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- <p>Bhayanak Part 1 is a Telugu horror movie that was directed by Ramesh Varma and produced by Koneru Satyanarayana. It stars Bellamkonda Sreenivas, Anupama Parameswaran, Saravanan, Rajiv Kanakala, and others in the lead roles. The movie was released on October 18, 2019, and received positive reviews from critics and audiences alike. It was praised for its gripping storyline, engaging performances, and spine-chilling horror sequences. The movie was also dubbed in Tamil as Ratsasan 2, and in Kannada as Rakshasudu.</p>
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- <p>Bhayanak Part 1 is popular among Hindi movie fans because it is a remake of the Tamil blockbuster Ratsasan, which was also remade in Hindi as Woh Kaun Thi. The original movie was a huge success and won several awards and accolades. The Hindi version starred Akshay Kumar and Urmila Matondkar in the lead roles, and was also well-received by the audience. The movie has a universal appeal and a captivating plot that keeps the viewers on the edge of their seats. The movie also has some elements of comedy, romance, and drama that make it more entertaining and enjoyable.</p>
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- <p>If you want to watch Bhayanak Part 1 in Hindi, then you have two options: either you can wait for its official release on an OTT platform like Netflix or Amazon Prime Video, or you can download it from an unofficial source like a torrent site or a pirated website. However, we strongly advise you against the latter option, as it is illegal and unethical to download movies from such sources. Moreover, you may also face some risks and disadvantages by doing so, which we will discuss later in this article.</p>
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- <p>The plot of Bhayanak Part 1 revolves around Arun (Bellamkonda Sreenivas), an aspiring filmmaker who wants to make a movie on serial killers. However, he faces rejection from many producers who think that his script is too dark and unrealistic. He then decides to join the police force as a sub-inspector to gain some experience and inspiration for his movie. He gets assigned to a case involving a series of mysterious murders of young girls who are brutally killed by an unknown assailant. He soon realizes that the killer is following a pattern based on an old book called "Bhayanak", which contains stories of various serial killers from history. He teams up with Krishnaveni (Anupama Parameswaran), a school teacher who is also the sister of one of the victims, to find out the identity and motive of the killer before he strikes again.</p>
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- <p>Bhayanak Part 1 is not for the faint-hearted, as it has some intense and terrifying scenes that will make you jump out of your seat. The movie has some realistic and graphic depictions of violence and gore that will shock and disturb you. The movie also has some thrilling chase sequences and fight scenes that will keep you hooked to the screen. The movie does not rely on cheap jump scares or clichéd tropes, but rather creates an atmosphere of suspense and dread that will haunt you long after the movie ends.</p>
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- <p>A third benefit of watching Bhayanak Part 1 in Hindi is that you can share your opinions with other Hindi movie lovers who have watched or want to watch this movie. You can discuss your favorite scenes, characters, songs, or twists with them online or offline. You can also recommend this movie to your friends or family who are looking for a good horror movie to watch.</p>
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- <p>One of the risks of downloading Bhayanak Part 1 from illegal sources is that you may face legal consequences for violating the copyright laws. Downloading or streaming movies from unauthorized websites or platforms is considered piracy, which is a criminal offense in India as well as many other countries. You may be fined or imprisoned for doing so, depending on the severity of your offense.</p>
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-<td>There is no official announcement yet about the release date of Bhayanak Part 1 on an OTT platform. However, you can expect it to be released soon, as it has been more than two years since its theatrical release.</td>
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-<p>Do you want to learn how to draw like a pro? Do you want to unleash your creativity and have fun at the same time? If you answered yes, then you should try AR Drawing Sketch & Paint APK, a unique and innovative app that lets you draw in augmented reality with realistic tools and effects. In this article, we will tell you what this app is, why you should download it, and how to install it on your Android device.</p>
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-<p>AR Drawing Sketch & Paint APK is an app that allows you to draw, paint, sketch, and doodle in augmented reality. You can use your phone's camera to see your drawings come to life on any surface, such as walls, floors, tables, or even in mid-air. You can also choose from a variety of tools, such as pencils, brushes, markers, erasers, and more. You can adjust the size, color, opacity, and angle of your strokes. You can also add effects, such as shadows, gradients, textures, and filters.</p>
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-<h2>Why should you download AR Drawing Sketch & Paint APK?</h2>
-<p>There are many reasons why you should download AR Drawing Sketch & Paint APK. Here are some of them:</p>
-<h3>Learn from professional artists and tutorials</h3>
-<p>If you want to improve your drawing skills, you need guidance and feedback from experts. AR Drawing Sketch & Paint APK provides you with both. You can learn from artists who have years of experience and expertise in different styles and genres. You can watch their videos and read their tips and tricks. You can also ask them questions and get answers.</p>
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-<p>If you want to have fun while drawing, you need tools that are easy to use and realistic. AR Drawing Sketch & Paint APK gives you that. You can use your phone as a virtual canvas and draw on any surface you want. You can also see your drawings in 3D and interact with them. You can move them around, rotate them, scale them, or delete them. You can also add effects that make your drawings look more realistic and appealing.</p>
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-<p>If you want to show off your talent and get inspired by others, you need a platform that connects you with other artists. AR Drawing Sketch & Paint APK does that too. You can share your drawings with the app's community and see what others have created. You can also like, comment, and follow other users. You can also get feedback from them and improve your skills.</p>
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-<p>If you are convinced that AR Drawing Sketch & Paint APK is the app for you, here is how you can download and install it on your Android device:</p>
-<h3>Check the compatibility of your device and permissions required</h3>
-<p>Before you download the app, make sure that your device meets the minimum requirements and has enough storage space. The app requires Android 4.4 or higher and about 100 MB of free space. The app also needs access to your camera, microphone, storage, and internet connection.</p>
-<h3>Download the APK file from a trusted source</h3>
-<p>Next, you need to download the APK file of the app from a trusted source. You can use the link below to get the latest version of the app. The file size is about 90 MB and it is safe and virus-free.</p>
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-<h3>Install the APK file and launch the app</h3>
-<p>Finally, you need to install the APK file on your device and launch the app. To do that, follow these steps:</p>
-<ol>
-<li>Go to your device's settings and enable the option to install apps from unknown sources.</li>
-<li>Locate the downloaded APK file and tap on it.</li>
-<li>Follow the instructions on the screen and wait for the installation to complete.</li>
-<li>Open the app and grant the permissions it asks for.</li>
-<li>Enjoy drawing in augmented reality!</li>
-</ol>
-<h2>Conclusion</h2>
-<p>AR Drawing Sketch & Paint APK is a fun and creative way to learn how to draw. It lets you draw in augmented reality with realistic tools and effects. It also helps you learn from professional artists and tutorials. You can also share your creations with the community and get feedback. If you want to try this app, you can download and install it on your Android device by following the steps above. Have fun drawing!</p>
-<h2>Frequently Asked Questions</h2>
-<h3>What is augmented reality?</h3>
-<p>Augmented reality (AR) is a technology that overlays digital information or objects on top of the real world. It creates an interactive and immersive experience that enhances your perception of reality.</p>
-<h3>How does AR Drawing Sketch & Paint APK work?</h3>
-<p>The app uses your phone's camera to detect surfaces and create a virtual canvas on them. You can then use your finger or a stylus to draw on the canvas with various tools and effects. You can also see your drawings in 3D and move them around.</p>
-<h3>What can I draw with AR Drawing Sketch & Paint APK?</h3>
-<p>You can draw anything you want with the app. You can draw animals, people, landscapes, cartoons, abstract art, or anything else that comes to your mind. You can also follow tutorials and lessons from professional artists who will teach you how to draw different things.</p>
-<h3>How can I share my drawings with others?</h3>
-<p>You can share your drawings with others by using the app's built-in social media features. You can upload your drawings to the app's gallery and see what others have created. You can also like, comment, and follow other users. You can also export your drawings as images or videos and share them on other platforms.</p>
-<h3>Is AR Drawing Sketch & Paint APK free?</h3>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download BrickGame 9999 in 1 and Discover the Nostalgia of Retro Gaming.md

