all new nodes working under EXR tab category

This reverts commit 46010e1dd5.

comfy-nodes/external_exr.py

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

comfy-nodes/external_exr_input.py

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

comfy-nodes/external_exr_output.py

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

comfy-nodes/external_exr_sequence_input.py

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

comfy-nodes/external_exr_sequence_output.py

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

comfy-nodes/external_lora.py

@@ -1,8 +1,4 @@
 import folder_paths
-from PIL import Image, ImageOps
-import numpy as np
-import torch
-import folder_paths
 
 
 class AnyType(str):
@@ -41,6 +37,10 @@ class ComfyUIDeployExternalLora:
                     "STRING",
                     {"multiline": False, "default": ""},
                 ),
+                "bearer_token": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
             },
         }
 
@@ -57,6 +57,7 @@ class ComfyUIDeployExternalLora:
         display_name=None,
         description=None,
         lora_url=None,
+        bearer_token=None,
     ):
         import requests
         import os
@@ -84,9 +85,13 @@ class ComfyUIDeployExternalLora:
                     + " to "
                     + destination_path
                 )
+                headers = {"User-Agent": "Mozilla/5.0"}
+                if bearer_token:
+                    headers["Authorization"] = f"Bearer {bearer_token}"
+                    print("using bearer token")
                 response = requests.get(
                     lora_url,
-                    headers={"User-Agent": "Mozilla/5.0"},
+                    headers=headers,
                     allow_redirects=True,
                 )
                 with open(destination_path, "wb") as out_file:

comfy-nodes/external_number_slider_int.py

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

comfy-nodes/external_seed.py

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

comfy-nodes/external_video.py

@@ -748,36 +748,64 @@ 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",),
+                "vae": ("VAE",),
+                "default_video": (sorted(files),),
+                "display_name": (
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ),
+                "description": (
+                    "STRING",
+                    {"multiline": True, "default": ""},
+                ),
+                "default_value_url": ("STRING", {"image_preview": True, "default": ""}),
+            },
+            "hidden": {"unique_id": "UNIQUE_ID"},
+        }
 
     CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
 
@@ -804,16 +832,21 @@ class ComfyUIDeployExternalVideo:
         select_every_nth = kwargs.get("select_every_nth")
         meta_batch = kwargs.get("meta_batch")
         unique_id = kwargs.get("unique_id")
-
+        default_value_url = kwargs.get("default_value_url")
 
         input_dir = folder_paths.get_input_directory()
-        if input_id.startswith("http"):
+        if input_id.startswith("http") or (
+            default_value_url and default_value_url.startswith("http")
+        ):
             import requests
 
-            print("Fetching video from URL: ", input_id)
-            response = requests.get(input_id, stream=True)
+            # Use input_id if it's a URL, otherwise use default_value_url
+            url = input_id if input_id.startswith("http") else default_value_url
+
+            print("Fetching video from URL: ", url)
+            response = requests.get(url, stream=True)
             file_size = int(response.headers.get("Content-Length", 0))
-            file_extension = input_id.split(".")[-1].split("?")[
+            file_extension = url.split(".")[-1].split("?")[
                 0
             ]  # Extract extension and handle URLs with parameters
             if file_extension not in video_extensions:

comfy-nodes/http_exr_input.py

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

comfy-nodes/http_exr_output.py

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

comfy-nodes/http_exr_sequence_input.py

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

comfy-nodes/http_exr_sequence_output.py

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

custom_routes.py

@@ -158,6 +158,8 @@ from logging import basicConfig, getLogger
 # Check for an environment variable to enable/disable Logfire
 use_logfire = os.environ.get("USE_LOGFIRE", "false").lower() == "true"
 
+API_KEY_COMFY_ORG = os.environ.get("API_KEY_COMFY_ORG", None)
+
 if use_logfire:
     try:
         import logfire
@@ -283,6 +285,9 @@ def post_prompt(json_data):
         if "extra_data" in json_data:
             extra_data = json_data["extra_data"]
 
+        if API_KEY_COMFY_ORG is not None:
+            extra_data["api_key_comfy_org"] = API_KEY_COMFY_ORG
+
         if "client_id" in json_data:
             extra_data["client_id"] = json_data["client_id"]
         if valid[0]:
@@ -316,7 +321,7 @@ def randomSeed(num_digits=15):
     return random.randint(range_start, range_end)
 
 
-def apply_random_seed_to_workflow(workflow_api):
+def apply_random_seed_to_workflow(workflow_api, workflow):
     """
     Applies a random seed to each element in the workflow_api that has a 'seed' input.
 
