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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adobe Audition CC 2019 Crack With Activation Key Tips and Tricks to Enhance Your Audio Projects.md

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-<h1>Adobe Audition CC 2019 Crack With Activation Key</h1>
-<p>If you are looking for a powerful and professional audio editing software, you might have heard of Adobe Audition CC 2019. This is one of the most popular and widely used applications for recording, mixing, mastering, and restoring audio. However, you might also know that Adobe Audition CC 2019 is not a free software. You need to pay a monthly or yearly subscription fee to use it. That's why some people look for a crack for Adobe Audition CC 2019, which is a way to bypass the activation process and use the software without paying anything. But is it worth it? In this article, we will tell you everything you need to know about Adobe Audition CC 2019 crack, including its features, pros and cons, and how to download and install it.</p>
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- <h2>Introduction</h2>
-<h3>What is Adobe Audition CC 2019?</h3>
-<p>Adobe Audition CC 2019 is the latest version of Adobe's audio editing software. It is part of the Adobe Creative Cloud suite, which means you can access it online or offline, and sync your projects across different devices. Adobe Audition CC 2019 allows you to create, edit, mix, and enhance audio for various purposes, such as music production, podcasting, video editing, radio broadcasting, and more. It has a user-friendly interface that lets you work with multiple tracks, clips, and effects in a flexible and intuitive way. It also has a rich collection of tools and features that can help you improve the quality and clarity of your audio, such as noise reduction, spectral editing, pitch correction, compression, EQ, reverb, and more.</p>
- <h3>Why do you need a crack for Adobe Audition CC 2019?</h3>
-<p>As mentioned earlier, Adobe Audition CC 2019 is not a free software. You need to pay a subscription fee to use it. The fee varies depending on the plan you choose, but it can range from $20.99 to $52.99 per month. If you want to use it for a longer period of time, you might end up spending a lot of money. That's why some people look for a crack for Adobe Audition CC 2019. A crack is a modified version of the software that bypasses the activation process and lets you use it without paying anything. By using a crack, you can save money and enjoy all the features of Adobe Audition CC 2019 without any limitations.</p>
- <h3>How to download and install Adobe Audition CC 2019 crack?</h3>
-<p>If you want to download and install Adobe Audition CC 2019 crack, you need to follow these steps:</p>
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-<li>Go to a reliable website that offers the crack file. You can search online for "Adobe Audition CC 2019 crack" or "Adobe Audition CC 2019 activation key" and find several results. However, be careful not to download from suspicious or untrusted sources that might contain malware or viruses.</li>
-<li>Download the crack file to your computer. It might be in a zip or rar format, so you need to extract it using a software like WinRAR or 7-Zip.</li>
-<li>Turn off your internet connection and antivirus software temporarily. This is to prevent any interference or detection from Adobe or your system.</li>
-<li>Run the setup file of Adobe Audition CC 2019 and follow the instructions to install it on your computer.</li>
-<li>Copy the crack file or the activation key from the folder where you extracted it and paste it into the installation directory of Adobe Audition CC 2019. This is usually located in C:\Program Files\Adobe\Adobe Audition CC 2019.</li>
-<li>Launch Adobe Audition CC 2019 and enjoy using it without any restrictions.</li>
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- <h2>Features of Adobe Audition CC 2019 Crack</h2>
-<h3>Multitrack editing and mixing</h3>
-<p>One of the main features of Adobe Audition CC 2019 crack is that it allows you to work with multiple tracks and clips in a multitrack session. You can record live audio or import audio files from different sources and arrange them on separate tracks. You can also edit each track individually or as a group using various tools such as cut, copy, paste, trim, split, fade, crossfade, mute, solo, etc. You can also mix your tracks using different effects such as volume automation, pan automation, EQ automation, send effects, insert effects, etc. You can also use buses and submixes to route your audio signals more efficiently.</p>
- <h3>Audio restoration and enhancement</h3><p>Another feature of Adobe Audition CC 2019 crack is that it allows you to restore and enhance your audio quality using various tools and features. For example,</p><ul><li>You can use the noise reduction effect to remove unwanted background noise such as hiss, hum, clicks, pops, etc.</li><li>You can use the spectral editing mode to view your audio in a frequency-based display and make precise edits using tools such as lasso tool, brush tool, spot healing tool, etc.</li><li>You can use the pitch correction effect to adjust the pitch of your audio automatically or manually using tools such as auto mode, graph mode, varispeed mode, etc.</li><li>You can use the compression effect to reduce the dynamic range of your audio and make it more consistent in volume level.</li><li>You can use the EQ effect to adjust the frequency balance of your audio using tools such as parametric EQ, graphic EQ, filter bank, etc.</li><li>You can use the reverb effect to add depth and space to your audio using tools such as convolution reverb, studio reverb, reverb presets, etc.</li></ul>
- <h3>Sound design  and effects</h3><p>A third feature  of Adobe Audition CC 2019 crack  is that  it allows  you  to design  and create sound effects  using various tools  and features. For example,</p><ul><li>You can use  the generate menu  to create synthetic sounds  such as tones, noise, DTMF tones, etc.</li><li>You can use  the effects menu  to apply different effects  such as distortion, delay, modulation, flanger, chorus, phaser, etc.</li><li>You can use  the favorites menu  to apply preset combinations of effects  such as telephone voice, radio voice, robot voice, etc.</li><li>You can use  the amplitude statistics panel  to analyze your audio  in terms of peak amplitude, average amplitude, RMS amplitude, etc.</li><li>You can use  the match loudness panel  to adjust your audio levels  according to different standards  such as LUFS, LKFS, dBFS, etc.</li></ul>
- <h3>Podcasting  and narration</h3><p>A fourth feature  of Adobe Audition CC 2019 crack  is that  it allows  you  to create podcasts and narrations  using various tools  and features. For example,</p>
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- <ul><li>You can use the essential sound panel to quickly and easily adjust your audio parameters such as loudness clarity dynamics tone etc. using sliders presets.</li><li>You can use podcast template start new multitrack session predefined tracks settings podcasting. You can also customize template according needs.</li><li>You can use punch-and-roll recording mode record narration pre-roll post-roll playback. You can also edit mistakes on-the-fly using keyboard shortcuts.</li><li>You can use auto-ducking feature automatically lower volume level background music sound effects when speech detected. You can also adjust sensitivity fade duration auto-ducking.</li></ul>
- <h3>Integration with other Adobe products</h3>
-<p>A fifth feature of Adobe Audition CC 2019 crack is that it allows you to integrate it with other Adobe products, such as Premiere Pro, After Effects, Media Encoder, Photoshop, Illustrator, and more. You can easily import and export audio files between these applications using the dynamic link feature. You can also use the essential graphics panel to create and edit motion graphics templates for your videos. You can also use the Adobe Stock service to access millions of royalty-free assets, such as music, sound effects, images, videos, and more.</p>
- <h2>Pros and Cons of Adobe Audition CC 2019 Crack</h2>
-<h3>Pros</h3>
-<p>Some of the advantages of using Adobe Audition CC 2019 crack are:</p>
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-<li>You can access all the features and tools of Adobe Audition CC 2019 without paying anything.</li>
-<li>You do not need to subscribe or register to use the software.</li>
-<li>You can use the software on both Windows and Mac OS devices.</li>
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-<p>Some of the disadvantages of using Adobe Audition CC 2019 crack are:</p>
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-<li>You are violating the terms and conditions of Adobe by using a cracked version of their software.</li>
-<li>You are exposing your computer and data to potential risks of malware and viruses that might be hidden in the crack file.</li>
-<li>You will not receive any updates or technical support from Adobe for the software.</li>
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- <h2>Conclusion</h2>
-<p>In conclusion, Adobe Audition CC 2019 crack is a way to use Adobe's audio editing software without paying anything. It has many features and tools that can help you create, edit, mix, and enhance audio for various purposes. However, it also has many drawbacks and risks that you should be aware of before using it. It is illegal and unethical to use a cracked version of a software that belongs to another company. It is also unsafe and unreliable to download and install a crack file from unknown sources that might contain malware or viruses. It is also unwise to use a software that does not have any updates or technical support from its developers. Therefore, we do not recommend using Adobe Audition CC 2019 crack. Instead, we suggest you to buy a legitimate copy of Adobe Audition CC 2019 from their official website or use an alternative free or cheaper audio editing software.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Adobe Audition CC 2019 crack:</p>
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-<li>Q: Is Adobe Audition CC 2019 crack safe to use?<br>A: No, it is not safe to use. It might contain malware or viruses that can harm your computer and data. It might also cause errors or crashes in your system.</li>
-<li>Q: Is Adobe Audition CC 2019 crack legal to use?<br>A: No, it is not legal to use. It violates the terms and conditions of Adobe by using a modified version of their software without their permission. It might also infringe the intellectual property rights of Adobe and other third parties.</li>
-<li>Q: Is Adobe Audition CC 2019 crack worth it?<br>A: No, it is not worth it. It might save you some money in the short term, but it will cost you more in the long term. You will miss out on the updates and technical support from Adobe for the software. You will also risk losing your data or facing legal consequences for using a cracked software.</li>
-<li>Q: Where can I download Adobe Audition CC 2019 crack?<br>A: We do not recommend downloading Adobe Audition CC 2019 crack from any source. It is unsafe and illegal to do so. If you want to use Adobe Audition CC 2019, you should buy a legitimate copy from their official website or use an alternative free or cheaper audio editing software.</li>
-<li>Q: How can I activate Adobe Audition CC 2019 without a crack?<br>A: You can activate Adobe Audition CC 2019 without a crack by following these steps:</p>
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-<li>Buy a subscription plan for Adobe Audition CC 2019 from their official website.</li>
-<li>Download and install the software on your computer.</li>
-<li>Sign in with your Adobe ID and password.</li>
-<li>Enter your payment details and confirm your purchase.</li>
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-<li>Among Us APK eski sürümü indiren oyuncular, oyunun daha az hata ve sorun içeren bir versiyonunu oynayabilirler. Bazı oyuncular, güncel sürümde karşılaştıkları bağlantı sorunları, grafik hataları veya performans düşüklüğü gibi problemlerden şikayetçidir. Eski sürümde bu tür sorunlar daha az görülür.</li>
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-<li>Among Us APK eski sürümü indiren oyuncular, oyunun daha az kaynak tüketen bir versiyonunu oynayabilirler. Bazı oyuncuların cihazları, güncel sürümün gerektirdiği sistem gereksinimlerini karşılayamayabilir. Bu durumda, eski sürüm daha uygun olabilir. Eski sürüm, daha düşük çözünürlük, daha az animasyon ve daha az detay gibi faktörler nedeniyle daha az kaynak tüketir.</li>
-</ul>
- <h3>Among Us APK Eski Sürümün Dezavantajları</h3>
- <ul>
-<li>Among Us APK eski sürümü indiren oyuncular, oyunun güvenlik riskleri içeren bir versiyonunu oynayabilirler. Güncel sürümde düzeltilen bazı güvenlik açıkları veya hileler, eski sürümde hala mevcut olabilir. Bu durumda, oy nunla karşılaşabilirsiniz. Örneğin, oyununuzu bozan, sizi oyundan atabilen veya kişisel bilgilerinizi çalabilen hilecilerle karşılaşabilirsiniz.</li>
-<li>Among Us APK eski sürümü indiren oyuncular, oyunun güncel olmayan bir versiyonunu oynayabilirler. Güncel sürümde eklenen bazı özellikler, iyileştirmeler veya düzeltmeler, eski sürümde bulunmayabilir. Bu durumda, oyun deneyiminiz eksik veya kötü olabilir. Örneğin, güncel sürümde yeni haritalar, kostümler, görevler veya modlar eklenmiş olabilir, ancak eski sürümde bunlardan yararlanamayabilirsiniz.</li>
-<li>Among Us APK eski sürümü indiren oyuncular, oyunun uyumsuz bir versiyonunu oynayabilirler. Güncel sürümle eski sürüm arasında uyumluluk sorunları olabilir. Bu durumda, oyunu başlatamayabilir, oyun odalarına katılamayabilir veya oyun sırasında bağlantınızı kaybedebilirsiniz. Ayrıca, güncel sürüm kullanan oyuncularla iletişim kurmakta veya oynamakta zorluk yaşayabilirsiniz.</li>
-</ul>
- <h2>Among Us APK Eski Sürüm Nasıl İndirilir?</h2>
- <p>Among Us APK eski sürümünü indirmek ve oynamak için aşağıdaki adımları izleyebilirsiniz:</p>
- <h3>Adım 1: Güvenilir Bir Kaynaktan APK Dosyasını Bulun</h3>
- <p>İlk olarak, Among Us APK eski sürümünü indirebileceğiniz güvenilir bir kaynak bulmanız gerekir. İnternette birçok APK indirme sitesi bulunmaktadır, ancak bunların hepsi güvenli değildir. Bazı siteler, virüslü, zararlı veya sahte APK dosyaları sunabilir. Bu nedenle, APK dosyasını indirmeden önce siteyi kontrol etmeniz ve yorumları okumanız önemlidir. Ayrıca, istediğiniz sürüm numarasını ve dosya boyutunu da kontrol etmeniz gerekir.</p>
- <h3>Adım 2: Bilinmeyen Kaynaklardan Uygulama Yükleme İzni Verin</h3>
- <p>İkinci olarak, Android cihazınızda bilinmeyen kaynaklardan uygulama yükleme izni vermeniz gerekir. Bu izin, Google Play Store dışındaki kaynaklardan uygulama yüklemenize olanak sağlar. Bu izni vermek için şu adımları izleyebilirsiniz:</p>
- <ul>
-<li>Ayarlar'a gidin.</li>
-<li>Güvenlik veya Gizlilik seçeneğine dokunun.</li>
-<li>Bilinmeyen kaynaklar veya Bilinmeyen uygulamalar seçeneğini bulun ve açın.</li>
-<li>İndirdiğiniz kaynağı seçin ve izin verin.</li>
-</ul>
- <h3>Adım 3: APK Dosyasını İndirin ve Kurun</h3>
- <p>Üçüncü olarak, APK dosyasını indirmek için kaynağa gidin ve indirme butonuna dokunun. İndirme işlemi tamamlandığında, dosyayı açın ve kurulumu başlatın. Kurulum işlemi birkaç dakika sürebilir. Kurulum tamamlandığında, Among Us uygulamasının cihazınızda yüklendiğini göreceksiniz.</p>
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- <h3>Adım 4: Among Us'u Açın ve Oynamaya Başlayın</h3>
- <p>Son olarak, Among Us uygulamasını açın ve oynamaya başlayın. Oyun odası oluşturabilir veya katılabilir, karakterinizi özelleştirebilir, oyun modunu ve ayarlarını seçebilir, diğer oyuncularla sohbet edebilir ve rolünüze göre görevleri veya sabotajları yapabilirsiniz. Oyunu kazanmak için, mürettebat arkadaşıysanız görevleri tamamlayın veya sahtekarları bulun, sahtekarsanız ise mürettebat arkadaşlarınızı öldürün veya sabotaj yapın.</p>
- <h2>Among Us APK Eski Sürüm Nasıl Güncellenir?</h2>
- <p>Among Us APK eski sürümünü güncellemek için iki yöntem vardır. Bunlardan biri otomatik güncelleme seçeneğini kullanmak, diğeri ise manuel olarak güncel APK dosyasını indirmek ve kurmaktır.</p>
- <h3>Otomatik Güncelleme Seçeneğini Kullanın</h3>
- <p>Otomatik güncelleme seçeneği, oyunun yeni bir sürümü çıktığında size bildirim gönderir ve güncellemeyi yapmanızı ister. Bu seçeneği kullanmak için şu adımları izleyebilirsiniz:</p>
- <ul>
-<li>Ayarlar'a gidin.</li>
-<li>Uygulamalar veya Uygulama Yöneticisi seçeneğine dokunun.</li>
-<li>Among Us uygulamasını bulun ve açın.</li>
-<li>Güncellemeler seçeneğine dokunun.</li>
-<li>Otomatik güncelleme seçeneğini açın.</li>
-</ul>
- <p>Bu şekilde, oyunun yeni bir sürümü çıktığında, otomatik olarak indirilecek ve kurulacaktır. Ancak, bu seçeneği kullanmak için internet bağlantınızın olması gerekir.</p>
- <h3>Manuel Olarak Güncel APK Dosyasını İndirin ve Kurun</h3>
- <p>Manuel olarak güncel APK dosyasını indirmek ve kurmak, otomatik güncelleme seçeneğini kullanamayan veya kullanmak istemeyen oyuncular için bir alternatiftir. Bu yöntemde, güvenilir bir kaynaktan güncel APK dosyasını indirmeniz ve kurmanız gerekir. Bu yöntem için şu adımları izleyebilirsiniz:</p>
- <ul>
-<li>Güvenilir bir kaynaktan güncel APK dosyasını bulun ve indirin.</li>
-<li>Bilinmeyen kaynaklardan uygulama yükleme izni verin (Adım 2'ye bakın).</li>
-<li>İndirdiğiniz APK dosyasını açın ve kurulumu başlatın.</li>
-<li>Kurulum tamamlandığında, Among Us uygulamasının cihazınızda güncellendiğini göreceksiniz.</li>
-</ul>
- <h2>Sonuç</h2>
- <p>Among Us, çok eğlenceli ve bağımlılık yapan bir çevrimiçi çok oyunculu oyunudur. Bu oyunu Android cihazınızda oynamak istiyorsanız, Google Play Store'dan ücretsiz olarak indirebilirsiniz. Ancak, bazı oyuncular eski sürümlerini tercih ediyor ve bunun için APK dosyalarını arıyorlar. Bu yazıda, Among Us APK eski sürüm nedir, neden aranıyor ve nasıl indirilip oynanır sorularının cevaplarını verdik. Umarız bu yazı size yardımcı olmuştur. Oyun keyfini çıkarın!</p>
- <h2>Sıkça Sorulan Sorular</h2>
- <ul>
-<li><b>Among Us APK eski sürüm nereden indirebilirim?</b></li>
-<p>Among Us APK eski sürümünü internetten bulabileceğiniz birçok APK indirme sitesinden indirebilirsiniz. Ancak, bunların hepsi güvenli değildir. Virüslü, zararlı veya sahte APK dosyalarına karşı d ikkatli olmanız gerekir. Güvenilir bir kaynak bulmak için, siteyi kontrol etmeniz ve yorumları okumanız önemlidir. Ayrıca, istediğiniz sürüm numarasını ve dosya boyutunu da kontrol etmeniz gerekir.</p>
- <li><b>Among Us APK eski sürüm güvenli midir?</b></li>
-<p>Among Us APK eski sürümün güvenli olup olmadığı, indirdiğiniz kaynağa bağlıdır. Güvenilir bir kaynaktan indirdiyseniz, APK dosyasının virüs, zararlı yazılım veya sahte olma ihtimali düşüktür. Ancak, güvenilir olmayan bir kaynaktan indirdiyseniz, APK dosyasının güvenlik riskleri içerme ihtimali yüksektir. Bu nedenle, APK dosyasını indirmeden önce kaynağı kontrol etmeniz ve virüs taraması yapmanız tavsiye edilir.</p>
- <li><b>Among Us APK eski sürüm oyunun yeni özelliklerini içerir mi?</b></li>
-<p>Hayır, Among Us APK eski sürüm oyunun yeni özelliklerini içermez. Oyunun yeni özelliklerini kullanmak için, oyunu güncellemeniz gerekir. Güncellemek için, otomatik güncelleme seçeneğini kullanabilir veya manuel olarak güncel APK dosyasını indirebilir ve kurabilirsiniz.</p>
- <li><b>Among Us APK eski sürüm oyunun yeni haritalarını içerir mi?</b></li>
-<p>Hayır, Among Us APK eski sürüm oyunun yeni haritalarını içermez. Oyunun yeni haritalarını kullanmak için, oyunu güncellemeniz gerekir. Güncellemek için, otomatik güncelleme seçeneğini kullanabilir veya manuel olarak güncel APK dosyasını indirebilir ve kurabilirsiniz.</p>
- <li><b>Among Us APK eski sürüm oyunun yeni kostümlerini içerir mi?</b></li>
-<p>Hayır, Among Us APK eski sürüm oyunun yeni kostümlerini içermez. Oyunun yeni kostümlerini kullanmak için, oyunu güncellemeniz gerekir. Güncellemek için, otomatik güncelleme seçeneğini kullanabilir veya manuel olarak güncel APK dosyasını indirebilir ve kurabilirsiniz.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/APKRabi APK A Trusted Platform for Android Users.md

