fix(upload outputs): skip images/gifs/files/mesh when env var is true

The env var is `CD_BYPASS_UPLOAD`.
When that variables is `True`, we don't upload the media to our comfy
deploy s3 bucket.

There are 2 steps.
1. save the file into our s3 bucket
2. save the saving into our database.

When `CD_BYPASS_UPLOAD` is True:
1. Skip the save file into our s3 bucket
2. Skip the save into our database

Previously we were skipping the step 1, but not the step 2. So that is
the reason of why we keep seeing the comfy deploy URL when fetching the
run details:

```
outputs: [
  {
    data:{
      gifs: [
        {
          url: "https://comfy-deploy-output.s3.amazonaws.com/video.mp4"
        }
      ],
      text: [
        "A text that you displayed with show text node"
      ]
    }
  }
]
```

With the new changes we don't save that into our database, and fetching
the details of a run will look like this:
```
outputs: [
  {
    data:{
      text: [
        "A text that you displayed with show text node"
      ]
    }
  }
]
```

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

comfy-nodes/external_lora.py

@@ -5,14 +5,6 @@ import torch
 import folder_paths
 
 
-class AnyType(str):
-    def __ne__(self, __value: object) -> bool:
-        return False
-
-
-WILDCARD = AnyType("*")
-
-
 class ComfyUIDeployExternalLora:
     @classmethod
     def INPUT_TYPES(s):
@@ -25,81 +17,40 @@ class ComfyUIDeployExternalLora:
             },
             "optional": {
                 "default_lora_name": (folder_paths.get_filename_list("loras"),),
-                "lora_save_name": (  # if `default_lora_name` is a link to download a file, we will attempt to save it with this name
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
-                "lora_url": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
             },
         }
 
-    RETURN_TYPES = (WILDCARD,)
+    RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
     RETURN_NAMES = ("path",)
 
     FUNCTION = "run"
 
     CATEGORY = "deploy"
 
-    def run(
-        self,
-        input_id,
-        default_lora_name=None,
-        lora_save_name=None,
-        display_name=None,
-        description=None,
-        lora_url=None,
-    ):
+    def run(self, input_id, default_lora_name=None):
         import requests
         import os
         import uuid
 
-        if lora_url:
-            if lora_url.startswith("http"):
-                if lora_save_name:
-                    existing_loras = folder_paths.get_filename_list("loras")
-                    # Check if lora_save_name exists in the list
-                    if lora_save_name in existing_loras:
-                        print(f"using lora: {lora_save_name}")
-                        return (lora_save_name,)
-                else:
-                    lora_save_name = str(uuid.uuid4()) + ".safetensors"
-                print(lora_save_name)
-                print(folder_paths.folder_names_and_paths["loras"][0][0])
-                destination_path = os.path.join(
-                    folder_paths.folder_names_and_paths["loras"][0][0], lora_save_name
-                )
-                print(destination_path)
-                print(
-                    "Downloading external lora - "
-                    + lora_url
-                    + " to "
-                    + destination_path
-                )
-                response = requests.get(
-                    lora_url,
-                    headers={"User-Agent": "Mozilla/5.0"},
-                    allow_redirects=True,
-                )
-                with open(destination_path, "wb") as out_file:
-                    out_file.write(response.content)
-                print(f"Ext Lora loading: {lora_url} to {lora_save_name}")
-                return (lora_save_name,)
-            else:
-                print(f"Ext Lora loading: {lora_url}")
-                return (lora_url,)
+        if default_lora_name.startswith("http"):
+            unique_filename = str(uuid.uuid4()) + ".safetensors"
+            print(unique_filename)
+            print(folder_paths.folder_names_and_paths["loras"][0][0])
+            destination_path = os.path.join(
+                folder_paths.folder_names_and_paths["loras"][0][0], unique_filename
+            )
+            print(destination_path)
+            print("Downloading external lora - " + input_id + " to " + destination_path)
+            response = requests.get(
+                input_id,
+                headers={"User-Agent": "Mozilla/5.0"},
+                allow_redirects=True,
+            )
+            with open(destination_path, "wb") as out_file:
+                out_file.write(response.content)
+            return (unique_filename,)
         else:
-            print(f"Ext Lora loading: {default_lora_name}")
+            print(f"using lora: {default_lora_name}")
             return (default_lora_name,)
 
