Benjamin Herb
126ebfc10e
- use h/w tuples to specify tiling size and overlap to allow for finer
control
- added OneOrTwoValues class to handle CLI input of single values or h/w
pairs
591 lines
25 KiB
Python
591 lines
25 KiB
Python
#!/usr/bin/env python3
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"""
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Standalone SeedVR2 Video Upscaler CLI Script
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"""
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import sys
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import os
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import argparse
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import time
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import multiprocessing as mp
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# Set up path before any other imports to fix module resolution
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script_dir = os.path.dirname(os.path.abspath(__file__))
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if script_dir not in sys.path:
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sys.path.insert(0, script_dir)
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# Set environment variable so all spawned processes can find modules
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os.environ['PYTHONPATH'] = script_dir + ':' + os.environ.get('PYTHONPATH', '')
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# Ensure safe CUDA usage with multiprocessing
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if mp.get_start_method(allow_none=True) != 'spawn':
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mp.set_start_method('spawn', force=True)
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# -------------------------------------------------------------
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# 1) Gestion VRAM (cudaMallocAsync) déjà en place
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os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "backend:cudaMallocAsync")
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# 2) Pré-parse de la ligne de commande pour récupérer --cuda_device
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_pre_parser = argparse.ArgumentParser(add_help=False)
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_pre_parser.add_argument("--cuda_device", type=str, default=None)
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_pre_args, _ = _pre_parser.parse_known_args()
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if _pre_args.cuda_device is not None:
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device_list_env = [x.strip() for x in _pre_args.cuda_device.split(',') if x.strip()!='']
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if len(device_list_env) == 1:
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# Single GPU: restrict visibility now
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os.environ["CUDA_VISIBLE_DEVICES"] = device_list_env[0]
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# -------------------------------------------------------------
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# 3) Imports lourds (torch, etc.) après la configuration env
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import torch
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import cv2
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import numpy as np
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from datetime import datetime
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from pathlib import Path
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from src.utils.downloads import download_weight
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from src.utils.debug import Debug
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debug = Debug(enabled=False) # Default to disabled, can be enabled via CLI
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def extract_frames_from_video(video_path, skip_first_frames=0, load_cap=None, prepend_frames=0):
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"""
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Extract frames from video and convert to tensor format
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Args:
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video_path (str): Path to input video
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skip_first_frame (bool): Skip the first frame during extraction
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load_cap (int): Maximum number of frames to load (None for all)
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Returns:
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torch.Tensor: Frames tensor in format [T, H, W, C] (Float16, normalized 0-1)
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"""
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debug.log(f"Extracting frames from video: {video_path}", category="file")
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if not os.path.exists(video_path):
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raise FileNotFoundError(f"Video file not found: {video_path}")
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# Open video
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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raise ValueError(f"Cannot open video file: {video_path}")
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# Get video properties
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fps = cap.get(cv2.CAP_PROP_FPS)
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frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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debug.log(f"Video info: {frame_count} frames, {width}x{height}, {fps:.2f} FPS", category="info")
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if skip_first_frames:
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debug.log(f"Will skip first {skip_first_frames} frames", category="info")
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if load_cap:
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debug.log(f"Will load maximum {load_cap} frames", category="info")
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if prepend_frames:
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debug.log(f"Will prepend {prepend_frames} frames to the video", category="info")
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frames = []
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frame_idx = 0
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frames_loaded = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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# Skip first frame if requested
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if frame_idx < skip_first_frames:
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frame_idx += 1
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continue
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if skip_first_frames > 0 and frame_idx == skip_first_frames:
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debug.log(f"Skipped first {skip_first_frames} frames", category="info")
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# Check load cap
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if load_cap is not None and load_cap > 0 and frames_loaded >= load_cap:
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debug.log(f"Reached load cap of {load_cap} frames", category="info")
