Delete assets/core.py

assets/core.py

@@ -1,906 +0,0 @@
-import os
-import cv2
-import glob
-import time
-import torch
-import shutil
-import argparse
-import platform
-import datetime
-import subprocess
-import insightface
-import onnxruntime
-import numpy as np
-import gradio as gr
-import threading
-import queue
-from tqdm import tqdm
-import concurrent.futures
-from moviepy.editor import VideoFileClip
-
-from face_swapper import Inswapper, paste_to_whole
-from face_analyser import detect_conditions, get_analysed_data, swap_options_list
-from face_parsing import init_parsing_model, get_parsed_mask, mask_regions, mask_regions_to_list
-from face_enhancer import get_available_enhancer_names, load_face_enhancer_model, cv2_interpolations
-from utils import trim_video, StreamerThread, ProcessBar, open_directory, split_list_by_lengths, merge_img_sequence_from_ref, create_image_grid
-
-## ------------------------------ USER ARGS ------------------------------
-
-parser = argparse.ArgumentParser(description="Swap-Mukham Face Swapper")
-parser.add_argument("--out_dir", help="Default Output directory", default=os.getcwd())
-parser.add_argument("--batch_size", help="Gpu batch size", default=32)
-parser.add_argument("--cuda", action="store_true", help="Enable cuda", default=False)
-parser.add_argument(
-    "--colab", action="store_true", help="Enable colab mode", default=False
-)
-user_args = parser.parse_args()
-
-## ------------------------------ DEFAULTS ------------------------------
-
-USE_COLAB = user_args.colab
-USE_CUDA = user_args.cuda
-DEF_OUTPUT_PATH = user_args.out_dir
-BATCH_SIZE = int(user_args.batch_size)
-WORKSPACE = None
-OUTPUT_FILE = None
-CURRENT_FRAME = None
-STREAMER = None
-DETECT_CONDITION = "best detection"
-DETECT_SIZE = 640
-DETECT_THRESH = 0.6
-NUM_OF_SRC_SPECIFIC = 10
-MASK_INCLUDE = [
-    "Skin",
-    "R-Eyebrow",
-    "L-Eyebrow",
-    "L-Eye",
-    "R-Eye",
-    "Nose",
-    "Mouth",
-    "L-Lip",
-    "U-Lip"
-]
-MASK_SOFT_KERNEL = 17
-MASK_SOFT_ITERATIONS = 10
-MASK_BLUR_AMOUNT = 0.1
-MASK_ERODE_AMOUNT = 0.15
-
-FACE_SWAPPER = None
-FACE_ANALYSER = None
-FACE_ENHANCER = None
-FACE_PARSER = None
-FACE_ENHANCER_LIST = ["NONE"]
-FACE_ENHANCER_LIST.extend(get_available_enhancer_names())
-FACE_ENHANCER_LIST.extend(cv2_interpolations)
-
-## ------------------------------ SET EXECUTION PROVIDER ------------------------------
-# Note: Non CUDA users may change settings here
-
-PROVIDER = ["CPUExecutionProvider"]
-
-if USE_CUDA:
-    available_providers = onnxruntime.get_available_providers()
-    if "CUDAExecutionProvider" in available_providers:
-        print("\n********** Running on CUDA **********\n")
-        PROVIDER = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-    else:
-        USE_CUDA = False
-        print("\n********** CUDA unavailable running on CPU **********\n")
-else:
-    USE_CUDA = False
-    print("\n********** Running on CPU **********\n")
-
-device = "cuda" if USE_CUDA else "cpu"
-EMPTY_CACHE = lambda: torch.cuda.empty_cache() if device == "cuda" else None
-
-## ------------------------------ LOAD MODELS ------------------------------
-
-def load_face_analyser_model(name="buffalo_l"):
-    global FACE_ANALYSER
-    if FACE_ANALYSER is None:
-        FACE_ANALYSER = insightface.app.FaceAnalysis(name=name, providers=PROVIDER)
-        FACE_ANALYSER.prepare(
-            ctx_id=0, det_size=(DETECT_SIZE, DETECT_SIZE), det_thresh=DETECT_THRESH
-        )
-
-
-def load_face_swapper_model(path="./assets/pretrained_models/inswapper_128.onnx"):
-    global FACE_SWAPPER
-    if FACE_SWAPPER is None:
-        batch = int(BATCH_SIZE) if device == "cuda" else 1
-        FACE_SWAPPER = Inswapper(model_file=path, batch_size=batch, providers=PROVIDER)
-
-
-def load_face_parser_model(path="./assets/pretrained_models/79999_iter.pth"):
