Merge branch 'main' into dev

comfy-nodes/comfy_std_output.py

@@ -1,46 +0,0 @@
-import json
-
-
-class AnyType(str):
-    """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
-
-    def __ne__(self, __value: object) -> bool:
-        return False
-
-
-any = AnyType("*")
-
-
-class ComfyDeployStdOutputAny:
-    @classmethod
-    def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
-        return {
-            "required": {
-                "name": ("STRING", {"default": "ComfyUI"}),
-                "source": (any, {}),  # Use "*" to accept any input type
-            },
-        }
-
-    CATEGORY = "output"
-    RETURN_TYPES = ()
-    FUNCTION = "run"
-    OUTPUT_NODE = True
-
-    def run(self, name, source=None):
-        value = "None"
-        if source is not None:
-            try:
-                value = json.dumps(source)
-            except Exception:
-                try:
-                    value = str(source)
-                except Exception:
-                    value = "source exists, but could not be serialized."
-
-        return {"ui": {name: (value,)}}
-
-
-NODE_CLASS_MAPPINGS = {"ComfyDeployStdOutputAny": ComfyDeployStdOutputAny}
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ComfyDeployStdOutputAny": "Standard Any Output (ComfyDeploy)"
-}

comfy-nodes/comfy_std_output_image.py

@@ -1,92 +0,0 @@
-import os
-import json
-import numpy as np
-from PIL import Image
-from PIL.PngImagePlugin import PngInfo
-import folder_paths
-
-
-class ComfyDeployStdOutputImage:
-    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}),
-            },
-            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-        }
-
-    RETURN_TYPES = ()
-    FUNCTION = "run"
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "output"
-    DESCRIPTION = "Saves the input images to your ComfyUI output directory."
-
-    def run(
-        self,
-        images,
-        filename_prefix="ComfyUI",
-        file_type="png",
-        quality=80,
-        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}
-            )
-            counter += 1
-
-        return {"ui": {"images": results}}
-
-
-NODE_CLASS_MAPPINGS = {"ComfyDeployStdOutputImage": ComfyDeployStdOutputImage}
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ComfyDeployStdOutputImage": "Standard Image Output (ComfyDeploy)"
-}

comfy-nodes/external_boolean.py

@@ -8,16 +8,6 @@ class ComfyUIDeployExternalBoolean:
                     {"multiline": False, "default": "input_bool"},
                 ),
                 "default_value": ("BOOLEAN", {"default": False})
-            },
-            "optional": {
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
@@ -26,7 +16,7 @@ class ComfyUIDeployExternalBoolean:
     
     FUNCTION = "run"
     
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
         print(f"Node '{input_id}' processing with switch set to {default_value}")
         return [default_value]
     

comfy-nodes/external_checkpoints.py

@@ -5,12 +5,6 @@ import torch
 import folder_paths
 from tqdm import tqdm
 
-class AnyType(str):
-    def __ne__(self, __value: object) -> bool:
-        return False
-        
-WILDCARD = AnyType("*")
-
 class ComfyUIDeployExternalCheckpoint:
     @classmethod
     def INPUT_TYPES(s):
@@ -23,25 +17,17 @@ class ComfyUIDeployExternalCheckpoint:
             },
             "optional": {
                 "default_value": (folder_paths.get_filename_list("checkpoints"), ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
-    RETURN_TYPES = (WILDCARD,)
+    RETURN_TYPES = (folder_paths.get_filename_list("checkpoints"),)
     RETURN_NAMES = ("path",)
 
     FUNCTION = "run"
 
     CATEGORY = "deploy"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
         import requests
         import os
         import uuid

comfy-nodes/external_face_model.py

@@ -1,108 +0,0 @@
-from PIL import Image, ImageOps
-import numpy as np
-import torch
-import folder_paths
-
-
-class AnyType(str):
-    def __ne__(self, __value: object) -> bool:
-        return False
-
-
-WILDCARD = AnyType("*")
-
-
-class ComfyUIDeployExternalFaceModel:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_id": (
-                    "STRING",
-                    {"multiline": False, "default": "input_reactor_face_model"},
-                ),
-            },
-            "optional": {
-                "default_face_model_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "face_model_save_name": (  # if `default_face_model_name` is a link to download a file, we will attempt to save it with this name
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-                "face_model_url": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-            },
-        }
-
-    RETURN_TYPES = (WILDCARD,)
-    RETURN_NAMES = ("path",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "deploy"
-
-    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

@@ -15,14 +15,6 @@ class ComfyUIDeployExternalImage:
             },
             "optional": {
                 "default_value": ("IMAGE",),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
@@ -33,7 +25,7 @@ class ComfyUIDeployExternalImage:
 
     CATEGORY = "image"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
         image = default_value
         try:
             if input_id.startswith('http'):

comfy-nodes/external_image_alpha.py

@@ -15,14 +15,6 @@ class ComfyUIDeployExternalImageAlpha:
             },
             "optional": {
                 "default_value": ("IMAGE",),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
@@ -33,7 +25,7 @@ class ComfyUIDeployExternalImageAlpha:
 
     CATEGORY = "image"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
         image = default_value
         try:
             if input_id.startswith('http'):

comfy-nodes/external_image_batch.py

@@ -21,14 +21,6 @@ class ComfyUIDeployExternalImageBatch:
             },
             "optional": {
                 "default_value": ("IMAGE",),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
@@ -39,34 +31,14 @@ class ComfyUIDeployExternalImageBatch:
 
     CATEGORY = "image"
     
-    def process_image(self, image):
+    def run(self, input_id, images=None, default_value=None):
-        image = ImageOps.exif_transpose(image)
-        image = image.convert("RGB")
-        image = np.array(image).astype(np.float32) / 255.0
-        image_tensor = torch.from_numpy(image)[None,]
-        return image_tensor
-    
-    def run(self, input_id, images=None, default_value=None, display_name=None, description=None):
-        import requests
-        import zipfile
-        import io
-        
         processed_images = []
         try:
             images_list = json.loads(images)  # Assuming images is a JSON array string
             print(images_list)
             for img_input in images_list:
-                if img_input.startswith('http') and img_input.endswith('.zip'):
+                if img_input.startswith('http'):
-                    print("Fetching zip file from url: ", img_input)
+                    import requests
-                    response = requests.get(img_input)
-                    zip_file = zipfile.ZipFile(io.BytesIO(response.content))
-                    for file_name in zip_file.namelist():
-                        if file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
-                            with zip_file.open(file_name) as file:
-                                image = Image.open(file)
-                                image = self.process_image(image)
-                                processed_images.append(image)
-                elif  img_input.startswith('http'):
                     from io import BytesIO
                     print("Fetching image from url: ", img_input)
                     response = requests.get(img_input)

comfy-nodes/external_lora.py

@@ -5,14 +5,6 @@ import torch
 import folder_paths
 
 
-class AnyType(str):
-    def __ne__(self, __value: object) -> bool:
-        return False
-
-
-WILDCARD = AnyType("*")
-
-
 class ComfyUIDeployExternalLora:
     @classmethod
     def INPUT_TYPES(s):
@@ -25,69 +17,38 @@ class ComfyUIDeployExternalLora:
             },
             "optional": {
                 "default_lora_name": (folder_paths.get_filename_list("loras"),),
-                "lora_save_name": (  # if `default_lora_name` is a link to download a file, we will attempt to save it with this name
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-                "lora_url": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
             },
         }
 
-    RETURN_TYPES = (WILDCARD,)
+    RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
     RETURN_NAMES = ("path",)
 
     FUNCTION = "run"
 
