merge

* feat(text): create node for external text list 

This is to send a list of texts to other nodes

* refactor: remove prints and rename variable

* style: update comment

* refactor: remove unused optional inputs

comfy-nodes/external_checkpoints.py

@@ -5,6 +5,12 @@ import torch
 import folder_paths
 from tqdm import tqdm
 
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+        
+WILDCARD = AnyType("*")
+
 class ComfyUIDeployExternalCheckpoint:
     @classmethod
     def INPUT_TYPES(s):
@@ -20,7 +26,7 @@ class ComfyUIDeployExternalCheckpoint:
             }
         }
 
-    RETURN_TYPES = (folder_paths.get_filename_list("checkpoints"),)
+    RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
 
     FUNCTION = "run"

comfy-nodes/external_lora.py

@@ -5,6 +5,14 @@ import torch
 import folder_paths
 
 
+class AnyType(str):
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+
+WILDCARD = AnyType("*")
+
+
 class ComfyUIDeployExternalLora:
     @classmethod
     def INPUT_TYPES(s):
@@ -17,27 +25,38 @@ class ComfyUIDeployExternalLora:
             },
             "optional": {
                 "default_lora_name": (folder_paths.get_filename_list("loras"),),
+                "lora_save_name": ( # if `default_lora_name` is a link to download a file, we will attempt to save it with this name
+                    "STRING",
+                    {"multiline": False, "default": ""},
+                ), 
             },
         }
 
-    RETURN_TYPES = (folder_paths.get_filename_list("loras"),)
+    RETURN_TYPES = (WILDCARD,)
     RETURN_NAMES = ("path",)
 
     FUNCTION = "run"
 
     CATEGORY = "deploy"
 
-    def run(self, input_id, default_lora_name=None):
+    def run(self, input_id, default_lora_name=None, lora_save_name=None):
         import requests
         import os
         import uuid
 
         if default_lora_name.startswith("http"):
-            unique_filename = str(uuid.uuid4()) + ".safetensors"
-            print(unique_filename)
+            if lora_save_name:
+                existing_loras = folder_paths.get_filename_list("loras")
+                # Check if lora_save_name exists in the list
+                if lora_save_name in existing_loras:
+                    print(f"using lora: {lora_save_name}")
+                    return (lora_save_name,)
+            else:
+                lora_save_name = str(uuid.uuid4()) + ".safetensors"
+            print(lora_save_name)
             print(folder_paths.folder_names_and_paths["loras"][0][0])
             destination_path = os.path.join(
-                folder_paths.folder_names_and_paths["loras"][0][0], unique_filename
+                folder_paths.folder_names_and_paths["loras"][0][0], lora_save_name
             )
             print(destination_path)
             print("Downloading external lora - " + input_id + " to " + destination_path)
@@ -48,7 +67,7 @@ class ComfyUIDeployExternalLora:
             )
             with open(destination_path, "wb") as out_file:
                 out_file.write(response.content)
-            return (unique_filename,)
+            return (lora_save_name,)
         else:
             print(f"using lora: {default_lora_name}")
             return (default_lora_name,)

comfy-nodes/external_text_list.py

@@ -0,0 +1,43 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+import json
+
+class ComfyUIDeployExternalTextList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": 'input_text_list'},
+                ),
+                 "text": (
+                    "STRING",
+                    {"multiline": True, "default": "[]"},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("text",)
+
+    OUTPUT_IS_LIST = (True,)
+
+    FUNCTION = "run"
+
+    CATEGORY = "text"
+
+    def run(self, input_id, text=None):
+        text_list = []
+        try:
+            text_list = json.loads(text)  # Assuming text is a JSON array string
+        except Exception as e:
+            print(f"Error processing images: {e}")
+            pass
+        return [text_list]
+
+
+NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalTextList": ComfyUIDeployExternalTextList}
+NODE_DISPLAY_NAME_MAPPINGS = {"ComfyUIDeployExternalTextList": "External Text List (ComfyUI Deploy)"}

comfy-nodes/external_video.py

@@ -1,10 +1,15 @@
-# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite and is meant to work with
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
+# Intended to work with https://github.com/NicholasKao1029/ComfyUI-VideoHelperSuite/tree/main
 import os
 import itertools
 import numpy as np
 import torch
+from typing import Union
+from torch import Tensor
 import cv2
+import psutil
 
+from collections.abc import Mapping
 import folder_paths
 from comfy.utils import common_upscale
 
@@ -90,13 +95,25 @@ if gifski_path is None:
         gifski_path = shutil.which("gifski")
 
 
+def is_safe_path(path):
+    if "VHS_STRICT_PATHS" not in os.environ:
+        return True
+    basedir = os.path.abspath(".")
+    try:
+        common_path = os.path.commonpath([basedir, path])
+    except:
+        # Different drive on windows
+        return False
+    return common_path == basedir
+
+
 def get_sorted_dir_files_from_directory(
     directory: str,
     skip_first_images: int = 0,
     select_every_nth: int = 1,
     extensions: Iterable = None,
 ):
-    directory = directory.strip()
+    directory = strip_path(directory)
     dir_files = os.listdir(directory)
     dir_files = sorted(dir_files)
     dir_files = [os.path.join(directory, x) for x in dir_files]
@@ -177,18 +194,59 @@ def requeue_workflow(requeue_required=(-1, True)):
 
 
 def get_audio(file, start_time=0, duration=0):
-    args = [ffmpeg_path, "-v", "error", "-i", file]
+    args = [ffmpeg_path, "-i", file]
     if start_time > 0:
         args += ["-ss", str(start_time)]
     if duration > 0:
         args += ["-t", str(duration)]
     try:
+        # TODO: scan for sample rate and maintain
         res = subprocess.run(
-            args + ["-f", "wav", "-"], stdout=subprocess.PIPE, check=True
-        ).stdout
+            args + ["-f", "f32le", "-"], capture_output=True, check=True
+        )
+        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
+        match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
     except subprocess.CalledProcessError as e:
-        return False
-    return res
+        raise Exception(
+            f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
+        )
+    if match:
+        ar = int(match.group(1))
+        # NOTE: Just throwing an error for other channel types right now
+        # Will deal with issues if they come
+        ac = {"mono": 1, "stereo": 2}[match.group(2)]
+    else:
+        ar = 44100
+        ac = 2
+    audio = audio.reshape((-1, ac)).transpose(0, 1).unsqueeze(0)
+    return {"waveform": audio, "sample_rate": ar}
+
+
+class LazyAudioMap(Mapping):
+    def __init__(self, file, start_time, duration):
+        self.file = file
+        self.start_time = start_time
+        self.duration = duration
+        self._dict = None
+
+    def __getitem__(self, key):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return self._dict[key]
+
+    def __iter__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return iter(self._dict)
+
+    def __len__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return len(self._dict)
+
+
+def lazy_get_audio(file, start_time=0, duration=0):
+    return LazyAudioMap(file, start_time, duration)
 
 
 def lazy_eval(func):
@@ -230,6 +288,19 @@ def validate_sequence(path):
     return False
 