-    global FACE_PARSER
-    if FACE_PARSER is None:
-        FACE_PARSER = init_parsing_model(path, device=device)
-
-
-load_face_analyser_model()
-load_face_swapper_model()
-
-## ------------------------------ MAIN PROCESS ------------------------------
-
-
-def process(
-    input_type,
-    image_path,
-    video_path,
-    directory_path,
-    source_path,
-    output_path,
-    output_name,
-    keep_output_sequence,
-    condition,
-    age,
-    distance,
-    face_enhancer_name,
-    enable_face_parser,
-    mask_includes,
-    mask_soft_kernel,
-    mask_soft_iterations,
-    blur_amount,
-    erode_amount,
-    face_scale,
-    enable_laplacian_blend,
-    crop_top,
-    crop_bott,
-    crop_left,
-    crop_right,
-    *specifics,
-):
-    global WORKSPACE
-    global OUTPUT_FILE
-    global PREVIEW
-    WORKSPACE, OUTPUT_FILE, PREVIEW = None, None, None
-
-    ## ------------------------------ GUI UPDATE FUNC ------------------------------
-
-    def ui_before():
-        return (
-            gr.update(visible=True, value=PREVIEW),
-            gr.update(interactive=False),
-            gr.update(interactive=False),
-            gr.update(visible=False),
-        )
-
-    def ui_after():
-        return (
-            gr.update(visible=True, value=PREVIEW),
-            gr.update(interactive=True),
-            gr.update(interactive=True),
-            gr.update(visible=False),
-        )
-
-    def ui_after_vid():
-        return (
-            gr.update(visible=False),
-            gr.update(interactive=True),
-            gr.update(interactive=True),
-            gr.update(value=OUTPUT_FILE, visible=True),
-        )
-
-    start_time = time.time()
-    total_exec_time = lambda start_time: divmod(time.time() - start_time, 60)
-    get_finsh_text = lambda start_time: f"✔️ Completed in {int(total_exec_time(start_time)[0])} min {int(total_exec_time(start_time)[1])} sec."
-
-    ## ------------------------------ PREPARE INPUTS & LOAD MODELS ------------------------------
-
-    
-
-    yield "### \n 💊 Loading face analyser model...", *ui_before()
-    load_face_analyser_model()
-
-    yield "### \n 👑 Loading face swapper model...", *ui_before()
-    load_face_swapper_model()
-
-    if face_enhancer_name != "NONE":
-        if face_enhancer_name not in cv2_interpolations:
-            yield f"### \n 🔮 Loading {face_enhancer_name} model...", *ui_before()
-        FACE_ENHANCER = load_face_enhancer_model(name=face_enhancer_name, device=device)
-    else:
-        FACE_ENHANCER = None
-
-    if enable_face_parser:
-        yield "### \n 🧲 Loading face parsing model...", *ui_before()
-        load_face_parser_model()
-
-    includes = mask_regions_to_list(mask_includes)
-    specifics = list(specifics)
-    half = len(specifics) // 2
-    sources = specifics[:half]
-    specifics = specifics[half:]
-    if crop_top > crop_bott:
-        crop_top, crop_bott = crop_bott, crop_top
-    if crop_left > crop_right:
-        crop_left, crop_right = crop_right, crop_left
-    crop_mask = (crop_top, 511-crop_bott, crop_left, 511-crop_right)
-
-    def swap_process(image_sequence):
-        ## ------------------------------ CONTENT CHECK ------------------------------
-
-    
-        yield "### \n 📡 Analysing face data...", *ui_before()
-        if condition != "Specific Face":
-            source_data = source_path, age
-        else:
-            source_data = ((sources, specifics), distance)
-        analysed_targets, analysed_sources, whole_frame_list, num_faces_per_frame = get_analysed_data(
-            FACE_ANALYSER,
-            image_sequence,
-            source_data,
-            swap_condition=condition,
-            detect_condition=DETECT_CONDITION,
-            scale=face_scale
