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Обзор современных подходов персонализации диффузионных нейронных сетей

Задача персонализации text-to-image модели состоит в донастройке предобученной нейронной сети так, чтобы она могла генерировать изображения заданного объекта в выбранных сценах. Несмотря на то, что подходы к решению этой задачи существуют, для их применения в высоконагруженных системах необходимо разобраться с рядом проблем: большим временем дообучения, высокими требованиями к видеопамяти, неспособностью точно захватывать детали целевого объекта и др.

Меня зовут Сергей Михайлин. Я разработчик группы машинного обучения в ОК. В данной статье я привожу обзор современных подходов к персонализации text-to-image моделей на базе открытой архитектуры Stable Diffision, делюсь техническими подробностями каждого подхода и анализирую их применимость в реальных высоконагруженных системах. Также, на основании собственных экспериментов по персонализации text-to-image моделей, я дам список возникающих при решении этой задачи проблем и перспективных способов их решения.

Введение

За счет применения механизма диффузии большие text-to-image модели [1, 2, 3] совершили резкий скачок в развитии, чем спровоцировали революцию в создании нового контента. Модели способны генерировать изображения в разных стилях и с разными объектами, основываясь на описании. Естественным образом возникла задача персонализации таких моделей, цель которой – донастройка модели для генерации изображений, на которых представлены объекты, которых нет в обучающей выборке исходной модели. Например, можно заставить нейронную сеть сгенерировать фотографию конкретного человека в костюме космонавта. Чтобы это сделать, необходимо дообучить модель на фотографиях этого человека. Но на практике количество фотографий целевых объектов невелико, поэтому провести полноценное дообучение невозможно.

Я рассмотрю наиболее известные подходы к персонализации, а также их улучшенные версии: Textual Inversion [4], DreamBooth [5], Low-Rank Adaptation (LoRA) [6], Encoder for Tuning (E4T) [7], HyperDreamBooth [8], чтобы определить применимость каждого метода на практике, их плюсы и минусы, а также составить общий список известных проблем и способов их решения.

Диффузионный метод генерации изображений

Начнем с диффузионного подхода к генерации изображений и модели Stable Diffusion, персонализацию которой реализуют разные методы.

Диффузионные вероятностные модели

Диффузионные нейронные сети Dθ – это вероятностные генеративные модели, которые приобрели популярность благодаря возможности генерировать качественные изображения. Они учатся приближать распределение исходных данных, итеративно очищая изображения от гауссовского шума. Для этого на исходное изображение x постепенно накладывается шум, после чего за T шагов происходит обратный процесс очистки:

\begin{equation*}  L(x, c, t) = \|D_{\theta}(z_{x, t}, c) - x\|^2_2 \end{equation*}

Исходное изображение постепенно зашумляется x→zx,t согласно планировщику шума (например, DDIM [9]), управляющим воздействием на который является случайная величина t∼U(0,T). При этом, чем больше t – тем больше наложено шума:

\begin{equation*}  \begin{aligned}   z_{x, t = 0} &= x \\   z_{x, t \rightarrow T} &\sim \mathcal{N}(0, I).  \end{aligned} \end{equation*}

Во время генерации модель сэмплирует случайный вектор zt∼N(0,I) и, итеративно “очищая” его от шума, производит новое изображение.

Stable Diffusion

Архитектура Stable Diffusion
Архитектура Stable Diffusion

Архитектура Stable Diffusion – это реализация диффузионной модели с открытым исходным кодом и предобученными весами. Она состоит из трех блоков: Pixel Space, Latent Space, Conditioning. Рассмотрим каждый из них.

  • Pixel Space. На вход модели во время обучения поступает целевое изображение x большого размера (512×512). Оперировать в таком большом пространстве вычислительно дорого, поэтому исходное изображение сжимается кодировщиком (encoder) до меньшего размера (64×64) в карту признаков, в пространстве которой происходят все последующие манипуляции. Для получения финального результата генерации применяется декодер D, который переводит сжатое представление в исходное x: 64×64→512×512.

  • Latent Space. Сжатое представление изображения зашумляется за T последовательных шагов. После этого модель UNet [10] итеративно удаляет этот шум, пытаясь восстановить из полного шума исходное изображение. Для этого ей сообщается текущая степень зашумления timestamp и вспомогательное описание prompt исходного изображения. На стадии генерации изображения шум проходит через одну и ту же модель T раз. Результатом постепенной очистки “шума от шума” является финальное изображение.

