Merge branch 'main' into dev

comfy-nodes/external_checkpoints.py

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

comfy-nodes/external_lora.py

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

comfy-nodes/external_text_list.py

@@ -1,43 +0,0 @@
-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

@@ -1,15 +1,10 @@
-# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
-# Intended to work with https://github.com/NicholasKao1029/ComfyUI-VideoHelperSuite/tree/main
+# credit goes to https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite and is meant to work with
 import os
 import itertools
 import numpy as np
 import torch
-from typing import Union
-from torch import Tensor
 import cv2
-import psutil
 
-from collections.abc import Mapping
 import folder_paths
 from comfy.utils import common_upscale
 
@@ -95,25 +90,13 @@ if gifski_path is None:
         gifski_path = shutil.which("gifski")
 
 
-def is_safe_path(path):
-    if "VHS_STRICT_PATHS" not in os.environ:
-        return True
-    basedir = os.path.abspath(".")
-    try:
-        common_path = os.path.commonpath([basedir, path])
-    except:
-        # Different drive on windows
-        return False
-    return common_path == basedir
-
-
 def get_sorted_dir_files_from_directory(
     directory: str,
     skip_first_images: int = 0,
     select_every_nth: int = 1,
     extensions: Iterable = None,
 ):
-    directory = strip_path(directory)
+    directory = directory.strip()
     dir_files = os.listdir(directory)
     dir_files = sorted(dir_files)
     dir_files = [os.path.join(directory, x) for x in dir_files]
@@ -194,59 +177,18 @@ def requeue_workflow(requeue_required=(-1, True)):
 
 
 def get_audio(file, start_time=0, duration=0):
-    args = [ffmpeg_path, "-i", file]
+    args = [ffmpeg_path, "-v", "error", "-i", file]
     if start_time > 0:
         args += ["-ss", str(start_time)]
     if duration > 0:
         args += ["-t", str(duration)]
     try:
-        # TODO: scan for sample rate and maintain
         res = subprocess.run(
-            args + ["-f", "f32le", "-"], capture_output=True, check=True
-        )
-        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
-        match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
+            args + ["-f", "wav", "-"], stdout=subprocess.PIPE, check=True
+        ).stdout
     except subprocess.CalledProcessError as e:
-        raise Exception(
-            f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
-        )
-    if match:
-        ar = int(match.group(1))
-        # NOTE: Just throwing an error for other channel types right now
-        # Will deal with issues if they come
-        ac = {"mono": 1, "stereo": 2}[match.group(2)]
-    else:
-        ar = 44100
-        ac = 2
-    audio = audio.reshape((-1, ac)).transpose(0, 1).unsqueeze(0)
-    return {"waveform": audio, "sample_rate": ar}
-
-
-class LazyAudioMap(Mapping):
-    def __init__(self, file, start_time, duration):
-        self.file = file
-        self.start_time = start_time
-        self.duration = duration
-        self._dict = None
-
-    def __getitem__(self, key):
-        if self._dict is None:
-            self._dict = get_audio(self.file, self.start_time, self.duration)
-        return self._dict[key]
-
-    def __iter__(self):
-        if self._dict is None:
-            self._dict = get_audio(self.file, self.start_time, self.duration)
-        return iter(self._dict)
-
-    def __len__(self):
-        if self._dict is None:
-            self._dict = get_audio(self.file, self.start_time, self.duration)
-        return len(self._dict)
-
-
-def lazy_get_audio(file, start_time=0, duration=0):
-    return LazyAudioMap(file, start_time, duration)
+        return False
+    return res
 
 
 def lazy_eval(func):
@@ -288,19 +230,6 @@ def validate_sequence(path):
     return False
 
 
-def strip_path(path):
-    # This leaves whitespace inside quotes and only a single "
-    # thus ' ""test"' -> '"test'
-    # consider path.strip(string.whitespace+"\"")
-    # or weightier re.fullmatch("[\\s\"]*(.+?)[\\s\"]*", path).group(1)
-    path = path.strip()
-    if path.startswith('"'):
-        path = path[1:]
-    if path.endswith('"'):
-        path = path[:-1]
-    return path
-
-
 def hash_path(path):
     if path is None:
         return "input"
@@ -357,145 +286,6 @@ def target_size(
     return (width, height)
 