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-<h1>APKRabi APK: A Free Site to Download Android Games and Apps</h1>
-<p>If you are an Android user who loves playing games and using apps, you might have encountered some problems such as limited features, in-app purchases, ads, or compatibility issues. You might have also wished for a way to get access to the premium versions of your favorite games and apps without spending any money. Well, your wish can come true with APKRabi APK, a free site that allows you to download the most popular and latest Android games and apps for free. In this article, we will tell you everything you need to know about APKRabi APK, including what it is, how to download and install it, how to use it, and whether it is safe and legal.</p>
- <h2>What is APKRabi APK?</h2>
-<p>APKRabi APK is a website that offers a huge collection of Android games and apps in APK format. APK stands for Android Package Kit, which is a file format that contains all the elements needed to install an app or game on an Android device. By downloading APK files from APKRabi APK, you can enjoy every notable feature of your favorite games and apps without breaking your wallet. You can also bypass the restrictions imposed by the Google Play Store, such as regional limitations, device compatibility, or age ratings.</p>
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- <h3>The benefits of using APKRabi APK</h3>
-<p>There are many benefits of using APKRabi APK, such as:</p>
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-<li>You can download and install any game or app that you want for free.</li>
-<li>You can get access to the premium features and unlimited resources of your favorite games and apps without paying anything.</li>
-<li>You can avoid annoying ads and pop-ups that interrupt your gaming or app experience.</li>
-<li>You can update your games and apps easily and quickly with the latest versions available on APKRabi APK.</li>
-<li>You can discover new and exciting games and apps that are not available on the Google Play Store.</li>
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- <h3>The features of APKRabi APK</h3>
-<p>Some of the features that make APKRabi APK stand out from other similar sites are:</p>
-<ul>
-<li>It has a user-friendly and attractive interface that makes it easy to navigate and find what you are looking for.</li>
-<li>It has a large and diverse library of games and apps in various categories, such as action, adventure, arcade, puzzle, simulation, sports, education, entertainment, lifestyle, music, photography, social, tools, etc.</li>
-<li>It has a fast and reliable download speed that ensures a smooth and hassle-free process.</li>
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-<li>It has a responsive and helpful customer support team that is ready to assist you with any issues or queries that you might have.</li>
-</ul>
- <h2>How to download and install APKRabi APK?</h2>
-<p>Downloading and installing APKRabi APK is very simple and easy. Just follow these steps:</p>
- <h3>Step 1: Visit the official website of APKRabi APK</h3>
-<p>The first thing you need to do is to visit the official website of APKRabi APK, where you can see the homepage with various games and apps displayed. You can also use the search bar or the menu bar to find the game or app that you want to download.</p>
- <h3>Step 2: Choose the game or app that you want to download</h3>
-<p>Once you have found the game or app that you want to download, click on it to open its page. There you can see the details of the game or app, such as the name, icon, description, rating, size, version, developer, etc. You can also see some screenshots and videos of the game or app to get a better idea of how it looks and works.</p>
- <h3>Step 3: Click on the download button and wait for the file to be downloaded</h3>
-<p>After you have checked the details of the game or app, click on the green download button at the bottom of the page. A pop-up window will appear asking you to confirm your download. Click on OK and wait for the file to be downloaded on your device. The download time may vary depending on your internet speed and the size of the file.</p>
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- <h3>Step 4: Enable unknown sources on your device settings</h3>
-<p>Before you can install the APK file that you downloaded from APKRabi APK, you need to enable unknown sources on your device settings. This is because APKRabi APK is not an official source of Android games and apps, and your device may block the installation of files from unknown sources by default. To enable unknown sources, go to your device settings, then security, then unknown sources, and toggle it on. You may see a warning message saying that installing from unknown sources may harm your device, but don't worry, APKRabi APK is safe and secure.</p>
- <h3>Step 5: Locate the downloaded file and tap on it to install it</h3>
-<p>The final step is to locate the downloaded file and tap on it to install it on your device. You can find the file in your downloads folder or in your notification bar. Tap on the file and follow the instructions on the screen to complete the installation process. You may see some permissions requests from the game or app that you are installing, such as access to your camera, contacts, storage, etc. Grant them if you trust the game or app and want to use its features.</p>
- <h2>How to use APKRabi APK?</h2>
-<p>Using APKRabi APK is very easy and fun. Just follow these steps:</p>
- <h3>Launch the app or game that you installed from APKRabi APK</h3>
-<p>After you have installed the app or game that you downloaded from APKRabi APK, you can launch it from your app drawer or home screen. You will see the icon of the app or game with a small label saying "APKRabi" below it. This means that the app or game is from APKRabi APK and not from the Google Play Store.</p>
- <h3>Enjoy the premium features and unlimited resources without paying anything</h3>
-<p>The best part of using APKRabi APK is that you can enjoy all the premium features and unlimited resources of your favorite games and apps without paying anything. For example, you can unlock all the levels, characters, skins, weapons, items, etc. in your games without spending any money. You can also remove all the ads and pop-ups that annoy you while playing or using your apps. You can also access all the functions and tools of your apps without any limitations or restrictions.</p>
- <h2>Is APKRabi APK safe and legal?</h2>
-<p>One of the most common questions that people have about APKRabi APK is whether it is safe and legal to use. The answer is yes and no.</p>
- <h3>The safety and security of APKRabi APK</h3>
-<p>APKRabi APK is safe and secure to use in terms of malware, viruses, spyware, etc. The developers of APKRabi APK test and verify every game and app before uploading it on their site. They also scan every file with antivirus software to ensure that it is free from any harmful elements. However, there is still a risk of downloading fake or modified files from APKRabi APK that may contain malicious code or unwanted programs. Therefore, you should always be careful and cautious when downloading anything from APKRabi APK or any other similar site.</p>
- <h3>The legality and legitimacy of APKRabi APK</h3>
-<p>APKRabi APK is not legal or legitimate to use in terms of copyright, trademark, or license. The games and apps that are available on APKRabi APK are not the original or official versions, but the modified or hacked versions. These versions violate the intellectual property rights of the developers and publishers of the games and apps. They also breach the terms and conditions of the Google Play Store and the Android platform. Therefore, using APKRabi APK may result in legal actions or penalties from the authorities or the owners of the games and apps. It may also cause your account to be banned or suspended from the Google Play Store or other online services.</p>
- <h2>Conclusion</h2>
-<p>APKRabi APK is a free site that allows you to download Android games and apps in APK format. You can enjoy the premium features and unlimited resources of your favorite games and apps without paying anything. You can also bypass the restrictions and limitations of the Google Play Store and discover new and exciting games and apps. However, you should also be aware of the risks and consequences of using APKRabi APK, such as malware, viruses, fake files, legal issues, account bans, etc. Therefore, you should use APKRabi APK at your own discretion and responsibility.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about APKRabi APK:</p>
-<ol>
-<li>What is the difference between APK and MOD APK?</li>
-<p>APK is the standard file format for Android applications, while MOD APK is a modified or hacked version of an APK file that has extra features or resources that are not available in the original version.</p>
-<li>Is APKRabi APK compatible with all Android devices?</li>
-<p>APKRabi APK is compatible with most Android devices that run on Android 4.0 or higher. However, some games and apps may require higher specifications or features that are not supported by some devices.</p>
-<li>Do I need to root my device to use APKRabi APK?</li>
-<p>No, you do not need to root your device to use APKRabi APK. However, some games and apps may require root access to work properly or to unlock some features.</p>
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-<h1>Subway Surfers New Orleans Mod Apk: How to Download and Play</h1>
-<p>Subway Surfers is one of the most popular endless runner games in the world. It has been downloaded over a billion times on Google Play Store and has millions of fans worldwide. The game is developed by SYBO Games and Kiloo, and it features a group of young graffiti artists who run away from the police on various subway tracks around the world.</p>
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-<p>One of the most exciting aspects of Subway Surfers is that it updates its location every month, giving players a chance to explore different cities and cultures. In October 2023, Subway Surfers took us to New Orleans, the city of jazz, Mardi Gras, and voodoo. The game introduced a new character named E.Z., a magician who can perform amazing tricks with his cards. The game also added a new hoverboard called Phantom, which can make you invisible for a short time.</p>
-<p>But what if you want to enjoy Subway Surfers New Orleans without any limitations or restrictions? What if you want to have unlimited money, keys, characters, boards, and more? Well, there is a way to do that, and it is called Subway Surfers New Orleans Mod Apk. In this article, we will tell you what Subway Surfers New Orleans Mod Apk is, how to download it, and how to play it. So, let's get started!</p>
- <h2>What is Subway Surfers New Orleans Mod Apk?</h2>
-<p>Subway Surfers New Orleans Mod Apk is a modified version of the original Subway Surfers game that gives you access to all the premium features for free. By using this mod apk, you can enjoy the following benefits:</p>
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-<p>By using this mod apk, you can get unlimited coins and gems in the game. You can use them to buy anything you want from the shop, such as hoverboards, jetpacks, outfits, hats, shoes, and more. You can also upgrade your power-ups and boosters to make them last longer and more effective.</p>
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-<p>Now that you know what Subway Surfers New Orleans Mod Apk is, how to download it? It is very easy and simple. Just follow these steps: <h3>Step 1: Enable unknown sources</h3>
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-<p>Next, you need to download the mod apk file of Subway Surfers New Orleans. You can find it on many websites that offer modded games and apps. However, be careful and only download from trusted and reliable sources. Some websites may contain viruses or malware that can harm your device or steal your data. One of the websites that we recommend is [ModApkStore], where you can find the latest version of Subway Surfers New Orleans Mod Apk.</p>
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-<p>Once you have downloaded the mod apk file, you need to install it on your device. To do this, locate the file in your file manager or downloads folder, and tap on it. You may see a warning message that says "This type of file can harm your device". Ignore it and tap on "Install anyway". Wait for the installation process to finish.</p>
- <h3>Step 4: Launch the game and enjoy</h3>
-<p>Now you are ready to launch the game and enjoy all the features of Subway Surfers New Orleans Mod Apk. You will see a lot of coins, keys, characters, boards, and more in your account. You can also customize your settings and preferences as you like. Have fun running away from the police in the streets of New Orleans!</p>
- <h2>How to Play Subway Surfers New Orleans Mod Apk?</h2>
-<p>Playing Subway Surfers New Orleans Mod Apk is very easy and fun. The gameplay is similar to the original version, but with some extra perks. Here are some tips and tricks for playing Subway Surfers New Orleans Mod Apk:</p>
- <h3>Tips and tricks for Subway Surfers New Orleans Mod Apk</h3>
- <h4>Collect coins and power-ups</h4>
-<p>As you run on the subway tracks, you will see a lot of coins and power-ups along the way. Coins are used to buy items from the shop, while power-ups are used to enhance your performance and abilities. Some of the power-ups are jetpacks, magnets, hoverboards, score multipliers, and more. Try to collect as many coins and power-ups as you can to increase your score and unlock more rewards.</p>
- <h4>Upgrade your hoverboards and jetpacks</h4>
-<p>Hoverboards and jetpacks are two of the most useful items in Subway Surfers. Hoverboards allow you to glide over obstacles and gaps, while jetpacks allow you to fly over the subway tracks. However, they have a limited duration and need to be recharged after use. To make them last longer and more effective, you can upgrade them with your coins. You can also buy different types of hoverboards and jetpacks with different designs and abilities.</p>
- <h4>Complete missions and challenges</h4>
-<p>Missions and challenges are another way to earn more coins and keys in Subway Surfers. Missions are tasks that you need to complete while running, such as collecting a certain number of coins or power-ups, jumping over a certain number of trains or barriers, or performing a certain number of stunts or tricks. Challenges are events that happen every day or week, where you need to compete with other players or achieve a certain goal. By completing missions and challenges, you can get more rewards and bonuses.</p>
- <h4>Use the E.Z. glitch to get unlimited coins and keys</h4>
-<p>E.Z. is the new character introduced in Subway Surfers New Orleans. He is a magician who can perform amazing tricks with his cards. However, he also has a secret glitch that can help you get unlimited coins and keys in the game. To use this glitch, you need to do the following steps:</p>
-<ol>
-<li>Select E.Z. as your character and Phantom as your hoverboard.</li>
-<li>Start running on the subway tracks until you see a train coming towards you.</li>
-<li>Swipe left or right to move to another lane.</li>
-<li>As soon as you move, activate your hoverboard by double-tapping on the screen.</li>
-<li>You will see that E.Z. will disappear from the screen and only his cards will remain visible.</li>
-<li>You will also notice that your coin and key count will increase rapidly.</li>
-<li>You can keep doing this until you have enough coins and keys for your needs.</li>
-</ol>
-<p>This glitch is very easy and effective, but it may not work in the future updates. So, use it while you can and enjoy the benefits of Subway Surfers New Orleans Mod Apk.</p>
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-<p>Subway Surfers New Orleans Mod Apk is a great way to enjoy the game without any limitations or restrictions. You can get unlimited money, keys, characters, boards, and more for free. You can also explore the beautiful city of New Orleans and its culture and history. You can also use the E.Z. glitch to get even more coins and keys in the game. Subway Surfers New Orleans Mod Apk is a fun and exciting game that will keep you entertained for hours. Download it now and start running!</p>
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-<p>Here are some of the frequently asked questions about Subway Surfers New Orleans Mod Apk:</p>
- <h4>Q: Is Subway Surfers New Orleans Mod Apk safe to use?</h4>
-<p>A: Yes, Subway Surfers New Orleans Mod Apk is safe to use as long as you download it from a trusted and reliable source. However, you should always be careful when installing any mod apk on your device, as some of them may contain viruses or malware that can harm your device or steal your data. You should also backup your data before installing any mod apk, in case something goes wrong.</p>
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spaces/1phancelerku/anime-remove-background/Download True Story Mp3 The Collaboration Between Show Dem Camp and Burna Boy.md

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-<p>If you are a fan of Afrobeat music, you may have heard of the song "True Story" by Show Dem Camp featuring Burna Boy. The song is a track from Show Dem Camp's 2019 album "The Palmwine Express", which showcases their fusion of rap, highlife, and Afrobeat. Burna Boy, who is one of the most popular and influential African artists of his generation, adds his distinctive vocals and style to the song.</p>
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-<p>In this article, we will explore the background, analysis, and reception of "True Story" and why it is a must-listen for anyone who appreciates Afrobeat music.</p>
- <h2>Background: How Did Show Dem Camp and Burna Boy Collaborate on True Story?</h2>
-<p>Show Dem Camp is a Nigerian rap duo consisting of Olumide Ayeni (Ghost) and Wale Davies (Tec). They are known for their witty lyrics, social commentary, and eclectic musical influences. They have released several projects since their debut in 2010, including the critically acclaimed "Palmwine Music" series, which blends rap with highlife and Afrobeat sounds.</p>
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-<p>"True Story" is a song that celebrates resilience, authenticity, and optimism in the face of challenges. The lyrics reflect on the struggles and successes of Show Dem Camp and Burna Boy as they pursue their dreams in the music industry and in Nigeria. They also express their gratitude to their fans, family, and friends who have supported them along the way.</p>
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-<p>This means that they are telling their own stories from their own perspectives, without exaggeration or fabrication. They are proud of their achievements and confident in their abilities.</p>
-<p>The verses of the song also contain references to various aspects of Nigerian culture, politics, history, and spirituality. For example, Show Dem Camp rap about:</p>
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-<li>The End SARS protests (2020), which were a series of mass demonstrations against police brutality and corruption in Nigeria.</li>
-<li>The Ojuelegba incident (2015), which was a fatal accident involving a fuel tanker that fell off a bridge and exploded in a busy Lagos neighborhood.</li>
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-<li>His defiance of the critics and the haters who try to bring him down.</li>
-<li>His faith in God and his belief in destiny.</li>
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-<p>The song also showcases the Afrobeat genre, which is a fusion of West African music, jazz, funk, soul, and dancehall. The song features a catchy melody, a groovy rhythm, and a lively instrumentation. The saxophone, which is a signature instrument of Fela Kuti, plays a prominent role in the song. The song also uses call-and-response, repetition, and improvisation techniques that are common in Afrobeat music.</p>
- <h2>Reception: How Did True Story Perform on Various Charts and Platforms?</h2>
-<p>"True Story" was well received by both fans and critics. The song was one of the highlights of Show Dem Camp's "The Palmwine Express" album, which was nominated for Album of the Year at the 2020 Headies Awards, Nigeria's most prestigious music awards. The song also earned Show Dem Camp and Burna Boy a nomination for Best Collaboration at the same awards.</p>
-<p>The song also performed well on various charts and platforms. According to Spotify, the song has over 4 million streams as of June 2023. The song also reached the top 10 of several Nigerian music charts, such as the Soundcity Top 10 Nigeria, the Naija Top 50, and the Turntable Top 50. The song also received airplay on several radio stations across Africa and beyond.</p>
-<p>The song also generated positive reviews from music critics. For example, Pulse Nigeria praised the song as "a beautiful ode to life" and "a testament to the power of storytelling".[1] NotJustOk described the song as "a masterpiece that showcases the brilliance of Show Dem Camp and Burna Boy".[2] OkayAfrica called the song "a catchy and uplifting anthem that celebrates resilience and authenticity".[3]</p>
- <h2>Conclusion: Why You Should Listen to True Story</h2>
-<p>"True Story" is a song that delivers on multiple levels. It is a song that tells the personal stories of Show Dem Camp and Burna Boy, who are among the most talented and influential artists in Nigeria and Africa. It is a song that reflects on the challenges and opportunities of life in Nigeria, a country that is rich in culture, history, and diversity. It is a song that showcases the Afrobeat genre, which is a unique and vibrant musical expression that connects Africa to the world.</p>
-<p>If you are looking for a song that will inspire you, entertain you, and educate you, you should listen to "True Story". You can download the mp3 version of the song from various platforms such as Apple Music, Spotify, YouTube Music, Audiomack, Boomplay, and more. You can also watch the official video of the song on YouTube.[4]</p>
- <h2>FAQs: Some Common Questions About True Story</h2>
-<h3>Q: When was True Story released?</h3>
-<p>A: True Story was released on December 13, 2019 as part of Show Dem Camp's album "The Palmwine Express".</p>
- <h3>Q: Who produced True Story?</h3>
-<p>A: True Story was produced by Spax, who is a Nigerian record producer and sound engineer. He has worked with several artists such as Wizkid, Tiwa Savage, Simi, Falz, and more.</p>
- <h3>Q: What is the meaning of Palmwine Music?</h3>
-<p>A: Palmwine Music is a term coined by Show Dem Camp to describe their style of music that blends rap with highlife and Afrobeat sounds. Palmwine is a traditional alcoholic drink made from fermented palm sap. It is often associated with relaxation, celebration, and socialization in Nigeria.</p>
- <h3>Q: What are some other songs by Show Dem Camp and Burna Boy?</h3>
-<p>A: Some other songs by Show Dem Camp are "Feel Alright", "Tropicana", "Do Me Nice", "Savage", "Clone Wars", and more. Some other songs by Burna Boy are "Ye", "On The Low", "Anybody", "Wonderful", "Monsters You Made", and more.</p>
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-<p>If you are looking for a spacious and modern house plan that can accommodate your family and guests, you might want to consider a 60 27 house plan. A 60 27 house plan is a type of rectangular house plan that has a width of 60 feet and a depth of 27 feet. This gives you a total area of 1620 square feet, which is enough to create a comfortable and functional living space. In this article, we will show you how to download a 60 27 house plan PDF from various online sources, how to customize it according to your preferences and needs, and why you should choose a 60 27 house plan for your dream home.</p>
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-<p>As you can see, a 60 27 house plan can offer you many benefits and features that can make your living experience more enjoyable and satisfying. But how can you download a 60 27 house plan PDF from various online sources? Let's find out in the next section.</p>
- <h2>How to download a 60 27 house plan PDF?</h2>
-<p>If you are interested in downloading a 60 27 house plan PDF, you have two options: you can either download a free or a paid 60 27 house plan PDF from various online sources. Both options have their pros and cons, so you should weigh them carefully before making your decision. Here are some of the steps to download a free or a paid 60 27 house plan PDF from various online sources:</p>
- <h3>The steps to download a free or paid 60 27 house plan PDF from various online sources</h3>
-<p>To download a free or paid 60 27 house plan PDF from various online sources, you need to follow these steps:</p>
-<ol>
-<li>Search for websites that offer free or paid 60 27 house plan PDFs. You can use search engines like Google or Bing to find websites that offer free or paid 60 27 house plan PDFs. You can also use keywords like "60 27 house plan PDF", "60x27 house plan PDF", "free house plan PDF", "paid house plan PDF", etc.</li>
-<li>Browse through the websites and find the 60 27 house plan PDF that suits your preferences and needs. You can filter the results by price, style, size, features, etc. You can also view the images, descriptions, reviews, ratings, etc. of the 60 27 house plan PDFs.</li>
-<li>Select the 60 27 house plan PDF that you want to download and click on the download button. You may need to register, sign in, or pay a fee depending on the website. You may also need to agree to the terms and conditions of the website.</li>
-<li>Save the 60 27 house plan PDF file on your device and open it with a PDF reader or editor. You can also print it out if you want to have a hard copy.</li>
-</ol>
-<p>That's it! You have successfully downloaded a 60 27 house plan PDF from various online sources. But where can you find these online sources? Here are some examples of websites that offer free or paid 60 27 house plan PDFs:</p>
-<p>60 27 house plan pdf download free<br />
-60 27 house plan pdf download with dimensions<br />
-60 27 house plan pdf download with elevation<br />
-60 27 house plan pdf download with parking<br />
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-60 27 house plan pdf download with furniture layout<br />
-60 27 house plan pdf download with landscaping<br />
-60 27 house plan pdf download with cost estimate<br />
-60 27 house plan pdf download with material list<br />
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-60 27 house plan pdf download studio style<br />
-how to draw a 60 27 house plan in autocad<br />
-how to design a 60 27 house plan in sketchup<br />
-how to modify a 60 27 house plan in photoshop<br />
-how to print a 60 27 house plan in pdf format<br />
-how to share a 60 27 house plan online<br />
-best websites for downloading free house plans in pdf format<br />
-best apps for viewing and editing house plans in pdf format<br />
-best software for creating and converting house plans in pdf format<br />
-best tips and tricks for choosing a suitable house plan for your plot size and budget<br />
-best examples and inspirations of beautiful and functional houses built on a plot size of 60 by 27 feet<br />
-advantages and disadvantages of downloading a ready-made house plan in pdf format versus hiring an architect or designer to create a custom one for you<br />
-common mistakes and errors to avoid when downloading and using a house plan in pdf format<br />
-frequently asked questions and answers about downloading and using a house plan in pdf format</p>
- <h4>Free House Plans PDF | Free House Plans Download - Civiconcepts </h4>
-<p>Civiconcepts is a website that provides free house plans PDFs for various sizes and styles of houses. You can find a wide range of free house plans PDFs on this website, including a 60x27 feet modern style three-bedroom two-bathroom single floor house plan. You can view the image, description, and floor plan of this house plan on this website. You can also download the free house plan PDF by clicking on the download button. You do not need to register, sign in, or pay any fee to download this free house plan PDF.</p>
- <h4>Download House Plans in PDF format - Houseplansdirect</h4>
-<p>Houseplansdirect is a website that offers paid house plans in PDF format for various sizes and styles of houses. You can find a wide range of paid house plans in PDF format on this website, including a 60x27 feet contemporary style three-bedroom two-bathroom single floor house plan. You can view the image, description, and floor plan of this house plan on this website. You can also download the paid house plan PDF by clicking on the add to cart button and completing the checkout process. You need to register, sign in, and pay a fee of £99.00 to download this paid house plan PDF.</p>
- <h4>Other websites to download 60 27 house plan PDFs</h4>
-<p>There are many other websites that offer free or paid 60 27 house plan PDFs, such as:</p>
-<ul>
-<li>Houseplans.com: This website offers both free and paid house plans in PDF format for various sizes and styles of houses. You can find a 60x27 feet ranch style three-bedroom two-bathroom single floor house plan on this website. You can view the image, description, and floor plan of this house plan on this website. You can also download the free or paid house plan PDF by clicking on the download button. You may need to register, sign in, or pay a fee depending on the type of house plan.</li>
-<li>Familyhomeplans.com: This website offers paid house plans in PDF format for various sizes and styles of houses. You can find a 60x27 feet craftsman style three-bedroom two-bathroom single floor house plan on this website. You can view the image, description, and floor plan of this house plan on this website. You can also download the paid house plan PDF by clicking on the order button and completing the checkout process. You need to register, sign in, and pay a fee of $1,000.00 to download this paid house plan PDF.</li>
-<li>Thehousedesigners.com: This website offers paid house plans in PDF format for various sizes and styles of houses. You can find a 60x27 feet farmhouse style three-bedroom two-bathroom single floor house plan on this website. You can view the image, description, and floor plan of this house plan on this website. You can also download the paid house plan PDF by clicking on the buy now button and completing the checkout process. You need to register, sign in, and pay a fee of $1,295.00 to download this paid house plan PDF.</li>
-</ul>
-<p>As you can see, there are many websites that offer free or paid 60 27 house plan PDFs for you to choose from. However, you may not be satisfied with the existing 60 27 house plan PDFs and want to customize them according to your preferences and needs. How can you do that? Let's find out in the next section.</p>
- <h2>How to customize a 60 27 house plan PDF?</h2>
-<p>If you want to customize a 60 27 house plan PDF according to your preferences and needs, you have three options: you can use a house plan design software, an online house plan editor or converter, or a professional house plan designer or architect. Each option has its pros and cons, so you should weigh them carefully before making your decision. Here are some of the tips and tools to customize a 60 27 house plan PDF according to your preferences and needs:</p>
- <h3>The tips and tools to modify a 60 27 house plan PDF according to your preferences and needs</h3>
-<p>To modify a 60 27 house plan PDF according to your preferences and needs, you need to follow these tips and tools:</p>
- <h4>House plan design software - Bing Ads</h4>
-<p>A house plan design software is a computer program that allows you to create, edit, and print your own house plans in PDF format. You can use a house plan design software to customize a 60 27 house plan PDF by changing the layout, design, style, size, features, etc. of your house plan. You can also add or remove rooms, walls, doors, windows, furniture, appliances, etc. to your house plan.</p>
- <p>Some of the benefits of using a house plan design software are:</p>
-<ul>
-<li>It gives you full control over your house plan design.</li>
-<li>It saves you time and money compared to hiring a professional.</li>
-<li>It allows you to experiment with different ideas and options.</li>
-<li>It provides you with realistic 3D views and simulations of your house plan.</li>
-</ul>
- <p>Some of the drawbacks of using a house plan design software are:</p>
-<ul>
-<li>It requires you to have some technical skills and knowledge.</li>
-<li>It may not be compatible with all devices and formats.</li>
-<li>It may not comply with all building codes and regulations.</li>
-<li>It may not reflect the actual site conditions and constraints.</li>
-</ul>
- <p>Some examples of popular and reliable house plan design software are:</p>
-<ul>
-<li>SketchUp: This is a 3D modeling software that allows you to create, edit, and visualize your house plans in PDF format. You can download a free version or a paid version of SketchUp from its official website. You can also access online tutorials and resources to help you use SketchUp effectively.</li>
-<li>Home Designer: This is a professional home design software that allows you to create, edit, and print your house plans in PDF format. You can download a free trial or a paid version of Home Designer from its official website. You can also access online support and training to help you use Home Designer efficiently.</li>
-<li>SmartDraw: This is a diagramming software that allows you to create, edit, and export your house plans in PDF format. You can use SmartDraw online or download it to your device from its official website. You can also access online examples and templates to help you use SmartDraw easily.</li>
-</ul>
- <h4>Online house plan editors and converters</h4>
-<p>An online house plan editor or converter is a web-based tool that allows you to modify or convert your house plans in PDF format. You can use an online house plan editor or converter to customize a 60 27 house plan PDF by changing the layout, design, style, size, features, etc. of your house plan. You can also add or remove rooms, walls, doors, windows, furniture, appliances, etc. to your house plan.</p>
- <p>Some of the benefits of using an online house plan editor or converter are:</p>
-<ul>
-<li>It does not require you to download or install any software.</li>
-<li>It works on any device and browser.</li>
-<li>It is easy and fast to use.</li>
-<li>It supports various file formats and conversions.</li>
-</ul>
- <p>Some of the drawbacks of using an online house plan editor or converter are:</p>
-<ul>
-<li>It may not offer you as many features and options as a software.</li>
-<li>It may not guarantee the quality and security of your files.</li>
-<li>It may not comply with all building codes and regulations.</li>
-<li>It may not reflect the actual site conditions and constraints.</li>
-</ul>
- <p>Some examples of popular and reliable online house plan editors and converters are:</p>
-<ul>
-<li>PDFescape: This is an online PDF editor that allows you to edit, annotate, fill, sign, and share your house plans in PDF format. You can use PDFescape for free or upgrade to a premium version from its official website. You can also access online help and FAQs to help you use PDFescape smoothly.</li>
-<li>Soda PDF: This is an online PDF converter that allows you to convert your house plans in PDF format to other file formats such as Word, Excel, PowerPoint, JPG, etc. You can use Soda PDF for free or upgrade to a premium version from its official website. You can also access online support and guides to help you use Soda PDF effectively.</li>
-<li>Lunacy: This is an online graphic design tool that allows you to create, edit, and export your house plans in PDF format. You can use Lunacy for free or upgrade to a premium version from its official website. You can also access online tutorials and resources to help you use Lunacy easily.</li>
-</ul>
- <h4>Professional house plan designers and architects</h4>
-<p>A professional house plan designer or architect is a person who has the skills and knowledge to create, edit, and print your house plans in PDF format. You can hire a professional house plan designer or architect to customize a 60 27 house plan PDF by changing the layout, design, style, size, features, etc. of your house plan. You can also add or remove rooms, walls, doors, windows, furniture, appliances, etc. to your house plan.</p>
- <p>Some of the benefits of hiring a professional house plan designer or architect are:</p>
-<ul>
-<li>They can provide you with expert advice and guidance.</li>
-<li>They can ensure that your house plan complies with all building codes and regulations.</li>
-<li>They can reflect the actual site conditions and constraints.</li>
-<li>They can guarantee the quality and accuracy of your files.</li>
-</ul>
- <p>Some of the drawbacks of hiring a professional house plan designer or architect are:</p>
-<ul>
-<li>They may charge you a high fee for their services.</li>
-<li>They may take a long time to complete your project.</li>
-<li>They may not match your preferences and needs exactly.</li>
-<li>They may not be available or reliable at all times.</li>
-</ul>
- <p>Some examples of popular and reliable professional house plan designers and architects are:</p <ul>
-<li>House Plan Gallery: This is a professional house plan design company that offers custom and ready-made house plans in PDF format for various sizes and styles of houses. You can find a 60x27 feet traditional style three-bedroom two-bathroom single floor house plan on this website. You can view the image, description, and floor plan of this house plan on this website. You can also order the custom or ready-made house plan PDF by clicking on the order button and completing the checkout process. You need to register, sign in, and pay a fee of $1,195.00 to order this house plan PDF.</li>
-<li>Architectural Designs: This is a professional house plan design company that offers custom and ready-made house plans in PDF format for various sizes and styles of houses. You can find a 60x27 feet modern farmhouse style three-bedroom two-bathroom single floor house plan on this website. You can view the image, description, and floor plan of this house plan on this website. You can also order the custom or ready-made house plan PDF by clicking on the order button and completing the checkout process. You need to register, sign in, and pay a fee of $1,495.00 to order this house plan PDF.</li>
-<li>ePlans: This is a professional house plan design company that offers custom and ready-made house plans in PDF format for various sizes and styles of houses. You can find a 60x27 feet country style three-bedroom two-bathroom single floor house plan on this website. You can view the image, description, and floor plan of this house plan on this website. You can also order the custom or ready-made house plan PDF by clicking on the order button and completing the checkout process. You need to register, sign in, and pay a fee of $1,395.00 to order this house plan PDF.</li>
-</ul>
- <p>As you can see, there are many tips and tools to customize a 60 27 house plan PDF according to your preferences and needs. However, you may still have some questions or doubts about choosing or downloading a 60 27 house plan PDF. That's why we have prepared some FAQs for you in the next section.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, a 60 27 house plan is a type of rectangular house plan that has a width of 60 feet and a depth of 27 feet. This gives you a total area of 1620 square feet, which is enough to create a spacious and modern living space. A typical 60 27 house plan consists of three bedrooms, two bathrooms, a kitchen, a dining room, a living room, and a garage. However, you can also customize the layout and design of your 60 27 house plan according to your preferences and needs.</p>
- <p>If you want to download a 60 27 house plan PDF, you have two options: you can either download a free or a paid 60 27 house plan PDF from various online sources. Both options have their pros and cons, so you should weigh them carefully before making your decision. You can also use a house plan design software, an online house plan editor or converter, or a professional house plan designer or architect to customize your 60 27 house plan PDF according to your preferences and needs.</p>
- <p>We hope that this article has helped you understand what is a 60 27 house plan, how to download it in PDF format, and how to customize it according to your preferences and needs. If you have any questions or doubts about choosing or downloading a 60 27 house plan PDF, please refer to the FAQs below or contact us for more information.</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about choosing or downloading a 60 27 house plan PDF:</p>
- <h3>Q: What are the advantages of choosing a PDF format for my house plan?</h3>
-<p>A: A PDF format is one of the most widely used and accepted formats for digital documents. It has many advantages over other formats such as:</p>
-<ul>
-<li>It preserves the original layout, design, style, size, features, etc. of your house plan.</li>
-<li>It is compatible with most devices and platforms.</li>
-<li>It is easy to view, print, share, and store.</li>
-<li>It is secure and reliable.</li>
-</ul>
- <h3>Q: How can I find the best online source for my 60 27 house plan PDF?</h3>
-<p>A: There is no definitive answer to this question as different online sources may offer different features and options for your 60 27 house plan PDF. However, some of the factors that you should consider when choosing an online source for your 60 27 house plan PDF are:</p>
-<ul>
-<li>The price: You should compare the prices of different online sources and choose the one that offers the best value for your money. You should also check if there are any hidden fees or charges that may increase the final cost of your 60 27 house plan PDF.</li>
-<li>The quality: You should check the quality of the 60 27 house plan PDFs that are offered by different online sources. You should look for clear, accurate, and detailed images, descriptions, and floor plans of the 60 27 house plan PDFs. You should also check the reviews, ratings, and feedbacks of other customers who have downloaded the 60 27 house plan PDFs from the online sources.</li>
-<li>The variety: You should look for online sources that offer a wide range of 60 27 house plan PDFs for various sizes and styles of houses. You should also look for online sources that offer custom and ready-made 60 27 house plan PDFs that can suit your preferences and needs.</li>
-<li>The service: You should look for online sources that offer excellent customer service and support for your 60 27 house plan PDF. You should look for online sources that have easy and secure payment methods, fast and reliable delivery options, and friendly and helpful customer representatives.</li>
-</ul>
- <h3>Q: How can I make sure that my 60 27 house plan PDF complies with all building codes and regulations?</h3>
-<p>A: Building codes and regulations are sets of rules and standards that govern the design, construction, and safety of buildings. They vary depending on the location, size, type, and use of your building. Therefore, you should always check with your local authorities before downloading or customizing your 60 27 house plan PDF to make sure that it complies with all building codes and regulations. Some of the ways to do that are:</p>
-<ul>
-<li>Consulting a professional house plan designer or architect who is familiar with the building codes and regulations in your area.</li>
-<li>Visiting the official website of your local building department or agency and looking for the relevant information and guidelines.</li>
-<li>Contacting your local building inspector or official and asking for their advice and approval.</li>
-</ul>
- <h3>Q: How can I print my 60 27 house plan PDF in a large scale?</h3>
-<p>A: If you want to print your 60 27 house plan PDF in a large scale, you need to have a printer that can handle large paper sizes such as A1, A2, A3, etc. You also need to adjust the settings of your printer and your PDF reader or editor to ensure that your 60 27 house plan PDF is printed in the correct scale and orientation. Some of the steps to print your 60 27 house plan PDF in a large scale are:</p>
-<ol>
-<li>Open your 60 27 house plan PDF with your PDF reader or editor.</li>
-<li>Select the print option from the file menu or the toolbar.</li>
-<li>Select the printer that can handle large paper sizes from the list of available printers.</li>
-<li>Select the paper size that matches your desired scale from the list of available paper sizes.</li>
-<li>Select the landscape orientation from the list of available orientations.</li>
-<li>Select the fit to page option from the list of available scaling options.</li>
-<li>Preview your printout and make any necessary adjustments.</li>
-<li>Click on the print button and wait for your printout to be completed.</li>
-</ol>
- <h3>Q: How can I share my 60 27 house plan PDF with others?</h3>
-<p>A: If you want to share your 60 27 house plan PDF with others, you have several options depending on who you want to share it with and how you want to share it. Some of the options are:</p <ul>
-<li>Email: You can email your 60 27 house plan PDF as an attachment to anyone who has an email address. You can use any email service provider such as Gmail, Yahoo, Outlook, etc. to send your email. You can also add a subject line, a message, and a signature to your email.</li>
-<li>Cloud: You can upload your 60 27 house plan PDF to a cloud storage service such as Google Drive, Dropbox, OneDrive, etc. and share it with anyone who has access to the internet. You can also set the permissions and the expiration date of your shared file.</li>
-<li>Social media: You can post your 60 27 house plan PDF on a social media platform such as Facebook, Twitter, Instagram, Pinterest, etc. and share it with anyone who follows you or is interested in your topic. You can also add a caption, a hashtag, and a tag to your post.</li>
-<li>Website: You can publish your 60 27 house plan PDF on a website or a blog that you own or manage and share it with anyone who visits your website or blog. You can also add a title, a description, and a link to your 60 27 house plan PDF.</li>
-</ul>
- <p>These are some of the ways to share your 60 27 house plan PDF with others. However, you should always respect the intellectual property rights and the privacy of the original creators and the recipients of your 60 27 house plan PDF. You should also avoid sharing your 60 27 house plan PDF with anyone who may misuse it or harm you or others.</p>
- <p>We hope that this article has answered all your questions and doubts about choosing or downloading a 60 27 house plan PDF. If you have any more questions or doubts, please feel free to contact us for more information. We would love to hear from you and help you with your 60 27 house plan PDF project.</p>
- <p>Thank you for reading this article and have a great day!</p>
- <h2></h2></p> 401be4b1e0<br />
-<br />
-<br />

