all new nodes working under EXR tab category

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/http_exr_input.py

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

comfy-nodes/http_exr_output.py

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

comfy-nodes/http_exr_sequence_input.py

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

comfy-nodes/http_exr_sequence_output.py

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

comfy-nodes/output_exr.py

@@ -1,78 +0,0 @@
-# In file: comfyui-deploy/comfy-nodes/output_exr.py
-
-import os
-import numpy as np
-import folder_paths
-
-# Try to set up OpenCV for EXR writing.
-try:
-    os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
-    import cv2
-    OPENCV_AVAILABLE = True
-except ImportError:
-    print("Warning: OpenCV not found for ComfyDeployOutputEXR. Please add opencv-python-headless to requirements.txt")
-    OPENCV_AVAILABLE = False
-
-# ALIGNED: Renamed class to match project conventions
-class ComfyDeployOutputEXR:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE", ),
-                "filename_prefix": ("STRING", {"default": "ComfyDeploy_EXR"})
-            },
-            # ADDED: Optional output_id for consistency with other ComfyDeploy nodes
-            "optional": {
-                "output_id": ("STRING", {"multiline": False, "default": "output_exr"}),
-            },
-        }
-    
-    RETURN_TYPES = ()
-    # ALIGNED: Changed function name to 'run'
-    FUNCTION = "run"
-    OUTPUT_NODE = True
-    # ALIGNED: Matched the category name
-    CATEGORY = "🔗ComfyDeploy"
-    DESCRIPTION = "Saves the input images as EXR (HDR) files."
-
-    def run(self, images, filename_prefix="ComfyDeploy_EXR", output_id="output_exr"):
-        if not OPENCV_AVAILABLE:
-            raise ImportError("OpenCV is required to save EXR files. Please ensure opencv-python-headless is in requirements.txt.")
-
-        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 image in images:
-            image_np = image.cpu().numpy()
-
-            if image_np.dtype != np.float32:
-                 image_np = image_np.astype(np.float32)
-
-            file = f"{filename}_{counter:05}.exr"
-            file_path = os.path.join(full_output_folder, file)
-
-            image_np_bgr = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
-            cv2.imwrite(file_path, image_np_bgr)
-
-            results.append({
-                "filename": file,
-                "subfolder": subfolder,
-                "type": self.type,
-                "output_id": output_id, # ADDED
-            })
-            counter += 1
-
-        return {"ui": {"images": results}}
-
-# ALIGNED: Mappings are defined at the bottom of the node file in this project
-NODE_CLASS_MAPPINGS = {"ComfyDeployOutputEXR": ComfyDeployOutputEXR}
-NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployOutputEXR": "EXR Output (ComfyDeploy)"}

custom_routes.py

@@ -31,7 +31,6 @@ import torch
 import psutil
 from collections import OrderedDict
 import io
-from urllib.parse import urlencode
 
 # Global session
 client_session = None
@@ -265,7 +264,7 @@ def clear_current_prompt(sid):
     streaming_prompt_metadata[sid].running_prompt_ids.clear()
 
 
-async def post_prompt(json_data):
+def post_prompt(json_data):
     prompt_server = server.PromptServer.instance
     json_data = prompt_server.trigger_on_prompt(json_data)
 
@@ -281,48 +280,7 @@ async def post_prompt(json_data):
 
     if "prompt" in json_data:
         prompt = json_data["prompt"]
-        prompt_id = json_data.get("prompt_id") or str(uuid.uuid4())
+        valid = execution.validate_prompt(prompt)
-
-        partial_execution_targets = None
-        if "partial_execution_targets" in json_data:
-            partial_execution_targets = json_data["partial_execution_targets"]
-
-        # Handle different validate_prompt signatures (newest to oldest)
-        valid = None
-        last_error = None
-
-        # v0.3.48 (3 args)
-        try:
-            valid = await execution.validate_prompt(
-                prompt_id, prompt, partial_execution_targets
-            )
-        except TypeError as e:
-            last_error = e
-            logger.debug(
-                f"validate_prompt with 3 params not supported, trying with 2. Debug: {last_error}"
-            )
-
-        # v0.3.45 - 0.3.47 (2 args)
-        if valid is None:
-            try:
-                valid = await execution.validate_prompt(prompt_id, prompt)
-            except TypeError as e:
-                last_error = e
-                logger.debug(
-                    f"validate_prompt with 2 params not supported, trying legacy signature. Debug: {last_error}"
-                )
-
-        # v0.3.44 or older (1 arg)
-        if valid is None:
-            try:
-                valid = execution.validate_prompt(prompt)
-            except TypeError as e:
-                last_error = e
-                logger.error(
-                    f"validate_prompt failed with all signatures. Last error: {last_error}"
-                )
-                raise
-
         extra_data = {}
         if "extra_data" in json_data:
             extra_data = json_data["extra_data"]
@@ -333,6 +291,8 @@ async def post_prompt(json_data):
         if "client_id" in json_data:
             extra_data["client_id"] = json_data["client_id"]
         if valid[0]:
+            # if the prompt id is provided
+            prompt_id = json_data.get("prompt_id") or str(uuid.uuid4())
             outputs_to_execute = valid[2]
             prompt_server.prompt_queue.put(
                 (number, prompt_id, prompt, extra_data, outputs_to_execute)
@@ -539,15 +499,15 @@ def send_prompt(sid: str, inputs: StreamingPrompt):
 
     prompt_id = str(uuid.uuid4())
 