 
+def strip_path(path):
+    # This leaves whitespace inside quotes and only a single "
+    # thus ' ""test"' -> '"test'
+    # consider path.strip(string.whitespace+"\"")
+    # or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1)
+    path = path.strip()
+    if path.startswith('"'):
+        path = path[1:]
+    if path.endswith('"'):
+        path = path[:-1]
+    return path
+
+
 def hash_path(path):
     if path is None:
         return "input"
@@ -286,6 +357,145 @@ def target_size(
     return (width, height)
 
 
+def validate_index(
+    index: int,
+    length: int = 0,
+    is_range: bool = False,
+    allow_negative=False,
+    allow_missing=False,
+) -> int:
+    # if part of range, do nothing
+    if is_range:
+        return index
+    # otherwise, validate index
+    # validate not out of range - only when latent_count is passed in
+    if length > 0 and index > length - 1 and not allow_missing:
+        raise IndexError(f"Index '{index}' out of range for {length} item(s).")
+    # if negative, validate not out of range
+    if index < 0:
+        if not allow_negative:
+            raise IndexError(f"Negative indeces not allowed, but was '{index}'.")
+        conv_index = length + index
+        if conv_index < 0 and not allow_missing:
+            raise IndexError(
+                f"Index '{index}', converted to '{conv_index}' out of range for {length} item(s)."
+            )
+        index = conv_index
+    return index
+
+
+def convert_to_index_int(
+    raw_index: str,
+    length: int = 0,
+    is_range: bool = False,
+    allow_negative=False,
+    allow_missing=False,
+) -> int:
+    try:
+        return validate_index(
+            int(raw_index),
+            length=length,
+            is_range=is_range,
+            allow_negative=allow_negative,
+            allow_missing=allow_missing,
+        )
+    except ValueError as e:
+        raise ValueError(f"Index '{raw_index}' must be an integer.", e)
+
+
+def convert_str_to_indexes(
+    indexes_str: str, length: int = 0, allow_missing=False
+) -> list[int]:
+    if not indexes_str:
+        return []
+    int_indexes = list(range(0, length))
+    allow_negative = length > 0
+    chosen_indexes = []
+    # parse string - allow positive ints, negative ints, and ranges separated by ':'
+    groups = indexes_str.split(",")
+    groups = [g.strip() for g in groups]
+    for g in groups:
+        # parse range of indeces (e.g. 2:16)
+        if ":" in g:
+            index_range = g.split(":", 2)
+            index_range = [r.strip() for r in index_range]
+
+            start_index = index_range[0]
+            if len(start_index) > 0:
+                start_index = convert_to_index_int(
+                    start_index,
+                    length=length,
+                    is_range=True,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            else:
+                start_index = 0
+            end_index = index_range[1]
+            if len(end_index) > 0:
+                end_index = convert_to_index_int(
+                    end_index,
+                    length=length,
+                    is_range=True,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            else:
+                end_index = length
+            # support step as well, to allow things like reversing, every-other, etc.
+            step = 1
+            if len(index_range) > 2:
+                step = index_range[2]
+                if len(step) > 0:
+                    step = convert_to_index_int(
+                        step,
+                        length=length,
+                        is_range=True,
+                        allow_negative=True,
+                        allow_missing=True,
+                    )
+                else:
+                    step = 1
+            # if latents were passed in, base indeces on known latent count
+            if len(int_indexes) > 0:
+                chosen_indexes.extend(int_indexes[start_index:end_index][::step])
+            # otherwise, assume indeces are valid
+            else:
+                chosen_indexes.extend(list(range(start_index, end_index, step)))
+        # parse individual indeces
+        else:
+            chosen_indexes.append(
+                convert_to_index_int(
+                    g,
+                    length=length,
+                    allow_negative=allow_negative,
+                    allow_missing=allow_missing,
+                )
+            )
+    return chosen_indexes
+
+
+def select_indexes(input_obj: Union[Tensor, list], idxs: list):
+    if type(input_obj) == Tensor:
+        return input_obj[idxs]
+    else:
+        return [input_obj[i] for i in idxs]
+
+
+def select_indexes_from_str(
+    input_obj: Union[Tensor, list], indexes: str, err_if_missing=True, err_if_empty=True
+):
+    real_idxs = convert_str_to_indexes(
+        indexes, len(input_obj), allow_missing=not err_if_missing
+    )
+    if err_if_empty and len(real_idxs) == 0:
+        raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
+    return select_indexes(input_obj, real_idxs)
+
+
+###
+
+
 def cv_frame_generator(
     video,
     force_rate,
@@ -295,9 +505,10 @@ def cv_frame_generator(
     meta_batch=None,
     unique_id=None,
 ):
-    video_cap = cv2.VideoCapture(video)
+    video_cap = cv2.VideoCapture(strip_path(video))
     if not video_cap.isOpened():
         raise ValueError(f"{video} could not be loaded with cv.")
+    pbar = None
 
     # extract video metadata
     fps = video_cap.get(cv2.CAP_PROP_FPS)
@@ -319,6 +530,8 @@ def cv_frame_generator(
         target_frame_time = 1 / force_rate
 
     yield (width, height, fps, duration, total_frames, target_frame_time)
+    if meta_batch is not None:
+        yield min(frame_load_cap, total_frames)
 
     time_offset = target_frame_time - base_frame_time
     while video_cap.isOpened():
@@ -349,7 +562,8 @@ def cv_frame_generator(
         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         # convert frame to comfyui's expected format
         # TODO: frame contains no exif information. Check if opencv2 has already applied
-        frame = np.array(frame, dtype=np.float32) / 255.0
+        frame = np.array(frame, dtype=np.float32)
+        torch.from_numpy(frame).div_(255)
         if prev_frame is not None:
             inp = yield prev_frame
             if inp is not None:
@@ -357,6 +571,8 @@ def cv_frame_generator(
                 return
         prev_frame = frame
         frames_added += 1
+        if pbar is not None:
+            pbar.update_absolute(frames_added, frame_load_cap)
         # if cap exists and we've reached it, stop processing frames
         if frame_load_cap > 0 and frames_added >= frame_load_cap:
             break
@@ -367,6 +583,17 @@ def cv_frame_generator(
         yield prev_frame
 
