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external_video.py
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external_video.py
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# 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
### Utils
import hashlib
from typing import Iterable
import shutil
import subprocess
import re
import uuid
import server
from tqdm import tqdm
BIGMIN = -(2**53 - 1)
BIGMAX = 2**53 - 1
DIMMAX = 8192
def ffmpeg_suitability(path):
try:
version = subprocess.run(
[path, "-version"], check=True, capture_output=True
).stdout.decode("utf-8")
except:
return 0
score = 0
# rough layout of the importance of various features
simple_criterion = [
("libvpx", 20),
("264", 10),
("265", 3),
("svtav1", 5),
("libopus", 1),
]
for criterion in simple_criterion:
if version.find(criterion[0]) >= 0:
score += criterion[1]
# obtain rough compile year from copyright information
copyright_index = version.find("2000-2")
if copyright_index >= 0:
copyright_year = version[copyright_index + 6 : copyright_index + 9]
if copyright_year.isnumeric():
score += int(copyright_year)
return score
if "VHS_FORCE_FFMPEG_PATH" in os.environ:
ffmpeg_path = os.environ.get("VHS_FORCE_FFMPEG_PATH")
else:
ffmpeg_paths = []
try:
from imageio_ffmpeg import get_ffmpeg_exe
imageio_ffmpeg_path = get_ffmpeg_exe()
ffmpeg_paths.append(imageio_ffmpeg_path)
except:
if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
raise
if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
ffmpeg_path = imageio_ffmpeg_path
else:
system_ffmpeg = shutil.which("ffmpeg")
if system_ffmpeg is not None:
ffmpeg_paths.append(system_ffmpeg)
if os.path.isfile("ffmpeg"):
ffmpeg_paths.append(os.path.abspath("ffmpeg"))
if os.path.isfile("ffmpeg.exe"):
ffmpeg_paths.append(os.path.abspath("ffmpeg.exe"))
if len(ffmpeg_paths) == 0:
ffmpeg_path = None
elif len(ffmpeg_paths) == 1:
# Evaluation of suitability isn't required, can take sole option
# to reduce startup time
ffmpeg_path = ffmpeg_paths[0]
else:
ffmpeg_path = max(ffmpeg_paths, key=ffmpeg_suitability)
gifski_path = os.environ.get("VHS_GIFSKI", None)
if gifski_path is None:
gifski_path = os.environ.get("JOV_GIFSKI", None)
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)
dir_files = os.listdir(directory)
dir_files = sorted(dir_files)
dir_files = [os.path.join(directory, x) for x in dir_files]
dir_files = list(filter(lambda filepath: os.path.isfile(filepath), dir_files))
# filter by extension, if needed
if extensions is not None:
extensions = list(extensions)
new_dir_files = []
for filepath in dir_files:
ext = "." + filepath.split(".")[-1]
if ext.lower() in extensions:
new_dir_files.append(filepath)
dir_files = new_dir_files
# start at skip_first_images
dir_files = dir_files[skip_first_images:]
dir_files = dir_files[0::select_every_nth]
return dir_files
# modified from https://stackoverflow.com/questions/22058048/hashing-a-file-in-python
def calculate_file_hash(filename: str, hash_every_n: int = 1):
# Larger video files were taking >.5 seconds to hash even when cached,
# so instead the modified time from the filesystem is used as a hash
h = hashlib.sha256()
h.update(filename.encode())
h.update(str(os.path.getmtime(filename)).encode())
return h.hexdigest()
prompt_queue = server.PromptServer.instance.prompt_queue
def requeue_workflow_unchecked():
"""Requeues the current workflow without checking for multiple requeues"""
currently_running = prompt_queue.currently_running
(_, _, prompt, extra_data, outputs_to_execute) = next(
iter(currently_running.values())
)
# Ensure batch_managers are marked stale
prompt = prompt.copy()
for uid in prompt:
if prompt[uid]["class_type"] == "VHS_BatchManager":
prompt[uid]["inputs"]["requeue"] = (
prompt[uid]["inputs"].get("requeue", 0) + 1
)