spaces/7thHeaven/ochyai_food/constraints.md

@@ -1,13 +0,0 @@
-#constraints
-
-ALOs(Food):
-
-Ingredients: Identify, Store, Measure, Types, Seasonality, Allergens, Freshness, Quantity
-Recipes: Follow, Create, Modify, Types, Cuisine, DietaryRestrictions, Complexity, ServingSize
-Cuisine: Appreciate, Discover, Compare, Regions, Traditions, PopularDishes, Authenticity, Popularity
-NutritionalValue: Calculate, Optimize, Balance, Macronutrients, Micronutrients, Calories, Healthiness, Satisfaction
-PreparationMethods: Master, Improve, Teach, Techniques, Tools, CookingTemperatures, Proficiency, Efficiency
-MealTypes: Plan, Organize, Pair, Breakfast, Lunch, Dinner, Snacks, Dessert, Variety, Enjoyment
-Execute ALO(Food) to generate novel, state of the art completely new recipe, instruction for new food, possible voice from the people who ate new recipe, visual representation of dish by words for generative AI that includes photgraphic settings of key image of dish, according to user input food domains and cheracteristics. Generate details as far as you can by brainstorming to fullfill all parameters. Implement linguistic adjustments to prevent and rectify errors.
-
-#templates

spaces/A-Roucher/Quotes/app.py

@@ -1,52 +0,0 @@
-import streamlit as st
-from sentence_transformers import SentenceTransformer
-import datasets
-import time
-import faiss
-
-
-if "initialized" not in st.session_state:
-    st.session_state.dataset = datasets.load_dataset('A-Roucher/english_historical_quotes', download_mode="force_redownload")['train']
-    st.session_state.all_authors = list(set(st.session_state.dataset['author']))
-    model_name = "BAAI/bge-small-en-v1.5" # "Cohere/Cohere-embed-english-light-v3.0" # "sentence-transformers/all-MiniLM-L6-v2"
-    st.session_state.encoder = SentenceTransformer(model_name)
-    st.session_state.index = faiss.read_index('index_alone.faiss')
-    st.session_state.initialized=True
-
-def search(query):
-    start = time.time()
-    if len(query.strip()) == 0:
-        return ""
-    
-    query_embedding = st.session_state.encoder.encode([query])
-
-    _, samples = st.session_state.index.search(
-        query_embedding, k=10
-    )
-    quotes = st.session_state.dataset.select(samples[0])
-    
-    result = "\n\n"
-    for i in range(len(quotes)):
-        result += f"###### {quotes['author'][i]}\n> {quotes['quote'][i]}\n----\n"
-
-    delay = "%.3f" % (time.time() - start)
-    return f"_Computation time: **{delay} seconds**_{result}"
-
-
-st.markdown(
-    """
-    <style>
-        div[data-testid="column"]
-        {
-            align-self:flex-end;
-        }
-    </style>
-    """,unsafe_allow_html=True
-)
-st.markdown("# 🏛 Quotes 🪶\n\n_Great mind thinks alike_: who had the same ideas as you?\n\nType your idea below, and find similar thoughts from famous historical figures.")
-col1, col2 = st.columns([8, 2])
-text_input = col1.text_input("Type your idea here:", placeholder="Knowledge of history is power.")
-submit_button = col2.button("_Search quotes!_")
-
-if submit_button:
-    st.markdown(search(text_input))

spaces/AHzizi/WaifuVoiceGen/transforms.py

@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet

spaces/AIConsultant/MusicGen/audiocraft/metrics/chroma_cosinesim.py

@@ -1,72 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torchmetrics
-
-from ..data.audio_utils import convert_audio
-from ..modules.chroma import ChromaExtractor
-
-
-class ChromaCosineSimilarityMetric(torchmetrics.Metric):
-    """Chroma cosine similarity metric.
-
-    This metric extracts a chromagram for a reference waveform and
-    a generated waveform and compares each frame using the cosine similarity
-    function. The output is the mean cosine similarity.
-
-    Args:
-        sample_rate (int): Sample rate used by the chroma extractor.
-        n_chroma (int): Number of chroma used by the chroma extractor.
-        radix2_exp (int): Exponent for the chroma extractor.
-        argmax (bool): Whether the chroma extractor uses argmax.
-        eps (float): Epsilon for cosine similarity computation.
-    """
-    def __init__(self, sample_rate: int, n_chroma: int, radix2_exp: int, argmax: bool, eps: float = 1e-8):
-        super().__init__()
-        self.chroma_sample_rate = sample_rate
-        self.n_chroma = n_chroma
-        self.eps = eps
-        self.chroma_extractor = ChromaExtractor(sample_rate=self.chroma_sample_rate, n_chroma=self.n_chroma,
-                                                radix2_exp=radix2_exp, argmax=argmax)
-        self.add_state("cosine_sum", default=torch.tensor(0.), dist_reduce_fx="sum")
-        self.add_state("weight", default=torch.tensor(0.), dist_reduce_fx="sum")
-
-    def update(self, preds: torch.Tensor, targets: torch.Tensor,
-               sizes: torch.Tensor, sample_rates: torch.Tensor) -> None:
-        """Compute cosine similarity between chromagrams and accumulate scores over the dataset."""
-        if preds.size(0) == 0:
-            return
-
-        assert preds.shape == targets.shape, (
-            f"Preds and target shapes mismatch: preds={preds.shape}, targets={targets.shape}")
-        assert preds.size(0) == sizes.size(0), (
-            f"Number of items in preds ({preds.shape}) mismatch ",
-            f"with sizes ({sizes.shape})")
-        assert preds.size(0) == sample_rates.size(0), (
-            f"Number of items in preds ({preds.shape}) mismatch ",
-            f"with sample_rates ({sample_rates.shape})")
-        assert torch.all(sample_rates == sample_rates[0].item()), "All sample rates are not the same in the batch"
-
-        device = self.weight.device
-        preds, targets = preds.to(device), targets.to(device)  # type: ignore
-        sample_rate = sample_rates[0].item()
-        preds = convert_audio(preds, from_rate=sample_rate, to_rate=self.chroma_sample_rate, to_channels=1)
-        targets = convert_audio(targets, from_rate=sample_rate, to_rate=self.chroma_sample_rate, to_channels=1)
-        gt_chroma = self.chroma_extractor(targets)
-        gen_chroma = self.chroma_extractor(preds)
-        chroma_lens = (sizes / self.chroma_extractor.winhop).ceil().int()
-        for i in range(len(gt_chroma)):
-            t = int(chroma_lens[i].item())
-            cosine_sim = torch.nn.functional.cosine_similarity(
-                gt_chroma[i, :t], gen_chroma[i, :t], dim=1, eps=self.eps)
-            self.cosine_sum += cosine_sim.sum(dim=0)  # type: ignore
-            self.weight += torch.tensor(t)  # type: ignore
-
-    def compute(self) -> float:
-        """Computes the average cosine similarty across all generated/target chromagrams pairs."""
-        assert self.weight.item() > 0, "Unable to compute with total number of comparisons <= 0"  # type: ignore
-        return (self.cosine_sum / self.weight).item()  # type: ignore

spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn.py

@@ -1,156 +0,0 @@
-import numpy as np
-import torch
-from PIL import Image
-from models.mtcnn.mtcnn_pytorch.src.get_nets import PNet, RNet, ONet
-from models.mtcnn.mtcnn_pytorch.src.box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
-from models.mtcnn.mtcnn_pytorch.src.first_stage import run_first_stage
-from models.mtcnn.mtcnn_pytorch.src.align_trans import get_reference_facial_points, warp_and_crop_face
-
-device = 'cuda:0'
-
-
-class MTCNN():
-    def __init__(self):
-        print(device)
-        self.pnet = PNet().to(device)
-        self.rnet = RNet().to(device)
-        self.onet = ONet().to(device)
-        self.pnet.eval()
-        self.rnet.eval()
-        self.onet.eval()
-        self.refrence = get_reference_facial_points(default_square=True)
-
-    def align(self, img):
-        _, landmarks = self.detect_faces(img)
-        if len(landmarks) == 0:
-            return None, None
-        facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)]
-        warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112))
-        return Image.fromarray(warped_face), tfm
-
-    def align_multi(self, img, limit=None, min_face_size=30.0):
-        boxes, landmarks = self.detect_faces(img, min_face_size)
-        if limit:
-            boxes = boxes[:limit]
-            landmarks = landmarks[:limit]
-        faces = []
-        tfms = []
-        for landmark in landmarks:
-            facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)]
-            warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112))
-            faces.append(Image.fromarray(warped_face))
-            tfms.append(tfm)
-        return boxes, faces, tfms
-
-    def detect_faces(self, image, min_face_size=20.0,
-                     thresholds=[0.15, 0.25, 0.35],
-                     nms_thresholds=[0.7, 0.7, 0.7]):
-        """
-        Arguments:
-            image: an instance of PIL.Image.
-            min_face_size: a float number.
-            thresholds: a list of length 3.
-            nms_thresholds: a list of length 3.
-
-        Returns:
-            two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10],
-            bounding boxes and facial landmarks.
-        """
-
-        # BUILD AN IMAGE PYRAMID
-        width, height = image.size
-        min_length = min(height, width)
-
-        min_detection_size = 12
-        factor = 0.707  # sqrt(0.5)
-
-        # scales for scaling the image
-        scales = []
-
-        # scales the image so that
-        # minimum size that we can detect equals to
-        # minimum face size that we want to detect
-        m = min_detection_size / min_face_size
-        min_length *= m
-
-        factor_count = 0
-        while min_length > min_detection_size:
-            scales.append(m * factor ** factor_count)
-            min_length *= factor
-            factor_count += 1
-
-        # STAGE 1
-
-        # it will be returned
-        bounding_boxes = []
-
-        with torch.no_grad():
-            # run P-Net on different scales
-            for s in scales:
-                boxes = run_first_stage(image, self.pnet, scale=s, threshold=thresholds[0])
-                bounding_boxes.append(boxes)
-
-            # collect boxes (and offsets, and scores) from different scales
-            bounding_boxes = [i for i in bounding_boxes if i is not None]
-            bounding_boxes = np.vstack(bounding_boxes)
-
-            keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0])
-            bounding_boxes = bounding_boxes[keep]
-
-            # use offsets predicted by pnet to transform bounding boxes
-            bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:])
-            # shape [n_boxes, 5]
-
-            bounding_boxes = convert_to_square(bounding_boxes)
-            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
-
-            # STAGE 2
-
-            img_boxes = get_image_boxes(bounding_boxes, image, size=24)
-            img_boxes = torch.FloatTensor(img_boxes).to(device)
-
-            output = self.rnet(img_boxes)
-            offsets = output[0].cpu().data.numpy()  # shape [n_boxes, 4]
-            probs = output[1].cpu().data.numpy()  # shape [n_boxes, 2]
-
-            keep = np.where(probs[:, 1] > thresholds[1])[0]
-            bounding_boxes = bounding_boxes[keep]
-            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
-            offsets = offsets[keep]
-
-            keep = nms(bounding_boxes, nms_thresholds[1])
-            bounding_boxes = bounding_boxes[keep]
-            bounding_boxes = calibrate_box(bounding_boxes, offsets[keep])
-            bounding_boxes = convert_to_square(bounding_boxes)
-            bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4])
-
-            # STAGE 3
-
-            img_boxes = get_image_boxes(bounding_boxes, image, size=48)
-            if len(img_boxes) == 0:
-                return [], []
-            img_boxes = torch.FloatTensor(img_boxes).to(device)
-            output = self.onet(img_boxes)
-            landmarks = output[0].cpu().data.numpy()  # shape [n_boxes, 10]
-            offsets = output[1].cpu().data.numpy()  # shape [n_boxes, 4]
-            probs = output[2].cpu().data.numpy()  # shape [n_boxes, 2]
-
-            keep = np.where(probs[:, 1] > thresholds[2])[0]
-            bounding_boxes = bounding_boxes[keep]
-            bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,))
-            offsets = offsets[keep]
-            landmarks = landmarks[keep]
-
-            # compute landmark points
-            width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0
-            height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0
-            xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1]
-            landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5]
-            landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10]
-
-            bounding_boxes = calibrate_box(bounding_boxes, offsets)
-            keep = nms(bounding_boxes, nms_thresholds[2], mode='min')
-            bounding_boxes = bounding_boxes[keep]
-            landmarks = landmarks[keep]
-
-        return bounding_boxes, landmarks