-    # prompt = {
+    prompt = {
-    #     "prompt": workflow_api,
+        "prompt": workflow_api,
-    #     "client_id": sid,  # "comfy_deploy_instance", #api.client_id
+        "client_id": sid,  # "comfy_deploy_instance", #api.client_id
-    #     "prompt_id": prompt_id,
+        "prompt_id": prompt_id,
-    #     "extra_data": {"extra_pnginfo": {"workflow": workflow}},
+        "extra_data": {"extra_pnginfo": {"workflow": workflow}},
-    # }
+    }
 
     try:
-        # res = post_prompt(prompt)
+        res = post_prompt(prompt)
         inputs.running_prompt_ids.add(prompt_id)
         prompt_metadata[prompt_id] = SimplePrompt(
             status_endpoint=inputs.status_endpoint,
@@ -558,7 +518,7 @@ def send_prompt(sid: str, inputs: StreamingPrompt):
     except Exception as e:
         error_type = type(e).__name__
         stack_trace_short = traceback.format_exc().strip().split("\n")[-2]
-        # stack_trace = traceback.format_exc().strip()
+        stack_trace = traceback.format_exc().strip()
         logger.info(f"error: {error_type}, {e}")
         logger.info(f"stack trace: {stack_trace_short}")
 
@@ -634,7 +594,7 @@ async def comfy_deploy_run(request):
     )
 
     try:
-        res = await post_prompt(prompt)
+        res = post_prompt(prompt)
     except Exception as e:
         error_type = type(e).__name__
         stack_trace_short = traceback.format_exc().strip().split("\n")[-2]
@@ -703,7 +663,7 @@ async def stream_prompt(data, token):
     # log('info', "Begin prompt", prompt=prompt)
 
     try:
-        res = await post_prompt(prompt)
+        res = post_prompt(prompt)
     except Exception as e:
         error_type = type(e).__name__
         stack_trace_short = traceback.format_exc().strip().split("\n")[-2]
@@ -1305,11 +1265,22 @@ def handle_execute(class_type, last_node_id, prompt_id, server, unique_id):
 
 try:
     origin_execute = execution.execute
-    is_async = asyncio.iscoroutinefunction(origin_execute)
 
-    if is_async:
+    def swizzle_execute(
-
+        server,
-        async def swizzle_execute(
+        dynprompt,
+        caches,
+        current_item,
+        extra_data,
+        executed,
+        prompt_id,
+        execution_list,
+        pending_subgraph_results,
+    ):
+        unique_id = current_item
+        class_type = dynprompt.get_node(unique_id)["class_type"]
+        last_node_id = server.last_node_id
+        result = origin_execute(
             server,
             dynprompt,
             caches,
@@ -1319,61 +1290,12 @@ try:
             prompt_id,
             execution_list,
             pending_subgraph_results,
-            pending_async_nodes,
+        )
-        ):
+        handle_execute(class_type, last_node_id, prompt_id, server, unique_id)
-            unique_id = current_item
+        return result
-            class_type = dynprompt.get_node(unique_id)["class_type"]
-            last_node_id = server.last_node_id
-
-            result = await origin_execute(
-                server,
-                dynprompt,
-                caches,
-                current_item,
-                extra_data,
-                executed,
-                prompt_id,
-                execution_list,
-                pending_subgraph_results,
-                pending_async_nodes,
-            )
-
-            handle_execute(class_type, last_node_id, prompt_id, server, unique_id)
-            return result
-    else:
-
-        def swizzle_execute(
-            server,
-            dynprompt,
-            caches,
-            current_item,
-            extra_data,
-            executed,
-            prompt_id,
-            execution_list,
-            pending_subgraph_results,
-        ):
-            unique_id = current_item
-            class_type = dynprompt.get_node(unique_id)["class_type"]
-            last_node_id = server.last_node_id
-
-            result = origin_execute(
-                server,
-                dynprompt,
-                caches,
-                current_item,
-                extra_data,
-                executed,
-                prompt_id,
-                execution_list,
-                pending_subgraph_results,
-            )
-
-            handle_execute(class_type, last_node_id, prompt_id, server, unique_id)
-            return result
 
     execution.execute = swizzle_execute
-except Exception:
+except Exception as e:
     pass
 
 
@@ -1517,6 +1439,10 @@ async def send_json_override(self, event, data, sid=None):
             logger.info(format_table(headers, table_data))
             # print("========================\n")
 