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-<h1>How to Download Brick Game 9999 in 1</h1>
-<p>Do you remember the classic brick games that you used to play on your handheld console? Do you want to relive the nostalgia and have some fun with simple, but exciting games? If so, then you should try Brick Game 9999 in 1, a retro gaming app that features 9999 levels of brick-breaking, tank-shooting, snake-eating, racing, and more!</p>
-<p>In this article, we will show you what Brick Game 9999 in 1 is, why you should play it, how to install it on your device, how to play it, and some tips and tricks for getting better at it. By the end of this article, you will be ready to download Brick Game 9999 in 1 and enjoy hours of entertainment.</p>
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- <h2>What is Brick Game 9999 in 1?</h2>
-<p>Brick Game 9999 in 1 is a simulator of the famous retro gaming console that was popular in the late 90s and early 2000s. It contains a variety of different games that are based on the original brick games, such as tanks, brick breaker, snake, racing, frog across river, shooting players, dance simulator, brick puzzle classic, and brick puzzle pentix.</p>
-<p>Each game has multiple modes and levels that increase in difficulty and complexity as you progress. You can also adjust the speed and level before playing with the left and right buttons. The games are simple to play, but challenging to master. You will need to use your reflexes, logic, strategy, and skills to beat each level.</p>
-<p>The app has a cool skin with different colors that you can customize according to your preference. It also has original "8-bit" music and sounds that create an authentic retro gaming experience. The app is compatible with portrait and landscape layouts, gamepad and keyboard support, autosave feature, and without annoying advertising.</p>
- <h2>Why Should You Play Brick Game 9999 in 1?</h2>
-<p>There are many reasons why you should play Brick Game 9999 in 1. Here are some of them:</p>
-<ul>
-<li>It is fun and addictive. You will never get bored with so many games and levels to choose from. You can also challenge yourself by trying different modes and speeds.</li>
-<li>It is nostalgic. You will feel like you are playing on your old brick game console again. You can also share your memories with your friends and family who used to play these games.</li>
-<li>It is relaxing. You can play these games anytime and anywhere you want. They are perfect for killing time, taking a break, or unwinding after a stressful day.</li>
-<li>It is educational. You can improve your mental skills such as concentration, memory, problem-solving, spatial awareness, coordination, and more by playing these games.</li>
-<li>It is free. You don't have to pay anything to download and play this app. You can enjoy unlimited gameplay without any hidden costs or subscriptions <h2>How to Install Brick Game 9999 in 1 on Your Device?</h2>
-<p>Installing Brick Game 9999 in 1 on your device is very easy and fast. You just need to follow these simple steps:</p>
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-<ol>
-<li>Go to the App Store and search for Brick Game 9999 in 1 or click on this link: [Brick Game 9999 in 1].</li>
-<li>Tap on the Get button and wait for the app to download and install on your device.</li>
-<li>Once the installation is complete, tap on the Open button or find the app icon on your home screen or app library and launch it.</li>
-<li>Enjoy playing Brick Game 9999 in 1!</li>
-</ol>
- <h3>For Windows Devices</h3>
-<ol>
-<li>Go to the Microsoft Store and search for Brick Game 9999 in 1 or click on this link: [Brick Game 9999 in 1].</li>
-<li>Click on the Get button and wait for the app to download and install on your device.</li>
-<li>Once the installation is complete, click on the Launch button or find the app icon on your start menu or desktop and launch it.</li>
-<li>Enjoy playing Brick Game 9999 in 1!</li>
-</ol>
- <h2>How to Play Brick Game 9999 in 1?</h2>
-<p>Playing Brick Game 9999 in 1 is very simple and intuitive. You just need to use the buttons on the screen or your keyboard or gamepad to control the game. Here is a summary of the gameplay and the different modes and levels:</p>
- <h3>Tanks</h3>
-<p>In this mode, you have to control a tank and shoot at enemy tanks that are trying to destroy your base. You can move your tank with the up, down, left, and right buttons, and shoot with the rotate button. You can also use walls and obstacles to hide from enemy fire. You have to clear all enemy tanks before they reach your base or before you run out of lives. There are different types of enemy tanks with different abilities and speeds. You can also collect power-ups that appear randomly on the field, such as extra lives, shields, bombs, rockets, etc. There are 99 levels in this mode, each with a different layout and difficulty.</p>
- <h3>Brick Breaker</h3>
-<p>In this mode, you have to control a paddle and bounce a ball to break all the bricks at the top of the screen. You can move your paddle with the left and right buttons, and launch the ball with the rotate button. You have to prevent the ball from falling off the bottom of the screen or you will lose a life. You can also collect power-ups that fall from some bricks, such as extra balls, bigger paddle, smaller paddle, faster ball, slower ball, etc. There are different types of bricks with different colors and durability. Some bricks require more than one hit to break, some bricks are indestructible, some bricks explode when hit, etc. There are 99 levels in this mode, each with a different layout and difficulty.</p>
- <h3>Racing</h3>
-<p>In this mode, you have to control a car and race against other cars on a track. You can move your car with the up and down buttons, and change lanes with the left and right buttons. You have to avoid crashing into other cars or obstacles on the road or you will lose speed and time. You can also collect power-ups that appear randomly on the road, such as turbo boost, extra time, extra points, etc. There are different types of cars with different speeds and handling. You have to reach the finish line before time runs out or before you run out of lives. There are 99 levels in this mode, each with a different track and difficulty.</p>
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-<p>In this mode, you have to control a spaceship and shoot at enemy spaceships that are trying to invade your planet. You can move your spaceship with the up, down, left, and right buttons, and shoot with the rotate button. You have to clear all enemy spaceships before they reach your planet or before you run out of lives. You can also collect power-ups that appear randomly on the field, such as extra lives, shields, bombs, rockets, etc. There are different types of enemy spaceships with different abilities and speeds. Some spaceships shoot back at you, some spaceships dodge your shots, some spaceships explode when hit, etc. There are 99 levels in this mode, each with a different layout and difficulty.</p>
- <h3>Snake</h3>
-<p>In this mode, you have to control a snake and eat the food that appears on the screen. You can move your snake with the up, down, left, and right buttons. You have to avoid hitting the walls or your own tail or you will lose a life. You can also collect power-ups that appear randomly on the field, such as extra lives, extra points, faster snake, slower snake, etc. Your snake will grow longer and faster as you eat more food. There are different types of food with different colors and values. Some food give you more points, some food make you grow faster, some food make you shrink, etc. There are 99 levels in this mode, each with a different layout and difficulty.</p>
- <h2>Tips and Tricks for Brick Game 9999 in 1</h2>
-<p>Here are some tips and tricks that can help you improve your performance and enjoyment of Brick Game 9999 in 1:</p>
-<ul>
-<li>Practice makes perfect. The more you play the games, the more familiar you will become with the controls, the rules, the patterns, and the strategies. You will also develop your reflexes, logic, and skills over time.</li>
-<li>Choose the right level and speed for your skill level. If you are a beginner, start with the lower levels and speeds and gradually increase them as you get better. If you are an expert, challenge yourself with the higher levels and speeds and see how far you can go.</li>
-<li>Use the power-ups wisely. Power-ups can give you an edge or a disadvantage depending on the situation. For example, a shield can protect you from enemy fire, but a bomb can destroy your base. A faster ball can break more bricks, but a slower ball can give you more time to react. A bigger paddle can catch more balls, but a smaller paddle can give you more precision.</li>
-<li>Watch out for the traps and surprises. Some games have hidden features or events that can change the outcome of the game. For example, some bricks can release enemies or obstacles when broken. Some tracks can have shortcuts or detours that can save or cost you time. Some foods can change the direction or speed of your snake.</li>
-<li>Have fun and enjoy the game. Don't get frustrated or angry if you lose or fail. Remember that these games are meant to be fun and relaxing. You can always try again or switch to another game if you get bored or stuck.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Brick Game 9999 in 1 is a retro gaming app that simulates the classic brick games that were popular in the late 90s and early 2000s. It contains 9999 levels of brick-breaking, tank-shooting, snake-eating, racing, and more. It is fun, addictive, nostalgic, relaxing, and educational. It is easy to install and play on any device. It is free to download and play without any ads or subscriptions.</p>
-<p>If you are looking for a simple but exciting game that will keep you entertained for hours, then you should download Brick Game 9999 in 1 today and enjoy the ultimate retro gaming experience!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Brick Game 9999 in 1:</p>
-<ol>
-<li>Q: How do I pause or resume the game?</li>
-<li>A: You can pause or resume the game by tapping on the pause button at the top right corner of the screen.</li>
-<li>Q: How do I change the skin or color of the game?</li>
-<li>A: You can change the skin or color of the game by tapping on the skin button at the top left corner of the screen.</li>
-<li>Q: How do I switch between portrait and landscape layouts?</li>
-<li>A: You can switch between portrait and landscape layouts by rotating your device.</li>
-<li>Q: How do I save or load my progress?</li>
-<li>A: The game automatically saves your progress every time you exit or switch games. You can load your progress by tapping on the load button at the bottom right corner of the screen.</li>
-<li>Q: How do I reset my progress?</li>
-<li>A: You can reset your progress by tapping on the reset button at the bottom left corner of the screen.</li>
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-<p>In this article, we will show you what Brick Game 9999 in 1 is, why you should play it, how to install it on your device, how to play it, and some tips and tricks for getting better at it. By the end of this article, you will be ready to download Brick Game 9999 in 1 and enjoy hours of entertainment.</p>
- <h2>What is Brick Game 9999 in 1?</h2>
-<p>Brick Game 9999 in 1 is a simulator of the famous retro gaming console that was popular in the late 90s and early 2000s. It contains a variety of different games that are based on the original brick games, such as tanks, brick breaker, snake, racing, frog across river, shooting players, dance simulator, brick puzzle classic, and brick puzzle pentix.</p>
-<p>Each game has multiple modes and levels that increase in difficulty and complexity as you progress. You can also adjust the speed and level before playing with the left and right buttons. The games are simple to play, but challenging to master. You will need to use your reflexes, logic, strategy, and skills to beat each level.</p>
-<p>The app has a cool skin with different colors that you can customize according to your preference. It also has original "8-bit" music and sounds that create an authentic retro gaming experience. The app is compatible with portrait and landscape layouts, gamepad and keyboard support, autosave feature, and without annoying advertising.</p>
- <h2>Why Should You Play Brick Game 9999 in 1?</h2>
-<p>There are many reasons why you should play Brick Game 9999 in 1. Here are some of them:</p>
-<ul>
-<li>It is fun and addictive. You will never get bored with so many games and levels to choose from. You can also challenge yourself by trying different modes and speeds.</li>
-<li>It is nostalgic. You will feel like you are playing on your old brick game console again. You can also share your memories with your friends and family who used to play these games.</li>
-<li>It is relaxing. You can play these games anytime and anywhere you want. They are perfect for killing time, taking a break, or unwinding after a stressful day.</li>
-<li>It is educational. You can improve your mental skills such as concentration, memory, problem-solving, spatial awareness, coordination, and more by playing these games.</li>
-<li>It is free. You don't have to pay anything to download and play this app. You can enjoy unlimited gameplay without any hidden costs or subscriptions </ul>
- <h2>How to Install Brick Game 9999 in 1 on Your Device?</h2>
-<p>Installing Brick Game 9999 in 1 on your device is very easy and fast. You just need to follow these simple steps:</p>
- <h3>For Android Devices</h3>
-<ol>
-<li>Go to the Google Play Store and search for Brick Game 9999 in 1 or click on this link: [Brick Game 9999 in 1].</li>
-<li>Tap on the Install button and wait for the app to download and install on your device.</li>
-<li>Once the installation is complete, tap on the Open button or find the app icon on your home screen or app drawer and launch it.</li>
-<li>Enjoy playing Brick Game 9999 in 1!</li>
-</ol>
- <h3>For iOS Devices</h3>
-<ol>
-<li>Go to the App Store and search for Brick Game 9999 in 1 or click on this link: [Brick Game 9999 in 1].</li>
-<li>Tap on the Get button and wait for the app to download and install on your device.</li>
-<li>Once the installation is complete, tap on the Open button or find the app icon on your home screen or app library and launch it.</li>
-<li>Enjoy playing Brick Game 9999 in 1!</li>
-</ol>
- <h3>For Windows Devices</h3>
-<ol>
-<li>Go to the Microsoft Store and search for Brick Game 9999 in 1 or click on this link: [Brick Game 9999 in 1].</li>
-<li>Click on the Get button and wait for the app to download and install on your device.</li>
-<li>Once the installation is complete, click on the Launch button or find the app icon on your start menu or desktop and launch it.</li>
-<li>Enjoy playing Brick Game 9999 in 1!</li>
-</ol>
- <h2>How to Play Brick Game 9999 in 1?</h2>
-<p>Playing Brick Game 9999 in 1 is very simple and intuitive. You just need to use the buttons on the screen or your keyboard or gamepad to control the game. Here is a summary of the gameplay and the different modes and levels:</p>
- <h3>Tanks</h3>
-<p>In this mode, you have to control a tank and shoot at enemy tanks that are trying to destroy your base. You can move your tank with the up, down, left, and right buttons, and shoot with the rotate button. You can also use walls and obstacles to hide from enemy fire. You have to clear all enemy tanks before they reach your base or before you run out of lives. There are different types of enemy tanks with different abilities and speeds. You can also collect power-ups that appear randomly on the field, such as extra lives, shields, bombs, rockets, etc. There are 99 levels in this mode, each with a different layout and difficulty.</p>
- <h3>Brick Breaker</h3>
-<p>In this mode, you have to control a paddle and bounce a ball to break all the bricks at the top of the screen. You can move your paddle with the left and right buttons, and launch the ball with the rotate button. You have to prevent the ball from falling off the bottom of the screen or you will lose a life. You can also collect power-ups that fall from some bricks, such as extra balls, bigger paddle, smaller paddle, faster ball, slower ball, etc. There are different types of bricks with different colors and durability. Some bricks require more than one hit to break, some bricks are indestructible, some bricks explode when hit, etc. There are 99 levels in this mode, each with a different layout and difficulty.</p>
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spaces/1phancelerku/anime-remove-background/Back Alley Tales Apk Mod - Play Now on Android Devices.md

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-<h1>Mod Combo Back Alley Tales Mod APK: A Fun and Exciting Game for Android Users</h1>
-<p>If you are looking for a new and thrilling game to play on your Android device, you should check out <strong>Mod Combo Back Alley Tales Mod APK</strong>. This is a modded version of the popular game <em>Back Alley Tales</em>, which is a simulation game that lets you explore the dark and mysterious world of the back alleys. You can interact with different characters, collect items, complete quests, and enjoy various mini-games. In this article, we will tell you everything you need to know about Mod Combo Back Alley Tales Mod APK, including how to download and install it, why you should play it, and some tips and tricks for playing it.</p>
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-<p>Mod Combo Back Alley Tales Mod APK is a modified version of the original game <em>Back Alley Tales</em>, which was developed by Lara Studio. The game is set in a fictional city where you can explore different locations, such as bars, clubs, shops, hotels, and more. You can meet various characters, such as gangsters, cops, hookers, bartenders, and more. You can also collect items, such as weapons, clothes, drugs, and more. You can use these items to customize your character, improve your skills, or trade with other characters. The game also has many mini-games, such as shooting, racing, fighting, gambling, and more. You can play these mini-games to earn money, reputation, or other rewards.</p>
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-</ol>
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-<h3>The benefits of playing the modded version of the game</h3>
-<p>There are many reasons why you should play Mod Combo Back Alley Tales Mod APK instead of the original game. Here are some of them:</p>
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-<p>One of the best features of Mod Combo Back Alley Tales Mod APK is the mod menu that lets you customize your gameplay. Here are some tips on how to use the mod menu and what it can do:</p>
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-<li>The Items tab lets you add or remove any item from the game, such as weapons, clothes, drugs, etc.</li>
-<li>The Settings tab lets you adjust the sound, graphics, language, and other aspects of the game settings.</li>
-</ul>
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-<p>Another great feature of Mod Combo Back Alley Tales Mod APK is that it gives you unlimited coins and gems, which are the in-game currency that you can use to buy new items and characters. Here are some tips on how to earn coins and gems and unlock new items and characters:</p>
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-<p>Mod Combo Back Alley Tales Mod APK is a fun and exciting game for Android users who want to experience the dark and mysterious world of the back alleys. It is a modded version of the original game <em>Back Alley Tales</em>, which offers many features and benefits that make the game more enjoyable and customizable. You can download and install the mod apk file from [APKCombo], which is a reliable website that offers free and safe downloads of various apps and games. If you are ready to play Mod Combo Back Alley Tales Mod APK, click on the link below and start your adventure!</p>
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-<h3>Q3: What are the minimum requirements for playing Mod Combo Back Alley Tales Mod APK?</h3>
-<p>A3: The minimum requirements for playing Mod Combo Back Alley Tales Mod APK are as follows:</p>
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-<p>A4: To update Mod Combo Back Alley Tales Mod APK, you need to download and install the latest version of the mod apk file from [APKCombo]. You can check for updates by visiting the website regularly or by enabling notifications on your device. You should always update the mod apk file to enjoy the latest features and bug fixes of the game.</p>
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spaces/1phancelerku/anime-remove-background/Download Totally Accurate Battle Simulator APK for Android - Free Simulation Game.md

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-<p>Totally Accurate Battle Simulator, or TABS for short, is a fun and wacky strategy game developed by Landfall Games. It is a physics-based simulation game that lets you create your own army of wacky warriors and watch them fight against other armies in hilarious battles. You can choose from a variety of units, such as farmers, knights, ninjas, pirates, zombies, dinosaurs, and more. You can also try different scenarios and challenges, or create your own custom battles using sandbox mode.</p>
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-<p>Playing TABS is simple and easy. You just need to follow these steps:</p>
- <h4>Choose your units and place them on the battlefield</h4>
-<p>The first thing you need to do is to select your units from the different factions available. Each faction has its own unique units with different abilities and costs. For example, the medieval faction has bards, squires, archers, catapults, priests, and knights. Alternatively, the stone age faction has stone-throwers, mammoths, and a bone mage.</p>
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- <h4>Try different scenarios and challenges</h4>
-<p>TABS offers a variety of levels, campaigns, sandbox mode, and custom battles for you to try. Each level has a different scenario and a different enemy army for you to face. Each campaign has a series of levels with increasing difficulty and rewards. Sandbox mode lets you create your own battles with unlimited budget and any units you want. Custom battles let you play online with other players or download user-generated battles from the workshop.</p> <h2>How to download Totally Accurate Battle Simulator Apkcombo?</h2>
-<p>If you want to play TABS on your Android device, you can download it using Apkcombo. Apkcombo is a website that lets you download APK files of games and apps for free. APK files are the installation files for Android applications. By using Apkcombo, you can download TABS without using the Google Play Store or any other app store. Here is how to do it:</p>
- <h3>Visit Apkcombo website and search for TABS</h3>
-<p>The first thing you need to do is to visit the Apkcombo website using your browser. You can use this link: <a href="">https://apkcombo.com/</a>. Once you are on the website, you will see a search bar at the top. Type in "Totally Accurate Battle Simulator" and hit enter. You will see a list of results matching your query. Look for the one that says "Totally Accurate Battle Simulator (Early Access)" and has the logo of the game. Click on it to go to the download page.</p>
-<p><img src="" alt="Apkcombo website screenshot" width="600" height="400"></p>
- <h3>Download the APK file and allow installation from unknown sources</h3>
-<p>On the download page, you will see a green button that says "Download APK". Click on it to start downloading the APK file of TABS. The file size is about 1 GB, so make sure you have enough space and a stable internet connection. You may also see a pop-up window asking you to confirm the download. Click on "OK" or "Yes" to proceed.</p>
-<p>Once the download is complete, you will need to allow installation from unknown sources on your device. This is because APK files are not from the official app store and may be considered unsafe by your device. To do this, go to your device settings and look for security or privacy options. Find the option that says "Allow installation from unknown sources" or something similar and enable it. You may also see a warning message telling you about the risks of installing unknown apps. Click on "OK" or "Yes" to continue.</p>
-<p><img src="" alt="Allow installation from unknown sources screenshot" width="600" height="400"></p>
- <h3>Install the game and enjoy</h3>
-<p>Now that you have downloaded the APK file and allowed installation from unknown sources, you can install the game on your device. To do this, go to your file manager or downloads folder and look for the APK file of TABS. It should have a name like "com.landfallgames.tabs.apk". Tap on it to open it and start the installation process. You may also see a pop-up window asking you to confirm the installation. Click on "Install" or "Yes" to proceed.</p>
-<p>The installation may take a few minutes, depending on your device speed and performance. Once it is done, you will see a message saying "App installed" or something similar. You will also see an option to open the game or close the window. Click on "Open" to launch the game and enjoy.</p>
-<p><img src="" alt="Installation complete screenshot" width="600" height="400"></p> <h2>What are the benefits of using Apkcombo?</h2>
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-<p>Totally Accurate Battle Simulator is a fun and wacky game, but it can also be challenging and tricky at times. Here are some tips and tricks for playing TABS better:</p>
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-<li><b>Experiment with different units and combinations:</b> TABS has a lot of units for you to choose from, each with its own strengths and weaknesses. You can mix and match different units from different factions, or stick to one faction for a themed army. Experimenting with different units and combinations can help you find the best strategy for each level and scenario.</li>
-<li><b>Watch replays and learn from mistakes:</b> TABS has a replay feature that lets you watch your previous battles again. You can see what went wrong or right, and learn from your mistakes or successes. Watching replays can help you improve your skills and tactics.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Totally Accurate Battle Simulator Apkcombo is a fun and wacky strategy game that lets you create your own army of wacky warriors and watch them fight against other armies in hilarious battles. You can choose from a variety of units, such as farmers, knights, ninjas, pirates, zombies, dinosaurs, and more. You can also try different scenarios and challenges, or create your own custom battles using sandbox mode.</p>
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-<p>We hope this article has helped you learn more about Totally Accurate Battle Simulator Apkcombo. If you have any questions or comments, feel free to leave them below. And if you enjoyed this article, please share it with your friends and family.</p>
- <h2>FAQs</h2>
-<h3>What are the system requirements for Totally Accurate Battle Simulator?</h3>
-<p>The minimum system requirements for running TABS on Android devices are:</p>
-<ul>
-<li>Android 5.0 or higher</li>
-<li>2 GB of RAM</li>
-<li>1 GB of free storage space</li>
-</ul>
-<p>The recommended system requirements for running TABS on Android devices are:</p>
-<ul>
-<li>Android 8.0 or higher</li>
-<li>4 GB of RAM</li>
-<li>2 GB of free storage space</li>
-</ul>
- <h3>Is Totally Accurate Battle Simulator free?</h3>
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- <h3>Can I play Totally Accurate Battle Simulator online with other players?</h3>
-<p>No, Totally Accurate Battle Simulator does not have a multiplayer mode yet. However, the developers have said that they may add multiplayer mode in future updates. For now, you can play online with other players or download user-generated battles from the workshop. You can also share your own battles with other players using the workshop feature.</p>
- <h3>How can I contact the developers of Totally Accurate Battle Simulator?</h3>
-<p>If you want to contact the developers of TABS, you can use the following methods:</p>
-<ul>
-<li>Email: <a href="mailto:[email protected]">[email protected]</a></li>
-<li>Website: <a href="https://landfall.se/">https://landfall.se/</a></li>
-<li>Twitter: <a href="https://twitter.com/landfallgames">@landfallgames</a></li>
-<li>Facebook: <a href="https://www.facebook.com/landfallgames/">https://www.facebook.com/landfallgames/</a></li>
-<li>YouTube: <a href="https://www.youtube.com/channel/UCfWd1Tk9wJZcAQXdaYlYYRA">https://www.youtube.com/channel/UCfWd1Tk9wJZcAQXdaYlYYRA</a></li>
-</ul>
- <h3>Where can I find more information about Totally Accurate Battle Simulator?</h3>
-<p>If you want to find more information about TABS, you can visit the following sources:</p>
-<ul>
-<li>Official website: <a href="https://tabs.landfall.se/">https://tabs.landfall.se/</a></li>
-<li>YouTube channel: <a href="https://www.youtube.com/channel/UCfWd1Tk9wJZcAQXdaYlYYRA">https://www.youtube.com/channel/UCfWd1Tk9wJZcAQXdaYlYYRA</a></li>
-<li>Reddit community: <a href="https://www.reddit.com/r/AccurateBattleSim/">https://www.reddit.com/r/AccurateBattleSim/</a></li>
-<li>Steam page: <a href="https://store.steampowered.com/app/508440/Totally_Accurate_Battle_Simulator/">https://store.steampowered.com/app/508440/Totally_Accurate_Battle_Simulator/</a></li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />

spaces/232labs/VToonify/vtoonify/model/raft/demo.py

@@ -1,75 +0,0 @@
-import sys
-sys.path.append('core')
-
-import argparse
-import os
-import cv2
-import glob
-import numpy as np
-import torch
-from PIL import Image
-
-from raft import RAFT
-from utils import flow_viz
-from utils.utils import InputPadder
-
-
-
-DEVICE = 'cuda'
-
-def load_image(imfile):
-    img = np.array(Image.open(imfile)).astype(np.uint8)
-    img = torch.from_numpy(img).permute(2, 0, 1).float()
-    return img[None].to(DEVICE)
-
-
-def viz(img, flo):
-    img = img[0].permute(1,2,0).cpu().numpy()
-    flo = flo[0].permute(1,2,0).cpu().numpy()
-    
-    # map flow to rgb image
-    flo = flow_viz.flow_to_image(flo)
-    img_flo = np.concatenate([img, flo], axis=0)
-
-    # import matplotlib.pyplot as plt
-    # plt.imshow(img_flo / 255.0)
-    # plt.show()
-
-    cv2.imshow('image', img_flo[:, :, [2,1,0]]/255.0)
-    cv2.waitKey()
-
-
-def demo(args):
-    model = torch.nn.DataParallel(RAFT(args))
-    model.load_state_dict(torch.load(args.model))
-
-    model = model.module
-    model.to(DEVICE)
-    model.eval()
-
-    with torch.no_grad():
-        images = glob.glob(os.path.join(args.path, '*.png')) + \
-                 glob.glob(os.path.join(args.path, '*.jpg'))
-        
-        images = sorted(images)
-        for imfile1, imfile2 in zip(images[:-1], images[1:]):
-            image1 = load_image(imfile1)
-            image2 = load_image(imfile2)
-
-            padder = InputPadder(image1.shape)
-            image1, image2 = padder.pad(image1, image2)
-
-            flow_low, flow_up = model(image1, image2, iters=20, test_mode=True)
-            viz(image1, flow_up)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--model', help="restore checkpoint")
-    parser.add_argument('--path', help="dataset for evaluation")
-    parser.add_argument('--small', action='store_true', help='use small model')
-    parser.add_argument('--mixed_precision', action='store_true', help='use mixed precision')
-    parser.add_argument('--alternate_corr', action='store_true', help='use efficent correlation implementation')
-    args = parser.parse_args()
-
-    demo(args)

spaces/2hack2furious/anonymizer/README.md

@@ -1,13 +0,0 @@
----
-title: Anonymizer
-emoji: 🕵️
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/4eJIoBek/Stable_Diffusion_1.4_openvino/demo.py

@@ -1,74 +0,0 @@
-# -- coding: utf-8 --`
-import argparse
-import os
-# engine
-from stable_diffusion_engine import StableDiffusionEngine
-# scheduler
-from diffusers import LMSDiscreteScheduler, PNDMScheduler
-# utils
-import cv2
-import numpy as np
-
-
-def main(args):
-    if args.seed is not None:
-        np.random.seed(args.seed)
-    if args.init_image is None:
-        scheduler = LMSDiscreteScheduler(
-            beta_start=args.beta_start,
-            beta_end=args.beta_end,
-            beta_schedule=args.beta_schedule,
-            tensor_format="np"
-        )
-    else:
-        scheduler = PNDMScheduler(
-            beta_start=args.beta_start,
-            beta_end=args.beta_end,
-            beta_schedule=args.beta_schedule,
-            skip_prk_steps = True,
-            tensor_format="np"
-        )
-    engine = StableDiffusionEngine(
-        model = args.model,
-        scheduler = scheduler,
-        tokenizer = args.tokenizer
-    )
-    image = engine(
-        prompt = args.prompt,
-        init_image = None if args.init_image is None else cv2.imread(args.init_image),
-        mask = None if args.mask is None else cv2.imread(args.mask, 0),
-        strength = args.strength,
-        num_inference_steps = args.num_inference_steps,
-        guidance_scale = args.guidance_scale,
-        eta = args.eta
-    )
-    cv2.imwrite(args.output, image)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    # pipeline configure
-    parser.add_argument("--model", type=str, default="4eJIoBek/stable-diffusion-v1-4-openvino-fp32", help="model name")
-    # randomizer params
-    parser.add_argument("--seed", type=int, default=None, help="random seed for generating consistent images per prompt")
-    # scheduler params
-    parser.add_argument("--beta-start", type=float, default=0.00085, help="LMSDiscreteScheduler::beta_start")
-    parser.add_argument("--beta-end", type=float, default=0.012, help="LMSDiscreteScheduler::beta_end")
-    parser.add_argument("--beta-schedule", type=str, default="scaled_linear", help="LMSDiscreteScheduler::beta_schedule")
-    # diffusion params
-    parser.add_argument("--num-inference-steps", type=int, default=32, help="num inference steps")
-    parser.add_argument("--guidance-scale", type=float, default=7.5, help="guidance scale")
-    parser.add_argument("--eta", type=float, default=0.0, help="eta")
-    # tokenizer
-    parser.add_argument("--tokenizer", type=str, default="openai/clip-vit-large-patch14", help="tokenizer")
-    # prompt
-    parser.add_argument("--prompt", type=str, default="Street-art painting of Emilia Clarke in style of Banksy, photorealism", help="prompt")
-    # img2img params
-    parser.add_argument("--init-image", type=str, default=None, help="path to initial image")
-    parser.add_argument("--strength", type=float, default=0.5, help="how strong the initial image should be noised [0.0, 1.0]")
-    # inpainting
-    parser.add_argument("--mask", type=str, default=None, help="mask of the region to inpaint on the initial image")
-    # output name
-    parser.add_argument("--output", type=str, default="output.png", help="output image name")
-    args = parser.parse_args()
-    main(args)