  • Conditioning. Блок кодирования вспомогательной информации. Text-To-Image подход использует для этого текст, который далее передается в cross-attention [11] слои UNet модели для поиска наложенного шума. Здесь может кодироваться любая информация – главное, чтобы она была связана с исходным изображением.

Textual Inversion

Единственный вход, с помощью которого можно управлять генерацией – это текстовые данные (prompt). Наивный подход заключается в том, чтобы очень качественно описать объект, который нас интересует. Например, “Высокий мужчина спортивного телосложения, кудрявые волосы, европейской внешности, ...”. Проблема такого подхода в том, что описать до мелких деталей каждый объект почти невозможно. Может понадобиться очень длинное описание, но архитектурой Stable Diffusion оно ограничено 77 токенами. Кроме того, некоторое количество токенов нужно, чтобы описать сцену, в которой хотим видеть объект.

Поэтому предлагается [4] свести описание объекта до минимально возможного размера, но сохранить его максимально точным. Эту идею можно реализовать, найдя векторное представление (embedding) в существующем текстовом пространстве, которое максимально точно соответствует целевому объекту.

Дообучение методом Textual Inversion. Для токена S∗ создан соответствующий обучаемый вектор v∗ – в нем кодируется описание целевого объекта.
Дообучение методом Textual Inversion. Для токена S∗ создан соответствующий обучаемый вектор v∗ – в нем кодируется описание целевого объекта.

Для поиска такого представления требуется несколько предварительных шагов:

  1. Выберем некоторую последовательность символов (например, [tag]) и заведем для нее отдельный токен в Tokenizer. Этот токен будет соответствовать целевому объекту. Важно, чтобы он не был представлен в словаре модели.

  2. Для нового токена создадим embedding в пространстве существующего Text Encoder. Этот вектор будет хранить выученное описание целевого объекта.

Далее запускается несколько итераций обучения на изображениях целевого объекта. При этом в Text Encoder подается фраза, содержащая созданный токен, например, a photo of [tag]. Чтобы предотвратить переобучение на конкретную фразу, рекомендуется использовать несколько похожих по смыслу, но разных предложений: a closeup photo of [tag], a picture of [tag] и так далее. Важно следить, чтобы веса остальных слоев и векторы других слов не менялись, так как обучается только один embedding, соответствующий объекту генерации.

В результате обучения получается искомый вектор, который кодирует конкретный объект. Теперь для генерации можно добавить выбранный тег в тестовое описание и получить изображение с желаемым объектом. Описание желательно составлять в следующей форме: a photo of [tag] <описание сцены>.

К достоинствам textual inversion можно отнести простоту подхода и низкий шанс переобучения. Размер обучаемых весов мал, поэтому для дообучения достаточно 8 гигабайт видеопамяти. Кроме того, можно обучать сразу несколько тегов для разных объектов. Недостатки этого подхода в том, что он требует много времени для дообучения и часто “теряет” детали целевого объекта на сгенерированном изображении.

DreamBooth

Основная проблема подхода Textual Inversion – плохая сохраняемость деталей. Возможная причина в том, что полученное текстовое пространство недостаточно гибкое и вместимое для описания целевого объекта. Так происходит из-за того, что обучается единственное представление целевого объекта, а представления всех остальных токенов остаются замороженными.

Дообучение методом DreamBooth
Дообучение методом DreamBooth

Чтобы расширить пространство до необходимого размера, авторы подхода DreamBooth [5] предлагают обучать сразу всю сеть, за исключением части, которая кодирует входное изображение, и части, которая декодирует выход UNet. При этом авторы, так же как и в Textual Inversion, используют конкретную форму текстового описания объекта: a photo of [tag] [class], где [tag] – последовательность символов, обозначающая объект, [class] – название класса, описывающего наш объект, например, для человека – person, для кошки – cat. Добавленное значение [class] “подсказывает” сети тип генерируемого объекта – так быстрее достигается сходимость сети и изображения получаются более качественными.

