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Путеводитель для диффузионок. Как заставить нейросети качественно редактировать изображения

Привет, Хабр! Меня зовут Вадим, я — младший научный сотрудник группы Controllable Generative AI лаборатории FusionBrain в AIRI. Последние несколько лет я занимаюсь исследованием генеративных моделей в контексте задачи редактирования фотографий. Мы с командой накопили большую экспертизу в этом и хотели бы поделиться ей.

Совсем недавно мы выложили препринт статьи, которую мы представим на ECCV этой осенью (сама статья, её код, demo на HuggingFace). Там мы предложили метод редактирования реальных изображений с помощью диффузионных моделей, который достигает лучшего среди всех методов компромисса между качеством редактирования и сохранением структуры исходного изображения, а также эффективен с вычислительной точки зрения. В данной статье я хотел бы рассказать о том, почему приходится делать такой выбор, и как мы эту проблему обошли. Приятного чтения!

Введение

Думаю, в 2024 году никого не нужно убеждать, что мы живем в эпоху победившего генеративного искусственного интеллекта, по крайней мере, в областях рисования изображений и написания текстов. Пока бизнес разбирается в том, заменят ли нейросети художников, появляются новые модели, а также совершенствуются старые.

Основных архитектур не так много: вариацонные автокодировщики (VAE), модели на основе потоков (Flow-based models), генеративно-состязательные сети (GAN) и, наконец, диффузионные модели. Последние демонстрируют высокое качество генерации, благодаря чему их активно используют в text-to-image задачах. Dall-E, Stable Diffusion, Midjourney — одни из самых выдающихся представителей диффузионок.

Но, несмотря на впечатляющие результаты, диффузионные модели всё еще сложно применить к задаче редактирования реальных изображений. Скажем, поменять цвет пиджака на вашей фотографии или заменить кошку собакой на семейном портрете сложнее, чем сгенерировать их. Основная проблема заключается в том, чтобы найти баланс между тем, чтобы отредактированное изображение отражало ожидаемые изменения и сохраняло исходную структуру, а также те части, которые редактировать не следует.

Для каждой архитектуры задача о редактировании решается по-своему. Не так давно мой коллега Денис Бобков выпустил большой и подробный разбор того, как мы это делаем для GAN на примере модификации модели StyleGAN. Здесь же я поделюсь достижениями нашей группы на ниве диффузионных моделей. Но для начала пару слов о том, что это вообще такое.

Диффузия

О принципе работы диффузионных моделей можно догадаться по их названию. Вспомним физику: если мы откроем флакон с духами, запах через какое-то время начнёт распространяться по воздуху. Это происходит из-за броуновского движения пахучих молекул. Хотя это движение хаотичное, частицы в среднем стремятся покинуть область с высокой концентрацией, то есть флакон — происходит диффузия.

Обратите внимание на то, как меняется вероятностное распределение молекул: сначала оно отражает некоторую пространственную структуру (внутренность флакона), но после вскрытия размывается, в пределе сходясь к константе по всему объёму. Это можно представить, как размытие информации в шум.

Источник: Gravelcliff / gifer.com

Если не касаться проблемы детерминизма, которому угрожает квантовая механика, и оставаться в рамках классических уравнений движения, то мы можем сопоставить друг другу начальное и конечное состояния газов, если будем рассуждать в терминах координат и скоростей всех-всех молекул в нашей задаче. Другими словами, остаётся теоретическая возможность восстановить начальное распределение из шума, хотя на практике такое реализовать невозможно из-за астрономического числа взаимодействующих тел.

Теперь перенесёмся в компьютерные науки. Представьте, что с самого начала у нас есть какое-то осмысленное изображение. Распределение пикселей при этом демонстрирует некоторую структуру. Затем мы проводим последовательность размытий с помощью гауссовых функций (молекулы в газе также диффундируют «по гауссу»), в результате чего картинка превращается в шум, а распределение становится случайным (как белый шум в телевизоре) — такую цепочку преобразований я буду называть forward траекторией.

С первого взгляда такое преобразование кажется бессмысленным, зачем из хорошей фотографии делать белый шум? Ответ кроется в том, что диффузионная модель способна обращать вспять данный процесс, то есть преобразовывать белый шум малыми шагами до высококачественного изображения (backward траектория).

Представьте, что у нас есть нейросеть, которая на вход принимает зашумленную картинку, а на выходе выдает шум, который необходимо вычесть из изображения, чтобы оно стало менее искаженным. Данная операция называется расшумлением. Чтобы научить модель расшумлять необходимо собрать огромное количество картинок. Сам процесс обучения нейросети заключается в том, что на каждом шаге мы случайным образом выбираем изображения нашего набора данных, зашумляем его и оптимизируем параметры нейросети так, чтобы предсказание нейросети при подаче зашумленного изображения было как можно ближе к исходному.