+            timeline = format_execution_timeline(NODE_EXECUTION_TIMES)
+            logger.info(f"\nNode Execution Timeline:\n{timeline}")
+            # Clear the execution times for the next run
+
     # the last executing event is none, then the workflow is finished
     if event == "executing" and data.get("node") is None:
         mark_prompt_done(prompt_id=prompt_id)
@@ -2132,7 +2058,6 @@ async def upload_in_background(
                 ("model_file", "format", "application/octet-stream"),
                 ("result", "format", "application/octet-stream"),
                 ("text_file", "format", "text/plain"),
-                ("audio", "format", "audio/mpeg"),
             ]:
                 items = data.get(file_type, [])
 
@@ -2220,7 +2145,6 @@ async def update_run_with_output(
             or "model_file" in data
             or "result" in data
             or "text_file" in data
-            or "audio" in data
         )
     if bypass_upload and have_upload_media:
         print(
@@ -2829,57 +2753,53 @@ class UploadQueue:
                     logger.error(f"Upload failed: {str(e)}")
                     logger.error(traceback.format_exc())
                 finally:
-                    async with self.lock:  # Acquire lock to protect shared dict access
+                    # Remove this upload from tracking
-                        if prompt_id in self.pending_uploads:
+                    if prompt_id in self.pending_uploads:
-                            self.pending_uploads[prompt_id].discard(upload_id)
+                        self.pending_uploads[prompt_id].discard(upload_id)
+                        # Remove from node tracking if applicable
+                        if (
+                            node_id
+                            and prompt_id in self.node_uploads
+                            and node_id in self.node_uploads[prompt_id]
+                        ):
+                            self.node_uploads[prompt_id][node_id].discard(upload_id)
 
-                            if (
+                            # If this was the last upload for this node, clean up node data
-                                node_id
+                            if not self.node_uploads[prompt_id][node_id]:
-                                and prompt_id in self.node_uploads
+                                del self.node_uploads[prompt_id][node_id]
-                                and node_id in self.node_uploads[prompt_id]
+                                if prompt_id in self.node_output_data:
-                            ):
+                                    if node_id in self.node_output_data[prompt_id]:
-                                self.node_uploads[prompt_id][node_id].discard(upload_id)
+                                        if self.node_output_data[prompt_id][node_id]["data"]:
-
+                                            # Send final node data to API before cleanup
-                                if not self.node_uploads[prompt_id][node_id]:
+                                            if prompt_metadata[prompt_id].status_endpoint:
-                                    del self.node_uploads[prompt_id][node_id]
+                                                body = {
-
+                                                    "run_id": prompt_id,
-                                    if (
+                                                    "output_data": self.node_output_data[
-                                        prompt_id in self.node_output_data
-                                        and node_id in self.node_output_data[prompt_id]
-                                    ):
-                                        node_data = self.node_output_data[prompt_id][
-                                            node_id
-                                        ]
-                                        if node_data["data"]:
-                                            body = {
-                                                "run_id": prompt_id,
-                                                "output_data": node_data["data"],
-                                                "node_meta": {"node_id": node_id},
-                                            }
-                                            try:
-                                                await async_request_with_retry(
-                                                    "POST",
-                                                    prompt_metadata[
                                                         prompt_id
-                                                    ].status_endpoint,
+                                                    ][node_id]["data"],
-                                                    token=prompt_metadata[
+                                                    "node_meta": {"node_id": node_id},
-                                                        prompt_id
+                                                }
-                                                    ].token,
+                                                try:
-                                                    json=body,
+                                                    await async_request_with_retry(
-                                                )
+                                                        "POST",
-                                            except Exception as e:
+                                                        prompt_metadata[
-                                                logger.error(
+                                                            prompt_id
-                                                    f"Failed to send final node data: {str(e)}"
+                                                        ].status_endpoint,
-                                                )
+                                                        token=prompt_metadata[prompt_id].token,
-
+                                                        json=body,
-                                        # Safe to delete now (re-check not strictly needed with lock, but harmless)
+                                                    )
+                                                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)
+
                         # If no more pending uploads for this prompt and it's done, update status
-                        if (
+                        if not self.pending_uploads[prompt_id] and is_prompt_done(
-                            prompt_id in self.pending_uploads
+                            prompt_id
-                            and not self.pending_uploads[prompt_id]
-                            and is_prompt_done(prompt_id)
                         ):
                             # Clean up all data for this prompt
                             if prompt_id in self.node_uploads:
@@ -2892,12 +2812,9 @@ class UploadQueue:
                             loop.create_task(update_run(prompt_id, Status.SUCCESS))
                             loop.create_task(send("success", {"prompt_id": prompt_id}))
 
-                    # Mark task as done (outside lock to avoid holding it unnecessarily)
+                    # Mark task as done
                     self.queue.task_done()
 