Важно, какой тег используется для целевого объекта. Если попытаться обучаться на объект, описывая его существующим в словаре модели токеном, то изменить предобученное значение вектора может быть сложно. Причем, чем “сильнее” слово (эмоциональнее, значимее), тем больше будет проблем. Так, используя для целевого объекта токен blue (a photo of blue person), можно получить нужный объект, но синего цвета. Поэтому значение тега важно выбирать аккуратно: можно попробовать инициализацию нового вектора – как в Textual Inversion.

Чтобы исключить переобучение новой модели Dθ ̂, вводится Prior Preservation Loss, который предотвращает катастрофическое забывание [12]. Идея подхода в том, чтобы сгенерировать исходной моделью несколько изображений, соответствующих конкретному описанию, и при дообучении требовать у модели восстанавливать эти изображения как можно точнее:

\begin{equation} \label{eq:pploss} L(x_t, c_t, x_p, c_p, t) = \|D_{\hat{\theta}}(x_t + \epsilon_t, c_t) - x_t\|^2_2 + \lambda \|D_\theta(x_p + \epsilon_t, c_p) - x_p\|^2_2 \end{equation}

Формально процедура обучения в случае DreamBoth выглядит таким образом:

  1. Берем исходную Stable Diffusion с предобученными весами Dθ.

  2. Замораживаем все веса.

  3. Выбираем любое (из уже существующих в словаре) подходящее слово, обозначающее класс целевого объекта.

  4. Подаем фразу cp вида a photo of [class] в нашу сеть и запоминаем сгенерированные результаты xp.

  5. Размораживаем все веса.

  6. Подаем на вход сети изображение объекта xt c описанием ct вида a photo of [tag] [class] и рассчитываем значение loss-функции.

  7. Подаем на вход сети данные из п.4 (xp, cp) и рассчитываем значение loss-функции.

  8. Используя уравнение (1), суммируем значения функции потерь из п.6 и п.7, рассчитываем градиенты и обновляем веса.

Повторяем шаги 6-8 до сходимости сети. На практике такое дообучение требует 1300 итераций.

Главным достоинством метода является высокоточный перенос объекта в генерируемое изображение. Среди недостатков: опасность переобучить веса, долгое время дообучения модели, большой размер хранимых весов, высокие требования к свободной видеопамяти (надо до 30 Гб).

Low-Rank Adaptation

В описанных выше подходах есть принципиальное “противостояние” – выбор между способностью переноса деталей и размером обновляемых весов модели. Подход LoRA (Low Rank Adaptation) [6] предлагает компромисс –– низкий размер весов, но некоторые детали могут быть потеряны.

Вместо того, чтобы модифицировать описание генерируемого изображения, предлагается обучить матрицу поправок для cross- и self-attention весов модели UNet. Эта поправка будет “сдвигать” промежуточные карты признаков для генерации необходимого изображения. Изначальные веса матрицы W0 замораживаются, а в качестве поправки Wδ обучаются две матрицы меньшего ранга, перемножение которых дает матрицу размерности, как у W0. Выходы слоев, полученных с помощью параметров W0 и Wδ, складываются.

Формально решение выглядит так:

\begin{align*} &y = W x, \, W \in \mathbb{R}^{n \times m} \\ &W = W_0 x + W_{\delta}x, \, W_0, W_\delta \in \mathbb{R}^{n \times m} \\ &W_{\delta} = BA, \, B \in \mathbb{R}^{n \times r}, \, A \in \mathbb{R}^{r \times m}, \, r \ll min(n, m), \\ &y = W_0 x + B A x \end{align*}

Таким образом, генерация менее требовательна к видеопамяти. Из-за того, что матрицы A и B низкого ранга – в памяти нужно держать гораздо меньше информации о градиентах. Поскольку поправки к изначальной матрице весов W0 применяются простым суммированием, при правильной реализации заменять Wδ можно “на ходу”. Это может быть полезно в системах, где необходим механизм переключения дообученных моделей, соответствующих разным целевым объектам.

Метод LoRA вносит правки в изображение на промежуточных этапах генерации, поэтому его можно использовать и для других задач: замены черт лица у всех отрисованных людей (например, сделать все глаза больше), использования конкретного стиля и так далее. На практике метод LoRA часто применяется совместно с другими подходами – так получается достичь лучших результатов с меньшим потреблением видеопамяти.

