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.gitattributes

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+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

app.py

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+import os
+import cv2
+import numpy as np
+from PIL import Image
+import urllib.request
+import gradio as gr
+import insightface
+from insightface.app import FaceAnalysis
+from gfpgan import GFPGANer
+
+# --- Загрузка моделей при первом старте ---
+def download_file(url: str, filename: str):
+    if not os.path.exists(filename):
+        print(f"Скачиваю {filename} из {url}...")
+        urllib.request.urlretrieve(url, filename)
+        print(f"{filename} скачан.")
+
+# Пути к моделям
+INSWAPPER_URL = "https://huggingface.co/ezioruan/inswapper_128.onnx/resolve/main/inswapper_128.onnx"
+INSWAPPER_PATH = "inswapper_128.onnx"
+GFPGAN_URL = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth"
+GFPGAN_PATH = "GFPGANv1.3.pth"
+
+download_file(INSWAPPER_URL, INSWAPPER_PATH)
+download_file(GFPGAN_URL, GFPGAN_PATH)
+
+assert os.path.exists(INSWAPPER_PATH), "inswapper_128.onnx не найден."
+assert os.path.exists(GFPGAN_PATH), "GFPGANv1.3.pth не найден."
+
+# --- Инициализация моделей ---
+try:
+    import torch
+    has_gpu = torch.cuda.is_available()
+except Exception:
+    has_gpu = False
+
+ctx_id = 0 if has_gpu else -1  # 0 = GPU, -1 = CPU
+print("GPU доступен" if has_gpu else "Работаем на CPU")
+
+providers = ['CPUExecutionProvider']  # Только CPU для бесплатного тарифа
+
+app_face = FaceAnalysis(name="buffalo_l", providers=providers)
+app_face.prepare(ctx_id=ctx_id, det_size=(640, 640))
+
+swapper = insightface.model_zoo.get_model(INSWAPPER_PATH, providers=providers)
+face_enhancer = GFPGANer(
+    model_path=GFPGAN_PATH,
+    upscale=1,
+    arch="clean",
+    channel_multiplier=2,
+    bg_upsampler=None,
+)
+
+MAX_PREVIEWS = 8  # Максимум маленьких превью
+
+# --- Тексты для локализации ---
+TEXTS = {
+    "ru": {
+        "lang_radio_label": "Язык / Language",
+        "title_md": "# FaceSwap Pro\nСлева — фото **донора**, справа — фото, где нужно заменить лица.",
+        "step1_title": "### 1. Фото донора",
+        "step1_input_label": "Загрузите фото донора (может быть несколько лиц)",
+        "step1_donor_choice_label": "Выберите лицо-донора",
+        "step2_title": "### 2. Фото, которое меняем",
+        "step2_input_label": "Загрузите изображение для замены лиц",
+        "step2_target_choices_label": "Выберите лица для замены (если ничего не выбрано — меняем все)",
+        "step3_title": "### 3. Настройки и экспорт",
+        "use_enh_label": "Улучшить качество результата (GFPGAN)",
+        "eta_initial": "Оценка времени появится после обнаружения лиц.",
+        "fmt_label": "Формат файла для скачивания",
+        "run_btn": "Запустить замену",
+        "download_btn": "Скачать результат",
+        "before_after_label": "До / После",
+        "eta_fmt_sec": "Оценочное время обработки: ~{sec} сек.",
+        "eta_fmt_min": "Оценочное время обработки: ~{min} мин {sec} сек.",
+        "msg_need_images": "Загрузите оба изображения, чтобы начать обработку.",
+        "msg_no_donor_faces": "На фото донора не найдено ни одного лица.",
+        "msg_no_target_faces": "На целевом фото не найдено ни одного лица.",
+        "msg_prep": "Подготовка к обработке...",
+        "msg_swap_step": "Замена лица {i} из {n}",
+        "msg_enh": "Улучшение качества (GFPGAN)...",
+        "msg_done": "Готово. Обработано лиц: {n}.",
+        "progress_done": "Готово!",
+        "donor_option": "Донор {i}",
+        "target_option": "Лицо {i}",
+    },
+    "en": {
+        "lang_radio_label": "Language / Язык",
+        "title_md": "# FaceSwap Pro\nLeft — **donor photo**, right — photo where faces will be replaced.",
+        "step1_title": "### 1. Donor photo",
