from .runners import AccurateModeRunner, FastModeRunner, BalancedModeRunner, InterpolationModeRunner, InterpolationModeSingleFrameRunner
from .data import VideoData, get_video_fps, save_video, search_for_images
import os
import gradio as gr
def check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder):
frames_guide = VideoData(video_guide, video_guide_folder)
frames_style = VideoData(video_style, video_style_folder)
message = ""
if len(frames_guide) < len(frames_style):
message += f"The number of frames mismatches. Only the first {len(frames_guide)} frames of style video will be used.\n"
frames_style.set_length(len(frames_guide))
elif len(frames_guide) > len(frames_style):
message += f"The number of frames mismatches. Only the first {len(frames_style)} frames of guide video will be used.\n"
frames_guide.set_length(len(frames_style))
height_guide, width_guide = frames_guide.shape()
height_style, width_style = frames_style.shape()
if height_guide != height_style or width_guide != width_style:
message += f"The shape of frames mismatches. The frames in style video will be resized to (height: {height_guide}, width: {width_guide})\n"
frames_style.set_shape(height_guide, width_guide)
return frames_guide, frames_style, message
def smooth_video(
video_guide,
video_guide_folder,
video_style,
video_style_folder,
mode,
window_size,
batch_size,
tracking_window_size,
output_path,
fps,
minimum_patch_size,
num_iter,
guide_weight,
initialize,
progress = None,
):
# input
frames_guide, frames_style, message = check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder)
if len(message) > 0:
print(message)
# output
if output_path == "":
if video_style is None:
output_path = os.path.join(video_style_folder, "output")
else:
output_path = os.path.join(os.path.split(video_style)[0], "output")
os.makedirs(output_path, exist_ok=True)
print("No valid output_path. Your video will be saved here:", output_path)
elif not os.path.exists(output_path):
os.makedirs(output_path, exist_ok=True)
print("Your video will be saved here:", output_path)
frames_path = os.path.join(output_path, "frames")
video_path = os.path.join(output_path, "video.mp4")
os.makedirs(frames_path, exist_ok=True)
# process
if mode == "Fast" or mode == "Balanced":
tracking_window_size = 0
ebsynth_config = {
"minimum_patch_size": minimum_patch_size,
"threads_per_block": 8,
"num_iter": num_iter,
"gpu_id": 0,
"guide_weight": guide_weight,
"initialize": initialize,
"tracking_window_size": tracking_window_size,
}
if mode == "Fast":
FastModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
elif mode == "Balanced":
BalancedModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
elif mode == "Accurate":
AccurateModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path)
# output
try:
fps = int(fps)
except:
fps = get_video_fps(video_style) if video_style is not None else 30
print("Fps:", fps)
print("Saving video...")
video_path = save_video(frames_path, video_path, num_frames=len(frames_style), fps=fps)
print("Success!")
print("Your frames are here:", frames_path)
print("Your video is here:", video_path)
return output_path, fps, video_path
class KeyFrameMatcher:
def __init__(self):
pass
def extract_number_from_filename(self, file_name):
result = []
number = -1
for i in file_name:
if ord(i)>=ord("0") and ord(i)<=ord("9"):
if number == -1:
number = 0
number = number*10 + ord(i) - ord("0")
else:
if number != -1:
result.append(number)
number = -1
if number != -1:
result.append(number)
result = tuple(result)
return result
def extract_number_from_filenames(self, file_names):
numbers = [self.extract_number_from_filename(file_name) for file_name in file_names]
min_length = min(len(i) for i in numbers)
for i in range(min_length-1, -1, -1):
if len(set(number[i] for number in numbers))==len(file_names):
return [number[i] for number in numbers]
return list(range(len(file_names)))
def match_using_filename(self, file_names_a, file_names_b):
file_names_b_set = set(file_names_b)
matched_file_name = []
for file_name in file_names_a:
if file_name not in file_names_b_set:
matched_file_name.append(None)
else:
matched_file_name.append(file_name)
return matched_file_name
def match_using_numbers(self, file_names_a, file_names_b):
numbers_a = self.extract_number_from_filenames(file_names_a)
numbers_b = self.extract_number_from_filenames(file_names_b)
numbers_b_dict = {number: file_name for number, file_name in zip(numbers_b, file_names_b)}
matched_file_name = []
