mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 14:58:12 +00:00
398 lines
20 KiB
Python
398 lines
20 KiB
Python
from .runners import AccurateModeRunner, FastModeRunner, BalancedModeRunner, InterpolationModeRunner, InterpolationModeSingleFrameRunner
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from .data import VideoData, get_video_fps, save_video, search_for_images
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import os
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import gradio as gr
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def check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder):
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frames_guide = VideoData(video_guide, video_guide_folder)
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frames_style = VideoData(video_style, video_style_folder)
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message = ""
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if len(frames_guide) < len(frames_style):
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message += f"The number of frames mismatches. Only the first {len(frames_guide)} frames of style video will be used.\n"
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frames_style.set_length(len(frames_guide))
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elif len(frames_guide) > len(frames_style):
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message += f"The number of frames mismatches. Only the first {len(frames_style)} frames of guide video will be used.\n"
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frames_guide.set_length(len(frames_style))
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height_guide, width_guide = frames_guide.shape()
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height_style, width_style = frames_style.shape()
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if height_guide != height_style or width_guide != width_style:
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message += f"The shape of frames mismatches. The frames in style video will be resized to (height: {height_guide}, width: {width_guide})\n"
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frames_style.set_shape(height_guide, width_guide)
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return frames_guide, frames_style, message
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def smooth_video(
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video_guide,
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video_guide_folder,
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video_style,
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video_style_folder,
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mode,
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window_size,
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batch_size,
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tracking_window_size,
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output_path,
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fps,
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minimum_patch_size,
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num_iter,
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guide_weight,
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initialize,
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progress = None,
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):
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# input
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frames_guide, frames_style, message = check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder)
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if len(message) > 0:
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print(message)
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# output
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if output_path == "":
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if video_style is None:
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output_path = os.path.join(video_style_folder, "output")
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else:
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output_path = os.path.join(os.path.split(video_style)[0], "output")
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os.makedirs(output_path, exist_ok=True)
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print("No valid output_path. Your video will be saved here:", output_path)
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elif not os.path.exists(output_path):
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os.makedirs(output_path, exist_ok=True)
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print("Your video will be saved here:", output_path)
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frames_path = os.path.join(output_path, "frames")
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video_path = os.path.join(output_path, "video.mp4")
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os.makedirs(frames_path, exist_ok=True)
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# process
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if mode == "Fast" or mode == "Balanced":
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tracking_window_size = 0
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ebsynth_config = {
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"minimum_patch_size": minimum_patch_size,
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"threads_per_block": 8,
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"num_iter": num_iter,
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"gpu_id": 0,
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"guide_weight": guide_weight,
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"initialize": initialize,
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"tracking_window_size": tracking_window_size,
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}
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if mode == "Fast":
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FastModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
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elif mode == "Balanced":
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BalancedModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
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elif mode == "Accurate":
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AccurateModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
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# output
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try:
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fps = int(fps)
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except:
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fps = get_video_fps(video_style) if video_style is not None else 30
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print("Fps:", fps)
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print("Saving video...")
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video_path = save_video(frames_path, video_path, num_frames=len(frames_style), fps=fps)
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print("Success!")
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print("Your frames are here:", frames_path)
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print("Your video is here:", video_path)
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return output_path, fps, video_path
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class KeyFrameMatcher:
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def __init__(self):
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pass
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def extract_number_from_filename(self, file_name):
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result = []
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number = -1
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for i in file_name:
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if ord(i)>=ord("0") and ord(i)<=ord("9"):
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if number == -1:
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number = 0
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number = number*10 + ord(i) - ord("0")
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else:
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if number != -1:
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result.append(number)
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number = -1
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if number != -1:
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result.append(number)
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result = tuple(result)
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return result
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def extract_number_from_filenames(self, file_names):
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numbers = [self.extract_number_from_filename(file_name) for file_name in file_names]
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min_length = min(len(i) for i in numbers)
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for i in range(min_length-1, -1, -1):
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if len(set(number[i] for number in numbers))==len(file_names):
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return [number[i] for number in numbers]
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return list(range(len(file_names)))
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def match_using_filename(self, file_names_a, file_names_b):
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file_names_b_set = set(file_names_b)
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matched_file_name = []
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for file_name in file_names_a:
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if file_name not in file_names_b_set:
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matched_file_name.append(None)
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else:
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matched_file_name.append(file_name)
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return matched_file_name
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def match_using_numbers(self, file_names_a, file_names_b):
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numbers_a = self.extract_number_from_filenames(file_names_a)
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numbers_b = self.extract_number_from_filenames(file_names_b)
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numbers_b_dict = {number: file_name for number, file_name in zip(numbers_b, file_names_b)}
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matched_file_name = []