+        "step1_input_label": "Upload donor photo (can contain multiple faces)",
+        "step1_donor_choice_label": "Choose donor face",
+        "step2_title": "### 2. Target photo",
+        "step2_input_label": "Upload image where faces will be replaced",
+        "step2_target_choices_label": "Choose faces to replace (if none selected — replace all)",
+        "step3_title": "### 3. Settings & Export",
+        "use_enh_label": "Enhance result quality (GFPGAN)",
+        "eta_initial": "Time estimate will appear after detecting faces.",
+        "fmt_label": "Download file format",
+        "run_btn": "Start swapping",
+        "download_btn": "Download result",
+        "before_after_label": "Before / After",
+        "eta_fmt_sec": "Estimated processing time: ~{sec} sec.",
+        "eta_fmt_min": "Estimated processing time: ~{min} min {sec} sec.",
+        "msg_need_images": "Please upload both images to start.",
+        "msg_no_donor_faces": "No faces detected on donor image.",
+        "msg_no_target_faces": "No faces detected on target image.",
+        "msg_prep": "Preparing for processing...",
+        "msg_swap_step": "Replacing face {i} of {n}",
+        "msg_enh": "Enhancing quality (GFPGAN)...",
+        "msg_done": "Done. Faces processed: {n}.",
+        "progress_done": "Done!",
+        "donor_option": "Donor {i}",
+        "target_option": "Face {i}",
+    },
+}
+
+def T(lang: str, key: str, **kwargs) -> str:
+    s = TEXTS[lang][key]
+    return s.format(**kwargs)
+
+# --- Основная логика обработки ---
+def detect_faces_generic(img):
+    if img is None:
+        return [], []
+    bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    faces = app_face.get(bgr)
+    faces = sorted(faces, key=lambda f: f.bbox[0])
+    previews = []
+    for f in faces:
+        x1, y1, x2, y2 = map(int, f.bbox)
+        h, w = bgr.shape[:2]
+        x1 = max(0, x1); y1 = max(0, y1)
+        x2 = min(w, x2); y2 = min(h, y2)
+        crop = bgr[y1:y2, x1:x2]
+        previews.append(cv2.cvtColor(crop, cv2.COLOR_BGR2RGB))
+    return previews, faces
+
+def update_donor_faces(img, lang: str):
+    previews, faces = detect_faces_generic(img)
+    updates = []
+    for i in range(MAX_PREVIEWS):
+        if i < len(previews):
+            updates.append(gr.update(value=previews[i], visible=True))
+        else:
+            updates.append(gr.update(value=None, visible=False))
+    labels = [T(lang, "donor_option", i=i + 1) for i in range(len(previews))]
+    default = labels[0] if labels else None
+    radio_update = gr.update(choices=labels, value=default)
+    return updates + [radio_update, faces]
+
+def update_target_faces(img, use_enhancer: bool, lang: str):
+    previews, faces = detect_faces_generic(img)
+    updates = []
+    for i in range(MAX_PREVIEWS):
+        if i < len(previews):
+            updates.append(gr.update(value=previews[i], visible=True))
+        else:
+            updates.append(gr.update(value=None, visible=False))
+    labels = [T(lang, "target_option", i=i + 1) for i in range(len(previews))]
+    default = labels if labels else []
+    checkbox_update = gr.update(choices=labels, value=default)
+    eta_text = estimate_time(lang, len(faces), use_enhancer)
+    return updates + [checkbox_update, faces, eta_text]
+
+def estimate_time(lang: str, num_faces: int, use_enhancer: bool) -> str:
+    if num_faces <= 0:
+        return TEXTS[lang]["eta_initial"]
+    base_per_face = 4 if has_gpu else 10
+    extra_per_face = 6 if use_enhancer else 0
+    total = num_faces * (base_per_face + extra_per_face)
+    if total < 60:
+        return T(lang, "eta_fmt_sec", sec=int(total))
+    m = int(total // 60)
+    s = int(total % 60)
+    return T(lang, "eta_fmt_min", min=m, sec=s)
+
+# --- Интерфейс Gradio ---
+custom_css = """