 
+def batched(it, n):
+    while batch := tuple(itertools.islice(it, n)):
+        yield batch
+
+
+def batched_vae_encode(images, vae, frames_per_batch):
+    for batch in batched(images, frames_per_batch):
+        image_batch = torch.from_numpy(np.array(batch))
+        yield from vae.encode(image_batch).numpy()
+
+
 def load_video_cv(
     video: str,
     force_rate: int,
@@ -378,6 +605,8 @@ def load_video_cv(
     select_every_nth: int,
     meta_batch=None,
     unique_id=None,
+    memory_limit_mb=None,
+    vae=None,
 ):
     if meta_batch is None or unique_id not in meta_batch.inputs:
         gen = cv_frame_generator(
@@ -401,30 +630,89 @@ def load_video_cv(
                 total_frames,
                 target_frame_time,
             )
+            meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
 
     else:
         (gen, width, height, fps, duration, total_frames, target_frame_time) = (
             meta_batch.inputs[unique_id]
         )
 
+    memory_limit = None
+    if memory_limit_mb is not None:
+        memory_limit *= 2**20
+    else:
+        # TODO: verify if garbage collection should be performed here.
+        # leaves ~128 MB unreserved for safety
+        try:
+            memory_limit = (
+                psutil.virtual_memory().available + psutil.swap_memory().free
+            ) - 2**27
+        except:
+            print(
+                "Failed to calculate available memory. Memory load limit has been disabled"
+            )
+    if memory_limit is not None:
+        if vae is not None:
+            # space required to load as f32, exist as latent with wiggle room, decode to f32
+            max_loadable_frames = int(
+                memory_limit // (width * height * 3 * (4 + 4 + 1 / 10))
+            )
+        else:
+            # TODO: use better estimate for when vae is not None
+            # Consider completely ignoring for load_latent case?
+            max_loadable_frames = int(memory_limit // (width * height * 3 * (0.1)))
         if meta_batch is not None:
+            if meta_batch.frames_per_batch > max_loadable_frames:
+                raise RuntimeError(
+                    f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory"
+                )
             gen = itertools.islice(gen, meta_batch.frames_per_batch)
+        else:
+            original_gen = gen
+            gen = itertools.islice(gen, max_loadable_frames)
+    downscale_ratio = getattr(vae, "downscale_ratio", 8)
+    frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
+    if force_size != "Disabled" or vae is not None:
+        new_size = target_size(
+            width, height, force_size, custom_width, custom_height, downscale_ratio
+        )
+        if new_size[0] != width or new_size[1] != height:
 
+            def rescale(frame):
+                s = torch.from_numpy(
+                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
+                )
+                s = s.movedim(-1, 1)
+                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+                return s.movedim(1, -1).numpy()
+
+            gen = itertools.chain.from_iterable(
+                map(rescale, batched(gen, frames_per_batch))
+            )
+    else:
+        new_size = width, height
+    if vae is not None:
+        gen = batched_vae_encode(gen, vae, frames_per_batch)
+        vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
+        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
+    else:
         # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
         images = torch.from_numpy(
-        np.fromiter(gen, np.dtype((np.float32, (height, width, 3))))
+            np.fromiter(gen, np.dtype((np.float32, (new_size[1], new_size[0], 3))))
         )
+    if meta_batch is None and memory_limit is not None:
+        try:
+            next(original_gen)
+            raise RuntimeError(
+                f"Memory limit hit after loading {len(images)} frames. Stopping execution."
+            )
+        except StopIteration:
+            pass
     if len(images) == 0:
         raise RuntimeError("No frames generated")
-    if force_size != "Disabled":
-        new_size = target_size(width, height, force_size, custom_width, custom_height)
-        if new_size[0] != width or new_size[1] != height:
-            s = images.movedim(-1, 1)
-            s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
-            images = s.movedim(1, -1)
 
     # Setup lambda for lazy audio capture
-    audio = lambda: get_audio(
+    audio = lazy_get_audio(
         video,
         skip_first_frames * target_frame_time,
         frame_load_cap * target_frame_time * select_every_nth,
@@ -440,13 +728,16 @@ def load_video_cv(
         "loaded_fps": 1 / target_frame_time,
         "loaded_frame_count": len(images),
         "loaded_duration": len(images) * target_frame_time,
-        "loaded_width": images.shape[2],
-        "loaded_height": images.shape[1],
+        "loaded_width": new_size[0],
+        "loaded_height": new_size[1],
     }
-
-    return (images, len(images), lazy_eval(audio), video_info)
+    if vae is None:
+        return (images, len(images), audio, video_info, None)
+    else:
+        return (None, len(images), audio, video_info, {"samples": images})
 
 
+# modeled after Video upload node
 class ComfyUIDeployExternalVideo:
     @classmethod
     def INPUT_TYPES(s):
@@ -457,68 +748,38 @@ class ComfyUIDeployExternalVideo:
                 file_parts = f.split(".")
                 if len(file_parts) > 1 and (file_parts[-1] in video_extensions):
                     files.append(f)
-        return {
-            "required": {
+        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},
-                ),
+                     "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_value": (sorted(files),),
                 },
-            "hidden": {"unique_id": "UNIQUE_ID"},
+                "hidden": {
+                    "unique_id": "UNIQUE_ID"
+                },
                 }
 
     CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
 
-    RETURN_TYPES = (
-        "IMAGE",
-        "INT",
-        "VHS_AUDIO",
-        "VHS_VIDEOINFO",
-    )
+    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
     RETURN_NAMES = (
         "IMAGE",
         "frame_count",
         "audio",
         "video_info",
+        "LATENT",
     )
 