-        )
-
-        ## ------------------------------ SWAP FUNC ------------------------------
-
-        yield "### \n ⚙️ Generating faces...", *ui_before()
-        preds = []
-        matrs = []
-        count = 0
-        global PREVIEW
-        for batch_pred, batch_matr in FACE_SWAPPER.batch_forward(whole_frame_list, analysed_targets, analysed_sources):
-            preds.extend(batch_pred)
-            matrs.extend(batch_matr)
-            EMPTY_CACHE()
-            count += 1
-
-            if USE_CUDA:
-                image_grid = create_image_grid(batch_pred, size=128)
-                PREVIEW = image_grid[:, :, ::-1]
-                yield f"### \n ⚙️ Generating face Batch {count}", *ui_before()
-
-        ## ------------------------------ FACE ENHANCEMENT ------------------------------
-
-        generated_len = len(preds)
-        if face_enhancer_name != "NONE":
-            yield f"### \n 📐 Upscaling faces with {face_enhancer_name}...", *ui_before()
-            for idx, pred in tqdm(enumerate(preds), total=generated_len, desc=f"Upscaling with {face_enhancer_name}"):
-                enhancer_model, enhancer_model_runner = FACE_ENHANCER
-                pred = enhancer_model_runner(pred, enhancer_model)
-                preds[idx] = cv2.resize(pred, (512,512))
-        EMPTY_CACHE()
-
-        ## ------------------------------ FACE PARSING ------------------------------
-
-        if enable_face_parser:
-            yield "### \n 🖇️ Face-parsing mask...", *ui_before()
-            masks = []
-            count = 0
-            for batch_mask in get_parsed_mask(FACE_PARSER, preds, classes=includes, device=device, batch_size=BATCH_SIZE, softness=int(mask_soft_iterations)):
-                masks.append(batch_mask)
-                EMPTY_CACHE()
-                count += 1
-
-                if len(batch_mask) > 1:
-                    image_grid = create_image_grid(batch_mask, size=128)
-                    PREVIEW = image_grid[:, :, ::-1]
-                    yield f"### \n ✏️ Face parsing Batch {count}", *ui_before()
-            masks = np.concatenate(masks, axis=0) if len(masks) >= 1 else masks
-        else:
-            masks = [None] * generated_len
-
-        ## ------------------------------ SPLIT LIST ------------------------------
-
-        split_preds = split_list_by_lengths(preds, num_faces_per_frame)
-        del preds
-        split_matrs = split_list_by_lengths(matrs, num_faces_per_frame)
-        del matrs
-        split_masks = split_list_by_lengths(masks, num_faces_per_frame)
-        del masks
-
-        ## ------------------------------ PASTE-BACK ------------------------------
-
-        yield "### \n 🛠️ Pasting back...", *ui_before()
-        def post_process(frame_idx, frame_img, split_preds, split_matrs, split_masks, enable_laplacian_blend, crop_mask, blur_amount, erode_amount):
-            whole_img_path = frame_img
-            whole_img = cv2.imread(whole_img_path)
-            blend_method = 'laplacian' if enable_laplacian_blend else 'linear'
-            for p, m, mask in zip(split_preds[frame_idx], split_matrs[frame_idx], split_masks[frame_idx]):
-                p = cv2.resize(p, (512,512))
-                mask = cv2.resize(mask, (512,512)) if mask is not None else None
-                m /= 0.25
-                whole_img = paste_to_whole(p, whole_img, m, mask=mask, crop_mask=crop_mask, blend_method=blend_method, blur_amount=blur_amount, erode_amount=erode_amount)
-            cv2.imwrite(whole_img_path, whole_img)
-
-        def concurrent_post_process(image_sequence, *args):
-            with concurrent.futures.ThreadPoolExecutor() as executor:
-                futures = []
-                for idx, frame_img in enumerate(image_sequence):