Теперь, получая на вход случайный шум, такая модель начнет поэтапно «извлекать» из чистого шума осмысленные изображения в духе обучающего датасета.

Источник: Vaibhav Singh et al. / learnopencv.com

В описанной выше схеме работы диффузионной модели на вход принимается только шум, и мы никак не можем контролировать финальный результат. Однако данную модель можно дополнить специальным модулем, который будет принимать на вход текстовое описание. Добавив такой модуль в процесс обучения нейросети мы сможем влиять на процесс генерации с помощью описаний (Вау!). Если вы хотите глубже погрузиться в тему с подробными математическими формулами, то можно это сделать в этой статье. А мы двигаемся дальше.

Постановка задачи и математика

Итак, теперь нам бы хотелось решать вот такую задачу: подать на вход модели картинку и текст, в котором сказано, что именно там нужно изменить. Существующие подходы к этой проблеме можно разделить на три основные группы.

Подходы из первой категории используют дообучение всей диффузионной модели на входное изображение, чтобы сохранить его структуру и все необходимые детали при редактировании. Хотя такая стратегия дает хорошие результаты, процесс оптимизации делает их очень длительными, что не позволяет применять их на практике. Примеры: тут, тут и тут.

Следующие две группы методов основываются на корректировке forward и backward траекторий. В одной упор делается на кэшировании промежуточных представлений во время этапа зашумления, далее данные представление во время процесса расшумления подставляются в промежуточные слои нейросети. Такой способ позволяет достаточно дешевым способом сохранить бóльшую часть изображения без дополнительных оптимизаций, откорректировав backward процесс. Главная беда этого подхода — отсутствие универсальности и высокая чувствительность к подбору гиперпараметров, из-за этого данный метод не позволяет получать стабильные результаты (примеры тут и тут).

Во последней же группе методов основная идея заключается в построении высококачественной реконструкции исходного изображением путем модификации алгоритма семплирования для backward и forward траекторий. Но основным недостатком все же является слабая способность к редактируемости (примеры тут и тут).

В целом, ситуация складывается такая, что нам приходится выбирать между скоростью метода, его универсальностью и итоговым качеством. Во время первых подходов к задаче мы выявили, что идеи построения точной реконструкции и кэширования промежуточных представлений достаточно изящно можно объединить в концепции гайдеров. С одной стороны такой метод лишается чувствительности к гиперпараметрам и становится более универсальным, а с другой — мы пропускаем этап создания идеальной реконструкции, что ускоряет итоговый результат.

Вернемся к началу и разберёмся, как работает диффузионная модель, более подробно.

Процесс зашумления

Пусть наши данные (картинки) поступают из распределения $x_0 \sim q(x)$ . Определим процесс зашумления, в котором каждый шаг добавляется небольшое количество гауссовского шума к изначальному изображению для каждого из шагов. Формально это выражается так:

$q(x_t | x_{t - 1}) = \mathcal{N}(x_t | \sqrt{1 - \beta_t}x_{t - 1}, \beta_t \mathbf{I}) \\ x_t = \sqrt{1 - \beta} x_{t - 1} + \sqrt{\beta} \epsilon \\ \epsilon \sim \mathcal{N}(0, \mathbf{I}), ~~~ \beta \in (0, 1).$

Зависимость x_t можно выразить иначе (это понадобится нам в дальнейшем):

$x_t \sim q(x_t | x_0) = \mathcal{N}(x_t | \sqrt{\bar{\alpha_t}}x_0, (1 - \bar{\alpha_t})\mathbf{I})\\ \bar{\alpha_t} = \prod\limits_{i=1}^t (1 - \beta_i).$

Тогда правдоподобие всей цепочки зашумления может быть выражено следующим образом:

$q(x_{1:T} | x_0) = \prod\limits_{t = 1}^T q(x_t|x_{t - 1}).$

Из формулы шага видно, что по мере увеличения шага коэффициент $\alpha_t$ стремится к 0, и, следовательно, стремится к нулю среднее x_t . При достаточно больших распределение превращается в распределение чистого шума. Теперь мы поняли, как математически можно описать этап зашумления, но что делать с расшумлением?

Цепочка преобразований данных в прямом и обратном диффузионном процессе. Источник: Ho et al. / arXiv, 2020 с дополнительными обозначениями.