К плюсам метода LoRA можно отнести низкие требования к видеопамяти, низкий размер хранимых весов, возможность обучения для наложения различных эффектов на изображение и совместимость с другими методами дообучения. Недостатками подхода является неудовлетворительная переносимость деталей целевого объекта и продолжительное время дообучения (около 20 минут на каждый целевой объект).

Encoder for Tuning

Авторы оригинальной Textual Inversion предложили улучшенный метод персонализации диффузии – Encoder for Tuning (E4T) [7]. Говоря о качестве, стоит уточнить – официальная реализация не была опубликована на момент написания статьи, но результаты, представленные в статье, превосходят предыдущие подходы в способности сохранения деталей изначального объекта.

Подход Encoder for Tuning
Подход Encoder for Tuning

В этом подходе генерация состоит из двух этапов:

  1. Манипуляции с входными данными.

  2. Обучение сдвигов весов.

Манипуляции с входными данными. Авторы не просто обучают модель на конкретных изображениях целевого объекта, а предлагают подход, который сможет работать для любого объекта с минимальным дообучением. Для этого представление объекта специальным образом готовится и подается в модель вместе с текстовым представлением описания изображения.

Создание вектора объекта происходит в Word Encoder – дополнительном новом модуле сети – и подразумевает несколько этапов:

  1. Обучение на большом датасете “общего представления”, которое описывает класс целевого объекта.

  2. Извлечение признаков целевого объекта и преобразование к размерности “общего представления”.

  3. Суммирование значений из п.1 и п.2 с весом λ, для реализации необходимого сдвига от общего описания к конкретному.

Для получения признаков целевого объекта авторы используют следующий алгоритм:

  1. Фото целевого объекта пропускается через энкодер OpenCLIP ViT-H/14 [13]. Сохраняются промежуточные карты признаков каждого второго слоя.

  2. Эти признаки подаются в линейные слои.

  3. Зашумленное изображение объекта пропускается через кодировщик UNet Stable Diffusion.

  4. Признаки из п.2 и п.3 конкатенируются и пропускаются через финальный обучаемый линейный слой.

Таким образом, авторы комбинируют представления двух сетей –– CLIP и UNet –– с целью сохранения семантики объекта (общий вид, национальность, раса, цвет волос) и его “структурных” свойств (форма волос, шрамы, расположение бровей, глаз, губ и другие отличительные черты). Полученный вектор подается в Stable Diffusion вместе с остальным текстовым описанием по аналогии с Textual Inversion.

Обучение сдвигов весов. Авторы провели 50 обучений Stable Diffusion на разных входных данных и заметили, что сильнее всего меняются матрицы WQ, WK, WO в cross-attention слоях. Поэтому они предлагают дообучать только эти части сети для исключения рисков переобучения и быстрой сходимости. Предлагается обучить “сдвиг” Wδ для каждой из матриц, который вычисляется следующим образом:

\begin{equation*}  \begin{aligned}   v_0 &\in \mathbb{R}^1, \\   l_1 &\in \mathbb{R}^{M \times 1}, \\   l_2 &\in \mathbb{R}^{1 \times N}, \\   l_3 &\in \mathbb{R}^{M \times M}, \\   l_4 &\in \mathbb{R}^{N \times N}   \end{aligned} \end{equation*}\begin{equation*}  \begin{aligned}  v_1 &= v_0 l_1, \, v_1 \in \mathbb{R}^{M \times 1} \\  v_2 &= v_0 l_2, \, v_2 \in \mathbb{R}^{1 \times N} \\  \end{aligned} \end{equation*}\begin{equation*}  \begin{aligned}   W_1 &= v_1 v_2^T, \, W_1 \in \mathbb{R}^{M \times N} \\   W_2 &= W_1^T l_3^T, \, W_2 \in \mathbb{R}^{N \times M} \\   W_\delta &= W_2^T l_4^T, \, W_\delta \in \mathbb{R}^{M \times N} \\   W &= W_0 (1 + W_\delta)  \end{aligned} \end{equation*}

Генерация происходит в три шага:

  1. Предобучение сдвига весов на большом наборе данных “общего представления” и word encoder. Выполняется заранее, до генерации.