 
-def validate_index(
-    index: int,
-    length: int = 0,
-    is_range: bool = False,
-    allow_negative=False,
-    allow_missing=False,
-) -> int:
-    # if part of range, do nothing
-    if is_range:
-        return index
-    # otherwise, validate index
-    # validate not out of range - only when latent_count is passed in
-    if length > 0 and index > length - 1 and not allow_missing:
-        raise IndexError(f"Index '{index}' out of range for {length} item(s).")
-    # if negative, validate not out of range
-    if index < 0:
-        if not allow_negative:
-            raise IndexError(f"Negative indeces not allowed, but was '{index}'.")
-        conv_index = length + index
-        if conv_index < 0 and not allow_missing:
-            raise IndexError(
-                f"Index '{index}', converted to '{conv_index}' out of range for {length} item(s)."
-            )
-        index = conv_index
-    return index
-
-
-def convert_to_index_int(
-    raw_index: str,
-    length: int = 0,
-    is_range: bool = False,
-    allow_negative=False,
-    allow_missing=False,
-) -> int:
-    try:
-        return validate_index(
-            int(raw_index),
-            length=length,
-            is_range=is_range,
-            allow_negative=allow_negative,
-            allow_missing=allow_missing,
-        )
-    except ValueError as e:
-        raise ValueError(f"Index '{raw_index}' must be an integer.", e)
-
-
-def convert_str_to_indexes(
-    indexes_str: str, length: int = 0, allow_missing=False
-) -> list[int]:
-    if not indexes_str:
-        return []
-    int_indexes = list(range(0, length))
-    allow_negative = length > 0
-    chosen_indexes = []
-    # parse string - allow positive ints, negative ints, and ranges separated by ':'
-    groups = indexes_str.split(",")
-    groups = [g.strip() for g in groups]
-    for g in groups:
-        # parse range of indeces (e.g. 2:16)
-        if ":" in g:
-            index_range = g.split(":", 2)
-            index_range = [r.strip() for r in index_range]
-
-            start_index = index_range[0]
-            if len(start_index) > 0:
-                start_index = convert_to_index_int(
-                    start_index,
-                    length=length,
-                    is_range=True,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            else:
-                start_index = 0
-            end_index = index_range[1]
-            if len(end_index) > 0:
-                end_index = convert_to_index_int(
-                    end_index,
-                    length=length,
-                    is_range=True,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            else:
-                end_index = length
-            # support step as well, to allow things like reversing, every-other, etc.
-            step = 1
-            if len(index_range) > 2:
-                step = index_range[2]
-                if len(step) > 0:
-                    step = convert_to_index_int(
-                        step,
-                        length=length,
-                        is_range=True,
-                        allow_negative=True,
-                        allow_missing=True,
-                    )
-                else:
-                    step = 1
-            # if latents were passed in, base indeces on known latent count
-            if len(int_indexes) > 0:
-                chosen_indexes.extend(int_indexes[start_index:end_index][::step])
-            # otherwise, assume indeces are valid
-            else:
-                chosen_indexes.extend(list(range(start_index, end_index, step)))
-        # parse individual indeces
-        else:
-            chosen_indexes.append(
-                convert_to_index_int(
-                    g,
-                    length=length,
-                    allow_negative=allow_negative,
-                    allow_missing=allow_missing,
-                )
-            )
-    return chosen_indexes
-
-
-def select_indexes(input_obj: Union[Tensor, list], idxs: list):
-    if type(input_obj) == Tensor:
-        return input_obj[idxs]
-    else:
-        return [input_obj[i] for i in idxs]
-
-
-def select_indexes_from_str(
-    input_obj: Union[Tensor, list], indexes: str, err_if_missing=True, err_if_empty=True
-):
-    real_idxs = convert_str_to_indexes(
-        indexes, len(input_obj), allow_missing=not err_if_missing
-    )
-    if err_if_empty and len(real_idxs) == 0:
-        raise Exception(f"Nothing was selected based on indexes found in '{indexes}'.")
-    return select_indexes(input_obj, real_idxs)
-
-
-###
-
-
 def cv_frame_generator(
     video,
     force_rate,
@@ -505,10 +295,9 @@ def cv_frame_generator(
     meta_batch=None,
     unique_id=None,
 ):
-    video_cap = cv2.VideoCapture(strip_path(video))
+    video_cap = cv2.VideoCapture(video)
     if not video_cap.isOpened():
         raise ValueError(f"{video} could not be loaded with cv.")
-    pbar = None
 