     CATEGORY = "deploy"
 
-    def run(
+    def run(self, input_id, default_lora_name=None):
-        self,
-        input_id,
-        default_lora_name=None,
-        lora_save_name=None,
-        display_name=None,
-        description=None,
-        lora_url=None,
-    ):
         import requests
         import os
         import uuid
 
-        if lora_url and lora_url.startswith("http"):
+        if default_lora_name.startswith("http"):
-            if lora_save_name:
+            unique_filename = str(uuid.uuid4()) + ".safetensors"
-                existing_loras = folder_paths.get_filename_list("loras")
+            print(unique_filename)
-                # 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
+                folder_paths.folder_names_and_paths["loras"][0][0], unique_filename
             )
             print(destination_path)
-            print("Downloading external lora - " + lora_url + " to " + destination_path)
+            print("Downloading external lora - " + input_id + " to " + destination_path)
             response = requests.get(
-                lora_url,
+                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 (lora_save_name,)
+            return (unique_filename,)
         else:
             print(f"using lora: {default_lora_name}")
             return (default_lora_name,)

comfy-nodes/external_number.py

@@ -16,15 +16,7 @@ class ComfyUIDeployExternalNumber:
             "optional": {
                 "default_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "default": 0, "min": -2147483647, "max": 2147483647, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 0, "step": 0.01},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
                 ),
             }
         }
@@ -36,7 +28,7 @@ class ComfyUIDeployExternalNumber:
 
     CATEGORY = "number"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
         try:
             float_value = float(input_id)
             print("my number", float_value)

comfy-nodes/external_number_int.py

@@ -16,15 +16,7 @@ class ComfyUIDeployExternalNumberInt:
             "optional": {
                 "default_value": (
                     "INT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0},
+                    {"multiline": True, "display": "number", "default": 0},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
                 ),
             }
         }
@@ -36,7 +28,7 @@ class ComfyUIDeployExternalNumberInt:
 
     CATEGORY = "number"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
         if not input_id or (isinstance(input_id, str) and not input_id.strip().isdigit()):
             return [default_value]
         return [int(input_id)]

comfy-nodes/external_number_slider.py

@@ -11,23 +11,15 @@ class ComfyUIDeployExternalNumberSlider:
             "optional": {
                 "default_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0.5, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 0.5, "step": 0.01},
                 ),
                 "min_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 0, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 0, "step": 0.01},
                 ),
                 "max_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "min": -2147483647, "max": 2147483647, "default": 1, "step": 0.01},
+                    {"multiline": True, "display": "number", "default": 1, "step": 0.01},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
                 ),
             }
         }
@@ -39,7 +31,7 @@ class ComfyUIDeployExternalNumberSlider:
 
     CATEGORY = "number"
 
-    def run(self, input_id, default_value=None, min_value=0, max_value=1, display_name=None, description=None):
+    def run(self, input_id, default_value=None, min_value=0, max_value=1):
         try:
             float_value = float(input_id)
             if min_value <= float_value <= max_value:

comfy-nodes/external_text.py

@@ -18,14 +18,6 @@ class ComfyUIDeployExternalText:
                     "STRING",
                     {"multiline": True, "default": ""},
                 ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
@@ -36,7 +28,7 @@ class ComfyUIDeployExternalText:
 
     CATEGORY = "text"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None):
+    def run(self, input_id, default_value=None):
         return [default_value]
 
 

comfy-nodes/external_text_any.py

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

@@ -1,15 +1,10 @@
-# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite and is meant to work with
-# Intended to work with https://github.com/NicholasKao1029/ComfyUI-VideoHelperSuite/tree/main
 import os
 import itertools
 import numpy as np
 import torch
-from typing import Union
-from torch import Tensor
 import cv2
-import psutil
 
-from collections.abc import Mapping
 import folder_paths
 from comfy.utils import common_upscale
 
@@ -95,25 +90,13 @@ if gifski_path is None:
         gifski_path = shutil.which("gifski")
 
 
-def is_safe_path(path):
-    if "VHS_STRICT_PATHS" not in os.environ:
-        return True
-    basedir = os.path.abspath(".")
-    try:
-        common_path = os.path.commonpath([basedir, path])
-    except:
-        # Different drive on windows
-        return False
-    return common_path == basedir
-
-
 def get_sorted_dir_files_from_directory(
     directory: str,
     skip_first_images: int = 0,
     select_every_nth: int = 1,
     extensions: Iterable = None,
 ):
-    directory = strip_path(directory)
+    directory = directory.strip()
     dir_files = os.listdir(directory)
     dir_files = sorted(dir_files)
     dir_files = [os.path.join(directory, x) for x in dir_files]
@@ -194,59 +177,18 @@ def requeue_workflow(requeue_required=(-1, True)):
 
 
 def get_audio(file, start_time=0, duration=0):
-    args = [ffmpeg_path, "-i", file]
+    args = [ffmpeg_path, "-v", "error", "-i", file]
     if start_time > 0:
         args += ["-ss", str(start_time)]
     if duration > 0:
         args += ["-t", str(duration)]
     try:
-        # TODO: scan for sample rate and maintain
         res = subprocess.run(
-            args + ["-f", "f32le", "-"], capture_output=True, check=True
+            args + ["-f", "wav", "-"], stdout=subprocess.PIPE, check=True
-        )
+        ).stdout
-        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
-        match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
     except subprocess.CalledProcessError as e:
-        raise Exception(
+        return False
-            f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
+    return res
-        )
-    if match:
-        ar = int(match.group(1))
-        # NOTE: Just throwing an error for other channel types right now
-        # Will deal with issues if they come
-        ac = {"mono": 1, "stereo": 2}[match.group(2)]
-    else:
-        ar = 44100
-        ac = 2
-    audio = audio.reshape((-1, ac)).transpose(0, 1).unsqueeze(0)
-    return {"waveform": audio, "sample_rate": ar}
-
-
-class LazyAudioMap(Mapping):
-    def __init__(self, file, start_time, duration):
-        self.file = file
-        self.start_time = start_time
-        self.duration = duration
-        self._dict = None
-
-    def __getitem__(self, key):
-        if self._dict is None:
-            self._dict = get_audio(self.file, self.start_time, self.duration)
-        return self._dict[key]
-
-    def __iter__(self):
-        if self._dict is None:
-            self._dict = get_audio(self.file, self.start_time, self.duration)
-        return iter(self._dict)
-
-    def __len__(self):
-        if self._dict is None:
-            self._dict = get_audio(self.file, self.start_time, self.duration)
-        return len(self._dict)
-
-
-def lazy_get_audio(file, start_time=0, duration=0):
-    return LazyAudioMap(file, start_time, duration)
 
 
 def lazy_eval(func):
@@ -288,19 +230,6 @@ def validate_sequence(path):
     return False
 
 
-def strip_path(path):
-    # This leaves whitespace inside quotes and only a single "
-    # thus ' ""test"' -> '"test'
-    # consider path.strip(string.whitespace+"\"")
-    # or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1)
-    path = path.strip()
-    if path.startswith('"'):
-        path = path[1:]
-    if path.endswith('"'):
-        path = path[:-1]
-    return path
-
-
 def hash_path(path):
     if path is None:
         return "input"
@@ -357,145 +286,6 @@ def target_size(
     return (width, height)
 