# execution.py has guards for concurrency, but server doesn't.
# TODO: Check that this won't be an issue
number = -server.PromptServer.instance.number
server.PromptServer.instance.number += 1
prompt_id = str(server.uuid.uuid4())
prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
requeue_guard = [None, 0, 0, {}]
def requeue_workflow(requeue_required=(-1, True)):
assert len(prompt_queue.currently_running) == 1
global requeue_guard
(run_number, _, prompt, _, _) = next(iter(prompt_queue.currently_running.values()))
if requeue_guard[0] != run_number:
# Calculate a count of how many outputs are managed by a batch manager
managed_outputs = 0
for bm_uid in prompt:
if prompt[bm_uid]["class_type"] == "VHS_BatchManager":
for output_uid in prompt:
if prompt[output_uid]["class_type"] in ["VHS_VideoCombine"]:
for inp in prompt[output_uid]["inputs"].values():
if inp == [bm_uid, 0]:
managed_outputs += 1
requeue_guard = [run_number, 0, managed_outputs, {}]
requeue_guard[1] = requeue_guard[1] + 1
requeue_guard[3][requeue_required[0]] = requeue_required[1]
if requeue_guard[1] == requeue_guard[2] and max(requeue_guard[3].values()):
requeue_workflow_unchecked()
def get_audio(file, start_time=0, duration=0):
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", "f32le", "-"], capture_output=True, check=True
)
audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
match = re.search(", (\\d+) Hz, (\\w+), ", res.stderr.decode("utf-8"))
except subprocess.CalledProcessError as e:
raise Exception(
f"VHS failed to extract audio from {file}:\n" + e.stderr.decode("utf-8")
)
if match:
ar = int(match.group(1))
# NOTE: Just throwing an error for other channel types right now
# Will deal with issues if they come
ac = {"mono": 1, "stereo": 2}[match.group(2)]
else:
ar = 44100
ac = 2
audio = audio.reshape((-1, ac)).transpose(0, 1).unsqueeze(0)
return {"waveform": audio, "sample_rate": ar}
class LazyAudioMap(Mapping):
def __init__(self, file, start_time, duration):
self.file = file
self.start_time = start_time
self.duration = duration
self._dict = None
def __getitem__(self, key):
if self._dict is None:
self._dict = get_audio(self.file, self.start_time, self.duration)
return self._dict[key]
def __iter__(self):
if self._dict is None:
self._dict = get_audio(self.file, self.start_time, self.duration)
return iter(self._dict)
def __len__(self):
if self._dict is None:
self._dict = get_audio(self.file, self.start_time, self.duration)
return len(self._dict)
def lazy_get_audio(file, start_time=0, duration=0):
return LazyAudioMap(file, start_time, duration)
def lazy_eval(func):
class Cache:
def __init__(self, func):
self.res = None
self.func = func
def get(self):
if self.res is None:
self.res = self.func()
return self.res
cache = Cache(func)
return lambda: cache.get()
def is_url(url):
return url.split("://")[0] in ["http", "https"]
def validate_sequence(path):
# Check if path is a valid ffmpeg sequence that points to at least one file
(path, file) = os.path.split(path)
if not os.path.isdir(path):
return False
match = re.search("%0?\d+d", file)
if not match:
return False
seq = match.group()
if seq == "%d":
seq = "\\\\d+"
else:
seq = "\\\\d{%s}" % seq[1:-1]
file_matcher = re.compile(re.sub("%0?\d+d", seq, file))
for file in os.listdir(path):
if file_matcher.fullmatch(file):
return True
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"
if is_url(path):
return "url"
return calculate_file_hash(path.strip('"'))
def validate_path(path, allow_none=False, allow_url=True):
if path is None:
return allow_none
if is_url(path):
# Probably not feasible to check if url resolves here
return True if allow_url else "URLs are unsupported for this path"
if not os.path.isfile(path.strip('"')):
return "Invalid file path: {}".format(path)
return True
### Utils
video_extensions = ["webm", "mp4", "mkv", "gif"]
def is_gif(filename) -> bool:
file_parts = filename.split(".")
return len(file_parts) > 1 and file_parts[-1] == "gif"
def target_size(
width, height, force_size, custom_width, custom_height
) -> tuple[int, int]:
if force_size == "Custom":
return (custom_width, custom_height)
elif force_size == "Custom Height":
force_size = "?x" + str(custom_height)
elif force_size == "Custom Width":
force_size = str(custom_width) + "x?"