     FUNCTION = "load_video"

custom_routes.py

@@ -22,17 +22,98 @@ from typing import Dict, List, Union, Any, Optional
 from PIL import Image
 import copy
 import struct
+from aiohttp import ClientError
+import atexit
+
+# Global session
+client_session = None
+
+# def create_client_session():
+#     global client_session
+#     if client_session is None:
+#         client_session = aiohttp.ClientSession()
+        
+async def ensure_client_session():
+    global client_session
+    if client_session is None:
+        client_session = aiohttp.ClientSession()
+
+async def cleanup():
+    global client_session
+    if client_session:
+        await client_session.close()
+        
+def exit_handler():
+    print("Exiting the application. Initiating cleanup...")
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(cleanup())
+
+atexit.register(exit_handler)
+
+max_retries = int(os.environ.get('MAX_RETRIES', '3'))
+retry_delay_multiplier = float(os.environ.get('RETRY_DELAY_MULTIPLIER', '2'))
+
+print(f"max_retries: {max_retries}, retry_delay_multiplier: {retry_delay_multiplier}")
+
+async def async_request_with_retry(method, url, **kwargs):
+    global client_session
+    await ensure_client_session()
+    retry_delay = 1  # Start with 1 second delay
+
+    for attempt in range(max_retries):
+        try:
+            async with client_session.request(method, url, **kwargs) as response:
+                response.raise_for_status()
+                return response
+        except ClientError as e:
+            if attempt == max_retries - 1:
+                logger.error(f"Request failed after {max_retries} attempts: {e}")
+                # raise
+            logger.warning(f"Request failed (attempt {attempt + 1}/{max_retries}): {e}")
+            await asyncio.sleep(retry_delay)
+            retry_delay *= retry_delay_multiplier  # Exponential backoff
 
 from logging import basicConfig, getLogger
+
+# Check for an environment variable to enable/disable Logfire
+use_logfire = os.environ.get('USE_LOGFIRE', 'false').lower() == 'true'
+
+if use_logfire:
+    try:
         import logfire
-# if os.environ.get('LOGFIRE_TOKEN', None) is not None:
         logfire.configure(
             send_to_logfire="if-token-present"
         )
-# basicConfig(handlers=[logfire.LogfireLoggingHandler()])
-logfire_handler = logfire.LogfireLoggingHandler()
+        logger = logfire
+    except ImportError:
+        print("Logfire not installed or disabled. Using standard Python logger.")
+        use_logfire = False
+
+if not use_logfire:
+    # Use a standard Python logger when Logfire is disabled or not available
     logger = getLogger("comfy-deploy")
-logger.addHandler(logfire_handler)
+    basicConfig(level="INFO")  # You can adjust the logging level as needed
+
+def log(level, message, **kwargs):
+    if use_logfire:
+        getattr(logger, level)(message, **kwargs)
+    else:
+        getattr(logger, level)(f"{message} {kwargs}")
+        
+# For a span, you might need to create a context manager
+from contextlib import contextmanager
+
+@contextmanager
+def log_span(name):
+    if use_logfire:
+        with logger.span(name):
+            yield
+    else:
+        yield
+    #     logger.info(f"Start: {name}")
+    #     yield
+    #     logger.info(f"End: {name}")
+
 
 from globals import StreamingPrompt, Status, sockets, SimplePrompt, streaming_prompt_metadata, prompt_metadata
 
@@ -73,11 +154,11 @@ def clear_current_prompt(sid):
     prompt_server = server.PromptServer.instance
     to_delete = list(streaming_prompt_metadata[sid].running_prompt_ids)  # Convert set to list
 
-    logger.info("clearning out prompt: ", to_delete)
+    logger.info(f"clearing out prompt: {to_delete}")
     for id_to_delete in to_delete:
         delete_func = lambda a: a[1] == id_to_delete
         prompt_server.prompt_queue.delete_queue_item(delete_func)
-        logger.info("deleted prompt: ", id_to_delete, prompt_server.prompt_queue.get_tasks_remaining())
+        logger.info(f"deleted prompt: {id_to_delete}, remaining tasks: {prompt_server.prompt_queue.get_tasks_remaining()}")
 
     streaming_prompt_metadata[sid].running_prompt_ids.clear()
 
@@ -268,7 +349,7 @@ async def comfy_deploy_run(request):
 
     status = 200
 
-    if "node_errors" in res and res["node_errors"]:
+    if "node_errors" in res and res["node_errors"] is not None:
         # Even tho there are node_errors it can still be run
         status = 400
         await update_run_with_output(prompt_id, {
@@ -306,7 +387,7 @@ async def stream_prompt(data):
         workflow_api=workflow_api
     )
 
-    logfire.info("Begin prompt", prompt=prompt)
+    # log('info', "Begin prompt", prompt=prompt)
 
     try:
         res = post_prompt(prompt)
@@ -329,7 +410,7 @@ async def stream_prompt(data):
 
     status = 200
 
-    if "node_errors" in res and res["node_errors"]:
+    if "node_errors" in res and res["node_errors"] is not None:
         # Even tho there are node_errors it can still be run
         status = 400
         await update_run_with_output(prompt_id, {
@@ -359,8 +440,8 @@ async def stream_response(request):
     prompt_id = data.get("prompt_id")
     comfy_message_queues[prompt_id] = asyncio.Queue()
 
-    with logfire.span('Streaming Run'):
-        logfire.info('Streaming prompt')
+    with log_span('Streaming Run'):
+        log('info', 'Streaming prompt')
 
         try:
             result = await stream_prompt(data=data)
@@ -373,7 +454,7 @@ async def stream_response(request):
                     if not comfy_message_queues[prompt_id].empty():
                         data = await comfy_message_queues[prompt_id].get()
 
-                        logfire.info(data["event"], data=json.dumps(data))
+                        # log('info', data["event"], data=json.dumps(data))
                         # logger.info("listener", data)
                         await response.write(f"event: event_update\ndata: {json.dumps(data)}\n\n".encode('utf-8'))
                         await response.drain()  # Ensure the buffer is flushed
@@ -384,10 +465,10 @@ async def stream_response(request):
 
                 await asyncio.sleep(0.1)  # Adjust the sleep duration as needed
         except asyncio.CancelledError:
-            logfire.info("Streaming was cancelled")
+            log('info', "Streaming was cancelled")
             raise
         except Exception as e:
-            logfire.error("Streaming error", error=e)
+            log('error', "Streaming error", error=e)
         finally:
             # event_emitter.off("send_json", task)
             await response.write_eof()
@@ -482,10 +563,9 @@ async def upload_file_endpoint(request):
 
     if get_url:
         try:
-            async with aiohttp.ClientSession() as session:
             headers = {'Authorization': f'Bearer {token}'}
             params = {'file_size': file_size, 'type': file_type}
-                async with session.get(get_url, params=params, headers=headers) as response:
+            response = await async_request_with_retry('GET', get_url, params=params, headers=headers)
             if response.status == 200:
                 content = await response.json()
                 upload_url = content["upload_url"]
@@ -496,7 +576,7 @@ async def upload_file_endpoint(request):
                         # "x-amz-acl": "public-read",
                         "Content-Length": str(file_size)
                     }
-                            async with session.put(upload_url, data=f, headers=headers) as upload_response:
+                    upload_response = await async_request_with_retry('PUT', upload_url, data=f, headers=headers)
                     if upload_response.status == 200:
                         return web.json_response({
                             "message": "File uploaded successfully",
@@ -588,9 +668,7 @@ async def update_realtime_run_status(realtime_id: str, status_endpoint: str, sta
     if (status_endpoint is None):
         return
     # requests.post(status_endpoint, json=body)
-    async with aiohttp.ClientSession() as session:
-        async with session.post(status_endpoint, json=body) as response:
-            pass
+    await async_request_with_retry('POST', status_endpoint, json=body)
 