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for number in numbers_a:
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if number in numbers_b_dict:
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matched_file_name.append(numbers_b_dict[number])
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else:
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matched_file_name.append(None)
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return matched_file_name
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def match_filenames(self, file_names_a, file_names_b):
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matched_file_name = self.match_using_filename(file_names_a, file_names_b)
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if sum([i is not None for i in matched_file_name]) > 0:
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return matched_file_name
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matched_file_name = self.match_using_numbers(file_names_a, file_names_b)
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return matched_file_name
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def detect_frames(frames_path, keyframes_path):
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if not os.path.exists(frames_path) and not os.path.exists(keyframes_path):
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return "Please input the directory of guide video and rendered frames"
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elif not os.path.exists(frames_path):
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return "Please input the directory of guide video"
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elif not os.path.exists(keyframes_path):
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return "Please input the directory of rendered frames"
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frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)]
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keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)]
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if len(frames)==0:
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return f"No images detected in {frames_path}"
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if len(keyframes)==0:
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return f"No images detected in {keyframes_path}"
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matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes)
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max_filename_length = max([len(i) for i in frames])
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if sum([i is not None for i in matched_keyframes])==0:
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message = ""
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for frame, matched_keyframe in zip(frames, matched_keyframes):
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message += frame + " " * (max_filename_length - len(frame) + 1)
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message += "--> No matched keyframes\n"
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else:
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message = ""
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for frame, matched_keyframe in zip(frames, matched_keyframes):
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message += frame + " " * (max_filename_length - len(frame) + 1)
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if matched_keyframe is None:
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message += "--> [to be rendered]\n"
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else:
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message += f"--> {matched_keyframe}\n"
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return message
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def check_input_for_interpolating(frames_path, keyframes_path):
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# search for images
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frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)]
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keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)]
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# match frames
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matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes)
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file_list = [file_name for file_name in matched_keyframes if file_name is not None]
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index_style = [i for i, file_name in enumerate(matched_keyframes) if file_name is not None]
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frames_guide = VideoData(None, frames_path)
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frames_style = VideoData(None, keyframes_path, file_list=file_list)
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# match shape
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message = ""
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height_guide, width_guide = frames_guide.shape()
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height_style, width_style = frames_style.shape()
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if height_guide != height_style or width_guide != width_style:
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message += f"The shape of frames mismatches. The rendered keyframes will be resized to (height: {height_guide}, width: {width_guide})\n"
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frames_style.set_shape(height_guide, width_guide)
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return frames_guide, frames_style, index_style, message
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def interpolate_video(
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frames_path,
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keyframes_path,
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output_path,
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fps,
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batch_size,
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tracking_window_size,
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minimum_patch_size,
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num_iter,
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guide_weight,
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initialize,
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progress = None,
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):
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# input
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frames_guide, frames_style, index_style, message = check_input_for_interpolating(frames_path, keyframes_path)
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if len(message) > 0:
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print(message)
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# output
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if output_path == "":
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output_path = os.path.join(keyframes_path, "output")
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os.makedirs(output_path, exist_ok=True)
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print("No valid output_path. Your video will be saved here:", output_path)
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elif not os.path.exists(output_path):
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os.makedirs(output_path, exist_ok=True)
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print("Your video will be saved here:", output_path)
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output_frames_path = os.path.join(output_path, "frames")
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output_video_path = os.path.join(output_path, "video.mp4")
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os.makedirs(output_frames_path, exist_ok=True)
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# process
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ebsynth_config = {
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"minimum_patch_size": minimum_patch_size,
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"threads_per_block": 8,
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"num_iter": num_iter,
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"gpu_id": 0,
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"guide_weight": guide_weight,
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"initialize": initialize,
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"tracking_window_size": tracking_window_size
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}
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if len(index_style)==1:
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InterpolationModeSingleFrameRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path)
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else:
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InterpolationModeRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path)
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try:
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fps = int(fps)
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except:
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fps = 30
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print("Fps:", fps)
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print("Saving video...")
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video_path = save_video(output_frames_path, output_video_path, num_frames=len(frames_guide), fps=fps)
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print("Success!")
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print("Your frames are here:", output_frames_path)
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print("Your video is here:", video_path)
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return output_path, fps, video_path
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def on_ui_tabs():
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with gr.Blocks(analytics_enabled=False) as ui_component:
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with gr.Tab("Blend"):
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gr.Markdown("""
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# Blend
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Given a guide video and a style video, this algorithm will make the style video fluent according to the motion features of the guide video. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/208d902d-6aba-48d7-b7d5-cd120ebd306d) to see the example. Note that this extension doesn't support long videos. Please use short videos (e.g., several seconds). The algorithm is mainly designed for 512*512 resolution. Please use a larger `Minimum patch size` for higher resolution.