if force_size != "Disabled":
force_size = force_size.split("x")
if force_size[0] == "?":
width = (width * int(force_size[1])) // height
# Limit to a multple of 8 for latent conversion
width = int(width) + 4 & ~7
height = int(force_size[1])
elif force_size[1] == "?":
height = (height * int(force_size[0])) // width
height = int(height) + 4 & ~7
width = int(force_size[0])
else:
width = int(force_size[0])
height = int(force_size[1])
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,
frame_load_cap,
skip_first_frames,
select_every_nth,
meta_batch=None,
unique_id=None,
):
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)
width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
total_frames = int(video_cap.get(cv2.CAP_PROP_FRAME_COUNT))
duration = total_frames / fps
# set video_cap to look at start_index frame
total_frame_count = 0
total_frames_evaluated = -1
frames_added = 0
base_frame_time = 1 / fps
prev_frame = None
if force_rate == 0:
target_frame_time = base_frame_time
else:
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():
if time_offset < target_frame_time:
is_returned = video_cap.grab()
# if didn't return frame, video has ended
if not is_returned:
break
time_offset += base_frame_time
if time_offset < target_frame_time:
continue
time_offset -= target_frame_time
# if not at start_index, skip doing anything with frame
total_frame_count += 1
if total_frame_count <= skip_first_frames:
continue
else:
total_frames_evaluated += 1
# if should not be selected, skip doing anything with frame
if total_frames_evaluated % select_every_nth != 0:
continue
# opencv loads images in BGR format (yuck), so need to convert to RGB for ComfyUI use
# follow up: can videos ever have an alpha channel?
# To my testing: No. opencv has no support for alpha
unused, frame = video_cap.retrieve()
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)
if prev_frame is not None:
inp = yield prev_frame
if inp is not None:
# ensure the finally block is called
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
if meta_batch is not None:
meta_batch.inputs.pop(unique_id)
meta_batch.has_closed_inputs = True
if prev_frame is not None:
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,
force_size: str,
custom_width: int,
custom_height: int,
frame_load_cap: int,
skip_first_frames: int,
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(
video,
force_rate,
frame_load_cap,
skip_first_frames,
select_every_nth,
meta_batch,
unique_id,
)
(width, height, fps, duration, total_frames, target_frame_time) = next(gen)
if meta_batch is not None:
meta_batch.inputs[unique_id] = (
gen,
width,
height,
fps,
duration,
total_frames,
target_frame_time,
)
meta_batch.total_frames = min(meta_batch.total_frames, next(gen))
else:
(gen, width, height, fps, duration, total_frames, target_frame_time) = (
meta_batch.inputs[unique_id]
)
memory_limit = None
if memory_limit_mb is not None:
memory_limit *= 2**20
else:
# TODO: verify if garbage collection should be performed here.
# leaves ~128 MB unreserved for safety
try:
memory_limit = (
psutil.virtual_memory().available + psutil.swap_memory().free
) - 2**27
except:
print(
"Failed to calculate available memory. Memory load limit has been disabled"
)
if memory_limit is not None:
if vae is not None:
# space required to load as f32, exist as latent with wiggle room, decode to f32
max_loadable_frames = int(
memory_limit // (width * height * 3 * (4 + 4 + 1 / 10))
)
else:
# TODO: use better estimate for when vae is not None
# Consider completely ignoring for load_latent case?
max_loadable_frames = int(memory_limit // (width * height * 3 * (0.1)))
if meta_batch is not None:
if meta_batch.frames_per_batch > max_loadable_frames:
raise RuntimeError(
f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory"
)
gen = itertools.islice(gen, meta_batch.frames_per_batch)
else:
original_gen = gen
gen = itertools.islice(gen, max_loadable_frames)
downscale_ratio = getattr(vae, "downscale_ratio", 8)
frames_per_batch = (1920 * 1080 * 16) // (width * height) or 1
if force_size != "Disabled" or vae is not None:
new_size = target_size(
width, height, force_size, custom_width, custom_height, downscale_ratio
)
if new_size[0] != width or new_size[1] != height:
def rescale(frame):
s = torch.from_numpy(
np.fromiter(frame, np.dtype((np.float32, (height, width, 3))))
)
s = s.movedim(-1, 1)
s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
return s.movedim(1, -1).numpy()
gen = itertools.chain.from_iterable(
map(rescale, batched(gen, frames_per_batch))
)
else:
new_size = width, height
if vae is not None:
gen = batched_vae_encode(gen, vae, frames_per_batch)
vw, vh = new_size[0] // downscale_ratio, new_size[1] // downscale_ratio
images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (4, vh, vw)))))
else:
# Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
images = torch.from_numpy(
np.fromiter(gen, np.dtype((np.float32, (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")
# Setup lambda for lazy audio capture
audio = lazy_get_audio(
video,
skip_first_frames * target_frame_time,
frame_load_cap * target_frame_time * select_every_nth,
)
# Adjust target_frame_time for select_every_nth
target_frame_time *= select_every_nth
video_info = {
"source_fps": fps,
"source_frame_count": total_frames,
"source_duration": duration,
"source_width": width,
"source_height": height,
"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],
}
if vae is None:
return (images, len(images), audio, video_info, None)
else:
return (None, len(images), audio, video_info, {"samples": images})
# modeled after Video upload node
class ComfyUIDeployExternalVideo:
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
files = []
for f in os.listdir(input_dir):
if os.path.isfile(os.path.join(input_dir, f)):
file_parts = f.split(".")
