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Н Новости

Как я обучал LoRA на стиле South Park для Flux: сбор кадров через MPV, особенности FluxGym и эксперименты с рангами

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Генеративные нейросети уже изменили мир цифрового искусства, но настоящая магия начинается, когда ты сам берешь их под контроль. Сегодня расскажу о своем эксперименте по обучению LoRA на стиле South Park — от сбора датасета до финальной модели. Поделюсь реальным опытом, техническими нюансами и самое главное — что конкретно сработало, а что оказалось пустой тратой времени.

Меня зовут Илья, я основатель онлайн-нейросети для создания изображений ArtGeneration.me, техноблогер и нейро-евангелист.

Идея обучить LoRA на стиле мультсериала пришла ко мне случайно. На глаза попался новый анимационный сериал «Ваш дружелюбный сосед Человек-паук», и я подумал: «Было бы классно обучить LoRA именно на этом стиле!». Я уже обучал LoRA на отдельных персонажах и простых стилях, но на таких сложных и комплексных особо ещё не тренировал.

Но стиль человека-паука показался мне слишком сложным для первого эксперимента такого рода. Решил сначала потренироваться на чем-то попроще. И тут удачно подвернулась спешл-серия South Park! Стиль South Park простой, узнаваемый, многие его любят (включая меня). На Civitai уже была одна LoRA South Park, так что я подумал — если смог кто-то другой, то и я смогу!

Спойлер: всё оказалось гораздо сложнее, чем я думал. Но обо всём по порядку.

❯ Как собрать датасет, не сдохнув от скуки

Первое, что нужно для обучения LoRA — качественный датасет. У меня была FullHD-серия South Park — идеальное качество для набора скриншотов. Осталось только придумать, как эти скриншоты делать быстро и удобно.

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Для просмотра видео я использую MPV. Раньше сидел на MPC-HC, но он стал подтормаживать на некоторых 4K рипах, так что я переехал на MPV. Он не только быстрее, но и поддерживает кучу всяких приколюшек типа скриптинга. Хотя для наших целей хватит и встроенной функции скриншотов.

Функция сохранения кадров в MPV активируется нажатием клавиши S (только на английской раскладке, что важно). Но чтобы не хранить скриншоты где попало, стоит настроить плеер. Создаём файл конфигурации по пути C:\Users\[имя_пользователя]\AppData\Roaming\mpv\mpv.conf (можно быстро перейти через Win+R → %APPDATA%\mpv → Enter). Если папки mpv нет – создайте её.

Вот содержимое файла mpv.conf:

screenshot-directory="C:/Users/user/Pictures/Screenshots/mpv"
screenshot-template="%F/%P"
screenshot-format=png
save-position-on-quit=yes
resume-playback=yes

(Замените user на ваше имя пользователя)

Что делает каждая строчка: screenshot-directory задаёт путь для скриншотов, screenshot-template определяет формат имени (где %F - имя видео, %P - позиция), screenshot-format выбирает PNG для лучшего качества, а две последние строки заставляют плеер запоминать где вы остановились и автоматически продолжать с этого места при следующем запуске. Таким образом мы решаем и проблему скриншотов, и вечный вопрос — «а где я остановился в прошлый раз?».

Вооружившись настроенным MPV, я посмотрел несколько серий South Park, нажимая S в ключевые моменты. Это, кстати, гораздо веселее, чем может показаться — сидишь себе, ржёшь над Картманом и заодно собираешь датасет.

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В итоге у меня набралось около 150 скриншотов. Но для качественного обучения мало просто надёргать кадров — нужно тщательно их отфильтровать: убрать смазанные кадры, выкинуть неудачные ракурсы, оставить только типичные для стиля примеры. Для тренировки LoRA на персонажа обычно достаточно ~30 изображений, а вот для стиля нужно больше — до 200. У меня осталось около 120 кадров после фильтрации.

❯ Подготовка изображений к обучению

Теперь встал вопрос обработки. Обучение модели будет проходить в разрешении 1024×1024, а мои скриншоты были другого формата. Без паники! Python-скрипт спешит на помощь!

Для тех, кто никогда не работал с Python, вот краткая инструкция: скачайте и установите Python с официального сайта, при установке поставьте галочку «Add Python to PATH», потом откройте командную строку (Win+R, введите «cmd») и выполните команду pip install pillow для установки библиотеки обработки изображений.