 

comfy-nodes/external_number.py

@@ -16,15 +16,7 @@ class ComfyUIDeployExternalNumber:
             "optional": {
                 "default_value": (
                     "FLOAT",
-                    {"multiline": True, "display": "number", "default": 0, "min": -2147483647, "max": 2147483647, "step": 0.01},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
+                    {"multiline": True, "display": "number", "default": 0, "step": 0.01},
                 ),
             }
         }
@@ -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},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
+                    {"multiline": True, "display": "number", "default": 0},
                 ),
             }
         }
@@ -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},
-                ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
+                    {"multiline": True, "display": "number", "default": 1, "step": 0.01},
                 ),
             }
         }
@@ -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_string_combine.py

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

comfy-nodes/external_text.py

@@ -18,14 +18,6 @@ class ComfyUIDeployExternalText:
                     "STRING",
                     {"multiline": True, "default": ""},
                 ),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": True, "default": ""},
-                ),
             }
         }
 
@@ -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
-# Intended to work with https://github.com/NicholasKao1029/ComfyUI-VideoHelperSuite/tree/main
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite and is meant to work with
 import os
 import itertools
 import numpy as np
 import torch
-from typing import Union
-from torch import Tensor
 import cv2
-import psutil
 
-from collections.abc import Mapping
 import folder_paths
 from comfy.utils import common_upscale
 
@@ -95,25 +90,13 @@ if gifski_path is None:
         gifski_path = shutil.which("gifski")
 
 
-def is_safe_path(path):
-    if "VHS_STRICT_PATHS" not in os.environ:
-        return True
-    basedir = os.path.abspath(".")
-    try:
-        common_path = os.path.commonpath([basedir, path])
-    except:
-        # Different drive on windows
-        return False
-    return common_path == basedir
-
-
 def get_sorted_dir_files_from_directory(
     directory: str,
     skip_first_images: int = 0,
     select_every_nth: int = 1,
     extensions: Iterable = None,
 ):
-    directory = strip_path(directory)
+    directory = directory.strip()
     dir_files = os.listdir(directory)
     dir_files = sorted(dir_files)
     dir_files = [os.path.join(directory, x) for x in dir_files]
@@ -194,59 +177,18 @@ def requeue_workflow(requeue_required=(-1, True)):
 
 
 def get_audio(file, start_time=0, duration=0):
-    args = [ffmpeg_path, "-i", file]
+    args = [ffmpeg_path, "-v", "error", "-i", file]
     if start_time > 0:
         args += ["-ss", str(start_time)]
     if duration > 0:
         args += ["-t", str(duration)]
     try:
-        # TODO: scan for sample rate and maintain
         res = subprocess.run(
-            args + ["-f", "f32le", "-"], capture_output=True, check=True
-        )
-        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
-        match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
+            args + ["-f", "wav", "-"], stdout=subprocess.PIPE, check=True
+        ).stdout
     except subprocess.CalledProcessError as e:
-        raise Exception(
-            f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
-        )
-    if match:
-        ar = int(match.group(1))
-        # NOTE: Just throwing an error for other channel types right now
-        # Will deal with issues if they come
-        ac = {"mono": 1, "stereo": 2}[match.group(2)]
-    else:
-        ar = 44100
-        ac = 2
-    audio = audio.reshape((-1, ac)).transpose(0, 1).unsqueeze(0)
-    return {"waveform": audio, "sample_rate": ar}
-
-
-class LazyAudioMap(Mapping):
-    def __init__(self, file, start_time, duration):
-        self.file = file
-        self.start_time = start_time
-        self.duration = duration
-        self._dict = None
-
-    def __getitem__(self, key):
-        if self._dict is None:
-            self._dict = get_audio(self.file, self.start_time, self.duration)
-        return self._dict[key]
-
-    def __iter__(self):
-        if self._dict is None:
-            self._dict = get_audio(self.file, self.start_time, self.duration)
-        return iter(self._dict)
-
-    def __len__(self):
-        if self._dict is None:
-            self._dict = get_audio(self.file, self.start_time, self.duration)
-        return len(self._dict)
-
-
-def lazy_get_audio(file, start_time=0, duration=0):
-    return LazyAudioMap(file, start_time, duration)
+        return False
+    return res
 
 
 def lazy_eval(func):
@@ -288,19 +230,6 @@ def validate_sequence(path):
     return False
 