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break
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# Convert BGR to RGB
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# Convert to float32 and normalize to 0-1
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frame = frame.astype(np.float32) / 255.0
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frames.append(frame)
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frame_idx += 1
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frames_loaded += 1
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if debug.enabled and frames_loaded % 100 == 0:
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total_to_load = min(frame_count, load_cap) if load_cap else frame_count
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debug.log(f"Extracted {frames_loaded}/{total_to_load} frames", category="file")
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cap.release()
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if len(frames) == 0:
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raise ValueError(f"No frames extracted from video: {video_path}")
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debug.log(f"Extracted {len(frames)} frames", category="success")
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# preprend frames if requested (reverse of the first few frames)
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if prepend_frames > 0:
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prepend_frames = min(prepend_frames, len(frames))
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frames = frames[-prepend_frames:] + frames
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# Convert to tensor [T, H, W, C] and cast to Float16 for ComfyUI compatibility
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frames_tensor = torch.from_numpy(np.stack(frames)).to(torch.float16)
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debug.log(f"Frames tensor shape: {frames_tensor.shape}, dtype: {frames_tensor.dtype}", category="memory")
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return frames_tensor, fps
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def save_frames_to_video(frames_tensor, output_path, fps=30.0):
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"""
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Save frames tensor to video file
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Args:
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frames_tensor (torch.Tensor): Frames in format [T, H, W, C] (Float16, 0-1)
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output_path (str): Output video path
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fps (float): Output video FPS
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"""
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debug.log(f"Saving {frames_tensor.shape[0]} frames to video: {output_path}", category="file")
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# Ensure output directory exists
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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# Convert tensor to numpy and denormalize
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frames_np = frames_tensor.cpu().numpy()
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frames_np = (frames_np * 255.0).astype(np.uint8)
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# Get video properties
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T, H, W, C = frames_np.shape
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# Initialize video writer
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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out = cv2.VideoWriter(output_path, fourcc, fps, (W, H))
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if not out.isOpened():
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raise ValueError(f"Cannot create video writer for: {output_path}")
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# Write frames
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for i, frame in enumerate(frames_np):
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# Convert RGB to BGR for OpenCV
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frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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out.write(frame_bgr)
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if debug.enabled and (i + 1) % 100 == 0:
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debug.log(f"Saved {i + 1}/{T} frames", category="file")
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out.release()
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debug.log(f"Video saved successfully: {output_path}", category="success")
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def save_frames_to_png(frames_tensor, output_dir, base_name):
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"""
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Save frames tensor as sequential PNG images.
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Args:
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frames_tensor (torch.Tensor): Frames in format [T, H, W, C] (Float16, 0-1)
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output_dir (str): Directory to save PNGs
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base_name (str): Base name for output files (without extension)
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"""
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debug.log(f"Saving {frames_tensor.shape[0]} frames as PNGs to directory: {output_dir}", category="file")
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# Ensure output directory exists
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os.makedirs(output_dir, exist_ok=True)
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# Convert to numpy uint8 RGB
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frames_np = (frames_tensor.cpu().numpy() * 255.0).astype(np.uint8)
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total = frames_np.shape[0]
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digits = max(5, len(str(total))) # at least 5 digits
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for idx, frame in enumerate(frames_np):
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filename = f"{base_name}_{idx:0{digits}d}.png"
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file_path = os.path.join(output_dir, filename)
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# Convert RGB to BGR for cv2
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frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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cv2.imwrite(file_path, frame_bgr)
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if debug.enabled and (idx + 1) % 100 == 0:
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debug.log(f"Saved {idx + 1}/{total} PNGs", category="file")
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debug.log(f"PNG saving completed: {total} files in '{output_dir}'", category="success")
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def apply_temporal_overlap_blending(frames_tensor, batch_size, overlap):
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"""
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Blend frames with temporal overlap in pixel space and remove duplicates.