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""")
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with gr.Row():
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with gr.Column():
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with gr.Tab("Guide video"):
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video_guide = gr.Video(label="Guide video")
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with gr.Tab("Guide video (images format)"):
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video_guide_folder = gr.Textbox(label="Guide video (images format)", value="")
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with gr.Column():
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with gr.Tab("Style video"):
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video_style = gr.Video(label="Style video")
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with gr.Tab("Style video (images format)"):
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video_style_folder = gr.Textbox(label="Style video (images format)", value="")
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with gr.Column():
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output_path = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of style video")
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fps = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps")
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video_output = gr.Video(label="Output video", interactive=False, show_share_button=True)
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btn = gr.Button(value="Blend")
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with gr.Row():
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with gr.Column():
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gr.Markdown("# Settings")
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mode = gr.Radio(["Fast", "Balanced", "Accurate"], label="Inference mode", value="Fast", interactive=True)
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window_size = gr.Slider(label="Sliding window size", value=15, minimum=1, maximum=1000, step=1, interactive=True)
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batch_size = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True)
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tracking_window_size = gr.Slider(label="Tracking window size (only for accurate mode)", value=0, minimum=0, maximum=10, step=1, interactive=True)
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gr.Markdown("## Advanced Settings")
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minimum_patch_size = gr.Slider(label="Minimum patch size (odd number)", value=5, minimum=5, maximum=99, step=2, interactive=True)
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num_iter = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True)
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guide_weight = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True)
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initialize = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True)
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with gr.Column():
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gr.Markdown("""
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# Reference
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* Output directory: the directory to save the video.
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* Inference mode
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|Mode|Time|Memory|Quality|Frame by frame output|Description|
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|Fast|■|■■■|■■|No|Blend the frames using a tree-like data structure, which requires much RAM but is fast.|
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|Balanced|■■|■|■■|Yes|Blend the frames naively.|
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|Accurate|■■■|■|■■■|Yes|Blend the frames and align them together for higher video quality. When [batch size] >= [sliding window size] * 2 + 1, the performance is the best.|
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* Sliding window size: our algorithm will blend the frames in a sliding windows. If the size is n, each frame will be blended with the last n frames and the next n frames. A large sliding window can make the video fluent but sometimes smoggy.
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* Batch size: a larger batch size makes the program faster but requires more VRAM.
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* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough.
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* Advanced settings
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* Minimum patch size (odd number): the minimum patch size used for patch matching. (Default: 5)
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* Number of iterations: the number of iterations of patch matching. (Default: 5)
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* Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10)
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* NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity)
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""")
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btn.click(
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smooth_video,
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inputs=[
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video_guide,
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video_guide_folder,
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video_style,
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video_style_folder,
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mode,
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window_size,
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batch_size,
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tracking_window_size,
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output_path,
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fps,
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minimum_patch_size,
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num_iter,
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guide_weight,
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initialize
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],
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outputs=[output_path, fps, video_output]
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)
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with gr.Tab("Interpolate"):
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gr.Markdown("""
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# Interpolate
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Given a guide video and some rendered keyframes, this algorithm will render the remaining frames. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/3490c5b4-8f67-478f-86de-f9adc2ace16a) to see the example. The algorithm is experimental and is only tested for 512*512 resolution.
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""")
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with gr.Row():
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with gr.Column():
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with gr.Row():
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with gr.Column():
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video_guide_folder_ = gr.Textbox(label="Guide video (images format)", value="")
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with gr.Column():
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rendered_keyframes_ = gr.Textbox(label="Rendered keyframes (images format)", value="")
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with gr.Row():
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detected_frames = gr.Textbox(label="Detected frames", value="Please input the directory of guide video and rendered frames", lines=9, max_lines=9, interactive=False)
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video_guide_folder_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames)
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rendered_keyframes_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames)
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with gr.Column():
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output_path_ = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of rendered keyframes")
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fps_ = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps")
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video_output_ = gr.Video(label="Output video", interactive=False, show_share_button=True)
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btn_ = gr.Button(value="Interpolate")
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with gr.Row():
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with gr.Column():
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gr.Markdown("# Settings")
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batch_size_ = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True)
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tracking_window_size_ = gr.Slider(label="Tracking window size", value=0, minimum=0, maximum=10, step=1, interactive=True)
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gr.Markdown("## Advanced Settings")
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minimum_patch_size_ = gr.Slider(label="Minimum patch size (odd number, larger is better)", value=15, minimum=5, maximum=99, step=2, interactive=True)
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num_iter_ = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True)
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guide_weight_ = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True)
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initialize_ = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True)
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with gr.Column():
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gr.Markdown("""
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# Reference
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* Output directory: the directory to save the video.
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* Batch size: a larger batch size makes the program faster but requires more VRAM.
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* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough.
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* Advanced settings
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* Minimum patch size (odd number): the minimum patch size used for patch matching. **This parameter should be larger than that in blending. (Default: 15)**
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* Number of iterations: the number of iterations of patch matching. (Default: 5)
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* Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10)
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* NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity)
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""")
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btn_.click(
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interpolate_video,
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inputs=[
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video_guide_folder_,
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rendered_keyframes_,
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output_path_,
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fps_,
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batch_size_,
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tracking_window_size_,
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minimum_patch_size_,
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num_iter_,
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guide_weight_,
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initialize_,
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],
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outputs=[output_path_, fps_, video_output_]
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)
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return [(ui_component, "FastBlend", "FastBlend_ui")]
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