@@ -329,6 +334,41 @@ def apply_random_seed_to_workflow(workflow_api):
                 # If seed is a list, it's an input from another node (generally `external number int`)
                 if isinstance(workflow_api[key]["inputs"]["seed"], list):
                     continue
+
+                # Check node type in workflow to determine if we should randomize
+                node_id = key
+                should_skip = (
+                    False  # Add a flag to track if we should skip randomization
+                )
+
+                for node in workflow["nodes"]:
+                    if str(node["id"]) == node_id and node["type"] == "KSampler":
+                        # Check if this node has widgets_values and if seed setting is not "fixed"
+                        if "widgets_values" in node and len(node["widgets_values"]) > 1:
+                            seed_mode = node["widgets_values"][1]
+                            if seed_mode == "fixed":
+                                # Skip randomization for fixed seeds
+                                logger.info(
+                                    f"Skipping random seed for KSampler (node {node_id}) as it's set to fixed"
+                                )
+                                should_skip = True  # Set the flag to skip randomization
+                                break  # Exit the inner loop
+
+                            # Apply random seed for non-fixed seeds (randomize, iter, etc.)
+                            workflow_api[key]["inputs"]["seed"] = randomSeed()
+                            logger.info(
+                                f"Applied random seed {workflow_api[key]['inputs']['seed']} to KSampler (node {node_id})"
+                            )
+                            should_skip = (
+                                True  # Set the flag to skip default randomization
+                            )
+                            break  # Exit the inner loop
+                        break  # This break will skip checking other nodes if widgets_values doesn't exist
+
+                # Skip the rest of the code for this key if we already handled it
+                if should_skip:
+                    continue
+
                 # Special case for SONICSampler
                 if workflow_api[key]["class_type"] == "SONICSampler":
                     workflow_api[key]["inputs"]["seed"] = randomSeed("sonic")
@@ -436,6 +476,12 @@ def apply_inputs_to_workflow(workflow_api: Any, inputs: Any, sid: str = None):
                 if value["class_type"] == "ComfyUIDeployExternalEXR":
                     value["inputs"]["exr_file"] = new_value
 
+                if value["class_type"] == "ComfyUIDeployExternalSeed":
+                    logger.info(
+                        f"Applied random seed {new_value} to {value['class_type']}"
+                    )
+                    value["inputs"]["default_value"] = new_value
+
 
 def send_prompt(sid: str, inputs: StreamingPrompt):
     # workflow_api = inputs.workflow_api
@@ -443,7 +489,7 @@ def send_prompt(sid: str, inputs: StreamingPrompt):
     workflow = copy.deepcopy(inputs.workflow)
 
     # Random seed
-    apply_random_seed_to_workflow(workflow_api)
+    apply_random_seed_to_workflow(workflow_api, workflow)
 
     logger.info("getting inputs", inputs.inputs)
 
@@ -529,7 +575,7 @@ async def comfy_deploy_run(request):
     workflow = data.get("workflow")
 
     # Now it handles directly in here
-    apply_random_seed_to_workflow(workflow_api)
+    apply_random_seed_to_workflow(workflow_api, workflow)
     apply_inputs_to_workflow(workflow_api, inputs)
 
     prompt = {
@@ -596,7 +642,7 @@ async def stream_prompt(data, token):
     gpu_event_id = data.get("gpu_event_id", None)
 
     # Now it handles directly in here
-    apply_random_seed_to_workflow(workflow_api)
+    apply_random_seed_to_workflow(workflow_api, workflow)
     apply_inputs_to_workflow(workflow_api, inputs)
 
     prompt = {
@@ -2010,13 +2056,14 @@ async def upload_in_background(
                 ("files", "content_type", "image/png"),
                 ("gifs", "format", "image/gif"),
                 ("model_file", "format", "application/octet-stream"),
+                ("result", "format", "application/octet-stream"),
                 ("text_file", "format", "text/plain"),
             ]:
                 items = data.get(file_type, [])
 
