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"
+            if lora_save_name:
-            print(unique_filename)
+                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
+            args + ["-f", "f32le", "-"], capture_output=True, check=True
-        ).stdout
+        )
+        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
+        raise Exception(
-    return res
+            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]
         )
 
-    if meta_batch is not None:
+    memory_limit = None
-        gen = itertools.islice(gen, meta_batch.frames_per_batch)
+    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:
 
-    # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+            def rescale(frame):
-    images = torch.from_numpy(
+                s = torch.from_numpy(
-        np.fromiter(gen, np.dtype((np.float32, (height, width, 3))))
+                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
-    )
+                )
+                s = s.movedim(-1, 1)
+                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+                return s.movedim(1, -1).numpy()
+
+            gen = itertools.chain.from_iterable(
+                map(rescale, batched(gen, frames_per_batch))
+            )
+    else:
+        new_size = width, height
+    if vae is not None:
+        gen = batched_vae_encode(gen, vae, frames_per_batch)
+        vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
+        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
+    else:
+        # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+        images = torch.from_numpy(
+            np.fromiter(gen, np.dtype((np.float32, (new_size[1], new_size[0], 3))))
+        )
+    if meta_batch is None and memory_limit is not None:
+        try:
+            next(original_gen)
+            raise RuntimeError(
+                f"Memory limit hit after loading {len(images)} frames. Stopping execution."
+            )
+        except StopIteration:
+            pass
     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_width": new_size[0],
-        "loaded_height": images.shape[1],
+        "loaded_height": new_size[1],
     }
-
+    if vae is None:
-    return (images, len(images), lazy_eval(audio), video_info)
+        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 {
+        return {"required": {
-            "required": {
+                    "input_id": (
-                "input_id": (
+                        "STRING",
-                    "STRING",
+                        {"multiline": False, "default": "input_video"},
-                    {"multiline": False, "default": "input_video"},
+                    ),
-                ),
+                     "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
-                "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"],),
-                "force_size": (
+                     "custom_width": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
-                    [
+                     "custom_height": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
-                        "Disabled",
+                     "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
-                        "Custom Height",
+                     "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
-                        "Custom Width",
+                     "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
-                        "Custom",
+                     },
-                        "256x?",
+                "optional": {
-                        "?x256",
+                    "meta_batch": ("VHS_BatchManager",),
-                        "256x256",
+                    "vae": ("VAE",),
-                        "512x?",
+                    "default_value": (sorted(files),),
-                        "?x512",
+                },
-                        "512x512",
+                "hidden": {
-                    ],
+                    "unique_id": "UNIQUE_ID"
-                ),
+                },
-                "custom_width": (
+                }
-                    "INT",
-                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
-                ),
-                "custom_height": (
-                    "INT",
-                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
-                ),
-                "frame_load_cap": (
-                    "INT",
-                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
-                ),
-                "skip_first_frames": (
-                    "INT",
-                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
-                ),
-                "select_every_nth": (
-                    "INT",
-                    {"default": 1, "min": 1, "max": BIGMAX, "step": 1},
-                ),
-            },
-            "optional": {
-                "meta_batch": ("VHS_BatchManager",),
-                "default_value": (sorted(files),),
-            },
-            "hidden": {"unique_id": "UNIQUE_ID"},
-        }
 
     CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
 
-    RETURN_TYPES = (
+    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
-        "IMAGE",
-        "INT",
-        "VHS_AUDIO",
-        "VHS_VIDEOINFO",
-    )
     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
-import logfire
+
-# if os.environ.get('LOGFIRE_TOKEN', None) is not None:
+# Check for an environment variable to enable/disable Logfire
-logfire.configure(
+use_logfire = os.environ.get('USE_LOGFIRE', 'false').lower() == 'true'
-    send_to_logfire="if-token-present"
+
-)
+if use_logfire:
-# basicConfig(handlers=[logfire.LogfireLoggingHandler()])
+    try:
-logfire_handler = logfire.LogfireLoggingHandler()
+        import logfire
-logger = getLogger("comfy-deploy")
+        logfire.configure(
-logger.addHandler(logfire_handler)
+            send_to_logfire="if-token-present"
+        )
+        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")
+    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'):
+    with log_span('Streaming Run'):
-        logfire.info('Streaming prompt')
+        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()
@@ -482,34 +563,33 @@ async def upload_file_endpoint(request):
 
     if get_url:
         try:
-            async with aiohttp.ClientSession() as session:
+            headers = {'Authorization': f'Bearer {token}'}
-                headers = {'Authorization': f'Bearer {token}'}
+            params = {'file_size': file_size, 'type': file_type}
-                params = {'file_size': file_size, 'type': file_type}
+            response = await async_request_with_retry('GET', get_url, params=params, headers=headers)
-                async with session.get(get_url, params=params, headers=headers) as response:
+            if response.status == 200:
-                    if response.status == 200:
+                content = await response.json()
-                        content = await response.json()
+                upload_url = content["upload_url"]
-                        upload_url = content["upload_url"]
 