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Args:
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frames_tensor (torch.Tensor): [T, H, W, C], Float16 in [0,1]
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batch_size (int): Frames per batch used during generation
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overlap (int): Overlapping frames between consecutive batches
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Returns:
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torch.Tensor: Blended frames [T, H, W, C] with duplicates removed
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"""
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T = frames_tensor.shape[0]
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if overlap <= 0 or batch_size <= overlap or T <= batch_size:
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return frames_tensor
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device = frames_tensor.device
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dtype = frames_tensor.dtype
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output = frames_tensor[:batch_size]
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input_pos = batch_size
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while input_pos < T:
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remaining_frames = T - input_pos
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current_batch_size = min(batch_size, remaining_frames)
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if current_batch_size <= overlap:
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break
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current_batch = frames_tensor[input_pos:input_pos + current_batch_size]
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prev_tail = output[-overlap:] # overlap frames from previous output
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cur_head = current_batch[:overlap] # overlap frames from current batch
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# Crossfade (clamping so that the blending only happens in the center of the overlap)
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w_prev = torch.clamp(torch.linspace(2.0, -1.0, steps=overlap, device=device, dtype=dtype), 0.0, 1.0).view(overlap, 1, 1, 1)
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w_cur = 1.0 - w_prev
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blended = prev_tail * w_prev + cur_head * w_cur
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# Replace the last overlap frames in output with blended result
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output = torch.cat([output[:-overlap], blended], dim=0)
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# Append the non-overlapping part of current batch (if any)
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if overlap < current_batch_size:
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non_overlapping = current_batch[overlap:]
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output = torch.cat([output, non_overlapping], dim=0)
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input_pos += current_batch_size
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return output
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def _worker_process(proc_idx, device_id, frames_np, shared_args, return_queue):
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"""Worker process that performs upscaling on a slice of frames using a dedicated GPU."""
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# 1. Limit CUDA visibility to the chosen GPU BEFORE importing torch-heavy deps
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os.environ["CUDA_VISIBLE_DEVICES"] = str(device_id)
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# Keep same cudaMallocAsync setting
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os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "backend:cudaMallocAsync")
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import torch # local import inside subprocess
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from src.core.model_manager import configure_runner
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from src.core.generation import generation_loop
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# Create debug instance for this worker process
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worker_debug = Debug(enabled=shared_args["debug"])
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# Reconstruct frames tensor
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frames_tensor = torch.from_numpy(frames_np).to(torch.float16)
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# Prepare runner
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model_dir = shared_args["model_dir"]
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model_name = shared_args["model"]
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# ensure model weights present (each process checks but very fast if already downloaded)
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worker_debug.log(f"Configuring runner for device {device_id}", category="general")
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runner = configure_runner(model_name, model_dir, shared_args["preserve_vram"], worker_debug, block_swap_config=shared_args["block_swap_config"], vae_tiling_enabled=shared_args["vae_tiling_enabled"], vae_tile_size=shared_args["vae_tile_size"], vae_tile_overlap=shared_args["vae_tile_overlap"])
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# Run generation
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result_tensor = generation_loop(
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runner=runner,
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images=frames_tensor,
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cfg_scale=shared_args["cfg_scale"],
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seed=shared_args["seed"],
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res_w=shared_args["res_w"],
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batch_size=shared_args["batch_size"],
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preserve_vram=shared_args["preserve_vram"],
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temporal_overlap=shared_args["temporal_overlap"],
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debug=worker_debug,
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block_swap_config=shared_args["block_swap_config"]
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)
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# Send back result as numpy array to avoid CUDA transfers
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return_queue.put((proc_idx, result_tensor.cpu().numpy()))
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def _gpu_processing(frames_tensor, device_list, args):
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"""Split frames and process them in parallel on multiple GPUs."""