                 for item in items:
                     # if is model_file, just add it to the data
-                    if file_type == "model_file":
+                    if file_type == "model_file" or file_type == "result":
                         if isinstance(item, str):
                             filename = os.path.basename(item)
                             # Extract folder name from the path
@@ -2096,6 +2143,7 @@ async def update_run_with_output(
             or "files" in data
             or "gifs" in data
             or "model_file" in data
+            or "result" in data
             or "text_file" in data
         )
     if bypass_upload and have_upload_media:
@@ -2719,30 +2767,32 @@ class UploadQueue:
                             # If this was the last upload for this node, clean up node data
                             if not self.node_uploads[prompt_id][node_id]:
                                 del self.node_uploads[prompt_id][node_id]
-                                if self.node_output_data[prompt_id][node_id]["data"]:
-                                    # Send final node data to API before cleanup
-                                    if prompt_metadata[prompt_id].status_endpoint:
-                                        body = {
-                                            "run_id": prompt_id,
-                                            "output_data": self.node_output_data[
-                                                prompt_id
-                                            ][node_id]["data"],
-                                            "node_meta": {"node_id": node_id},
-                                        }
-                                        try:
-                                            await async_request_with_retry(
-                                                "POST",
-                                                prompt_metadata[
-                                                    prompt_id
-                                                ].status_endpoint,
-                                                token=prompt_metadata[prompt_id].token,
-                                                json=body,
-                                            )
-                                        except Exception as e:
-                                            logger.error(
-                                                f"Failed to send final node data: {str(e)}"
-                                            )
-                                del self.node_output_data[prompt_id][node_id]
+                                if prompt_id in self.node_output_data:
+                                    if node_id in self.node_output_data[prompt_id]:
+                                        if self.node_output_data[prompt_id][node_id]["data"]:
+                                            # Send final node data to API before cleanup
+                                            if prompt_metadata[prompt_id].status_endpoint:
+                                                body = {
+                                                    "run_id": prompt_id,
+                                                    "output_data": self.node_output_data[
+                                                        prompt_id
+                                                    ][node_id]["data"],
+                                                    "node_meta": {"node_id": node_id},
+                                                }
+                                                try:
+                                                    await async_request_with_retry(
+                                                        "POST",
+                                                        prompt_metadata[
+                                                            prompt_id
+                                                        ].status_endpoint,
+                                                        token=prompt_metadata[prompt_id].token,
+                                                        json=body,
+                                                    )
+                                                except Exception as e:
+                                                    logger.error(
+                                                        f"Failed to send final node data: {str(e)}"
+                                                    )
+                                        del self.node_output_data[prompt_id][node_id]
 
                         # Send status update
                         await self.update_queue_status(prompt_id)

globals.py

@@ -56,11 +56,27 @@ streaming_prompt_metadata: dict[str, StreamingPrompt] = {}
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
     UNENCODED_PREVIEW_IMAGE = 2
+    EXR_IMAGE = 4
 
 
 max_output_id_length = 24
 
 
+async def send_exr(image_data, sid=None, output_id: str = None):
+    max_length = max_output_id_length
+    output_id = output_id[:max_length]
+    padded_output_id = output_id.ljust(max_length, "\x00")
+    encoded_output_id = padded_output_id.encode("ascii", "replace")
+
+    bytesIO = BytesIO()
+    # 10 bytes for the output_id
+    bytesIO.write(encoded_output_id)
+    bytesIO.write(image_data)
+
+    preview_bytes = bytesIO.getvalue()
+    await send_bytes(BinaryEventTypes.EXR_IMAGE, preview_bytes, sid=sid)
+
+
 async def send_image(image_data, sid=None, output_id: str = None):
     max_length = max_output_id_length
     output_id = output_id[:max_length]

web-plugin/index.js

@@ -5,7 +5,7 @@ LGraphNode = LiteGraph.LGraphNode;
 import { ComfyDialog, $el } from "../../scripts/ui.js";
 
 import { generateDependencyGraph } from "https://esm.sh/comfyui-json@0.1.25";
-import { ComfyDeploy } from "https://esm.sh/comfydeploy@2.0.0-beta.69";
+// import { ComfyDeploy } from "https://esm.sh/comfydeploy@2.0.0-beta.69";
 
 const styles = `
 .comfydeploy-menu-item {
@@ -330,8 +330,7 @@ async function convertToInput(node, widget, config) {
     inputNode.configure({
       widgets_values: [inputId, widget.value, JSON.stringify(options)],
     });
-  } else
-  {
+  } else {
     inputNode.configure({
       widgets_values: [inputId, widget.value],
     });
@@ -345,7 +344,7 @@ async function convertToInput(node, widget, config) {
     console.log(options);
     inputNode.widgets.find((x) => x.name == "default_value").options.values = options;
   }
- 
+
   app.graph.add(inputNode);
   inputNode.connect(0, node, index);
 