-                    # Send status update (also outside lock)
-                    await self.update_queue_status(prompt_id)
-
             except Exception as e:
                 logger.error(f"Error in upload worker: {str(e)}")
                 logger.error(traceback.format_exc())
@@ -2968,317 +2885,3 @@ def format_execution_timeline(execution_times):
         current_time += duration
 
     return format_table(headers, rows)
-
-
-@server.PromptServer.instance.routes.get("/comfyui-deploy/auth-response")
-async def auth_response_proxy(request):
-    request_id = request.rel_url.query.get("request_id")
-    api_url = request.rel_url.query.get("api_url", "https://api.comfydeploy.com")
-
-    if not request_id:
-        return web.json_response({"error": "request_id is required"}, status=400)
-
-    target_url = f"{api_url}/api/platform/comfyui/auth-response?request_id={request_id}"
-
-    try:
-        await ensure_client_session()
-        async with client_session.get(target_url) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)
-
-
-@server.PromptServer.instance.routes.post("/comfyui-deploy/workflow")
-async def create_workflow_proxy(request):
-    data = await request.json()
-    name = data.get("name")
-    workflow_json = data.get("workflow_json")
-    workflow_api = data.get("workflow_api")
-    machine_id = data.get("machine_id")
-    api_url = data.get("api_url", "https://api.comfydeploy.com")
-
-    auth_header = request.headers.get("Authorization")
-    if not auth_header:
-        return web.json_response(
-            {"error": "Authorization header is required"}, status=401
-        )
-
-    if not name or not workflow_json or not workflow_api:
-        return web.json_response(
-            {"error": "name, workflow_json, workflow_api are required"}, status=400
-        )
-
-    target_url = f"{api_url}/api/workflow"
-
-    request_body = {
-        "name": name,
-        "workflow_json": json.dumps(workflow_json),
-        "workflow_api": json.dumps(workflow_api),
-        "machine_id": machine_id,
-    }
-
-    try:
-        await ensure_client_session()
-        async with client_session.post(
-            target_url,
-            json=request_body,
-            headers={
-                "Content-Type": "application/json",
-                "Authorization": auth_header,
-            },
-        ) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)
-
-
-@server.PromptServer.instance.routes.post("/comfyui-deploy/workflow/version")
-async def create_workflow_version_proxy(request):
-    data = await request.json()
-    workflow_id = data.get("workflow_id")
-    workflow = data.get("workflow")
-    workflow_api = data.get("workflow_api")
-    comment = data.get("comment", "")
-    api_url = data.get("api_url", "https://api.comfydeploy.com")
-
-    auth_header = request.headers.get("Authorization")
-
-    if not auth_header:
-        return web.json_response(
-            {"error": "Authorization header is required"}, status=401
-        )
-
-    target_url = f"{api_url}/api/workflow/{workflow_id}/version"
-
-    request_body = {
-        "workflow": workflow,
-        "workflow_api": workflow_api,
-        "comment": comment,
-    }
-
-    try:
-        await ensure_client_session()
-        async with client_session.post(
-            target_url,
-            json=request_body,
-            headers={
-                "Content-Type": "application/json",
-                "Authorization": auth_header,
-            },
-        ) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)
-
-
-@server.PromptServer.instance.routes.get("/comfyui-deploy/workflows")
-async def get_workflows_proxy(request):
-    api_url = request.rel_url.query.get("api_url", "https://api.comfydeploy.com")
-    search = request.rel_url.query.get("search", "")
-    limit = request.rel_url.query.get("limit", 10)
-    offset = request.rel_url.query.get("offset", 0)
-    auth_header = request.headers.get("Authorization")
-
-    if not auth_header:
-        return web.json_response(
-            {"error": "Authorization header is required"}, status=401
-        )
-
-    # Build query parameters properly
-    params = {}
-    if search:
-        params["search"] = search
-    if limit:
-        params["limit"] = limit
-    if offset:
-        params["offset"] = offset
-
-    target_url = f"{api_url}/api/workflows"
-    if params:
-        target_url += f"?{urlencode(params)}"
-
-    try:
-        await ensure_client_session()
-        async with client_session.get(
-            target_url,
-            headers={
-                "Content-Type": "application/json",
-                "Authorization": auth_header,
-            },
-        ) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)
-
-
-# for getting a workflow by id
-@server.PromptServer.instance.routes.get("/comfyui-deploy/workflow")
-async def get_workflow_proxy(request):
-    workflow_id = request.rel_url.query.get("workflow_id")
-    api_url = request.rel_url.query.get("api_url", "https://api.comfydeploy.com")