 
-def validate_index(
-    index: int,
-    length: int = 0,
-    is_range: bool = False,
-    allow_negative=False,
-    allow_missing=False,
-) -> int:
-    # if part of range, do nothing
-    if is_range:
-        return index
-    # otherwise, validate index
-    # validate not out of range - only when latent_count is passed in
-    if length > 0 and index > length - 1 and not allow_missing:
-        raise IndexError(f"Index '{index}' out of range for {length} item(s).")
-    # if negative, validate not out of range
-    if index < 0:
-        if not allow_negative:
-            raise IndexError(f"Negative indeces not allowed, but was '{index}'.")
-        conv_index = length + index
-        if conv_index < 0 and not allow_missing:
-            raise IndexError(
-                f"Index '{index}', converted to '{conv_index}' out of range for {length} item(s)."
-            )
-        index = conv_index
-    return index
-
-
-def convert_to_index_int(
-    raw_index: str,
-    length: int = 0,
-    is_range: bool = False,
-    allow_negative=False,
-    allow_missing=False,
-) -> int:
-    try:
-        return validate_index(
-            int(raw_index),
-            length=length,
-            is_range=is_range,
-            allow_negative=allow_negative,
-            allow_missing=allow_missing,
-        )
-    except ValueError as e:
-        raise ValueError(f"Index '{raw_index}' must be an integer.", e)
-
-
-def convert_str_to_indexes(
-    indexes_str: str, length: int = 0, allow_missing=False
-) -> list[int]:
-    if not indexes_str:
-        return []
-    int_indexes = list(range(0, length))
-    allow_negative = length > 0
-    chosen_indexes = []
-    # parse string - allow positive ints, negative ints, and ranges separated by ':'
-    groups = indexes_str.split(",")
-    groups = [g.strip() for g in groups]
-    for g in groups:
-        # parse range of indeces (e.g. 2:16)
-        if ":" in g:
-            index_range = g.split(":", 2)
-            index_range = [r.strip() for r in index_range]
-
-            start_index = index_range[0]
-            if len(start_index) > 0:
-                start_index = convert_to_index_int(
-                    start_index,
-                    length=length,
-                    is_range=True,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            else:
-                start_index = 0
-            end_index = index_range[1]
-            if len(end_index) > 0:
-                end_index = convert_to_index_int(
-                    end_index,
-                    length=length,
-                    is_range=True,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            else:
-                end_index = length
-            # support step as well, to allow things like reversing, every-other, etc.
-            step = 1
-            if len(index_range) > 2:
-                step = index_range[2]
-                if len(step) > 0:
-                    step = convert_to_index_int(
-                        step,
-                        length=length,
-                        is_range=True,
-                        allow_negative=True,
-                        allow_missing=True,
-                    )
-                else:
-                    step = 1
-            # if latents were passed in, base indeces on known latent count
-            if len(int_indexes) > 0:
-                chosen_indexes.extend(int_indexes[start_index:end_index][::step])
-            # otherwise, assume indeces are valid
-            else:
-                chosen_indexes.extend(list(range(start_index, end_index, step)))
-        # parse individual indeces
-        else:
-            chosen_indexes.append(
-                convert_to_index_int(
-                    g,
-                    length=length,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            )
-    return chosen_indexes
-
-
-def select_indexes(input_obj: Union[Tensor, list], idxs: list):
-    if type(input_obj) == Tensor:
-        return input_obj[idxs]
-    else:
-        return [input_obj[i] for i in idxs]
-
-
-def select_indexes_from_str(
-    input_obj: Union[Tensor, list], indexes: str, err_if_missing=True, err_if_empty=True
-):
-    real_idxs = convert_str_to_indexes(
-        indexes, len(input_obj), allow_missing=not err_if_missing
-    )
-    if err_if_empty and len(real_idxs) == 0:
-        raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
-    return select_indexes(input_obj, real_idxs)
-
-
-###
-
-
 def cv_frame_generator(
     video,
     force_rate,
@@ -505,10 +295,9 @@ def cv_frame_generator(
     meta_batch=None,
     unique_id=None,
 ):
-    video_cap = cv2.VideoCapture(strip_path(video))
+    video_cap = cv2.VideoCapture(video)
     if not video_cap.isOpened():
         raise ValueError(f"{video} could not be loaded with cv.")
-    pbar = None
 
     # extract video metadata
     fps = video_cap.get(cv2.CAP_PROP_FPS)
@@ -530,8 +319,6 @@ def cv_frame_generator(
         target_frame_time = 1 / force_rate
 
     yield (width, height, fps, duration, total_frames, target_frame_time)
-    if meta_batch is not None:
-        yield min(frame_load_cap, total_frames)
 
     time_offset = target_frame_time - base_frame_time
     while video_cap.isOpened():
@@ -562,8 +349,7 @@ def cv_frame_generator(
         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         # convert frame to comfyui's expected format
         # TODO: frame contains no exif information. Check if opencv2 has already applied
-        frame = np.array(frame, dtype=np.float32)
+        frame = np.array(frame, dtype=np.float32) / 255.0
-        torch.from_numpy(frame).div_(255)
         if prev_frame is not None:
             inp = yield prev_frame
             if inp is not None:
@@ -571,8 +357,6 @@ def cv_frame_generator(
                 return
         prev_frame = frame
         frames_added += 1
-        if pbar is not None:
-            pbar.update_absolute(frames_added, frame_load_cap)
         # if cap exists and we've reached it, stop processing frames
         if frame_load_cap > 0 and frames_added >= frame_load_cap:
             break
@@ -583,17 +367,6 @@ def cv_frame_generator(
         yield prev_frame
 
 
-def batched(it, n):
-    while batch := tuple(itertools.islice(it, n)):
-        yield batch
-
-
-def batched_vae_encode(images, vae, frames_per_batch):
-    for batch in batched(images, frames_per_batch):
-        image_batch = torch.from_numpy(np.array(batch))
-        yield from vae.encode(image_batch).numpy()
-
-
 def load_video_cv(
     video: str,
     force_rate: int,
@@ -605,8 +378,6 @@ def load_video_cv(
     select_every_nth: int,
     meta_batch=None,
     unique_id=None,
-    memory_limit_mb=None,
-    vae=None,
 ):
     if meta_batch is None or unique_id not in meta_batch.inputs:
         gen = cv_frame_generator(
@@ -630,89 +401,30 @@ def load_video_cv(
                 total_frames,
                 target_frame_time,
             )
-            meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
 
     else:
         (gen, width, height, fps, duration, total_frames, target_frame_time) = (
             meta_batch.inputs[unique_id]
         )
 
-    memory_limit = None
+    if meta_batch is not None:
-    if memory_limit_mb is not None:
+        gen = itertools.islice(gen, meta_batch.frames_per_batch)
-        memory_limit *= 2**20
-    else:
-        # TODO: verify if garbage collection should be performed here.
-        # leaves ~128 MB unreserved for safety
-        try:
-            memory_limit = (
-                psutil.virtual_memory().available + psutil.swap_memory().free
-            ) - 2**27
-        except:
-            print(
-                "Failed to calculate available memory. Memory load limit has been disabled"
-            )
-    if memory_limit is not None:
-        if vae is not None:
-            # space required to load as f32, exist as latent with wiggle room, decode to f32
-            max_loadable_frames = int(
-                memory_limit // (width * height * 3 * (4 + 4 + 1 / 10))
-            )
-        else:
-            # TODO: use better estimate for when vae is not None
-            # Consider completely ignoring for load_latent case?
-            max_loadable_frames = int(memory_limit // (width * height * 3 * (0.1)))
-        if meta_batch is not None:
-            if meta_batch.frames_per_batch > max_loadable_frames:
-                raise RuntimeError(
-                    f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory"
-                )
-            gen = itertools.islice(gen, meta_batch.frames_per_batch)
-        else:
-            original_gen = gen
-            gen = itertools.islice(gen, max_loadable_frames)
-    downscale_ratio = getattr(vae, "downscale_ratio", 8)
-    frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
-    if force_size != "Disabled" or vae is not None:
-        new_size = target_size(
-            width, height, force_size, custom_width, custom_height, downscale_ratio
-        )
-        if new_size[0] != width or new_size[1] != height:
 
