mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
394 lines
15 KiB
Python
394 lines
15 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
|
|
import argparse
|
|
import os
|
|
import os.path as osp
|
|
import torchvision.transforms.functional as TF
|
|
import torch.nn.functional as F
|
|
import cv2
|
|
import tempfile
|
|
import imageio
|
|
import torch
|
|
import decord
|
|
from PIL import Image
|
|
import numpy as np
|
|
from rembg import remove, new_session
|
|
import random
|
|
import ffmpeg
|
|
import os
|
|
import tempfile
|
|
import subprocess
|
|
import json
|
|
from functools import lru_cache
|
|
|
|
|
|
from PIL import Image
|
|
video_info_cache = []
|
|
def seed_everything(seed: int):
|
|
random.seed(seed)
|
|
np.random.seed(seed)
|
|
torch.manual_seed(seed)
|
|
if torch.cuda.is_available():
|
|
torch.cuda.manual_seed(seed)
|
|
if torch.backends.mps.is_available():
|
|
torch.mps.manual_seed(seed)
|
|
|
|
def resample(video_fps, video_frames_count, max_target_frames_count, target_fps, start_target_frame ):
|
|
import math
|
|
|
|
video_frame_duration = 1 /video_fps
|
|
target_frame_duration = 1 / target_fps
|
|
|
|
target_time = start_target_frame * target_frame_duration
|
|
frame_no = math.ceil(target_time / video_frame_duration)
|
|
cur_time = frame_no * video_frame_duration
|
|
frame_ids =[]
|
|
while True:
|
|
if max_target_frames_count != 0 and len(frame_ids) >= max_target_frames_count :
|
|
break
|
|
diff = round( (target_time -cur_time) / video_frame_duration , 5)
|
|
add_frames_count = math.ceil( diff)
|
|
frame_no += add_frames_count
|
|
if frame_no >= video_frames_count:
|
|
break
|
|
frame_ids.append(frame_no)
|
|
cur_time += add_frames_count * video_frame_duration
|
|
target_time += target_frame_duration
|
|
frame_ids = frame_ids[:max_target_frames_count]
|
|
return frame_ids
|
|
|
|
import os
|
|
from datetime import datetime
|
|
|
|
def get_file_creation_date(file_path):
|
|
# On Windows
|
|
if os.name == 'nt':
|
|
return datetime.fromtimestamp(os.path.getctime(file_path))
|
|
# On Unix/Linux/Mac (gets last status change, not creation)
|
|
else:
|
|
stat = os.stat(file_path)
|
|
return datetime.fromtimestamp(stat.st_birthtime if hasattr(stat, 'st_birthtime') else stat.st_mtime)
|
|
|
|
def truncate_for_filesystem(s, max_bytes=255):
|
|
if len(s.encode('utf-8')) <= max_bytes: return s
|
|
l, r = 0, len(s)
|
|
while l < r:
|
|
m = (l + r + 1) // 2
|
|
if len(s[:m].encode('utf-8')) <= max_bytes: l = m
|
|
else: r = m - 1
|
|
return s[:l]
|
|
|
|
@lru_cache(maxsize=100)
|
|
def get_video_info(video_path):
|
|
global video_info_cache
|
|
import cv2
|
|
cap = cv2.VideoCapture(video_path)
|
|
|
|
# Get FPS
|
|
fps = round(cap.get(cv2.CAP_PROP_FPS))
|
|
|
|
# Get resolution
|
|
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
|
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
|
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
|
cap.release()
|
|
|
|
return fps, width, height, frame_count
|
|
|
|
def get_video_frame(file_name: str, frame_no: int, return_last_if_missing: bool = False, return_PIL = True) -> torch.Tensor:
|
|
"""Extract nth frame from video as PyTorch tensor normalized to [-1, 1]."""
