Merge branch 'main' into dev

.gitignore

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

README.md

@@ -2,9 +2,6 @@
 
 Open source comfyui deployment platform, a `vercel` for generative workflow infra. (serverless hosted gpu with vertical intergation with comfyui)
 
-> [!NOTE]  
-> Im looking for creative hacker to join ComfyDeploy's core team! DM me on [twitter](https://x.com/BennyKokMusic)
-
 Join [Discord](https://discord.gg/EEYcQmdYZw) to chat more or visit [Comfy Deploy](https://comfydeploy.com/) to get started!
 
 Check out our latest [nextjs starter kit](https://github.com/BennyKok/comfyui-deploy-next-example) with Comfy Deploy

comfy-nodes/external_audio.py

@@ -1,57 +0,0 @@
-import os
-import io
-import torchaudio
-from folder_paths import get_annotated_filepath
-
-class ComfyUIDeployExternalAudio:
-    RETURN_TYPES = ("AUDIO",)
-    RETURN_NAMES = ("audio",)
-    FUNCTION = "load_audio"
-    
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "input_id": (
-                    "STRING",
-                    {"multiline": False, "default": "input_audio"},
-                ),
-                "audio_file": ("STRING", {"default": ""}),
-            },
-            "optional": {
-                "default_value": ("AUDIO",),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-            }
-        }
-    
-    @classmethod
-    def VALIDATE_INPUTS(s, audio_file, **kwargs):
-        return True
-
-    def load_audio(self, input_id, audio_file, default_value=None, display_name=None, description=None):
-        if audio_file and audio_file != "":
-            if audio_file.startswith(('http://', 'https://')):
-                # Handle URL input
-                import requests
-                response = requests.get(audio_file)
-                audio_data = io.BytesIO(response.content)
-                waveform, sample_rate = torchaudio.load(audio_data)
-            else:
-                # Handle local file
-                audio_path = get_annotated_filepath(audio_file)
-                waveform, sample_rate = torchaudio.load(audio_path)
-            
-            audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
-            return (audio,)
-        else:
-            return (default_value,)
-
-NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalAudio": ComfyUIDeployExternalAudio}
-NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalAudio": "External Audio (ComfyUI Deploy)"}

comfy-nodes/external_boolean.py

@@ -8,16 +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_exr.py

@@ -1,109 +0,0 @@
-import os
-import io
-import cv2 as cv
-import numpy as np
-import torch
-import requests
-from folder_paths import get_annotated_filepath
-
-class ComfyUIDeployExternalEXR:
-    RETURN_TYPES = ("IMAGE", "MASK")
-    RETURN_NAMES = ("image", "mask") 
-    FUNCTION = "load_exr"
-    
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "input_id": (
-                    "STRING",
-                    {"multiline": False, "default": "input_exr"},
-                ),
-                "exr_file": ("STRING", {"default": ""}),
-                "tonemap": (["linear", "sRGB", "Reinhard"], {"default": "sRGB"}),
-            },
-            "optional": {
-                "default_image": ("IMAGE",),
-                "default_mask": ("MASK",),
-                "display_name": (
-                    "STRING", 
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-            }
-        }
-
-    @classmethod
-    def VALIDATE_INPUTS(s, exr_file, **kwargs):
-        return True
-
-    def sRGBtoLinear(self, npArray):
-        less = npArray <= 0.0404482362771082
-        npArray[less] = npArray[less] / 12.92
-        npArray[~less] = np.power((npArray[~less] + 0.055) / 1.055, 2.4)
-
-    def linearToSRGB(self, npArray):
-        less = npArray <= 0.0031308
-        npArray[less] = npArray[less] * 12.92
-        npArray[~less] = np.power(npArray[~less], 1/2.4) * 1.055 - 0.055
-
-    def load_exr(self, input_id, exr_file, tonemap="sRGB", 
-                 default_image=None, default_mask=None,
-                 display_name=None, description=None):
-        try:
-            if exr_file and exr_file != "":
-                if exr_file.startswith(('http://', 'https://')):
-                    # Handle URL input
-                    response = requests.get(exr_file)
-                    # Write to temp buffer
-                    buffer = io.BytesIO(response.content)
-                    nparr = np.frombuffer(buffer.getvalue(), np.uint8)
-                    image = cv.imdecode(nparr, cv.IMREAD_UNCHANGED).astype(np.float32)
-                else:
-                    # Handle local file
-                    exr_path = get_annotated_filepath(exr_file)
-                    image = cv.imread(exr_path, cv.IMREAD_UNCHANGED).astype(np.float32)
-
-                if len(image.shape) == 2:
-                    image = np.repeat(image[..., np.newaxis], 3, axis=2)
-                
-                # Extract RGB and flip channels
-                rgb = np.flip(image[:,:,:3], 2).copy()
-                
-                # Apply tonemapping
-                if tonemap == "sRGB":
-                    self.linearToSRGB(rgb)
-                    rgb = np.clip(rgb, 0, 1)
-                elif tonemap == "Reinhard":
-                    rgb = np.clip(rgb, 0, None)
-                    rgb = rgb / (rgb + 1)
-                    self.linearToSRGB(rgb)
-                    rgb = np.clip(rgb, 0, 1)
-                
-                rgb = torch.unsqueeze(torch.from_numpy(rgb), 0)
-                
-                # Handle alpha/mask
-                mask = torch.zeros((1, image.shape[0], image.shape[1]), dtype=torch.float32)
-                if image.shape[2] > 3:
-                    mask[0] = torch.from_numpy(np.clip(image[:,:,3], 0, 1))
-
-                return (rgb, mask)
-            else:
-                # Return defaults if no file provided
-                return (default_image, default_mask)
-
-        except Exception as e:
-            print(f"Error loading EXR: {str(e)}")
-            # Return defaults on error
-            return (default_image, default_mask)
-
-NODE_CLASS_MAPPINGS = {
-    "ComfyUIDeployExternalEXR": ComfyUIDeployExternalEXR
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ComfyUIDeployExternalEXR": "External EXR (ComfyUI Deploy)"
-}