-                    future = executor.submit(post_process, idx, frame_img, *args)
-                    futures.append(future)
-
-                for future in tqdm(concurrent.futures.as_completed(futures), total=len(futures), desc="Pasting back"):
-                    result = future.result()
-
-        concurrent_post_process(
-            image_sequence,
-            split_preds,
-            split_matrs,
-            split_masks,
-            enable_laplacian_blend,
-            crop_mask,
-            blur_amount,
-            erode_amount
-        )
-
-
-    ## ------------------------------ IMAGE ------------------------------
-
-    if input_type == "Image":
-        target = cv2.imread(image_path)
-        output_file = os.path.join(output_path, output_name + ".png")
-        cv2.imwrite(output_file, target)
-
-        for info_update in swap_process([output_file]):
-            yield info_update
-
-        OUTPUT_FILE = output_file
-        WORKSPACE = output_path
-        PREVIEW = cv2.imread(output_file)[:, :, ::-1]
-
-        yield get_finsh_text(start_time), *ui_after()
-
-    ## ------------------------------ VIDEO ------------------------------
-
-    elif input_type == "Video":
-        temp_path = os.path.join(output_path, output_name, "sequence")
-        os.makedirs(temp_path, exist_ok=True)
-
-        yield "### \n 💽 Extracting video frames...", *ui_before()
-        image_sequence = []
-        cap = cv2.VideoCapture(video_path)
-        curr_idx = 0
-        while True:
-            ret, frame = cap.read()
-            if not ret:break
-            frame_path = os.path.join(temp_path, f"frame_{curr_idx}.jpg")
-            cv2.imwrite(frame_path, frame)
-            image_sequence.append(frame_path)
-            curr_idx += 1
-        cap.release()
-        cv2.destroyAllWindows()
-
-        for info_update in swap_process(image_sequence):
-            yield info_update
-
-        yield "### \n 🔗 Merging sequence...", *ui_before()
-        output_video_path = os.path.join(output_path, output_name + ".mp4")
-        merge_img_sequence_from_ref(video_path, image_sequence, output_video_path)
-
-        if os.path.exists(temp_path) and not keep_output_sequence:
-            yield "### \n 🚽 Removing temporary files...", *ui_before()
-            shutil.rmtree(temp_path)
-
-        WORKSPACE = output_path
-        OUTPUT_FILE = output_video_path
-
-        yield get_finsh_text(start_time), *ui_after_vid()
-
-    ## ------------------------------ DIRECTORY ------------------------------
-
-    elif input_type == "Directory":
-        extensions = ["jpg", "jpeg", "png", "bmp", "tiff", "ico", "webp"]
-        temp_path = os.path.join(output_path, output_name)
-        if os.path.exists(temp_path):
-            shutil.rmtree(temp_path)
-        os.mkdir(temp_path)
-
-        file_paths =[]
-        for file_path in glob.glob(os.path.join(directory_path, "*")):
-            if any(file_path.lower().endswith(ext) for ext in extensions):
-                img = cv2.imread(file_path)
-                new_file_path = os.path.join(temp_path, os.path.basename(file_path))
-                cv2.imwrite(new_file_path, img)
-                file_paths.append(new_file_path)
-
-        for info_update in swap_process(file_paths):
-            yield info_update
-
-        PREVIEW = cv2.imread(file_paths[-1])[:, :, ::-1]
-        WORKSPACE = temp_path
-        OUTPUT_FILE = file_paths[-1]
-
-        yield get_finsh_text(start_time), *ui_after()
-
-    ## ------------------------------ STREAM ------------------------------
-
-    elif input_type == "Stream":
-        pass
-
-
-## ------------------------------ GRADIO FUNC ------------------------------
-
-
-def update_radio(value):
-    if value == "Image":
-        return (
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        )