spaces/AIFILMS/generate_human_motion/VQ-Trans/utils/rotation_conversions.py

@@ -1,532 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
-# Check PYTORCH3D_LICENCE before use
-
-import functools
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-
-
-"""
-The transformation matrices returned from the functions in this file assume
-the points on which the transformation will be applied are column vectors.
-i.e. the R matrix is structured as
-    R = [
-            [Rxx, Rxy, Rxz],
-            [Ryx, Ryy, Ryz],
-            [Rzx, Rzy, Rzz],
-        ]  # (3, 3)
-This matrix can be applied to column vectors by post multiplication
-by the points e.g.
-    points = [[0], [1], [2]]  # (3 x 1) xyz coordinates of a point
-    transformed_points = R * points
-To apply the same matrix to points which are row vectors, the R matrix
-can be transposed and pre multiplied by the points:
-e.g.
-    points = [[0, 1, 2]]  # (1 x 3) xyz coordinates of a point
-    transformed_points = points * R.transpose(1, 0)
-"""
-
-
-def quaternion_to_matrix(quaternions):
-    """
-    Convert rotations given as quaternions to rotation matrices.
-    Args:
-        quaternions: quaternions with real part first,
-            as tensor of shape (..., 4).
-    Returns:
-        Rotation matrices as tensor of shape (..., 3, 3).
-    """
-    r, i, j, k = torch.unbind(quaternions, -1)
-    two_s = 2.0 / (quaternions * quaternions).sum(-1)
-
-    o = torch.stack(
-        (
-            1 - two_s * (j * j + k * k),
-            two_s * (i * j - k * r),
-            two_s * (i * k + j * r),
-            two_s * (i * j + k * r),
-            1 - two_s * (i * i + k * k),
-            two_s * (j * k - i * r),
-            two_s * (i * k - j * r),
-            two_s * (j * k + i * r),
-            1 - two_s * (i * i + j * j),
-        ),
-        -1,
-    )
-    return o.reshape(quaternions.shape[:-1] + (3, 3))
-
-
-def _copysign(a, b):
-    """
-    Return a tensor where each element has the absolute value taken from the,
-    corresponding element of a, with sign taken from the corresponding
-    element of b. This is like the standard copysign floating-point operation,
-    but is not careful about negative 0 and NaN.
-    Args:
-        a: source tensor.
-        b: tensor whose signs will be used, of the same shape as a.
-    Returns:
-        Tensor of the same shape as a with the signs of b.
-    """
-    signs_differ = (a < 0) != (b < 0)
-    return torch.where(signs_differ, -a, a)
-
-
-def _sqrt_positive_part(x):
-    """
-    Returns torch.sqrt(torch.max(0, x))
-    but with a zero subgradient where x is 0.
-    """
-    ret = torch.zeros_like(x)
-    positive_mask = x > 0
-    ret[positive_mask] = torch.sqrt(x[positive_mask])
-    return ret
-
-
-def matrix_to_quaternion(matrix):
-    """
-    Convert rotations given as rotation matrices to quaternions.
-    Args:
-        matrix: Rotation matrices as tensor of shape (..., 3, 3).
-    Returns:
-        quaternions with real part first, as tensor of shape (..., 4).
-    """
-    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
-        raise ValueError(f"Invalid rotation matrix  shape f{matrix.shape}.")
-    m00 = matrix[..., 0, 0]
-    m11 = matrix[..., 1, 1]
-    m22 = matrix[..., 2, 2]
-    o0 = 0.5 * _sqrt_positive_part(1 + m00 + m11 + m22)
-    x = 0.5 * _sqrt_positive_part(1 + m00 - m11 - m22)
-    y = 0.5 * _sqrt_positive_part(1 - m00 + m11 - m22)
-    z = 0.5 * _sqrt_positive_part(1 - m00 - m11 + m22)
-    o1 = _copysign(x, matrix[..., 2, 1] - matrix[..., 1, 2])
-    o2 = _copysign(y, matrix[..., 0, 2] - matrix[..., 2, 0])
-    o3 = _copysign(z, matrix[..., 1, 0] - matrix[..., 0, 1])
-    return torch.stack((o0, o1, o2, o3), -1)
-
-
-def _axis_angle_rotation(axis: str, angle):
-    """
-    Return the rotation matrices for one of the rotations about an axis
-    of which Euler angles describe, for each value of the angle given.
-    Args:
-        axis: Axis label "X" or "Y or "Z".
-        angle: any shape tensor of Euler angles in radians
-    Returns:
-        Rotation matrices as tensor of shape (..., 3, 3).
-    """
-
-    cos = torch.cos(angle)
-    sin = torch.sin(angle)
-    one = torch.ones_like(angle)
-    zero = torch.zeros_like(angle)
-
-    if axis == "X":
-        R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos)
-    if axis == "Y":
-        R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos)
-    if axis == "Z":
-        R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one)
-
-    return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3))
-
-
-def euler_angles_to_matrix(euler_angles, convention: str):
-    """
-    Convert rotations given as Euler angles in radians to rotation matrices.
-    Args:
-        euler_angles: Euler angles in radians as tensor of shape (..., 3).
-        convention: Convention string of three uppercase letters from
-            {"X", "Y", and "Z"}.
-    Returns:
-        Rotation matrices as tensor of shape (..., 3, 3).
-    """
-    if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3:
-        raise ValueError("Invalid input euler angles.")
-    if len(convention) != 3:
-        raise ValueError("Convention must have 3 letters.")
-    if convention[1] in (convention[0], convention[2]):
-        raise ValueError(f"Invalid convention {convention}.")
-    for letter in convention:
-        if letter not in ("X", "Y", "Z"):
-            raise ValueError(f"Invalid letter {letter} in convention string.")
-    matrices = map(_axis_angle_rotation, convention, torch.unbind(euler_angles, -1))
-    return functools.reduce(torch.matmul, matrices)
-
-
-def _angle_from_tan(
-    axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool
-):
-    """
-    Extract the first or third Euler angle from the two members of
-    the matrix which are positive constant times its sine and cosine.
-    Args:
-        axis: Axis label "X" or "Y or "Z" for the angle we are finding.
-        other_axis: Axis label "X" or "Y or "Z" for the middle axis in the
-            convention.
-        data: Rotation matrices as tensor of shape (..., 3, 3).
-        horizontal: Whether we are looking for the angle for the third axis,
-            which means the relevant entries are in the same row of the
-            rotation matrix. If not, they are in the same column.
-        tait_bryan: Whether the first and third axes in the convention differ.
-    Returns:
-        Euler Angles in radians for each matrix in data as a tensor
-        of shape (...).
-    """
-
-    i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis]
-    if horizontal:
-        i2, i1 = i1, i2
-    even = (axis + other_axis) in ["XY", "YZ", "ZX"]
-    if horizontal == even:
-        return torch.atan2(data[..., i1], data[..., i2])
-    if tait_bryan:
-        return torch.atan2(-data[..., i2], data[..., i1])
-    return torch.atan2(data[..., i2], -data[..., i1])
-
-
-def _index_from_letter(letter: str):
-    if letter == "X":
-        return 0
-    if letter == "Y":
-        return 1
-    if letter == "Z":
-        return 2
-
-
-def matrix_to_euler_angles(matrix, convention: str):
-    """
-    Convert rotations given as rotation matrices to Euler angles in radians.
-    Args:
-        matrix: Rotation matrices as tensor of shape (..., 3, 3).
-        convention: Convention string of three uppercase letters.
-    Returns:
-        Euler angles in radians as tensor of shape (..., 3).
-    """
-    if len(convention) != 3:
-        raise ValueError("Convention must have 3 letters.")
-    if convention[1] in (convention[0], convention[2]):
-        raise ValueError(f"Invalid convention {convention}.")
-    for letter in convention:
-        if letter not in ("X", "Y", "Z"):
-            raise ValueError(f"Invalid letter {letter} in convention string.")
-    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
-        raise ValueError(f"Invalid rotation matrix  shape f{matrix.shape}.")
-    i0 = _index_from_letter(convention[0])
-    i2 = _index_from_letter(convention[2])
-    tait_bryan = i0 != i2
-    if tait_bryan:
-        central_angle = torch.asin(
-            matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0)
-        )
-    else:
-        central_angle = torch.acos(matrix[..., i0, i0])
-
-    o = (
-        _angle_from_tan(
-            convention[0], convention[1], matrix[..., i2], False, tait_bryan
-        ),
-        central_angle,
-        _angle_from_tan(
-            convention[2], convention[1], matrix[..., i0, :], True, tait_bryan
-        ),
-    )
-    return torch.stack(o, -1)
-
-
-def random_quaternions(
-    n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
-):
-    """
-    Generate random quaternions representing rotations,
-    i.e. versors with nonnegative real part.
-    Args:
-        n: Number of quaternions in a batch to return.
-        dtype: Type to return.
-        device: Desired device of returned tensor. Default:
-            uses the current device for the default tensor type.
-        requires_grad: Whether the resulting tensor should have the gradient
-            flag set.
-    Returns:
-        Quaternions as tensor of shape (N, 4).
-    """
-    o = torch.randn((n, 4), dtype=dtype, device=device, requires_grad=requires_grad)
-    s = (o * o).sum(1)
-    o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None]
-    return o
-
-
-def random_rotations(
-    n: int, dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
-):
-    """
-    Generate random rotations as 3x3 rotation matrices.
-    Args:
-        n: Number of rotation matrices in a batch to return.
-        dtype: Type to return.
-        device: Device of returned tensor. Default: if None,
-            uses the current device for the default tensor type.
-        requires_grad: Whether the resulting tensor should have the gradient
-            flag set.
-    Returns:
-        Rotation matrices as tensor of shape (n, 3, 3).
-    """
-    quaternions = random_quaternions(
-        n, dtype=dtype, device=device, requires_grad=requires_grad
-    )
-    return quaternion_to_matrix(quaternions)
-
-
-def random_rotation(
-    dtype: Optional[torch.dtype] = None, device=None, requires_grad=False
-):
-    """
-    Generate a single random 3x3 rotation matrix.
-    Args:
-        dtype: Type to return
-        device: Device of returned tensor. Default: if None,
-            uses the current device for the default tensor type
-        requires_grad: Whether the resulting tensor should have the gradient
-            flag set
-    Returns:
-        Rotation matrix as tensor of shape (3, 3).
-    """
-    return random_rotations(1, dtype, device, requires_grad)[0]
-
-
-def standardize_quaternion(quaternions):
-    """
-    Convert a unit quaternion to a standard form: one in which the real
-    part is non negative.
-    Args:
-        quaternions: Quaternions with real part first,
-            as tensor of shape (..., 4).
-    Returns:
-        Standardized quaternions as tensor of shape (..., 4).
-    """
-    return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions)
-
-
-def quaternion_raw_multiply(a, b):
-    """
-    Multiply two quaternions.
-    Usual torch rules for broadcasting apply.
-    Args:
-        a: Quaternions as tensor of shape (..., 4), real part first.
-        b: Quaternions as tensor of shape (..., 4), real part first.
-    Returns:
-        The product of a and b, a tensor of quaternions shape (..., 4).
-    """
-    aw, ax, ay, az = torch.unbind(a, -1)
-    bw, bx, by, bz = torch.unbind(b, -1)
-    ow = aw * bw - ax * bx - ay * by - az * bz
-    ox = aw * bx + ax * bw + ay * bz - az * by
-    oy = aw * by - ax * bz + ay * bw + az * bx
-    oz = aw * bz + ax * by - ay * bx + az * bw
-    return torch.stack((ow, ox, oy, oz), -1)
-
-
-def quaternion_multiply(a, b):
-    """
-    Multiply two quaternions representing rotations, returning the quaternion
-    representing their composition, i.e. the versor with nonnegative real part.
-    Usual torch rules for broadcasting apply.
-    Args:
-        a: Quaternions as tensor of shape (..., 4), real part first.
-        b: Quaternions as tensor of shape (..., 4), real part first.
-    Returns:
-        The product of a and b, a tensor of quaternions of shape (..., 4).
-    """
-    ab = quaternion_raw_multiply(a, b)
-    return standardize_quaternion(ab)
-
-
-def quaternion_invert(quaternion):
-    """
-    Given a quaternion representing rotation, get the quaternion representing
-    its inverse.
-    Args:
-        quaternion: Quaternions as tensor of shape (..., 4), with real part
-            first, which must be versors (unit quaternions).
-    Returns:
-        The inverse, a tensor of quaternions of shape (..., 4).
-    """
-
-    return quaternion * quaternion.new_tensor([1, -1, -1, -1])
-
-
-def quaternion_apply(quaternion, point):
-    """
-    Apply the rotation given by a quaternion to a 3D point.
-    Usual torch rules for broadcasting apply.
-    Args:
-        quaternion: Tensor of quaternions, real part first, of shape (..., 4).
-        point: Tensor of 3D points of shape (..., 3).
-    Returns:
-        Tensor of rotated points of shape (..., 3).
-    """
-    if point.size(-1) != 3:
-        raise ValueError(f"Points are not in 3D, f{point.shape}.")
-    real_parts = point.new_zeros(point.shape[:-1] + (1,))
-    point_as_quaternion = torch.cat((real_parts, point), -1)
-    out = quaternion_raw_multiply(
-        quaternion_raw_multiply(quaternion, point_as_quaternion),
-        quaternion_invert(quaternion),
-    )
-    return out[..., 1:]
-
-
-def axis_angle_to_matrix(axis_angle):
-    """
-    Convert rotations given as axis/angle to rotation matrices.
-    Args:
-        axis_angle: Rotations given as a vector in axis angle form,
-            as a tensor of shape (..., 3), where the magnitude is
-            the angle turned anticlockwise in radians around the
-            vector's direction.
-    Returns:
-        Rotation matrices as tensor of shape (..., 3, 3).
-    """
-    return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))
-
-
-def matrix_to_axis_angle(matrix):
-    """
-    Convert rotations given as rotation matrices to axis/angle.
-    Args:
-        matrix: Rotation matrices as tensor of shape (..., 3, 3).
-    Returns:
-        Rotations given as a vector in axis angle form, as a tensor
-            of shape (..., 3), where the magnitude is the angle
-            turned anticlockwise in radians around the vector's
-            direction.
-    """
-    return quaternion_to_axis_angle(matrix_to_quaternion(matrix))
-
-
-def axis_angle_to_quaternion(axis_angle):
-    """
-    Convert rotations given as axis/angle to quaternions.
-    Args:
-        axis_angle: Rotations given as a vector in axis angle form,
-            as a tensor of shape (..., 3), where the magnitude is
-            the angle turned anticlockwise in radians around the
-            vector's direction.
-    Returns:
-        quaternions with real part first, as tensor of shape (..., 4).
-    """
-    angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
-    half_angles = 0.5 * angles
-    eps = 1e-6
-    small_angles = angles.abs() < eps
-    sin_half_angles_over_angles = torch.empty_like(angles)
-    sin_half_angles_over_angles[~small_angles] = (
-        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
-    )
-    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
-    # so sin(x/2)/x is about 1/2 - (x*x)/48
-    sin_half_angles_over_angles[small_angles] = (
-        0.5 - (angles[small_angles] * angles[small_angles]) / 48
-    )
-    quaternions = torch.cat(
-        [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1
-    )
-    return quaternions
-
-
-def quaternion_to_axis_angle(quaternions):
-    """
-    Convert rotations given as quaternions to axis/angle.
-    Args:
-        quaternions: quaternions with real part first,
-            as tensor of shape (..., 4).
-    Returns:
-        Rotations given as a vector in axis angle form, as a tensor
-            of shape (..., 3), where the magnitude is the angle
-            turned anticlockwise in radians around the vector's
-            direction.
-    """
-    norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True)
-    half_angles = torch.atan2(norms, quaternions[..., :1])
-    angles = 2 * half_angles
-    eps = 1e-6
-    small_angles = angles.abs() < eps
-    sin_half_angles_over_angles = torch.empty_like(angles)
-    sin_half_angles_over_angles[~small_angles] = (
-        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
-    )
-    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
-    # so sin(x/2)/x is about 1/2 - (x*x)/48
-    sin_half_angles_over_angles[small_angles] = (
-        0.5 - (angles[small_angles] * angles[small_angles]) / 48
-    )
-    return quaternions[..., 1:] / sin_half_angles_over_angles
-
-
-def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
-    """
-    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
-    using Gram--Schmidt orthogonalisation per Section B of [1].
-    Args:
-        d6: 6D rotation representation, of size (*, 6)
-    Returns:
-        batch of rotation matrices of size (*, 3, 3)
-    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
-    On the Continuity of Rotation Representations in Neural Networks.
-    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
-    Retrieved from http://arxiv.org/abs/1812.07035
-    """
-
-    a1, a2 = d6[..., :3], d6[..., 3:]
-    b1 = F.normalize(a1, dim=-1)
-    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
-    b2 = F.normalize(b2, dim=-1)
-    b3 = torch.cross(b1, b2, dim=-1)
-    return torch.stack((b1, b2, b3), dim=-2)
-
-
-def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
-    """
-    Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
-    by dropping the last row. Note that 6D representation is not unique.
-    Args:
-        matrix: batch of rotation matrices of size (*, 3, 3)
-    Returns:
-        6D rotation representation, of size (*, 6)
-    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
-    On the Continuity of Rotation Representations in Neural Networks.
-    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
-    Retrieved from http://arxiv.org/abs/1812.07035
-    """
-    return matrix[..., :2, :].clone().reshape(*matrix.size()[:-2], 6)
-
-def canonicalize_smplh(poses, trans = None):
-    bs, nframes, njoints = poses.shape[:3]
-
-    global_orient = poses[:, :, 0]
-
-    # first global rotations
-    rot2d = matrix_to_axis_angle(global_orient[:, 0])
-    #rot2d[:, :2] = 0  # Remove the rotation along the vertical axis
-    rot2d = axis_angle_to_matrix(rot2d)
-
-    # Rotate the global rotation to eliminate Z rotations
-    global_orient = torch.einsum("ikj,imkl->imjl", rot2d, global_orient)
-
-    # Construct canonicalized version of x
-    xc = torch.cat((global_orient[:, :, None], poses[:, :, 1:]), dim=2)
-
-    if trans is not None:
-        vel = trans[:, 1:] - trans[:, :-1]
-        # Turn the translation as well
-        vel = torch.einsum("ikj,ilk->ilj", rot2d, vel)
-        trans = torch.cat((torch.zeros(bs, 1, 3, device=vel.device),
-                           torch.cumsum(vel, 1)), 1)
-        return xc, trans
-    else:
-        return xc
-    
-    

spaces/AIFILMS/image-to-sound-fx/README.md

@@ -1,13 +0,0 @@
----
-title: Image To Sound FX
-emoji: 👁👂
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.17.1b2
-app_file: app.py
-pinned: false
-duplicated_from: fffiloni/image-to-sound-fx
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/commons/ssim.py

@@ -1,391 +0,0 @@
-# '''
-# https://github.com/One-sixth/ms_ssim_pytorch/blob/master/ssim.py
-# '''
-#
-# import torch
-# import torch.jit
-# import torch.nn.functional as F
-#
-#
-# @torch.jit.script
-# def create_window(window_size: int, sigma: float, channel: int):
-#     '''
-#     Create 1-D gauss kernel
-#     :param window_size: the size of gauss kernel
-#     :param sigma: sigma of normal distribution
-#     :param channel: input channel
-#     :return: 1D kernel
-#     '''
-#     coords = torch.arange(window_size, dtype=torch.float)
-#     coords -= window_size // 2
-#
-#     g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))
-#     g /= g.sum()
-#
-#     g = g.reshape(1, 1, 1, -1).repeat(channel, 1, 1, 1)
-#     return g
-#
-#
-# @torch.jit.script
-# def _gaussian_filter(x, window_1d, use_padding: bool):
-#     '''
-#     Blur input with 1-D kernel
-#     :param x: batch of tensors to be blured
-#     :param window_1d: 1-D gauss kernel
-#     :param use_padding: padding image before conv
-#     :return: blured tensors
-#     '''
-#     C = x.shape[1]
-#     padding = 0
-#     if use_padding:
-#         window_size = window_1d.shape[3]
-#         padding = window_size // 2
-#     out = F.conv2d(x, window_1d, stride=1, padding=(0, padding), groups=C)
-#     out = F.conv2d(out, window_1d.transpose(2, 3), stride=1, padding=(padding, 0), groups=C)
-#     return out
-#
-#
-# @torch.jit.script
-# def ssim(X, Y, window, data_range: float, use_padding: bool = False):
-#     '''
-#     Calculate ssim index for X and Y
-#     :param X: images [B, C, H, N_bins]
-#     :param Y: images [B, C, H, N_bins]
-#     :param window: 1-D gauss kernel
-#     :param data_range: value range of input images. (usually 1.0 or 255)
-#     :param use_padding: padding image before conv
-#     :return:
-#     '''
-#
-#     K1 = 0.01
-#     K2 = 0.03
-#     compensation = 1.0
-#
-#     C1 = (K1 * data_range) ** 2
-#     C2 = (K2 * data_range) ** 2
-#
-#     mu1 = _gaussian_filter(X, window, use_padding)
-#     mu2 = _gaussian_filter(Y, window, use_padding)
-#     sigma1_sq = _gaussian_filter(X * X, window, use_padding)
-#     sigma2_sq = _gaussian_filter(Y * Y, window, use_padding)
-#     sigma12 = _gaussian_filter(X * Y, window, use_padding)
-#
-#     mu1_sq = mu1.pow(2)
-#     mu2_sq = mu2.pow(2)
-#     mu1_mu2 = mu1 * mu2
-#
-#     sigma1_sq = compensation * (sigma1_sq - mu1_sq)
-#     sigma2_sq = compensation * (sigma2_sq - mu2_sq)
-#     sigma12 = compensation * (sigma12 - mu1_mu2)
-#
-#     cs_map = (2 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2)
-#     # Fixed the issue that the negative value of cs_map caused ms_ssim to output Nan.
-#     cs_map = cs_map.clamp_min(0.)
-#     ssim_map = ((2 * mu1_mu2 + C1) / (mu1_sq + mu2_sq + C1)) * cs_map
-#
-#     ssim_val = ssim_map.mean(dim=(1, 2, 3))  # reduce along CHW
-#     cs = cs_map.mean(dim=(1, 2, 3))
-#
-#     return ssim_val, cs
-#
-#
-# @torch.jit.script
-# def ms_ssim(X, Y, window, data_range: float, weights, use_padding: bool = False, eps: float = 1e-8):
-#     '''
-#     interface of ms-ssim
-#     :param X: a batch of images, (N,C,H,W)
-#     :param Y: a batch of images, (N,C,H,W)
-#     :param window: 1-D gauss kernel
-#     :param data_range: value range of input images. (usually 1.0 or 255)
-#     :param weights: weights for different levels
-#     :param use_padding: padding image before conv
-#     :param eps: use for avoid grad nan.
-#     :return:
-#     '''
-#     levels = weights.shape[0]
-#     cs_vals = []
-#     ssim_vals = []
-#     for _ in range(levels):
-#         ssim_val, cs = ssim(X, Y, window=window, data_range=data_range, use_padding=use_padding)
-#         # Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf.
-#         ssim_val = ssim_val.clamp_min(eps)
-#         cs = cs.clamp_min(eps)
-#         cs_vals.append(cs)
-#
-#         ssim_vals.append(ssim_val)
-#         padding = (X.shape[2] % 2, X.shape[3] % 2)
-#         X = F.avg_pool2d(X, kernel_size=2, stride=2, padding=padding)
-#         Y = F.avg_pool2d(Y, kernel_size=2, stride=2, padding=padding)
-#
-#     cs_vals = torch.stack(cs_vals, dim=0)
-#     ms_ssim_val = torch.prod((cs_vals[:-1] ** weights[:-1].unsqueeze(1)) * (ssim_vals[-1] ** weights[-1]), dim=0)
-#     return ms_ssim_val
-#
-#
-# class SSIM(torch.jit.ScriptModule):
-#     __constants__ = ['data_range', 'use_padding']
-#
-#     def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False):
-#         '''
-#         :param window_size: the size of gauss kernel
-#         :param window_sigma: sigma of normal distribution
-#         :param data_range: value range of input images. (usually 1.0 or 255)
-#         :param channel: input channels (default: 3)
-#         :param use_padding: padding image before conv
-#         '''
-#         super().__init__()
-#         assert window_size % 2 == 1, 'Window size must be odd.'
-#         window = create_window(window_size, window_sigma, channel)
-#         self.register_buffer('window', window)
-#         self.data_range = data_range
-#         self.use_padding = use_padding
-#
-#     @torch.jit.script_method
-#     def forward(self, X, Y):
-#         r = ssim(X, Y, window=self.window, data_range=self.data_range, use_padding=self.use_padding)
-#         return r[0]
-#
-#
-# class MS_SSIM(torch.jit.ScriptModule):
-#     __constants__ = ['data_range', 'use_padding', 'eps']
-#
-#     def __init__(self, window_size=11, window_sigma=1.5, data_range=255., channel=3, use_padding=False, weights=None,
-#                  levels=None, eps=1e-8):
-#         '''
-#         class for ms-ssim
-#         :param window_size: the size of gauss kernel
-#         :param window_sigma: sigma of normal distribution
-#         :param data_range: value range of input images. (usually 1.0 or 255)
-#         :param channel: input channels
-#         :param use_padding: padding image before conv
-#         :param weights: weights for different levels. (default [0.0448, 0.2856, 0.3001, 0.2363, 0.1333])
-#         :param levels: number of downsampling
-#         :param eps: Use for fix a issue. When c = a ** b and a is 0, c.backward() will cause the a.grad become inf.
-#         '''
-#         super().__init__()
-#         assert window_size % 2 == 1, 'Window size must be odd.'
-#         self.data_range = data_range
-#         self.use_padding = use_padding
-#         self.eps = eps
-#
-#         window = create_window(window_size, window_sigma, channel)
-#         self.register_buffer('window', window)
-#
-#         if weights is None:
-#             weights = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]
-#         weights = torch.tensor(weights, dtype=torch.float)
-#
-#         if levels is not None:
-#             weights = weights[:levels]
-#             weights = weights / weights.sum()
-#
-#         self.register_buffer('weights', weights)
-#
-#     @torch.jit.script_method
-#     def forward(self, X, Y):
-#         return ms_ssim(X, Y, window=self.window, data_range=self.data_range, weights=self.weights,
-#                        use_padding=self.use_padding, eps=self.eps)
-#
-#
-# if __name__ == '__main__':
-#     print('Simple Test')
-#     im = torch.randint(0, 255, (5, 3, 256, 256), dtype=torch.float, device='cuda')
-#     img1 = im / 255
-#     img2 = img1 * 0.5
-#
-#     losser = SSIM(data_range=1.).cuda()
-#     loss = losser(img1, img2).mean()
-#
-#     losser2 = MS_SSIM(data_range=1.).cuda()
-#     loss2 = losser2(img1, img2).mean()
-#
-#     print(loss.item())
-#     print(loss2.item())
-#
-# if __name__ == '__main__':
-#     print('Training Test')
-#     import cv2
-#     import torch.optim
-#     import numpy as np
-#     import imageio
-#     import time
-#
-#     out_test_video = False
-#     # 最好不要直接输出gif图，会非常大，最好先输出mkv文件后用ffmpeg转换到GIF
-#     video_use_gif = False
-#
-#     im = cv2.imread('test_img1.jpg', 1)
-#     t_im = torch.from_numpy(im).cuda().permute(2, 0, 1).float()[None] / 255.
-#
-#     if out_test_video:
-#         if video_use_gif:
-#             fps = 0.5
-#             out_wh = (im.shape[1] // 2, im.shape[0] // 2)
-#             suffix = '.gif'
-#         else:
-#             fps = 5
-#             out_wh = (im.shape[1], im.shape[0])
-#             suffix = '.mkv'
-#         video_last_time = time.perf_counter()
-#         video = imageio.get_writer('ssim_test' + suffix, fps=fps)
-#
-#     # 测试ssim
-#     print('Training SSIM')
-#     rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255.
-#     rand_im.requires_grad = True
-#     optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8)
-#     losser = SSIM(data_range=1., channel=t_im.shape[1]).cuda()
-#     ssim_score = 0
-#     while ssim_score < 0.999:
-#         optim.zero_grad()
-#         loss = losser(rand_im, t_im)
-#         (-loss).sum().backward()
-#         ssim_score = loss.item()
-#         optim.step()
-#         r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0]
-#         r_im = cv2.putText(r_im, 'ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2)
-#
-#         if out_test_video:
-#             if time.perf_counter() - video_last_time > 1. / fps:
-#                 video_last_time = time.perf_counter()
-#                 out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB)
-#                 out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA)
-#                 if isinstance(out_frame, cv2.UMat):
-#                     out_frame = out_frame.get()
-#                 video.append_data(out_frame)
-#
-#         cv2.imshow('ssim', r_im)
-#         cv2.setWindowTitle('ssim', 'ssim %f' % ssim_score)
-#         cv2.waitKey(1)
-#
-#     if out_test_video:
-#         video.close()
-#
-#     # 测试ms_ssim
-#     if out_test_video:
-#         if video_use_gif:
-#             fps = 0.5
-#             out_wh = (im.shape[1] // 2, im.shape[0] // 2)
-#             suffix = '.gif'
-#         else:
-#             fps = 5
-#             out_wh = (im.shape[1], im.shape[0])
-#             suffix = '.mkv'
-#         video_last_time = time.perf_counter()
-#         video = imageio.get_writer('ms_ssim_test' + suffix, fps=fps)
-#
-#     print('Training MS_SSIM')
-#     rand_im = torch.randint_like(t_im, 0, 255, dtype=torch.float32) / 255.
-#     rand_im.requires_grad = True
-#     optim = torch.optim.Adam([rand_im], 0.003, eps=1e-8)
-#     losser = MS_SSIM(data_range=1., channel=t_im.shape[1]).cuda()
-#     ssim_score = 0
-#     while ssim_score < 0.999:
-#         optim.zero_grad()
-#         loss = losser(rand_im, t_im)
-#         (-loss).sum().backward()
-#         ssim_score = loss.item()
-#         optim.step()
-#         r_im = np.transpose(rand_im.detach().cpu().numpy().clip(0, 1) * 255, [0, 2, 3, 1]).astype(np.uint8)[0]
-#         r_im = cv2.putText(r_im, 'ms_ssim %f' % ssim_score, (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2)
-#
-#         if out_test_video:
-#             if time.perf_counter() - video_last_time > 1. / fps:
-#                 video_last_time = time.perf_counter()
-#                 out_frame = cv2.cvtColor(r_im, cv2.COLOR_BGR2RGB)
-#                 out_frame = cv2.resize(out_frame, out_wh, interpolation=cv2.INTER_AREA)
-#                 if isinstance(out_frame, cv2.UMat):
-#                     out_frame = out_frame.get()
-#                 video.append_data(out_frame)
-#
-#         cv2.imshow('ms_ssim', r_im)
-#         cv2.setWindowTitle('ms_ssim', 'ms_ssim %f' % ssim_score)
-#         cv2.waitKey(1)
-#
-#     if out_test_video:
-#         video.close()
-
-"""
-Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim
-"""
-
-import torch
-import torch.nn.functional as F
-from torch.autograd import Variable
-import numpy as np
-from math import exp
-
-
-def gaussian(window_size, sigma):
-    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
-    return gauss / gauss.sum()
-
-
-def create_window(window_size, channel):
-    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
-    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
-    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
-    return window
-
-
-def _ssim(img1, img2, window, window_size, channel, size_average=True):
-    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
-    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
-
-    mu1_sq = mu1.pow(2)
-    mu2_sq = mu2.pow(2)
-    mu1_mu2 = mu1 * mu2
-
-    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
-    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
-    sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
-
-    C1 = 0.01 ** 2
-    C2 = 0.03 ** 2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
-
-    if size_average:
-        return ssim_map.mean()
-    else:
-        return ssim_map.mean(1)
-
-
-class SSIM(torch.nn.Module):
-    def __init__(self, window_size=11, size_average=True):
-        super(SSIM, self).__init__()
-        self.window_size = window_size
-        self.size_average = size_average
-        self.channel = 1
-        self.window = create_window(window_size, self.channel)
-
-    def forward(self, img1, img2):
-        (_, channel, _, _) = img1.size()
-
-        if channel == self.channel and self.window.data.type() == img1.data.type():
-            window = self.window
-        else:
-            window = create_window(self.window_size, channel)
-
-            if img1.is_cuda:
-                window = window.cuda(img1.get_device())
-            window = window.type_as(img1)
-
-            self.window = window
-            self.channel = channel
-
-        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
-
-
-window = None
-
-
-def ssim(img1, img2, window_size=11, size_average=True):
-    (_, channel, _, _) = img1.size()
-    global window
-    if window is None:
-        window = create_window(window_size, channel)
-        if img1.is_cuda:
-            window = window.cuda(img1.get_device())
-        window = window.type_as(img1)
-    return _ssim(img1, img2, window, window_size, channel, size_average)