@@ -615,9 +614,9 @@ const ext = {
     //   return r
     // };
   },
-  
+
   // async nodeCreated(node) {
- 
+
   // },
 
   registerCustomNodes() {
@@ -1396,85 +1395,6 @@ async function deployWorkflow() {
   }
 }
 
-// Add this function to refresh the workflows list
-function refreshWorkflowsList(el) {
-  const workflowsList = el.querySelector("#workflows-list");
-  const workflowsLoading = el.querySelector("#workflows-loading");
-
-  workflowsLoading.style.display = "flex";
-  workflowsList.style.display = "none";
-  workflowsList.innerHTML = "";
-
-  client.workflows
-    .getAll({
-      page: "1",
-      pageSize: "10",
-    })
-    .then((result) => {
-      workflowsLoading.style.display = "none";
-      workflowsList.style.display = "block";
-
-      if (result.length === 0) {
-        workflowsList.innerHTML =
-          "<li style='color: #bdbdbd;'>No workflows found</li>";
-        return;
-      }
-
-      result.forEach((workflow) => {
-        const li = document.createElement("li");
-        li.style.marginBottom = "15px";
-        li.style.padding = "15px";
-        li.style.backgroundColor = "#2a2a2a";
-        li.style.borderRadius = "8px";
-        li.style.boxShadow = "0 2px 4px rgba(0,0,0,0.1)";
-
-        const lastRun = workflow.runs[0];
-        const lastRunStatus = lastRun ? lastRun.status : "No runs";
-        const statusColor =
-          lastRunStatus === "success"
-            ? "#4CAF50"
-            : lastRunStatus === "error"
-              ? "#F44336"
-              : "#FFC107";
-
-        const timeAgo = getTimeAgo(new Date(workflow.updatedAt));
-
-        li.innerHTML = `
-          <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px;">
-            <div style="flex: 1; overflow: hidden; text-overflow: ellipsis; white-space: nowrap;">
-              <strong style="font-size: 18px; color: #e0e0e0;">${workflow.name}</strong>
-            </div>
-            <span style="font-size: 12px; color: ${statusColor}; margin-left: 10px;">Last run: ${lastRunStatus}</span>
-          </div>
-          <div style="font-size: 14px; color: #bdbdbd; margin-bottom: 10px;">Last updated ${timeAgo}</div>
-          <div style="display: flex; gap: 10px;">
-            <button class="open-cloud-btn" style="padding: 5px 10px; background-color: #4CAF50; color: white; border: none; border-radius: 4px; cursor: pointer;">Open in Cloud</button>
-            <button class="load-api-btn" style="padding: 5px 10px; background-color: #2196F3; color: white; border: none; border-radius: 4px; cursor: pointer;">Load Workflow</button>
-          </div>
-        `;
-
-        const openCloudBtn = li.querySelector(".open-cloud-btn");
-        openCloudBtn.onclick = () =>
-          window.open(
-            `${getData().endpoint}/workflows/${workflow.id}?workspace=true`,
-            "_blank",
-          );
-
-        const loadApiBtn = li.querySelector(".load-api-btn");
-        loadApiBtn.onclick = () => loadWorkflowApi(workflow.versions[0].id);
-
-        workflowsList.appendChild(li);
-      });
-    })
-    .catch((error) => {
-      console.error("Error fetching workflows:", error);
-      workflowsLoading.style.display = "none";
-      workflowsList.style.display = "block";
-      workflowsList.innerHTML =
-        "<li style='color: #F44336;'>Error fetching workflows</li>";
-    });
-}
-
 function addButton() {
   const menu = document.querySelector(".comfy-menu");
 