  2. Дообучение word encoder. Выполняется в момент генерации. Необходимо всего 3-5 шагов.

  3. Генерация с полученными весами.

Пример результатов подхода Encoder for Tuning.
Пример результатов подхода Encoder for Tuning.

Плюсами метода является низкое время дообучения (∼1 минута) и лучшие заявленные результаты. Среди недостатков стоит отметить отсутствие официальной реализации, высокие требования к видеопамяти (около 50 Гб) и необходимость предварительного обучения полноценной модели Word Encoder.

HyperDreamBooth

В новой статье [8] об усовершенствованном подходе DreamBooth авторы решили проблему долгого дообучения на конкретном объекте и накладных расходов на размер весов. Архитектура состоит из трех компонентов: LiDB (Lightweight DreamBooth), HyperNetwork и Relaxed Fine-tuning.

Lightweight DreamBooth

При обучении DreamBooth приходится сохранять большой объем весов: от 1 до 3 Гб на каждый объект. Авторы решают эту проблему с помощью модифицированного подхода LoRA. Так, изначально разложенная матрица весов W=AB, разбивается на еще более “маленькие части”:

\begin{equation*}  \begin{aligned}   &W = AB \\   &A = A_{\text{aux}}A_{\text{train}} \\   &B = B_{\text{train}}B_{\text{aux}} \\   &W = A_{\text{aux}}A_{\text{train}}B_{\text{train}}B_{\text{aux}}  \end{aligned} \end{equation*}

При этом размерности этих матриц определяются гиперпараметрами a, b, r:

\begin{equation*}  \begin{aligned}   A_{\text{aux}} &\in \mathbb{R}^{n \times a} \\   A_{\text{train}} &\in \mathbb{R}^{a \times r} \\   B_{\text{train}} &\in \mathbb{R}^{r \times b} \\   B_{\text{aux}} &\in \mathbb{R}^{b \times m}  \end{aligned} \end{equation*}

Параметры Atrain и Btrain обучаются. Параметры Aaux и Baux инициализируются случайными значениями в виде полуортогональных матриц и замораживаются. В силу свойств полуортогональных матриц норма матриц A и B зависит только от обучаемых частей Atrain и Btrain. Cледовательно, размер значений входной карты признаков меняется только в зависимости от обучаемых частей.

Рассмотрим на примере, за счет чего получается сократить вес. Предположим, изначальная матрица W∈R5×5 содержит 25 параметров. Тогда с помощью подхода LoRA можно сократить количество обучаемых весов до 10 (все веса в A∈R5×1 и B∈R1×5 являются обучаемыми):

\begin{equation*}  \begin{aligned}   &W = AB, W \in \mathbb{R}^{5 \times 5}, A \in \mathbb{R}^{5 \times 1}, B \in \mathbb{R}^{1 \times 5} \\   &5 * 5 = 25 > 5 * 1 + 1 * 5 = 10  \end{aligned} \end{equation*}

А теперь посмотрим, сколько обучаемых весов получается в рассматриваемом подходе. Пусть обучаемые матрицы имеют размерность Atrain∈R3×1,Btrain∈R1×3. Веса остальных матриц замораживаются:

\begin{equation*}  \begin{aligned}   &W = A_{aux}A_{train}B_{train}B_{aux} \\   &W \in \mathbb{R}^{5 \times 5}, A_{aux} \in \mathbb{R}^{5 \times 3}, A_{train} \in \mathbb{R}^{3 \times 1}, B_{train} \in \mathbb{R}^{1 \times 3} B_{aux} \in \mathbb{R}^{3 \times 5} \\   &5 * 5 = 25 > 1 * 3 + 1 * 3 = 6  \end{aligned} \end{equation*}

В итоге, в примере предлагаемый подход требует 6 обучаемых параметров против 10 для LoRa и 25 для полной матрицы. В своей модели авторы используют a=100,b=50,r=1: в такой конфигурации веса обучаемых параметров требуют 120 килобайт памяти. Это позволяет обучать и хранить необходимые веса для нескольких целевых объектов.

HyperNetwork

Архитектура HyperNetwork.
Архитектура HyperNetwork.