     # extract video metadata
     fps = video_cap.get(cv2.CAP_PROP_FPS)
@@ -530,8 +319,6 @@ def cv_frame_generator(
         target_frame_time = 1 / force_rate
 
     yield (width, height, fps, duration, total_frames, target_frame_time)
-    if meta_batch is not None:
-        yield min(frame_load_cap, total_frames)
 
     time_offset = target_frame_time - base_frame_time
     while video_cap.isOpened():
@@ -562,8 +349,7 @@ def cv_frame_generator(
         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         # convert frame to comfyui's expected format
         # TODO: frame contains no exif information. Check if opencv2 has already applied
-        frame = np.array(frame, dtype=np.float32)
-        torch.from_numpy(frame).div_(255)
+        frame = np.array(frame, dtype=np.float32) / 255.0
         if prev_frame is not None:
             inp = yield prev_frame
             if inp is not None:
@@ -571,8 +357,6 @@ def cv_frame_generator(
                 return
         prev_frame = frame
         frames_added += 1
-        if pbar is not None:
-            pbar.update_absolute(frames_added, frame_load_cap)
         # if cap exists and we've reached it, stop processing frames
         if frame_load_cap > 0 and frames_added >= frame_load_cap:
             break
@@ -583,17 +367,6 @@ def cv_frame_generator(
         yield prev_frame
 
 
-def batched(it, n):
-    while batch := tuple(itertools.islice(it, n)):
-        yield batch
-
-
-def batched_vae_encode(images, vae, frames_per_batch):
-    for batch in batched(images, frames_per_batch):
-        image_batch = torch.from_numpy(np.array(batch))
-        yield from vae.encode(image_batch).numpy()
-
-
 def load_video_cv(
     video: str,
     force_rate: int,
@@ -605,8 +378,6 @@ def load_video_cv(
     select_every_nth: int,
     meta_batch=None,
     unique_id=None,
-    memory_limit_mb=None,
-    vae=None,
 ):
     if meta_batch is None or unique_id not in meta_batch.inputs:
         gen = cv_frame_generator(
@@ -630,89 +401,30 @@ def load_video_cv(
                 total_frames,
                 target_frame_time,
             )
-            meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
 
     else:
         (gen, width, height, fps, duration, total_frames, target_frame_time) = (
             meta_batch.inputs[unique_id]
         )
 
-    memory_limit = None
-    if memory_limit_mb is not None:
-        memory_limit *= 2**20
-    else:
-        # TODO: verify if garbage collection should be performed here.
-        # leaves ~128 MB unreserved for safety
-        try:
-            memory_limit = (
-                psutil.virtual_memory().available + psutil.swap_memory().free
-            ) - 2**27
-        except:
-            print(
-                "Failed to calculate available memory. Memory load limit has been disabled"
-            )
-    if memory_limit is not None:
-        if vae is not None:
-            # space required to load as f32, exist as latent with wiggle room, decode to f32
-            max_loadable_frames = int(
-                memory_limit // (width * height * 3 * (4 + 4 + 1 / 10))
-            )
-        else:
-            # TODO: use better estimate for when vae is not None
-            # Consider completely ignoring for load_latent case?
-            max_loadable_frames = int(memory_limit // (width * height * 3 * (0.1)))
-        if meta_batch is not None:
-            if meta_batch.frames_per_batch > max_loadable_frames:
-                raise RuntimeError(
-                    f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory"
-                )
-            gen = itertools.islice(gen, meta_batch.frames_per_batch)
-        else:
-            original_gen = gen
-            gen = itertools.islice(gen, max_loadable_frames)
-    downscale_ratio = getattr(vae, "downscale_ratio", 8)
-    frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
-    if force_size != "Disabled" or vae is not None:
-        new_size = target_size(
-            width, height, force_size, custom_width, custom_height, downscale_ratio
-        )
-        if new_size[0] != width or new_size[1] != height:
+    if meta_batch is not None:
+        gen = itertools.islice(gen, meta_batch.frames_per_batch)
 