Теперь создайте текстовый файл с названием resize_images.py, вставьте в него код ниже, поместите файл в папку со скриншотами и запустите двойным кликом:

from PIL import Image
import os

# Создаем выходную директорию, если её нет
output_dir = "outputs"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

# Получаем все файлы изображений из текущей директории
image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff', '.webp']
image_files = []
for file in os.listdir('.'):
    if any(file.lower().endswith(ext) for ext in image_extensions) and os.path.isfile(file):
        image_files.append(file)

# Обрабатываем каждое изображение
for i, file in enumerate(image_files):
    try:
        # Открываем изображение
        img = Image.open(file)
        
        # Принудительно конвертируем в RGB (убираем прозрачность)
        img = img.convert('RGB')
        
        # Получаем размеры изображения
        width, height = img.size
        
        # Определяем новые размеры, сохраняя соотношение сторон
        if width > height:
            # Если ширина больше высоты, устанавливаем ширину = 1024
            new_width = 1024
            new_height = int(height * (new_width / width))
        else:
            # Если высота больше ширины, устанавливаем высоту = 1024
            new_height = 1024
            new_width = int(width * (new_height / height))
        
        # Изменяем размер изображения с сохранением пропорций
        resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
        
        # Сохраняем с порядковым номером в формате PNG с максимальным качеством
        output_path = os.path.join(output_dir, f"{i+1:03d}.png")
        resized_img.save(output_path, format='PNG', optimize=True, compress_level=0)
        
        print(f"Обработано {file} -> {output_path} ({new_width}x{new_height})")
        
    except Exception as e:
        print(f"Ошибка при обработке {file}: {e}")

print(f"Завершена обработка {len(image_files)} изображений.")

Этот скрипт делает несколько полезных вещей: сохраняет пропорции изображений, убирает прозрачность (чтобы не было проблем при обучении), нумерует файлы последовательно и оптимизирует PNG для лучшего качества. После запуска вы получите в папке outputs все обработанные изображения.

❯ Создание описаний для изображений

Следующий шаг — создание текстовых описаний (по-английски это называется captioning) для изображений. Нейросети учатся на парах «картинка + описание», и от качества описаний очень зависит результат.

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Если бы я делал LoRA для чего-то безобидного, то использовал бы Florence-2 от Microsoft. Эта модель шикарно описывает обычные сцены и довольно быстрая. Но с South Park ситуация сложнее — там NSFW-контент, который Florence-2 не сможет нормально обработать (стесняется).

Я перепробовал кучу инструментов для создания описаний и в итоге остановился на Joy Caption Alpha Two. Эта модель меня покорила тем, что в ней есть более 19 различных настроек, разные типы описаний (включая стили Midjourney и Fusion) и основа на визуальной языковой модели, что даёт более подробные и точные описания.

Но возникла проблема — Joy Caption обрабатывает изображения только по одному, а у меня их 120! Сначала я искал готовые решения для пакетной обработки, но нашел только несколько cli, которые у меня даже не запустились. Пришлось закатать рукава и сделать свою локальную версию, за одно прикрутил к ней перевод и пакетный режим обработки.

Несколько вечеров кодинга (и некоторое количество психованных удалений файлов) — и я смог сделать работающую локальную версию с мультирежимом. Я хорошо потрудился, чтобы превратить это в портативную версию, которая запускается даже на видеокартах с 12 ГБ памяти.

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Результатом стал JoyCaption Ultimate α2, который я выложил на канал Нейрософт, где публикуются репаки и портативные версии различных нейросетей. Моя модификация умеет генерировать описания в 9 разных режимах, поддерживает расширенные инструкции, разные стили и длину описаний, а также позволяет визуально проверить и исправить неудачные промпты в пакетном режиме.

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Обработка 120 изображений заняла около 5 минут на RTX 4090. Главное — результат получился отличный, с корректными описаниями всех особенностей стиля South Park. На выходе мы получаем папку с результатами, в которой лежат все картинки и у каждой есть txt файл с промптом.

❯ Запуск обучения в FluxGym

Теперь, когда у меня был готовый датасет с картинками и описаниями, можно было приступать к самому обучению. Для этого я использовал FluxGym, установленный через Pinokio.

Pinokio — это удобный инсталлер для различных нейросетей. Установка проста: заходите на сайт, скачиваете, запускаете. Через интерфейс Pinokio находите FluxGym, жмёте Install, ждёте загрузки компонентов — и вуаля, у вас есть рабочий инструмент для обучения LoRA специально под модель Flux.

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После запуска FluxGym появляется окошко с настройками. Я закинул свой датасет и настроил такие параметры:

  • Ранг: 4 (мне казалось, что для простого стиля South Park этого достаточно)

  • Эпохи: 13

  • Повторения: 10

  • Генерация сэмплов каждые 500 шагов

  • Добавил параметры --w 1280 --h 768 --s 20 для настройки превью, чтобы они генерировались с нормальным разрешением, а не стандартным 512×512

Запустил обучение и стал с нетерпением ждать результатов... И тут произошёл первый шок.