comfy-nodes/external_face_model.py

@@ -1,108 +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

@@ -15,15 +15,6 @@ class ComfyUIDeployExternalImage:
             },
             "optional": {
                 "default_value": ("IMAGE",),
-                "display_name": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "description": (
-                    "STRING",
-                    {"multiline": False, "default": ""},
-                ),
-                "default_value_url": ("STRING", {"image_preview": True, "default": ""}),
             }
         }
 
@@ -34,44 +25,32 @@ class ComfyUIDeployExternalImage:
 
     CATEGORY = "image"
 
-    def run(self, input_id, default_value=None, display_name=None, description=None, default_value_url=None):
+    def run(self, input_id, default_value=None):
         image = default_value
-        
-        # Try both input_id and default_value_url
-        urls_to_try = [url for url in [input_id, default_value_url] if url]
-        
-        print(default_value_url)
-        
-        for url in urls_to_try:
-            try:
-                if url.startswith('http'):
-                    import requests
-                    from io import BytesIO
-                    print(f"Fetching image from url: {url}")
-                    response = requests.get(url)
-                    image = Image.open(BytesIO(response.content))
-                    break
-                elif url.startswith(('data:image/png;base64,', 'data:image/jpeg;base64,', 'data:image/jpg;base64,')):
-                    import base64
-                    from io import BytesIO
-                    print("Decoding base64 image")
-                    base64_image = url[url.find(",")+1:]
-                    decoded_image = base64.b64decode(base64_image)
-                    image = Image.open(BytesIO(decoded_image))
-                    break
-            except:
-                continue
-        
-        if image is not None:
-            try:
-                image = ImageOps.exif_transpose(image)
-                image = image.convert("RGB")
-                image = np.array(image).astype(np.float32) / 255.0
-                image = torch.from_numpy(image)[None,]
-            except:
-                pass
-                
-        return [image]
+        try:
+            if input_id.startswith('http'):
+                import requests
+                from io import BytesIO
+                print("Fetching image from url: ", input_id)
+                response = requests.get(input_id)
+                image = Image.open(BytesIO(response.content))
+            elif input_id.startswith('data:image/png;base64,') or input_id.startswith('data:image/jpeg;base64,') or input_id.startswith('data:image/jpg;base64,'):
+                import base64
+                from io import BytesIO
+                print("Decoding base64 image")
+                base64_image = input_id[input_id.find(",")+1:]
+                decoded_image = base64.b64decode(base64_image)
+                image = Image.open(BytesIO(decoded_image))
+            else:
+                raise ValueError("Invalid image url provided.")
+
+            image = ImageOps.exif_transpose(image)
+            image = image.convert("RGB")
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            return [image]
+        except:
+            return [image]
 
 
 NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalImage": ComfyUIDeployExternalImage}

comfy-nodes/external_image_alpha.py

@@ -15,14 +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)"}

comfy-nodes/output_image.py

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

globals.py

@@ -6,12 +6,10 @@ from PIL import Image, ImageOps
 from io import BytesIO
 from pydantic import BaseModel as PydanticBaseModel
 