|
|
cap = cv2.VideoCapture(file_name)
|
|
|
|
if not cap.isOpened():
|
|
raise ValueError(f"Cannot open video: {file_name}")
|
|
|
|
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
|
|
|
# Handle out of bounds
|
|
if frame_no >= total_frames or frame_no < 0:
|
|
if return_last_if_missing:
|
|
frame_no = total_frames - 1
|
|
else:
|
|
cap.release()
|
|
raise IndexError(f"Frame {frame_no} out of bounds (0-{total_frames-1})")
|
|
|
|
# Get frame
|
|
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_no)
|
|
ret, frame = cap.read()
|
|
cap.release()
|
|
|
|
if not ret:
|
|
raise ValueError(f"Failed to read frame {frame_no}")
|
|
|
|
# Convert BGR->RGB, reshape to (C,H,W), normalize to [-1,1]
|
|
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
|
if return_PIL:
|
|
return Image.fromarray(frame)
|
|
else:
|
|
return (torch.from_numpy(frame).permute(2, 0, 1).float() / 127.5) - 1.0
|
|
# def get_video_frame(file_name, frame_no):
|
|
# decord.bridge.set_bridge('torch')
|
|
# reader = decord.VideoReader(file_name)
|
|
|
|
# frame = reader.get_batch([frame_no]).squeeze(0)
|
|
# img = Image.fromarray(frame.numpy().astype(np.uint8))
|
|
# return img
|
|
|
|
def convert_image_to_video(image):
|
|
if image is None:
|
|
return None
|
|
|
|
# Convert PIL/numpy image to OpenCV format if needed
|
|
if isinstance(image, np.ndarray):
|
|
# Gradio images are typically RGB, OpenCV expects BGR
|
|
img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
|
|
else:
|
|
# Handle PIL Image
|
|
img_array = np.array(image)
|
|
img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
|
|
|
|
height, width = img_bgr.shape[:2]
|
|
|
|
# Create temporary video file (auto-cleaned by Gradio)
|
|
with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_video:
|
|
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
|
|
out = cv2.VideoWriter(temp_video.name, fourcc, 30.0, (width, height))
|
|
out.write(img_bgr)
|
|
out.release()
|
|
return temp_video.name
|
|
|
|
def resize_lanczos(img, h, w):
|
|
img = (img + 1).float().mul_(127.5)
|
|
img = Image.fromarray(np.clip(img.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8))
|
|
img = img.resize((w,h), resample=Image.Resampling.LANCZOS)
|
|
img = torch.from_numpy(np.array(img).astype(np.float32)).movedim(-1, 0)
|
|
img = img.div(127.5).sub_(1)
|
|
return img
|
|
|
|
def remove_background(img, session=None):
|
|
if session ==None:
|
|
session = new_session()
|
|
img = Image.fromarray(np.clip(255. * img.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8))
|
|
img = remove(img, session=session, alpha_matting = True, bgcolor=[255, 255, 255, 0]).convert('RGB')
|
|
return torch.from_numpy(np.array(img).astype(np.float32) / 255.0).movedim(-1, 0)
|
|
|
|
|
|
def convert_image_to_tensor(image):
|
|
return torch.from_numpy(np.array(image).astype(np.float32)).div_(127.5).sub_(1.).movedim(-1, 0)
|
|
|
|
def convert_tensor_to_image(t, frame_no = -1):
|
|
t = t[:, frame_no] if frame_no >= 0 else t
|
|
return Image.fromarray(t.clone().add_(1.).mul_(127.5).permute(1,2,0).to(torch.uint8).cpu().numpy())
|
|
|
|
def save_image(tensor_image, name, frame_no = -1):
|
|
convert_tensor_to_image(tensor_image, frame_no).save(name)
|
|
|
|
def get_outpainting_full_area_dimensions(frame_height,frame_width, outpainting_dims):
|
|
outpainting_top, outpainting_bottom, outpainting_left, outpainting_right= outpainting_dims
|
|
frame_height = int(frame_height * (100 + outpainting_top + outpainting_bottom) / 100)
|
|
frame_width = int(frame_width * (100 + outpainting_left + outpainting_right) / 100)
|
|
return frame_height, frame_width
|
|
|
|
def get_outpainting_frame_location(final_height, final_width, outpainting_dims, block_size = 8):
|
|
outpainting_top, outpainting_bottom, outpainting_left, outpainting_right= outpainting_dims
|
|
raw_height = int(final_height / ((100 + outpainting_top + outpainting_bottom) / 100))