-            def rescale(frame):
+    # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
-                s = torch.from_numpy(
+    images = torch.from_numpy(
-                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
+        np.fromiter(gen, np.dtype((np.float32, (height, width, 3))))
-                )
+    )
-                s = s.movedim(-1, 1)
-                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
-                return s.movedim(1, -1).numpy()
-
-            gen = itertools.chain.from_iterable(
-                map(rescale, batched(gen, frames_per_batch))
-            )
-    else:
-        new_size = width, height
-    if vae is not None:
-        gen = batched_vae_encode(gen, vae, frames_per_batch)
-        vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
-        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
-    else:
-        # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
-        images = torch.from_numpy(
-            np.fromiter(gen, np.dtype((np.float32, (new_size[1], new_size[0], 3))))
-        )
-    if meta_batch is None and memory_limit is not None:
-        try:
-            next(original_gen)
-            raise RuntimeError(
-                f"Memory limit hit after loading {len(images)} frames. Stopping execution."
-            )
-        except StopIteration:
-            pass
     if len(images) == 0:
         raise RuntimeError("No frames generated")
+    if force_size != "Disabled":
+        new_size = target_size(width, height, force_size, custom_width, custom_height)
+        if new_size[0] != width or new_size[1] != height:
+            s = images.movedim(-1, 1)
+            s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+            images = s.movedim(1, -1)
 
     # Setup lambda for lazy audio capture
-    audio = lazy_get_audio(
+    audio = lambda: get_audio(
         video,
         skip_first_frames * target_frame_time,
         frame_load_cap * target_frame_time * select_every_nth,
@@ -728,16 +440,13 @@ def load_video_cv(
         "loaded_fps": 1 / target_frame_time,
         "loaded_frame_count": len(images),
         "loaded_duration": len(images) * target_frame_time,
-        "loaded_width": new_size[0],
+        "loaded_width": images.shape[2],
-        "loaded_height": new_size[1],
+        "loaded_height": images.shape[1],
     }
-    if vae is None:
+
-        return (images, len(images), audio, video_info, None)
+    return (images, len(images), lazy_eval(audio), video_info)
-    else:
-        return (None, len(images), audio, video_info, {"samples": images})
 
 
-# modeled after Video upload node
 class ComfyUIDeployExternalVideo:
     @classmethod
     def INPUT_TYPES(s):
@@ -748,46 +457,68 @@ class ComfyUIDeployExternalVideo:
                 file_parts = f.split(".")
                 if len(file_parts) > 1 and (file_parts[-1] in video_extensions):
                     files.append(f)
-        return {"required": {
+        return {
-                    "input_id": (
+            "required": {
-                        "STRING",
+                "input_id": (
-                        {"multiline": False, "default": "input_video"},
+                    "STRING",
-                    ),
+                    {"multiline": False, "default": "input_video"},
-                     "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
+                ),
-                     "force_size": (["Disabled", "Custom Height", "Custom Width", "Custom", "256x?", "?x256", "256x256", "512x?", "?x512", "512x512"],),
+                "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
-                     "custom_width": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
+                "force_size": (
-                     "custom_height": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
+                    [
-                     "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                        "Disabled",
-                     "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                        "Custom Height",
-                     "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
+                        "Custom Width",
-                     },
+                        "Custom",
-                "optional": {
+                        "256x?",
-                    "meta_batch": ("VHS_BatchManager",),
+                        "?x256",
-                    "vae": ("VAE",),
+                        "256x256",
-                    "default_video": (sorted(files),),
+                        "512x?",
-                    "display_name": (
+                        "?x512",
-                        "STRING",
+                        "512x512",
-                        {"multiline": False, "default": ""},
+                    ],
-                    ),
+                ),
-                    "description": (
+                "custom_width": (
-                        "STRING",
+                    "INT",
-                        {"multiline": True, "default": ""},
+                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
-                    ),
+                ),
-                },
+                "custom_height": (
-                "hidden": {
+                    "INT",
-                    "unique_id": "UNIQUE_ID"
+                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
-                },
+                ),
-                }
+                "frame_load_cap": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
+                ),
+                "skip_first_frames": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
+                ),
+                "select_every_nth": (
+                    "INT",
+                    {"default": 1, "min": 1, "max": BIGMAX, "step": 1},
+                ),
+            },
+            "optional": {
+                "meta_batch": ("VHS_BatchManager",),
+                "default_value": (sorted(files),),
+            },
+            "hidden": {"unique_id": "UNIQUE_ID"},
+        }
 
     CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
 
-    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
+    RETURN_TYPES = (
+        "IMAGE",
+        "INT",
+        "VHS_AUDIO",
+        "VHS_VIDEOINFO",
+    )
     RETURN_NAMES = (
         "IMAGE",
         "frame_count",
         "audio",
         "video_info",
-        "LATENT",
     )
 
     FUNCTION = "load_video"
@@ -804,6 +535,8 @@ class ComfyUIDeployExternalVideo:
         meta_batch = kwargs.get("meta_batch")
         unique_id = kwargs.get("unique_id")
 
+        video = kwargs.get("default_value")
+        video_path = folder_paths.get_annotated_filepath(video.strip('"'))
 
         input_dir = folder_paths.get_input_directory()
         if input_id.startswith("http"):
@@ -833,11 +566,8 @@ class ComfyUIDeployExternalVideo:
                     leave=True,
                 ):
                     out_file.write(chunk)
-        else:
+
-            video = kwargs.get("default_video", None)
+        print("video path: ", video_path)
-            if video is None:
-                raise "No default video given and no external video provided"
-            video_path = folder_paths.get_annotated_filepath(video.strip('"'))
 
         return load_video_cv(
             video=video_path,

comfy-nodes/model_list.py

@@ -1,60 +0,0 @@
-import folder_paths
-class AnyType(str):
-    def __ne__(self, __value: object) -> bool:
-        return False
-
-from os import walk
-
-WILDCARD = AnyType("*")
-
-MODEL_EXTENSIONS = {
-    "safetensors": "SafeTensors file format",
-    "ckpt": "Checkpoint file",
-    "pth": "PyTorch serialized file",
-    "pkl": "Pickle file",
-    "onnx": "ONNX file",
-}
-
-def fetch_files(path):
-    for (dirpath, dirnames, filenames) in walk(path):
-        fs = []
-        if len(dirnames) > 0:
-            for dirname in dirnames:
-                fs.extend(fetch_files(f"{dirpath}/{dirname}"))
-        for filename in filenames:
-            # Remove "./models/" from the beginning of dirpath
-            relative_dirpath = dirpath.replace("./models/", "", 1)
-            file_path = f"{relative_dirpath}/{filename}"
-            
-            # Only add files that are known model extensions
-            file_extension = filename.split('.')[-1].lower()
-            if file_extension in MODEL_EXTENSIONS:
-                fs.append(file_path)
-
-        return fs
-allModels = fetch_files("./models")
-
-class ComfyUIDeployModalList:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": (allModels, ),
-            }
-        }
-
-    RETURN_TYPES = (WILDCARD,)
-    RETURN_NAMES = ("model",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "model"
-
-    def run(self, model=""):
-        # Split the model path by '/' and select the last item
-        model_name = model.split('/')[-1]
-        return [model_name]
-
-
-NODE_CLASS_MAPPINGS = {"ComfyUIDeployModelList": ComfyUIDeployModalList}
-NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployModelList": "Model List (ComfyUI Deploy)"}

globals.py

@@ -6,12 +6,10 @@ from PIL import Image, ImageOps
 from io import BytesIO
 from pydantic import BaseModel as PydanticBaseModel
 