Процесс расшумления

Формулы, описанные в предыдущей главе, позволяют из исходного изображения получить зашумленное. Чтобы произвести шаг расшумления, необходимо иметь оценку на обратный шаг:

$x_{t-1} \sim q(x_{t-1} | x_t)$

Однако на практике такое распределение оценить почти не представляется возможным, из-за этого интерес решению этой задачи невелик. Основным способом оценки данного распределения являются диффузионные модели (наконец-то мы дошли до них!). Введем такое распределение и параметризуем его нейросетью:

$p_\theta(x_{0:T}) = p(x_T) \prod\limits_{t=1}^T p_\theta(x_{t-1} | x_t), \\ p_\theta(x_{t-1} | x_t) = \mathcal{N}(x_{t - 1} | \mu_\theta(x_t, t), \mathbf\Sigma_\theta(x_t, t))$

Такая модель обучается следующим способом — каждую итерацию мы случайно выбираем из наших данных изображение ( x_0 ), генерируем случайный шум ( $\epsilon_t$ ), подаем в модель и делаем шаг оптимизации по обычной функции потерь — L_2 -лоссу.

$L_t = || \epsilon_t - \epsilon_\theta(x_t, t) ||_2 = \\ || \epsilon_t - \epsilon_\theta(\sqrt{\bar{\alpha_t}}x_0 + (1 - \bar{\alpha_t})\epsilon_t,~ t) ||_2$

Таким образом мы научим нейросеть правильно расшумлять изображения.

В данном введении я описал все достаточно высокоуровнево и без выводов, если хотите полноценно погрузиться в математику, которая стоит за данными моделями, то советую посетить данный ресурс.

Про наш метод

Начну издалека. Представим, что у нас есть такая функция $\nabla_x\log p(x)$ , и мы хотим обусловить распределение на . В этом нам поможет байесовский вывод:

$\nabla_x\log p(x | y) = \nabla_x\log\dfrac{p(y|x)p(x)}{p(y)} \\ = \nabla_x\log p(y|x) + \nabla_x\log p(x) - \nabla_x\log p(y) \\ = | \nabla_x\log p(y) = 0 | = \nabla_x\log p(x) + \nabla_x\log p(y|x)$

Другими словами, если мы имеем доступ к градиенту логарифма распределения для данных, то мы можем обусловить данный градиент на что угодно через нехитрое преобразование выше. Для чего же это нужно?

Вспомним диффузионные модели и редактирование на их основе. После того, как обучение модели завершено, нейросеть начинает предсказывать нам не что иное, как градиент логарифма правдоподобия данных $\nabla_x\log p(x)$ . Мы решили использовать это свойство и возможность обуславливать распределение через прием сверху, который называется guidance.

Вернусь на главу раньше и напомню, что изначально наше исследование было направлено на методы редактирования, основанные на кешировании внутренних представлений исходных изображений при forward процессе и подстановке их при редактировании на backward. А что, если не подставлять их, а использовать в качестве гайденса на backward траектории, подумали мы? И сделали!

План такой:

на forward этапе мы кэшируем внутренние представления диффузионной модели
на backward этапе (генерации) при каждом шаге расшумления мы модифицируем предсказание модели с помощью дополнительного слагаемого:

$\nabla_{x_t} \log \mathcal{N} (u_t |\hat{u}_t, x_t) = \nabla_{x_t} ||u_t(x_t) - \hat{u}_t||_2,$

где u_t — внутренние представления в момент генерации. Таким образом, вместо того, чтобы явно заменить u_t на $\hat{u}_t$ , как делали предшествующие методы, мы модифицируем траекторию генерации так, чтобы эти представления менее болезненным образом приближались друг к другу. Мы назвали этот метод Guide-and-Rescale.

Теперь перейдем к экспериментам.

Эксперименты

Для того, чтобы всесторонне сравнить наш подход с другими методами, мы выбрали 4 различных типа редактирования:

Локальное изменение внешности и одежды человека
Изменение эмоций человека
Замена одного животного на другое
Глобальная стилизация изображения

Для каждого типа мы собрали 20 примеров редактирования, на которых проводили тесты.

Мы также сравнивали методы исходя из того, насколько похожи отредактированные ими изображения на реальные. Например, мы превращали собак в кошек и оценивали схожесть результата с настоящими кошками в совокупности (FID). Для этой части экспериментов данных потребовалось больше: 500 примеров собак и кошек из датасета AFHQ.

В качестве конкурентов мы выбрали подходы, лежащие в отрытом доступе: NPI, NPI Prox и NTI, совмещенные с P2P, MasaCtrl, ProxMasaCtrl, PnP и EDICT. Мы использовали оригинальный код авторов с параметрами по умолчанию, рекомендованными в описании каждого метода. Для нашего метода мы также фиксировали гиперпараметры во время оценки.

Визуальное сравнение нашего метода с бейзлайнами при различных типах редактирования можно оценить ниже:

Наш подход показывает более стабильные результаты, чем существующие методы, и позволяет достичь лучшего компромисса между качеством редактирования и сохранением структуры исходного изображения.

