Upload core.py

assets/core.py

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+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)