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[Перевод] Эй, компьютер, создай-ка мне шрифт

Это история о том, как я с нуля осваивал создание генеративных моделей МО, попутно обучая компьютер создавать шрифты. Да, настоящие типографские шрифты, состоящие из набора заглавных глифов. Созданная мной модель получает на входе описание шрифта и создаёт на выходе файл с их готовым набором. Назвал я свой проект FontoGen.

Выше вы видите несколько примеров шрифтов, сгенерированных моделью FontoGen.

Ну а дальше я подробно опишу всю историю.

Я построю свой луна-парк — с блэкджеком и шлюхами! — Bender Bending Rodríguez

Вступление

В начале 2023 года, когда ИИ начал активно заполонять интернет, я, как и многие другие, очень заинтересовался этой темой. Меня затянуло в этот мир, где я создавал мемы с помощью Stable Diffusion, обучал LoRa на лицах моих друзей и подстраивал модели трансляции текста в речь, имитирующие известные голоса.

В один момент моё внимание привлекла технология генерации SVG на основе текста, которая оказалась куда более сложной задачей в сравнении с генерацией из того же текста растровых изображений. И сложность заключается не только в самом формате. Эта технология также позволяет представлять точную форму множеством различных способов. А поскольку я хотел научиться создавать генеративные модели МО с нуля, это направление стало моим проектом выходного дня.

Идея

Когда я начал изучать различные способы генерации SVG, то наткнулся на работу IconShop, в которой учёным удалось достичь весьма впечатлительных результатов. Мне потребовалось какое-то время, чтобы воспроизвести их путём создания модели на основе взятого из этой работы описания. Когда я всё же смог достичь достаточно близких результатов, то понял, что процесс генерации шрифтов может быть аналогичен процессу генерации SVG, и приступил к работе над этим проектом.

Итоговый результат

В сравнении с генерацией SVG-картинок, шрифты генерировать и проще, и сложнее одновременно. Проще в том смысле, что в шрифтах нет цветного компонента, присущего изображениям SVG. Сложность же в том, что один шрифт состоит из множества глифов, и все эти глифы должны быть стилистически согласованы. Сохранение общей стилистики оказалось серьёзной проблемой, которую я опишу ниже.

Архитектура модели

Итак, в основу моей модели лёг описанный в работе IconShop метод генерации SVG. В итоге получилась «sequence-to-sequence» модель, обученная на последовательности, состоящей из текстовых эмбеддингов, сопровождаемых эмбеддингами шрифтов.

Последовательность ввода

▍ Текстовые эмбеддинги

Для создания текстовых эмбеддингов я использовал предварительно обученную модель BERT, которая помогает передать «смысл» промпта. Последовательность текста ограничена 16 токенами, которые в случае BERT примерно соответствуют такому же количеству слов. И хотя теоретически промпт мог бы быть длиннее, для моей конфигурации с одним GPU ограничения памяти стали значительной проблемой. Итак, все текстовые описания шрифтов, присутствующих в датасете, при помощи GPT-3 были обобщены во множество ключевых слов.

▍ Эмбеддинги шрифтов

Для того, чтобы создать эмбеддинги шрифтов, шрифты сначала нужно преобразовать в последовательность токенов по аналогии с тем, как текст токенизируется при помощи токенизатора BERT. В этом проекте я рассматривал только формы глифов, игнорируя ширину, высоту, смещение и прочие полезные метаданные, присутствующие в файлах шрифтов. Каждый глиф претерпевал даунсэмплинг до размера 150х150 пикселей и нормализовался. Путём экспериментов я выяснил, что уменьшение до размера 150х150 сохраняет все особенности шрифта с минимальной деформацией глифов, которая при меньших размерах становилась уже более выраженной.

Для парсинга файлов шрифтов я использовал библиотеку Python fonttools, которая ловко преобразует каждый глиф в последовательность кривых, линий и команд перемещения, где каждую команду можно сопровождать или не сопровождать очередной точкой. Для получения минимального пригодного к использованию шрифта я решил ограничить набор глифов следующими:

ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789.,!?

В итоге словарь модели включил 22 547 токенов:

40 глифов,
5 операций перемещения по линиям: moveTo, lineTo, qCurveTo, curveTo, closePath,
2 токена для представления EOS (end of sequence, конец последовательности) и PAD (padding, заполнение),
150^2 = 22 500 точек.

Пример последовательности токенов шрифта

Затем последовательность токенов с помощью матриц эмбеддингов преобразуется в вектор эмбеддинга. Кроме того, как предлагает работа SkexGen, для координат x и y я использовал отдельные матрицы. Заключительным шагом стало применение позиционных эмбеддингов.