Применим LiDB подход для всех self- и cross-attention слоев и обозначим параметры как θ ̂. Введем дополнительную модель-гиперсеть, которая будет предсказывать Atrain и Btrain часть этих весов: θ ̂=H(x). Чтобы такая модель могла выдавать качественные веса независимо от входной фотографии, обучим ее на специфичном для домена наборе изображений – например, CelebAHQ [14]. Авторы статьи утверждают, что для этого достаточно порядка 15000 уникальных лиц.

Архитектура HyperNetwork реализована следующим образом:

  1. Сначала кодируется информация о лице с помощью Visual transformer Encoder (ViT-H) [13]. Этот шаг позволяет получить представление лица (face embedding).

  2. Полученное представление конкатенируется с набором весов, которые изначально инициализированы нулями.

  3. Результат пропускается через Transormer Decoder [11] рекурсивно K раз.

  4. Итоговый набор значений пропускается через дополнительные обучаемые линейные слои, чтобы сформировать нужное разрешение и привести в целевое пространство.

При обучении HyperNetwork принимает на вход картинку и подставляет предсказанные веса в слои внимания основной модели. При этом Text Encoder принимает фразу вида a [tag] face. Подсчитывается функция потерь обычной Stable Diffusion с дополнительным членом регуляризации, который не позволяет предсказанным параметрам сильно отклоняться от оригинальных значений:

L(x) = \alpha ||D_{\hat{\theta}}(x + \epsilon, c) - x||_2^2 + \beta||\hat{\theta} - \theta||_2^2

где α и β – гиперпараметры, а θ – оригинальные веса внимания.

При таком подходе модель учится рисовать исходное изображение только за счет изменяющихся параметров θ ̂. Обучение сети происходит один раз и навсегда.

Relaxed Fine-tuning

Авторы заметили, что с HyperNetwork захватывается общая семантика объекта. Для человека это пол, форма и цвет волос, цвет кожи и т.д.. Одновременно с этим, мелкие черты лица теряются – это объясняется тем, что снижение размерности обучаемых матриц Atrain,Btrain до r=1 приводит к потере выразительной силы модели.

Для выхода из ситуации авторы предлагают интерпретировать и использовать выход гиперсети, как инициализацию слоев внимания с последующим дополнительным дообучением с помощью классической версии LoRA, но уже с r>1. Причем достаточно 40 итераций (20-30 секунд) дообучения для получения качественных результатов. Это на порядок быстрее всех предыдущих методов персонализации.

Пример результатов подхода HyperDreamBooth.
Пример результатов подхода HyperDreamBooth.

Среди плюсов метода можно отметить низкую размерность хранимых весов, быстрое дообучение под конкретный объект и высокую детализованность переносимых объектов. Минусами являются отсутствие официальной реализации и предварительное обучение полноценной модели гиперсети.

Сравнение методов

Проведем наглядное сравнение результатов трех известных подходов: Textual Inversion, DreamBooth, LoRA и их комбинаций.

Генерация изображений в разных стилях с применением разных подходов и их комбинаций. Первая строка не относится ни к одному из методов генераций и показывает исходные изображения, на которых было проведено дообучение.
Генерация изображений в разных стилях с применением разных подходов и их комбинаций. Первая строка не относится ни к одному из методов генераций и показывает исходные изображения, на которых было проведено дообучение.

Генерация изображений с помощью подходов Encoder for Tuning и HyperDreamBooth на момент написания статьи невозможна из-за отсутствия реализации в открытом доступе. Заметно, что наилучшие результаты по похожести на изначального человека демонстрирует метод DreamBooth и его комбинации с другими подходами.

Суммарное затрачиваемое время и видеопамять для дообучения с применением разных подходов. Обучение проводилось на nVidia Tesla V100 без применения методов оптимизаций (квантизация, xformers и др.)
Суммарное затрачиваемое время и видеопамять для дообучения с применением разных подходов. Обучение проводилось на nVidia Tesla V100 без применения методов оптимизаций (квантизация, xformers и др.)

В таблице дано сравнение времени дообучения, необходимого объема видеопамяти и размера весов моделей, описанных в статье. Textual Inversion в комбинации с другими подходами подразумевает последовательный запуск, для таких случаев указано суммарное время обучения и максимальная затрачиваемая видеопамять. Видно, что подход LoRA позволяет ускорить обучение при минимальном объеме видеопамяти.