spaces/801artistry/RVC801/guidml.py

@@ -1,710 +0,0 @@
-"""
-0416后的更新：
-    引入config中half
-    重建npy而不用填写
-    v2支持
-    无f0模型支持
-    修复
-
-    int16：
-    增加无索引支持
-    f0算法改harvest(怎么看就只有这个会影响CPU占用)，但是不这么改效果不好
-"""
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-
-Config = Config()
-
-import torch_directml
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch_directml.device(torch_directml.default_device())
-current_dir = os.getcwd()
-
-
-class RVC:
-    def __init__(
-        self, key, hubert_path, pth_path, index_path, npy_path, index_rate
-    ) -> None:
-        """
-        初始化
-        """
-        try:
-            self.f0_up_key = key
-            self.time_step = 160 / 16000 * 1000
-            self.f0_min = 50
-            self.f0_max = 1100
-            self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-            self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-            self.sr = 16000
-            self.window = 160
-            if index_rate != 0:
-                self.index = faiss.read_index(index_path)
-                # self.big_npy = np.load(npy_path)
-                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
-                print("index search enabled")
-            self.index_rate = index_rate
-            model_path = hubert_path
-            print("load model(s) from {}".format(model_path))
-            models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-                [model_path],
-                suffix="",
-            )
-            self.model = models[0]
-            self.model = self.model.to(device)
-            if Config.is_half:
-                self.model = self.model.half()
-            else:
-                self.model = self.model.float()
-            self.model.eval()
-            cpt = torch.load(pth_path, map_location="cpu")
-            self.tgt_sr = cpt["config"][-1]
-            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-            self.if_f0 = cpt.get("f0", 1)
-            self.version = cpt.get("version", "v1")
-            if self.version == "v1":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs256NSFsid(
-                        *cpt["config"], is_half=Config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-            elif self.version == "v2":
-                if self.if_f0 == 1:
-                    self.net_g = SynthesizerTrnMs768NSFsid(
-                        *cpt["config"], is_half=Config.is_half
-                    )
-                else:
-                    self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-            del self.net_g.enc_q
-            print(self.net_g.load_state_dict(cpt["weight"], strict=False))
-            self.net_g.eval().to(device)
-            if Config.is_half:
-                self.net_g = self.net_g.half()
-            else:
-                self.net_g = self.net_g.float()
-        except:
-            print(traceback.format_exc())
-
-    def get_f0(self, x, f0_up_key, inp_f0=None):
-        x_pad = 1
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0, t = pyworld.harvest(
-            x.astype(np.double),
-            fs=self.sr,
-            f0_ceil=f0_max,
-            f0_floor=f0_min,
-            frame_period=10,
-        )
-        f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-        f0 = signal.medfilt(f0, 3)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def infer(self, feats: torch.Tensor) -> np.ndarray:
-        """
-        推理函数
-        """
-        audio = feats.clone().cpu().numpy()
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-        if Config.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        inputs = {
-            "source": feats.to(device),
-            "padding_mask": padding_mask.to(device),
-            "output_layer": 9 if self.version == "v1" else 12,
-        }
-        torch.cuda.synchronize()
-        with torch.no_grad():
-            logits = self.model.extract_features(**inputs)
-            feats = (
-                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
-            )
-
-        ####索引优化
-        try:
-            if (
-                hasattr(self, "index")
-                and hasattr(self, "big_npy")
-                and self.index_rate != 0
-            ):
-                npy = feats[0].cpu().numpy().astype("float32")
-                score, ix = self.index.search(npy, k=8)
-                weight = np.square(1 / score)
-                weight /= weight.sum(axis=1, keepdims=True)
-                npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-                if Config.is_half:
-                    npy = npy.astype("float16")
-                feats = (
-                    torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
-                    + (1 - self.index_rate) * feats
-                )
-            else:
-                print("index search FAIL or disabled")
-        except:
-            traceback.print_exc()
-            print("index search FAIL")
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        torch.cuda.synchronize()
-        print(feats.shape)
-        if self.if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio, self.f0_up_key)
-            p_len = min(feats.shape[1], 13000, pitch.shape[0])  # 太大了爆显存
-        else:
-            pitch, pitchf = None, None
-            p_len = min(feats.shape[1], 13000)  # 太大了爆显存
-        torch.cuda.synchronize()
-        # print(feats.shape,pitch.shape)
-        feats = feats[:, :p_len, :]
-        if self.if_f0 == 1:
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
-            pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
-        p_len = torch.LongTensor([p_len]).to(device)
-        ii = 0  # sid
-        sid = torch.LongTensor([ii]).to(device)
-        with torch.no_grad():
-            if self.if_f0 == 1:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
-                    .data.cpu()
-                    .float()
-                )
-            else:
-                infered_audio = (
-                    self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
-                )
-        torch.cuda.synchronize()
-        return infered_audio
-
-
-class GUIConfig:
-    def __init__(self) -> None:
-        self.hubert_path: str = ""
-        self.pth_path: str = ""
-        self.index_path: str = ""
-        self.npy_path: str = ""
-        self.pitch: int = 12
-        self.samplerate: int = 44100
-        self.block_time: float = 1.0  # s
-        self.buffer_num: int = 1
-        self.threhold: int = -30
-        self.crossfade_time: float = 0.08
-        self.extra_time: float = 0.04
-        self.I_noise_reduce = False
-        self.O_noise_reduce = False
-        self.index_rate = 0.3
-
-
-class GUI:
-    def __init__(self) -> None:
-        self.config = GUIConfig()
-        self.flag_vc = False
-
-        self.launcher()
-
-    def load(self):
-        (
-            input_devices,
-            output_devices,
-            input_devices_indices,
-            output_devices_indices,
-        ) = self.get_devices()
-        try:
-            with open("values1.json", "r") as j:
-                data = json.load(j)
-        except:
-            with open("values1.json", "w") as j:
-                data = {
-                    "pth_path": "",
-                    "index_path": "",
-                    "sg_input_device": input_devices[
-                        input_devices_indices.index(sd.default.device[0])
-                    ],
-                    "sg_output_device": output_devices[
-                        output_devices_indices.index(sd.default.device[1])
-                    ],
-                    "threhold": "-45",
-                    "pitch": "0",
-                    "index_rate": "0",
-                    "block_time": "1",
-                    "crossfade_length": "0.04",
-                    "extra_time": "1",
-                }
-        return data
-
-    def launcher(self):
-        data = self.load()
-        sg.theme("LightBlue3")
-        input_devices, output_devices, _, _ = self.get_devices()
-        layout = [
-            [
-                sg.Frame(
-                    title=i18n("Load model"),
-                    layout=[
-                        [
-                            sg.Input(
-                                default_text="hubert_base.pt",
-                                key="hubert_path",
-                                disabled=True,
-                            ),
-                            sg.FileBrowse(
-                                i18n("Hubert Model"),
-                                initial_folder=os.path.join(os.getcwd()),
-                                file_types=(("pt files", "*.pt"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text=data.get("pth_path", ""),
-                                key="pth_path",
-                            ),
-                            sg.FileBrowse(
-                                i18n("Select the .pth file"),
-                                initial_folder=os.path.join(os.getcwd(), "weights"),
-                                file_types=(("weight files", "*.pth"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text=data.get("index_path", ""),
-                                key="index_path",
-                            ),
-                            sg.FileBrowse(
-                                i18n("Select the .index file"),
-                                initial_folder=os.path.join(os.getcwd(), "logs"),
-                                file_types=(("index files", "*.index"),),
-                            ),
-                        ],
-                        [
-                            sg.Input(
-                                default_text="你不需要填写这个You don't need write this.",
-                                key="npy_path",
-                                disabled=True,
-                            ),
-                            sg.FileBrowse(
-                                i18n("Select the .npy file"),
-                                initial_folder=os.path.join(os.getcwd(), "logs"),
-                                file_types=(("feature files", "*.npy"),),
-                            ),
-                        ],
-                    ],
-                )
-            ],
-            [
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("Input device")),
-                            sg.Combo(
-                                input_devices,
-                                key="sg_input_device",
-                                default_value=data.get("sg_input_device", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Output device")),
-                            sg.Combo(
-                                output_devices,
-                                key="sg_output_device",
-                                default_value=data.get("sg_output_device", ""),
-                            ),
-                        ],
-                    ],
-                    title=i18n("Audio device (please use the same type of driver)"),
-                )
-            ],
-            [
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("Response threshold")),
-                            sg.Slider(
-                                range=(-60, 0),
-                                key="threhold",
-                                resolution=1,
-                                orientation="h",
-                                default_value=data.get("threhold", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Pitch settings")),
-                            sg.Slider(
-                                range=(-24, 24),
-                                key="pitch",
-                                resolution=1,
-                                orientation="h",
-                                default_value=data.get("pitch", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Index Rate")),
-                            sg.Slider(
-                                range=(0.0, 1.0),
-                                key="index_rate",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("index_rate", ""),
-                            ),
-                        ],
-                    ],
-                    title=i18n("General settings"),
-                ),
-                sg.Frame(
-                    layout=[
-                        [
-                            sg.Text(i18n("Sample length")),
-                            sg.Slider(
-                                range=(0.1, 3.0),
-                                key="block_time",
-                                resolution=0.1,
-                                orientation="h",
-                                default_value=data.get("block_time", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Fade length")),
-                            sg.Slider(
-                                range=(0.01, 0.15),
-                                key="crossfade_length",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("crossfade_length", ""),
-                            ),
-                        ],
-                        [
-                            sg.Text(i18n("Extra推理时长")),
-                            sg.Slider(
-                                range=(0.05, 3.00),
-                                key="extra_time",
-                                resolution=0.01,
-                                orientation="h",
-                                default_value=data.get("extra_time", ""),
-                            ),
-                        ],
-                        [
-                            sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
-                            sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
-                        ],
-                    ],
-                    title=i18n("Performance settings"),
-                ),
-            ],
-            [
-                sg.Button(i18n("开始音频Convert"), key="start_vc"),
-                sg.Button(i18n("停止音频Convert"), key="stop_vc"),
-                sg.Text(i18n("Inference time (ms):")),
-                sg.Text("0", key="infer_time"),
-            ],
-        ]
-        self.window = sg.Window("RVC - GUI", layout=layout)
-        self.event_handler()
-
-    def event_handler(self):
-        while True:
-            event, values = self.window.read()
-            if event == sg.WINDOW_CLOSED:
-                self.flag_vc = False
-                exit()
-            if event == "start_vc" and self.flag_vc == False:
-                if self.set_values(values) == True:
-                    print("using_cuda:" + str(torch.cuda.is_available()))
-                    self.start_vc()
-                    settings = {
-                        "pth_path": values["pth_path"],
-                        "index_path": values["index_path"],
-                        "sg_input_device": values["sg_input_device"],
-                        "sg_output_device": values["sg_output_device"],
-                        "threhold": values["threhold"],
-                        "pitch": values["pitch"],
-                        "index_rate": values["index_rate"],
-                        "block_time": values["block_time"],
-                        "crossfade_length": values["crossfade_length"],
-                        "extra_time": values["extra_time"],
-                    }
-                    with open("values1.json", "w") as j:
-                        json.dump(settings, j)
-            if event == "stop_vc" and self.flag_vc == True:
-                self.flag_vc = False
-
-    def set_values(self, values):
-        if len(values["pth_path"].strip()) == 0:
-            sg.popup(i18n("Select the pth file"))
-            return False
-        if len(values["index_path"].strip()) == 0:
-            sg.popup(i18n("Select the index file"))
-            return False
-        pattern = re.compile("[^\x00-\x7F]+")
-        if pattern.findall(values["hubert_path"]):
-            sg.popup(i18n("The hubert model path must not contain Chinese characters"))
-            return False
-        if pattern.findall(values["pth_path"]):
-            sg.popup(i18n("The pth file path must not contain Chinese characters."))
-            return False
-        if pattern.findall(values["index_path"]):
-            sg.popup(i18n("The index file path must not contain Chinese characters."))
-            return False
-        self.set_devices(values["sg_input_device"], values["sg_output_device"])
-        self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
-        self.config.pth_path = values["pth_path"]
-        self.config.index_path = values["index_path"]
-        self.config.npy_path = values["npy_path"]
-        self.config.threhold = values["threhold"]
-        self.config.pitch = values["pitch"]
-        self.config.block_time = values["block_time"]
-        self.config.crossfade_time = values["crossfade_length"]
-        self.config.extra_time = values["extra_time"]
-        self.config.I_noise_reduce = values["I_noise_reduce"]
-        self.config.O_noise_reduce = values["O_noise_reduce"]
-        self.config.index_rate = values["index_rate"]
-        return True
-
-    def start_vc(self):
-        torch.cuda.empty_cache()
-        self.flag_vc = True
-        self.block_frame = int(self.config.block_time * self.config.samplerate)
-        self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
-        self.sola_search_frame = int(0.012 * self.config.samplerate)
-        self.delay_frame = int(0.01 * self.config.samplerate)  # 往前预留0.02s
-        self.extra_frame = int(self.config.extra_time * self.config.samplerate)
-        self.rvc = None
-        self.rvc = RVC(
-            self.config.pitch,
-            self.config.hubert_path,
-            self.config.pth_path,
-            self.config.index_path,
-            self.config.npy_path,
-            self.config.index_rate,
-        )
-        self.input_wav: np.ndarray = np.zeros(
-            self.extra_frame
-            + self.crossfade_frame
-            + self.sola_search_frame
-            + self.block_frame,
-            dtype="float32",
-        )
-        self.output_wav: torch.Tensor = torch.zeros(
-            self.block_frame, device=device, dtype=torch.float32
-        )
-        self.sola_buffer: torch.Tensor = torch.zeros(
-            self.crossfade_frame, device=device, dtype=torch.float32
-        )
-        self.fade_in_window: torch.Tensor = torch.linspace(
-            0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
-        )
-        self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
-        self.resampler1 = tat.Resample(
-            orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
-        )
-        self.resampler2 = tat.Resample(
-            orig_freq=self.rvc.tgt_sr,
-            new_freq=self.config.samplerate,
-            dtype=torch.float32,
-        )
-        thread_vc = threading.Thread(target=self.soundinput)
-        thread_vc.start()
-
-    def soundinput(self):
-        """
-        接受音频输入
-        """
-        with sd.Stream(
-            channels=2,
-            callback=self.audio_callback,
-            blocksize=self.block_frame,
-            samplerate=self.config.samplerate,
-            dtype="float32",
-        ):
-            while self.flag_vc:
-                time.sleep(self.config.block_time)
-                print("Audio block passed.")
-        print("ENDing VC")
-
-    def audio_callback(
-        self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
-    ):
-        """
-        音频处理
-        """
-        start_time = time.perf_counter()
-        indata = librosa.to_mono(indata.T)
-        if self.config.I_noise_reduce:
-            indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
-
-        """noise gate"""
-        frame_length = 2048
-        hop_length = 1024
-        rms = librosa.feature.rms(
-            y=indata, frame_length=frame_length, hop_length=hop_length
-        )
-        db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
-        # print(rms.shape,db.shape,db)
-        for i in range(db_threhold.shape[0]):
-            if db_threhold[i]:
-                indata[i * hop_length : (i + 1) * hop_length] = 0
-        self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
-
-        # infer
-        print("input_wav:" + str(self.input_wav.shape))
-        # print('infered_wav:'+str(infer_wav.shape))
-        infer_wav: torch.Tensor = self.resampler2(
-            self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
-        )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
-            device
-        )
-        print("infer_wav:" + str(infer_wav.shape))
-
-        # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
-        cor_nom = F.conv1d(
-            infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
-            self.sola_buffer[None, None, :],
-        )
-        cor_den = torch.sqrt(
-            F.conv1d(
-                infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
-                ** 2,
-                torch.ones(1, 1, self.crossfade_frame, device=device),
-            )
-            + 1e-8
-        )
-        sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
-        print("sola offset: " + str(int(sola_offset)))
-
-        # crossfade
-        self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
-        self.output_wav[: self.crossfade_frame] *= self.fade_in_window
-        self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
-        if sola_offset < self.sola_search_frame:
-            self.sola_buffer[:] = (
-                infer_wav[
-                    -self.sola_search_frame
-                    - self.crossfade_frame
-                    + sola_offset : -self.sola_search_frame
-                    + sola_offset
-                ]
-                * self.fade_out_window
-            )
-        else:
-            self.sola_buffer[:] = (
-                infer_wav[-self.crossfade_frame :] * self.fade_out_window
-            )
-
-        if self.config.O_noise_reduce:
-            outdata[:] = np.tile(
-                nr.reduce_noise(
-                    y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
-                ),
-                (2, 1),
-            ).T
-        else:
-            outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
-        total_time = time.perf_counter() - start_time
-        self.window["infer_time"].update(int(total_time * 1000))
-        print("infer time:" + str(total_time))
-
-    def get_devices(self, update: bool = True):
-        """获取设备列表"""
-        if update:
-            sd._terminate()
-            sd._initialize()
-        devices = sd.query_devices()
-        hostapis = sd.query_hostapis()
-        for hostapi in hostapis:
-            for device_idx in hostapi["devices"]:
-                devices[device_idx]["hostapi_name"] = hostapi["name"]
-        input_devices = [
-            f"{d['name']} ({d['hostapi_name']})"
-            for d in devices
-            if d["max_input_channels"] > 0
-        ]
-        output_devices = [
-            f"{d['name']} ({d['hostapi_name']})"
-            for d in devices
-            if d["max_output_channels"] > 0
-        ]
-        input_devices_indices = [
-            d["index"] if "index" in d else d["name"]
-            for d in devices
-            if d["max_input_channels"] > 0
-        ]
-        output_devices_indices = [
-            d["index"] if "index" in d else d["name"]
-            for d in devices
-            if d["max_output_channels"] > 0
-        ]
-        return (
-            input_devices,
-            output_devices,
-            input_devices_indices,
-            output_devices_indices,
-        )
-
-    def set_devices(self, input_device, output_device):
-        """设置输出设备"""
-        (
-            input_devices,
-            output_devices,
-            input_device_indices,
-            output_device_indices,
-        ) = self.get_devices()
-        sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
-        sd.default.device[1] = output_device_indices[
-            output_devices.index(output_device)
-        ]
-        print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
-        print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
-
-
-gui = GUI()

spaces/801artistry/RVC801/infer/lib/infer_pack/models.py

@@ -1,1174 +0,0 @@
-import math
-import logging
-
-logger = logging.getLogger(__name__)
-
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
-
-from infer.lib.infer_pack import attentions, commons, modules
-from infer.lib.infer_pack.commons import get_padding, init_weights
-has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        if uv.device.type == "privateuseone":  # for DirectML
-            uv = uv.float()
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        if hasattr(self, "ddtype") == False:
-            self.ddtype = self.l_linear.weight.dtype
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        # print(x.dtype,sine_wavs.dtype,self.l_linear.weight.dtype)
-        # if self.is_half:
-        #     sine_wavs = sine_wavs.half()
-        # sine_merge = self.l_tanh(self.l_linear(sine_wavs.to(x)))
-        # print(sine_wavs.dtype,self.ddtype)
-        if sine_wavs.dtype != self.ddtype:
-            sine_wavs = sine_wavs.to(self.ddtype)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            if has_xpu and x.dtype == torch.bfloat16:
-                x = F.pad(x.to(dtype=torch.float16), (0, n_pad), "reflect").to(dtype=torch.bfloat16)
-            else:
-                x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap

spaces/801artistry/RVC801/lib/infer_pack/transforms.py

@@ -1,209 +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=1.0,
-    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=1.0,
-    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=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    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/modules/lstm.py