 
-def strip_path(path):
-    # This leaves whitespace inside quotes and only a single "
-    # thus ' ""test"' -> '"test'
-    # consider path.strip(string.whitespace+"\"")
-    # or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1)
-    path = path.strip()
-    if path.startswith('"'):
-        path = path[1:]
-    if path.endswith('"'):
-        path = path[:-1]
-    return path
-
-
 def hash_path(path):
     if path is None:
         return "input"
@@ -357,145 +286,6 @@ def target_size(
     return (width, height)
 
 
-def validate_index(
-    index: int,
-    length: int = 0,
-    is_range: bool = False,
-    allow_negative=False,
-    allow_missing=False,
-) -> int:
-    # if part of range, do nothing
-    if is_range:
-        return index
-    # otherwise, validate index
-    # validate not out of range - only when latent_count is passed in
-    if length > 0 and index > length - 1 and not allow_missing:
-        raise IndexError(f"Index '{index}' out of range for {length} item(s).")
-    # if negative, validate not out of range
-    if index < 0:
-        if not allow_negative:
-            raise IndexError(f"Negative indeces not allowed, but was '{index}'.")
-        conv_index = length + index
-        if conv_index < 0 and not allow_missing:
-            raise IndexError(
-                f"Index '{index}', converted to '{conv_index}' out of range for {length} item(s)."
-            )
-        index = conv_index
-    return index
-
-
-def convert_to_index_int(
-    raw_index: str,
-    length: int = 0,
-    is_range: bool = False,
-    allow_negative=False,
-    allow_missing=False,
-) -> int:
-    try:
-        return validate_index(
-            int(raw_index),
-            length=length,
-            is_range=is_range,
-            allow_negative=allow_negative,
-            allow_missing=allow_missing,
-        )
-    except ValueError as e:
-        raise ValueError(f"Index '{raw_index}' must be an integer.", e)
-
-
-def convert_str_to_indexes(
-    indexes_str: str, length: int = 0, allow_missing=False
-) -> list[int]:
-    if not indexes_str:
-        return []
-    int_indexes = list(range(0, length))
-    allow_negative = length > 0
-    chosen_indexes = []
-    # parse string - allow positive ints, negative ints, and ranges separated by ':'
-    groups = indexes_str.split(",")
-    groups = [g.strip() for g in groups]
-    for g in groups:
-        # parse range of indeces (e.g. 2:16)
-        if ":" in g:
-            index_range = g.split(":", 2)
-            index_range = [r.strip() for r in index_range]
-
-            start_index = index_range[0]
-            if len(start_index) > 0:
-                start_index = convert_to_index_int(
-                    start_index,
-                    length=length,
-                    is_range=True,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            else:
-                start_index = 0
-            end_index = index_range[1]
-            if len(end_index) > 0:
-                end_index = convert_to_index_int(
-                    end_index,
-                    length=length,
-                    is_range=True,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            else:
-                end_index = length
-            # support step as well, to allow things like reversing, every-other, etc.
-            step = 1
-            if len(index_range) > 2:
-                step = index_range[2]
-                if len(step) > 0:
-                    step = convert_to_index_int(
-                        step,
-                        length=length,
-                        is_range=True,
-                        allow_negative=True,
-                        allow_missing=True,
-                    )
-                else:
-                    step = 1
-            # if latents were passed in, base indeces on known latent count
-            if len(int_indexes) > 0:
-                chosen_indexes.extend(int_indexes[start_index:end_index][::step])
-            # otherwise, assume indeces are valid
-            else:
-                chosen_indexes.extend(list(range(start_index, end_index, step)))
-        # parse individual indeces
-        else:
-            chosen_indexes.append(
-                convert_to_index_int(
-                    g,
-                    length=length,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            )
-    return chosen_indexes
-
-
-def select_indexes(input_obj: Union[Tensor, list], idxs: list):
-    if type(input_obj) == Tensor:
-        return input_obj[idxs]
-    else:
-        return [input_obj[i] for i in idxs]
-
-
-def select_indexes_from_str(
-    input_obj: Union[Tensor, list], indexes: str, err_if_missing=True, err_if_empty=True
-):
-    real_idxs = convert_str_to_indexes(
-        indexes, len(input_obj), allow_missing=not err_if_missing
-    )
-    if err_if_empty and len(real_idxs) == 0:
-        raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
-    return select_indexes(input_obj, real_idxs)
-
-
-###
-
-
 def cv_frame_generator(
     video,
     force_rate,
@@ -505,10 +295,9 @@ def cv_frame_generator(
     meta_batch=None,
     unique_id=None,
 ):
-    video_cap = cv2.VideoCapture(strip_path(video))
+    video_cap = cv2.VideoCapture(video)
     if not video_cap.isOpened():
         raise ValueError(f"{video} could not be loaded with cv.")
-    pbar = None
 