for number in numbers_a:
if number in numbers_b_dict:
matched_file_name.append(numbers_b_dict[number])
else:
matched_file_name.append(None)
return matched_file_name
def match_filenames(self, file_names_a, file_names_b):
matched_file_name = self.match_using_filename(file_names_a, file_names_b)
if sum([i is not None for i in matched_file_name]) > 0:
return matched_file_name
matched_file_name = self.match_using_numbers(file_names_a, file_names_b)
return matched_file_name
def detect_frames(frames_path, keyframes_path):
if not os.path.exists(frames_path) and not os.path.exists(keyframes_path):
return "Please input the directory of guide video and rendered frames"
elif not os.path.exists(frames_path):
return "Please input the directory of guide video"
elif not os.path.exists(keyframes_path):
return "Please input the directory of rendered frames"
frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)]
keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)]
if len(frames)==0:
return f"No images detected in {frames_path}"
if len(keyframes)==0:
return f"No images detected in {keyframes_path}"
matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes)
max_filename_length = max([len(i) for i in frames])
if sum([i is not None for i in matched_keyframes])==0:
message = ""
for frame, matched_keyframe in zip(frames, matched_keyframes):
message += frame + " " * (max_filename_length - len(frame) + 1)
message += "--> No matched keyframes\n"
else:
message = ""
for frame, matched_keyframe in zip(frames, matched_keyframes):
message += frame + " " * (max_filename_length - len(frame) + 1)
if matched_keyframe is None:
message += "--> [to be rendered]\n"
else:
message += f"--> {matched_keyframe}\n"
return message
def check_input_for_interpolating(frames_path, keyframes_path):
# search for images
frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)]
keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)]
# match frames
matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes)
file_list = [file_name for file_name in matched_keyframes if file_name is not None]
index_style = [i for i, file_name in enumerate(matched_keyframes) if file_name is not None]
frames_guide = VideoData(None, frames_path)
frames_style = VideoData(None, keyframes_path, file_list=file_list)
# match shape
message = ""
height_guide, width_guide = frames_guide.shape()
height_style, width_style = frames_style.shape()
if height_guide != height_style or width_guide != width_style:
message += f"The shape of frames mismatches. The rendered keyframes will be resized to (height: {height_guide}, width: {width_guide})\n"
frames_style.set_shape(height_guide, width_guide)
return frames_guide, frames_style, index_style, message
def interpolate_video(
frames_path,
keyframes_path,
output_path,
fps,
batch_size,
tracking_window_size,
minimum_patch_size,
num_iter,
guide_weight,
initialize,
progress = None,
):
# input
frames_guide, frames_style, index_style, message = check_input_for_interpolating(frames_path, keyframes_path)
if len(message) > 0:
print(message)
# output
if output_path == "":
output_path = os.path.join(keyframes_path, "output")
os.makedirs(output_path, exist_ok=True)
print("No valid output_path. Your video will be saved here:", output_path)
elif not os.path.exists(output_path):
os.makedirs(output_path, exist_ok=True)
print("Your video will be saved here:", output_path)
output_frames_path = os.path.join(output_path, "frames")
output_video_path = os.path.join(output_path, "video.mp4")
os.makedirs(output_frames_path, exist_ok=True)
# process
ebsynth_config = {
"minimum_patch_size": minimum_patch_size,
"threads_per_block": 8,
"num_iter": num_iter,
"gpu_id": 0,
"guide_weight": guide_weight,
"initialize": initialize,
"tracking_window_size": tracking_window_size
}
if len(index_style)==1:
InterpolationModeSingleFrameRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path)
else:
InterpolationModeRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path)
try:
fps = int(fps)
except:
fps = 30
print("Fps:", fps)
print("Saving video...")
video_path = save_video(output_frames_path, output_video_path, num_frames=len(frames_guide), fps=fps)
print("Success!")
print("Your frames are here:", output_frames_path)
print("Your video is here:", video_path)
return output_path, fps, video_path
def on_ui_tabs():
with gr.Blocks(analytics_enabled=False) as ui_component:
with gr.Tab("Blend"):
gr.Markdown("""
# Blend
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.