❯ Дневник Роршаха, 4 апреля: результаты разваливались на глазах

Результаты были... мягко говоря, неутешительными. Фоны получались более-менее нормальными, но персонажи — просто ужас какой-то. Месиво из десятков одинаковых Картманов, наложенных друг на друга, деформированные лица, непонятные конечности.

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«Нет, это не очередной трип Паркера и Стоуна, это были мои плохо натренированные LoRA», — думал я, глядя на эту цифровую какофонию. Раньше я тренировал LoRA на персонажах, и никаких проблем не возникало. Почему же сейчас всё пошло не так?

❯ Эксперименты с параметрами обучения

Гуглинг подсказал, что для стилей, возможно, нужен более высокий ранг LoRA. Это влияет на глубину обучения — чем выше ранг, тем глубже модель может изучить особенности стиля.

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Я решил попробовать обучение с рангом 128. Результаты стали лучше, но объем модели раздулся до полутора гигабайт! Решил попробовать компромиссный вариант: ранг 64, при котором LoRA весит примерно 500-600 МБ, что уже приемлемо.

Также я кардинально снизил скороть обучения. По умолчанию в FluxGym используется --learning_rate 8e-4, а я уменьшил до --learning_rate 2e-4, то есть в 4 раза. Это должно было предотвратить переобучение, но увеличило время тренировки. Вместо нескольких часов обучение заняло почти полдня. Но ради качества можно и подождать.

В процессе экспериментов я также пришел к выводу, что лучше ставить количество повторов равным 1, а желаемую продолжительность обучения регулировать числом эпох. Это даёт бóльшую гибкость и упрощает анализ результатов.

❯ Звёздный дневник, 38 мая 3.057 года: переученность видна невооружённым глазом

Примерно на 260-й эпохе я начал замечать явные признаки переобучения — множество зрачков в глазах персонажей, смазанные формы, снижение качества изображений. Пора было останавливаться. К тому времени обучение шло уже около суток.

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Я решил остановиться на 255-й эпохе, и у меня получилось 90 файлов моделей с разных этапов обучения. Но как теперь понять, какая из них лучшая?

Автоматизация тестирования моделей

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Для начала я написал простой скрипт, который создал мне список всех файлов в папке:

import os
from datetime import datetime

# Получаем текущую папку, откуда запущен скрипт
current_folder = os.getcwd()

# Имя выходного файла
output_file = 'file_list.txt'

# Открываем файл для записи
with open(output_file, 'w', encoding='utf-8') as f:
    f.write(f"Список файлов в текущей папке: {current_folder}\n")
    f.write(f"Дата: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
    f.write("-" * 50 + "\n\n")
    
    # Получаем список всех файлов в текущей папке и подпапках
    for root, dirs, files in os.walk(current_folder):
        # Получаем относительный путь от текущей папки
        rel_path = os.path.relpath(root, current_folder)
        
        if rel_path != '.':
            f.write(f"\nПапка: {rel_path}\n")
        else:
            f.write("Текущая папка:\n")
        
        # Записываем все файлы из этой папки
        for file in sorted(files):
            f.write(f"  {file}\n")

print(f"Список файлов сохранен в {output_file}")

Затем скормив в ЛЛМ список, я составил последовательность для тестирования с равномерной выборкой примерно каждой 30-й эпохи:

<lora:sp64-000003:1>, <lora:sp64-000030:1>, <lora:sp64-000060:1>, <lora:sp64-000087:1>, <lora:sp64-000114:1>, <lora:sp64-000144:1>, <lora:sp64-000171:1>, <lora:sp64-000198:1>, <lora:sp64-000228:1>, <lora:sp64-000255:1>

Для тестирования я использовал Stable Diffusion WebUI Forge. Хоть этот форк A1111 и устаревает, он всё ещё удобен для многих задач. Особенно круто в нём работает скрипт X/Y/Z plot, который позволяет автоматически протестировать разные LoRA и получить наглядную таблицу.

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Я использовал функцию PROMPT S/R (Search and Replace), чтобы автоматически перебрать все варианты LoRA. Для тестов я использовал такие промпты:

Digital drawing in South Park style A fat boy sits astride a cow, with a red barn behind him
Digital drawing in South Park style a policeman is sitting in a strip club, a naked stripper is showing her breasts on stage.
Digital drawing in South Park style tricycle chase, fat boy with glasses rides after red-haired boy in green ushanka hat, cinematic

Результаты тестирования, я небрежно сложил на онлайн доску, можно посмотреть.

❯ Ошибка новичка при тестировании

После первых тестов я понял свою глупую ошибку — я тестировал на промте, похожем на те, что были в моём датасете! Так делать нельзя, ведь это не показывает реальную гибкость модели.