-            def rescale(frame):
-                s = torch.from_numpy(
-                    np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
-                )
-                s = s.movedim(-1, 1)
-                s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
-                return s.movedim(1, -1).numpy()
-
-            gen = itertools.chain.from_iterable(
-                map(rescale, batched(gen, frames_per_batch))
-            )
-    else:
-        new_size = width, height
-    if vae is not None:
-        gen = batched_vae_encode(gen, vae, frames_per_batch)
-        vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
-        images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
-    else:
-        # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
-        images = torch.from_numpy(
-            np.fromiter(gen, np.dtype((np.float32, (new_size[1], new_size[0], 3))))
-        )
-    if meta_batch is None and memory_limit is not None:
-        try:
-            next(original_gen)
-            raise RuntimeError(
-                f"Memory limit hit after loading {len(images)} frames. Stopping execution."
-            )
-        except StopIteration:
-            pass
+    # Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
+    images = torch.from_numpy(
+        np.fromiter(gen, np.dtype((np.float32, (height, width, 3))))
+    )
     if len(images) == 0:
         raise RuntimeError("No frames generated")
+    if force_size != "Disabled":
+        new_size = target_size(width, height, force_size, custom_width, custom_height)
+        if new_size[0] != width or new_size[1] != height:
+            s = images.movedim(-1, 1)
+            s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
+            images = s.movedim(1, -1)
 
     # Setup lambda for lazy audio capture
-    audio = lazy_get_audio(
+    audio = lambda: get_audio(
         video,
         skip_first_frames * target_frame_time,
         frame_load_cap * target_frame_time * select_every_nth,
@@ -728,16 +440,13 @@ def load_video_cv(
         "loaded_fps": 1 / target_frame_time,
         "loaded_frame_count": len(images),
         "loaded_duration": len(images) * target_frame_time,
-        "loaded_width": new_size[0],
-        "loaded_height": new_size[1],
+        "loaded_width": images.shape[2],
+        "loaded_height": images.shape[1],
     }
-    if vae is None:
-        return (images, len(images), audio, video_info, None)
-    else:
-        return (None, len(images), audio, video_info, {"samples": images})
+
+    return (images, len(images), lazy_eval(audio), video_info)
 
 
-# modeled after Video upload node
 class ComfyUIDeployExternalVideo:
     @classmethod
     def INPUT_TYPES(s):
@@ -748,38 +457,68 @@ class ComfyUIDeployExternalVideo:
                 file_parts = f.split(".")
                 if len(file_parts) > 1 and (file_parts[-1] in video_extensions):
                     files.append(f)
-        return {"required": {
-                    "input_id": (
-                        "STRING",
-                        {"multiline": False, "default": "input_video"},
-                    ),
-                     "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
-                     "force_size": (["Disabled", "Custom Height", "Custom Width", "Custom", "256x?", "?x256", "256x256", "512x?", "?x512", "512x512"],),
-                     "custom_width": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
-                     "custom_height": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
-                     "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
-                     "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
-                     "select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
-                     },
-                "optional": {
-                    "meta_batch": ("VHS_BatchManager",),
-                    "vae": ("VAE",),
-                    "default_value": (sorted(files),),
-                },
-                "hidden": {
-                    "unique_id": "UNIQUE_ID"
-                },
-                }
+        return {
+            "required": {
+                "input_id": (
+                    "STRING",
+                    {"multiline": False, "default": "input_video"},
+                ),
+                "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
+                "force_size": (
+                    [
+                        "Disabled",
+                        "Custom Height",
+                        "Custom Width",
+                        "Custom",
+                        "256x?",
+                        "?x256",
+                        "256x256",
+                        "512x?",
+                        "?x512",
+                        "512x512",
+                    ],
+                ),
+                "custom_width": (
+                    "INT",
+                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
+                ),
+                "custom_height": (
+                    "INT",
+                    {"default": 512, "min": 0, "max": DIMMAX, "step": 8},
+                ),
+                "frame_load_cap": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
+                ),
+                "skip_first_frames": (
+                    "INT",
+                    {"default": 0, "min": 0, "max": BIGMAX, "step": 1},
+                ),
+                "select_every_nth": (
+                    "INT",
+                    {"default": 1, "min": 1, "max": BIGMAX, "step": 1},
+                ),
+            },
+            "optional": {
+                "meta_batch": ("VHS_BatchManager",),
+                "default_value": (sorted(files),),
+            },
+            "hidden": {"unique_id": "UNIQUE_ID"},
+        }
 
     CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
 
-    RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
+    RETURN_TYPES = (
+        "IMAGE",
+        "INT",
+        "VHS_AUDIO",
+        "VHS_VIDEOINFO",
+    )
     RETURN_NAMES = (
         "IMAGE",
         "frame_count",
         "audio",
         "video_info",
-        "LATENT",
     )
 
     FUNCTION = "load_video"

custom_routes.py

@@ -22,98 +22,17 @@ 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
-        logfire.configure(
-            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}")
-
+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 = getLogger("comfy-deploy")
+logger.addHandler(logfire_handler)
 
 from globals import StreamingPrompt, Status, sockets, SimplePrompt, streaming_prompt_metadata, prompt_metadata
 