if len(file_parts) > 1 and (file_parts[-1] in video_extensions):
files.append(f)
return {"required": {
"input_id": (
"STRING",
{"multiline": False, "default": "input_video"},
),
"force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
"force_size": (["Disabled", "Custom Height", "Custom Width", "Custom", "256x?", "?x256", "256x256", "512x?", "?x512", "512x512"],),
"custom_width": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
"custom_height": ("INT", {"default": 512, "min": 0, "max": DIMMAX, "step": 8}),
"frame_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
"skip_first_frames": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
"select_every_nth": ("INT", {"default": 1, "min": 1, "max": BIGMAX, "step": 1}),
},
"optional": {
"meta_batch": ("VHS_BatchManager",),
"vae": ("VAE",),
"default_video": (sorted(files),),
"display_name": (
"STRING",
{"multiline": False, "default": ""},
),
"description": (
"STRING",
{"multiline": True, "default": ""},
),
},
"hidden": {
"unique_id": "UNIQUE_ID"
},
}
CATEGORY = "Video Helper Suite 🎥🅥🅗🅢"
RETURN_TYPES = ("IMAGE", "INT", "AUDIO", "VHS_VIDEOINFO", "LATENT")
RETURN_NAMES = (
"IMAGE",
"frame_count",
"audio",
"video_info",
"LATENT",
)
FUNCTION = "load_video"
def load_video(self, **kwargs):
input_id = kwargs.get("input_id")
force_rate = kwargs.get("force_rate")
force_size = kwargs.get("force_size", "Disabled")
custom_width = kwargs.get("custom_width")
custom_height = kwargs.get("custom_height")
frame_load_cap = kwargs.get("frame_load_cap")
skip_first_frames = kwargs.get("skip_first_frames")
select_every_nth = kwargs.get("select_every_nth")
meta_batch = kwargs.get("meta_batch")
unique_id = kwargs.get("unique_id")
input_dir = folder_paths.get_input_directory()
if input_id.startswith("http"):
import requests
print("Fetching video from URL: ", input_id)
response = requests.get(input_id, stream=True)
file_size = int(response.headers.get("Content-Length", 0))
file_extension = input_id.split(".")[-1].split("?")[
0
] # Extract extension and handle URLs with parameters
if file_extension not in video_extensions:
file_extension = ".mp4"
unique_filename = str(uuid.uuid4()) + "." + file_extension
video_path = os.path.join(input_dir, unique_filename)
chunk_size = 1024 # 1 Kibibyte
num_bars = int(file_size / chunk_size)
with open(video_path, "wb") as out_file:
for chunk in tqdm(
response.iter_content(chunk_size=chunk_size),
total=num_bars,
unit="KB",
desc="Downloading",
leave=True,
):
out_file.write(chunk)
else:
video = kwargs.get("default_video", None)
if video is None:
raise "No default video given and no external video provided"
video_path = folder_paths.get_annotated_filepath(video.strip('"'))
return load_video_cv(
video=video_path,
force_rate=force_rate,
force_size=force_size,
custom_width=custom_width,
custom_height=custom_height,
frame_load_cap=frame_load_cap,
skip_first_frames=skip_first_frames,
select_every_nth=select_every_nth,
meta_batch=meta_batch,
unique_id=unique_id,
)
@classmethod
def IS_CHANGED(s, video, **kwargs):
image_path = folder_paths.get_annotated_filepath(video)
return calculate_file_hash(image_path)
NODE_CLASS_MAPPINGS = {"ComfyUIDeployExternalVideo": ComfyUIDeployExternalVideo}
NODE_DISPLAY_NAME_MAPPINGS = {
"ComfyUIDeployExternalVideo": "External Video (ComfyUI Deploy x VHS)"
}