 @server.PromptServer.instance.routes.get('/comfyui-deploy/ws')
 async def websocket_handler(request):
@@ -611,9 +689,8 @@ async def websocket_handler(request):
     status_endpoint = request.rel_url.query.get('status_endpoint', None)
 
     if auth_token is not None and get_workflow_endpoint_url is not None:
-        async with aiohttp.ClientSession() as session:
         headers = {'Authorization': f'Bearer {auth_token}'}
-            async with session.get(get_workflow_endpoint_url, headers=headers) as response:
+        response = await async_request_with_retry('GET', get_workflow_endpoint_url, headers=headers)
         if response.status == 200:
             workflow = await response.json()
 
@@ -805,13 +882,14 @@ async def send_json_override(self, event, data, sid=None):
 
             prompt_metadata[prompt_id].progress.add(node)
             calculated_progress = len(prompt_metadata[prompt_id].progress) / len(prompt_metadata[prompt_id].workflow_api)
+            calculated_progress = round(calculated_progress, 2)
             # logger.info("calculated_progress", calculated_progress)
 
             if prompt_metadata[prompt_id].last_updated_node is not None and prompt_metadata[prompt_id].last_updated_node == node:
                 return
             prompt_metadata[prompt_id].last_updated_node = node
             class_type = prompt_metadata[prompt_id].workflow_api[node]['class_type']
-            logger.info(f"updating run live status {class_type}")
+            logger.info(f"At: {calculated_progress * 100}% - {class_type}")
             await send("live_status", {
                 "prompt_id": prompt_id,
                 "current_node": class_type,
@@ -836,14 +914,15 @@ async def send_json_override(self, event, data, sid=None):
         # await update_run_with_output(prompt_id, data)
 
     if event == 'executed' and 'node' in data and 'output' in data:
-        logger.info(f"executed {data}")
         if prompt_id in prompt_metadata:
             node = data.get('node')
             class_type = prompt_metadata[prompt_id].workflow_api[node]['class_type']
-            logger.info(f"executed {class_type}")
+            logger.info(f"Executed {class_type} {data}")
             if class_type == "PreviewImage":
-                logger.info("skipping preview image")
+                logger.info("Skipping preview image")
                 return
+        else:
+            logger.info(f"Executed {data}")
             
         await update_run_with_output(prompt_id, data.get('output'), node_id=data.get('node'))
         # await update_run_with_output(prompt_id, data.get('output'), node_id=data.get('node'))
@@ -864,7 +943,7 @@ async def update_run_live_status(prompt_id, live_status, calculated_progress: fl
     if (status_endpoint is None):
         return
 
-    logger.info(f"progress {calculated_progress}")
+    # logger.info(f"progress {calculated_progress}")
 
     body = {
         "run_id": prompt_id,
@@ -883,9 +962,7 @@ async def update_run_live_status(prompt_id, live_status, calculated_progress: fl
         })
 
     # requests.post(status_endpoint, json=body)
-    async with aiohttp.ClientSession() as session:
-        async with session.post(status_endpoint, json=body) as response:
-            pass
+    await async_request_with_retry('POST', status_endpoint, json=body)
 
 
 async def update_run(prompt_id: str, status: Status):
@@ -916,9 +993,7 @@ async def update_run(prompt_id: str, status: Status):
         try:
             # requests.post(status_endpoint, json=body)
             if (status_endpoint is not None):
-                async with aiohttp.ClientSession() as session:
-                    async with session.post(status_endpoint, json=body) as response:
-                        pass
+                await async_request_with_retry('POST', status_endpoint, json=body)
 
             if (status_endpoint is not None) and cd_enable_run_log and (status == Status.SUCCESS or status == Status.FAILED):
                 try:
@@ -948,9 +1023,7 @@ async def update_run(prompt_id: str, status: Status):
                             ]
                         }
 
-                        async with aiohttp.ClientSession() as session:
-                            async with session.post(status_endpoint, json=body) as response:
-                                pass
+                        await async_request_with_retry('POST', status_endpoint, json=body)
                         # requests.post(status_endpoint, json=body)
                 except Exception as log_error:
                     logger.info(f"Error reading log file: {log_error}")
@@ -998,7 +1071,7 @@ async def upload_file(prompt_id, filename, subfolder=None, content_type="image/p
     filename = os.path.basename(filename)
     file = os.path.join(output_dir, filename)
 
-    logger.info(f"uploading file {file}")
+    logger.info(f"Uploading file {file}")
 
     file_upload_endpoint = prompt_metadata[prompt_id].file_upload_endpoint
 
@@ -1024,18 +1097,17 @@ async def upload_file(prompt_id, filename, subfolder=None, content_type="image/p
             "Content-Length": str(len(data)),
         }
         # response = requests.put(ok.get("url"), headers=headers, data=data)
-        async with aiohttp.ClientSession() as session:
-            async with session.put(ok.get("url"), headers=headers, data=data) as response:
+        response = await async_request_with_retry('PUT', ok.get("url"), headers=headers, data=data)
         logger.info(f"Upload file response status: {response.status}, status text: {response.reason}")
         end_time = time.time()  # End timing after the request is complete
         logger.info("Upload time: {:.2f} seconds".format(end_time - start_time))
 
 def have_pending_upload(prompt_id):
     if prompt_id in prompt_metadata and len(prompt_metadata[prompt_id].uploading_nodes) > 0:
-        logger.info(f"have pending upload {len(prompt_metadata[prompt_id].uploading_nodes)}")
+        logger.info(f"Have pending upload {len(prompt_metadata[prompt_id].uploading_nodes)}")
         return True
 
-    logger.info("no pending upload")
+    logger.info("No pending upload")
     return False
 
 def mark_prompt_done(prompt_id):
@@ -1093,7 +1165,7 @@ async def update_file_status(prompt_id: str, data, uploading, have_error=False,
         else:
             prompt_metadata[prompt_id].uploading_nodes.discard(node_id)
 