▍ Трансформер

Моя модель — это авторегрессионный декодер-трансформер, состоящий из 16 слоёв и 8 блоков. Размер модели 512, в результате чего мы получаем 73,7 миллиона параметров.

  | Name  | Type     | Params
-----------------------------------
0 | model | Fontogen | 73.7 M
-----------------------------------
73.7 M    Обучаемые параметры
0         Необучаемые параметры
73.7 M    Всего параметров
294.728   Общий остаточный размер параметров (МБ)

Потери я вычислил с помощью простого метода перекрёстной энтропии, проигнорировав токен заполнения.

▍ Механизм внимания

При каждой генерации очередной части глифа есть ряд факторов, которые определяют, какой токен будет следующим. Первым делом форму глифа определяет заданный модели промпт. Затем модели нужно учесть все ранее сгенерированные для этого глифа токены. И, наконец, чтобы обеспечить согласованность стиля, ей нужно также учесть все глифы, которые были созданы перед этим.

В ходе первых экспериментов, где использовалось лишь небольшое число глифов, я начал с глобального внимания (global attention). Тем не менее по мере увеличения последовательности этот подход становился всё менее практичным, указывая на необходимость использовать разрежённое внимание (sparse attention). Изучив различные варианты, я остановил свой выбор на механизме BigBird. Этот метод поддерживает глобальное внимание, позволяя сфокусироваться на изначальном промпте, и скользящее внимание (window attention), когда модель просматривает N предыдущих токенов, определяя стиль нескольких предшествовавших глифов.

Механизм внимания BigBird

Учитывая, что один глиф может иметь различное число токенов, я настроил механизм внимания на учитывание не менее 3 последних глифов. И хотя в основном этот подход обеспечивал успешное сохранение общего стиля шрифта, в некоторых сложных случаях стиль плавно скатывался в неисправимые каракули.

Сложности каллиграфии

Обучение

Для обучения модели я собрал датасет из 71 000 шрифтов. Среди них 60% были отнесены к неопределённой категории, а 20% сопровождались более длинными описаниями, которые GPT-3.5 сжал в несколько ключевых слов. Кроме того, я добавил 15% шрифтов, в которых промпт содержал только имя шрифта, и 5% вообще без какого-либо описания, чтобы модель научилась генерировать шрифты без промпта.

Ввиду высоких требований к памяти, моя Nvidia 4090 с 24G VRAM смогла уместить в один пакет лишь две последовательности шрифтов, причём я часто наблюдал взрыв градиента. Решить проблему помогло использование аккумуляции градиента и градиентного клиппинга. Модель обучалась в течение 50 эпох, на что ушло 127 часов. Я перезапускал обучение после 36 эпох и прогонял её ещё в течение 14, снизив аккумуляцию градиента. Останавливалось обучение, когда потери при валидации практически переставали снижаться.

Потери при валидации в течение первых 36 эпох

Потери при валидации в течение оставшихся 14 эпох

Повышение производительности

Для меня было очень важно добиться высокой скорости обучения, так как я проводил его на одном GPU, что требовало довольно много времени.

При первой итерации я обрабатывал файлы шрифтов и текстовые описания на каждом шагу непосредственно внутри модели. И хотя структура этой базы данных упрощала прототипирование, это также означало, что одни и те же задачи нужно повторять снова и снова, растягивая процесс обучения. Кроме того, загрузка в память языковой модели BERT означала, что она займёт часть драгоценной VRAM. Максимально сдвинув весь процесс в сторону этапа предварительной обработки датасета, я смог втрое увеличить производительность.
Изначально модель опиралась на трансформеры с платформы Hugging Face. Перенос кода на xFormers привёл к очевидному повышению скорости и эффективности потребления памяти.

Вместо заключения

Поставленной цели я достиг. Я научился создавать генеративные модели трансформеров и реализовал такую, которая в качестве побочного эффекта умеет генерировать шрифты. Но есть ещё много разных идей, которые я не попробовал. Например, что, если мою модель интегрировать в существующие редакторы шрифтов, чтобы дизайнеры могли просто создавать один глиф А, а модель бы генерировала все остальные. Или, возможно, редактор шрифтов мог бы просто предлагать контрольные точки для кривых Безье по ходу их отрисовки. Горизонт идей очень широк, и здесь ещё многое предстоит исследовать.

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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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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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27.08.24 12:50 James889900

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02.09.24 20:24 [email protected]

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06.09.24 01:35 Celinagarcia

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06.09.24 01:44 Celinagarcia

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16.09.24 00:10 marcusaustin

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16.09.24 00:11 marcusaustin

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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

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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