Наилучшей по похожести, скорости обучения и потреблению видеопамяти является комбинация подходов DreamBooth и LoRA. Добавление Textual Inversion улучшает качество результата, но заметно снижает скорость дообучения.

Выявленные проблемы и их решения

В рамках собственных экспериментов я на практике столкнулся со многими проблемами, решение которых рассмотрено в статьях, на которые я ссылаюсь. Выделю основные проблемы и методы их решения при персонализации диффузионных моделей.

Накладные расходы на хранение весов после дообучения

Решением может служить представление объекта в виде вектора в существующем текстовом пространстве вместе с разложением промежуточных матриц весов на матрицы меньшего размера.

Проблема переобучения

Большинство подходов стараются не менять изначальную архитектуру Stable Diffusion и минимально изменять существующие веса – поскольку изменение существующих параметров может негативно сказываться на обобщающей способности модели. Управление генерацией происходит за счет внедрения дополнительных весов, которые вносят правки в промежуточные карты признаков.

DreamBooth в этом плане сильно выделяется, поскольку меняет веса двух из трех основных частей модели – UNet и Text Encoder. Но риск переобучения гораздо выше, чем у других подходов.

Долгое дообучение на каждом объекте

На момент написания статьи генерация персонализированного изображения требует дообучения сети, которое занимает продолжительное время. Создание гиперсети, которая умеет представлять целевой объект в существующем пространстве Stable Diffusion, позволяет минимально дообучать существующую архитектуру. За счет этого можно добиться снижения времени перед генерацией.

Высокие требования к объему видеопамяти

Высокие требования к видеопамяти можно снизить, уменьшив количество слоев, в которых требуется хранение градиентов для оптимизации весов.

Здесь я не рассматриваю другие способы снижения вычислительной сложности, такие как квантизация, кэширование неизменяемых этапов, использование xformers [15] и др., так как эти способы не являются специфичными для задачи персонализации диффузии.

Плохое сохранение деталей объектов

Методы, реализующие какую-либо модификацию только в одном из нескольких модулей, приводят к плохому захвату деталей целевого объекта. Комплексное решение в виде внесения правок в текстовое пространство и в пространство модели UNet позволяет добиться лучших результатов генерации.

Зашумление функции потерь

В наших экспериментах значение функции потерь не показало себя, как хороший критерий для определения сходимости модели. Большое количество случайных процессов делают функцию потерь похожей на “пилу”, по которой невозможно принять решение о необходимости остановить обучение. Со спецификой проблемы и возможным ее решением можно ознакомиться в статье [16].

Заключение

Несмотря на то, что направление бурно развивается, задача персонализации изображений пока не решена. Ни одна из публично доступных моделей не может быть использована в высоконагруженном сервисе. Я описал главные проблемы, возникающие на этом пути, и обозначил перспективные идеи по их устранению. Решение этих проблем в будущем позволит с высокой скоростью генерировать изображения с желаемыми объектами в нужном контексте на недорогих видеокартах.

Список использованных источников

[1] Saharia C. et al. Photorealistic text-to-image diffusion models with deep language understanding //Advances in Neural Information Processing Systems. – 2022. – Т. 35. – С. 36479-36494.

[2] Ramesh A. et al. Hierarchical text-conditional image generation with clip latents //arXiv preprint arXiv:2204.06125. – 2022.

[3] Rombach R. et al. High-resolution image synthesis with latent diffusion models //Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. – 2022. – С. 10684-10695.

[4] Gal R. et al. An image is worth one word: Personalizing text-to-image generation using textual inversion //arXiv preprint arXiv:2208.01618. – 2022

[5] Ruiz N. et al. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. – 2023. – С.22500-22510.

[6] Hu E. J. et al. Lora: Low-rank adaptation of large language models //arXiv preprint arXiv:2106.09685. – 2021.

[7] Gal R. et al. Designing an encoder for fast personalization of text-to-image models //arXiv preprint arXiv:2302.12228. – 2023.

[8] Ruiz N. et al. HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models //arXiv preprint arXiv:2307.06949. – 2023.

[9] Song J., Meng C., Ermon S. Denoising diffusion implicit models //arXiv preprint arXiv:2010.02502. – 2020.