-    logger.info(prompt_metadata[prompt_id].uploading_nodes)
+    logger.info(f"Remaining uploads: {prompt_metadata[prompt_id].uploading_nodes}")
     # Update the remote status
 
     if have_error:
@@ -1177,7 +1249,7 @@ async def update_run_with_output(prompt_id, data, node_id=None):
 
     if have_upload_media:
         try:
-            logger.info(f"\nhave_upload {have_upload} {node_id}")
+            logger.info(f"\nHave_upload {have_upload_media} Node Id: {node_id}")
 
             if have_upload_media:
                 await update_file_status(prompt_id, data, True, node_id=node_id)
@@ -1190,9 +1262,7 @@ async def update_run_with_output(prompt_id, data, node_id=None):
 
     # requests.post(status_endpoint, json=body)
     if status_endpoint is not None:
-        async with aiohttp.ClientSession() as session:
-            async with session.post(status_endpoint, json=body) as response:
-                pass
+        await async_request_with_retry('POST', status_endpoint, json=body)
 
     await send('outputs_uploaded', {
         "prompt_id": prompt_id

requirements.txt

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

web-plugin/index.js

@@ -19,15 +19,15 @@ function dispatchAPIEventData(data) {
     // Custom parse error
     if (msg.error) {
         let message = msg.error.message;
-    if (msg.error.details)
-      message += ": " + msg.error.details;
-    for (const [nodeID, nodeError] of Object.entries(
-      msg.node_errors,
-    )) {
+        if (msg.error.details) message += ": " + msg.error.details;
+        for (const [nodeID, nodeError] of Object.entries(msg.node_errors)) {
             message += "\n" + nodeError.class_type + ":";
             for (const errorReason of nodeError.errors) {
                 message +=
-          "\n    - " + errorReason.message + ": " + errorReason.details;
+                    "\n    - " +
+                    errorReason.message +
+                    ": " +
+                    errorReason.details;
             }
         }
 
@@ -47,32 +47,38 @@ function dispatchAPIEventData(data) {
                 // window.name = this.clientId; // use window name so it isnt reused when duplicating tabs
                 // sessionStorage.setItem("clientId", this.clientId); // store in session storage so duplicate tab can load correct workflow
             }
-      api.dispatchEvent(new CustomEvent("status", { detail: msg.data.status }));
+            api.dispatchEvent(
+                new CustomEvent("status", { detail: msg.data.status })
+            );
             break;
         case "progress":
-      api.dispatchEvent(new CustomEvent("progress", { detail: msg.data }));
+            api.dispatchEvent(
+                new CustomEvent("progress", { detail: msg.data })
+            );
             break;
         case "executing":
             api.dispatchEvent(
-        new CustomEvent("executing", { detail: msg.data.node }),
+                new CustomEvent("executing", { detail: msg.data.node })
             );
             break;
         case "executed":
-      api.dispatchEvent(new CustomEvent("executed", { detail: msg.data }));
+            api.dispatchEvent(
+                new CustomEvent("executed", { detail: msg.data })
+            );
             break;
         case "execution_start":
             api.dispatchEvent(
-        new CustomEvent("execution_start", { detail: msg.data }),
+                new CustomEvent("execution_start", { detail: msg.data })
             );
             break;
         case "execution_error":
             api.dispatchEvent(
-        new CustomEvent("execution_error", { detail: msg.data }),
+                new CustomEvent("execution_error", { detail: msg.data })
             );
             break;
         case "execution_cached":
             api.dispatchEvent(
-        new CustomEvent("execution_cached", { detail: msg.data }),
+                new CustomEvent("execution_cached", { detail: msg.data })
             );
             break;
         default:
@@ -146,11 +152,13 @@ const ext = {
         }
 
         if (!workflow_version_id) {
-      console.error("No workflow_version_id provided in query parameters.");
+            console.error(
+                "No workflow_version_id provided in query parameters."
+            );
         } else {
             loadingDialog.showLoading(
                 "Loading workflow from " + org_display,
-        "Please wait...",
+                "Please wait..."
             );
             fetch(endpoint + "/api/workflow-version/" + workflow_version_id, {
                 method: "GET",
@@ -163,7 +171,10 @@ const ext = {
                     const data = await res.json();
                     const { workflow, workflow_id, error } = data;
                     if (error) {
-            infoDialog.showMessage("Unable to load this workflow", error);
+                        infoDialog.showMessage(
+                            "Unable to load this workflow",
+                            error
+                        );
                         return;
                     }
 
@@ -186,7 +197,7 @@ const ext = {
                     window.history.replaceState(
                         {},
                         document.title,
-            window.location.pathname,
+                        window.location.pathname
                     );
                 });
         }
@@ -209,22 +220,37 @@ const ext = {
                 ComfyWidgets.STRING(
                     this,
                     "workflow_name",
-          ["", { default: this.properties.workflow_name, multiline: false }],
-          app,
+                    [
+                        "",
+                        {
+                            default: this.properties.workflow_name,
+                            multiline: false,
+                        },
+                    ],
+                    app
                 );
 
                 ComfyWidgets.STRING(
                     this,
                     "workflow_id",
-          ["", { default: this.properties.workflow_id, multiline: false }],
-          app,
+                    [
+                        "",
+                        {
+                            default: this.properties.workflow_id,
+                            multiline: false,
+                        },
+                    ],
+                    app
                 );
 
                 ComfyWidgets.STRING(
                     this,
                     "version",
-          ["", { default: this.properties.version, multiline: false }],
-          app,
+                    [
+                        "",
+                        { default: this.properties.version, multiline: false },
+                    ],
+                    app
                 );
 
                 // this.widgets.forEach((w) => {
@@ -251,7 +277,7 @@ const ext = {
                 title_mode: LiteGraph.NORMAL_TITLE,
                 title: "Comfy Deploy",
                 collapsable: true,
-      }),
+            })
         );
 