-
 class BaseModel(PydanticBaseModel):
     class Config:
         arbitrary_types_allowed = True
         
-
 class Status(Enum):
     NOT_STARTED = "not-started"
     RUNNING = "running"
@@ -19,7 +17,6 @@ class Status(Enum):
     FAILED = "failed"
     UPLOADING = "uploading"
 
-
 class StreamingPrompt(BaseModel):
     workflow_api: Any
     auth_token: str
@@ -27,52 +24,42 @@ class StreamingPrompt(BaseModel):
     running_prompt_ids: set[str] = set()
     status_endpoint: Optional[str]
     file_upload_endpoint: Optional[str]
-    workflow: Any
-    gpu_event_id: Optional[str] = None
-
     
 class SimplePrompt(BaseModel):
     status_endpoint: Optional[str]
     file_upload_endpoint: Optional[str]
     
-    token: Optional[str]
-
     workflow_api: dict
     status: Status = Status.NOT_STARTED
     progress: set = set()
-    last_updated_node: Optional[str] = None
+    last_updated_node: Optional[str] = None,
     uploading_nodes: set = set()
     done: bool = False
-    is_realtime: bool = False
+    is_realtime: bool = False,
-    start_time: Optional[float] = None
+    start_time: Optional[float] = None,
-    gpu_event_id: Optional[str] = None
-
 
 sockets = dict()
 prompt_metadata: dict[str, SimplePrompt] = {}
 streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
 
-
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
     UNENCODED_PREVIEW_IMAGE = 2
     
-
 max_output_id_length = 24
 
-
+async def send_image(image_data, sid=None, output_id:str = None):
-async def send_image(image_data, sid=None, output_id: str = None):
     max_length = max_output_id_length
     output_id = output_id[:max_length]
-    padded_output_id = output_id.ljust(max_length, "\x00")
+    padded_output_id = output_id.ljust(max_length, '\x00')
-    encoded_output_id = padded_output_id.encode("ascii", "replace")
+    encoded_output_id = padded_output_id.encode('ascii', 'replace')
     
     image_type = image_data[0]
     image = image_data[1]
     max_size = image_data[2]
     quality = image_data[3]
     if max_size is not None:
-        if hasattr(Image, "Resampling"):
+        if hasattr(Image, 'Resampling'):
             resampling = Image.Resampling.BILINEAR
         else:
             resampling = Image.ANTIALIAS
@@ -101,18 +88,12 @@ async def send_image(image_data, sid=None, output_id: str = None):
     preview_bytes = bytesIO.getvalue()
     await send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
         
-
 async def send_socket_catch_exception(function, message):
     try:
         await function(message)
-    except (
+    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
-        aiohttp.ClientError,
-        aiohttp.ClientPayloadError,
-        ConnectionResetError,
-    ) as err:
         print("send error:", err)
 
-
 def encode_bytes(event, data):
     if not isinstance(event, int):
         raise RuntimeError(f"Binary event types must be integers, got {event}")
@@ -122,7 +103,6 @@ def encode_bytes(event, data):
     message.extend(data)
     return message
 
-
 async def send_bytes(event, data, sid=None):
     message = encode_bytes(event, data)
     

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-deploy"
 description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
-version = "1.1.0"
+version = "1.0.0"
 license = "LICENSE"
 dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
 

requirements.txt

@@ -2,5 +2,4 @@ aiofiles
 pydantic
 opencv-python
 imageio-ffmpeg
-brotli
+logfire
-# logfire

web-plugin/index.js

@@ -2,7 +2,6 @@ import { app } from "./app.js";
 import { api } from "./api.js";
 import { ComfyWidgets, LGraphNode } from "./widgets.js";
 import { generateDependencyGraph } from "https://esm.sh/[email protected]";
-import { ComfyDeploy } from "https://esm.sh/[email protected]";
 
 const loadingIcon = `<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 24 24"><g fill="none" stroke="#888888" stroke-linecap="round" stroke-width="2"><path stroke-dasharray="60" stroke-dashoffset="60" stroke-opacity=".3" d="M12 3C16.9706 3 21 7.02944 21 12C21 16.9706 16.9706 21 12 21C7.02944 21 3 16.9706 3 12C3 7.02944 7.02944 3 12 3Z"><animate fill="freeze" attributeName="stroke-dashoffset" dur="1.3s" values="60;0"/></path><path stroke-dasharray="15" stroke-dashoffset="15" d="M12 3C16.9706 3 21 7.02944 21 12"><animate fill="freeze" attributeName="stroke-dashoffset" dur="0.3s" values="15;0"/><animateTransform attributeName="transform" dur="1.5s" repeatCount="indefinite" type="rotate" values="0 12 12;360 12 12"/></path></g></svg>`;
 
@@ -20,8 +19,11 @@ function dispatchAPIEventData(data) {
   // Custom parse error
   if (msg.error) {
     let message = msg.error.message;
-    if (msg.error.details) message += ": " + msg.error.details;
+    if (msg.error.details)
-    for (const [nodeID, nodeError] of Object.entries(msg.node_errors)) {
+      message += ": " + msg.error.details;
+    for (const [nodeID, nodeError] of Object.entries(
+      msg.node_errors,
+    )) {
       message += "\n" + nodeError.class_type + ":";
       for (const errorReason of nodeError.errors) {
         message +=
@@ -83,52 +85,11 @@ function dispatchAPIEventData(data) {
   }
 }
 
-const context = {
-  selectedWorkflowInfo: null,
-};
-// let selectedWorkflowInfo = {
-//   workflow_id: "05da8f2b-63af-4c0c-86dd-08d01ec512b7",
-//   machine_id: "45ac5f85-b7b6-436f-8d97-2383b25485f3",
-//   native_run_api_endpoint: "http://localhost:3011/api/run",
-// };
-
-async function getSelectedWorkflowInfo() {
-  const workflow_info_promise = new Promise((resolve) => {
-    try {
-      const handleMessage = (event) => {
-        try {
-          const message = JSON.parse(event.data);
-          if (message.type === "workflow_info") {
-            resolve(message.data);
-            window.removeEventListener("message", handleMessage);
-          }
-        } catch (error) {
-          console.error(error);
-          resolve(undefined);
-        }
-      };
-      window.addEventListener("message", handleMessage);
-      sendEventToCD("workflow_info");
-    } catch (error) {
-      console.error(error);
-      resolve(undefined);
-    }
-  });
-
-  return workflow_info_promise;
-}
-
-function setSelectedWorkflowInfo(info) {
-  context.selectedWorkflowInfo = info;
-}
-
 /** @typedef {import('../../../web/types/comfy.js').ComfyExtension} ComfyExtension*/
 /** @type {ComfyExtension} */
 const ext = {
   name: "BennyKok.ComfyUIDeploy",
 
-  native_mode: false,
-
   init(app) {
     addButton();
 
@@ -138,17 +99,16 @@ const ext = {
     const org_display = queryParams.get("org_display");
     const origin = queryParams.get("origin");
     const workspace_mode = queryParams.get("workspace_mode");
-    this.native_mode = queryParams.get("native_mode") === "true";
 
     if (workspace_mode) {
       document.querySelector(".comfy-menu").style.display = "none";
 
       sendEventToCD("cd_plugin_onInit");
 
-      // app.queuePrompt = ((originalFunction) => async () => {
+      app.queuePrompt = ((originalFunction) => async () => {
-      //   // const prompt = await app.graphToPrompt();
+        // const prompt = await app.graphToPrompt();
-      //   sendEventToCD("cd_plugin_onQueuePromptTrigger");
+        sendEventToCD("cd_plugin_onQueuePromptTrigger");
-      // })(app.queuePrompt);
+      })(app.queuePrompt);
 
       // // Intercept the onkeydown event
       // window.addEventListener(
@@ -234,13 +194,11 @@ const ext = {
 
   registerCustomNodes() {
     /** @type {LGraphNode}*/
-    class ComfyDeploy extends LGraphNode {
+    class ComfyDeploy {
+      color = LGraphCanvas.node_colors.yellow.color;
+      bgcolor = LGraphCanvas.node_colors.yellow.bgcolor;
+      groupcolor = LGraphCanvas.node_colors.yellow.groupcolor;
       constructor() {
-        super();
-        this.color = LGraphCanvas.node_colors.yellow.color;
-        this.bgcolor = LGraphCanvas.node_colors.yellow.bgcolor;
-        this.groupcolor = LGraphCanvas.node_colors.yellow.groupcolor;
-
         if (!this.properties) {
           this.properties = {};
           this.properties.workflow_name = "";
@@ -248,75 +206,50 @@ const ext = {
           this.properties.version = "";
         }
 
-        this.addWidget(
+        ComfyWidgets.STRING(
-          "text",
+          this,
           "workflow_name",
-          this.properties.workflow_name,
+          ["", { default: this.properties.workflow_name, multiline: false }],
-          (v) => {
+          app,
-            this.properties.workflow_name = v;
-          },
-          { multiline: false },
         );
 
-        this.addWidget(
+        ComfyWidgets.STRING(
-          "text",
+          this,
           "workflow_id",
-          this.properties.workflow_id,
+          ["", { default: this.properties.workflow_id, multiline: false }],
-          (v) => {
+          app,
-            this.properties.workflow_id = v;
-          },
-          { multiline: false },
         );
 
-        this.addWidget(
+        ComfyWidgets.STRING(
-          "text",
+          this,
           "version",
-          this.properties.version,
+          ["", { default: this.properties.version, multiline: false }],
-          (v) => {
+          app,
-            this.properties.version = v;
-          },
-          { multiline: false },
         );
 
+        // this.widgets.forEach((w) => {
+        //   // w.computeSize = () => [200,10]
+        //   w.computedHeight = 2;
+        // })
+
         this.widgets_start_y = 10;
+        this.setSize(this.computeSize());
+
+        // const config = {  };
+
+        // console.log(this);
         this.serialize_widgets = true;
         this.isVirtualNode = true;
       }
-
-      onExecute() {
-        // This method is called when the node is executed
-        // You can add any necessary logic here
-      }
-
-      onSerialize(o) {
-        // This method is called when the node is being serialized
-        // Ensure all necessary data is saved
-        if (!o.properties) {
-          o.properties = {};
-        }
-        o.properties.workflow_name = this.properties.workflow_name;
-        o.properties.workflow_id = this.properties.workflow_id;
-        o.properties.version = this.properties.version;
-      }
-
-      onConfigure(o) {
-        // This method is called when the node is being configured (e.g., when loading a saved graph)
-        // Ensure all necessary data is restored
-        if (o.properties) {
-          this.properties = { ...this.properties, ...o.properties };
-          this.widgets[0].value = this.properties.workflow_name || "";
-          this.widgets[1].value = this.properties.workflow_id || "";
-          this.widgets[2].value = this.properties.version || "1";
-        }
-      }
     }
 
-    // Register the node type
+    // Load default visibility
+
     LiteGraph.registerNodeType(
       "ComfyDeploy",
       Object.assign(ComfyDeploy, {
-        title: "Comfy Deploy",
         title_mode: LiteGraph.NORMAL_TITLE,
+        title: "Comfy Deploy",
         collapsable: true,
       }),
     );
@@ -328,116 +261,26 @@ const ext = {
     // const graphCanvas = document.getElementById("graph-canvas");
 
     window.addEventListener("message", async (event) => {
-      //   console.log("message", event);
       try {
         const message = JSON.parse(event.data);
         if (message.type === "graph_load") {
           const comfyUIWorkflow = message.data;
-          // console.log("recieved: ", comfyUIWorkflow);
+          console.log("recieved: ", comfyUIWorkflow);
           // Assuming there's a method to load the workflow data into the ComfyUI
           // This part of the code would depend on how the ComfyUI expects to receive and process the workflow data
           // For demonstration, let's assume there's a loadWorkflow method in the ComfyUI API
           if (comfyUIWorkflow && app && app.loadGraphData) {
-            try {
-              await window["app"].ui.settings.setSettingValueAsync(
-                "Comfy.Validation.Workflows",
-                false,
-              );
-            } catch (error) {
-              console.warning(
-                "Error setting validation to false, is fine to ignore this",
-                error,
-              );
-            }
-            console.log("loadGraphData");
             app.loadGraphData(comfyUIWorkflow);
           }
         } else if (message.type === "deploy") {
           // deployWorkflow();
           const prompt = await app.graphToPrompt();
-          // api.handlePromptGenerated(prompt);
           sendEventToCD("cd_plugin_onDeployChanges", prompt);
         } else if (message.type === "queue_prompt") {
           const prompt = await app.graphToPrompt();
-          if (typeof api.handlePromptGenerated === "function") {
-            api.handlePromptGenerated(prompt);
-          } else {
-            console.warn("api.handlePromptGenerated is not a function");
-          }
           sendEventToCD("cd_plugin_onQueuePrompt", prompt);
-        } else if (message.type === "get_prompt") {
-          const prompt = await app.graphToPrompt();
-          sendEventToCD("cd_plugin_onGetPrompt", prompt);
         } else if (message.type === "event") {
           dispatchAPIEventData(message.data);
-        } else if (message.type === "add_node") {
-          console.log("add node", message.data);
-          app.graph.beforeChange();
-          var node = LiteGraph.createNode(message.data.type);
-          node.configure({
-            widgets_values: message.data.widgets_values,
-          });
-
-          console.log("node", node);
-
-          const graphMouse = app.canvas.graph_mouse;
-
-          node.pos = [graphMouse[0], graphMouse[1]];
-
-          app.graph.add(node);
-          app.graph.afterChange();
-        } else if (message.type === "zoom_to_node") {
-          const nodeId = message.data.nodeId;
-          const position = message.data.position;
-
-          const node = app.graph.getNodeById(nodeId);
-          if (!node) return;
-
-          const canvas = app.canvas;
-          const targetScale = 1;
-          const targetOffsetX =
-            canvas.canvas.width / 4 - position[0] - node.size[0] / 2;
-          const targetOffsetY =
-            canvas.canvas.height / 4 - position[1] - node.size[1] / 2;
-
-          const startScale = canvas.ds.scale;
-          const startOffsetX = canvas.ds.offset[0];
-          const startOffsetY = canvas.ds.offset[1];
-
-          const duration = 400; // Animation duration in milliseconds
-          const startTime = Date.now();
-
-          function easeOutCubic(t) {
-            return 1 - Math.pow(1 - t, 3);
-          }
-
-          function lerp(start, end, t) {
-            return start * (1 - t) + end * t;
-          }
-
-          function animate() {
-            const currentTime = Date.now();
-            const elapsedTime = currentTime - startTime;
-            const t = Math.min(elapsedTime / duration, 1);
-
-            const easedT = easeOutCubic(t);
-
-            const currentScale = lerp(startScale, targetScale, easedT);
-            const currentOffsetX = lerp(startOffsetX, targetOffsetX, easedT);
-            const currentOffsetY = lerp(startOffsetY, targetOffsetY, easedT);
-
-            canvas.setZoom(currentScale);
-            canvas.ds.offset = [currentOffsetX, currentOffsetY];
-            canvas.draw(true, true);
-
-            if (t < 1) {
-              requestAnimationFrame(animate);
-            }
-          }
-
-          animate();
-        } else if (message.type === "workflow_info") {
-          setSelectedWorkflowInfo(message.data);
         }
         // else if (message.type === "refresh") {
         //   sendEventToCD("cd_plugin_onRefresh");
@@ -445,6 +288,10 @@ const ext = {
       } catch (error) {
         // console.error("Error processing message:", error);
       }
+
+      // if (!event.data.flow || Object.entries(event.data.flow).length <= 0)
+      //   return;
+      //   updateBlendshapesPrompts(event.data.flow);
     });
 