-    auth_header = request.headers.get("Authorization")
-
-    if not auth_header:
-        return web.json_response(
-            {"error": "Authorization header is required"}, status=401
-        )
-
-    target_url = f"{api_url}/api/workflow/{workflow_id}"
-
-    try:
-        await ensure_client_session()
-        async with client_session.get(
-            target_url, headers={"Authorization": auth_header}
-        ) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)
-
-
-# for getting a machine by id
-@server.PromptServer.instance.routes.get("/comfyui-deploy/machine")
-async def get_machine_proxy(request):
-    machine_id = request.rel_url.query.get("machine_id")
-    api_url = request.rel_url.query.get("api_url", "https://api.comfydeploy.com")
-    auth_header = request.headers.get("Authorization")
-
-    if not auth_header:
-        return web.json_response(
-            {"error": "Authorization header is required"}, status=401
-        )
-
-    target_url = f"{api_url}/api/machine/{machine_id}"
-
-    try:
-        await ensure_client_session()
-        async with client_session.get(
-            target_url, headers={"Authorization": auth_header}
-        ) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)
-
-
-# for fetching docker steps from current snapshot
-@server.PromptServer.instance.routes.post("/comfyui-deploy/snapshot-to-docker")
-async def snapshot_to_docker_proxy(request):
-    data = await request.json()
-    snapshot = data.get("snapshot")
-    api_url = data.get("api_url", "https://api.comfydeploy.com")
-    auth_header = request.headers.get("Authorization")
-
-    if not auth_header:
-        return web.json_response(
-            {"error": "Authorization header is required"}, status=401
-        )
-
-    target_url = f"{api_url}/api/snapshot-to-docker"
-
-    request_body = snapshot
-
-    try:
-        await ensure_client_session()
-        async with client_session.post(
-            target_url, json=request_body, headers={"Authorization": auth_header}
-        ) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)
-
-
-# update a serverless machine with machine id
-@server.PromptServer.instance.routes.post("/comfyui-deploy/machine/update")
-async def update_machine_proxy(request):
-    data = await request.json()
-    machine_id = data.get("machine_id")
-    comfyui_version = data.get("comfyui_version", None)
-    docker_steps = data.get("docker_steps")
-    api_url = data.get("api_url", "https://api.comfydeploy.com")
-    auth_header = request.headers.get("Authorization")
-
-    if not auth_header:
-        return web.json_response(
-            {"error": "Authorization header is required"}, status=401
-        )
-
-    target_url = f"{api_url}/api/machine/serverless/{machine_id}"
-
-    request_body = {"docker_command_steps": docker_steps}
-
-    if comfyui_version:
-        request_body["comfyui_version"] = comfyui_version
-
-    try:
-        await ensure_client_session()
-        async with client_session.patch(
-            target_url, json=request_body, headers={"Authorization": auth_header}
-        ) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)
-
-
-@server.PromptServer.instance.routes.post("/comfyui-deploy/machine/create")
-async def create_machine_proxy(request):
-    data = await request.json()
-    name = data.get("name")
-    docker_command_steps = data.get("docker_command_steps")
-    comfyui_version = data.get("comfyui_version")
-    api_url = data.get("api_url", "https://api.comfydeploy.com")
-    auth_header = request.headers.get("Authorization")
-
-    if not auth_header:
-        return web.json_response(
-            {"error": "Authorization header is required"}, status=401
-        )
-
-    target_url = f"{api_url}/api/machine/serverless"
-
-    request_body = {
-        "name": name,
-        "docker_command_steps": docker_command_steps,
-        "comfyui_version": comfyui_version,
-        "gpu": "A10G",
-    }
-
-    try:
-        await ensure_client_session()
-        async with client_session.post(
-            target_url, json=request_body, headers={"Authorization": auth_header}
-        ) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)
-
-
-# get latest comfyui version
-@server.PromptServer.instance.routes.get("/comfyui-deploy/comfyui-version")
-async def get_comfyui_version_proxy(request):
-    api_url = request.rel_url.query.get("api_url", "https://api.comfydeploy.com")
-    auth_header = request.headers.get("Authorization")
-
-    if not auth_header:
-        return web.json_response(
-            {"error": "Authorization header is required"}, status=401
-        )
-
-    target_url = f"{api_url}/api/latest-hashes"
-
-    try:
-        await ensure_client_session()
-        async with client_session.get(
-            target_url, headers={"Authorization": auth_header}
-        ) as response:
-            json_data = await response.json()
-            return web.json_response(json_data, status=response.status)
-    except Exception as e:
-        return web.json_response({"error": str(e)}, status=500)