-    elif value == "Video":
-        return (
-            gr.update(visible=False),
-            gr.update(visible=True),
-            gr.update(visible=False),
-        )
-    elif value == "Directory":
-        return (
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-        )
-    elif value == "Stream":
-        return (
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=True),
-        )
-
-
-def swap_option_changed(value):
-    if value.startswith("Age"):
-        return (
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=True),
-        )
-    elif value == "Specific Face":
-        return (
-            gr.update(visible=False),
-            gr.update(visible=True),
-            gr.update(visible=False),
-        )
-    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
-
-
-def video_changed(video_path):
-    sliders_update = gr.Slider.update
-    button_update = gr.Button.update
-    number_update = gr.Number.update
-
-    if video_path is None:
-        return (
-            sliders_update(minimum=0, maximum=0, value=0),
-            sliders_update(minimum=1, maximum=1, value=1),
-            number_update(value=1),
-        )
-    try:
-        clip = VideoFileClip(video_path)
-        fps = clip.fps
-        total_frames = clip.reader.nframes
-        clip.close()
-        return (
-            sliders_update(minimum=0, maximum=total_frames, value=0, interactive=True),
-            sliders_update(
-                minimum=0, maximum=total_frames, value=total_frames, interactive=True
-            ),
-            number_update(value=fps),
-        )
-    except:
-        return (
-            sliders_update(value=0),
-            sliders_update(value=0),
-            number_update(value=1),
-        )
-
-
-def analyse_settings_changed(detect_condition, detection_size, detection_threshold):
-    yield "### \n 💡 Applying new values..."
-    global FACE_ANALYSER
-    global DETECT_CONDITION
-    DETECT_CONDITION = detect_condition
-    FACE_ANALYSER = insightface.app.FaceAnalysis(name="buffalo_l", providers=PROVIDER)
-    FACE_ANALYSER.prepare(
-        ctx_id=0,
-        det_size=(int(detection_size), int(detection_size)),
-        det_thresh=float(detection_threshold),
-    )
-    yield f"### \n ✔️ Applied detect condition:{detect_condition}, detection size: {detection_size}, detection threshold: {detection_threshold}"
-
-
-def stop_running():
-    global STREAMER
-    if hasattr(STREAMER, "stop"):
-        STREAMER.stop()
-        STREAMER = None
-    return "Cancelled"
-
-
-def slider_changed(show_frame, video_path, frame_index):
-    if not show_frame:
-        return None, None
-    if video_path is None:
-        return None, None
-    clip = VideoFileClip(video_path)
-    frame = clip.get_frame(frame_index / clip.fps)
-    frame_array = np.array(frame)
-    clip.close()
-    return gr.Image.update(value=frame_array, visible=True), gr.Video.update(
-        visible=False
-    )
-
-
-def trim_and_reload(video_path, output_path, output_name, start_frame, stop_frame):
-    yield video_path, f"### \n 🛠️ Trimming video frame {start_frame} to {stop_frame}..."
-    try:
-        output_path = os.path.join(output_path, output_name)
-        trimmed_video = trim_video(video_path, output_path, start_frame, stop_frame)
-        yield trimmed_video, "### \n ✔️ Video trimmed and reloaded."
-    except Exception as e:
-        print(e)
-        yield video_path, "### \n ❌ Video trimming failed. See console for more info."
-
-
-## ------------------------------ GRADIO GUI ------------------------------
-
-css = """
-footer{display:none !important}
-"""
-
-with gr.Blocks(css=css) as interface:
-    gr.Markdown("# 🧸 Deepfake Faceswap")
-    gr.Markdown("### 📥 insightface inswapper bypass NSFW.")