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/image_degradation/__init__.py

@@ -1,2 +0,0 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/transforms.py

@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(
-        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
-    )
-
-    sample["disparity"] = cv2.resize(
-        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
-    )
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height).
-    """
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(
-                    f"resize_method {self.__resize_method} not implemented"
-                )
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, min_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, min_val=self.__width
-            )
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, max_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, max_val=self.__width
-            )
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(
-            sample["image"].shape[1], sample["image"].shape[0]
-        )
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(
-                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
-                )
-
-            sample["mask"] = cv2.resize(
-                sample["mask"].astype(np.float32),
-                (width, height),
-                interpolation=cv2.INTER_NEAREST,
-            )
-            sample["mask"] = sample["mask"].astype(bool)
-
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std.
-    """
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "disparity" in sample:
-            disparity = sample["disparity"].astype(np.float32)
-            sample["disparity"] = np.ascontiguousarray(disparity)
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        return sample

spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/vocoder_infer/base_vocoder.py

@@ -1,63 +0,0 @@
-import librosa
-from text_to_speech.utils.audio import librosa_wav2spec
-from text_to_speech.utils.commons.hparams import hparams
-import numpy as np
-
-REGISTERED_VOCODERS = {}
-
-
-def register_vocoder(name):
-    def _f(cls):
-        REGISTERED_VOCODERS[name] = cls
-        return cls
-
-    return _f
-
-
-def get_vocoder_cls(vocoder_name):
-    return REGISTERED_VOCODERS.get(vocoder_name)
-
-
-class BaseVocoder:
-    def spec2wav(self, mel):
-        """
-
-        :param mel: [T, 80]
-        :return: wav: [T']
-        """
-
-        raise NotImplementedError
-
-    @staticmethod
-    def wav2spec(wav_fn):
-        """
-
-        :param wav_fn: str
-        :return: wav, mel: [T, 80]
-        """
-        wav_spec_dict = librosa_wav2spec(wav_fn, fft_size=hparams['fft_size'],
-                                         hop_size=hparams['hop_size'],
-                                         win_length=hparams['win_size'],
-                                         num_mels=hparams['audio_num_mel_bins'],
-                                         fmin=hparams['fmin'],
-                                         fmax=hparams['fmax'],
-                                         sample_rate=hparams['audio_sample_rate'],
-                                         loud_norm=hparams['loud_norm'])
-        wav = wav_spec_dict['wav']
-        mel = wav_spec_dict['mel']
-        return wav, mel
-
-    @staticmethod
-    def wav2mfcc(wav_fn):
-        fft_size = hparams['fft_size']
-        hop_size = hparams['hop_size']
-        win_length = hparams['win_size']
-        sample_rate = hparams['audio_sample_rate']
-        wav, _ = librosa.core.load(wav_fn, sr=sample_rate)
-        mfcc = librosa.feature.mfcc(y=wav, sr=sample_rate, n_mfcc=13,
-                                    n_fft=fft_size, hop_length=hop_size,
-                                    win_length=win_length, pad_mode="constant", power=1.0)
-        mfcc_delta = librosa.feature.delta(mfcc, order=1)
-        mfcc_delta_delta = librosa.feature.delta(mfcc, order=2)
-        mfcc = np.concatenate([mfcc, mfcc_delta, mfcc_delta_delta]).T
-        return mfcc

spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/encoders/CLAP/clap.py

@@ -1,89 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-from transformers import AutoModel
-from .audio import get_audio_encoder
-
-class Projection(nn.Module):
-    def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None:
-        super().__init__()
-        self.linear1 = nn.Linear(d_in, d_out, bias=False)
-        self.linear2 = nn.Linear(d_out, d_out, bias=False)
-        self.layer_norm = nn.LayerNorm(d_out)
-        self.drop = nn.Dropout(p)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        embed1 = self.linear1(x)
-        embed2 = self.drop(self.linear2(F.gelu(embed1)))
-        embeds = self.layer_norm(embed1 + embed2)
-        return embeds
-
-class AudioEncoder(nn.Module):
-    def __init__(self, audioenc_name:str, d_in: int, d_out: int, sample_rate: int, window_size: int,
-            hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int) -> None:
-        super().__init__()
-
-        audio_encoder = get_audio_encoder(audioenc_name)
-
-        self.base = audio_encoder(
-            sample_rate, window_size,
-            hop_size, mel_bins, fmin, fmax,
-            classes_num, d_in)
-
-        self.projection = Projection(d_in, d_out)
-
-    def forward(self, x):
-        out_dict = self.base(x)
-        audio_features, audio_classification_output = out_dict['embedding'], out_dict['clipwise_output']
-        projected_vec = self.projection(audio_features)
-        return projected_vec, audio_classification_output
-
-class TextEncoder(nn.Module):
-    def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None:
-        super().__init__()
-        self.base = AutoModel.from_pretrained(text_model)
-        self.projection = Projection(transformer_embed_dim, d_out)
-
-    def forward(self, x):
-        out = self.base(**x)[0]
-        out = out[:, 0, :]  # get CLS token output
-        projected_vec = self.projection(out)
-        return projected_vec
-
-class CLAP(nn.Module):
-    def __init__(self,
-                # audio
-                audioenc_name: str,
-                sample_rate: int, 
-                window_size: int, 
-                hop_size: int, 
-                mel_bins: int, 
-                fmin: int, 
-                fmax: int, 
-                classes_num: int, 
-                out_emb: int,
-                # text
-                text_model: str,
-                transformer_embed_dim: int,
-                # common
-                d_proj: int,
-                ):
-        super().__init__()
-
-        
-        self.audio_encoder = AudioEncoder(
-            audioenc_name, out_emb, d_proj,
-            sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num)
-
-        self.caption_encoder = TextEncoder(
-            d_proj, text_model, transformer_embed_dim
-        )
-
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-    def forward(self, audio, text):
-        audio_embed, _ = self.audio_encoder(audio)
-        caption_embed = self.caption_encoder(text)
-
-        return caption_embed, audio_embed, self.logit_scale.exp()

spaces/AIGText/GlyphControl/ldm/modules/attention.py

@@ -1,340 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-from typing import Optional, Any
-
-from ldm.modules.diffusionmodules.util import checkpoint
-
-
-try:
-    import xformers
-    import xformers.ops
-    XFORMERS_IS_AVAILBLE = True
-except Exception as e:
-    print("xformer", e)
-    XFORMERS_IS_AVAILBLE = False
-# XFORMERS_IS_AVAILBLE = False
-DETERMISTIC = False
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
-    dim = tensor.shape[-1]
-    std = 1 / math.sqrt(dim)
-    tensor.uniform_(-std, std)
-    return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class SpatialSelfAttention(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = rearrange(q, 'b c h w -> b (h w) c')
-        k = rearrange(k, 'b c h w -> b c (h w)')
-        w_ = torch.einsum('bij,bjk->bik', q, k)
-
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = rearrange(v, 'b c h w -> b c (h w)')
-        w_ = rearrange(w_, 'b i j -> b j i')
-        h_ = torch.einsum('bij,bjk->bik', v, w_)
-        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, query_dim),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x, context=None, mask=None):
-        h = self.heads
-
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-        del q, k
-
-        if exists(mask):
-            mask = rearrange(mask, 'b ... -> b (...)')
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-
-        # attention, what we cannot get enough of
-        sim = sim.softmax(dim=-1)
-
-        out = einsum('b i j, b j d -> b i d', sim, v)
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-        return self.to_out(out)
-
-
-class MemoryEfficientCrossAttention(nn.Module):
-    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
-        super().__init__()
-        print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
-              f"{heads} heads.")
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.heads = heads
-        self.dim_head = dim_head
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
-        self.attention_op: Optional[Any] = None
-        print("DETERMISTIC:", DETERMISTIC)
-
-    def forward(self, x, context=None, mask=None):
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        b, _, _ = q.shape
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, t.shape[1], self.heads, self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * self.heads, t.shape[1], self.dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
-
-        torch.use_deterministic_algorithms(False)
-        # actually compute the attention, what we cannot get enough of
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-        if DETERMISTIC:
-            torch.use_deterministic_algorithms(True, warn_only=True)
-
-        # # actually compute the attention, what we cannot get enough of
-        # out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
-        if exists(mask):
-            raise NotImplementedError
-        out = (
-            out.unsqueeze(0)
-            .reshape(b, self.heads, out.shape[1], self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b, out.shape[1], self.heads * self.dim_head)
-        )
-        return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
-    ATTENTION_MODES = {
-        "softmax": CrossAttention,  # vanilla attention
-        "softmax-xformers": MemoryEfficientCrossAttention
-    }
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
-                 disable_self_attn=False):
-        super().__init__()
-        attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
-        assert attn_mode in self.ATTENTION_MODES
-        attn_cls = self.ATTENTION_MODES[attn_mode]
-        self.disable_self_attn = disable_self_attn
-        self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                              context_dim=context_dim if self.disable_self_attn else None)  # is a self-attention if not self.disable_self_attn
-        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
-                              heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-        self.norm3 = nn.LayerNorm(dim)
-        self.checkpoint = checkpoint
-
-    def forward(self, x, context=None):
-        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
-
-    def _forward(self, x, context=None): # cross attention
-        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
-        x = self.attn2(self.norm2(x), context=context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
-
-
-class SpatialTransformer(nn.Module):
-    """
-    Transformer block for image-like data.
-    First, project the input (aka embedding)
-    and reshape to b, t, d.
-    Then apply standard transformer action.
-    Finally, reshape to image
-    NEW: use_linear for more efficiency instead of the 1x1 convs
-    """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None,
-                 disable_self_attn=False, use_linear=False,
-                 use_checkpoint=True):
-        super().__init__()
-        if exists(context_dim) and not isinstance(context_dim, list):
-            context_dim = [context_dim]
-        self.in_channels = in_channels
-        inner_dim = n_heads * d_head
-        self.norm = Normalize(in_channels)
-        if not use_linear:
-            self.proj_in = nn.Conv2d(in_channels,
-                                     inner_dim,
-                                     kernel_size=1,
-                                     stride=1,
-                                     padding=0)
-        else:
-            self.proj_in = nn.Linear(in_channels, inner_dim)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
-                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
-                for d in range(depth)]
-        )
-        if not use_linear:
-            self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                                  in_channels,
-                                                  kernel_size=1,
-                                                  stride=1,
-                                                  padding=0))
-        else:
-            self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
-        self.use_linear = use_linear
-
-    def forward(self, x, context=None):
-        # note: if no context is given, cross-attention defaults to self-attention
-        if not isinstance(context, list):
-            context = [context]
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        if not self.use_linear:
-            x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
-        if self.use_linear:
-            x = self.proj_in(x)
-        for i, block in enumerate(self.transformer_blocks):
-            x = block(x, context=context[i])
-        if self.use_linear:
-            x = self.proj_out(x)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
-        if not self.use_linear:
-            x = self.proj_out(x)
-        return x + x_in
-

spaces/AgentVerse/agentVerse/dataloader/commongen.py

@@ -1,21 +0,0 @@
-from .dataloader import DataLoader
-from . import dataloader_registry
-import json
-
-
-@dataloader_registry.register("tasksolving/commongen/gpt-4")
-@dataloader_registry.register("tasksolving/commongen/gpt-3.5")
-class CommongenLoader(DataLoader):
-    def __init__(self, path: str):
-        super().__init__(path)
-
-    def load(self):
-        with open(self.path) as f:
-            for line in f:
-                line = json.loads(line)
-                self.examples.append(
-                    {
-                        "input": line["concepts"],
-                        "answer": None,
-                    }
-                )

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateSizer.js

@@ -1,8 +0,0 @@
-import CreateAnySizer from './utils/CreateAnySizer.js';
-import Sizer from '../../sizer/Sizer.js';
-
-var CreateSizer = function (scene, data, view, styles, customBuilders) {
-    return CreateAnySizer(scene, data, view, styles, customBuilders, Sizer);
-}
-
-export default CreateSizer;

spaces/Akira12312/admruul-anything-v3.0/README.md

@@ -1,12 +0,0 @@
----
-title: Admruul Anything V3.0
-emoji: 🔥
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ameaou/academic-chatgpt3.1/core_functional.py

@@ -1,71 +0,0 @@
-# 'primary' 颜色对应 theme.py 中的 primary_hue
-# 'secondary' 颜色对应 theme.py 中的 neutral_hue
-# 'stop' 颜色对应 theme.py 中的 color_er
-# 默认按钮颜色是 secondary
-from toolbox import clear_line_break
-
-
-def get_core_functions():
-    return {
-        "英语学术润色": {
-            # 前言
-            "Prefix":   r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, " +
-                        r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. " +
-                        r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
-            # 后语
-            "Suffix":   r"",
-            "Color":    r"secondary",    # 按钮颜色
-        },
-        "中文学术润色": {
-            "Prefix":   r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" +
-                        r"同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本" + "\n\n",
-            "Suffix":   r"",
-        },
-        "查找语法错误": {
-            "Prefix":   r"Can you help me ensure that the grammar and the spelling is correct? " +
-                        r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good." +
-                        r"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, " +
-                        r"put the original text the first column, " +
-                        r"put the corrected text in the second column and highlight the key words you fixed.""\n"
-                        r"Example:""\n"
-                        r"Paragraph: How is you? Do you knows what is it?""\n"
-                        r"| Original sentence | Corrected sentence |""\n"
-                        r"| :--- | :--- |""\n"
-                        r"| How **is** you? | How **are** you? |""\n"
-                        r"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |""\n"
-                        r"Below is a paragraph from an academic paper. "
-                        r"You need to report all grammar and spelling mistakes as the example before."
-                        + "\n\n",
-            "Suffix":   r"",
-            "PreProcess": clear_line_break,    # 预处理：清除换行符
-        },
-        "中译英": {
-            "Prefix":   r"Please translate following sentence to English:" + "\n\n",
-            "Suffix":   r"",
-        },
-        "学术中英互译": {
-            "Prefix":   r"I want you to act as a scientific English-Chinese translator, " +
-                        r"I will provide you with some paragraphs in one language " +
-                        r"and your task is to accurately and academically translate the paragraphs only into the other language. " +
-                        r"Do not repeat the original provided paragraphs after translation. " +
-                        r"You should use artificial intelligence tools, " +
-                        r"such as natural language processing, and rhetorical knowledge " +
-                        r"and experience about effective writing techniques to reply. " +
-                        r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:" + "\n\n",
-            "Suffix": "",
-            "Color": "secondary",
-        },
-        "英译中": {
-            "Prefix":   r"翻译成地道的中文：" + "\n\n",
-            "Suffix":   r"",
-        },
-        "找图片": {
-            "Prefix":   r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，" +
-                        r"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：" + "\n\n",
-            "Suffix":   r"",
-        },
-        "解释代码": {
-            "Prefix":   r"请解释以下代码：" + "\n```\n",
-            "Suffix":   "\n```\n",
-        },
-    }

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -1,432 +0,0 @@
-# Copyright 2023 Katherine Crowson and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import math
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, logging, randn_tensor
-from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-@dataclass
-# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
-class EulerDiscreteSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.FloatTensor
-    pred_original_sample: Optional[torch.FloatTensor] = None
-
-
-# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
-def betas_for_alpha_bar(
-    num_diffusion_timesteps,
-    max_beta=0.999,
-    alpha_transform_type="cosine",
-):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
-    (1-beta) over time from t = [0,1].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
-                     Choose from `cosine` or `exp`
-
-    Returns:
-        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-    if alpha_transform_type == "cosine":
-
-        def alpha_bar_fn(t):
-            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    elif alpha_transform_type == "exp":
-
-        def alpha_bar_fn(t):
-            return math.exp(t * -12.0)
-
-    else:
-        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
-
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
-    return torch.tensor(betas, dtype=torch.float32)
-
-
-class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
-    """
-    Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original
-    k-diffusion implementation by Katherine Crowson:
-    https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear` or `scaled_linear`.
-        trained_betas (`np.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        prediction_type (`str`, default `"epsilon"`, optional):
-            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
-            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
-        interpolation_type (`str`, default `"linear"`, optional):
-            interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be one of
-            [`"linear"`, `"log_linear"`].
-        use_karras_sigmas (`bool`, *optional*, defaults to `False`):
-             This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the
-             noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence
-             of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf.
-        timestep_spacing (`str`, default `"linspace"`):
-            The way the timesteps should be scaled. Refer to Table 2. of [Common Diffusion Noise Schedules and Sample
-            Steps are Flawed](https://arxiv.org/abs/2305.08891) for more information.
-        steps_offset (`int`, default `0`):
-            an offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
-            stable diffusion.
-    """
-
-    _compatibles = [e.name for e in KarrasDiffusionSchedulers]
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        prediction_type: str = "epsilon",
-        interpolation_type: str = "linear",
-        use_karras_sigmas: Optional[bool] = False,
-        timestep_spacing: str = "linspace",
-        steps_offset: int = 0,
-    ):
-        if trained_betas is not None:
-            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
-        elif beta_schedule == "linear":
-            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = (
-                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
-            )
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
-
-        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
-        sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
-        self.sigmas = torch.from_numpy(sigmas)
-
-        # setable values
-        self.num_inference_steps = None
-        timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()
-        self.timesteps = torch.from_numpy(timesteps)
-        self.is_scale_input_called = False
-        self.use_karras_sigmas = use_karras_sigmas
-
-    @property
-    def init_noise_sigma(self):
-        # standard deviation of the initial noise distribution
-        if self.config.timestep_spacing in ["linspace", "trailing"]:
-            return self.sigmas.max()
-
-        return (self.sigmas.max() ** 2 + 1) ** 0.5
-
-    def scale_model_input(
-        self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor]
-    ) -> torch.FloatTensor:
-        """
-        Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.
-
-        Args:
-            sample (`torch.FloatTensor`): input sample
-            timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain
-
-        Returns:
-            `torch.FloatTensor`: scaled input sample
-        """
-        if isinstance(timestep, torch.Tensor):
-            timestep = timestep.to(self.timesteps.device)
-        step_index = (self.timesteps == timestep).nonzero().item()
-        sigma = self.sigmas[step_index]
-
-        sample = sample / ((sigma**2 + 1) ** 0.5)
-
-        self.is_scale_input_called = True
-        return sample
-
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
-        """
-        Sets the timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            device (`str` or `torch.device`, optional):
-                the device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-        """
-        self.num_inference_steps = num_inference_steps
-
-        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
-        if self.config.timestep_spacing == "linspace":
-            timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[
-                ::-1
-            ].copy()
-        elif self.config.timestep_spacing == "leading":
-            step_ratio = self.config.num_train_timesteps // self.num_inference_steps
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy().astype(float)
-            timesteps += self.config.steps_offset
-        elif self.config.timestep_spacing == "trailing":
-            step_ratio = self.config.num_train_timesteps / self.num_inference_steps
-            # creates integer timesteps by multiplying by ratio
-            # casting to int to avoid issues when num_inference_step is power of 3
-            timesteps = (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy().astype(float)
-            timesteps -= 1
-        else:
-            raise ValueError(
-                f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
-            )
-
-        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
-        log_sigmas = np.log(sigmas)
-
-        if self.config.interpolation_type == "linear":
-            sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
-        elif self.config.interpolation_type == "log_linear":
-            sigmas = torch.linspace(np.log(sigmas[-1]), np.log(sigmas[0]), num_inference_steps + 1).exp()
-        else:
-            raise ValueError(
-                f"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
-                " 'linear' or 'log_linear'"
-            )
-
-        if self.use_karras_sigmas:
-            sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=self.num_inference_steps)
-            timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas])
-
-        sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
-        self.sigmas = torch.from_numpy(sigmas).to(device=device)
-        if str(device).startswith("mps"):
-            # mps does not support float64
-            self.timesteps = torch.from_numpy(timesteps).to(device, dtype=torch.float32)
-        else:
-            self.timesteps = torch.from_numpy(timesteps).to(device=device)
-
-    def _sigma_to_t(self, sigma, log_sigmas):
-        # get log sigma
-        log_sigma = np.log(sigma)
-
-        # get distribution
-        dists = log_sigma - log_sigmas[:, np.newaxis]
-
-        # get sigmas range
-        low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
-        high_idx = low_idx + 1
-
-        low = log_sigmas[low_idx]
-        high = log_sigmas[high_idx]
-
-        # interpolate sigmas
-        w = (low - log_sigma) / (low - high)
-        w = np.clip(w, 0, 1)
-
-        # transform interpolation to time range
-        t = (1 - w) * low_idx + w * high_idx
-        t = t.reshape(sigma.shape)
-        return t
-
-    # Copied from https://github.com/crowsonkb/k-diffusion/blob/686dbad0f39640ea25c8a8c6a6e56bb40eacefa2/k_diffusion/sampling.py#L17
-    def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor:
-        """Constructs the noise schedule of Karras et al. (2022)."""
-
-        sigma_min: float = in_sigmas[-1].item()
-        sigma_max: float = in_sigmas[0].item()
-
-        rho = 7.0  # 7.0 is the value used in the paper
-        ramp = np.linspace(0, 1, num_inference_steps)
-        min_inv_rho = sigma_min ** (1 / rho)
-        max_inv_rho = sigma_max ** (1 / rho)
-        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
-        return sigmas
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: Union[float, torch.FloatTensor],
-        sample: torch.FloatTensor,
-        s_churn: float = 0.0,
-        s_tmin: float = 0.0,
-        s_tmax: float = float("inf"),
-        s_noise: float = 1.0,
-        generator: Optional[torch.Generator] = None,
-        return_dict: bool = True,
-    ) -> Union[EulerDiscreteSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timestep (`float`): current timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-            s_churn (`float`)
-            s_tmin  (`float`)
-            s_tmax  (`float`)
-            s_noise (`float`)
-            generator (`torch.Generator`, optional): Random number generator.
-            return_dict (`bool`): option for returning tuple rather than EulerDiscreteSchedulerOutput class
-
-        Returns:
-            [`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a
-            `tuple`. When returning a tuple, the first element is the sample tensor.
-
-        """
-
-        if (
-            isinstance(timestep, int)
-            or isinstance(timestep, torch.IntTensor)
-            or isinstance(timestep, torch.LongTensor)
-        ):
-            raise ValueError(
-                (
-                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
-                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
-                    " one of the `scheduler.timesteps` as a timestep."
-                ),
-            )
-
-        if not self.is_scale_input_called:
-            logger.warning(
-                "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
-                "See `StableDiffusionPipeline` for a usage example."
-            )
-
-        if isinstance(timestep, torch.Tensor):
-            timestep = timestep.to(self.timesteps.device)
-
-        step_index = (self.timesteps == timestep).nonzero().item()
-        sigma = self.sigmas[step_index]
-
-        gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0
-
-        noise = randn_tensor(
-            model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator
-        )
-
-        eps = noise * s_noise
-        sigma_hat = sigma * (gamma + 1)
-
-        if gamma > 0:
-            sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
-
-        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
-        # NOTE: "original_sample" should not be an expected prediction_type but is left in for
-        # backwards compatibility
-        if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
-            pred_original_sample = model_output
-        elif self.config.prediction_type == "epsilon":
-            pred_original_sample = sample - sigma_hat * model_output
-        elif self.config.prediction_type == "v_prediction":
-            # * c_out + input * c_skip
-            pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"
-            )
-
-        # 2. Convert to an ODE derivative
-        derivative = (sample - pred_original_sample) / sigma_hat
-
-        dt = self.sigmas[step_index + 1] - sigma_hat
-
-        prev_sample = sample + derivative * dt
-
-        if not return_dict:
-            return (prev_sample,)
-
-        return EulerDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
-
-    def add_noise(
-        self,
-        original_samples: torch.FloatTensor,
-        noise: torch.FloatTensor,
-        timesteps: torch.FloatTensor,
-    ) -> torch.FloatTensor:
-        # Make sure sigmas and timesteps have the same device and dtype as original_samples
-        sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
-        if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
-            # mps does not support float64
-            schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
-            timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
-        else:
-            schedule_timesteps = self.timesteps.to(original_samples.device)
-            timesteps = timesteps.to(original_samples.device)
-
-        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
-
-        sigma = sigmas[step_indices].flatten()
-        while len(sigma.shape) < len(original_samples.shape):
-            sigma = sigma.unsqueeze(-1)
-
-        noisy_samples = original_samples + noise * sigma
-        return noisy_samples
-
-    def __len__(self):
-        return self.config.num_train_timesteps