         ComfyDeploy.category = "deploy";
@@ -261,26 +287,121 @@ const ext = {
         // const graphCanvas = document.getElementById("graph-canvas");
 
         window.addEventListener("message", async (event) => {
+            //   console.log("message", event);
             try {
                 const message = JSON.parse(event.data);
                 if (message.type === "graph_load") {
                     const comfyUIWorkflow = message.data;
-          console.log("recieved: ", comfyUIWorkflow);
+                    // console.log("recieved: ", comfyUIWorkflow);
                     // Assuming there's a method to load the workflow data into the ComfyUI
                     // This part of the code would depend on how the ComfyUI expects to receive and process the workflow data
                     // For demonstration, let's assume there's a loadWorkflow method in the ComfyUI API
                     if (comfyUIWorkflow && app && app.loadGraphData) {
+                        console.log("loadGraphData");
                         app.loadGraphData(comfyUIWorkflow);
                     }
                 } else if (message.type === "deploy") {
                     // deployWorkflow();
                     const prompt = await app.graphToPrompt();
+                    // api.handlePromptGenerated(prompt);
                     sendEventToCD("cd_plugin_onDeployChanges", prompt);
                 } else if (message.type === "queue_prompt") {
                     const prompt = await app.graphToPrompt();
+                    if (typeof api.handlePromptGenerated === "function") {
+                        api.handlePromptGenerated(prompt);
+                    } else {
+                        console.warn(
+                            "api.handlePromptGenerated is not a function"
+                        );
+                    }
                     sendEventToCD("cd_plugin_onQueuePrompt", prompt);
+                } else if (message.type === "get_prompt") {
+                    const prompt = await app.graphToPrompt();
+                    sendEventToCD("cd_plugin_onGetPrompt", prompt);
                 } else if (message.type === "event") {
                     dispatchAPIEventData(message.data);
+                } else if (message.type === "add_node") {
+                    console.log("add node", message.data);
+                    app.graph.beforeChange();
+                    var node = LiteGraph.createNode(message.data.type);
+                    node.configure({
+                        widgets_values: message.data.widgets_values,
+                    });
+
+                    console.log("node", node);
+
+                    const graphMouse = app.canvas.graph_mouse;
+
+                    node.pos = [graphMouse[0], graphMouse[1]];
+
+                    app.graph.add(node);
+                    app.graph.afterChange();
+                } else if (message.type === "zoom_to_node") {
+                    const nodeId = message.data.nodeId;
+                    const position = message.data.position;
+
+                    const node = app.graph.getNodeById(nodeId);
+                    if (!node) return;
+
+                    const canvas = app.canvas;
+                    const targetScale = 1;
+                    const targetOffsetX =
+                        canvas.canvas.width / 4 -
+                        position[0] -
+                        node.size[0] / 2;
+                    const targetOffsetY =
+                        canvas.canvas.height / 4 -
+                        position[1] -
+                        node.size[1] / 2;
+
+                    const startScale = canvas.ds.scale;
+                    const startOffsetX = canvas.ds.offset[0];
+                    const startOffsetY = canvas.ds.offset[1];
+
+                    const duration = 400; // Animation duration in milliseconds
+                    const startTime = Date.now();
+
+                    function easeOutCubic(t) {
+                        return 1 - Math.pow(1 - t, 3);
+                    }
+
+                    function lerp(start, end, t) {
+                        return start * (1 - t) + end * t;
+                    }
+
+                    function animate() {
+                        const currentTime = Date.now();
+                        const elapsedTime = currentTime - startTime;
+                        const t = Math.min(elapsedTime / duration, 1);
+
+                        const easedT = easeOutCubic(t);
+
+                        const currentScale = lerp(
+                            startScale,
+                            targetScale,
+                            easedT
+                        );
+                        const currentOffsetX = lerp(
+                            startOffsetX,
+                            targetOffsetX,
+                            easedT
+                        );
+                        const currentOffsetY = lerp(
+                            startOffsetY,
+                            targetOffsetY,
+                            easedT
+                        );
+
+                        canvas.setZoom(currentScale);
+                        canvas.ds.offset = [currentOffsetX, currentOffsetY];
+                        canvas.draw(true, true);
+
+                        if (t < 1) {
+                            requestAnimationFrame(animate);
+                        }
+                    }
+
+                    animate();
                 }
                 // else if (message.type === "refresh") {
                 //   sendEventToCD("cd_plugin_onRefresh");
@@ -288,10 +409,6 @@ const ext = {
             } catch (error) {
                 // console.error("Error processing message:", error);
             }
-
-      // if (!event.data.flow || Object.entries(event.data.flow).length <= 0)
-      //   return;
-      //   updateBlendshapesPrompts(event.data.flow);
         });
 
         api.addEventListener("executed", (evt) => {
@@ -325,7 +442,7 @@ const ext = {
 
 function showError(title, message) {
     infoDialog.show(
-    `<h3 style="margin: 0px; color: red;">${title}</h3><br><span>${message}</span> `,
+        `<h3 style="margin: 0px; color: red;">${title}</h3><br><span>${message}</span> `
     );
 }
 
@@ -433,7 +550,7 @@ async function deployWorkflow() {
     if (deployMeta.length == 0) {
         const text = await inputDialog.input(
             "Create your deployment",
-      "Workflow name",
+            "Workflow name"
         );
         if (!text) return;
         console.log(text);
@@ -474,7 +591,7 @@ async function deployWorkflow() {
     <input id="reuse-hash" type="checkbox" checked>Reuse hash from last version</input>
     </label>
     </div>
-    `,
+    `
     );
     if (!ok) return;
 
@@ -493,7 +610,7 @@ async function deployWorkflow() {
     if (!snapshot) {
         showError(
             "Error when deploying",
-      "Unable to generate snapshot, please install ComfyUI Manager",
+            "Unable to generate snapshot, please install ComfyUI Manager"
         );
         return;
     }
@@ -514,7 +631,7 @@ async function deployWorkflow() {
                     "Content-Type": "application/json",
                     Authorization: "Bearer " + apiKey,
                 },
-      },
+            }
         )
             .then((x) => x.json())
             .catch(() => {
@@ -533,7 +650,7 @@ async function deployWorkflow() {
                 // Match previous hash for models
                 if (reuseHash && existing_workflow?.dependencies?.models) {
                     const previousModelHash = Object.entries(
-            existing_workflow?.dependencies?.models,
+                        existing_workflow?.dependencies?.models
                     ).flatMap(([key, value]) => {
                         return Object.values(value).map((x) => ({
                             ...x,
@@ -555,7 +672,9 @@ async function deployWorkflow() {
                 console.log(file);
                 loadingDialog.showLoading("Generating hash", file);
                 const hash = await fetch(
-          `/comfyui-deploy/get-file-hash?file_path=${encodeURIComponent(file)}`,
+                    `/comfyui-deploy/get-file-hash?file_path=${encodeURIComponent(
+                        file
+                    )}`
                 ).then((x) => x.json());
                 loadingDialog.showLoading("Generating hash", file);
                 console.log(hash);
@@ -565,18 +684,24 @@ async function deployWorkflow() {
                 console.log("Uploading ", file);
                 loadingDialog.showLoading("Uploading file", file);
                 try {
-          const { download_url } = await fetch(`/comfyui-deploy/upload-file`, {
+                    const { download_url } = await fetch(
+                        `/comfyui-deploy/upload-file`,
+                        {
                             method: "POST",
                             body: JSON.stringify({
                                 file_path: file,
                                 token: apiKey,
                                 url: endpoint + "/api/upload-url",
                             }),
-          })
+                        }
+                    )
                         .then((x) => x.json())
                         .catch(() => {
                             loadingDialog.close();
-              confirmDialog.confirm("Error", "Unable to upload file " + file);
+                            confirmDialog.confirm(
+                                "Error",
+                                "Unable to upload file " + file
+                            );
                         });
                     loadingDialog.showLoading("Uploaded file", file);
                     console.log(download_url);
@@ -613,8 +738,8 @@ async function deployWorkflow() {
       <iframe 
       style="z-index: 10; min-width: 600px; max-width: 1024px; min-height: 600px; border: none; background-color: transparent;"
       src="https://www.comfydeploy.com/dependency-graph?deps=${encodeURIComponent(
-        JSON.stringify(deps),
-      )}" />`,
+          JSON.stringify(deps)
+      )}" />`
             // createDynamicUIHtml(deps),
         );
         if (!depsOk) return;
@@ -675,7 +800,7 @@ async function deployWorkflow() {
         graph.change();
 