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num_devices = len(device_list)
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total_frames = frames_tensor.shape[0]
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# Create overlapping chunks (for multi GPU); ensures every chunk is
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# a multiple of batch_size (except last one) to avoid blending issues
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if args.temporal_overlap > 0 and num_devices > 1:
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chunk_with_overlap = total_frames // num_devices + args.temporal_overlap
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if args.batch_size > 1:
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chunk_with_overlap = ((chunk_with_overlap + args.batch_size - 1) // args.batch_size) * args.batch_size
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base_chunk_size = chunk_with_overlap - args.temporal_overlap
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chunks = []
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for i in range(num_devices):
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start_idx = i * base_chunk_size
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if i == num_devices - 1: # last chunk/device
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end_idx = total_frames
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else:
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end_idx = min(start_idx + chunk_with_overlap, total_frames)
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chunks.append(frames_tensor[start_idx:end_idx])
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else:
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chunks = torch.chunk(frames_tensor, num_devices, dim=0)
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manager = mp.Manager()
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return_queue = manager.Queue()
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workers = []
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shared_args = {
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"model": args.model,
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"model_dir": args.model_dir if args.model_dir is not None else "./models/SEEDVR2",
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"preserve_vram": args.preserve_vram,
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"debug": args.debug,
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"cfg_scale": 1.0,
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"seed": args.seed,
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"res_w": args.resolution,
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"batch_size": args.batch_size,
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"temporal_overlap": args.temporal_overlap,
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"block_swap_config": {
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'blocks_to_swap': args.blocks_to_swap,
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'use_none_blocking': args.use_none_blocking,
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'offload_io_components': args.offload_io_components,
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'cache_model': False, # No caching in CLI mode
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},
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"vae_tiling_enabled": args.vae_tiling_enabled,
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"vae_tile_size": args.vae_tile_size,
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"vae_tile_overlap": args.vae_tile_overlap,
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}
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for idx, (device_id, chunk_tensor) in enumerate(zip(device_list, chunks)):
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p = mp.Process(
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target=_worker_process,
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args=(idx, device_id, chunk_tensor.cpu().numpy(), shared_args, return_queue),
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)
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p.start()
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workers.append(p)
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results_np = [None] * num_devices
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collected = 0
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while collected < num_devices:
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proc_idx, res_np = return_queue.get()
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results_np[proc_idx] = res_np
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collected += 1
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for p in workers:
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p.join()
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# Concatenate results with overlap handling
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if args.temporal_overlap > 0 and num_devices > 1:
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# Reconstruct results considering overlap
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result_list = []
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overlap = args.temporal_overlap
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for idx, res_np in enumerate(results_np):
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if idx == 0:
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# First chunk: keep all frames
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result_list.append(torch.from_numpy(res_np).to(torch.float16))
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elif idx == num_devices - 1:
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# Last chunk: skip overlap frames at the beginning
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chunk_tensor = torch.from_numpy(res_np).to(torch.float16)
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if chunk_tensor.shape[0] > overlap:
