import os import itertools import numpy as np import torch from PIL import Image, ImageOps import cv2 import psutil import folder_paths from comfy.utils import common_upscale, ProgressBar from .logger import logger from .utils import BIGMAX, DIMMAX, calculate_file_hash, get_sorted_dir_files_from_directory, get_audio, lazy_eval, hash_path, validate_path, strip_path 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, downscale_ratio=8) -> tuple[int, int]: if force_size == "Disabled": pass elif force_size == "Custom Width": height *= custom_width/width width = custom_width elif force_size == "Custom Height": width *= custom_height/height height = custom_height else: width = custom_width height = custom_height width = int(width/downscale_ratio + 0.5) * downscale_ratio height = int(height/downscale_ratio + 0.5) * downscale_ratio return (width, height) 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 = ProgressBar(frame_load_cap) if frame_load_cap > 0 else 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) 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 #Python 3.12 adds an itertools.batched, but it's easily replicated for legacy support 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) 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: logger.warn("Failed to calculate available memory. Memory load limit has been disabled") #space required to load as f32, exist as latent with wiggle room, decode to f32 #TODO: fix when vae is not None if vae is not None: max_loadable_frames = int(memory_limit//(width*height*3*(4+4+1/10))) else: #Consider completely ignoring for load_latent case? max_loadable_frames = int(memory_limit//(width*height*3*(.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 = lambda : 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), lazy_eval(audio), video_info, None) else: return (None, len(images), lazy_eval(audio), video_info, {"samples": images}) class LoadVideoUpload: @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": { "video": (sorted(files),), "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",), }, "hidden": { "unique_id": "UNIQUE_ID" }, } CATEGORY = "Video Helper Suite 🎥🅥🅗🅢" RETURN_TYPES = ("IMAGE", "INT", "VHS_AUDIO", "VHS_VIDEOINFO", "LATENT") RETURN_NAMES = ("IMAGE", "frame_count", "audio", "video_info", "LATENT") FUNCTION = "load_video" def load_video(self, **kwargs): kwargs['video'] = folder_paths.get_annotated_filepath(strip_path(kwargs['video'])) return load_video_cv(**kwargs) @classmethod def IS_CHANGED(s, video, **kwargs): image_path = folder_paths.get_annotated_filepath(video) return calculate_file_hash(image_path) @classmethod def VALIDATE_INPUTS(s, video, force_size, **kwargs): if not folder_paths.exists_annotated_filepath(video): return "Invalid video file: {}".format(video) return True class LoadVideoPath: @classmethod def INPUT_TYPES(s): return { "required": { "video": ("STRING", {"default": "X://insert/path/here.mp4", "vhs_path_extensions": video_extensions}), "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",), }, "hidden": { "unique_id": "UNIQUE_ID" }, } CATEGORY = "Video Helper Suite 🎥🅥🅗🅢" RETURN_TYPES = ("IMAGE", "INT", "VHS_AUDIO", "VHS_VIDEOINFO", "LATENT") RETURN_NAMES = ("IMAGE", "frame_count", "audio", "video_info", "LATENT") FUNCTION = "load_video" def load_video(self, **kwargs): if kwargs['video'] is None or validate_path(kwargs['video']) != True: raise Exception("video is not a valid path: " + kwargs['video']) return load_video_cv(**kwargs) @classmethod def IS_CHANGED(s, video, **kwargs): return hash_path(video) @classmethod def VALIDATE_INPUTS(s, video, **kwargs): return validate_path(video, allow_none=True)