-                        with open(file_path, 'rb') as f:
+                with open(file_path, 'rb') as f:
-                            headers = {
+                    headers = {
-                                "Content-Type": file_type,
+                        "Content-Type": file_type,
-                                # "x-amz-acl": "public-read",
+                        # "x-amz-acl": "public-read",
-                                "Content-Length": str(file_size)
+                        "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:
+                    if upload_response.status == 200:
-                                    return web.json_response({
+                        return web.json_response({
-                                        "message": "File uploaded successfully",
+                            "message": "File uploaded successfully",
-                                        "download_url": content["download_url"]
+                            "download_url": content["download_url"]
-                                    })
+                        })
-                                else:
-                                    return web.json_response({
-                                        "error": f"Failed to upload file to {upload_url}. Status code: {upload_response.status}"
-                                    }, status=upload_response.status)
                     else:
                         return web.json_response({
-                            "error": f"Failed to fetch data from {get_url}. Status code: {response.status}"
+                            "error": f"Failed to upload file to {upload_url}. Status code: {upload_response.status}"
-                        }, status=response.status)
+                        }, status=upload_response.status)
+            else:
+                return web.json_response({
+                    "error": f"Failed to fetch data from {get_url}. Status code: {response.status}"
+                }, status=response.status)
         except Exception as e:
             return web.json_response({
                 "error": f"An error occurred while fetching data from {get_url}: {str(e)}"
@@ -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:
+    await async_request_with_retry('POST', status_endpoint, json=body)
-        async with session.post(status_endpoint, json=body) as response:
-            pass
 
 @server.PromptServer.instance.routes.get('/comfyui-deploy/ws')
 async def websocket_handler(request):
@@ -611,28 +689,27 @@ 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}'}
-            headers = {'Authorization': f'Bearer {auth_token}'}
+        response = await async_request_with_retry('GET', get_workflow_endpoint_url, headers=headers)
-            async with session.get(get_workflow_endpoint_url, headers=headers) as response:
+        if response.status == 200:
-                if response.status == 200:
+            workflow = await response.json()
-                    workflow = await response.json()
 
-                    logger.info(f"Loaded workflow version ${workflow['version']}")
+            logger.info(f"Loaded workflow version ${workflow['version']}")
 
-                    streaming_prompt_metadata[sid] = StreamingPrompt(
+            streaming_prompt_metadata[sid] = StreamingPrompt(
-                        workflow_api=workflow["workflow_api"],
+                workflow_api=workflow["workflow_api"],
-                        auth_token=auth_token,
+                auth_token=auth_token,
-                        inputs={},
+                inputs={},
-                        status_endpoint=status_endpoint,
+                status_endpoint=status_endpoint,
-                        file_upload_endpoint=request.rel_url.query.get('file_upload_endpoint', None),
+                file_upload_endpoint=request.rel_url.query.get('file_upload_endpoint', None),
-                    )
+            )
 
-                    await update_realtime_run_status(realtime_id, status_endpoint, Status.RUNNING)
+            await update_realtime_run_status(realtime_id, status_endpoint, Status.RUNNING)
-                    # await send("workflow_api", workflow_api, sid)
+            # await send("workflow_api", workflow_api, sid)
-                else:
+        else:
-                    error_message = await response.text()
+            error_message = await response.text()
-                    logger.info(f"Failed to fetch workflow endpoint. Status: {response.status}, Error: {error_message}")
+            logger.info(f"Failed to fetch workflow endpoint. Status: {response.status}, Error: {error_message}")
-                    # await send("error", {"message": error_message}, sid)
+            # await send("error", {"message": error_message}, sid)
 
     try:
         # Send initial state to the new client
@@ -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,
@@ -836,15 +914,16 @@ 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'))
         # update_run_with_output(prompt_id, data.get('output'))
@@ -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:
+    await async_request_with_retry('POST', status_endpoint, json=body)
-        async with session.post(status_endpoint, json=body) as response:
-            pass
 
 
 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:
+                await async_request_with_retry('POST', status_endpoint, json=body)
-                    async with session.post(status_endpoint, json=body) as response:
-                        pass
 
             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:
+                        await async_request_with_retry('POST', status_endpoint, json=body)
-                            async with session.post(status_endpoint, json=body) as response:
-                                pass
                         # 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:
+        response = await async_request_with_retry('PUT', ok.get("url"), headers=headers, data=data)
-            async with session.put(ok.get("url"), headers=headers, data=data) as response:
+        logger.info(f"Upload file response status: {response.status}, status text: {response.reason}")
-                logger.info(f"Upload file response status: {response.status}, status text: {response.reason}")
+        end_time = time.time()  # End timing after the request is complete
-                end_time = time.time()  # End timing after the request is complete
+        logger.info("Upload time: {:.2f} seconds".format(end_time - start_time))
-                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:
+        await async_request_with_retry('POST', status_endpoint, json=body)
-            async with session.post(status_endpoint, json=body) as response:
-                pass
 
     await send('outputs_uploaded', {
         "prompt_id": prompt_id

requirements.txt

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

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`,
     };