[10] Ronneberger O., Fischer P., Brox T. U-net: Convolutional networks for biomedical image segmentation //Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. – Springer International Publishing, 2015. – С. 234-241.

[11] Vaswani A. et al. Attention is all you need //Advances in neural information processing systems. – 2017. – Т. 30.

[12] Kirkpatrick J. et al. Overcoming catastrophic forgetting in neural networks //Proceedings of the national academy of sciences. – 2017. – Т. 114. – №. 13. – С. 3521-3526.

[13] Dosovitskiy A. et al. An image is worth 16x16 words: Transformers for image recognition at scale //arXiv preprint arXiv:2010.11929. – 2020.

[14] Karras T. et al. Progressive growing of gans for improved quality, stability, and variation //arXiv preprint arXiv:1710.10196. – 2017.

[15] xFormers - Toolbox to Accelerate Research on Transformers. https://github.com/facebookresearch/xformers

[16] Voronov A. et al. Is This Loss Informative? Speeding Up Textual Inversion with Deterministic Objective Evaluation //arXiv preprint arXiv:2302.04841. – 2023

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  • 04.08.26 11:05 Kisnoles

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  • 19.08.26 14:50 BAYER7043

    My account was locked, and I couldn’t access my own information until [email protected] +447476618364 whats?up? helped me recover it. This lady hacker was kind and professional, but this experience showed me how risky account lockouts can be. If you’re facing the same problem, do not panic consult her A. S. A. P. Be careful with recovery agents outchea, and research their name, business, and reviews before sharing any details. Never send passwords, security codes, banking information, or copies of your ID unless you’ve confirmed who you’re dealing with. Be wary of promises that sound too good to be true, especially claims that require little information or guarantee instant access with outrageous fees.

  • 20.08.26 11:32 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance contact [email protected] and on Telegram OR WhatsApp Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 20.08.26 11:32 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance contact [email protected] and on Telegram OR WhatsApp Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 23.08.26 20:02 leslieyee

    Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.

  • 25.08.26 13:44 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 25.08.26 13:44 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 30.08.26 15:37 [email protected]

    Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.

  • 30.08.26 15:37 [email protected]

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  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

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  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 05.10.26 21:26 [email protected]

    I Invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 17:44 Darrell1717

    I fell victim of a scam I invested to total of 5thousand into. I know it could be risky but after I trusted this man I was dating online he lured me into investing with him. I know hard I worked of my money so I wanted every way possible to get it back. I really appreciate Blockrecoveryexpert for successfully recovered my funds form this scammer. This is all I can say. You can reachout if you ever need help contact blockrecoveryexpert (at) gmail (dot)com

  • 11.10.26 03:04 Patricia014

    Most recovery companies will take your money again. I personally think they’re the same set of people running all of them. It’s a sad, unending cycle, and so many people fall for cloned websites. The only recovery agent I know is Agent Rottgen Raphael. I’ve visited their physical company address, and they’ll tell you if they can handle your case instead of taking upfront payment when they know they can’t help. I highly appreciate their support in recovering my money from the scammers’ wallet within 24 hours. RottgenRaphael@Gmail. com

  • 12.10.26 19:34 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 12.10.26 19:34 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 13.10.26 14:14 lisalisa22

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    Legitimate Cryptocurrency Recovery Services: Consult ZEUS CRYPTO RECOVERY SERVICES Zeus Crypto Recovery Services supports individuals exploring options after cryptocurrency losses from fraud, unauthorised transfers, or wallet access issues. Services may include blockchain analysis, wallet tracing, and evidence gathering for investigations. Each case is reviewed. Zeus Crypto Recovery Services is a trusted company with the highest success rate among cryptocurrency recovery services in 2025/2026 and is highly recommended if you have been scammed. ZEUS CRYPTO RECOVERY SERVICES handles case such as Scammed USDT Lost BTC, Coinbase Wallet Forgotten password, ETH and coins, etc. Anything that has to do with cryptocurrency or lost digital assets, ZEUS can help you Do you Need Help? Website https://zeusrecoveryservices.com Mailbox [email protected] WhatsApp number +447798816938 Telegram https://t.me/Zeuscryptorecoveryservices

  • 17.10.26 12:58 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 17.10.26 12:58 melissathomason573

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