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result_list.append(chunk_tensor[overlap:])
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else:
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# If chunk is smaller than overlap, skip it entirely
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pass
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else:
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# Middle chunks: skip overlap at beginning, keep overlap at end
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chunk_tensor = torch.from_numpy(res_np).to(torch.float16)
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if chunk_tensor.shape[0] > overlap:
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result_list.append(chunk_tensor[overlap:])
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if result_list:
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result_tensor = torch.cat(result_list, dim=0)
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else:
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result_tensor = torch.from_numpy(results_np[0]).to(torch.float16)
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else:
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# Original concatenation without overlap handling
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result_tensor = torch.from_numpy(np.concatenate(results_np, axis=0)).to(torch.float16)
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return result_tensor
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class OneOrTwoValues(argparse.Action):
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def __call__(self, parser, namespace, values, option_string=None):
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if len(values) not in [1, 2]:
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parser.error(f"{option_string} requires 1 or 2 arguments")
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if len(values) == 1:
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values = values[0]
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if ',' in values:
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values = [v.strip() for v in values.split(',') if v.strip()]
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else:
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values = values.split()
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try:
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result = tuple(int(v) for v in values)
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if len(result) == 1:
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result = (result[0], result[0]) # Convert single value to (h, w)
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setattr(namespace, self.dest, result)
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except ValueError:
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parser.error(f"{option_string} arguments must be integers")
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def parse_arguments():
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"""Parse command line arguments"""
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parser = argparse.ArgumentParser(description="SeedVR2 Video Upscaler CLI")
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parser.add_argument("--video_path", type=str, required=True,
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help="Path to input video file")
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parser.add_argument("--seed", type=int, default=100,
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help="Random seed for generation (default: 100)")
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parser.add_argument("--resolution", type=int, default=1072,
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help="Target resolution of the short side (default: 1072)")
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parser.add_argument("--batch_size", type=int, default=1,
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help="Number of frames per batch (default: 1)")
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parser.add_argument("--model", type=str, default="seedvr2_ema_3b_fp8_e4m3fn.safetensors",
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choices=[
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"seedvr2_ema_3b_fp16.safetensors",
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"seedvr2_ema_3b_fp8_e4m3fn.safetensors",
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"seedvr2_ema_7b_fp16.safetensors",
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"seedvr2_ema_7b_fp8_e4m3fn.safetensors"
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],
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help="Model to use (default: 3B FP8)")
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parser.add_argument("--model_dir", type=str, default="seedvr2_models",
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help="Directory containing the model files (default: use cache directory)")
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parser.add_argument("--skip_first_frames", type=int, default=0,
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help="Skip the first frames during processing")
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parser.add_argument("--load_cap", type=int, default=0,
|
|
help="Maximum number of frames to load from video (default: load all)")
|
|
parser.add_argument("--output", type=str, default=None,
|
|
help="Output path (default: auto-generated, if output_format is png, it will be a directory)")
|
|
parser.add_argument("--output_format", type=str, default="video", choices=["video", "png"],
|
|
help="Output format: 'video' (mp4) or 'png' images (default: video)")
|
|
parser.add_argument("--preserve_vram", action="store_true",
|
|
help="Enable VRAM preservation mode")
|
|
parser.add_argument("--debug", action="store_true",
|
|
help="Enable debug logging")
|
|
parser.add_argument("--cuda_device", type=str, default=None,
|
|
help="CUDA device id(s). Single id (e.g., '0') or comma-separated list '0,1' for multi-GPU")