@@ -1961,85 +1881,93 @@ export class ConfigDialog extends ComfyDialog {
 export const configDialog = new ConfigDialog();
 
 const currentOrigin = window.location.origin;
-const client = new ComfyDeploy({
-  bearerAuth: getData().apiKey,
-  serverURL: `${currentOrigin}/comfydeploy/api/`,
-});
+// const client = new ComfyDeploy({
+//   bearerAuth: getData().apiKey,
+//   serverURL: `${currentOrigin}/comfydeploy/api/`,
+// });
 
-app.extensionManager.registerSidebarTab({
-  id: "search",
-  icon: "pi pi-cloud-upload",
-  title: "Deploy",
-  tooltip: "Deploy and Configure",
-  type: "custom",
-  render: (el) => {
-    el.innerHTML = `
-      <div style="padding: 20px;">
-        <h3>Comfy Deploy</h3>
-        <div id="deploy-container" style="margin-bottom: 20px;"></div>
-        <div id="workflows-container" style="display: none;">
-          <h4>Your Workflows</h4>
-          <div id="workflows-loading" style="display: flex; justify-content: center; align-items: center; height: 100px;">
-            ${loadingIcon}
+// Check if the current URL hostname starts with localhost or 127.0.0.1
+const isLocalhost =
+  window.location.hostname === "localhost" ||
+  window.location.hostname === "127.0.0.1";
+
+// Only register the sidebar tab if we're on localhost
+if (isLocalhost) {
+  app.extensionManager.registerSidebarTab({
+    id: "search",
+    icon: "pi pi-cloud-upload",
+    title: "Deploy",
+    tooltip: "Deploy and Configure",
+    type: "custom",
+    render: (el) => {
+      el.innerHTML = `
+        <div style="padding: 20px;">
+          <h3>Comfy Deploy</h3>
+          <div id="deploy-container" style="margin-bottom: 20px;"></div>
+          <div id="workflows-container" style="display: none;">
+            <h4>Your Workflows</h4>
+            <div id="workflows-loading" style="display: flex; justify-content: center; align-items: center; height: 100px;">
+              ${loadingIcon}
+            </div>
+            <ul id="workflows-list" style="list-style-type: none; padding: 0; display: none;"></ul>
           </div>
-          <ul id="workflows-list" style="list-style-type: none; padding: 0; display: none;"></ul>
+          <div id="config-container"></div>
         </div>
-        <div id="config-container"></div>
-      </div>
-    `;
+      `;
 
-    // Add deploy button
-    const deployContainer = el.querySelector("#deploy-container");
-    const deployButton = document.createElement("button");
-    deployButton.id = "sidebar-deploy-button";
-    deployButton.style.display = "flex";
-    deployButton.style.alignItems = "center";
-    deployButton.style.justifyContent = "center";
-    deployButton.style.width = "100%";
-    deployButton.style.marginBottom = "10px";
-    deployButton.style.padding = "10px";
-    deployButton.style.fontSize = "16px";
-    deployButton.style.fontWeight = "bold";
-    deployButton.style.backgroundColor = "#4CAF50";
-    deployButton.style.color = "white";
-    deployButton.style.border = "none";
-    deployButton.style.borderRadius = "5px";
-    deployButton.style.cursor = "pointer";
-    deployButton.innerHTML = `<i class="pi pi-cloud-upload" style="margin-right: 8px;"></i><div id='sidebar-button-title'>Deploy</div>`;
-    deployButton.onclick = async () => {
-      await deployWorkflow();
-      // Refresh the workflows list after deployment
-      refreshWorkflowsList(el);
-    };
-    deployContainer.appendChild(deployButton);
+      // Add deploy button
+      const deployContainer = el.querySelector("#deploy-container");
+      const deployButton = document.createElement("button");
+      deployButton.id = "sidebar-deploy-button";
+      deployButton.style.display = "flex";
+      deployButton.style.alignItems = "center";
+      deployButton.style.justifyContent = "center";
+      deployButton.style.width = "100%";
+      deployButton.style.marginBottom = "10px";
+      deployButton.style.padding = "10px";
+      deployButton.style.fontSize = "16px";
+      deployButton.style.fontWeight = "bold";
+      deployButton.style.backgroundColor = "#4CAF50";
+      deployButton.style.color = "white";
+      deployButton.style.border = "none";
+      deployButton.style.borderRadius = "5px";
+      deployButton.style.cursor = "pointer";
+      deployButton.innerHTML = `<i class="pi pi-cloud-upload" style="margin-right: 8px;"></i><div id='sidebar-button-title'>Deploy</div>`;
+      deployButton.onclick = async () => {
+        await deployWorkflow();
+        // Refresh the workflows list after deployment
+        // refreshWorkflowsList(el);
+      };
+      deployContainer.appendChild(deployButton);
 