Для количественной оценки эффективности нашего метода мы решили использовать протокол оценки, аналогичный тому, что применяется в EDICT. Этот протокол измеряет два ключевых свойства метода.

Первое — это степень, в которой результат метода содержит желаемую правку. Для измерения этой характеристики мы используем метрику CLIP, которая рассчитывает соответствие между отредактированным изображением и инструкцией для редактирования. Второе свойство — это степень, в которой метод сохраняет структуру и детали исходного изображения, которые не должны меняться в процессе редактирования. Для этого мы используем метрику LPIPS между исходным изображением и его отредактированной версией.

Результаты этих метрик для нашего метода и других приведены в Таблице 1. Они показывают, что наш метод достигает наилучшего баланса между сохранением структуры исходного изображения и степенью редактирования. По метрике LPIPS мы показали наилучшие результаты, за исключением метода EDICT, который мы значительно превзошли по показателю CLIP. По показателю CLIP мы также лучше всех справляемся с задачей, кроме метода PnP, который сильно уступает в способности сохранять структуру исходного изображения по метрике LPIPS. Остальные подходы показывают одинаково худшие результаты по этим двум метрикам.

По сравнению с наиболее продвинутыми методами EDICT и PnP наш метод более эффективен с вычислительной точки зрения, что видно по времени работы при редактировании изображения. Для количественной оценки расстояния между распределениями отредактированных и реальных изображений мы рассчитали FID Score на задаче преобразования собаки в кошку. Как показано в Таблице 1, наш метод превосходит остальные по FID Score. MasaCtrl и ProxMasaCtrl показывают самый высокий FID. Наиболее близкий к нашему методу показатель FID Score имеет PnP, но он работает в несколько раз медленнее нашего метода.

Таблица 1. Сравнение методов по метрикам.

Провели мы и опыты на людях (*зловещий смех*)! Для пользователей мы подготовили всё те же 4 типа заданий, в которых люди оценивали, насколько наш метод предпочтительнее других, в форме вопроса о парном сравнении «наш методов против другого». Мы опросили 62 пользователя, которые ответили в общей сложности на 960 вопросов. В каждом вопросе было показано исходное изображение, инструкция к редактированию и два результата. Пользователи должны были ответить на два вопроса. Первый вопрос (Q1) — «Какое изображение лучше всего соответствует желаемому описанию редактирования?». Второй вопрос (Q2) — «Какое изображение лучше всего сохраняет общую структуру „Оригинального изображения“?».

Результаты исследования пользователей представлены в Таблице 2. Каждое значение представляет собой процент пользователей, которые предпочли наш метод по сравнению с соответствующим бейзлайном в первом столбце. По качеству редактирования наш метод предпочтительнее почти всех бейзлайнов и субъективно схож с P2P + NPI Prox. Однако по качеству сохранения наш метод лучше, чем P2P + NPI Prox. Подводя итог, можно сказать, что наш метод устойчив в широком диапазоне различных редактирований и субъективно дает лучшие результаты по качеству редактирования при сохранении областей исходного изображения, которые не должны быть отредактированы.

Таблица 2. Исследование на пользователях.

Заключение

Хочется отметить, что, несмотря на лучший баланс, универсальность и стабильность, наш метод все еще не идеален: необходимость каждый раз считать градиент от картинок требует достаточно много памяти и времени. Это мы планируем изучить и исправить в будущих исследованиях.

Надеюсь, данное чтение было понятным, интересным и познавательным для вас! Если появились вопросы, то готов ответить на них в телеграмме @MACderRu!

Статья: https://arxiv.org/abs/2409.01322

Код статьи на Github: https://github.com/FusionBrainLab/Guide-and-Rescale

HuggingFace demo: https://huggingface.co/spaces/AIRI-Institute/Guide-and-Rescale

Сайт конференции ECCV: https://eccv2024.ecva.net

Список авторов: Вадим Титов, Мадина Халматова, Александра Иванова, Дмитрий Ветров, Айбек Аланов. Анализ смежных работ в разделах 1 и 2 был получен Айбеком Алановым при поддержке гранта для исследовательских центров в области ИИ, предоставленного Аналитическим центром при Правительстве РФ Центру ИИ НИУ ВШЭ.

В качестве дополнительной литературы о text-to-image задачах порекомендую статью на Хабре, посвященную выходу модели Kandinsky 3.0.

Для сотрудничества можно обращаться к руководителю лаборатории Fusion Brain Андрею Кузнецову и руководителю группы «Контролируемый генеративный ИИ» Айбеку Аланову.

Следите за новостями в каналах: AI Brain, CompleteAI, AbstractDL, Dendi Math&AI.

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6 сент. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

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I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
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