-    with gr.Row():
-        with gr.Row():
-            with gr.Column(scale=0.4):
-                with gr.Tab("⚖️ Swap Condition"):
-                    swap_option = gr.Dropdown(
-                        swap_options_list,
-                        info="Choose which face or faces in the target image to swap.",
-                        multiselect=False,
-                        show_label=False,
-                        value=swap_options_list[0],
-                        interactive=True,
-                    )
-                    age = gr.Number(
-                        value=25, label="Value", interactive=True, visible=False
-                    )
-
-                with gr.Tab("🎛️ Detection Settings"):
-                    detect_condition_dropdown = gr.Dropdown(
-                        detect_conditions,
-                        label="Condition",
-                        value=DETECT_CONDITION,
-                        interactive=True,
-                        info="This condition is only used when multiple faces are detected on source or specific image.",
-                    )
-                    detection_size = gr.Number(
-                        label="Detection Size", value=DETECT_SIZE, interactive=True
-                    )
-                    detection_threshold = gr.Number(
-                        label="Detection Threshold",
-                        value=DETECT_THRESH,
-                        interactive=True,
-                    )
-                    apply_detection_settings = gr.Button("Apply settings")
-
-                with gr.Tab("♻️ Output Settings"):
-                    output_directory = gr.Text(
-                        label="Output Directory",
-                        value=DEF_OUTPUT_PATH,
-                        interactive=True,
-                    )
-                    output_name = gr.Text(
-                        label="Output Name", value="Result", interactive=True
-                    )
-                    keep_output_sequence = gr.Checkbox(
-                        label="Keep output sequence", value=False, interactive=True
-                    )
-
-                with gr.Tab("💎 Other Settings"):
-                    face_scale = gr.Slider(
-                        label="Face Scale",
-                        minimum=0,
-                        maximum=2,
-                        value=1,
-                        interactive=True,
-                    )
-
-                    face_enhancer_name = gr.Dropdown(
-                        FACE_ENHANCER_LIST, label="Face Enhancer", value="NONE", multiselect=False, interactive=True
-                    )
-
-                    with gr.Accordion("Advanced Mask", open=False):
-                        enable_face_parser_mask = gr.Checkbox(
-                            label="Enable Face Parsing",
-                            value=False,
-                            interactive=True,
-                        )
-
-                        mask_include = gr.Dropdown(
-                            mask_regions.keys(),
-                            value=MASK_INCLUDE,
-                            multiselect=True,
-                            label="Include",
-                            interactive=True,
-                        )
-                        mask_soft_kernel = gr.Number(
-                            label="Soft Erode Kernel",
-                            value=MASK_SOFT_KERNEL,
-                            minimum=3,
-                            interactive=True,
-                            visible = False
-                        )
-                        mask_soft_iterations = gr.Number(
-                            label="Soft Erode Iterations",
-                            value=MASK_SOFT_ITERATIONS,
-                            minimum=0,
-                            interactive=True,
-
-                        )
-
-
-                    with gr.Accordion("Crop Mask", open=False):
-                        crop_top = gr.Slider(label="Top", minimum=0, maximum=511, value=0, step=1, interactive=True)
-                        crop_bott = gr.Slider(label="Bottom", minimum=0, maximum=511, value=511, step=1, interactive=True)
-                        crop_left = gr.Slider(label="Left", minimum=0, maximum=511, value=0, step=1, interactive=True)
-                        crop_right = gr.Slider(label="Right", minimum=0, maximum=511, value=511, step=1, interactive=True)
-
-
-                    erode_amount = gr.Slider(
-                            label="Mask Erode",
-                            minimum=0,
-                            maximum=1,
-                            value=MASK_ERODE_AMOUNT,
-                            step=0.05,