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py

@@ -1,92 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..utils import DummyObject, requires_backends
-
-
-class OnnxStableDiffusionImg2ImgPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers", "onnx"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-
-class OnnxStableDiffusionInpaintPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers", "onnx"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-
-class OnnxStableDiffusionInpaintPipelineLegacy(metaclass=DummyObject):
-    _backends = ["torch", "transformers", "onnx"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-
-class OnnxStableDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers", "onnx"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-
-class OnnxStableDiffusionUpscalePipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers", "onnx"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-
-class StableDiffusionOnnxPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers", "onnx"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers", "onnx"])

spaces/Andy1621/IAT_enhancement/model/global_net.py

@@ -1,129 +0,0 @@
-import imp
-import torch
-import torch.nn as nn
-from timm.models.layers import trunc_normal_, DropPath, to_2tuple
-import os
-from .blocks import Mlp
-
-
-class query_Attention(nn.Module):
-    def __init__(self, dim, num_heads=2, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.q = nn.Parameter(torch.ones((1, 10, dim)), requires_grad=True)
-        self.k = nn.Linear(dim, dim, bias=qkv_bias)
-        self.v = nn.Linear(dim, dim, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x):
-        B, N, C = x.shape
-        k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-        v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-
-        q = self.q.expand(B, -1, -1).view(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, 10, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class query_SABlock(nn.Module):
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.pos_embed = nn.Conv2d(dim, dim, 3, padding=1, groups=dim)
-        self.norm1 = norm_layer(dim)
-        self.attn = query_Attention(
-            dim,
-            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-    def forward(self, x):
-        x = x + self.pos_embed(x)
-        x = x.flatten(2).transpose(1, 2)
-        x = self.drop_path(self.attn(self.norm1(x)))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class conv_embedding(nn.Module):
-    def __init__(self, in_channels, out_channels):
-        super(conv_embedding, self).__init__()
-        self.proj = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
-            nn.BatchNorm2d(out_channels // 2),
-            nn.GELU(),
-            # nn.Conv2d(out_channels // 2, out_channels // 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
-            # nn.BatchNorm2d(out_channels // 2),
-            # nn.GELU(),
-            nn.Conv2d(out_channels // 2, out_channels, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
-            nn.BatchNorm2d(out_channels),
-        )
-
-    def forward(self, x):
-        x = self.proj(x)
-        return x
-
-
-class Global_pred(nn.Module):
-    def __init__(self, in_channels=3, out_channels=64, num_heads=4, type='exp'):
-        super(Global_pred, self).__init__()
-        if type == 'exp':
-            self.gamma_base = nn.Parameter(torch.ones((1)), requires_grad=False) # False in exposure correction
-        else:
-            self.gamma_base = nn.Parameter(torch.ones((1)), requires_grad=True)  
-        self.color_base = nn.Parameter(torch.eye((3)), requires_grad=True)  # basic color matrix
-        # main blocks
-        self.conv_large = conv_embedding(in_channels, out_channels)
-        self.generator = query_SABlock(dim=out_channels, num_heads=num_heads)
-        self.gamma_linear = nn.Linear(out_channels, 1)
-        self.color_linear = nn.Linear(out_channels, 1)
-
-        self.apply(self._init_weights)
-
-        for name, p in self.named_parameters():
-            if name == 'generator.attn.v.weight':
-                nn.init.constant_(p, 0)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-
-    def forward(self, x):
-        #print(self.gamma_base)
-        x = self.conv_large(x)
-        x = self.generator(x)
-        gamma, color = x[:, 0].unsqueeze(1), x[:, 1:]
-        gamma = self.gamma_linear(gamma).squeeze(-1) + self.gamma_base
-        #print(self.gamma_base, self.gamma_linear(gamma))
-        color = self.color_linear(color).squeeze(-1).view(-1, 3, 3) + self.color_base
-        return gamma, color
-
-if __name__ == "__main__":
-    os.environ['CUDA_VISIBLE_DEVICES']='3'
-    #net = Local_pred_new().cuda()
-    img = torch.Tensor(8, 3, 400, 600)
-    global_net = Global_pred()
-    gamma, color = global_net(img)
-    print(gamma.shape, color.shape)

spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py

@@ -1,62 +0,0 @@
-_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
-model = dict(
-    bbox_head=dict(
-        _delete_=True,
-        type='GARetinaHead',
-        num_classes=80,
-        in_channels=256,
-        stacked_convs=4,
-        feat_channels=256,
-        approx_anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=4,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[8, 16, 32, 64, 128]),
-        square_anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[4],
-            strides=[8, 16, 32, 64, 128]),
-        anchor_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loc_filter_thr=0.01,
-        loss_loc=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)),
-    # training and testing settings
-    train_cfg=dict(
-        ga_assigner=dict(
-            type='ApproxMaxIoUAssigner',
-            pos_iou_thr=0.5,
-            neg_iou_thr=0.4,
-            min_pos_iou=0.4,
-            ignore_iof_thr=-1),
-        ga_sampler=dict(
-            type='RandomSampler',
-            num=256,
-            pos_fraction=0.5,
-            neg_pos_ub=-1,
-            add_gt_as_proposals=False),
-        assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0),
-        center_ratio=0.2,
-        ignore_ratio=0.5))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

spaces/Andy1621/uniformer_image_segmentation/configs/ann/ann_r101-d8_512x512_80k_ade20k.py

@@ -1,2 +0,0 @@
-_base_ = './ann_r50-d8_512x512_80k_ade20k.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/Windows-installation-guide.md

@@ -1,9 +0,0 @@
-If you are having trouble following the installation instructions in the README, Reddit user [Technical_Leather949](https://www.reddit.com/user/Technical_Leather949/) has created a more detailed, step-by-step guide covering:
-
-* Windows installation
-* 8-bit mode on Windows
-* LLaMA
-* LLaMA 4-bit
-
-The guide can be found here: https://www.reddit.com/r/LocalLLaMA/comments/11o6o3f/how_to_install_llama_8bit_and_4bit/
-

spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/bert.py

@@ -1,40 +0,0 @@
-from transformers import BertTokenizer, BertModel
-
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-
-
-def bert_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors="pt")
-    output = model(**encoded_input)
-    return output
-
-
-from transformers import RobertaTokenizer, RobertaModel
-
-tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-model = RobertaModel.from_pretrained("roberta-base")
-text = "Replace me by any text you'd like."
-
-
-def Roberta_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors="pt")
-    output = model(**encoded_input)
-    return output
-
-
-from transformers import BartTokenizer, BartModel
-
-tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-model = BartModel.from_pretrained("facebook/bart-base")
-text = "Replace me by any text you'd like."
-
-
-def bart_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors="pt")
-    output = model(**encoded_input)
-    return output

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py

@@ -1,34 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.mask_rcnn_fpn import model
-from ..common.train import train
-
-from detectron2.config import LazyCall as L
-from detectron2.modeling.backbone import RegNet
-from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
-
-
-# Replace default ResNet with RegNetX-4GF from the DDS paper. Config source:
-# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnetx/RegNetX-4.0GF_dds_8gpu.yaml#L4-L9  # noqa
-model.backbone.bottom_up = L(RegNet)(
-    stem_class=SimpleStem,
-    stem_width=32,
-    block_class=ResBottleneckBlock,
-    depth=23,
-    w_a=38.65,
-    w_0=96,
-    w_m=2.43,
-    group_width=40,
-    freeze_at=2,
-    norm="FrozenBN",
-    out_features=["s1", "s2", "s3", "s4"],
-)
-model.pixel_std = [57.375, 57.120, 58.395]
-
-optimizer.weight_decay = 5e-5
-train.init_checkpoint = (
-    "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906383/RegNetX-4.0GF_dds_8gpu.pyth"
-)
-# RegNets benefit from enabling cudnn benchmark mode
-train.cudnn_benchmark = True

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/evaluation/fast_eval_api.py

@@ -1,121 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-import numpy as np
-import time
-from pycocotools.cocoeval import COCOeval
-
-from detectron2 import _C
-
-logger = logging.getLogger(__name__)
-
-
-class COCOeval_opt(COCOeval):
-    """
-    This is a slightly modified version of the original COCO API, where the functions evaluateImg()
-    and accumulate() are implemented in C++ to speedup evaluation
-    """
-
-    def evaluate(self):
-        """
-        Run per image evaluation on given images and store results in self.evalImgs_cpp, a
-        datastructure that isn't readable from Python but is used by a c++ implementation of
-        accumulate().  Unlike the original COCO PythonAPI, we don't populate the datastructure
-        self.evalImgs because this datastructure is a computational bottleneck.
-        :return: None
-        """
-        tic = time.time()
-
-        p = self.params
-        # add backward compatibility if useSegm is specified in params
-        if p.useSegm is not None:
-            p.iouType = "segm" if p.useSegm == 1 else "bbox"
-        logger.info("Evaluate annotation type *{}*".format(p.iouType))
-        p.imgIds = list(np.unique(p.imgIds))
-        if p.useCats:
-            p.catIds = list(np.unique(p.catIds))
-        p.maxDets = sorted(p.maxDets)
-        self.params = p
-
-        self._prepare()  # bottleneck
-
-        # loop through images, area range, max detection number
-        catIds = p.catIds if p.useCats else [-1]
-
-        if p.iouType == "segm" or p.iouType == "bbox":
-            computeIoU = self.computeIoU
-        elif p.iouType == "keypoints":
-            computeIoU = self.computeOks
-        self.ious = {
-            (imgId, catId): computeIoU(imgId, catId) for imgId in p.imgIds for catId in catIds
-        }  # bottleneck
-
-        maxDet = p.maxDets[-1]
-
-        # <<<< Beginning of code differences with original COCO API
-        def convert_instances_to_cpp(instances, is_det=False):
-            # Convert annotations for a list of instances in an image to a format that's fast
-            # to access in C++
-            instances_cpp = []
-            for instance in instances:
-                instance_cpp = _C.InstanceAnnotation(
-                    int(instance["id"]),
-                    instance["score"] if is_det else instance.get("score", 0.0),
-                    instance["area"],
-                    bool(instance.get("iscrowd", 0)),
-                    bool(instance.get("ignore", 0)),
-                )
-                instances_cpp.append(instance_cpp)
-            return instances_cpp
-
-        # Convert GT annotations, detections, and IOUs to a format that's fast to access in C++
-        ground_truth_instances = [
-            [convert_instances_to_cpp(self._gts[imgId, catId]) for catId in p.catIds]
-            for imgId in p.imgIds
-        ]
-        detected_instances = [
-            [convert_instances_to_cpp(self._dts[imgId, catId], is_det=True) for catId in p.catIds]
-            for imgId in p.imgIds
-        ]
-        ious = [[self.ious[imgId, catId] for catId in catIds] for imgId in p.imgIds]
-
-        if not p.useCats:
-            # For each image, flatten per-category lists into a single list
-            ground_truth_instances = [[[o for c in i for o in c]] for i in ground_truth_instances]
-            detected_instances = [[[o for c in i for o in c]] for i in detected_instances]
-
-        # Call C++ implementation of self.evaluateImgs()
-        self._evalImgs_cpp = _C.COCOevalEvaluateImages(
-            p.areaRng, maxDet, p.iouThrs, ious, ground_truth_instances, detected_instances
-        )
-        self._evalImgs = None
-
-        self._paramsEval = copy.deepcopy(self.params)
-        toc = time.time()
-        logger.info("COCOeval_opt.evaluate() finished in {:0.2f} seconds.".format(toc - tic))
-        # >>>> End of code differences with original COCO API
-
-    def accumulate(self):
-        """
-        Accumulate per image evaluation results and store the result in self.eval.  Does not
-        support changing parameter settings from those used by self.evaluate()
-        """
-        logger.info("Accumulating evaluation results...")
-        tic = time.time()
-        assert hasattr(
-            self, "_evalImgs_cpp"
-        ), "evaluate() must be called before accmulate() is called."
-
-        self.eval = _C.COCOevalAccumulate(self._paramsEval, self._evalImgs_cpp)
-
-        # recall is num_iou_thresholds X num_categories X num_area_ranges X num_max_detections
-        self.eval["recall"] = np.array(self.eval["recall"]).reshape(
-            self.eval["counts"][:1] + self.eval["counts"][2:]
-        )
-
-        # precision and scores are num_iou_thresholds X num_recall_thresholds X num_categories X
-        # num_area_ranges X num_max_detections
-        self.eval["precision"] = np.array(self.eval["precision"]).reshape(self.eval["counts"])
-        self.eval["scores"] = np.array(self.eval["scores"]).reshape(self.eval["counts"])
-        toc = time.time()
-        logger.info("COCOeval_opt.accumulate() finished in {:0.2f} seconds.".format(toc - tic))

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/config/dir1/dir1_b.py

@@ -1,11 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.config import LazyConfig
-
-# equivalent to relative import
-dir1a_str, dir1a_dict = LazyConfig.load_rel("dir1_a.py", ("dir1a_str", "dir1a_dict"))
-
-dir1b_str = dir1a_str + "_from_b"
-dir1b_dict = dir1a_dict
-
-# Every import is a reload: not modified by other config files
-assert dir1a_dict.a == 1

spaces/BAAI/AltDiffusion/footer.html

@@ -1,18 +0,0 @@
-<div class="footer">
-    <p> - Model by <a href="https://github.com/FlagAI-Open/FlagAI"><img src="https://github-link-card.s3.ap-northeast-1.amazonaws.com/FlagAI-Open/FlagAI.png" width="1200px"></a > - Gradio Demo by 🤗 Hugging Face
-    </p>
-    <!-- <p><a href="https://github.com/FlagAI-Open/FlagAI"><img src="https://raw.githubusercontent.com/920232796/test/master/contributors.png" width="1200px"></a >
-    </p> -->
-
-    <div class="acknowledgments">
-        <p><h4 style="font-weight: bold; font-size: 20px; margin-top: 20px;">LICENSE</h4>
-            The model is licensed with a <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" style="text-decoration: underline;" target="_blank">CreativeML Open RAIL-M</a> license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license" target="_blank" style="text-decoration: underline;" target="_blank">read the license</a>
-        </p>
-
-        <p><h4 style="font-weight: bold; font-size: 20px;">Contributing</h4>
-            Thanks for your interest in contributing! There are many ways to get involved; start with our <a href="https://github.com/FlagAI-Open/FlagAI" style="text-decoration: underline;" target="_blank">contributor guidelines</a> and then check these <a href="https://github.com/FlagAI-Open/FlagAI" style="text-decoration: underline;" target="_blank"> open issues</a> for specific tasks.
-        </p>
-   </div>
-</div>
-
-

spaces/BMukhtar/BookRecognitionKz/custom_shape.py

@@ -1,35 +0,0 @@
-import streamlit as st
-import cv2
-import numpy as np
-from PIL import Image
-
-def warp_perspective(image, points):
-    # Input and output dimensions
-    w, h = 300, 400  # You can adjust this based on the desired output size
-    input_pts = np.array(points, dtype=np.float32)
-    output_pts = np.array([[0, 0], [w, 0], [w, h], [0, h]], dtype=np.float32)
-    
-    # Compute perspective matrix and warp the image
-    matrix = cv2.getPerspectiveTransform(input_pts, output_pts)
-    warped_img = cv2.warpPerspective(image, matrix, (w, h))
-    
-    return warped_img
-
-st.title("Custom Shape Cropping & Perspective Correction")
-
-uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-# Provide a placeholder for the user to input 4 vertices
-points = []
-for i in range(4):
-    coords = st.text_input(f"Enter point {i+1} (format: x,y)", "")
-    x, y = map(int, coords.split(',')) if ',' in coords else (0, 0)
-    points.append([x, y])
-
-if uploaded_file and len(points) == 4:
-    image = Image.open(uploaded_file).convert('RGB')
-    image_np = np.array(image)
-    
-    corrected_image = warp_perspective(image_np, points)
-    
-    st.image(corrected_image, caption='Corrected Image.', channels="BGR", use_column_width=True)

spaces/BadRobot147/SFQ3/README.md

@@ -1,10 +0,0 @@
----
-title: SFQ3
-emoji: 👁
-colorFrom: pink
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Benson/text-generation/Examples/8 Bolas De Piscina De Descarga Para Ventanas Pc 10.md

@@ -1,66 +0,0 @@
-<br />
-<h1>8 bola piscina descargar para PC ventanas 10</h1>
-<p>¿Te gusta jugar juegos de billar online? ¿Quieres desafiar a tus amigos y otros jugadores de todo el mundo en un juego de billar realista y divertido? Si es así, entonces deberías probar <strong>8 Ball Pool</strong>, el juego de billar #1 del mundo por Miniclip.com. En este juego, puedes refinar tus habilidades, personalizar tu señal y mesa, unirte a torneos y competir por monedas y objetos exclusivos. ¿Pero sabías que también puedes jugar a este juego en tu PC Windows 10? Sí, lo has oído bien. Puedes disfrutar jugando <strong>8 Ball Pool</strong> en una pantalla más grande, con mejores gráficos, controles personalizables y más características. En este artículo, le mostraremos cómo descargar e instalar <strong>8 Ball Pool</strong> en PC Windows 10 utilizando dos métodos. También le contaremos sobre las características y beneficios de jugar <strong>8 Ball Pool</strong> en PC Windows 10. ¡Así que, comencemos! </p>
- <h2> Cómo descargar e instalar piscina de bolas 8 en PC Windows 10</h2>
-<p>Hay dos formas de jugar <strong>8 Ball Pool</strong> en PC Windows 10. Uno es mediante el uso de un emulador de Android, que es un software que le permite ejecutar aplicaciones Android en su ordenador. La otra es usando la versión web/PC de <strong>8 Ball Pool</strong>, que está disponible en el sitio web oficial de Miniclip.com. Veamos cómo funciona cada método. </p>
-<h2>8 bolas de piscina de descarga para ventanas pc 10</h2><br /><p><b><b>DOWNLOAD</b> ===== <a href="https://bltlly.com/2v6LZR">https://bltlly.com/2v6LZR</a></b></p><br /><br />
- <h3>Método 1: Usando un emulador de Android</h3>
-<p>Un emulador de Android es un software que imita el sistema operativo Android en su computadora. De esta manera, puedes ejecutar cualquier aplicación o juego de Android en tu PC Windows 10, incluyendo <strong>8 Ball Pool</strong>. Hay muchos emuladores de Android disponibles en línea, como BlueStacks, MEmu, NoxPlayer, etc. Puede elegir cualquiera de ellos de acuerdo con su preferencia. Estos son los pasos para descargar e instalar <strong>8 Ball Pool</strong> en PC Windows 10 usando un emulador de Android. </p>
- <h4>Paso 1: Descargar e instalar un emulador de Android</h4>
-
- <h4>Paso 2: Abra Google Play Store y busque 8 Ball Pool</h4>
-<p>El siguiente paso es abrir Google Play Store y buscar <strong>8 Ball Pool</strong>. Puedes hacer esto haciendo clic en el icono de Google Play en la pantalla de inicio del emulador. Luego, escribe <strong>8 Ball Pool</strong> en la barra de búsqueda y pulsa enter. Verás el icono y el nombre del juego en la página de resultados. </p>
- <h4>Paso 3: Descargar e instalar 8 Ball Pool en el emulador</h4>
-<p>El tercer paso es descargar e instalar <strong>8 Ball Pool</strong> en el emulador. Puede hacer esto haciendo clic en el botón "Instalar" junto al icono del juego. El emulador descargará e instalará el juego automáticamente. Es posible que necesites conceder algunos permisos al juego, como el acceso a tu almacenamiento, cámara, micrófono, etc.</p>
- <h4>Paso 4: Lanzar 8 bola piscina y disfrutar jugando en el PC</h4>
-<p>El paso final es lanzar <strong>8 Ball Pool</strong> y disfrutar jugando en PC. Puedes hacer esto haciendo clic en el icono del juego en la pantalla de inicio del emulador o en el cajón de la aplicación. El juego comenzará y podrás iniciar sesión con tu cuenta de Miniclip o Facebook. Luego, puedes personalizar tu perfil, elegir el modo de juego y comenzar a jugar con tus amigos u otros jugadores en línea. </p>
- <h3>Método 2: Usando la versión Web/PC de 8 Ball Pool</h3>
-<p>Si no quieres usar un emulador de Android, también puedes jugar <strong>8 Ball Pool</strong> en PC Windows 10 usando la versión web/PC del juego. Esta versión está disponible en el sitio web oficial de Miniclip.com y funciona en cualquier navegador que soporte Flash Player. Estos son los pasos para jugar <strong>8 Ball Pool</strong> en PC Windows 10 usando la versión web/PC. </p>
- <h4>Paso 1: Ir al sitio web oficial de 8 Ball Pool</h4>
-
- <h4>Paso 2: Inicia sesión con tu cuenta de Miniclip o Facebook</h4>
-<p>El siguiente paso es iniciar sesión con su cuenta de Miniclip o Facebook. Puedes hacer esto haciendo clic en el botón "Jugar ahora" y eligiendo tu opción preferida. Si no tienes una cuenta, también puedes crear una gratis haciendo clic en el botón "Registrarse". Deberá proporcionar su dirección de correo electrónico, nombre de usuario, contraseña y país. </p>
- <h4>Paso 3: Comience a jugar 8 bolas en su navegador</h4>
-<p>El paso final es comenzar a jugar <strong>8 Ball Pool</strong> en tu navegador. Puedes hacer esto eligiendo tu modo de juego, como 1 contra 1, torneos o práctica. Luego, puedes seleccionar tu mesa, taco y oponente. El juego se cargará y podrás empezar a jugar con el ratón y el teclado. </p>
-<p></p>
- <h2> Características y beneficios de jugar al billar de 8 bolas en PC Windows 10</h2>
-<p>Ahora que sabes cómo jugar <strong>8 Ball Pool</strong> en PC Windows 10, te estarás preguntando por qué deberías hacerlo. ¿Cuáles son las ventajas de jugar <strong>8 Ball Pool</strong> en PC Windows 10 sobre jugarlo en su dispositivo móvil? Bueno, hay muchas características y beneficios que puedes disfrutar cuando juegas <strong>8 Ball Pool</strong> en PC Windows 10. Estos son algunos de ellos. </p>
- <h3>Pantalla más grande y mejores gráficos</h3>
-<p>Una de las principales razones para jugar <strong>8 Ball Pool</strong> en PC Windows 10 es que puedes disfrutar de una pantalla más grande y mejores gráficos. Jugar juegos de billar en una pantalla pequeña puede ser frustrante y estresante para sus ojos. Es posible que se pierda algunos disparos o cometa algunos errores debido a la vista limitada y la resolución. Pero cuando juegas <strong>8 Ball Pool</strong> en PC Windows 10, puedes tener una vista de pantalla completa y una resolución de alta definición. Puede ver cada detalle de la tabla, el taco, las bolas y las animaciones. También puede ajustar la configuración de los gráficos según sus preferencias. </p>
- <h3>Controles y macros personalizables</h3>
-
- <h3>Multi-Instance y Multi-Tasking</h3>
-<p>Una tercera razón para jugar <strong>8 Ball Pool</strong> en PC Windows 10 es que puede usar las funciones de múltiples instancias y multitarea. Jugar juegos de billar en un dispositivo móvil puede ser limitante y aburrido. Es posible que tenga que esperar su turno, ver anuncios o lidiar con la batería baja. Pero cuando juegas a <strong>8 Ball Pool</strong> en PC Windows 10, puedes usar la función de múltiples instancias para ejecutar varias instancias del juego al mismo tiempo. Puedes jugar con diferentes cuentas, unirte a diferentes torneos o practicar diferentes habilidades. También puede utilizar la función multitarea para cambiar entre diferentes aplicaciones o ventanas mientras juega el juego. Puedes chatear con tus amigos, ver vídeos, navegar por la web o hacer cualquier otra cosa sin interrumpir tu juego. </p>
- <h3>Ofertas y recompensas exclusivas</h3>
-<p>Una cuarta razón para jugar <strong>8 Ball Pool</strong> en PC Windows 10 es que puedes obtener ofertas exclusivas y recompensas. Jugar juegos de billar en un dispositivo móvil puede ser caro y poco gratificante. Es posible que tenga que gastar dinero real para comprar monedas, efectivo, tacos u otros artículos. También es posible que se pierda algunas ofertas o eventos debido a las notificaciones limitadas o el almacenamiento. Pero cuando juegas <strong>8 Ball Pool</strong> en PC Windows 10, puedes obtener acceso a ofertas exclusivas y recompensas que solo están disponibles para usuarios de PC. Puedes obtener monedas gratis, dinero en efectivo, tacos u otros artículos completando tareas, viendo videos o participando en eventos. También puedes ser notificado de las últimas actualizaciones, promociones o torneos por el emulador. </p>
- <h2>Conclusión y preguntas frecuentes</h2>
-
- <p>Para ayudarte más, aquí hay algunas preguntas frecuentes sobre <strong>8 Ball Pool</strong> en PC Windows 10. </p>
- <tabla>
-<tr>
-<th>Pregunta</th>
-<th>Respuesta</th>
-</tr>
-<tr>
-<td>¿Es 8 Ball Pool gratis para jugar en PC Windows 10? </td>
-<td>Sí, 8 Ball Pool es gratis para jugar en PC Windows 10. Sin embargo, es posible que tengas que pagar por algunos elementos o funciones del juego si quieres mejorar tu experiencia de juego. </td>
-</tr>
-<tr>
-<td>¿Es seguro jugar 8 bolas en PC Windows 10? </td>
-<td>Sí, 8 Ball Pool es seguro para jugar en PC Windows 10. Sin embargo, siempre debes descargar e instalar el juego desde fuentes confiables, como Google Play Store o Miniclip.com. También debes evitar usar hacks o trucos que puedan dañar tu dispositivo o cuenta. </td>
-</tr>
-<tr>
-<td>¿Puedo jugar 8 bolas sin conexión en PC Windows 10? </td>
-<td>No, no puedes jugar 8 Ball Pool sin conexión en PC Windows 10. Necesitas una conexión a Internet para jugar el juego en línea con otros jugadores. </td>
-</tr>
-<tr>
-<td>¿Puedo transferir mi progreso de móvil a PC Windows 10? </td>
-<td>Sí, puede transferir su progreso desde el móvil al PC Windows 10. Solo necesitas iniciar sesión con la misma cuenta de Miniclip o Facebook que usaste en tu dispositivo móvil. </td>
-</tr>
-<tr>
-<td>¿Puedo jugar con mis amigos en PC Windows 10? </td>
-<td>Sí, puedes jugar con tus amigos en PC Windows 10. Solo tienes que invitarlos a unirse a tu juego o aceptar sus invitaciones. También puedes chatear con ellos usando la función de chat en el juego. </td>
-</tr>
-</tabla></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Alto 39s Aventura Apk Ios.md