""")
with gr.Row():
with gr.Column():
with gr.Tab("Guide video"):
video_guide = gr.Video(label="Guide video")
with gr.Tab("Guide video (images format)"):
video_guide_folder = gr.Textbox(label="Guide video (images format)", value="")
with gr.Column():
with gr.Tab("Style video"):
video_style = gr.Video(label="Style video")
with gr.Tab("Style video (images format)"):
video_style_folder = gr.Textbox(label="Style video (images format)", value="")
with gr.Column():
output_path = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of style video")
fps = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps")
video_output = gr.Video(label="Output video", interactive=False, show_share_button=True)
btn = gr.Button(value="Blend")
with gr.Row():
with gr.Column():
gr.Markdown("# Settings")
mode = gr.Radio(["Fast", "Balanced", "Accurate"], label="Inference mode", value="Fast", interactive=True)
window_size = gr.Slider(label="Sliding window size", value=15, minimum=1, maximum=1000, step=1, interactive=True)
batch_size = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True)
tracking_window_size = gr.Slider(label="Tracking window size (only for accurate mode)", value=0, minimum=0, maximum=10, step=1, interactive=True)
gr.Markdown("## Advanced Settings")
minimum_patch_size = gr.Slider(label="Minimum patch size (odd number)", value=5, minimum=5, maximum=99, step=2, interactive=True)
num_iter = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True)
guide_weight = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True)
initialize = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True)
with gr.Column():
gr.Markdown("""
# Reference
* Output directory: the directory to save the video.
* Inference mode
|Mode|Time|Memory|Quality|Frame by frame output|Description|
|-|-|-|-|-|-|
|Fast|■|■■■|■■|No|Blend the frames using a tree-like data structure, which requires much RAM but is fast.|
|Balanced|■■|■|■■|Yes|Blend the frames naively.|
|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.|
* 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.
* Batch size: a larger batch size makes the program faster but requires more VRAM.
* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough.
* Advanced settings
* Minimum patch size (odd number): the minimum patch size used for patch matching. (Default: 5)
* Number of iterations: the number of iterations of patch matching. (Default: 5)
* Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10)
* NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity)
""")
btn.click(
smooth_video,
inputs=[
video_guide,
video_guide_folder,
video_style,
video_style_folder,
mode,
window_size,
batch_size,
tracking_window_size,
output_path,
fps,
minimum_patch_size,
num_iter,
guide_weight,
initialize
],
outputs=[output_path, fps, video_output]
)
with gr.Tab("Interpolate"):
gr.Markdown("""
# Interpolate
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.
""")
with gr.Row():
with gr.Column():
with gr.Row():
with gr.Column():
video_guide_folder_ = gr.Textbox(label="Guide video (images format)", value="")
with gr.Column():
rendered_keyframes_ = gr.Textbox(label="Rendered keyframes (images format)", value="")
with gr.Row():
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)
video_guide_folder_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames)
rendered_keyframes_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames)
with gr.Column():
output_path_ = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of rendered keyframes")
fps_ = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps")
video_output_ = gr.Video(label="Output video", interactive=False, show_share_button=True)
btn_ = gr.Button(value="Interpolate")
with gr.Row():
with gr.Column():
gr.Markdown("# Settings")
batch_size_ = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True)
tracking_window_size_ = gr.Slider(label="Tracking window size", value=0, minimum=0, maximum=10, step=1, interactive=True)
gr.Markdown("## Advanced Settings")
minimum_patch_size_ = gr.Slider(label="Minimum patch size (odd number, larger is better)", value=15, minimum=5, maximum=99, step=2, interactive=True)
num_iter_ = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True)
guide_weight_ = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True)
initialize_ = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True)
with gr.Column():
gr.Markdown("""
# Reference
* Output directory: the directory to save the video.
* Batch size: a larger batch size makes the program faster but requires more VRAM.
* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough.
* Advanced settings
* Minimum patch size (odd number): the minimum patch size used for patch matching. **This parameter should be larger than that in blending. (Default: 15)**
* Number of iterations: the number of iterations of patch matching. (Default: 5)
* Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10)
* NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity)
""")
btn_.click(
interpolate_video,
inputs=[
video_guide_folder_,
rendered_keyframes_,
output_path_,
fps_,
batch_size_,
tracking_window_size_,
minimum_patch_size_,
num_iter_,
guide_weight_,
initialize_,
],
outputs=[output_path_, fps_, video_output_]
)
return [(ui_component, "FastBlend", "FastBlend_ui")]