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]

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-deploy"
 description = "Open source comfyui deployment platform, a vercel for generative workflow infra."
-version = "2.3.2"
+version = "2.1.0"
 license = { file = "LICENSE" }
 dependencies = ["aiofiles", "pydantic", "opencv-python", "imageio-ffmpeg", "tabulate", "brotli"]
 

web-plugin/snapshot-utils.js

@@ -1,82 +0,0 @@
-// Snapshot Utilities
-// Centralized snapshot fetching with ComfyUI version fallback
-
-/**
- * Fetches the current snapshot with ComfyUI version fallback
- * If the snapshot response has null comfyui field, it will fetch the latest ComfyUI version
- * and update the snapshot with the comfyui_hash
- *
- * @param {Function} getDataFn - Function that returns { apiKey, apiUrl } for ComfyUI version API calls
- * @returns {Promise<Object>} - The snapshot data with comfyui field populated
- */
-export async function fetchSnapshot(getDataFn = null) {
-  try {
-    // Fetch the current snapshot
-    const response = await fetch("/snapshot/get_current");
-    if (!response.ok) {
-      throw new Error(`Snapshot fetch failed: ${response.status}`);
-    }
-
-    const snapshot = await response.json();
-
-    // Check if comfyui field is null and we have getDataFn for fallback
-    if (snapshot.comfyui === null && getDataFn) {
-      console.log(
-        "ComfyUI version is null in snapshot, fetching latest version..."
-      );
-
-      try {
-        const data = getDataFn();
-        if (data && data.apiKey) {
-          const comfyuiVersionResponse = await fetch(
-            `/comfyui-deploy/comfyui-version?api_url=${encodeURIComponent(
-              data.apiUrl || "https://api.comfydeploy.com"
-            )}`,
-            {
-              headers: {
-                Authorization: `Bearer ${data.apiKey}`,
-              },
-            }
-          );
-
-          if (comfyuiVersionResponse.ok) {
-            const versionData = await comfyuiVersionResponse.json();
-            if (versionData.comfyui_hash) {
-              console.log(
-                `Using ComfyUI hash from API: ${versionData.comfyui_hash}`
-              );
-              snapshot.comfyui = versionData.comfyui_hash;
-            }
-          } else {
-            console.warn(
-              "Failed to fetch ComfyUI version from API:",
-              comfyuiVersionResponse.status
-            );
-          }
-        }
-      } catch (error) {
-        console.warn("Error fetching ComfyUI version fallback:", error);
-        // Continue with original snapshot even if fallback fails
-      }
-    }
-
-    return snapshot;
-  } catch (error) {
-    console.error("Error fetching snapshot:", error);
-    throw error;
-  }
-}
-
-/**
- * Simple snapshot fetch without ComfyUI version fallback
- * Use this when you don't need the ComfyUI version fallback logic
- *
- * @returns {Promise<Object>} - The snapshot data as-is
- */
-export async function fetchSnapshotSimple() {
-  const response = await fetch("/snapshot/get_current");
-  if (!response.ok) {
-    throw new Error(`Snapshot fetch failed: ${response.status}`);
-  }
-  return response.json();
-}