|
|
parser.add_argument("--blocks_to_swap", type=int, default=0,
|
|
help="Number of blocks to swap for VRAM optimization (default: 0, disabled), up to 32 for 3B model, 36 for 7B")
|
|
parser.add_argument("--use_none_blocking", action="store_true",
|
|
help="Use non-blocking memory transfers for VRAM optimization")
|
|
parser.add_argument("--temporal_overlap", type=int, default=0,
|
|
help="Temporal overlap for processing (default: 0, no temporal overlap)")
|
|
parser.add_argument("--prepend_frames", type=int, default=0,
|
|
help="Number of frames to prepend to the video (default: 0). This can help with artifacts at the start of the video and are removed after processing")
|
|
parser.add_argument("--offload_io_components", action="store_true",
|
|
help="Offload IO components to CPU for VRAM optimization")
|
|
parser.add_argument("--vae_tiling_enabled", action="store_true",
|
|
help="Enable VAE tiling for improved VRAM usage")
|
|
parser.add_argument("--vae_tile_size", action=OneOrTwoValues, nargs='+', default=(512, 512),
|
|
help="VAE tile size (default: 512). Use single integer or two integers 'h w'. Only used if --vae_tiling_enabled is set")
|
|
parser.add_argument("--vae_tile_overlap", action=OneOrTwoValues, nargs='+', default=(128, 128),
|
|
help="VAE tile overlap (default: 128). Use single integer or two integers 'h w'. Only used if --vae_tiling_enabled is set")
|
|
return parser.parse_args()
|
|
|
|
|
|
def main():
|
|
"""Main CLI function"""
|
|
debug.log(f"SeedVR2 Video Upscaler CLI started at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", category="model", force=True)
|
|
|
|
# Parse arguments
|
|
args = parse_arguments()
|
|
debug.enabled = args.debug
|
|
|
|
debug.log("Arguments:", category="setup")
|
|
for key, value in vars(args).items():
|
|
debug.log(f" {key}: {value}", category="none")
|
|
|
|
if args.vae_tiling_enabled and (args.vae_tile_overlap[0] >= args.vae_tile_size[0] or args.vae_tile_overlap[1] >= args.vae_tile_size[1]):
|
|
print(f"Error: VAE tile overlap {args.vae_tile_overlap} must be smaller than tile size {args.vae_tile_size}")
|
|
sys.exit(1)
|
|
|
|
# Show actual CUDA device visibility
|
|
debug.log(f"CUDA_VISIBLE_DEVICES: {os.environ.get('CUDA_VISIBLE_DEVICES', 'Not set (all)')}", category="device")
|
|
if torch.cuda.is_available():
|
|
debug.log(f"torch.cuda.device_count(): {torch.cuda.device_count()}", category="device")
|
|
debug.log(f"Using device index 0 inside script (mapped to selected GPU)", category="device")
|
|
|
|
try:
|
|
# Ensure --output is a directory when using PNG format
|
|
if args.output_format == "png":
|
|
output_path_obj = Path(args.output)
|
|
if output_path_obj.suffix: # an extension is present, strip it
|
|
args.output = str(output_path_obj.with_suffix(''))
|
|
|
|
debug.log(f"Output will be saved to: {args.output}", category="file")
|
|
|
|
# Extract frames from video
|
|
debug.log(f"Extracting frames from video...", category="generation")
|
|
start_time = time.time()
|
|
frames_tensor, original_fps = extract_frames_from_video(
|
|
args.video_path,
|
|
args.skip_first_frames,
|
|
args.load_cap,
|
|
args.prepend_frames
|
|
)
|
|
|
|
debug.log(f"Frame extraction time: {time.time() - start_time:.2f}s", category="general")
|
|
# debug.log(f"Initial VRAM: {torch.cuda.memory_allocated() / 1024**3:.2f}GB", category="memory")
|
|
|
|
# Parse GPU list
|
|
device_list = [d.strip() for d in str(args.cuda_device).split(',') if d.strip()] if args.cuda_device else ["0"]
|
|
debug.log(f"Using devices: {device_list}", category="device")
|
|
processing_start = time.time()
|
|
download_weight(args.model, args.model_dir)
|
|
result = _gpu_processing(frames_tensor, device_list, args)
|
|
generation_time = time.time() - processing_start
|
|
|
|
debug.log(f"Generation time: {generation_time:.2f}s", category="general")
|
|
debug.log(f"Peak VRAM usage: {torch.cuda.max_memory_allocated() / 1024**3:.2f}GB", category="memory")
|
|
|
|
if args.temporal_overlap > 0:
|
|
debug.log(f"Applying temporal overlap with blending", category="generation")
|
|
result = apply_temporal_overlap_blending(result, args.batch_size, args.temporal_overlap)
|
|
debug.log(f"Result shape: {result.shape}, dtype: {result.dtype}", category="memory")
|
|
|
|
if args.prepend_frames > 0:
|
|
debug.log(f"Removing prepended ({args.prepend_frames}) frames from the results)", category="generation")
|
|
result = result[args.prepend_frames:]
|
|
debug.log(f"Result shape after removing prepended frames: {result.shape}", category="info")
|
|
|
|
# After generation_time calculation, choose saving method
|
|
if args.output_format == "png":
|
|
# Ensure output treated as directory
|
|
output_dir = args.output
|
|
base_name = Path(args.video_path).stem + "_upscaled"
|
|
debug.log(f"Saving PNG frames to directory: {output_dir}", category="file")
|
|
|
|
save_start = time.time()
|
|
save_frames_to_png(result, output_dir, base_name)
|
|
|
|
debug.log(f"Save time: {time.time() - save_start:.2f}s", category="general")
|
|
|
|
else:
|
|
# Save video
|
|
debug.log(f"Saving upscaled video to: {args.output}", category="file")
|
|
save_start = time.time()
|
|
save_frames_to_video(result, args.output, original_fps)
|
|
debug.log(f"Save time: {time.time() - save_start:.2f}s", category="general")
|
|
|
|
total_time = time.time() - start_time
|
|
debug.log(f"Upscaling completed successfully!", category="success", force=True)
|
|
if args.output_format == "png":
|
|
debug.log(f"PNG frames saved in directory: {args.output}", category="file", force=True)
|
|
else:
|
|
debug.log(f"Output saved to video: {args.output}", category="file", force=True)
|
|
debug.log(f"Total processing time: {total_time:.2f}s", category="timing", force=True)
|
|
debug.log(f"Average FPS: {len(frames_tensor) / generation_time:.2f} frames/sec", category="timing", force=True)
|
|
|
|
except Exception as e:
|
|
debug.log(f"Error during processing: {e}", category="error", force=True)
|
|
import traceback
|
|
traceback.print_exc()
|
|
sys.exit(1)
|
|
|
|
finally:
|
|
debug.log(f"Process {os.getpid()} terminating - VRAM will be automatically freed", category="cleanup", force=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |