870.8511352539062, 643.7430419921875],
+      "type": "SaveAnimatedWEBP",
+      "flags": {},
+      "order": 13,
+      "inputs": [{ "link": 56, "name": "images", "type": "IMAGE" }],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": ["ComfyUI", 16, false, 90, "default"]
+    },
+    {
+      "id": 51,
+      "pos": [-522.7415161132812, 959.3386840820312],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 5,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [100], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["width", 832, "Width", "The Width of the Video. "]
+    },
+    {
+      "id": 52,
+      "pos": [-518.9917602539062, 1207.9444580078125],
+      "mode": 0,
+      "size": [453.5999755859375, 200],
+      "type": "ComfyUIDeployExternalNumberInt",
+      "flags": {},
+      "order": 6,
+      "inputs": [],
+      "outputs": [
+        { "name": "value", "type": "INT", "links": [99], "slot_index": 0 }
+      ],
+      "properties": { "Node name for S&R": "ComfyUIDeployExternalNumberInt" },
+      "widgets_values": ["height", 480, "Height", "The Height of the Video. "]
+    }
+  ],
+  "config": {},
+  "groups": [
+    {
+      "id": 1,
+      "color": "#3f789e",
+      "flags": {},
+      "title": "Inputs",
+      "bounding": [
+        -560.9110717773438, 255.1485595703125, 500.94989013671875,
+        333.4786682128906
+      ],
+      "font_size": 24
+    },
+    {
+      "id": 2,
+      "color": "#b06634",
+      "flags": {},
+      "title": "Additional",
+      "bounding": [
+        -556.5305786132812, 619.87548828125, 761.2673950195312,
+        811.6837768554688
+      ],
+      "font_size": 24
+    }
+  ],
+  "version": 0.4,
+  "last_link_id": 100,
+  "last_node_id": 52
+}

globals.py

@@ -6,10 +6,12 @@ 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"
@@ -17,48 +19,76 @@ class Status(Enum):
     FAILED = "failed"
     UPLOADING = "uploading"
 
+
 class StreamingPrompt(BaseModel):
     workflow_api: Any
     auth_token: str
     inputs: dict[str, Union[str, bytes, Image.Image]]
     running_prompt_ids: set[str] = set()
-    status_endpoint: str
-    file_upload_endpoint: str
-    
+    status_endpoint: Optional[str]
+    file_upload_endpoint: Optional[str]
+    workflow: Any
+    gpu_event_id: Optional[str] = None
+
+
 class SimplePrompt(BaseModel):
-    status_endpoint: str
-    file_upload_endpoint: str
+    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,
+    is_realtime: bool = False
+    start_time: Optional[float] = None
+    gpu_event_id: Optional[str] = None
+
 
 sockets = dict()
 prompt_metadata: dict[str, SimplePrompt] = {}
 streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
 
+
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
     UNENCODED_PREVIEW_IMAGE = 2
-    
+    EXR_IMAGE = 4
+
+
 max_output_id_length = 24
 
-async def send_image(image_data, sid=None, output_id:str = None):
+
+async def send_exr(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")
+
+    bytesIO = BytesIO()
+    # 10 bytes for the output_id
+    bytesIO.write(encoded_output_id)
+    bytesIO.write(image_data)
+
+    preview_bytes = bytesIO.getvalue()
+    await send_bytes(BinaryEventTypes.EXR_IMAGE, preview_bytes, sid=sid)
+
+
+async def send_image(image_data, sid=None, output_id: str = None):
+    max_length = max_output_id_length
+    output_id = output_id[:max_length]
+    padded_output_id = output_id.ljust(max_length, "\x00")
+    encoded_output_id = padded_output_id.encode("ascii", "replace")
+
     image_type = image_data[0]
     image = image_data[1]
     max_size = image_data[2]
     quality = image_data[3]
     if max_size is not None:
-        if hasattr(Image, 'Resampling'):
+        if hasattr(Image, "Resampling"):
             resampling = Image.Resampling.BILINEAR
         else:
             resampling = Image.ANTIALIAS
@@ -82,17 +112,23 @@ 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}")
@@ -102,9 +138,10 @@ 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:
@@ -112,4 +149,4 @@ 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 = "1.0.0"
-license = "LICENSE"
-dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
+version = "2.1.0"
+license = { file = "LICENSE" }
+dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg", "tabulate", "brotli"]
 
 [project.urls]
 Repository = "https://github.com/BennyKok/comfyui-deploy"

requirements.txt

@@ -1,4 +1,7 @@
 aiofiles
 pydantic
 opencv-python
-imageio-ffmpeg
+imageio-ffmpeg
+brotli
+tabulate
+# logfire

web-plugin/api.js

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

web-plugin/app.js

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

web-plugin/widgets.js

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

web/package.json

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

web/src/components/customInputNodes.tsx

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

web/src/routes/registerCreateRunRoute.ts

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

web/src/server/createRun.ts

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