@@ -1,25 +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.
-
-from torch import nn
-
-
-class StreamableLSTM(nn.Module):
-    """LSTM without worrying about the hidden state, nor the layout of the data.
-    Expects input as convolutional layout.
-    """
-    def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True):
-        super().__init__()
-        self.skip = skip
-        self.lstm = nn.LSTM(dimension, dimension, num_layers)
-
-    def forward(self, x):
-        x = x.permute(2, 0, 1)
-        y, _ = self.lstm(x)
-        if self.skip:
-            y = y + x
-        y = y.permute(1, 2, 0)
-        return y

spaces/AIFILMS/StyleGANEX/configs/paths_config.py

@@ -1,25 +0,0 @@
-dataset_paths = {
-    'ffhq': 'data/train/ffhq/realign320x320/',
-    'ffhq_test': 'data/train/ffhq/realign320x320test/',
-    'ffhq1280': 'data/train/ffhq/realign1280x1280/',
-    'ffhq1280_test': 'data/train/ffhq/realign1280x1280test/',    
-    'ffhq_train_sketch': 'data/train/ffhq/realign640x640sketch/',
-    'ffhq_test_sketch': 'data/train/ffhq/realign640x640sketchtest/',
-    'ffhq_train_segmentation': 'data/train/ffhq/realign320x320mask/',
-    'ffhq_test_segmentation': 'data/train/ffhq/realign320x320masktest/',
-    'toonify_in': 'data/train/pixar/trainA/',
-    'toonify_out': 'data/train/pixar/trainB/',
-    'toonify_test_in': 'data/train/pixar/testA/',
-    'toonify_test_out': 'data/train/testB/',
-}
-
-model_paths = {
-    'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt',
-    'ir_se50': 'pretrained_models/model_ir_se50.pth',
-    'circular_face': 'pretrained_models/CurricularFace_Backbone.pth',
-    'mtcnn_pnet': 'pretrained_models/mtcnn/pnet.npy',
-    'mtcnn_rnet': 'pretrained_models/mtcnn/rnet.npy',
-    'mtcnn_onet': 'pretrained_models/mtcnn/onet.npy',
-    'shape_predictor': 'shape_predictor_68_face_landmarks.dat',
-    'moco': 'pretrained_models/moco_v2_800ep_pretrain.pth.tar'
-}

spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/detector.py

@@ -1,126 +0,0 @@
-import numpy as np
-import torch
-from torch.autograd import Variable
-from .get_nets import PNet, RNet, ONet
-from .box_utils import nms, calibrate_box, get_image_boxes, convert_to_square
-from .first_stage import run_first_stage
-
-
-def detect_faces(image, min_face_size=20.0,
-                 thresholds=[0.6, 0.7, 0.8],
-                 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.
-    """
-
-    # LOAD MODELS
-    pnet = PNet()
-    rnet = RNet()
-    onet = ONet()
-    onet.eval()
-
-    # 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, 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)
-
-        output = rnet(img_boxes)
-        offsets = output[0].data.numpy()  # shape [n_boxes, 4]
-        probs = output[1].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)
-        output = onet(img_boxes)
-        landmarks = output[0].data.numpy()  # shape [n_boxes, 10]
-        offsets = output[1].data.numpy()  # shape [n_boxes, 4]
-        probs = output[2].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/AIKey/ai_date/README.md

@@ -1,10 +0,0 @@
----
-title: Ai Date
-emoji: 🌍
-colorFrom: indigo
-colorTo: purple
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIatUIUC/CodeLATS/generators/factory.py

@@ -1,20 +0,0 @@
-from .py_generate import PyGenerator
-from .generator_types import Generator
-from .model import ModelBase, GPT4, GPT35, GPTDavinci
-
-def generator_factory(lang: str) -> Generator:
-    if lang == "py" or lang == "python":
-        return PyGenerator()
-    else:
-        raise ValueError(f"Invalid language for generator: {lang}")
-
-
-def model_factory(model_name: str) -> ModelBase:
-    if model_name == "gpt-4":
-        return GPT4()
-    elif model_name == "gpt-3.5-turbo-0613":
-        return GPT35()
-    elif model_name.startswith("text-davinci"):
-        return GPTDavinci(model_name)
-    else:
-        raise ValueError(f"Invalid model name: {model_name}")

spaces/AUBADA-ALARABI/poetry1/app.py

@@ -1,53 +0,0 @@
-import gc 
-import gradio as gr
-from transformers import pipeline, set_seed
-
-pipe = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
-#gc.collect()
-samples = [['أنت'
-           ,1.0, 50, 1.0, 1.0, 114],['هل غادر'
-            ,1.0, 50, 1.0, 1.0, 114 ],['ألا ليت'
-              ,1.0, 50, 1.0, 1.0, 114 ],['يا قدس'
-                 ,1.0, 50, 1.0, 1.0, 114],['عيد بأية حال'
-                   ,1.0, 50, 1.0, 1.0, 114],['لكل شيء إذا ما'
-                    ,1.0, 50, 1.0, 1.0, 114 ],['.'
-                       ,1.0, 50, 1.0, 1.0, 114]]
-                     
-notes = """
-- Enter a short prompt or select (click) one of the examples and click SEND 
-- Adjust parameters (temperture, top k, top p and penalty) through the slider (keep close to default values).
-- For the same seed (randomness), the same output is regenerated if other parameters are fixed. Seed should be 0 or more (not empty) 
-- Clear and enter new prompt or select another example and SEND to regenerate
-- The '.' means start a new line from no prompt (your prompt need not be long) 
-- Be patient: this runs on CPU (free tier)
-- Feedback (Twitter): @akhooli (https://twitter.com/akhooli/status/1611025232201977859) 
-- Note/Disclaimer: may generate unaccepted or inappropriate content. Use at your own risk.
-"""
-def sayPoetry(prompt, temp=1.0, topk = 50, topp = 1.0, penalty=1.0, seed=114):
-    if not int(seed) >= 0: seed=114
-    set_seed(seed)
-    gen = pipe(prompt, max_length=96, do_sample=True, temperature=temp, top_k=topk, top_p=topp, repetition_penalty=penalty,
-              min_length = 64, no_repeat_ngram_size = 3, return_full_text=True,
-               num_beams=5, num_return_sequences=1)[0]["generated_text"]
-    poetry =""
-    for line in gen.split('.')[:-1]:
-        poetry += line #+ "\n"
-    return poetry
-poetry = gr.Interface(fn=sayPoetry, 
-                    inputs=[
-                        gr.Textbox(label="Enter short prompt or select from examples:"), 
-                        gr.Slider(0.70, 1.2, step=0.01,value=1.0, label='control  temperature'),
-                        gr.Slider(25, 100, step=1,value=50, label='control  top k'),
-                        gr.Slider(0.80, 1.0, step=0.01,value=1.0, label='control  top p'),
-                        gr.Slider(0.90, 1.50, step=0.01,value=1.0, label='control  penalty'),
-                        gr.Number(value=139750, precision=0, label='Seed'),
-                    ], 
-                outputs=[gr.Textbox(label="Generated Poetry:")], 
-                    
-                allow_flagging='never',
-                title='Arabic Poetry Generation Demo (updated Jan. 2023)',
-                description = "A simple demo of AI generated poetry based on 1M poems fine-tuned using AraGPT2 (be patient, runs on cpu)",
-                examples=samples,
-                cache_examples=False,
-                article = notes)
-poetry.launch()         

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Aivvm.py

@@ -1,70 +0,0 @@
-from __future__ import annotations
-
-from ..requests import StreamSession
-from .base_provider import AsyncGeneratorProvider
-from ..typing import AsyncGenerator
-
-# to recreate this easily, send a post request to https://chat.aivvm.com/api/models
-models = {
-    'gpt-3.5-turbo': {'id': 'gpt-3.5-turbo', 'name': 'GPT-3.5'},
-    'gpt-3.5-turbo-0613': {'id': 'gpt-3.5-turbo-0613', 'name': 'GPT-3.5-0613'},
-    'gpt-3.5-turbo-16k': {'id': 'gpt-3.5-turbo-16k', 'name': 'GPT-3.5-16K'},
-    'gpt-3.5-turbo-16k-0613': {'id': 'gpt-3.5-turbo-16k-0613', 'name': 'GPT-3.5-16K-0613'},
-    'gpt-4': {'id': 'gpt-4', 'name': 'GPT-4'},
-    'gpt-4-0613': {'id': 'gpt-4-0613', 'name': 'GPT-4-0613'},
-    'gpt-4-32k': {'id': 'gpt-4-32k', 'name': 'GPT-4-32K'},
-    'gpt-4-32k-0613': {'id': 'gpt-4-32k-0613', 'name': 'GPT-4-32K-0613'},
-}
-
-class Aivvm(AsyncGeneratorProvider):
-    url                   = 'https://chat.aivvm.com'
-    supports_gpt_35_turbo = True
-    supports_gpt_4        = True
-    working = True
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool,
-        timeout: int = 30,
-        **kwargs
-    ) -> AsyncGenerator:
-        if not model:
-            model = "gpt-3.5-turbo"
-        elif model not in models:
-            raise ValueError(f"Model is not supported: {model}")
-
-        json_data = {
-            "model"       : models[model],
-            "messages"    : messages,
-            "key"         : "",
-            "prompt"      : kwargs.get("system_message", "You are ChatGPT, a large language model trained by OpenAI. Follow the user's instructions carefully. Respond using markdown."),
-            "temperature" : kwargs.get("temperature", 0.7)
-        }
-        headers = {
-            "Accept": "*/*",
-            "Origin": cls.url,
-            "Referer": f"{cls.url}/",
-        }
-        async with StreamSession(impersonate="chrome107", headers=headers, timeout=timeout) as session:
-            async with session.post(f"{cls.url}/api/chat", json=json_data) as response:
-                response.raise_for_status()
-                async for chunk in response.iter_content():
-                    if b'Access denied | chat.aivvm.com used Cloudflare' in chunk:
-                        raise ValueError("Rate Limit | use another provider")
-                    
-                    yield chunk.decode()
-
-    @classmethod
-    @property
-    def params(cls):
-        params = [
-            ('model', 'str'),
-            ('messages', 'list[dict[str, str]]'),
-            ('stream', 'bool'),
-            ('temperature', 'float'),
-        ]
-        param = ', '.join([': '.join(p) for p in params])
-        return f'g4f.provider.{cls.__name__} supports: ({param})'