|
|
height = int(raw_height / block_size) * block_size
|
|
extra_height = raw_height - height
|
|
|
|
raw_width = int(final_width / ((100 + outpainting_left + outpainting_right) / 100))
|
|
width = int(raw_width / block_size) * block_size
|
|
extra_width = raw_width - width
|
|
margin_top = int(outpainting_top/(100 + outpainting_top + outpainting_bottom) * final_height)
|
|
if extra_height != 0 and (outpainting_top + outpainting_bottom) != 0:
|
|
margin_top += int(outpainting_top / (outpainting_top + outpainting_bottom) * extra_height)
|
|
if (margin_top + height) > final_height or outpainting_bottom == 0: margin_top = final_height - height
|
|
margin_left = int(outpainting_left/(100 + outpainting_left + outpainting_right) * final_width)
|
|
if extra_width != 0 and (outpainting_left + outpainting_right) != 0:
|
|
margin_left += int(outpainting_left / (outpainting_left + outpainting_right) * extra_height)
|
|
if (margin_left + width) > final_width or outpainting_right == 0: margin_left = final_width - width
|
|
return height, width, margin_top, margin_left
|
|
|
|
def calculate_new_dimensions(canvas_height, canvas_width, image_height, image_width, fit_into_canvas, block_size = 16):
|
|
if fit_into_canvas == None:
|
|
# return image_height, image_width
|
|
return canvas_height, canvas_width
|
|
if fit_into_canvas:
|
|
scale1 = min(canvas_height / image_height, canvas_width / image_width)
|
|
scale2 = min(canvas_width / image_height, canvas_height / image_width)
|
|
scale = max(scale1, scale2)
|
|
else:
|
|
scale = (canvas_height * canvas_width / (image_height * image_width))**(1/2)
|
|
|
|
new_height = round( image_height * scale / block_size) * block_size
|
|
new_width = round( image_width * scale / block_size) * block_size
|
|
return new_height, new_width
|
|
|
|
def resize_and_remove_background(img_list, budget_width, budget_height, rm_background, ignore_first, fit_into_canvas = False ):
|
|
if rm_background:
|
|
session = new_session()
|
|
|
|
output_list =[]
|
|
for i, img in enumerate(img_list):
|
|
width, height = img.size
|
|
|
|
if fit_into_canvas:
|
|
white_canvas = np.ones((budget_height, budget_width, 3), dtype=np.uint8) * 255
|
|
scale = min(budget_height / height, budget_width / width)
|
|
new_height = int(height * scale)
|
|
new_width = int(width * scale)
|
|
resized_image= img.resize((new_width,new_height), resample=Image.Resampling.LANCZOS)
|
|
top = (budget_height - new_height) // 2
|
|
left = (budget_width - new_width) // 2
|
|
white_canvas[top:top + new_height, left:left + new_width] = np.array(resized_image)
|
|
resized_image = Image.fromarray(white_canvas)
|
|
else:
|
|
scale = (budget_height * budget_width / (height * width))**(1/2)
|
|
new_height = int( round(height * scale / 16) * 16)
|
|
new_width = int( round(width * scale / 16) * 16)
|
|
resized_image= img.resize((new_width,new_height), resample=Image.Resampling.LANCZOS)
|
|
if rm_background and not (ignore_first and i == 0) :
|
|
# resized_image = remove(resized_image, session=session, alpha_matting_erode_size = 1,alpha_matting_background_threshold = 70, alpha_foreground_background_threshold = 100, alpha_matting = True, bgcolor=[255, 255, 255, 0]).convert('RGB')
|
|
resized_image = remove(resized_image, session=session, alpha_matting_erode_size = 1, alpha_matting = True, bgcolor=[255, 255, 255, 0]).convert('RGB')
|
|
output_list.append(resized_image) #alpha_matting_background_threshold = 30, alpha_foreground_background_threshold = 200,
|
|
return output_list
|
|
|
|
|
|
|
|
|
|
def str2bool(v):
|
|
"""
|
|
Convert a string to a boolean.
|
|
|
|
Supported true values: 'yes', 'true', 't', 'y', '1'
|
|
Supported false values: 'no', 'false', 'f', 'n', '0'
|
|
|
|
Args:
|
|
v (str): String to convert.
|
|
|
|
Returns:
|
|
bool: Converted boolean value.
|
|
|
|
Raises:
|
|
argparse.ArgumentTypeError: If the value cannot be converted to boolean.