     api.addEventListener("executed", (evt) => {
@@ -457,25 +304,6 @@ const ext = {
       //   }
     });
 
-    if (this.native_mode) {
-      // console.log("native mode", window, window.app);
-      try {
-        await app.ui.settings.setSettingValueAsync("Comfy.UseNewMenu", "Top");
-        await app.ui.settings.setSettingValueAsync(
-          "Comfy.Sidebar.Size",
-          "small"
-        );
-        await app.ui.settings.setSettingValueAsync(
-          "Comfy.Sidebar.Location",
-          "right"
-        );
-        localStorage.setItem("Comfy.MenuPosition.Docked", "true");
-        console.log("native mode manmanman");
-      } catch (error) {
-        console.error("Error setting validation to false", error);
-      }
-    }
-
     app.graph.onAfterChange = ((originalFunction) =>
       async function () {
         const prompt = await app.graphToPrompt();
@@ -587,7 +415,6 @@ function createDynamicUIHtml(data) {
   return html;
 }
 
-// Modify the existing deployWorkflow function
 async function deployWorkflow() {
   const deploy = document.getElementById("deploy-button");
 
@@ -734,30 +561,30 @@ async function deployWorkflow() {
         console.log(hash);
         return hash.file_hash;
       },
-      //   handleFileUpload: async (file, hash, prevhash) => {
+      handleFileUpload: async (file, hash, prevhash) => {
-      //     console.log("Uploading ", file);
+        console.log("Uploading ", file);
-      //     loadingDialog.showLoading("Uploading file", file);
+        loadingDialog.showLoading("Uploading file", file);
-      //     try {
+        try {
-      //       const { download_url } = await fetch(`/comfyui-deploy/upload-file`, {
+          const { download_url } = await fetch(`/comfyui-deploy/upload-file`, {
-      //         method: "POST",
+            method: "POST",
-      //         body: JSON.stringify({
+            body: JSON.stringify({
-      //           file_path: file,
+              file_path: file,
-      //           token: apiKey,
+              token: apiKey,
-      //           url: endpoint + "/api/upload-url",
+              url: endpoint + "/api/upload-url",
-      //         }),
+            }),
-      //       })
+          })
-      //         .then((x) => x.json())
+            .then((x) => x.json())
-      //         .catch(() => {
+            .catch(() => {
-      //           loadingDialog.close();
+              loadingDialog.close();
-      //           confirmDialog.confirm("Error", "Unable to upload file " + file);
+              confirmDialog.confirm("Error", "Unable to upload file " + file);
-      //         });
+            });
-      //       loadingDialog.showLoading("Uploaded file", file);
+          loadingDialog.showLoading("Uploaded file", file);
-      //       console.log(download_url);
+          console.log(download_url);
-      //       return download_url;
+          return download_url;
-      //     } catch (error) {
+        } catch (error) {
-      //       return undefined;
+          return undefined;
-      //     }
+        }
-      //   },
+      },
       existingDependencies: existing_workflow.dependencies,
     });
 
@@ -782,15 +609,6 @@ async function deployWorkflow() {
       "Check dependencies",
       // JSON.stringify(deps, null, 2),
       `
-      <div>
-        You will need to create a cloud machine with the following configuration on ComfyDeploy
-        <ol style="text-align: left; margin-top: 10px;">
-            <li>Review the dependencies listed in the graph below</li>
-            <li>Create a new cloud machine with the required configuration</li>
-            <li>Install missing models and check missing files</li>
-            <li>Deploy your workflow to the newly created machine</li>
-        </ol>
-      </div>
       <div style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%);">${loadingIcon}</div>
       <iframe 
       style="z-index: 10; min-width: 600px; max-width: 1024px; min-height: 600px; border: none; background-color: transparent;"
@@ -860,14 +678,6 @@ async function deployWorkflow() {
       `<span style="color:green;">Deployed successfully!</span>  <a style="color:white;" target="_blank" href=${endpoint}/workflows/${data.workflow_id}>-> View here</a> <br/> <br/> Workflow ID: ${data.workflow_id} <br/> Workflow Name: ${workflow_name} <br/> Workflow Version: ${data.version} <br/>`,
     );
 
-    // // Refresh the workflows list in the sidebar
-    // const sidebarEl = document.querySelector(
-    //   '.comfy-sidebar-tab[data-id="search"]',
-    // );
-    // if (sidebarEl) {
-    //   refreshWorkflowsList(sidebarEl);
-    // }
-
     setTimeout(() => {
       title.textContent = "Deploy";
       title.style.color = "white";
@@ -885,85 +695,6 @@ async function deployWorkflow() {
   }
 }
 
-// Add this function to refresh the workflows list
-function refreshWorkflowsList(el) {
-  const workflowsList = el.querySelector("#workflows-list");
-  const workflowsLoading = el.querySelector("#workflows-loading");
-
-  workflowsLoading.style.display = "flex";
-  workflowsList.style.display = "none";
-  workflowsList.innerHTML = "";
-
-  client.workflows
-    .getAll({
-      page: "1",
-      pageSize: "10",
-    })
-    .then((result) => {
-      workflowsLoading.style.display = "none";
-      workflowsList.style.display = "block";
-
-      if (result.length === 0) {
-        workflowsList.innerHTML =
-          "<li style='color: #bdbdbd;'>No workflows found</li>";
-        return;
-      }
-
-      result.forEach((workflow) => {
-        const li = document.createElement("li");
-        li.style.marginBottom = "15px";
-        li.style.padding = "15px";
-        li.style.backgroundColor = "#2a2a2a";
-        li.style.borderRadius = "8px";
-        li.style.boxShadow = "0 2px 4px rgba(0,0,0,0.1)";
-
-        const lastRun = workflow.runs[0];
-        const lastRunStatus = lastRun ? lastRun.status : "No runs";
-        const statusColor =
-          lastRunStatus === "success"
-            ? "#4CAF50"
-            : lastRunStatus === "error"
-              ? "#F44336"
-              : "#FFC107";
-
-        const timeAgo = getTimeAgo(new Date(workflow.updatedAt));
-
-        li.innerHTML = `
-          <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px;">
-            <div style="flex: 1; overflow: hidden; text-overflow: ellipsis; white-space: nowrap;">
-              <strong style="font-size: 18px; color: #e0e0e0;">${workflow.name}</strong>
-            </div>
-            <span style="font-size: 12px; color: ${statusColor}; margin-left: 10px;">Last run: ${lastRunStatus}</span>
-          </div>
-          <div style="font-size: 14px; color: #bdbdbd; margin-bottom: 10px;">Last updated ${timeAgo}</div>
-          <div style="display: flex; gap: 10px;">
-            <button class="open-cloud-btn" style="padding: 5px 10px; background-color: #4CAF50; color: white; border: none; border-radius: 4px; cursor: pointer;">Open in Cloud</button>
-            <button class="load-api-btn" style="padding: 5px 10px; background-color: #2196F3; color: white; border: none; border-radius: 4px; cursor: pointer;">Load Workflow</button>
-          </div>
-        `;
-
-        const openCloudBtn = li.querySelector(".open-cloud-btn");
-        openCloudBtn.onclick = () =>
-          window.open(
-            `${getData().endpoint}/workflows/${workflow.id}?workspace=true`,
-            "_blank",
-          );
-
-        const loadApiBtn = li.querySelector(".load-api-btn");
-        loadApiBtn.onclick = () => loadWorkflowApi(workflow.versions[0].id);
-
-        workflowsList.appendChild(li);
-      });
-    })
-    .catch((error) => {
-      console.error("Error fetching workflows:", error);
-      workflowsLoading.style.display = "none";
-      workflowsList.style.display = "block";
-      workflowsList.innerHTML =
-        "<li style='color: #F44336;'>Error fetching workflows</li>";
-    });
-}
-
 function addButton() {
   const menu = document.querySelector(".comfy-menu");
 