-                            interactive=True,
-                        )
-
-                    blur_amount = gr.Slider(
-                            label="Mask Blur",
-                            minimum=0,
-                            maximum=1,
-                            value=MASK_BLUR_AMOUNT,
-                            step=0.05,
-                            interactive=True,
-                        )
-
-                    enable_laplacian_blend = gr.Checkbox(
-                        label="Laplacian Blending",
-                        value=True,
-                        interactive=True,
-                    )
-
-
-                source_image_input = gr.Image(
-                    label="Source face", type="filepath", interactive=True
-                )
-
-                with gr.Box(visible=False) as specific_face:
-                    for i in range(NUM_OF_SRC_SPECIFIC):
-                        idx = i + 1
-                        code = "\n"
-                        code += f"with gr.Tab(label='({idx})'):"
-                        code += "\n\twith gr.Row():"
-                        code += f"\n\t\tsrc{idx} = gr.Image(interactive=True, type='numpy', label='Source Face {idx}')"
-                        code += f"\n\t\ttrg{idx} = gr.Image(interactive=True, type='numpy', label='Specific Face {idx}')"
-                        exec(code)
-
-                    distance_slider = gr.Slider(
-                        minimum=0,
-                        maximum=2,
-                        value=0.6,
-                        interactive=True,
-                        label="Distance",
-                        info="Lower distance is more similar and higher distance is less similar to the target face.",
-                    )
-
-                with gr.Group():
-                    input_type = gr.Radio(
-                        ["Image", "Video"],
-                        label="Target Type",
-                        value="Image",
-                    )
-
-                    with gr.Box(visible=True) as input_image_group:
-                        image_input = gr.Image(
-                            label="Target Image", interactive=True, type="filepath"
-                        )
-
-                    with gr.Box(visible=False) as input_video_group:
-                        vid_widget = gr.Video if USE_COLAB else gr.Text
-                        video_input = gr.Video(
-                            label="Target Video", interactive=True
-                        )
-                        with gr.Accordion("🎨 Trim video", open=False):
-                            with gr.Column():
-                                with gr.Row():
-                                    set_slider_range_btn = gr.Button(
-                                        "Set frame range", interactive=True
-                                    )
-                                    show_trim_preview_btn = gr.Checkbox(
-                                        label="Show frame when slider change",
-                                        value=True,
-                                        interactive=True,
-                                    )
-
-                                video_fps = gr.Number(
-                                    value=30,
-                                    interactive=False,
-                                    label="Fps",
-                                    visible=False,
-                                )
-                                start_frame = gr.Slider(
-                                    minimum=0,
-                                    maximum=1,
-                                    value=0,
-                                    step=1,
-                                    interactive=True,
-                                    label="Start Frame",
-                                    info="",
-                                )
-                                end_frame = gr.Slider(
-                                    minimum=0,
-                                    maximum=1,
-                                    value=1,
-                                    step=1,
-                                    interactive=True,
-                                    label="End Frame",
-                                    info="",
-                                )
-                            trim_and_reload_btn = gr.Button(
-                                "Trim and Reload", interactive=True
-                            )
-
-                    with gr.Box(visible=False) as input_directory_group:
-                        direc_input = gr.Text(label="Path", interactive=True)
-
-            with gr.Column(scale=0.6):
-                info = gr.Markdown(value="...")