         infoDialog.show(
-      `<span style="color:green;">Deployed successfully!</span>  <a style="color:white;" target="_blank" href=${endpoint}/workflows/${data.workflow_id}>-> View here</a> <br/> <br/> Workflow ID: ${data.workflow_id} <br/> Workflow Name: ${workflow_name} <br/> Workflow Version: ${data.version} <br/>`,
+            `<span style="color:green;">Deployed successfully!</span>  <a style="color:white;" target="_blank" href=${endpoint}/workflows/${data.workflow_id}>-> View here</a> <br/> <br/> Workflow ID: ${data.workflow_id} <br/> Workflow Name: ${workflow_name} <br/> Workflow Version: ${data.version} <br/>`
         );
 
         setTimeout(() => {
@@ -872,17 +997,22 @@ export class InputDialog extends InfoDialog {
                         type: "button",
                         textContent: "Save",
                         onclick: () => {
-              const input = this.textElement.querySelector("#input").value;
+                            const input =
+                                this.textElement.querySelector("#input").value;
                             if (input.trim() === "") {
-                showError("Input validation", "Input cannot be empty");
+                                showError(
+                                    "Input validation",
+                                    "Input cannot be empty"
+                                );
                             } else {
                                 this.callback?.(input);
                                 this.close();
-                this.textElement.querySelector("#input").value = "";
+                                this.textElement.querySelector("#input").value =
+                                    "";
                             }
                         },
                     }),
-        ],
+                ]
             ),
         ];
     }
@@ -943,7 +1073,7 @@ export class ConfirmDialog extends InfoDialog {
                             this.close();
                         },
                     }),
-        ],
+                ]
             ),
         ];
     }
@@ -1000,7 +1130,7 @@ function getData(environment) {
 function saveData(data) {
     localStorage.setItem(
         "comfy_deploy_env_data_" + data.environment,
-    JSON.stringify(data),
+        JSON.stringify(data)
     );
 }
 
@@ -1015,7 +1145,9 @@ export class ConfigDialog extends ComfyDialog {
         this.element.style.paddingBottom = "20px";
 
         this.container = document.createElement("div");
-    this.element.querySelector(".comfy-modal-content").prepend(this.container);
+        this.element
+            .querySelector(".comfy-modal-content")
+            .prepend(this.container);
     }
 
     createButtons() {
@@ -1049,7 +1181,7 @@ export class ConfigDialog extends ComfyDialog {
                             this.close();
                         },
                     }),
-        ],
+                ]
             ),
         ];
     }
@@ -1061,7 +1193,8 @@ export class ConfigDialog extends ComfyDialog {
     }
 
     save(api_key, displayName) {
-    const deployOption = this.container.querySelector("#deployOption").value;
+        const deployOption =
+            this.container.querySelector("#deployOption").value;
         localStorage.setItem("comfy_deploy_env", deployOption);
 
         const endpoint = this.container.querySelector("#endpoint").value;
@@ -1093,8 +1226,12 @@ export class ConfigDialog extends ComfyDialog {
     <h3 style="margin: 0px;">Comfy Deploy Config</h3>
     <label style="color: white; width: 100%;">
       <select id="deployOption" style="margin: 8px 0px; width: 100%; height:30px; box-sizing: border-box;" >
-        <option value="cloud" ${data.environment === "cloud" ? "selected" : ""}>Cloud</option>
-        <option value="local" ${data.environment === "local" ? "selected" : ""}>Local</option>
+        <option value="cloud" ${
+            data.environment === "cloud" ? "selected" : ""
+        }>Cloud</option>
+        <option value="local" ${
+            data.environment === "local" ? "selected" : ""
+        }>Local</option>
       </select>
     </label>
       <label style="color: white; width: 100%;">
@@ -1112,7 +1249,9 @@ export class ConfigDialog extends ComfyDialog {
         }">
         <button id="loginButton" style="margin-top: 8px; width: 100%; height:40px; box-sizing: border-box; padding: 0px 6px;">
           ${
-            data.apiKey ? "Re-login with ComfyDeploy" : "Login with ComfyDeploy"
+              data.apiKey
+                  ? "Re-login with ComfyDeploy"
+                  : "Login with ComfyDeploy"
           }
         </button>
       </div>
@@ -1133,7 +1272,7 @@ export class ConfigDialog extends ComfyDialog {
                 clearInterval(poll);
                 infoDialog.showMessage(
                     "Timeout",
-          "Wait too long for the response, please try re-login",
+                    "Wait too long for the response, please try re-login"
                 );
             }, 30000); // Stop polling after 30 seconds
 
@@ -1144,14 +1283,15 @@ export class ConfigDialog extends ComfyDialog {
                         if (json.api_key) {
                             this.save(json.api_key, json.name);
                             this.close();
-              this.container.querySelector("#apiKey").value = json.api_key;
+                            this.container.querySelector("#apiKey").value =
+                                json.api_key;
                             // infoDialog.show();
                             clearInterval(this.poll);
                             clearTimeout(this.timeout);
                             // Refresh dialog
                             const a = await confirmDialog.confirm(
                                 "Authenticated",
-                `<div>You will be able to upload workflow to <button style="font-size: 18px; width: fit;">${json.name}</button></div>`,
+                                `<div>You will be able to upload workflow to <button style="font-size: 18px; width: fit;">${json.name}</button></div>`
                             );
                             configDialog.show();
                         }

web/package.json

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

web/src/routes/registerCreateRunRoute.ts

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

web/src/server/createRun.ts

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