@@ -1456,153 +1187,3 @@ export class ConfigDialog extends ComfyDialog {
 }
 
 export const configDialog = new ConfigDialog();
-
-const currentOrigin = window.location.origin;
-const client = new ComfyDeploy({
-  bearerAuth: getData().apiKey,
-  serverURL: `${currentOrigin}/comfydeploy/api/`,
-});
-
-app.extensionManager.registerSidebarTab({
-  id: "search",
-  icon: "pi pi-cloud-upload",
-  title: "Deploy",
-  tooltip: "Deploy and Configure",
-  type: "custom",
-  render: (el) => {
-    el.innerHTML = `
-      <div style="padding: 20px;">
-        <h3>Comfy Deploy</h3>
-        <div id="deploy-container" style="margin-bottom: 20px;"></div>
-        <div id="workflows-container">
-          <h4>Your Workflows</h4>
-          <div id="workflows-loading" style="display: flex; justify-content: center; align-items: center; height: 100px;">
-            ${loadingIcon}
-          </div>
-          <ul id="workflows-list" style="list-style-type: none; padding: 0; display: none;"></ul>
-        </div>
-        <div id="config-container"></div>
-      </div>
-    `;
-
-    // Add deploy button
-    const deployContainer = el.querySelector("#deploy-container");
-    const deployButton = document.createElement("button");
-    deployButton.id = "sidebar-deploy-button";
-    deployButton.style.display = "flex";
-    deployButton.style.alignItems = "center";
-    deployButton.style.justifyContent = "center";
-    deployButton.style.width = "100%";
-    deployButton.style.marginBottom = "10px";
-    deployButton.style.padding = "10px";
-    deployButton.style.fontSize = "16px";
-    deployButton.style.fontWeight = "bold";
-    deployButton.style.backgroundColor = "#4CAF50";
-    deployButton.style.color = "white";
-    deployButton.style.border = "none";
-    deployButton.style.borderRadius = "5px";
-    deployButton.style.cursor = "pointer";
-    deployButton.innerHTML = `<i class="pi pi-cloud-upload" style="margin-right: 8px;"></i><div id='sidebar-button-title'>Deploy</div>`;
-    deployButton.onclick = async () => {
-      await deployWorkflow();
-      // Refresh the workflows list after deployment
-      refreshWorkflowsList(el);
-    };
-    deployContainer.appendChild(deployButton);
-
-    // Add config button
-    const configContainer = el.querySelector("#config-container");
-    const configButton = document.createElement("button");
-    configButton.style.display = "flex";
-    configButton.style.alignItems = "center";
-    configButton.style.justifyContent = "center";
-    configButton.style.width = "100%";
-    configButton.style.padding = "8px";
-    configButton.style.fontSize = "14px";
-    configButton.style.backgroundColor = "#f0f0f0";
-    configButton.style.color = "#333";
-    configButton.style.border = "1px solid #ccc";
-    configButton.style.borderRadius = "5px";
-    configButton.style.cursor = "pointer";
-    configButton.innerHTML = `<i class="pi pi-cog" style="margin-right: 8px;"></i>Configure`;
-    configButton.onclick = () => {
-      configDialog.show();
-    };
-    deployContainer.appendChild(configButton);
-
-    // Fetch and display workflows
-    const workflowsList = el.querySelector("#workflows-list");
-    const workflowsLoading = el.querySelector("#workflows-loading");
-
-    refreshWorkflowsList(el);
-  },
-});
-
-function getTimeAgo(date) {
-  const seconds = Math.floor((new Date() - date) / 1000);
-  let interval = seconds / 31536000;
-  if (interval > 1) return Math.floor(interval) + " years ago";
-  interval = seconds / 2592000;
-  if (interval > 1) return Math.floor(interval) + " months ago";
-  interval = seconds / 86400;
-  if (interval > 1) return Math.floor(interval) + " days ago";
-  interval = seconds / 3600;
-  if (interval > 1) return Math.floor(interval) + " hours ago";
-  interval = seconds / 60;
-  if (interval > 1) return Math.floor(interval) + " minutes ago";
-  return Math.floor(seconds) + " seconds ago";
-}
-
-async function loadWorkflowApi(versionId) {
-  try {
-    const response = await client.comfyui.getWorkflowVersionVersionId({
-      versionId: versionId,
-    });
-    // Implement the logic to load the workflow API into the ComfyUI interface
-    console.log("Workflow API loaded:", response);
-    await window["app"].ui.settings.setSettingValueAsync(
-      "Comfy.Validation.Workflows",
-      false,
-    );
-    app.loadGraphData(response.workflow);
-    // You might want to update the UI or trigger some action in ComfyUI here
-  } catch (error) {
-    console.error("Error loading workflow API:", error);
-    // Show an error message to the user
-  }
-}
-
-const orginal_fetch_api = api.fetchApi;
-api.fetchApi = async (route, options) => {
-  console.log("Fetch API called with args:", route, options, ext.native_mode);
-
-  if (route.startsWith("/prompt") && ext.native_mode) {
-    const info = await getSelectedWorkflowInfo();
-    console.log("info", info);
-    if (info) {
-      const body = JSON.parse(options.body);
-
-      const data = {
-        client_id: body.client_id,
-        workflow_api_json: body.prompt,
-        workflow: body?.extra_data?.extra_pnginfo?.workflow,
-        is_native_run: true,
-        machine_id: info.machine_id,
-        workflow_id: info.workflow_id,
-        native_run_api_endpoint: info.native_run_api_endpoint,
-        gpu_event_id: info.gpu_event_id,
-      };
-
-      return await fetch("/comfyui-deploy/run", {
-        method: "POST",
-        headers: {
-          Authorization: `Bearer ${info.cd_token}`,
-          "Content-Type": "application/json",
-        },
-        body: JSON.stringify(data),
-      });
-    }
-  }
-
-  return await orginal_fetch_api.call(api, route, options);
-};

web/package.json

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

web/src/components/customInputNodes.tsx

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

web/src/routes/registerCreateRunRoute.ts

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

web/src/server/createRun.ts

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