-
-                with gr.Row():
-                    swap_button = gr.Button("🎯 Swap", variant="primary")
-                    cancel_button = gr.Button("❌ Cancel")
-
-                preview_image = gr.Image(label="Output", interactive=False)
-                preview_video = gr.Video(
-                    label="Output", interactive=False, visible=False
-                )
-
-                with gr.Row():
-                    output_directory_button = gr.Button(
-                        "💌", interactive=False, visible=False
-                    )
-                    output_video_button = gr.Button(
-                        "📽️", interactive=False, visible=False
-                    )
-
-                with gr.Box():
-                    with gr.Row():
-                        gr.Markdown(
-                            "### [🎭 Sponsor]"
-                        )
-                        gr.Markdown(
-                            "### [🖥️ Source code](https://huggingface.co/spaces/victorisgeek/SwapFace2Pon)"
-                        )
-                        gr.Markdown(
-                            "### [ 🧩 Playground](https://huggingface.co/spaces/victorisgeek/SwapFace2Pon)"
-                        )
-                        gr.Markdown(
-                            "### [📸 Run in Colab](https://colab.research.google.com/github/victorgeel/FaceSwapNoNfsw/blob/main/SwapFace.ipynb)"
-                        )
-                        gr.Markdown(
-                            "### [🤗 Modified Version](https://github.com/victorgeel/FaceSwapNoNfsw)"
-                        )
-
-    ## ------------------------------ GRADIO EVENTS ------------------------------
-
-    set_slider_range_event = set_slider_range_btn.click(
-        video_changed,
-        inputs=[video_input],
-        outputs=[start_frame, end_frame, video_fps],
-    )
-
-    trim_and_reload_event = trim_and_reload_btn.click(
-        fn=trim_and_reload,
-        inputs=[video_input, output_directory, output_name, start_frame, end_frame],
-        outputs=[video_input, info],
-    )
-
-    start_frame_event = start_frame.release(
-        fn=slider_changed,
-        inputs=[show_trim_preview_btn, video_input, start_frame],
-        outputs=[preview_image, preview_video],
-        show_progress=True,
-    )
-
-    end_frame_event = end_frame.release(
-        fn=slider_changed,
-        inputs=[show_trim_preview_btn, video_input, end_frame],
-        outputs=[preview_image, preview_video],
-        show_progress=True,
-    )
-
-    input_type.change(
-        update_radio,
-        inputs=[input_type],
-        outputs=[input_image_group, input_video_group, input_directory_group],
-    )
-    swap_option.change(
-        swap_option_changed,
-        inputs=[swap_option],
-        outputs=[age, specific_face, source_image_input],
-    )
-
-    apply_detection_settings.click(
-        analyse_settings_changed,
-        inputs=[detect_condition_dropdown, detection_size, detection_threshold],
-        outputs=[info],
-    )
-
-    src_specific_inputs = []
-    gen_variable_txt = ",".join(
-        [f"src{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)]
-        + [f"trg{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)]
-    )
-    exec(f"src_specific_inputs = ({gen_variable_txt})")
-    swap_inputs = [
-        input_type,
-        image_input,
-        video_input,
-        direc_input,
-        source_image_input,
-        output_directory,
-        output_name,
-        keep_output_sequence,
-        swap_option,
-        age,
-        distance_slider,
-        face_enhancer_name,
-        enable_face_parser_mask,
-        mask_include,
-        mask_soft_kernel,
-        mask_soft_iterations,
-        blur_amount,
-        erode_amount,
-        face_scale,
-        enable_laplacian_blend,
-        crop_top,
-        crop_bott,
-        crop_left,
-        crop_right,
-        *src_specific_inputs,
-    ]
-
-    swap_outputs = [
-        info,
-        preview_image,
-        output_directory_button,
-        output_video_button,
-        preview_video,
-    ]
-
-    swap_event = swap_button.click(
-        fn=process, inputs=swap_inputs, outputs=swap_outputs, show_progress=True
-    )
-
-    cancel_button.click(
-        fn=stop_running,
-        inputs=None,
-        outputs=[info],
-        cancels=[
-            swap_event,
-            trim_and_reload_event,
-            set_slider_range_event,
-            start_frame_event,
-            end_frame_event,
-        ],
-        show_progress=True,
-    )
-    output_directory_button.click(
-        lambda: open_directory(path=WORKSPACE), inputs=None, outputs=None
-    )
-    output_video_button.click(
-        lambda: open_directory(path=OUTPUT_FILE), inputs=None, outputs=None
-    )
-
-if __name__ == "__main__":
-    if USE_COLAB:
-        print("Running in colab mode")
-
-    interface.queue(concurrency_count=2, max_size=20).launch(share=USE_COLAB)