-
 class BaseModel(PydanticBaseModel):
     class Config:
         arbitrary_types_allowed = True
-
-
+        
 class Status(Enum):
     NOT_STARTED = "not-started"
     RUNNING = "running"
@@ -19,7 +17,6 @@ class Status(Enum):
     FAILED = "failed"
     UPLOADING = "uploading"
 
-
 class StreamingPrompt(BaseModel):
     workflow_api: Any
     auth_token: str
@@ -27,52 +24,42 @@ class StreamingPrompt(BaseModel):
     running_prompt_ids: set[str] = set()
     status_endpoint: Optional[str]
     file_upload_endpoint: Optional[str]
-    workflow: Any
-    gpu_event_id: Optional[str] = None
-
-
+    
 class SimplePrompt(BaseModel):
     status_endpoint: Optional[str]
     file_upload_endpoint: Optional[str]
-
-    token: Optional[str]
-
+    
     workflow_api: dict
     status: Status = Status.NOT_STARTED
     progress: set = set()
-    last_updated_node: Optional[str] = None
+    last_updated_node: Optional[str] = None,
     uploading_nodes: set = set()
     done: bool = False
-    is_realtime: bool = False
-    start_time: Optional[float] = None
-    gpu_event_id: Optional[str] = None
-
+    is_realtime: bool = False,
+    start_time: Optional[float] = None,
 
 sockets = dict()
 prompt_metadata: dict[str, SimplePrompt] = {}
 streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
 
-
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
     UNENCODED_PREVIEW_IMAGE = 2
-
-
+    
 max_output_id_length = 24
 
-
-async def send_image(image_data, sid=None, output_id: str = None):
+async def send_image(image_data, sid=None, output_id:str = None):
     max_length = max_output_id_length
     output_id = output_id[:max_length]
-    padded_output_id = output_id.ljust(max_length, "\x00")
-    encoded_output_id = padded_output_id.encode("ascii", "replace")
-
+    padded_output_id = output_id.ljust(max_length, '\x00')
+    encoded_output_id = padded_output_id.encode('ascii', 'replace')
+    
     image_type = image_data[0]
     image = image_data[1]
     max_size = image_data[2]
     quality = image_data[3]
     if max_size is not None:
-        if hasattr(Image, "Resampling"):
+        if hasattr(Image, 'Resampling'):
             resampling = Image.Resampling.BILINEAR
         else:
             resampling = Image.ANTIALIAS
@@ -96,23 +83,17 @@ async def send_image(image_data, sid=None, output_id: str = None):
     position_after = bytesIO.tell()
     bytes_written = position_after - position_before
     print(f"Bytes written: {bytes_written}")
-
+    
     image.save(bytesIO, format=image_type, quality=quality, compress_level=1)
     preview_bytes = bytesIO.getvalue()
     await send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid)
-
-
+        
 async def send_socket_catch_exception(function, message):
     try:
         await function(message)
-    except (
-        aiohttp.ClientError,
-        aiohttp.ClientPayloadError,
-        ConnectionResetError,
-    ) as err:
+    except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
         print("send error:", err)
 
-
 def encode_bytes(event, data):
     if not isinstance(event, int):
         raise RuntimeError(f"Binary event types must be integers, got {event}")
@@ -122,10 +103,9 @@ def encode_bytes(event, data):
     message.extend(data)
     return message
 
-
 async def send_bytes(event, data, sid=None):
     message = encode_bytes(event, data)
-
+    
     print("sending image to ", event, sid)
 
     if sid is None:
@@ -133,4 +113,4 @@ async def send_bytes(event, data, sid=None):
         for ws in _sockets:
             await send_socket_catch_exception(ws.send_bytes, message)
     elif sid in sockets:
-        await send_socket_catch_exception(sockets[sid].send_bytes, message)
+        await send_socket_catch_exception(sockets[sid].send_bytes, message)

pyproject.toml

@@ -1,8 +1,8 @@
 [project]
 name = "comfyui-deploy"
 description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
-version = "1.1.0"
-license = { file = "LICENSE" }
+version = "1.0.0"
+license = "LICENSE"
 dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg"]
 
 [project.urls]

requirements.txt

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

web-plugin/api.js

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

web-plugin/app.js

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

web-plugin/widgets.js

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

web/package.json

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

web/src/components/customInputNodes.tsx

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

web/src/routes/registerCreateRunRoute.ts

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

web/src/server/createRun.ts

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