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Я составил более короткие и совершенно другие промпты, и результаты оказались ГОРАЗДО лучше. Оказывается, проблема была не в моделях, а в моём тестировании!

❯ Финальный раунд экспериментов и открытия

После нескольких циклов тестирования я обнаружил несколько интересных закономерностей:

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LoRA на ранге 128 выглядит интереснее, чем на ранге 4 — она глубже изучает стиль и не так топорно его применяет. Высокий ранг позволяет модели улавливать более сложные и нюансированные особенности стиля.

Чем ниже ранг, тем быстрее происходит переобучение, что было для меня сюрпризом. Я ожидал, что модели с высоким рангом будут быстрее переобучаться из-за большего количества параметров. На практике оказалось наоборот — высокий ранг позволяет обучаться более «аккуратно», с меньшим риском жесткой фиксации на обучающих примерах.

В итоге я остановился на ранге 64 и эпохе 114, которая дала лучший баланс стилизации без переобучения. Удивительно, но это только примерно треть от всего обучения — более поздние эпохи давали признаки переобучения.

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Я проверил эту модель с разными весами (0.8 и 1.2), чтобы убедиться, что LoRA достаточно гибкая и работает предсказуемо при разных значениях. Результаты меня порадовали — даже при весе 0.8 стиль South Park был хорошо узнаваем, а при 1.2 не появлялись артефакты переобучения.

❯ Ключевые уроки из моего эксперимента

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Обучение LoRA на стили оказалось гораздо сложнее, чем я предполагал. Вот главные уроки, которые я извлек:

Для стилей нужен гораздо более высокий ранг, чем для персонажей. Если для персонажа часто хватает ранга 4-8, то для стиля лучше ставить 64-128. Это связано с тем, что стиль — это комплексный набор визуальных особенностей, которые затрагивают множество аспектов изображения.

c29a2df4266f5cd5da92454c7b5461cb.png

Чем ниже скорость обучения, тем более плавно происходит обучение, хотя и дольше. Для сложных стилей лучше уменьшить скорость в 3-4 раза от рекомендуемой и запастись терпением. Результат того стоит — меньше шансов получить переобученную модель.

Оптимальные эпохи часто находятся примерно в первой трети всего обучения. У меня лучший результат дала эпоха 114 из 255. Не бойтесь останавливать обучение раньше, если видите признаки переобучения.

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Никогда (серьезно, НИКОГДА) не тестируйте LoRA на промтах, похожих на те, что были в датасете! Это даст вам ложное представление о качестве модели. Тестировать нужно на новых, совершенно других промтах, чтобы проверить гибкость и универсальность обученной LoRA.

❯ Итоговый результат

67091cbfdbf8bf5709b2f1c90c6ea098.png

Готовую LoRA я опубликовал на Civitai: South Park Style Flux LoRA.

Все примеры и тесты можно посмотреть на доске: Примеры и тесты.

Обучение LoRA на стили оказалось намного сложнее, чем я ожидал, но результат того стоил. Теперь я гораздо лучше понимаю процесс и готов браться за более сложные стили — возможно, даже за тот самый сериал про человека-паука, который изначально меня вдохновил.

4fa978819698119f3dfa45ea7fc1314e.png

А какие LoRA вы обучали? Делитесь своим опытом в комментариях!

Я рассказываю больше о нейросетях у себя на YouTube, в телеграм и на Бусти. Буду рад вашей подписке и поддержке. Всех обнял и удачных генераций.


Новости, обзоры продуктов и конкурсы от команды Timeweb.Cloud — в нашем Telegram-канале

Опробовать ↩

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  • 14.10.25 08:46 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: 📧 Email: [email protected] 📱 Telegram: @Capitalcryptorecover Contact: [email protected] 📞 Call/Text: +1 (336) 390-6684 🌐 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 14.10.25 08:46 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: 📧 Email: [email protected] 📱 Telegram: @Capitalcryptorecover Contact: [email protected] 📞 Call/Text: +1 (336) 390-6684 🌐 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 14.10.25 08:46 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: 📧 Email: [email protected] 📱 Telegram: @Capitalcryptorecover Contact: [email protected] 📞 Call/Text: +1 (336) 390-6684 🌐 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.10.25 18:07 crypto

    Cryptocurrency's digital realm presents many opportunities, but it also conceals complex frauds. It is quite painful to lose your cryptocurrency to scam. You can feel harassed and lost as a result. If you have been the victim of a cryptocurrency scam, this guide explains what to do ASAP. Following these procedures will help you avoid further issues or get your money back. Communication with Marie ([email protected] and WhatsApp: +1 7127594675) can make all the difference.

  • 15.10.25 21:52 harristhomas7376

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