+.gradio-container { max-width: 1200px; margin: 0 auto; }
+.step-card { background: radial-gradient(circle at top left, #0b1220, #020617); border-radius: 18px; padding: 18px 20px; }
+.face-thumb img { width: 100%; height: 100%; object-fit: cover; border-radius: 8px; }
+.progress-ring { width: 120px; height: 120px; border-radius: 50%; display: flex; align-items: center; justify-content: center; }
+.progress-ring-text { font-size: 14px; color: #e5e7eb; text-align: center; }
+"""
+
+premium_theme = gr.themes.Default(
+    primary_hue="violet",
+    neutral_hue="slate",
+).set(
+    body_text_size="16px",
+    button_large_padding="12px 22px",
+)
+
+with gr.Blocks(title="FaceSwap Pro", css=custom_css) as demo:
+    lang_state = gr.State("ru")
+    lang_radio = gr.Radio(choices=["RU", "EN"], value="RU", label="Язык / Language")
+    title_md = gr.Markdown(TEXTS["ru"]["title_md"])
+
+    with gr.Row():
+        with gr.Column():
+            donor_img = gr.Image(label="Фото донора", type="numpy")
+            donor_previews = [gr.Image(visible=False, interactive=False, width=64, height=64) for _ in range(MAX_PREVIEWS)]
+            donor_choice = gr.Radio(label="Выберите лицо-донора", choices=[])
+        with gr.Column():
+            target_img = gr.Image(label="Целевое фото", type="numpy")
+            target_previews = [gr.Image(visible=False, interactive=False, width=64, height=64) for _ in range(MAX_PREVIEWS)]
+            target_choices = gr.CheckboxGroup(label="Выберите лица для замены", choices=[])
+
+    with gr.Row():
+        use_enh = gr.Checkbox(label="Улучшить качество результата (GFPGAN)", value=True)
+        fmt = gr.Dropdown(label="Формат файла для скачивания", choices=["png", "jpeg", "webp"], value="png")
+        run_btn = gr.Button("Запустить замену", variant="primary")
+        download_btn = gr.DownloadButton("Скачать результат")
+
+    result_img = gr.Image(label="Результат", interactive=False)
+    status_md = gr.Markdown("")
+    before_after = gr.Gallery(label="До / После", columns=2)
+
+    def switch_language(choice):
+        lang = "ru" if choice == "RU" else "en"
+        t = TEXTS[lang]
+        return (
+            lang,
+            gr.update(value=t["title_md"]),
+            gr.update(label=t["step1_input_label"]),
+            gr.update(label=t["step1_donor_choice_label"]),
+            gr.update(label=t["step2_input_label"]),
+            gr.update(label=t["step2_target_choices_label"]),
+            gr.update(label=t["use_enh_label"]),
+            gr.update(label=t["fmt_label"]),
+            gr.update(value=t["run_btn"]),
+            gr.update(value=t["download_btn"]),
+        )
+
+    lang_radio.change(
+        fn=switch_language,
+        inputs=lang_radio,
+        outputs=[
+            lang_state,
+            title_md,
+            donor_img,
+            donor_choice,
+            target_img,
+            target_choices,
+            use_enh,
+            fmt,
+            run_btn,
+            download_btn,
+        ],
+    )
+
+    donor_img.change(
+        fn=update_donor_faces,
+        inputs=[donor_img, lang_state],
+        outputs=donor_previews + [donor_choice],
+    )
+
+    target_img.change(
+        fn=update_target_faces,
+        inputs=[target_img, use_enh, lang_state],
+        outputs=target_previews + [target_choices],
+    )
+
+demo.launch(
+    share=False,
+    ssr_mode=False,
+    theme=premium_theme,
+    css=custom_css
+)

packages.txt

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+libglib2.0-0
+libgl1

requirements.txt

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+torch==2.4.1
+torchvision==0.19.1
+gradio==4.36.1
+insightface==0.7.3
+onnxruntime==1.16.3
+opencv-python-headless==4.8.1.78
+gfpgan==1.3.8
+basicsr==1.3.4.2
+facexlib==0.3.0
+Pillow==10.3.0
+huggingface_hub==0.23.0