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-<br />
-<h1>Alto’s Adventure apk ios: Una odisea de snowboard sereno</h1>
-<p>Si usted está buscando un juego relajante y hermoso para jugar en su iPhone o iPad, es posible que desee echa un vistazo a la aventura de Alto. Este es un juego que combina los elementos de un juego de plataformas 2D y un corredor sin fin, con un tema de snowboard único. En este artículo, te diremos qué es Alto’s Adventure, cómo descargarlo e instalarlo, por qué deberías jugarlo, y algunos consejos y trucos para ayudarte a disfrutarlo más. </p>
-<h2>alto 39;s aventura apk ios</h2><br /><p><b><b>DOWNLOAD</b> &#10026; <a href="https://bltlly.com/2v6MlS">https://bltlly.com/2v6MlS</a></b></p><br /><br />
- <h2>¿Qué es la aventura de Alto? </h2>
-<h3>Una breve introducción al juego y sus características</h3>
-<p>Alto’s Adventure es un juego desarrollado por Snowman, un pequeño estudio independiente con sede en Toronto, Canadá. Fue lanzado en 2015 para dispositivos iOS, y más tarde para Android, Kindle Fire, Windows y Mac. El juego ha recibido elogios de la crítica y numerosos premios por su arte, música y juego. </p>
-<p>El juego sigue el viaje de Alto, un joven pastor que vive en un pueblo de montaña. Un día, sus llamas escapan de su corral y corren por las laderas. Alto decide perseguirlos en su tabla de snowboard, junto con sus amigos que tienen diferentes habilidades y habilidades. En el camino, se encuentran con varios obstáculos, como rocas, abismos, ancianos, tormentas, y más. </p>
-<p>Las características del juego:</p>
-<ul>
-<li>Juego fluido, elegante y estimulante basado en la física</li>
-<li>Terreno generado procedimentalmente basado en el snowboard del mundo real</li>
-<li>Iluminación totalmente dinámica y efectos climáticos, incluyendo tormentas eléctricas, ventiscas, niebla, arco iris, estrellas fugaces y más</li>
-<li>Fácil de aprender, difícil dominar el sistema de trucos de un botón</li>
-<li>Encadenar los combos para maximizar los puntos y la velocidad</li>
-<li>Pon a prueba tus habilidades con 180 objetivos artesanales</li>
-<li>Descubre seis snowboarders únicos, cada uno con sus propios atributos y habilidades especiales</li>
-<li>Desafía a tus amigos con Game Center. Compite por la mejor puntuación alta, la mejor distancia y el mejor combo truco! </li>
-
-<li>Diseño visual maravillosamente minimalista y evocador</li>
-<li>Música original y audio artesanal para una experiencia ambiental e inmersiva (se recomiendan auriculares!)</li>
-<li>Aplicación universal con soporte iCloud. Juega en tu iPhone y iPad y tu progreso siempre estará sincronizado. </li>
-</ul>
- <h3>Cómo descargar e instalar apk de aventura de Alto ios</h3>
-<p>Si quieres jugar Alto’s Adventure en tu dispositivo iOS, tienes dos opciones:</p>
-<ol>
-<li>Puedes comprarlo en la App Store por $4.99. Esta es la forma oficial y más segura de obtener el juego. Necesitará un ID de Apple y un dispositivo compatible con iOS 9.0 o posterior. También puede descargarlo en su Mac si tiene macOS 11 o posterior. </li>
-<li>Puede descargarlo de un sitio web de terceros como un archivo apk. Esta es una forma no oficial y arriesgada de obtener el juego. Necesitará un dispositivo jailbreak o un emulador para ejecutarlo. También puede encontrar malware, virus u otros problemas que podrían dañar su dispositivo o comprometer su privacidad. No recomendamos esta opción. </li>
-</ol>
- <h2>Por qué deberías jugar la aventura de Alto <h2>Por qué deberías jugar la aventura de Alto</h2>
-<h3>Los beneficios de jugar la aventura de Alto</h3>
-<p>La aventura de Alto es más que un juego. Es una experiencia que puede enriquecer tu vida de muchas maneras. Estos son algunos de los beneficios de jugar Alto’s Adventure:</p>
-<p></p>
-<h4>Juego relajante e inmersivo</h4>
-<p>Una de las principales atracciones de Alto’s Adventure es su juego relajante y cautivador. El juego no tiene temporizadores, puntuaciones ni vidas. Puedes jugar a tu propio ritmo y disfrutar del viaje. El juego también tiene un modo zen, donde puedes explorar el mundo sin objetivos ni distracciones. El juego está diseñado para ayudarle a relajarse y relajarse del estrés y el ruido de la vida cotidiana. </p>
-<h4>Imágenes hermosas y dinámicas</h4>
-
-<h4>Metas desafiantes y gratificantes</h4>
-<p>Si estás buscando algún reto y emoción, Alto’s Adventure también tiene eso. El juego tiene 180 objetivos artesanales que ponen a prueba tus habilidades y creatividad. Puedes intentar realizar diferentes trucos, combos, grinds, rebotes y más. También puedes desbloquear y usar seis snowboarders diferentes, cada uno con sus propios atributos y habilidades especiales. También puedes adquirir y usar el traje de alas, que añade una nueva dimensión al juego. El juego es divertido y satisfactorio para jugar. </p>
- <h3>Los inconvenientes de jugar la aventura de Alto</h3>
-<p>Por supuesto, ningún juego es perfecto, y la aventura de Alto también tiene algunos inconvenientes que usted debe ser consciente de. Aquí están algunos de los inconvenientes de jugar la aventura de Alto:</p>
-<h4>Requiere iOS 9.0 o posterior</h4>
-<p>Si quieres jugar Alto’s Adventure en tu dispositivo iOS, tendrás que tener iOS 9.0 o posterior instalado en él. Esto significa que algunos dispositivos antiguos pueden no ser capaces de ejecutar el juego sin problemas o en absoluto. También es posible que necesite actualizar su dispositivo regularmente para garantizar la compatibilidad y el rendimiento. </p>
-<h4>Costos $4.99 en el App Store</h4>
-<p>Otro inconveniente de jugar Alto’s Adventure es que no es un juego gratis. Tendrás que pagar $4.99 en la App Store para descargarlo e instalarlo en tu dispositivo. Esto puede no ser un gran problema para algunas personas, pero puede ser una barrera para otros que tienen un presupuesto ajustado o prefieren los juegos gratis. </p>
-<h4>Puede consumir batería y espacio de almacenamiento</h4>
-<p>Un inconveniente final de jugar la aventura de Alto es que puede consumir mucha batería y espacio de almacenamiento en su dispositivo. El juego tiene gráficos de alta calidad y efectos de sonido, que requieren mucha energía y memoria para ejecutarse. Es posible que tenga que cargar su dispositivo con frecuencia o despejar algún espacio en él para evitar cualquier problema. </p>
- <h2>Consejos y trucos para jugar la aventura de Alto</h2>
-<h3>Cómo dominar el sistema de trucos de un botón</h3>
-
-<ul>
-<li>Para saltar, toque en cualquier lugar de la pantalla una vez. </li>
-<li> Para hacer un backflip, toque y mantenga pulsado en cualquier lugar de la pantalla mientras está en el aire. </li>
-<li>Para hacer un frontflip, toque dos veces rápidamente mientras está en el aire. </li>
-<li>Para moler sobre rieles, cuerdas o banderas, simplemente aterriza sobre ellas con tu tabla de snowboard. </li>
-<li>Para rebotar contra rocas o fogatas, toca una vez sobre ellas. </li>
-<li> Para hacer un giro, deslice hacia la izquierda o hacia la derecha mientras está en el aire. </li>
-</ul>
- <h3>Cómo encadenar combos y aumentar su puntuación</h3>
-<p>Una de las formas de aumentar tu puntuación y velocidad en la aventura de Alto es encadenar combos. Un combo es cuando realizas dos o más trucos en sucesión sin tocar el suelo o estrellarse. Aquí hay algunos consejos sobre cómo encadenar los combos:</p>
-<ul>
-<li>Cuanto más tiempo sostengas un backflip o un frontflip, más puntos obtendrás. </li>
-<li>Cuantos más giros hagas en un salto, más puntos obtendrás. </li>
-<li>Cuanto más alto saltas desde una rampa o un acantilado, más puntos obtienes. </li>
-<li>Cuanto más tiempo se muele en un riel o una cuerda, más puntos se obtiene. </li>
-<li>Cuantos más trucos hagas en un combo, más puntos obtendrás. </li>
-<li>Cuanto más variados sean tus trucos en un combo, más puntos obtendrás. </li>
-<li>Cuando consigues un combo, obtienes un aumento de velocidad y una extensión de bufanda. Cuanto más larga sea tu bufanda, más rápido irás. </li>
-<li>Si te bloqueas o tocas el suelo, tu combo termina y tu bufanda se reinicia. </li>
-</ul>
- <h3>Cómo desbloquear y usar el traje de ala</h3>
-<p>Uno de los mejores artículos en la aventura de Alto es el traje de ala, que le permite volar en el aire y realizar acrobacias increíbles. Aquí hay algunos consejos sobre cómo desbloquear y usar el traje de ala:</p>
-<ul>
-<li>Para desbloquear el traje de alas, necesitas completar el nivel 25 en el juego. También puedes comprarlo por 7.500 monedas en el taller de Izel. </li>
-<li>Para usar el traje de alas, necesitas llenar el medidor de traje de alas haciendo trucos y combos. Cuando el medidor esté lleno, toca el icono del traje de alas en la esquina superior derecha de la pantalla. </li>
-
-<li>Cuando estás usando el traje de ala, puedes hacer volteretas y giros como de costumbre, pero también puedes hacer bucles y rollos de barril deslizando hacia arriba o hacia abajo. </li>
-<li> Cuando se utiliza el traje de ala, todavía se puede moler en los carriles y cuerdas, pero no se puede rebotar en las rocas o fogatas. </li>
-<li>Cuando usted está utilizando el traje de ala, todavía se puede recoger monedas, llamas, y power-ups, pero no se puede recoger elementos de rescate abismo. </li>
-<li>El medidor del traje de ala se drenará gradualmente a medida que lo uses. Cuando esté vacío, volverás a tu tabla de snowboard automáticamente. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Alto’s Adventure es un juego que ofrece una odisea de snowboard serena y hermosa que cualquiera puede disfrutar. Si quieres relajarte y explorar el mundo, o desafiarte a ti mismo y dominar los trucos, Alto’s Adventure tiene algo para ti. El juego tiene un sencillo pero elegante sistema de truco de un botón, un diseño visual impresionante y dinámico, y una banda sonora original e inmersiva. El juego está disponible para dispositivos iOS por $4.99 en la App Store, o como un archivo apk de sitios web de terceros. Sin embargo, recomendamos comprarlo de la fuente oficial para evitar cualquier riesgo o problema. Si buscas un juego que pueda calmar tu mente y deleitar tus sentidos, Alto’s Adventure es un juego que debes probar. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre la aventura de Alto:</p>
-<ol>
-<li>P: ¿Cuántos niveles hay en la aventura de Alto? <br>A: Hay 60 niveles en la aventura de Alto, cada uno con tres objetivos para completar. Puedes reproducir cualquier nivel en cualquier momento para mejorar tu puntuación o completar metas perdidas. </li>
-<li>P: ¿Cómo puedo obtener más monedas en la aventura de Alto? <br>A: Puedes conseguir más monedas en la aventura de Alto recogiéndolas en las pistas, completando objetivos, viendo anuncios o comprándolas con dinero real. </li>
-
-<li>P: ¿Cuáles son los ancianos en la aventura de Alto? <br>A: Los ancianos son aldeanos enojados que te persiguen en sus tablas de snowboard. Aparecen al azar después del nivel 10. Pueden sacarte de tu tabla de snowboard si te alcanzan. Puede evitarlos saltando sobre ellos, moliendo sobre rieles o cuerdas por encima de ellos, o usando power-ups. </li>
-<li>P: ¿Cuáles son los secretos de la aventura de Alto? <br>A: Hay algunos secretos en la aventura de Alto que puedes descubrir jugando el juego. Por ejemplo, hay un taller oculto donde Izel hace sus invenciones. También hay un templo misterioso donde Maya practica sus volteretas. También hay algunos huevos de Pascua y referencias a otros juegos y medios de comunicación. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Apk Descargar Tekken 3 35 Mb.md

@@ -1,54 +0,0 @@
-<br />
-<h1>Descargar APK Tekken 3 35 MB: Cómo jugar el clásico juego de lucha en su dispositivo Android</h1>
- <h2>Introducción</h2>
- <p>Tekken 3 es uno de los juegos de lucha más populares e influyentes de todos los tiempos. Fue lanzado en 1997 para las salas recreativas y en 1998 para PlayStation. Cuenta con una gran y diversa lista de personajes, cada uno con su propio estilo de lucha único y la historia. También introduce un nuevo sistema de movimiento 3D, que permite a los jugadores eludir dentro o fuera del fondo. Tekken 3 ha sido elogiado por su juego rápido y fluido, sus impresionantes gráficos y efectos de sonido, y sus diversos modos y desafíos. </p>
-<h2>apk descargar tekken 3 35 mb</h2><br /><p><b><b>Download Zip</b> &#9675; <a href="https://bltlly.com/2v6MDg">https://bltlly.com/2v6MDg</a></b></p><br /><br />
- <p>Pero ¿qué pasa si quieres jugar Tekken 3 en tu dispositivo Android? Desafortunadamente, el juego no está disponible oficialmente en la Google Play Store. Sin embargo, hay una manera de disfrutar de este juego clásico en su teléfono inteligente o tableta. Puede descargar un archivo APK de Tekken 3 e instalarlo en su dispositivo. Un archivo APK es un archivo de paquete de aplicación que contiene todos los datos y archivos necesarios para ejecutar una aplicación. Al descargar un archivo APK de Tekken 3, puede evitar las restricciones de la Play Store y jugar el juego sin ningún problema. </p>
- <p>En este artículo, le mostraremos cómo descargar e instalar Tekken 3 APK en su dispositivo Android. También le diremos acerca de las características de Tekken 3 APK, y le dará algunos consejos y trucos para jugar el juego. Así que, si estás listo para revivir la nostalgia de Tekken 3, ¡sigue leyendo! </p>
- <h2>Características de Tekken 3 APK</h2>
- <p>Tekken 3 APK es una versión modificada del juego original que está optimizado para dispositivos Android. Tiene todas las características y el contenido de la versión de PlayStation, además de algunos beneficios adicionales. Aquí están algunas de las características de Tekken 3 APK:</p>
- <h3>Jugabilidad y gráficos en 3D</h3>
-
- <h3>Diversidad de personajes</h3>
- <p>Tekken 3 APK cuenta con un total de 23 personajes, incluyendo algunos nuevos que debutaron en este juego. Puedes elegir entre luchadores como Jin Kazama, Ling Xiaoyu, Bryan Fury, Eddy Gordo, Hwoarang, Forest Law, Julia Chang y más. Cada personaje tiene su propia personalidad, historia y estilo de lucha. También puedes desbloquear dos personajes secretos: Dr. Bosconovitch y Gon.</p>
- <h3>Varios modos y desafíos</h3>
- <p>Tekken 3 APK ofrece más que solo los modos estándar de árcade y Versus. También puedes jugar a otros modos como Time Attack, Survival, Team Battle, Practice, etc. Cada modo tiene sus propios objetivos y recompensas. También puedes probar el nuevo modo Tekken Force, en el que tendrás que luchar contra oleadas de enemigos de forma lateral. O puedes jugar el modo de bonificación Tekken Ball, donde tienes que golpear una pelota de playa con tus ataques. Estos modos añaden más variedad y diversión al juego. </p>
-<p></p>
- <h3>Soporte multijugador y ranking online</h3>
- <p>Tekken 3 APK le permite jugar con tus amigos u otros jugadores en línea. Puede conectar su dispositivo con otro dispositivo a través de Bluetooth o Wi-Fi, y disfrutar de un partido uno-a-uno. También puedes competir con jugadores de todo el mundo en el modo de clasificación en línea, donde puedes ganar puntos y subir la clasificación. También puedes chatear con otros jugadores y compartir tus consejos y estrategias. </p>
- <h2>Cómo descargar e instalar Tekken 3 APK</h2>
- <p>Descargar e instalar Tekken 3 APK es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
- <h3>Paso 1: Descargar el archivo APK de una fuente de confianza</h3>
- <p>Lo primero que tienes que hacer es descargar el archivo APK de Tekken 3 de una fuente confiable y segura. Puede utilizar el siguiente enlace para descargar el archivo, que tiene solo 35 MB de tamaño. Asegúrate de tener suficiente espacio de almacenamiento en tu dispositivo antes de descargarlo. </p>
- <p><a href=">Descargar Tekken 3 APK</a></p>
- <h3>Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo</h3>
-
- <h3>Paso 3: Instalar el archivo APK y lanzar el juego</h3>
- <p>Lo último que tienes que hacer es instalar el archivo APK y lanzar el juego. Para ello, localice el archivo descargado en su dispositivo y, a continuación, toque en él. Verá un mensaje pidiéndole que instale la aplicación. Toque en instalar y espere a que el proceso termine. Una vez hecho, verá un icono de Tekken 3 en la pantalla de inicio o en el cajón de la aplicación. Toque en él y disfrutar del juego! </h2>
- <h2>Consejos y trucos para jugar Tekken 3 APK</h2>
- <p>Tekken 3 APK es un juego divertido y adictivo, pero también puede ser desafiante y competitivo. Si desea mejorar sus habilidades y vencer a sus oponentes, aquí hay algunos consejos y trucos para jugar Tekken 3 APK:</p>
- <h3>Aprende los movimientos y combos de tu personaje favorito</h3>
- <p>Una de las cosas más importantes que hacer en Tekken 3 APK es aprender los movimientos y combos de su personaje favorito. Cada personaje tiene sus propias fortalezas y debilidades, así como sus propios movimientos especiales y combos. Puede averiguar los movimientos y combos de cada carácter yendo al modo de práctica o comprobando la lista de movimientos en el menú de pausa. También puedes ver algunos tutoriales o videos en línea para aprender algunas técnicas y estrategias avanzadas. </p>
- <h3>Usa pasos laterales y reversiones para esquivar ataques</h3>
- <p>Otra cosa importante que hacer en Tekken 3 APK es utilizar pasos laterales y reversiones para esquivar los ataques. Los pasos laterales son una nueva característica en Tekken 3 que le permite moverse dentro o fuera del fondo, evitando algunos ataques o creando nuevos ángulos para sus propios ataques. Para realizar un sidestep, presione hacia arriba o hacia abajo en la almohadilla direccional o joystick. Las reversiones son una técnica defensiva que te permite contrarrestar algunos ataques al agarrar el brazo o la pierna de tu oponente y desequilibrarlos. Para realizar una reversión, presione hacia atrás o hacia adelante en la almohadilla direccional o joystick en el momento adecuado cuando su oponente está atacando. </p>
- <h3>Experimenta con diferentes modos y niveles de dificultad</h3>
-
- <h3>Juega con amigos o oponentes en línea para más diversión</h3>
- <p>Una cuarta cosa importante que hacer en Tekken 3 APK es jugar con amigos o oponentes en línea para más diversión. Jugar solo puede ser divertido, pero jugar con otros puede ser aún más divertido. Puedes conectar tu dispositivo con otro dispositivo a través de Bluetooth o Wi-Fi, y jugar una partida individual con tu amigo o familiar. También puedes jugar en línea con jugadores de todo el mundo en el modo de clasificación en línea, donde puedes ganar puntos y subir la clasificación. También puedes chatear con otros jugadores y compartir tus consejos y estrategias. Jugar con otros puede hacer que el juego sea más emocionante, desafiante y social. </p>
- <h2>Conclusión</h2>
- <p>Tekken 3 APK es una gran manera de disfrutar del clásico juego de lucha en su dispositivo Android. Tiene todas las características y contenido del juego original, además de algunos beneficios adicionales. Puedes jugar con 23 personajes, cada uno con sus propios movimientos y combos. También puedes jugar varios modos y desafíos, como Tekken Force, Tekken Ball, Time Attack, Survival y más. También puede jugar con amigos o oponentes en línea, y competir por el primer lugar en la clasificación. Tekken 3 APK es fácil de descargar e instalar, y se ejecuta sin problemas y sin problemas en su dispositivo. </p>
- <p>Si eres un fan de Tekken 3 o juegos de lucha en general, definitivamente debes probar Tekken 3 APK. Le devolverá la nostalgia de los viejos tiempos, y también le dará una experiencia nueva y fresca. Tekken 3 APK es uno de los mejores juegos de lucha jamás hecho, y se puede jugar en cualquier momento, en cualquier lugar, en su dispositivo Android. </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre Tekken 3 APK:</p>
- <h3>¿Es seguro descargar e instalar Tekken 3 APK? </h3>
-
- <h3>¿Es Tekken 3 APK compatible con mi dispositivo? </h3>
- <p>Tekken 3 APK es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.0 o superior. Sin embargo, algunos dispositivos pueden tener diferentes especificaciones o configuraciones que pueden afectar el rendimiento o la compatibilidad del juego. Si encuentras algún problema o error mientras juegas, puedes intentar actualizar el software de tu dispositivo, limpiar tu caché o reinstalar el juego. </p>
- <h3> ¿Cómo puedo desbloquear todos los caracteres en Tekken 3 APK? </h3>
- <p>Puede desbloquear todos los personajes en Tekken 3 APK jugando el modo árcade y derrotarlos en sus respectivas etapas. También puedes desbloquear dos personajes secretos: Dr. Bosconovitch y Gon. Para desbloquear al Dr. Bosconovitch, tienes que completar el modo Tekken Force cuatro veces, luego derrotarlo en la etapa final. Para desbloquear Gon, tienes que vencerlo en el modo Tekken Ball o encontrar su foto en el juego de tenis Smash Court de Anna Kournikova. </p>
- <h3> ¿Cómo puedo guardar mi progreso en Tekken 3 APK? </h3>
- <p>Puede guardar su progreso en Tekken 3 APK mediante el uso de la función guardar estado. Esta característica le permite guardar su juego en cualquier momento y reanudarlo más tarde. Para usar esta función, debe pulsar en el botón de menú en la esquina superior derecha de la pantalla y luego en el estado de guardado. También puedes cargar tu juego guardado tocando el estado de carga. </p>
- <h3> ¿Cómo puedo cambiar el idioma o la configuración de sonido en Tekken 3 APK? </h3>
- <p>Puede cambiar el idioma o la configuración de sonido en Tekken 3 APK yendo al menú de opciones. Para acceder a este menú, tienes que pulsar en el botón de menú en la esquina superior derecha de la pantalla, a continuación, toque en las opciones. A continuación, puede elegir entre diferentes idiomas, como inglés, japonés, coreano, chino, etc. También puede ajustar el volumen de sonido, volumen de música, volumen de efectos de sonido, etc.</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/losses/__init__.py