web-plugin/workflow-list.js

@@ -1,417 +0,0 @@
-// Workflow list management
-let workflowsState = {
-  workflows: [],
-  offset: 0,
-  limit: 20,
-  loading: false,
-  hasMore: true,
-  initialized: false,
-  currentSearch: "",
-};
-
-// Make workflowsState accessible globally
-window.workflowsState = workflowsState;
-
-async function fetchWorkflows(getData, offset = 0, limit = 20, search = "") {
-  try {
-    const data = getData();
-    if (!data.apiKey) {
-      throw new Error("API key not configured");
-    }
-
-    const params = new URLSearchParams({
-      offset: offset.toString(),
-      limit: limit.toString(),
-      api_url: data.apiUrl || "https://api.comfydeploy.com",
-      ...(search && { search }),
-    });
-
-    const response = await fetch(`/comfyui-deploy/workflows?${params}`, {
-      method: "GET",
-      headers: {
-        Authorization: `Bearer ${data.apiKey}`,
-        "Content-Type": "application/json",
-      },
-    });
-
-    if (!response.ok) {
-      throw new Error(`Failed to fetch workflows: ${response.status}`);
-    }
-
-    const result = await response.json();
-    console.log("result", result);
-    return Array.isArray(result) ? result : [];
-  } catch (error) {
-    console.error("Error fetching workflows:", error);
-    return [];
-  }
-}
-
-function createWorkflowItem(workflow, getTimeAgo, getData) {
-  const li = document.createElement("li");
-  let loadingToast = null;
-  li.style.cssText = `
-      border-bottom: 1px solid #444;
-      background: transparent;
-      transition: all 0.2s ease;
-      cursor: pointer;
-    `;
-
-  li.addEventListener("mouseenter", () => {
-    li.style.background = "#333";
-  });
-
-  li.addEventListener("mouseleave", () => {
-    li.style.background = "transparent";
-  });
-
-  // Add click handler to fetch and load workflow data
-  li.addEventListener("click", async () => {
-    try {
-      const data = getData();
-      if (!data.apiKey) {
-        console.error("No API key configured");
-        return;
-      }
-
-      // Show loading toast
-      loadingToast = window.app.extensionManager.toast.add({
-        severity: "info",
-        summary: "Loading workflow...",
-        detail: `Loading "${workflow.name}"`,
-        life: 3000,
-      });
-
-      const params = new URLSearchParams({
-        workflow_id: workflow.id,
-        api_url: data.apiUrl || "https://api.comfydeploy.com",
-      });
-
-      const response = await fetch(`/comfyui-deploy/workflow?${params}`, {
-        method: "GET",
-        headers: {
-          Authorization: `Bearer ${data.apiKey}`,
-          "Content-Type": "application/json",
-        },
-      });
-
-      if (!response.ok) {
-        throw new Error(`Failed to fetch workflow: ${response.status}`);
-      }
-
-      const workflowData = await response.json();
-      console.log("Workflow data:", workflowData);
-
-      // Load the workflow into the graph
-      if (workflowData.versions && workflowData.versions.length > 0) {
-        const latestVersion = workflowData.versions[0];
-        if (latestVersion.workflow && window.app) {
-          // Load the workflow
-          window.app.loadGraphData(latestVersion.workflow);
-
-          // Wait a bit for the graph to fully load before checking for ComfyDeploy node
-          await new Promise((resolve) => setTimeout(resolve, 100));
-
-          // Check if ComfyDeploy node exists, if not add it back
-          const graph = window.app.graph;
-          let deployMeta = graph.findNodesByType("ComfyDeploy");
-
-          if (deployMeta.length === 0) {
-            // Add ComfyDeploy node with workflow metadata
-            graph.beforeChange();
-            const node = LiteGraph.createNode("ComfyDeploy");
-            node.configure({
-              widgets_values: [
-                workflow.name, // workflow_name
-                workflow.id, // workflow_id
-                latestVersion.version, // version
-              ],
-            });
-            node.pos = [0, 0];
-            graph.add(node);
-            graph.afterChange();
-
-            console.log(
-              `Added ComfyDeploy node with: name="${workflow.name}", id="${workflow.id}", version="${latestVersion.version}"`
-            );
-          }
-
-          // Show success toast
-          window.app.extensionManager.toast.add({
-            severity: "success",
-            summary: "Workflow loaded successfully",
-            detail: `Loaded "${workflow.name}" v${latestVersion.version}`,
-            life: 3000,
-          });
-        }
-      }
-    } catch (error) {
-      console.error("Error loading workflow:", error);
-      // Show error toast
-      window.app.extensionManager.toast.add({
-        severity: "error",
-        summary: "Failed to load workflow",
-        detail: error.message,
-        life: 5000,
-      });
-    } finally {
-      if (loadingToast) {
-        loadingToast.close();
-      }
-    }
-  });
-
-  const updatedDate = new Date(workflow.updated_at);
-  const timeAgo = getTimeAgo(updatedDate);
-
-  li.innerHTML = `
-      <div style="padding: 12px 16px;">
-        <div style="display: flex; align-items: flex-start; gap: 12px;">
-          ${
-            workflow.cover_image
-              ? `<img src="${workflow.cover_image}" 
-                     style="width: 40px; height: 40px; border-radius: 4px; object-fit: cover; flex-shrink: 0;"
-                     onerror="this.style.display='none'">`
-              : `<div style="width: 40px; height: 40px; border-radius: 4px; background: #444; flex-shrink: 0; display: flex; align-items: center; justify-content: center; font-size: 14px; color: #888;">
-                   ${workflow.name.charAt(0).toUpperCase()}
-                 </div>`
-          }
-          
-          <div style="flex: 1; min-width: 0;">
-            <div style="display: flex; align-items: center; gap: 8px; margin-bottom: 4px;">
-              <h4 style="margin: 0; font-size: 14px; font-weight: 400; color: #fff; white-space: nowrap; overflow: hidden; text-overflow: ellipsis;">
-                ${workflow.name}
-              </h4>
-              ${
-                workflow.pinned
-                  ? `<span style="color: #ffd700; font-size: 12px;">📌</span>`
-                  : ""
-              }
-            </div>
-            
-            ${
-              workflow.description
-                ? `<p style="margin: 0 0 8px 0; font-size: 12px; color: #bbb; line-height: 1.3; overflow: hidden; display: -webkit-box; -webkit-line-clamp: 2; -webkit-box-orient: vertical;">
-                     ${workflow.description}
-                   </p>`
-                : ""
-            }
-            