spaces/AfrodreamsAI/afrodreams/neural_style.py

@@ -1,509 +0,0 @@
-import os
-import copy
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import torchvision.transforms as transforms
-
-from PIL import Image
-from CaffeLoader import loadCaffemodel, ModelParallel
-
-import argparse
-parser = argparse.ArgumentParser()
-# Basic options
-parser.add_argument("-style_image", help="Style target image", default='examples/inputs/seated-nude.jpg')
-parser.add_argument("-style_blend_weights", default=None)
-parser.add_argument("-content_image", help="Content target image", default='examples/inputs/tubingen.jpg')
-parser.add_argument("-image_size", help="Maximum height / width of generated image", type=int, default=512)
-parser.add_argument("-gpu", help="Zero-indexed ID of the GPU to use; for CPU mode set -gpu = c", default=0)
-
-# Optimization options
-parser.add_argument("-content_weight", type=float, default=5e0)
-parser.add_argument("-style_weight", type=float, default=1e2)
-parser.add_argument("-normalize_weights", action='store_true')
-parser.add_argument("-tv_weight", type=float, default=1e-3)
-parser.add_argument("-num_iterations", type=int, default=1000)
-parser.add_argument("-init", choices=['random', 'image'], default='random')
-parser.add_argument("-init_image", default=None)
-parser.add_argument("-optimizer", choices=['lbfgs', 'adam'], default='adam')
-parser.add_argument("-learning_rate", type=float, default=1e0)
-parser.add_argument("-lbfgs_num_correction", type=int, default=100)
-
-# Output options
-parser.add_argument("-print_iter", type=int, default=50)
-parser.add_argument("-save_iter", type=int, default=100)
-parser.add_argument("-output_image", default='out.png')
-
-# Other options
-parser.add_argument("-style_scale", type=float, default=1.0)
-parser.add_argument("-original_colors", type=int, choices=[0, 1], default=0)
-parser.add_argument("-pooling", choices=['avg', 'max'], default='max')
-parser.add_argument("-model_file", type=str, default='models/vgg19-d01eb7cb.pth')
-parser.add_argument("-disable_check", action='store_true')
-parser.add_argument("-backend", choices=['nn', 'cudnn', 'mkl', 'mkldnn', 'openmp', 'mkl,cudnn', 'cudnn,mkl'], default='nn')
-parser.add_argument("-cudnn_autotune", action='store_true')
-parser.add_argument("-seed", type=int, default=-1)
-
-parser.add_argument("-content_layers", help="layers for content", default='relu4_2')
-parser.add_argument("-style_layers", help="layers for style", default='relu1_1,relu2_1,relu3_1,relu4_1,relu5_1')
-
-parser.add_argument("-multidevice_strategy", default='4,7,29')
-params = parser.parse_args()
-
-
-Image.MAX_IMAGE_PIXELS = 1000000000 # Support gigapixel images
-
-
-class TransferParams():
-    style_image = 'examples/inputs/seated-nude.jpg'
-    style_blend_weights = None
-    content_image = 'examples/inputs/tubingen.jpg'
-    image_size = 300
-    gpu = "c" #0
-    content_weight = 5e0
-    style_weight = 1e2
-    normalize_weights = False
-    tv_weight = 1e-3
-    num_iterations = 1000
-    init = 'random'
-    init_image = None
-    optimizer = 'adam'
-    learning_rate = 1e0
-    lbfgs_num_correction = 100
-    print_iter = 50
-    save_iter = 1000
-    output_image = 'out.png'
-    log_level = 10
-    style_scale = 1.0
-    original_colors = 0
-    pooling = 'max'
-    model_file = 'models/nin_imagenet.pth'#vgg16-00b39a1b.pth'
-    disable_check = False
-    backend = 'mkl'
-    cudnn_autotune = False
-    seed = -1
-    content_layers = 'relu0,relu3,relu7,relu12'#relu4_2'#
-    style_layers = 'relu0,relu3,relu7,relu12'#relu1_1,relu2_1,relu3_1,relu4_1,relu5_1'#'
-    multidevice_strategy = '4,7,29'
-
-def main():
-    transfer(params)
-
-def transfer(params):
-    dtype, multidevice, backward_device = setup_gpu()
- 
-
-    cnn, layerList = loadCaffemodel(params.model_file, params.pooling, params.gpu, params.disable_check)
-
-    content_image = preprocess(params.content_image, params.image_size).type(dtype)
-    style_image_input = params.style_image.split(',')
-    style_image_list, ext = [], [".jpg", ".jpeg", ".png", ".tiff"]
-    for image in style_image_input:
-        if os.path.isdir(image):
-            images = (image + "/" + file for file in os.listdir(image)
-            if os.path.splitext(file)[1].lower() in ext)
-            style_image_list.extend(images)
-        else:
-            style_image_list.append(image)
-    style_images_caffe = []
-    for image in style_image_list:
-        style_size = int(params.image_size * params.style_scale)
-        img_caffe = preprocess(image, style_size).type(dtype)
-        style_images_caffe.append(img_caffe)
-
-    if params.init_image != None:
-        image_size = (content_image.size(2), content_image.size(3))
-        init_image = preprocess(params.init_image, image_size).type(dtype)
-
-    # Handle style blending weights for multiple style inputs
-    style_blend_weights = []
-    if params.style_blend_weights == None:
-        # Style blending not specified, so use equal weighting
-        for i in style_image_list:
-            style_blend_weights.append(1.0)
-        for i, blend_weights in enumerate(style_blend_weights):
-            style_blend_weights[i] = int(style_blend_weights[i])
-    else:
-        style_blend_weights = params.style_blend_weights.split(',')
-        assert len(style_blend_weights) == len(style_image_list), \
-          "-style_blend_weights and -style_images must have the same number of elements!"
-
-    # Normalize the style blending weights so they sum to 1
-    style_blend_sum = 0
-    for i, blend_weights in enumerate(style_blend_weights):
-        style_blend_weights[i] = float(style_blend_weights[i])
-        style_blend_sum = float(style_blend_sum) + style_blend_weights[i]
-    for i, blend_weights in enumerate(style_blend_weights):
-        style_blend_weights[i] = float(style_blend_weights[i]) / float(style_blend_sum)
-
-    content_layers = params.content_layers.split(',')
-    style_layers = params.style_layers.split(',')
-
-    # Set up the network, inserting style and content loss modules
-    cnn = copy.deepcopy(cnn)
-    content_losses, style_losses, tv_losses = [], [], []
-    next_content_idx, next_style_idx = 1, 1
-    net = nn.Sequential()
-    c, r = 0, 0
-    if params.tv_weight > 0:
-        tv_mod = TVLoss(params.tv_weight).type(dtype)
-        net.add_module(str(len(net)), tv_mod)
-        tv_losses.append(tv_mod)
-
-    for i, layer in enumerate(list(cnn), 1):
-        if next_content_idx <= len(content_layers) or next_style_idx <= len(style_layers):
-            if isinstance(layer, nn.Conv2d):
-                net.add_module(str(len(net)), layer)
-
-                if layerList['C'][c] in content_layers:
-                    #print("Setting up content layer " + str(i) + ": " + str(layerList['C'][c]))
-                    loss_module = ContentLoss(params.content_weight)
-                    net.add_module(str(len(net)), loss_module)
-                    content_losses.append(loss_module)
-
-                if layerList['C'][c] in style_layers:
-                    #print("Setting up style layer " + str(i) + ": " + str(layerList['C'][c]))
-                    loss_module = StyleLoss(params.style_weight)
-                    net.add_module(str(len(net)), loss_module)
-                    style_losses.append(loss_module)
-                c+=1
-
-            if isinstance(layer, nn.ReLU):
-                net.add_module(str(len(net)), layer)
-
-                if layerList['R'][r] in content_layers:
-                    #print("Setting up content layer " + str(i) + ": " + str(layerList['R'][r]))
-                    loss_module = ContentLoss(params.content_weight)
-                    net.add_module(str(len(net)), loss_module)
-                    content_losses.append(loss_module)
-                    next_content_idx += 1
-
-                if layerList['R'][r] in style_layers:
-                    #print("Setting up style layer " + str(i) + ": " + str(layerList['R'][r]))
-                    loss_module = StyleLoss(params.style_weight)
-                    net.add_module(str(len(net)), loss_module)
-                    style_losses.append(loss_module)
-                    next_style_idx += 1
-                r+=1
-
-            if isinstance(layer, nn.MaxPool2d) or isinstance(layer, nn.AvgPool2d):
-                net.add_module(str(len(net)), layer)
-
-    if multidevice:
-        net = setup_multi_device(net)
-
-    # Capture content targets
-    for i in content_losses:
-        i.mode = 'capture'
-    #print("Capturing content targets")
-    print_torch(net, multidevice)
-    net(content_image)
-
-    # Capture style targets
-    for i in content_losses:
-        i.mode = 'None'
-
-    for i, image in enumerate(style_images_caffe):
-        #print("Capturing style target " + str(i+1))
-        for j in style_losses:
-            j.mode = 'capture'
-            j.blend_weight = style_blend_weights[i]
-        net(style_images_caffe[i])
-
-    # Set all loss modules to loss mode
-    for i in content_losses:
-        i.mode = 'loss'
-    for i in style_losses:
-        i.mode = 'loss'
-
-    # Maybe normalize content and style weights
-    if params.normalize_weights:
-        normalize_weights(content_losses, style_losses)
-
-    # Freeze the network in order to prevent
-    # unnecessary gradient calculations
-    for param in net.parameters():
-        param.requires_grad = False
-
-    # Initialize the image
-    if params.seed >= 0:
-        torch.manual_seed(params.seed)
-        torch.cuda.manual_seed_all(params.seed)
-        torch.backends.cudnn.deterministic=True
-    if params.init == 'random':
-        B, C, H, W = content_image.size()
-        img = torch.randn(C, H, W).mul(0.001).unsqueeze(0).type(dtype)
-    elif params.init == 'image':
-        if params.init_image != None:
-            img = init_image.clone()
-        else:
-            img = content_image.clone()
-    img = nn.Parameter(img)
-    
-    def maybe_print(t, loss):
-        if params.print_iter > 0 and t % params.print_iter == 0:
-            print("Iteration " + str(t) + " / "+ str(params.num_iterations))
-            for i, loss_module in enumerate(content_losses):
-                print("  Content " + str(i+1) + " loss: " + str(loss_module.loss.item()))
-            for i, loss_module in enumerate(style_losses):
-                print("  Style " + str(i+1) + " loss: " + str(loss_module.loss.item()))
-            print("  Total loss: " + str(loss.item()))
-
-    #final_image = ''
-    def maybe_save(t):
-        should_save = params.save_iter > 950 and t % params.save_iter == 0
-        should_save = should_save or t == params.num_iterations
-        if should_save:
-            output_filename, file_extension = os.path.splitext(params.output_image)
-            if t == params.num_iterations:
-                filename = output_filename + str(file_extension)
-            else:
-                filename = str(output_filename) + "_" + str(t) + str(file_extension)
-            disp = deprocess(img.clone())
-
-            # Maybe perform postprocessing for color-independent style transfer
-            if params.original_colors == 1:
-                disp = original_colors(deprocess(content_image.clone()), disp)
-
-
-            disp.save(str(filename))
-
-            return disp
-
-    # Function to evaluate loss and gradient. We run the net forward and
-    # backward to get the gradient, and sum up losses from the loss modules.
-    # optim.lbfgs internally handles iteration and calls this function many
-    # times, so we manually count the number of iterations to handle printing
-    # and saving intermediate results.
-    num_calls = [0]
-
-    def feval():
-        num_calls[0] += 1
-        optimizer.zero_grad()
-        net(img)
-        loss = 0
-
-        for mod in content_losses:
-            loss += mod.loss.to(backward_device)
-        for mod in style_losses:
-            loss += mod.loss.to(backward_device)
-        if params.tv_weight > 0:
-            for mod in tv_losses:
-                loss += mod.loss.to(backward_device)
-
-        loss.backward()
-
-        final_image = maybe_save(num_calls[0])
-        maybe_print(num_calls[0], loss)
-
-        return loss
-    ##print('the final image is', final_image)
-    optimizer, loopVal = setup_optimizer(img)
-    while num_calls[0] <= loopVal:
-         optimizer.step(feval)
-
-    
-# Configure the optimizer
-def setup_optimizer(img):
-    if params.optimizer == 'lbfgs':
-        print("Running optimization with L-BFGS")
-        optim_state = {
-            'max_iter': params.num_iterations,
-            'tolerance_change': -1,
-            'tolerance_grad': -1,
-        }
-        if params.lbfgs_num_correction != 100:
-            optim_state['history_size'] = params.lbfgs_num_correction
-        optimizer = optim.LBFGS([img], **optim_state)
-        loopVal = 1
-    elif params.optimizer == 'adam':
-        print("Running optimization with ADAM")
-        optimizer = optim.Adam([img], lr = params.learning_rate)
-        loopVal = params.num_iterations - 1
-    return optimizer, loopVal
-
-
-def setup_gpu():
-    def setup_cuda():
-        if 'cudnn' in params.backend:
-            torch.backends.cudnn.enabled = True
-            if params.cudnn_autotune:
-                torch.backends.cudnn.benchmark = True
-        else:
-            torch.backends.cudnn.enabled = False
-
-    def setup_cpu():
-        if 'mkl' in params.backend and 'mkldnn' not in params.backend:
-            torch.backends.mkl.enabled = True
-        elif 'mkldnn' in params.backend:
-            raise ValueError("MKL-DNN is not supported yet.")
-        elif 'openmp' in params.backend:
-            torch.backends.openmp.enabled = True
-
-    multidevice = False
-    if "," in str(params.gpu):
-        devices = params.gpu.split(',')
-        multidevice = True
-
-        if 'c' in str(devices[0]).lower():
-            backward_device = "cpu"
-            setup_cuda(), setup_cpu()
-        else:
-            backward_device = "cuda:" + devices[0]
-            setup_cuda()
-        dtype = torch.FloatTensor
-
-    #elif "c" not in str(params.gpu).lower():
-        #setup_cuda()
-       #dtype, backward_device = torch.cuda.FloatTensor, "cuda:" + str(params.gpu)
-    else:
-        setup_cpu()
-        dtype, backward_device = torch.FloatTensor, "cpu"
-    return dtype, multidevice, backward_device
-
-
-def setup_multi_device(net):
-    assert len(params.gpu.split(',')) - 1 == len(params.multidevice_strategy.split(',')), \
-      "The number of -multidevice_strategy layer indices minus 1, must be equal to the number of -gpu devices."
-
-    new_net = ModelParallel(net, params.gpu, params.multidevice_strategy)
-    return new_net
-
-
-# Preprocess an image before passing it to a model.
-# We need to rescale from [0, 1] to [0, 255], convert from RGB to BGR,
-# and subtract the mean pixel.
-def preprocess(image_name, image_size):
-    image = Image.open(image_name).convert('RGB')
-    if type(image_size) is not tuple:
-        image_size = tuple([int((float(image_size) / max(image.size))*x) for x in (image.height, image.width)])
-    Loader = transforms.Compose([transforms.Resize(image_size), transforms.ToTensor()])
-    rgb2bgr = transforms.Compose([transforms.Lambda(lambda x: x[torch.LongTensor([2,1,0])])])
-    Normalize = transforms.Compose([transforms.Normalize(mean=[103.939, 116.779, 123.68], std=[1,1,1])])
-    tensor = Normalize(rgb2bgr(Loader(image) * 256)).unsqueeze(0)
-    return tensor
-
-
-#  Undo the above preprocessing.
-def deprocess(output_tensor):
-    Normalize = transforms.Compose([transforms.Normalize(mean=[-103.939, -116.779, -123.68], std=[1,1,1])])
-    bgr2rgb = transforms.Compose([transforms.Lambda(lambda x: x[torch.LongTensor([2,1,0])])])
-    output_tensor = bgr2rgb(Normalize(output_tensor.squeeze(0).cpu())) / 256
-    output_tensor.clamp_(0, 1)
-    Image2PIL = transforms.ToPILImage()
-    image = Image2PIL(output_tensor.cpu())
-    return image
-
-
-# Combine the Y channel of the generated image and the UV/CbCr channels of the
-# content image to perform color-independent style transfer.
-def original_colors(content, generated):
-    content_channels = list(content.convert('YCbCr').split())
-    generated_channels = list(generated.convert('YCbCr').split())
-    content_channels[0] = generated_channels[0]
-    return Image.merge('YCbCr', content_channels).convert('RGB')
-
-
-# Print like Lua/Torch7
-def print_torch(net, multidevice):
-    if multidevice:
-        return
-    simplelist = ""
-    for i, layer in enumerate(net, 1):
-        simplelist = simplelist + "(" + str(i) + ") -> "
-    #print("nn.Sequential ( \n  [input -> " + simplelist + "output]")
-
-    def strip(x):
-        return str(x).replace(", ",',').replace("(",'').replace(")",'') + ", "
-    def n():
-        return "  (" + str(i) + "): " + "nn." + str(l).split("(", 1)[0]
-
-    for i, l in enumerate(net, 1):
-         if "2d" in str(l):
-             ks, st, pd = strip(l.kernel_size), strip(l.stride), strip(l.padding)
-             if "Conv2d" in str(l):
-                 ch = str(l.in_channels) + " -> " + str(l.out_channels)
-                 print(n() + "(" + ch + ", " + (ks).replace(",",'x', 1) + st + pd.replace(", ",')'))
-             elif "Pool2d" in str(l):
-                 st = st.replace("  ",' ') + st.replace(", ",')')
-                 print(n() + "(" + ((ks).replace(",",'x' + ks, 1) + st).replace(", ",','))
-         else:
-             print(n())
-    print(")")
-
-
-# Divide weights by channel size
-def normalize_weights(content_losses, style_losses):
-    for n, i in enumerate(content_losses):
-        i.strength = i.strength / max(i.target.size())
-    for n, i in enumerate(style_losses):
-        i.strength = i.strength / max(i.target.size())
-
-
-# Define an nn Module to compute content loss
-class ContentLoss(nn.Module):
-
-    def __init__(self, strength):
-        super(ContentLoss, self).__init__()
-        self.strength = strength
-        self.crit = nn.MSELoss()
-        self.mode = 'None'
-
-    def forward(self, input):
-        if self.mode == 'loss':
-            self.loss = self.crit(input, self.target) * self.strength
-        elif self.mode == 'capture':
-            self.target = input.detach()
-        return input
-
-
-class GramMatrix(nn.Module):
-
-    def forward(self, input):
-        B, C, H, W = input.size()
-        x_flat = input.view(C, H * W)
-        return torch.mm(x_flat, x_flat.t())
-
-
-# Define an nn Module to compute style loss
-class StyleLoss(nn.Module):
-
-    def __init__(self, strength):
-        super(StyleLoss, self).__init__()
-        self.target = torch.Tensor()
-        self.strength = strength
-        self.gram = GramMatrix()
-        self.crit = nn.MSELoss()
-        self.mode = 'None'
-        self.blend_weight = None
-
-    def forward(self, input):
-        self.G = self.gram(input)
-        self.G = self.G.div(input.nelement())
-        if self.mode == 'capture':
-            if self.blend_weight == None:
-                self.target = self.G.detach()
-            elif self.target.nelement() == 0:
-                self.target = self.G.detach().mul(self.blend_weight)
-            else:
-                self.target = self.target.add(self.blend_weight, self.G.detach())
-        elif self.mode == 'loss':
-            self.loss = self.strength * self.crit(self.G, self.target)
-        return input
-
-
-class TVLoss(nn.Module):
-
-    def __init__(self, strength):
-        super(TVLoss, self).__init__()
-        self.strength = strength
-
-    def forward(self, input):
-        self.x_diff = input[:,:,1:,:] - input[:,:,:-1,:]
-        self.y_diff = input[:,:,:,1:] - input[:,:,:,:-1]
-        self.loss = self.strength * (torch.sum(torch.abs(self.x_diff)) + torch.sum(torch.abs(self.y_diff)))
-        return input
-
-
-if __name__ == "__main__":
-    main()