@@ -154,11 +73,11 @@ 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(f"clearing out prompt: {to_delete}")
+    logger.info("clearning 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(f"deleted prompt: {id_to_delete}, remaining tasks: {prompt_server.prompt_queue.get_tasks_remaining()}")
+        logger.info("deleted prompt: ", id_to_delete, prompt_server.prompt_queue.get_tasks_remaining())
 
     streaming_prompt_metadata[sid].running_prompt_ids.clear()
 
@@ -349,7 +268,7 @@ async def comfy_deploy_run(request):
 
     status = 200
 
-    if "node_errors" in res and res["node_errors"] is not None:
+    if "node_errors" in res and res["node_errors"]:
         # Even tho there are node_errors it can still be run
         status = 400
         await update_run_with_output(prompt_id, {
@@ -387,7 +306,7 @@ async def stream_prompt(data):
         workflow_api=workflow_api
     )
 
-    # log('info', "Begin prompt", prompt=prompt)
+    logfire.info("Begin prompt", prompt=prompt)
 
     try:
         res = post_prompt(prompt)
@@ -410,7 +329,7 @@ async def stream_prompt(data):
 
     status = 200
 
-    if "node_errors" in res and res["node_errors"] is not None:
+    if "node_errors" in res and res["node_errors"]:
         # Even tho there are node_errors it can still be run
         status = 400
         await update_run_with_output(prompt_id, {
@@ -440,8 +359,8 @@ async def stream_response(request):
     prompt_id = data.get("prompt_id")
     comfy_message_queues[prompt_id] = asyncio.Queue()
 
-    with log_span('Streaming Run'):
-        log('info', 'Streaming prompt')
+    with logfire.span('Streaming Run'):
+        logfire.info('Streaming prompt')
 
         try:
             result = await stream_prompt(data=data)
@@ -454,7 +373,7 @@ async def stream_response(request):
                     if not comfy_message_queues[prompt_id].empty():
                         data = await comfy_message_queues[prompt_id].get()
 
-                        # log('info', data["event"], data=json.dumps(data))
+                        logfire.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
@@ -465,10 +384,10 @@ async def stream_response(request):
 
                 await asyncio.sleep(0.1)  # Adjust the sleep duration as needed
         except asyncio.CancelledError:
-            log('info', "Streaming was cancelled")
+            logfire.info("Streaming was cancelled")
             raise
         except Exception as e:
-            log('error', "Streaming error", error=e)
+            logfire.error("Streaming error", error=e)
         finally:
             # event_emitter.off("send_json", task)
             await response.write_eof()
@@ -563,33 +482,34 @@ async def upload_file_endpoint(request):
 
     if get_url:
         try:
-            headers = {'Authorization': f'Bearer {token}'}
-            params = {'file_size': file_size, 'type': file_type}
-            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"]
+            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:
+                    if response.status == 200:
+                        content = await response.json()
+                        upload_url = content["upload_url"]
 
-                with open(file_path, 'rb') as f:
-                    headers = {
-                        "Content-Type": file_type,
-                        # "x-amz-acl": "public-read",
-                        "Content-Length": str(file_size)
-                    }
-                    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",
-                            "download_url": content["download_url"]
-                        })
+                        with open(file_path, 'rb') as f:
+                            headers = {
+                                "Content-Type": file_type,
+                                # "x-amz-acl": "public-read",
+                                "Content-Length": str(file_size)
+                            }
+                            async with session.put(upload_url, data=f, headers=headers) as upload_response:
+                                if upload_response.status == 200:
+                                    return web.json_response({
+                                        "message": "File uploaded successfully",
+                                        "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 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}"
-                }, status=response.status)
+                            "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)}"
@@ -668,7 +588,9 @@ 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)
-    await async_request_with_retry('POST', status_endpoint, json=body)
+    async with aiohttp.ClientSession() as session:
+        async with session.post(status_endpoint, json=body) as response:
+            pass
 