-            <div style="display: flex; align-items: center; gap: 8px; margin-top: 8px;">
-              <img src="${workflow.user_icon}" 
-                   style="width: 16px; height: 16px; border-radius: 50%;"
-                   onerror="this.style.display='none'">
-              <span style="font-size: 11px; color: #888;">
-                ${workflow.user_name} • Updated ${timeAgo}
-              </span>
-            </div>
-          </div>
-        </div>
-      </div>
-    `;
-
-  return li;
-}
-
-async function loadMoreWorkflows(element, getData, getTimeAgo) {
-  if (workflowsState.loading || !workflowsState.hasMore) return;
-
-  workflowsState.loading = true;
-
-  const workflowsList = element.querySelector("#workflows-list");
-  const workflowsLoading = element.querySelector("#workflows-loading");
-
-  // Show loading indicator
-  workflowsLoading.style.display = "flex";
-
-  try {
-    const newWorkflows = await fetchWorkflows(
-      getData,
-      workflowsState.offset,
-      workflowsState.limit,
-      workflowsState.currentSearch
-    );
-
-    if (newWorkflows.length === 0) {
-      workflowsState.hasMore = false;
-    } else {
-      workflowsState.workflows.push(...newWorkflows);
-      workflowsState.offset += newWorkflows.length;
-
-      // Render new workflow items
-      newWorkflows.forEach((workflow) => {
-        const workflowItem = createWorkflowItem(workflow, getTimeAgo, getData);
-        workflowsList.appendChild(workflowItem);
-      });
-    }
-  } catch (error) {
-    console.error("Error loading more workflows:", error);
-  } finally {
-    workflowsState.loading = false;
-    workflowsLoading.style.display = "none";
-  }
-}
-
-function setupInfiniteScroll(container, element, getData, getTimeAgo) {
-  let isScrolling = false;
-
-  container.addEventListener("scroll", () => {
-    if (isScrolling) return;
-
-    const { scrollTop, scrollHeight, clientHeight } = container;
-
-    // Load more when scrolled to bottom (with 100px threshold)
-    if (scrollTop + clientHeight >= scrollHeight - 100) {
-      isScrolling = true;
-      loadMoreWorkflows(element, getData, getTimeAgo).finally(() => {
-        isScrolling = false;
-      });
-    }
-  });
-}
-
-async function initializeWorkflowsList(element, getData, getTimeAgo) {
-  const workflowsContainer = element.querySelector("#workflows-container");
-  const workflowsList = element.querySelector("#workflows-list");
-  const workflowsLoading = element.querySelector("#workflows-loading");
-
-  // Check if already initialized AND the DOM elements still exist
-  if (
-    workflowsState.initialized &&
-    workflowsList &&
-    workflowsList.children.length > 0
-  )
-    return;
-
-  try {
-    // Reset state (always reset when reinitializing)
-    workflowsState = {
-      workflows: [],
-      offset: 0,
-      limit: 20,
-      loading: false,
-      hasMore: true,
-      initialized: true,
-      currentSearch: "",
-    };
-
-    // Clear existing content in case of reinitialization
-    if (workflowsList) {
-      workflowsList.innerHTML = "";
-    }
-
-    // Show container and loading
-    workflowsContainer.style.display = "block";
-    workflowsLoading.style.display = "flex";
-
-    // Style the workflows list for full height scrolling
-    workflowsList.style.cssText = `
-        list-style-type: none;
-        padding: 0;
-        margin: 0;
-        height: calc(100vh - 550px);
-        overflow-y: auto;
-        scrollbar-width: thin;
-        scrollbar-color: #666 transparent;
-        border-top: 1px solid #444;
-      `;
-
-    // Add webkit scrollbar styles
-    const style = document.createElement("style");
-    style.textContent = `
-        #workflows-list::-webkit-scrollbar {
-          width: 6px;
-        }
-        #workflows-list::-webkit-scrollbar-track {
-          background: transparent;
-        }
-        #workflows-list::-webkit-scrollbar-thumb {
-          background: #666;
-          border-radius: 3px;
-        }
-        #workflows-list::-webkit-scrollbar-thumb:hover {
-          background: #777;
-        }
-      `;
-    document.head.appendChild(style);
-
-    // Setup infinite scroll
-    setupInfiniteScroll(workflowsList, element, getData, getTimeAgo);
-
-    // Load initial workflows
-    await loadMoreWorkflows(element, getData, getTimeAgo);
-
-    // Show the list
-    workflowsList.style.display = "block";
-  } catch (error) {
-    console.error("Error initializing workflows list:", error);
-    workflowsLoading.innerHTML = `
-        <div style="text-align: center; color: #e74c3c; font-size: 12px; padding: 20px;">
-          <div>Failed to load workflows</div>
-          <button onclick="initializeWorkflowsList(this.closest('.comfy-menu'), getData, getTimeAgo)" 
-                  style="margin-top: 8px; padding: 4px 8px; font-size: 11px; background: #f0f0f0; border: 1px solid #ccc; border-radius: 4px; cursor: pointer;">
-            Retry
-          </button>
-        </div>
-      `;
-  }
-}
-
-// Search functionality
-function addWorkflowSearch(element, getData, getTimeAgo) {
-  const workflowsContainer = element.querySelector("#workflows-container");
-  const h4 = workflowsContainer.querySelector("h4");
-
-  const searchContainer = document.createElement("div");
-  searchContainer.style.cssText = "margin-bottom: 12px;";
-
-  const searchInput = document.createElement("input");
-  searchInput.type = "text";
-  searchInput.placeholder = "Search workflows...";
-  searchInput.style.cssText = `
-      width: 100%;
-      padding: 8px 12px;
-      border: 1px solid #555;
-      border-radius: 6px;
-      font-size: 12px;
-      box-sizing: border-box;
-      background: #333;
-      color: #fff;
-    `;
-
-  let searchTimeout;
-  searchInput.addEventListener("input", (e) => {
-    clearTimeout(searchTimeout);
-    searchTimeout = setTimeout(async () => {
-      const searchTerm = e.target.value.trim();
-
-      // Update the tracked search term
-      workflowsState.currentSearch = searchTerm;
-
-      // Reset state for new search
-      workflowsState.workflows = [];
-      workflowsState.offset = 0;
-      workflowsState.hasMore = true;
-
-      // Clear current list
-      const workflowsList = element.querySelector("#workflows-list");
-      workflowsList.innerHTML = "";
-
-      // Load with search term
-      workflowsState.loading = false;
-      await loadMoreWorkflows(element, getData, getTimeAgo);
-    }, 300);
-  });
-
-  searchContainer.appendChild(searchInput);
-  h4.after(searchContainer);
-}
-
-// Export the functions
-export {
-  initializeWorkflowsList,
-  addWorkflowSearch,
-  workflowsState,
-  fetchWorkflows,
-  loadMoreWorkflows,
-};