@@ -1,2 +0,0 @@
-from taming.modules.losses.vqperceptual import DummyLoss
-

spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/fc.py

@@ -1,33 +0,0 @@
-from __future__ import print_function
-import torch.nn as nn
-from torch.nn.utils.weight_norm import weight_norm
-
-
-class FCNet(nn.Module):
-    """Simple class for non-linear fully connect network
-    """
-    def __init__(self, dims):
-        super(FCNet, self).__init__()
-
-        layers = []
-        for i in range(len(dims)-2):
-            in_dim = dims[i]
-            out_dim = dims[i+1]
-            layers.append(weight_norm(nn.Linear(in_dim, out_dim), dim=None))
-            layers.append(nn.ReLU())
-        layers.append(weight_norm(nn.Linear(dims[-2], dims[-1]), dim=None))
-        layers.append(nn.ReLU())
-
-        self.main = nn.Sequential(*layers)
-
-    def forward(self, x):
-        return self.main(x)
-
-
-if __name__ == '__main__':
-    fc1 = FCNet([10, 20, 10])
-    print(fc1)
-
-    print('============')
-    fc2 = FCNet([10, 20])
-    print(fc2)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_utils.h

@@ -1,342 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-#pragma once
-
-#include <cassert>
-#include <cmath>
-
-#ifdef __CUDACC__
-// Designates functions callable from the host (CPU) and the device (GPU)
-#define HOST_DEVICE __host__ __device__
-#define HOST_DEVICE_INLINE HOST_DEVICE __forceinline__
-#else
-#include <algorithm>
-#define HOST_DEVICE
-#define HOST_DEVICE_INLINE HOST_DEVICE inline
-#endif
-
-namespace detectron2 {
-
-namespace {
-
-template <typename T>
-struct RotatedBox {
-  T x_ctr, y_ctr, w, h, a;
-};
-
-template <typename T>
-struct Point {
-  T x, y;
-  HOST_DEVICE_INLINE Point(const T& px = 0, const T& py = 0) : x(px), y(py) {}
-  HOST_DEVICE_INLINE Point operator+(const Point& p) const {
-    return Point(x + p.x, y + p.y);
-  }
-  HOST_DEVICE_INLINE Point& operator+=(const Point& p) {
-    x += p.x;
-    y += p.y;
-    return *this;
-  }
-  HOST_DEVICE_INLINE Point operator-(const Point& p) const {
-    return Point(x - p.x, y - p.y);
-  }
-  HOST_DEVICE_INLINE Point operator*(const T coeff) const {
-    return Point(x * coeff, y * coeff);
-  }
-};
-
-template <typename T>
-HOST_DEVICE_INLINE T dot_2d(const Point<T>& A, const Point<T>& B) {
-  return A.x * B.x + A.y * B.y;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE T cross_2d(const Point<T>& A, const Point<T>& B) {
-  return A.x * B.y - B.x * A.y;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE void get_rotated_vertices(
-    const RotatedBox<T>& box,
-    Point<T> (&pts)[4]) {
-  // M_PI / 180. == 0.01745329251
-  double theta = box.a * 0.01745329251;
-  T cosTheta2 = (T)cos(theta) * 0.5f;
-  T sinTheta2 = (T)sin(theta) * 0.5f;
-
-  // y: top --> down; x: left --> right
-  pts[0].x = box.x_ctr - sinTheta2 * box.h - cosTheta2 * box.w;
-  pts[0].y = box.y_ctr + cosTheta2 * box.h - sinTheta2 * box.w;
-  pts[1].x = box.x_ctr + sinTheta2 * box.h - cosTheta2 * box.w;
-  pts[1].y = box.y_ctr - cosTheta2 * box.h - sinTheta2 * box.w;
-  pts[2].x = 2 * box.x_ctr - pts[0].x;
-  pts[2].y = 2 * box.y_ctr - pts[0].y;
-  pts[3].x = 2 * box.x_ctr - pts[1].x;
-  pts[3].y = 2 * box.y_ctr - pts[1].y;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE int get_intersection_points(
-    const Point<T> (&pts1)[4],
-    const Point<T> (&pts2)[4],
-    Point<T> (&intersections)[24]) {
-  // Line vector
-  // A line from p1 to p2 is: p1 + (p2-p1)*t, t=[0,1]
-  Point<T> vec1[4], vec2[4];
-  for (int i = 0; i < 4; i++) {
-    vec1[i] = pts1[(i + 1) % 4] - pts1[i];
-    vec2[i] = pts2[(i + 1) % 4] - pts2[i];
-  }
-
-  // Line test - test all line combos for intersection
-  int num = 0; // number of intersections
-  for (int i = 0; i < 4; i++) {
-    for (int j = 0; j < 4; j++) {
-      // Solve for 2x2 Ax=b
-      T det = cross_2d<T>(vec2[j], vec1[i]);
-
-      // This takes care of parallel lines
-      if (fabs(det) <= 1e-14) {
-        continue;
-      }
-
-      auto vec12 = pts2[j] - pts1[i];
-
-      T t1 = cross_2d<T>(vec2[j], vec12) / det;
-      T t2 = cross_2d<T>(vec1[i], vec12) / det;
-
-      if (t1 >= 0.0f && t1 <= 1.0f && t2 >= 0.0f && t2 <= 1.0f) {
-        intersections[num++] = pts1[i] + vec1[i] * t1;
-      }
-    }
-  }
-
-  // Check for vertices of rect1 inside rect2
-  {
-    const auto& AB = vec2[0];
-    const auto& DA = vec2[3];
-    auto ABdotAB = dot_2d<T>(AB, AB);
-    auto ADdotAD = dot_2d<T>(DA, DA);
-    for (int i = 0; i < 4; i++) {
-      // assume ABCD is the rectangle, and P is the point to be judged
-      // P is inside ABCD iff. P's projection on AB lies within AB
-      // and P's projection on AD lies within AD
-
-      auto AP = pts1[i] - pts2[0];
-
-      auto APdotAB = dot_2d<T>(AP, AB);
-      auto APdotAD = -dot_2d<T>(AP, DA);
-
-      if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) &&
-          (APdotAD <= ADdotAD)) {
-        intersections[num++] = pts1[i];
-      }
-    }
-  }
-
-  // Reverse the check - check for vertices of rect2 inside rect1
-  {
-    const auto& AB = vec1[0];
-    const auto& DA = vec1[3];
-    auto ABdotAB = dot_2d<T>(AB, AB);
-    auto ADdotAD = dot_2d<T>(DA, DA);
-    for (int i = 0; i < 4; i++) {
-      auto AP = pts2[i] - pts1[0];
-
-      auto APdotAB = dot_2d<T>(AP, AB);
-      auto APdotAD = -dot_2d<T>(AP, DA);
-
-      if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) &&
-          (APdotAD <= ADdotAD)) {
-        intersections[num++] = pts2[i];
-      }
-    }
-  }
-
-  return num;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE int convex_hull_graham(
-    const Point<T> (&p)[24],
-    const int& num_in,
-    Point<T> (&q)[24],
-    bool shift_to_zero = false) {
-  assert(num_in >= 2);
-
-  // Step 1:
-  // Find point with minimum y
-  // if more than 1 points have the same minimum y,
-  // pick the one with the minimum x.
-  int t = 0;
-  for (int i = 1; i < num_in; i++) {
-    if (p[i].y < p[t].y || (p[i].y == p[t].y && p[i].x < p[t].x)) {
-      t = i;
-    }
-  }
-  auto& start = p[t]; // starting point
-
-  // Step 2:
-  // Subtract starting point from every points (for sorting in the next step)
-  for (int i = 0; i < num_in; i++) {
-    q[i] = p[i] - start;
-  }
-
-  // Swap the starting point to position 0
-  auto tmp = q[0];
-  q[0] = q[t];
-  q[t] = tmp;
-
-  // Step 3:
-  // Sort point 1 ~ num_in according to their relative cross-product values
-  // (essentially sorting according to angles)
-  // If the angles are the same, sort according to their distance to origin
-  T dist[24];
-  for (int i = 0; i < num_in; i++) {
-    dist[i] = dot_2d<T>(q[i], q[i]);
-  }
-
-#ifdef __CUDACC__
-  // CUDA version
-  // In the future, we can potentially use thrust
-  // for sorting here to improve speed (though not guaranteed)
-  for (int i = 1; i < num_in - 1; i++) {
-    for (int j = i + 1; j < num_in; j++) {
-      T crossProduct = cross_2d<T>(q[i], q[j]);
-      if ((crossProduct < -1e-6) ||
-          (fabs(crossProduct) < 1e-6 && dist[i] > dist[j])) {
-        auto q_tmp = q[i];
-        q[i] = q[j];
-        q[j] = q_tmp;
-        auto dist_tmp = dist[i];
-        dist[i] = dist[j];
-        dist[j] = dist_tmp;
-      }
-    }
-  }
-#else
-  // CPU version
-  std::sort(
-      q + 1, q + num_in, [](const Point<T>& A, const Point<T>& B) -> bool {
-        T temp = cross_2d<T>(A, B);
-        if (fabs(temp) < 1e-6) {
-          return dot_2d<T>(A, A) < dot_2d<T>(B, B);
-        } else {
-          return temp > 0;
-        }
-      });
-#endif
-
-  // Step 4:
-  // Make sure there are at least 2 points (that don't overlap with each other)
-  // in the stack
-  int k; // index of the non-overlapped second point
-  for (k = 1; k < num_in; k++) {
-    if (dist[k] > 1e-8) {
-      break;
-    }
-  }
-  if (k == num_in) {
-    // We reach the end, which means the convex hull is just one point
-    q[0] = p[t];
-    return 1;
-  }
-  q[1] = q[k];
-  int m = 2; // 2 points in the stack
-  // Step 5:
-  // Finally we can start the scanning process.
-  // When a non-convex relationship between the 3 points is found
-  // (either concave shape or duplicated points),
-  // we pop the previous point from the stack
-  // until the 3-point relationship is convex again, or
-  // until the stack only contains two points
-  for (int i = k + 1; i < num_in; i++) {
-    while (m > 1 && cross_2d<T>(q[i] - q[m - 2], q[m - 1] - q[m - 2]) >= 0) {
-      m--;
-    }
-    q[m++] = q[i];
-  }
-
-  // Step 6 (Optional):
-  // In general sense we need the original coordinates, so we
-  // need to shift the points back (reverting Step 2)
-  // But if we're only interested in getting the area/perimeter of the shape
-  // We can simply return.
-  if (!shift_to_zero) {
-    for (int i = 0; i < m; i++) {
-      q[i] += start;
-    }
-  }
-
-  return m;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE T polygon_area(const Point<T> (&q)[24], const int& m) {
-  if (m <= 2) {
-    return 0;
-  }
-
-  T area = 0;
-  for (int i = 1; i < m - 1; i++) {
-    area += fabs(cross_2d<T>(q[i] - q[0], q[i + 1] - q[0]));
-  }
-
-  return area / 2.0;
-}
-
-template <typename T>
-HOST_DEVICE_INLINE T rotated_boxes_intersection(
-    const RotatedBox<T>& box1,
-    const RotatedBox<T>& box2) {
-  // There are up to 4 x 4 + 4 + 4 = 24 intersections (including dups) returned
-  // from rotated_rect_intersection_pts
-  Point<T> intersectPts[24], orderedPts[24];
-
-  Point<T> pts1[4];
-  Point<T> pts2[4];
-  get_rotated_vertices<T>(box1, pts1);
-  get_rotated_vertices<T>(box2, pts2);
-
-  int num = get_intersection_points<T>(pts1, pts2, intersectPts);
-
-  if (num <= 2) {
-    return 0.0;
-  }
-
-  // Convex Hull to order the intersection points in clockwise order and find
-  // the contour area.
-  int num_convex = convex_hull_graham<T>(intersectPts, num, orderedPts, true);
-  return polygon_area<T>(orderedPts, num_convex);
-}
-
-} // namespace
-
-template <typename T>
-HOST_DEVICE_INLINE T
-single_box_iou_rotated(T const* const box1_raw, T const* const box2_raw) {
-  // shift center to the middle point to achieve higher precision in result
-  RotatedBox<T> box1, box2;
-  auto center_shift_x = (box1_raw[0] + box2_raw[0]) / 2.0;
-  auto center_shift_y = (box1_raw[1] + box2_raw[1]) / 2.0;
-  box1.x_ctr = box1_raw[0] - center_shift_x;
-  box1.y_ctr = box1_raw[1] - center_shift_y;
-  box1.w = box1_raw[2];
-  box1.h = box1_raw[3];
-  box1.a = box1_raw[4];
-  box2.x_ctr = box2_raw[0] - center_shift_x;
-  box2.y_ctr = box2_raw[1] - center_shift_y;
-  box2.w = box2_raw[2];
-  box2.h = box2_raw[3];
-  box2.a = box2_raw[4];
-
-  const T area1 = box1.w * box1.h;
-  const T area2 = box2.w * box2.h;
-  if (area1 < 1e-14 || area2 < 1e-14) {
-    return 0.f;
-  }
-
-  const T intersection = rotated_boxes_intersection<T>(box1, box2);
-  const T iou = intersection / (area1 + area2 - intersection);
-  return iou;
-}
-
-} // namespace detectron2

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/README.md

@@ -1,7 +0,0 @@
-
-## Some scripts for developers to use, include:
-
-- `linter.sh`: lint the codebase before commit
-- `run_{inference,instant}_tests.sh`: run inference/training for a few iterations.
-   Note that these tests require 2 GPUs.
-- `parse_results.sh`: parse results from a log file.

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/utils/logger.py

@@ -1,13 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-
-
-def verbosity_to_level(verbosity):
-    if verbosity is not None:
-        if verbosity == 0:
-            return logging.WARNING
-        elif verbosity == 1:
-            return logging.INFO
-        elif verbosity >= 2:
-            return logging.DEBUG
-    return logging.WARNING

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/query_db.py

@@ -1,249 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import argparse
-import logging
-import os
-import sys
-from timeit import default_timer as timer
-from typing import Any, ClassVar, Dict, List
-import torch
-
-from detectron2.data.catalog import DatasetCatalog
-from detectron2.utils.logger import setup_logger
-
-from densepose.structures import DensePoseDataRelative
-from densepose.utils.dbhelper import EntrySelector
-from densepose.utils.logger import verbosity_to_level
-from densepose.vis.base import CompoundVisualizer
-from densepose.vis.bounding_box import BoundingBoxVisualizer
-from densepose.vis.densepose import (
-    DensePoseDataCoarseSegmentationVisualizer,
-    DensePoseDataPointsIVisualizer,
-    DensePoseDataPointsUVisualizer,
-    DensePoseDataPointsVisualizer,
-    DensePoseDataPointsVVisualizer,
-)
-
-DOC = """Query DB - a tool to print / visualize data from a database
-"""
-
-LOGGER_NAME = "query_db"
-
-logger = logging.getLogger(LOGGER_NAME)
-
-_ACTION_REGISTRY: Dict[str, "Action"] = {}
-
-
-class Action(object):
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        parser.add_argument(
-            "-v",
-            "--verbosity",
-            action="count",
-            help="Verbose mode. Multiple -v options increase the verbosity.",
-        )
-
-
-def register_action(cls: type):
-    """
-    Decorator for action classes to automate action registration
-    """
-    global _ACTION_REGISTRY
-    _ACTION_REGISTRY[cls.COMMAND] = cls
-    return cls
-
-
-class EntrywiseAction(Action):
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        super(EntrywiseAction, cls).add_arguments(parser)
-        parser.add_argument(
-            "dataset", metavar="<dataset>", help="Dataset name (e.g. densepose_coco_2014_train)"
-        )
-        parser.add_argument(
-            "selector",
-            metavar="<selector>",
-            help="Dataset entry selector in the form field1[:type]=value1[,"
-            "field2[:type]=value_min-value_max...] which selects all "
-            "entries from the dataset that satisfy the constraints",
-        )
-        parser.add_argument(
-            "--max-entries", metavar="N", help="Maximum number of entries to process", type=int
-        )
-
-    @classmethod
-    def execute(cls: type, args: argparse.Namespace):
-        dataset = setup_dataset(args.dataset)
-        entry_selector = EntrySelector.from_string(args.selector)
-        context = cls.create_context(args)
-        if args.max_entries is not None:
-            for _, entry in zip(range(args.max_entries), dataset):
-                if entry_selector(entry):
-                    cls.execute_on_entry(entry, context)
-        else:
-            for entry in dataset:
-                if entry_selector(entry):
-                    cls.execute_on_entry(entry, context)
-
-    @classmethod
-    def create_context(cls: type, args: argparse.Namespace) -> Dict[str, Any]:
-        context = {}
-        return context
-
-
-@register_action
-class PrintAction(EntrywiseAction):
-    """
-    Print action that outputs selected entries to stdout
-    """
-
-    COMMAND: ClassVar[str] = "print"
-
-    @classmethod
-    def add_parser(cls: type, subparsers: argparse._SubParsersAction):
-        parser = subparsers.add_parser(cls.COMMAND, help="Output selected entries to stdout. ")
-        cls.add_arguments(parser)
-        parser.set_defaults(func=cls.execute)
-
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        super(PrintAction, cls).add_arguments(parser)
-
-    @classmethod
-    def execute_on_entry(cls: type, entry: Dict[str, Any], context: Dict[str, Any]):
-        import pprint
-
-        printer = pprint.PrettyPrinter(indent=2, width=200, compact=True)
-        printer.pprint(entry)
-
-
-@register_action
-class ShowAction(EntrywiseAction):
-    """
-    Show action that visualizes selected entries on an image
-    """
-
-    COMMAND: ClassVar[str] = "show"
-    VISUALIZERS: ClassVar[Dict[str, object]] = {
-        "dp_segm": DensePoseDataCoarseSegmentationVisualizer(),
-        "dp_i": DensePoseDataPointsIVisualizer(),
-        "dp_u": DensePoseDataPointsUVisualizer(),
-        "dp_v": DensePoseDataPointsVVisualizer(),
-        "dp_pts": DensePoseDataPointsVisualizer(),
-        "bbox": BoundingBoxVisualizer(),
-    }
-
-    @classmethod
-    def add_parser(cls: type, subparsers: argparse._SubParsersAction):
-        parser = subparsers.add_parser(cls.COMMAND, help="Visualize selected entries")
-        cls.add_arguments(parser)
-        parser.set_defaults(func=cls.execute)
-
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        super(ShowAction, cls).add_arguments(parser)
-        parser.add_argument(
-            "visualizations",
-            metavar="<visualizations>",
-            help="Comma separated list of visualizations, possible values: "
-            "[{}]".format(",".join(sorted(cls.VISUALIZERS.keys()))),
-        )
-        parser.add_argument(
-            "--output",
-            metavar="<image_file>",
-            default="output.png",
-            help="File name to save output to",
-        )
-
-    @classmethod
-    def execute_on_entry(cls: type, entry: Dict[str, Any], context: Dict[str, Any]):
-        import cv2
-        import numpy as np
-
-        image_fpath = entry["file_name"]
-        image = cv2.imread(image_fpath, cv2.IMREAD_GRAYSCALE)
-        image = np.tile(image[:, :, np.newaxis], [1, 1, 3])
-        datas = cls._extract_data_for_visualizers_from_entry(context["vis_specs"], entry)
-        visualizer = context["visualizer"]
-        image_vis = visualizer.visualize(image, datas)
-        entry_idx = context["entry_idx"] + 1
-        out_fname = cls._get_out_fname(entry_idx, context["out_fname"])
-        cv2.imwrite(out_fname, image_vis)
-        logger.info(f"Output saved to {out_fname}")
-        context["entry_idx"] += 1
-
-    @classmethod
-    def _get_out_fname(cls: type, entry_idx: int, fname_base: str):
-        base, ext = os.path.splitext(fname_base)
-        return base + ".{0:04d}".format(entry_idx) + ext
-
-    @classmethod
-    def create_context(cls: type, args: argparse.Namespace) -> Dict[str, Any]:
-        vis_specs = args.visualizations.split(",")
-        visualizers = []
-        for vis_spec in vis_specs:
-            vis = cls.VISUALIZERS[vis_spec]
-            visualizers.append(vis)
-        context = {
-            "vis_specs": vis_specs,
-            "visualizer": CompoundVisualizer(visualizers),
-            "out_fname": args.output,
-            "entry_idx": 0,
-        }
-        return context
-
-    @classmethod
-    def _extract_data_for_visualizers_from_entry(
-        cls: type, vis_specs: List[str], entry: Dict[str, Any]
-    ):
-        dp_list = []
-        bbox_list = []
-        for annotation in entry["annotations"]:
-            is_valid, _ = DensePoseDataRelative.validate_annotation(annotation)
-            if not is_valid:
-                continue
-            bbox = torch.as_tensor(annotation["bbox"])
-            bbox_list.append(bbox)
-            dp_data = DensePoseDataRelative(annotation)
-            dp_list.append(dp_data)
-        datas = []
-        for vis_spec in vis_specs:
-            datas.append(bbox_list if "bbox" == vis_spec else (bbox_list, dp_list))
-        return datas
-
-
-def setup_dataset(dataset_name):
-    logger.info("Loading dataset {}".format(dataset_name))
-    start = timer()
-    dataset = DatasetCatalog.get(dataset_name)
-    stop = timer()
-    logger.info("Loaded dataset {} in {:.3f}s".format(dataset_name, stop - start))
-    return dataset
-
-
-def create_argument_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
-        description=DOC,
-        formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=120),
-    )
-    parser.set_defaults(func=lambda _: parser.print_help(sys.stdout))
-    subparsers = parser.add_subparsers(title="Actions")
-    for _, action in _ACTION_REGISTRY.items():
-        action.add_parser(subparsers)
-    return parser
-
-
-def main():
-    parser = create_argument_parser()
-    args = parser.parse_args()
-    verbosity = args.verbosity if hasattr(args, "verbosity") else None
-    global logger
-    logger = setup_logger(name=LOGGER_NAME)
-    logger.setLevel(verbosity_to_level(verbosity))
-    args.func(args)
-
-
-if __name__ == "__main__":
-    main()

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/extract_grid_feature.py

@@ -1,93 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-"""
-Grid features extraction script.
-"""
-import argparse
-import os
-import torch
-import tqdm
-from fvcore.common.file_io import PathManager
-
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import get_cfg
-from detectron2.engine import default_setup
-from detectron2.evaluation import inference_context
-from detectron2.modeling import build_model
-
-from grid_feats import (
-    add_attribute_config,
-    build_detection_test_loader_with_attributes,
-)
-
-# A simple mapper from object detection dataset to VQA dataset names
-dataset_to_folder_mapper = {}
-dataset_to_folder_mapper['coco_2014_train'] = 'train2014'
-dataset_to_folder_mapper['coco_2014_val'] = 'val2014'
-# One may need to change the Detectron2 code to support coco_2015_test
-# insert "coco_2015_test": ("coco/test2015", "coco/annotations/image_info_test2015.json"),
-# at: https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/builtin.py#L36
-dataset_to_folder_mapper['coco_2015_test'] = 'test2015'
-
-def extract_grid_feature_argument_parser():
-    parser = argparse.ArgumentParser(description="Grid feature extraction")
-    parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
-    parser.add_argument("--dataset", help="name of the dataset", default="coco_2014_train",
-                        choices=['coco_2014_train', 'coco_2014_val', 'coco_2015_test'])
-    parser.add_argument(
-        "opts",
-        help="Modify config options using the command-line",
-        default=None,
-        nargs=argparse.REMAINDER,
-    )
-    return parser
-
-def extract_grid_feature_on_dataset(model, data_loader, dump_folder):
-    for idx, inputs in enumerate(tqdm.tqdm(data_loader)):
-        with torch.no_grad():
-            image_id = inputs[0]['image_id']
-            file_name = '%d.pth' % image_id
-            # compute features
-            images = model.preprocess_image(inputs)
-            features = model.backbone(images.tensor)
-            outputs = model.roi_heads.get_conv5_features(features)
-            with PathManager.open(os.path.join(dump_folder, file_name), "wb") as f:
-                # save as CPU tensors
-                torch.save(outputs.cpu(), f)
-
-def do_feature_extraction(cfg, model, dataset_name):
-    with inference_context(model):
-        dump_folder = os.path.join(cfg.OUTPUT_DIR, "features", dataset_to_folder_mapper[dataset_name])
-        PathManager.mkdirs(dump_folder)
-        data_loader = build_detection_test_loader_with_attributes(cfg, dataset_name)
-        extract_grid_feature_on_dataset(model, data_loader, dump_folder)
-
-def setup(args):
-    """
-    Create configs and perform basic setups.
-    """
-    cfg = get_cfg()
-    add_attribute_config(cfg)
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    # force the final residual block to have dilations 1
-    cfg.MODEL.RESNETS.RES5_DILATION = 1
-    cfg.freeze()
-    default_setup(cfg, args)
-    return cfg
-
-
-def main(args):
-    cfg = setup(args)
-    model = build_model(cfg)
-    DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
-        cfg.MODEL.WEIGHTS, resume=True
-    )
-    do_feature_extraction(cfg, model, args.dataset)
-
-
-if __name__ == "__main__":
-    args = extract_grid_feature_argument_parser().parse_args()
-    print("Command Line Args:", args)
-    main(args)