|
|
"""
|
|
if isinstance(v, bool):
|
|
return v
|
|
v_lower = v.lower()
|
|
if v_lower in ('yes', 'true', 't', 'y', '1'):
|
|
return True
|
|
elif v_lower in ('no', 'false', 'f', 'n', '0'):
|
|
return False
|
|
else:
|
|
raise argparse.ArgumentTypeError('Boolean value expected (True/False)')
|
|
|
|
|
|
import sys, time
|
|
|
|
# Global variables to track download progress
|
|
_start_time = None
|
|
_last_time = None
|
|
_last_downloaded = 0
|
|
_speed_history = []
|
|
_update_interval = 0.5 # Update speed every 0.5 seconds
|
|
|
|
def progress_hook(block_num, block_size, total_size, filename=None):
|
|
"""
|
|
Simple progress bar hook for urlretrieve
|
|
|
|
Args:
|
|
block_num: Number of blocks downloaded so far
|
|
block_size: Size of each block in bytes
|
|
total_size: Total size of the file in bytes
|
|
filename: Name of the file being downloaded (optional)
|
|
"""
|
|
global _start_time, _last_time, _last_downloaded, _speed_history, _update_interval
|
|
|
|
current_time = time.time()
|
|
downloaded = block_num * block_size
|
|
|
|
# Initialize timing on first call
|
|
if _start_time is None or block_num == 0:
|
|
_start_time = current_time
|
|
_last_time = current_time
|
|
_last_downloaded = 0
|
|
_speed_history = []
|
|
|
|
# Calculate download speed only at specified intervals
|
|
speed = 0
|
|
if current_time - _last_time >= _update_interval:
|
|
if _last_time > 0:
|
|
current_speed = (downloaded - _last_downloaded) / (current_time - _last_time)
|
|
_speed_history.append(current_speed)
|
|
# Keep only last 5 speed measurements for smoothing
|
|
if len(_speed_history) > 5:
|
|
_speed_history.pop(0)
|
|
# Average the recent speeds for smoother display
|
|
speed = sum(_speed_history) / len(_speed_history)
|
|
|
|
_last_time = current_time
|
|
_last_downloaded = downloaded
|
|
elif _speed_history:
|
|
# Use the last calculated average speed
|
|
speed = sum(_speed_history) / len(_speed_history)
|
|
# Format file sizes and speed
|
|
def format_bytes(bytes_val):
|
|
for unit in ['B', 'KB', 'MB', 'GB']:
|
|
if bytes_val < 1024:
|
|
return f"{bytes_val:.1f}{unit}"
|
|
bytes_val /= 1024
|
|
return f"{bytes_val:.1f}TB"
|
|
|
|
file_display = filename if filename else "Unknown file"
|
|
|
|
if total_size <= 0:
|
|
# If total size is unknown, show downloaded bytes
|
|
speed_str = f" @ {format_bytes(speed)}/s" if speed > 0 else ""
|
|
line = f"\r{file_display}: {format_bytes(downloaded)}{speed_str}"
|
|
# Clear any trailing characters by padding with spaces
|
|
sys.stdout.write(line.ljust(80))
|
|
sys.stdout.flush()
|
|
return
|
|
|
|
downloaded = block_num * block_size
|
|
percent = min(100, (downloaded / total_size) * 100)
|
|
|
|
# Create progress bar (40 characters wide to leave room for other info)
|
|
bar_length = 40
|
|
filled = int(bar_length * percent / 100)
|
|
bar = '█' * filled + '░' * (bar_length - filled)
|
|
|
|
# Format file sizes and speed
|
|
def format_bytes(bytes_val):
|
|
for unit in ['B', 'KB', 'MB', 'GB']:
|
|
if bytes_val < 1024:
|
|
return f"{bytes_val:.1f}{unit}"
|
|
bytes_val /= 1024
|
|
return f"{bytes_val:.1f}TB"
|
|
|
|
speed_str = f" @ {format_bytes(speed)}/s" if speed > 0 else ""
|
|
|
|
# Display progress with filename first
|
|
line = f"\r{file_display}: [{bar}] {percent:.1f}% ({format_bytes(downloaded)}/{format_bytes(total_size)}){speed_str}"
|
|
# Clear any trailing characters by padding with spaces
|
|
sys.stdout.write(line.ljust(100))
|
|
sys.stdout.flush()
|
|
|
|
# Print newline when complete
|
|
if percent >= 100:
|
|
print()
|
|
|
|
# Wrapper function to include filename in progress hook
|
|
def create_progress_hook(filename):
|
|
"""Creates a progress hook with the filename included"""
|
|
global _start_time, _last_time, _last_downloaded, _speed_history
|
|
# Reset timing variables for new download
|
|
_start_time = None
|
|
_last_time = None
|
|
_last_downloaded = 0
|
|
_speed_history = []
|
|
|
|
def hook(block_num, block_size, total_size):
|
|
return progress_hook(block_num, block_size, total_size, filename)
|
|
return hook
|
|
|
|
|