     # extract video metadata
     fps = video_cap.get(cv2.CAP_PROP_FPS)
@@ -530,8 +319,6 @@ def cv_frame_generator(
         target_frame_time = 1 / force_rate
 
     yield (width, height, fps, duration, total_frames, target_frame_time)
-    if meta_batch is not None:
-        yield min(frame_load_cap, total_frames)
 
     time_offset = target_frame_time - base_frame_time
     while video_cap.isOpened():
@@ -562,8 +349,7 @@ def cv_frame_generator(
         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         # convert frame to comfyui's expected format
         # TODO: frame contains no exif information. Check if opencv2 has already applied
-        frame = np.array(frame, dtype=np.float32)
-        torch.from_numpy(frame).div_(255)
+        frame = np.array(frame, dtype=np.float32) / 255.0
         if prev_frame is not None:
             inp = yield prev_frame
             if inp is not None:
@@ -571,8 +357,6 @@ def cv_frame_generator(
                 return
         prev_frame = frame
         frames_added += 1
-        if pbar is not None:
-            pbar.update_absolute(frames_added, frame_load_cap)
         # if cap exists and we've reached it, stop processing frames
         if frame_load_cap > 0 and frames_added >= frame_load_cap:
             break
@@ -583,17 +367,6 @@ def cv_frame_generator(
         yield prev_frame
 
 
-def batched(it, n):
-    while batch := tuple(itertools.islice(it, n)):
-        yield batch
-
-
-def batched_vae_encode(images, vae, frames_per_batch):
-    for batch in batched(images, frames_per_batch):
-        image_batch = torch.from_numpy(np.array(batch))
-        yield from vae.encode(image_batch).numpy()
-
-
 def load_video_cv(
     video: str,
     force_rate: int,
@@ -605,8 +378,6 @@ def load_video_cv(
     select_every_nth: int,
     meta_batch=None,
     unique_id=None,
-    memory_limit_mb=None,
-    vae=None,
 ):
     if meta_batch is None or unique_id not in meta_batch.inputs:
         gen = cv_frame_generator(
@@ -630,89 +401,30 @@ def load_video_cv(
                 total_frames,
                 target_frame_time,
             )
-            meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
 
     else:
         (gen, width, height, fps, duration, total_frames, target_frame_time) = (
             meta_batch.inputs[unique_id]
         )
 
-    memory_limit = None
-    if memory_limit_mb is not None:
-        memory_limit *= 2**20
-    else:
-        # TODO: verify if garbage collection should be performed here.
-        # leaves ~128 MB unreserved for safety
-        try:
-            memory_limit = (
-                psutil.virtual_memory().available + psutil.swap_memory().free
-            ) - 2**27
-        except:
-            print(
-                "Failed to calculate available memory. Memory load limit has been disabled"
-            )
-    if memory_limit is not None:
-        if vae is not None:
-            # space required to load as f32, exist as latent with wiggle room, decode to f32
-            max_loadable_frames = int(
-                memory_limit // (width * height * 3 * (4 + 4 + 1 / 10))
-            )
-        else:
-            # TODO: use better estimate for when vae is not None
-            # Consider completely ignoring for load_latent case?
-            max_loadable_frames = int(memory_limit // (width * height * 3 * (0.1)))
-        if meta_batch is not None:
-            if meta_batch.frames_per_batch > max_loadable_frames:
-                raise RuntimeError(
-                    f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory"
-                )
-            gen = itertools.islice(gen, meta_batch.frames_per_batch)
-        else:
-            original_gen = gen
-            gen = itertools.islice(gen, max_loadable_frames)
-    downscale_ratio = getattr(vae, "downscale_ratio", 8)
-    frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
-    if force_size != "Disabled" or vae is not None:
-        new_size = target_size(
-            width, height, force_size, custom_width, custom_height, downscale_ratio
-        )
-        if new_size[0] != width or new_size[1] != height:
+    if meta_batch is not None:
+        gen = itertools.islice(gen, meta_batch.frames_per_batch)
 