 @server.PromptServer.instance.routes.get('/comfyui-deploy/ws')
 async def websocket_handler(request):
@@ -689,27 +611,28 @@ 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:
-        headers = {'Authorization': f'Bearer {auth_token}'}
-        response = await async_request_with_retry('GET', get_workflow_endpoint_url, headers=headers)
-        if response.status == 200:
-            workflow = await response.json()
+        async with aiohttp.ClientSession() as session:
+            headers = {'Authorization': f'Bearer {auth_token}'}
+            async with session.get(get_workflow_endpoint_url, headers=headers) as response:
+                if response.status == 200:
+                    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(
-                workflow_api=workflow["workflow_api"],
-                auth_token=auth_token,
-                inputs={},
-                status_endpoint=status_endpoint,
-                file_upload_endpoint=request.rel_url.query.get('file_upload_endpoint', None),
-            )
+                    streaming_prompt_metadata[sid] = StreamingPrompt(
+                        workflow_api=workflow["workflow_api"],
+                        auth_token=auth_token,
+                        inputs={},
+                        status_endpoint=status_endpoint,
+                        file_upload_endpoint=request.rel_url.query.get('file_upload_endpoint', None),
+                    )
 
-            await update_realtime_run_status(realtime_id, status_endpoint, Status.RUNNING)
-            # await send("workflow_api", workflow_api, sid)
-        else:
-            error_message = await response.text()
-            logger.info(f"Failed to fetch workflow endpoint. Status: {response.status}, Error: {error_message}")
-            # await send("error", {"message": error_message}, sid)
+                    await update_realtime_run_status(realtime_id, status_endpoint, Status.RUNNING)
+                    # await send("workflow_api", workflow_api, sid)
+                else:
+                    error_message = await response.text()
+                    logger.info(f"Failed to fetch workflow endpoint. Status: {response.status}, Error: {error_message}")
+                    # await send("error", {"message": error_message}, sid)
 
     try:
         # Send initial state to the new client
@@ -882,14 +805,13 @@ 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"At: {calculated_progress * 100}% - {class_type}")
+            logger.info(f"updating run live status {class_type}")
             await send("live_status", {
                 "prompt_id": prompt_id,
                 "current_node": class_type,
@@ -914,15 +836,14 @@ 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} {data}")
+            logger.info(f"executed {class_type}")
             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'))
@@ -943,7 +864,7 @@ 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,
@@ -962,7 +883,9 @@ async def update_run_live_status(prompt_id, live_status, calculated_progress: fl
         })
 
     # requests.post(status_endpoint, json=body)
-    await async_request_with_retry('POST', status_endpoint, json=body)
+    async with aiohttp.ClientSession() as session:
+        async with session.post(status_endpoint, json=body) as response:
+            pass
 
 
 async def update_run(prompt_id: str, status: Status):
@@ -993,7 +916,9 @@ async def update_run(prompt_id: str, status: Status):
         try:
             # requests.post(status_endpoint, json=body)
             if (status_endpoint is not None):
-                await async_request_with_retry('POST', status_endpoint, json=body)
+                async with aiohttp.ClientSession() as session:
+                    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:
@@ -1023,7 +948,9 @@ async def update_run(prompt_id: str, status: Status):
                             ]
                         }
 
-                        await async_request_with_retry('POST', status_endpoint, json=body)
+                        async with aiohttp.ClientSession() as session:
+                            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}")
@@ -1071,7 +998,7 @@ 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
 
@@ -1097,17 +1024,18 @@ 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)
-        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))
+        async with aiohttp.ClientSession() as session:
+            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}")
+                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):
@@ -1165,7 +1093,7 @@ 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(f"Remaining uploads: {prompt_metadata[prompt_id].uploading_nodes}")
+    logger.info(prompt_metadata[prompt_id].uploading_nodes)
     # Update the remote status
 
     if have_error:
@@ -1249,7 +1177,7 @@ async def update_run_with_output(prompt_id, data, node_id=None):
 
     if have_upload_media:
         try:
-            logger.info(f"\nHave_upload {have_upload_media} Node Id: {node_id}")
+            logger.info(f"\nhave_upload {have_upload} {node_id}")
 
             if have_upload_media:
                 await update_file_status(prompt_id, data, True, node_id=node_id)
@@ -1262,7 +1190,9 @@ async def update_run_with_output(prompt_id, data, node_id=None):
 
     # requests.post(status_endpoint, json=body)
     if status_endpoint is not None:
-        await async_request_with_retry('POST', status_endpoint, json=body)
+        async with aiohttp.ClientSession() as session:
+            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": "14.2",
+    "next": "14.1",
     "next-plausible": "^3.12.0",
     "next-themes": "^0.2.1",
     "next-usequerystate": "^1.13.2",

web/src/routes/registerCreateRunRoute.ts

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

web/src/server/createRun.ts

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