-    // Add config button
-    const configContainer = el.querySelector("#config-container");
-    const configButton = document.createElement("button");
-    configButton.style.display = "flex";
-    configButton.style.alignItems = "center";
-    configButton.style.justifyContent = "center";
-    configButton.style.width = "100%";
-    configButton.style.padding = "8px";
-    configButton.style.fontSize = "14px";
-    configButton.style.backgroundColor = "#f0f0f0";
-    configButton.style.color = "#333";
-    configButton.style.border = "1px solid #ccc";
-    configButton.style.borderRadius = "5px";
-    configButton.style.cursor = "pointer";
-    configButton.innerHTML = `<i class="pi pi-cog" style="margin-right: 8px;"></i>Configure`;
-    configButton.onclick = () => {
-      configDialog.show();
-    };
-    deployContainer.appendChild(configButton);
+      // Add config button
+      const configContainer = el.querySelector("#config-container");
+      const configButton = document.createElement("button");
+      configButton.style.display = "flex";
+      configButton.style.alignItems = "center";
+      configButton.style.justifyContent = "center";
+      configButton.style.width = "100%";
+      configButton.style.padding = "8px";
+      configButton.style.fontSize = "14px";
+      configButton.style.backgroundColor = "#f0f0f0";
+      configButton.style.color = "#333";
+      configButton.style.border = "1px solid #ccc";
+      configButton.style.borderRadius = "5px";
+      configButton.style.cursor = "pointer";
+      configButton.innerHTML = `<i class="pi pi-cog" style="margin-right: 8px;"></i>Configure`;
+      configButton.onclick = () => {
+        configDialog.show();
+      };
+      deployContainer.appendChild(configButton);
 
-    // Fetch and display workflows
-    const workflowsList = el.querySelector("#workflows-list");
-    const workflowsLoading = el.querySelector("#workflows-loading");
+      // Fetch and display workflows
+      const workflowsList = el.querySelector("#workflows-list");
+      const workflowsLoading = el.querySelector("#workflows-loading");
 
-    refreshWorkflowsList(el);
-  },
-});
+      // refreshWorkflowsList(el);
+    },
+  });
+}
 
 function getTimeAgo(date) {
   const seconds = Math.floor((new Date() - date) / 1000);
@@ -2056,24 +1984,24 @@ function getTimeAgo(date) {
   return Math.floor(seconds) + " seconds ago";
 }
 
-async function loadWorkflowApi(versionId) {
-  try {
-    const response = await client.comfyui.getWorkflowVersionVersionId({
-      versionId: versionId,
-    });
-    // Implement the logic to load the workflow API into the ComfyUI interface
-    console.log("Workflow API loaded:", response);
-    await window["app"].ui.settings.setSettingValueAsync(
-      "Comfy.Validation.Workflows",
-      true,
-    );
-    app.loadGraphData(response.workflow);
-    // You might want to update the UI or trigger some action in ComfyUI here
-  } catch (error) {
-    console.error("Error loading workflow API:", error);
-    // Show an error message to the user
-  }
-}
+// async function loadWorkflowApi(versionId) {
+//   try {
+//     const response = await client.comfyui.getWorkflowVersionVersionId({
+//       versionId: versionId,
+//     });
+//     // Implement the logic to load the workflow API into the ComfyUI interface
+//     console.log("Workflow API loaded:", response);
+//     await window["app"].ui.settings.setSettingValueAsync(
+//       "Comfy.Validation.Workflows",
+//       true,
+//     );
+//     app.loadGraphData(response.workflow);
+//     // You might want to update the UI or trigger some action in ComfyUI here
+//   } catch (error) {
+//     console.error("Error loading workflow API:", error);
+//     // Show an error message to the user
+//   }
+// }
 
 const orginal_fetch_api = api.fetchApi;
 api.fetchApi = async (route, options) => {