-            def rescale(frame):
-                s = torch.from_numpy(
-                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
-                )
-                s = s.movedim(-1, 1)
-                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
-                return s.movedim(1, -1).numpy()
-
-            gen = itertools.chain.from_iterable(
-                map(rescale, batched(gen, frames_per_batch))
-            )
-    else:
-        new_size = width, height
-    if vae is not None:
-        gen = batched_vae_encode(gen, vae, frames_per_batch)
-        vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
-        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
-    else:
-        # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
-        images = torch.from_numpy(
-            np.fromiter(gen, np.dtype((np.float32, (new_size[1], new_size[0], 3))))
-        )
-    if meta_batch is None and memory_limit is not None:
-        try:
-            next(original_gen)
-            raise RuntimeError(
-                f"Memory limit hit after loading {len(images)} frames. Stopping execution."
-            )
-        except StopIteration:
-            pass
+    # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+    images = torch.from_numpy(
+        np.fromiter(gen, np.dtype((np.float32, (height, width, 3))))
+    )
     if len(images) == 0:
         raise RuntimeError("No frames generated")
+    if force_size != "Disabled":
+        new_size = target_size(width, height, force_size, custom_width, custom_height)
+        if new_size[0] != width or new_size[1] != height:
+            s = images.movedim(-1, 1)
+            s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+            images = s.movedim(1, -1)
 
     # Setup lambda for lazy audio capture
-    audio = lazy_get_audio(
+    audio = lambda: get_audio(
         video,
         skip_first_frames * target_frame_time,
         frame_load_cap * target_frame_time * select_every_nth,
@@ -728,16 +440,13 @@ def load_video_cv(
         "loaded_fps": 1 / target_frame_time,
         "loaded_frame_count": len(images),
         "loaded_duration": len(images) * target_frame_time,
-        "loaded_width": new_size[0],
-        "loaded_height": new_size[1],
+        "loaded_width": images.shape[2],
+        "loaded_height": images.shape[1],
     }
-    if vae is None:
-        return (images, len(images), audio, video_info, None)
-    else:
-        return (None, len(images), audio, video_info, {"samples": images})
+
+    return (images, len(images), lazy_eval(audio), video_info)
 
 
-# modeled after Video upload node
 class ComfyUIDeployExternalVideo:
     @classmethod
     def INPUT_TYPES(s):
@@ -748,46 +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": {
-                    "input_id": (
-                        "STRING",
-                        {"multiline": False, "default": "input_video"},
-                    ),
-                     "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
-                     "force_size": (["Disabled", "Custom Height", "Custom Width", "Custom", "256x?", "?x256", "256x256", "512x?", "?x512", "512x512"],),
-                     "custom_width": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
-                     "custom_height": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
-                     "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
-                     "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
-                     "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
-                     },
-                "optional": {
-                    "meta_batch": ("VHS_BatchManager",),
-                    "vae": ("VAE",),
-                    "default_video": (sorted(files),),
-                    "display_name": (
-                        "STRING",
-                        {"multiline": False, "default": ""},
-                    ),
-                    "description": (
-                        "STRING",
-                        {"multiline": True, "default": ""},
-                    ),
-                },
-                "hidden": {
-                    "unique_id": "UNIQUE_ID"
-                },
-                }
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_video"},
+                ),
+                "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
+                "force_size": (
+                    [
+                        "Disabled",
+                        "Custom Height",
+                        "Custom Width",
+                        "Custom",
+                        "256x?",
+                        "?x256",
+                        "256x256",
+                        "512x?",
+                        "?x512",
+                        "512x512",
+                    ],
+                ),
+                "custom_width": (
+                    "INT",
+                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
+                ),
+                "custom_height": (
+                    "INT",
+                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
+                ),
+                "frame_load_cap": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
+                ),
+                "skip_first_frames": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
+                ),
+                "select_every_nth": (
+                    "INT",
+                    {"default": 1, "min": 1, "max": BIGMAX, "step": 1},
+                ),
+            },
+            "optional": {
+                "meta_batch": ("VHS_BatchManager",),
+                "default_value": (sorted(files),),
+            },
+            "hidden": {"unique_id": "UNIQUE_ID"},
+        }
 
     CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
 
-    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
+    RETURN_TYPES = (
+        "IMAGE",
+        "INT",
+        "VHS_AUDIO",
+        "VHS_VIDEOINFO",
+    )
     RETURN_NAMES = (
         "IMAGE",
         "frame_count",
         "audio",
         "video_info",
-        "LATENT",
     )
 
     FUNCTION = "load_video"
@@ -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)
-            if video is None:
-                raise "No default video given and no external video provided"
-            video_path = folder_paths.get_annotated_filepath(video.strip('"'))
+
+        print("video path: ", video_path)
 
         return load_video_cv(
             video=video_path,

comfy-nodes/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"
@@ -19,60 +17,48 @@ 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: Optional[str]
-    file_upload_endpoint: Optional[str]
-    workflow: Any
-    gpu_event_id: Optional[str] = None
-
-
+    status_endpoint: str
+    file_upload_endpoint: str
+    
 class SimplePrompt(BaseModel):
-    status_endpoint: Optional[str]
-    file_upload_endpoint: Optional[str]
-
-    token: Optional[str]
-
+    status_endpoint: str
+    file_upload_endpoint: str
     workflow_api: dict
     status: Status = Status.NOT_STARTED
     progress: set = set()
-    last_updated_node: Optional[str] = None
+    last_updated_node: Optional[str] = None,
     uploading_nodes: set = set()
     done: bool = False
-    is_realtime: bool = False
-    start_time: Optional[float] = None
-    gpu_event_id: Optional[str] = None
-
+    is_realtime: bool = False,
+    start_time: Optional[float] = None,
 
 sockets = dict()
 prompt_metadata: dict[str, SimplePrompt] = {}
 streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
 
-
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
     UNENCODED_PREVIEW_IMAGE = 2
-
-
+    
 max_output_id_length = 24
 
-
-async def send_image(image_data, sid=None, output_id: str = None):
+async def send_image(image_data, sid=None, output_id:str = None):
     max_length = max_output_id_length
     output_id = output_id[:max_length]
-    padded_output_id = output_id.ljust(max_length, "\x00")
-    encoded_output_id = padded_output_id.encode("ascii", "replace")
-
+    padded_output_id = output_id.ljust(max_length, '\x00')
+    encoded_output_id = padded_output_id.encode('ascii', 'replace')
+    
     image_type = image_data[0]
     image = image_data[1]
     max_size = image_data[2]
     quality = image_data[3]
     if max_size is not None:
-        if hasattr(Image, "Resampling"):
+        if hasattr(Image, 'Resampling'):
             resampling = Image.Resampling.BILINEAR
         else:
             resampling = Image.ANTIALIAS
@@ -96,23 +82,17 @@ async def send_image(image_data, sid=None, output_id: str = None):
     position_after = bytesIO.tell()
     bytes_written = position_after - position_before
     print(f"Bytes written: {bytes_written}")
-
+    
     image.save(bytesIO, format=image_type, quality=quality, compress_level=1)
     preview_bytes = bytesIO.getvalue()
     await send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
-
-
+        
 async def send_socket_catch_exception(function, message):
     try:
         await function(message)
-    except (
-        aiohttp.ClientError,
-        aiohttp.ClientPayloadError,
-        ConnectionResetError,
-    ) as err:
+    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
         print("send error:", err)
 
-
 def encode_bytes(event, data):
     if not isinstance(event, int):
         raise RuntimeError(f"Binary event types must be integers, got {event}")
@@ -122,10 +102,9 @@ def encode_bytes(event, data):
     message.extend(data)
     return message
 
-
 async def send_bytes(event, data, sid=None):
     message = encode_bytes(event, data)
-
+    
     print("sending image to ", event, sid)
 
     if sid is None:
@@ -133,4 +112,4 @@ 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

@@ -1,7 +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

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

web-plugin/api.js

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

web-plugin/app.js

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

web-plugin/widgets.js

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

web/package.json

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

web/src/components/customInputNodes.tsx

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

web/src/routes/registerCreateRunRoute.ts

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

web/src/server/createRun.ts

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