spaces/Agusbs98/automatic-ecg-diagnosis/predicts.py

@@ -1,118 +0,0 @@
-from libs import *
-import configVars
-from tools import tools
-from data import ECGDataset
-
-def procesar_archivo(format,number,unit,frec,file):
-    try:
-        prepare_data(format,number,unit,frec,file)
-        antonior92 = predict_antonior92()
-        CPSC = predict_CPSC_2018()
-        Chapman = predict_Chapman()
-        result = pd.DataFrame(data = [['Antonior92',antonior92],['CPSC-2018',CPSC],['Chapman',Chapman]],columns=['Red','Predicción'])
-        tools.ecgPlot("./datasets/pred.npy",500)
-        return result, "ecg.png"
-    except:
-        return pd.DataFrame(data = ["Se ha producido un error inesperado.","Compruebe que los datos de entrada sean correctos"],columns = ["ERROR."]), "error.jpg"
-
-
-def predict_CPSC_2018():
-    config = {
-    "ecg_leads":[
-        0, 1, 
-        6, 
-    ], 
-    "ecg_length":5000, 
-    "is_multilabel":True, 
-    }
-
-    train_loaders = {
-        "pred":torch.utils.data.DataLoader(
-            ECGDataset(
-                df_path = f"{configVars.pathCasos}pred.csv", data_path = f"{configVars.pathCasos}",
-                config = config, 
-                augment = False, 
-            ), 
-            timeout=0
-        )
-    }
-    save_ckp_dir = f"{configVars.pathModel}CPSC-2018"
-
-    pred = tools.LightX3ECG(
-                train_loaders, 
-                config,
-                save_ckp_dir, 
-                )
-    return pred if len(pred) != 0 else ['El archivo introducido no satisface ninguno de los criterios de clasificación']
-
-def predict_Chapman():
-    config = {
-    "ecg_leads":[
-        0, 1, 
-        6, 
-    ], 
-    "ecg_length":5000, 
-    "is_multilabel":False, 
-    }
-
-    train_loaders = {
-        "pred":torch.utils.data.DataLoader(
-            ECGDataset(
-                df_path = f"{configVars.pathCasos}pred.csv", data_path = f"{configVars.pathCasos}",
-                config = config, 
-                augment = False, 
-            ), 
-            timeout=0
-        )
-    }
-    save_ckp_dir = f"{configVars.pathModel}Chapman"
-
-    pred = tools.LightX3ECG(
-                train_loaders, 
-                config,
-                save_ckp_dir, 
-                )
-    return pred
-
-def predict_antonior92():
-    f = h5py.File(f"{configVars.pathCasos}pred.hdf5", 'r')
-    model = load_model(f"{configVars.pathModel}/antonior92/model.hdf5", compile=False)
-    model.compile(loss='binary_crossentropy', optimizer=Adam())
-    pred = model.predict(f['tracings'],  verbose=0)
-    optimal_thresholds = pd.read_csv(f"{configVars.pathThresholds}antonior92/optimal_thresholds_best.csv")
-    result = optimal_thresholds[optimal_thresholds["Threshold"]<=pred[0]]
-    result = result['Pred'].values.tolist()
-    f.close()
-    
-    return result if len(result) != 0 else ['Normal']
-
-def prepare_data(format,number,unit,frec,file):
-    units = {
-        'V':0.001,
-        'miliV':1,
-        'microV':1000,
-        'nanoV':1000000
-    }
-    if(format == 'XMLsierra'):
-        f = read_file(file.name)
-        df = pd.DataFrame()
-        for lead in f.leads:
-            df[lead.label]=lead.samples
-        data = df
-    elif(format == 'CSV'):
-        data = pd.read_csv(file.name,header = None)
-        
-    data = data[:-200]
-    data = data.T
-    leads = len(data)
-    frec = frec if frec>0 else 1
-    scale = 1/(number*units[unit])
-    ecg_preprocessed = tools.preprocess_ecg(data, frec, leads,
-                                            scale=scale,######### modificar para que segun la unidad introducida se pueda convertir los datos
-                                            use_all_leads=True,
-                                            remove_baseline=True)
-    tools.generateH5(ecg_preprocessed,
-             "pred.hdf5",new_freq=400,new_len=4096,
-             scale=2,sample_rate = frec)
-
-    np.save(f"{configVars.pathCasos}pred.npy",ecg_preprocessed )

spaces/Aloento/9Nine-VITS/residual_coupling_block.py

@@ -1,36 +0,0 @@
-from torch import nn
-
-import modules
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-               channels,
-               hidden_channels,
-               kernel_size,
-               dilation_rate,
-               n_layers,
-               n_flows=4,
-               gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x

spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/cpp/cppipc/ipc.cpp

@@ -1,701 +0,0 @@
-
-#include <type_traits>
-#include <cstring>
-#include <algorithm>
-#include <utility>          // std::pair, std::move, std::forward
-#include <atomic>
-#include <type_traits>      // aligned_storage_t
-#include <string>
-#include <vector>
-#include <array>
-#include <cassert>
-
-#include "libipc/ipc.h"
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/pool_alloc.h"
-#include "libipc/queue.h"
-#include "libipc/policy.h"
-#include "libipc/rw_lock.h"
-#include "libipc/waiter.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/utility/id_pool.h"
-#include "libipc/utility/scope_guard.h"
-#include "libipc/utility/utility.h"
-
-#include "libipc/memory/resource.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_array.h"
-
-namespace {
-
-using msg_id_t = std::uint32_t;
-using acc_t    = std::atomic<msg_id_t>;
-
-template <std::size_t DataSize, std::size_t AlignSize>
-struct msg_t;
-
-template <std::size_t AlignSize>
-struct msg_t<0, AlignSize> {
-    msg_id_t     cc_id_;
-    msg_id_t     id_;
-    std::int32_t remain_;
-    bool         storage_;
-};
-
-template <std::size_t DataSize, std::size_t AlignSize>
-struct msg_t : msg_t<0, AlignSize> {
-    std::aligned_storage_t<DataSize, AlignSize> data_ {};
-
-    msg_t() = default;
-    msg_t(msg_id_t cc_id, msg_id_t id, std::int32_t remain, void const * data, std::size_t size)
-        : msg_t<0, AlignSize> {cc_id, id, remain, (data == nullptr) || (size == 0)} {
-        if (this->storage_) {
-            if (data != nullptr) {
-                // copy storage-id
-                *reinterpret_cast<ipc::storage_id_t*>(&data_) =
-                     *static_cast<ipc::storage_id_t const *>(data);
-            }
-        }
-        else std::memcpy(&data_, data, size);
-    }
-};
-
-template <typename T>
-ipc::buff_t make_cache(T& data, std::size_t size) {
-    auto ptr = ipc::mem::alloc(size);
-    std::memcpy(ptr, &data, (ipc::detail::min)(sizeof(data), size));
-    return { ptr, size, ipc::mem::free };
-}
-
-struct cache_t {
-    std::size_t fill_;
-    ipc::buff_t buff_;
-
-    cache_t(std::size_t f, ipc::buff_t && b)
-        : fill_(f), buff_(std::move(b))
-    {}
-
-    void append(void const * data, std::size_t size) {
-        if (fill_ >= buff_.size() || data == nullptr || size == 0) return;
-        auto new_fill = (ipc::detail::min)(fill_ + size, buff_.size());
-        std::memcpy(static_cast<ipc::byte_t*>(buff_.data()) + fill_, data, new_fill - fill_);
-        fill_ = new_fill;
-    }
-};
-
-auto cc_acc() {
-    static ipc::shm::handle acc_h("__CA_CONN__", sizeof(acc_t));
-    return static_cast<acc_t*>(acc_h.get());
-}
-
-IPC_CONSTEXPR_ std::size_t align_chunk_size(std::size_t size) noexcept {
-    return (((size - 1) / ipc::large_msg_align) + 1) * ipc::large_msg_align;
-}
-
-IPC_CONSTEXPR_ std::size_t calc_chunk_size(std::size_t size) noexcept {
-    return ipc::make_align(alignof(std::max_align_t), align_chunk_size(
-           ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>)) + size));
-}
-
-struct chunk_t {
-    std::atomic<ipc::circ::cc_t> &conns() noexcept {
-        return *reinterpret_cast<std::atomic<ipc::circ::cc_t> *>(this);
-    }
-
-    void *data() noexcept {
-        return reinterpret_cast<ipc::byte_t *>(this)
-             + ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>));
-    }
-};
-
-struct chunk_info_t {
-    ipc::id_pool<> pool_;
-    ipc::spin_lock lock_;
-
-    IPC_CONSTEXPR_ static std::size_t chunks_mem_size(std::size_t chunk_size) noexcept {
-        return ipc::id_pool<>::max_count * chunk_size;
-    }
-
-    ipc::byte_t *chunks_mem() noexcept {
-        return reinterpret_cast<ipc::byte_t *>(this + 1);
-    }
-
-    chunk_t *at(std::size_t chunk_size, ipc::storage_id_t id) noexcept {
-        if (id < 0) return nullptr;
-        return reinterpret_cast<chunk_t *>(chunks_mem() + (chunk_size * id));
-    }
-};
-
-auto& chunk_storages() {
-    class chunk_handle_t {
-        ipc::shm::handle handle_;
-
-    public:
-        chunk_info_t *get_info(std::size_t chunk_size) {
-            if (!handle_.valid() &&
-                !handle_.acquire( ("__CHUNK_INFO__" + ipc::to_string(chunk_size)).c_str(), 
-                                  sizeof(chunk_info_t) + chunk_info_t::chunks_mem_size(chunk_size) )) {
-                ipc::error("[chunk_storages] chunk_shm.id_info_.acquire failed: chunk_size = %zd\n", chunk_size);
-                return nullptr;
-            }
-            auto info = static_cast<chunk_info_t*>(handle_.get());
-            if (info == nullptr) {
-                ipc::error("[chunk_storages] chunk_shm.id_info_.get failed: chunk_size = %zd\n", chunk_size);
-                return nullptr;
-            }
-            return info;
-        }
-    };
-    static ipc::map<std::size_t, chunk_handle_t> chunk_hs;
-    return chunk_hs;
-}
-
-chunk_info_t *chunk_storage_info(std::size_t chunk_size) {
-    auto &storages = chunk_storages();
-    std::decay_t<decltype(storages)>::iterator it;
-    {
-        static ipc::rw_lock lock;
-        IPC_UNUSED_ std::shared_lock<ipc::rw_lock> guard {lock};
-        if ((it = storages.find(chunk_size)) == storages.end()) {
-            using chunk_handle_t = std::decay_t<decltype(storages)>::value_type::second_type;
-            guard.unlock();
-            IPC_UNUSED_ std::lock_guard<ipc::rw_lock> guard {lock};
-            it = storages.emplace(chunk_size, chunk_handle_t{}).first;
-        }
-    }
-    return it->second.get_info(chunk_size);
-}
-
-std::pair<ipc::storage_id_t, void*> acquire_storage(std::size_t size, ipc::circ::cc_t conns) {
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return {};
-
-    info->lock_.lock();
-    info->pool_.prepare();
-    // got an unique id
-    auto id = info->pool_.acquire();
-    info->lock_.unlock();
-
-    auto chunk = info->at(chunk_size, id);
-    if (chunk == nullptr) return {};
-    chunk->conns().store(conns, std::memory_order_relaxed);
-    return { id, chunk->data() };
-}
-
-void *find_storage(ipc::storage_id_t id, std::size_t size) {
-    if (id < 0) {
-        ipc::error("[find_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return nullptr;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return nullptr;
-    return info->at(chunk_size, id)->data();
-}
-
-void release_storage(ipc::storage_id_t id, std::size_t size) {
-    if (id < 0) {
-        ipc::error("[release_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return;
-    info->lock_.lock();
-    info->pool_.release(id);
-    info->lock_.unlock();
-}
-
-template <ipc::relat Rp, ipc::relat Rc>
-bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::unicast>, 
-            std::atomic<ipc::circ::cc_t> &/*conns*/, ipc::circ::cc_t /*curr_conns*/, ipc::circ::cc_t /*conn_id*/) noexcept {
-    return true;
-}
-
-template <ipc::relat Rp, ipc::relat Rc>
-bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::broadcast>, 
-            std::atomic<ipc::circ::cc_t> &conns, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) noexcept {
-    auto last_conns = curr_conns & ~conn_id;
-    for (unsigned k = 0;;) {
-        auto chunk_conns  = conns.load(std::memory_order_acquire);
-        if (conns.compare_exchange_weak(chunk_conns, chunk_conns & last_conns, std::memory_order_release)) {
-            return (chunk_conns & last_conns) == 0;
-        }
-        ipc::yield(k);
-    }
-}
-
-template <typename Flag>
-void recycle_storage(ipc::storage_id_t id, std::size_t size, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) {
-    if (id < 0) {
-        ipc::error("[recycle_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
-        return;
-    }
-    std::size_t chunk_size = calc_chunk_size(size);
-    auto info = chunk_storage_info(chunk_size);
-    if (info == nullptr) return;
-
-    auto chunk = info->at(chunk_size, id);
-    if (chunk == nullptr) return;
-
-    if (!sub_rc(Flag{}, chunk->conns(), curr_conns, conn_id)) {
-        return;
-    }
-    info->lock_.lock();
-    info->pool_.release(id);
-    info->lock_.unlock();
-}
-
-template <typename MsgT>
-bool clear_message(void* p) {
-    auto msg = static_cast<MsgT*>(p);
-    if (msg->storage_) {
-        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg->remain_;
-        if (r_size <= 0) {
-            ipc::error("[clear_message] invalid msg size: %d\n", (int)r_size);
-            return true;
-        }
-        release_storage(
-            *reinterpret_cast<ipc::storage_id_t*>(&msg->data_),
-            static_cast<std::size_t>(r_size));
-    }
-    return true;
-}
-
-struct conn_info_head {
-
-    ipc::string name_;
-    msg_id_t    cc_id_; // connection-info id
-    ipc::detail::waiter cc_waiter_, wt_waiter_, rd_waiter_;
-    ipc::shm::handle acc_h_;
-
-    conn_info_head(char const * name)
-        : name_     {name}
-        , cc_id_    {(cc_acc() == nullptr) ? 0 : cc_acc()->fetch_add(1, std::memory_order_relaxed)}
-        , cc_waiter_{("__CC_CONN__" + name_).c_str()}
-        , wt_waiter_{("__WT_CONN__" + name_).c_str()}
-        , rd_waiter_{("__RD_CONN__" + name_).c_str()}
-        , acc_h_    {("__AC_CONN__" + name_).c_str(), sizeof(acc_t)} {
-    }
-
-    void quit_waiting() {
-        cc_waiter_.quit_waiting();
-        wt_waiter_.quit_waiting();
-        rd_waiter_.quit_waiting();
-    }
-
-    auto acc() {
-        return static_cast<acc_t*>(acc_h_.get());
-    }
-
-    auto& recv_cache() {
-        thread_local ipc::unordered_map<msg_id_t, cache_t> tls;
-        return tls;
-    }
-};
-
-template <typename W, typename F>
-bool wait_for(W& waiter, F&& pred, std::uint64_t tm) {
-    if (tm == 0) return !pred();
-    for (unsigned k = 0; pred();) {
-        bool ret = true;
-        ipc::sleep(k, [&k, &ret, &waiter, &pred, tm] {
-            ret = waiter.wait_if(std::forward<F>(pred), tm);
-            k   = 0;
-        });
-        if (!ret) return false; // timeout or fail
-        if (k == 0) break; // k has been reset
-    }
-    return true;
-}
-
-template <typename Policy,
-          std::size_t DataSize  = ipc::data_length,
-          std::size_t AlignSize = (ipc::detail::min)(DataSize, alignof(std::max_align_t))>
-struct queue_generator {
-
-    using queue_t = ipc::queue<msg_t<DataSize, AlignSize>, Policy>;
-
-    struct conn_info_t : conn_info_head {
-        queue_t que_;
-
-        conn_info_t(char const * name)
-            : conn_info_head{name}
-            , que_{("__QU_CONN__" +
-                    ipc::to_string(DataSize) + "__" +
-                    ipc::to_string(AlignSize) + "__" + name).c_str()} {
-        }
-
-        void disconnect_receiver() {
-            bool dis = que_.disconnect();
-            this->quit_waiting();
-            if (dis) {
-                this->recv_cache().clear();
-            }
-        }
-    };
-};
-
-template <typename Policy>
-struct detail_impl {
-
-using policy_t    = Policy;
-using flag_t      = typename policy_t::flag_t;
-using queue_t     = typename queue_generator<policy_t>::queue_t;
-using conn_info_t = typename queue_generator<policy_t>::conn_info_t;
-
-constexpr static conn_info_t* info_of(ipc::handle_t h) noexcept {
-    return static_cast<conn_info_t*>(h);
-}
-
-constexpr static queue_t* queue_of(ipc::handle_t h) noexcept {
-    return (info_of(h) == nullptr) ? nullptr : &(info_of(h)->que_);
-}
-
-/* API implementations */
-
-static void disconnect(ipc::handle_t h) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return;
-    }
-    que->shut_sending();
-    assert(info_of(h) != nullptr);
-    info_of(h)->disconnect_receiver();
-}
-
-static bool reconnect(ipc::handle_t * ph, bool start_to_recv) {
-    assert(ph != nullptr);
-    assert(*ph != nullptr);
-    auto que = queue_of(*ph);
-    if (que == nullptr) {
-        return false;
-    }
-    if (start_to_recv) {
-        que->shut_sending();
-        if (que->connect()) { // wouldn't connect twice
-            info_of(*ph)->cc_waiter_.broadcast();
-            return true;
-        }
-        return false;
-    }
-    // start_to_recv == false
-    if (que->connected()) {
-        info_of(*ph)->disconnect_receiver();
-    }
-    return que->ready_sending();
-}
-
-static bool connect(ipc::handle_t * ph, char const * name, bool start_to_recv) {
-    assert(ph != nullptr);
-    if (*ph == nullptr) {
-        *ph = ipc::mem::alloc<conn_info_t>(name);
-    }
-    return reconnect(ph, start_to_recv);
-}
-
-static void destroy(ipc::handle_t h) {
-    disconnect(h);
-    ipc::mem::free(info_of(h));
-}
-
-static std::size_t recv_count(ipc::handle_t h) noexcept {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return ipc::invalid_value;
-    }
-    return que->conn_count();
-}
-
-static bool wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        return false;
-    }
-    return wait_for(info_of(h)->cc_waiter_, [que, r_count] {
-        return que->conn_count() < r_count;
-    }, tm);
-}
-
-template <typename F>
-static bool send(F&& gen_push, ipc::handle_t h, void const * data, std::size_t size) {
-    if (data == nullptr || size == 0) {
-        ipc::error("fail: send(%p, %zd)\n", data, size);
-        return false;
-    }
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        ipc::error("fail: send, queue_of(h) == nullptr\n");
-        return false;
-    }
-    if (que->elems() == nullptr) {
-        ipc::error("fail: send, queue_of(h)->elems() == nullptr\n");
-        return false;
-    }
-    if (!que->ready_sending()) {
-        ipc::error("fail: send, que->ready_sending() == false\n");
-        return false;
-    }
-    ipc::circ::cc_t conns = que->elems()->connections(std::memory_order_relaxed);
-    if (conns == 0) {
-        ipc::error("fail: send, there is no receiver on this connection.\n");
-        return false;
-    }
-    // calc a new message id
-    auto acc = info_of(h)->acc();
-    if (acc == nullptr) {
-        ipc::error("fail: send, info_of(h)->acc() == nullptr\n");
-        return false;
-    }
-    auto msg_id   = acc->fetch_add(1, std::memory_order_relaxed);
-    auto try_push = std::forward<F>(gen_push)(info_of(h), que, msg_id);
-    if (size > ipc::large_msg_limit) {
-        auto   dat = acquire_storage(size, conns);
-        void * buf = dat.second;
-        if (buf != nullptr) {
-            std::memcpy(buf, data, size);
-            return try_push(static_cast<std::int32_t>(size) - 
-                            static_cast<std::int32_t>(ipc::data_length), &(dat.first), 0);
-        }
-        // try using message fragment
-        //ipc::log("fail: shm::handle for big message. msg_id: %zd, size: %zd\n", msg_id, size);
-    }
-    // push message fragment
-    std::int32_t offset = 0;
-    for (std::int32_t i = 0; i < static_cast<std::int32_t>(size / ipc::data_length); ++i, offset += ipc::data_length) {
-        if (!try_push(static_cast<std::int32_t>(size) - offset - static_cast<std::int32_t>(ipc::data_length),
-                      static_cast<ipc::byte_t const *>(data) + offset, ipc::data_length)) {
-            return false;
-        }
-    }
-    // if remain > 0, this is the last message fragment
-    std::int32_t remain = static_cast<std::int32_t>(size) - offset;
-    if (remain > 0) {
-        if (!try_push(remain - static_cast<std::int32_t>(ipc::data_length),
-                      static_cast<ipc::byte_t const *>(data) + offset, 
-                      static_cast<std::size_t>(remain))) {
-            return false;
-        }
-    }
-    return true;
-}
-
-static bool send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return send([tm](auto info, auto que, auto msg_id) {
-        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
-            if (!wait_for(info->wt_waiter_, [&] {
-                    return !que->push(
-                        [](void*) { return true; },
-                        info->cc_id_, msg_id, remain, data, size);
-                }, tm)) {
-                ipc::log("force_push: msg_id = %zd, remain = %d, size = %zd\n", msg_id, remain, size);
-                if (!que->force_push(
-                        clear_message<typename queue_t::value_t>,
-                        info->cc_id_, msg_id, remain, data, size)) {
-                    return false;
-                }
-            }
-            info->rd_waiter_.broadcast();
-            return true;
-        };
-    }, h, data, size);
-}
-
-static bool try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return send([tm](auto info, auto que, auto msg_id) {
-        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
-            if (!wait_for(info->wt_waiter_, [&] {
-                    return !que->push(
-                        [](void*) { return true; },
-                        info->cc_id_, msg_id, remain, data, size);
-                }, tm)) {
-                return false;
-            }
-            info->rd_waiter_.broadcast();
-            return true;
-        };
-    }, h, data, size);
-}
-
-static ipc::buff_t recv(ipc::handle_t h, std::uint64_t tm) {
-    auto que = queue_of(h);
-    if (que == nullptr) {
-        ipc::error("fail: recv, queue_of(h) == nullptr\n");
-        return {};
-    }
-    if (!que->connected()) {
-        // hasn't connected yet, just return.
-        return {};
-    }
-    auto& rc = info_of(h)->recv_cache();
-    for (;;) {
-        // pop a new message
-        typename queue_t::value_t msg;
-        if (!wait_for(info_of(h)->rd_waiter_, [que, &msg] {
-                return !que->pop(msg);
-            }, tm)) {
-            // pop failed, just return.
-            return {};
-        }
-        info_of(h)->wt_waiter_.broadcast();
-        if ((info_of(h)->acc() != nullptr) && (msg.cc_id_ == info_of(h)->cc_id_)) {
-            continue; // ignore message to self
-        }
-        // msg.remain_ may minus & abs(msg.remain_) < data_length
-        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg.remain_;
-        if (r_size <= 0) {
-            ipc::error("fail: recv, r_size = %d\n", (int)r_size);
-            return {};
-        }
-        std::size_t msg_size = static_cast<std::size_t>(r_size);
-        // large message
-        if (msg.storage_) {
-            ipc::storage_id_t buf_id = *reinterpret_cast<ipc::storage_id_t*>(&msg.data_);
-            void* buf = find_storage(buf_id, msg_size);
-            if (buf != nullptr) {
-                struct recycle_t {
-                    ipc::storage_id_t storage_id;
-                    ipc::circ::cc_t   curr_conns;
-                    ipc::circ::cc_t   conn_id;
-                } *r_info = ipc::mem::alloc<recycle_t>(recycle_t{
-                    buf_id, que->elems()->connections(std::memory_order_relaxed), que->connected_id()
-                });
-                if (r_info == nullptr) {
-                    ipc::log("fail: ipc::mem::alloc<recycle_t>.\n");
-                    return ipc::buff_t{buf, msg_size}; // no recycle
-                } else {
-                    return ipc::buff_t{buf, msg_size, [](void* p_info, std::size_t size) {
-                        auto r_info = static_cast<recycle_t *>(p_info);
-                        IPC_UNUSED_ auto finally = ipc::guard([r_info] {
-                            ipc::mem::free(r_info);
-                        });
-                        recycle_storage<flag_t>(r_info->storage_id, size, r_info->curr_conns, r_info->conn_id);
-                    }, r_info};
-                }
-            } else {
-                ipc::log("fail: shm::handle for large message. msg_id: %zd, buf_id: %zd, size: %zd\n", msg.id_, buf_id, msg_size);
-                continue;
-            }
-        }
-        // find cache with msg.id_
-        auto cac_it = rc.find(msg.id_);
-        if (cac_it == rc.end()) {
-            if (msg_size <= ipc::data_length) {
-                return make_cache(msg.data_, msg_size);
-            }
-            // gc
-            if (rc.size() > 1024) {
-                std::vector<msg_id_t> need_del;
-                for (auto const & pair : rc) {
-                    auto cmp = std::minmax(msg.id_, pair.first);
-                    if (cmp.second - cmp.first > 8192) {
-                        need_del.push_back(pair.first);
-                    }
-                }
-                for (auto id : need_del) rc.erase(id);
-            }
-            // cache the first message fragment
-            rc.emplace(msg.id_, cache_t { ipc::data_length, make_cache(msg.data_, msg_size) });
-        }
-        // has cached before this message
-        else {
-            auto& cac = cac_it->second;
-            // this is the last message fragment
-            if (msg.remain_ <= 0) {
-                cac.append(&(msg.data_), msg_size);
-                // finish this message, erase it from cache
-                auto buff = std::move(cac.buff_);
-                rc.erase(cac_it);
-                return buff;
-            }
-            // there are remain datas after this message
-            cac.append(&(msg.data_), ipc::data_length);
-        }
-    }
-}
-
-static ipc::buff_t try_recv(ipc::handle_t h) {
-    return recv(h, 0);
-}
-
-}; // detail_impl<Policy>
-
-template <typename Flag>
-using policy_t = ipc::policy::choose<ipc::circ::elem_array, Flag>;
-
-} // internal-linkage
-
-namespace ipc {
-
-template <typename Flag>
-ipc::handle_t chan_impl<Flag>::inited() {
-    ipc::detail::waiter::init();
-    return nullptr;
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::connect(ipc::handle_t * ph, char const * name, unsigned mode) {
-    return detail_impl<policy_t<Flag>>::connect(ph, name, mode & receiver);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::reconnect(ipc::handle_t * ph, unsigned mode) {
-    return detail_impl<policy_t<Flag>>::reconnect(ph, mode & receiver);
-}
-
-template <typename Flag>
-void chan_impl<Flag>::disconnect(ipc::handle_t h) {
-    detail_impl<policy_t<Flag>>::disconnect(h);
-}
-
-template <typename Flag>
-void chan_impl<Flag>::destroy(ipc::handle_t h) {
-    detail_impl<policy_t<Flag>>::destroy(h);
-}
-
-template <typename Flag>
-char const * chan_impl<Flag>::name(ipc::handle_t h) {
-    auto info = detail_impl<policy_t<Flag>>::info_of(h);
-    return (info == nullptr) ? nullptr : info->name_.c_str();
-}
-
-template <typename Flag>
-std::size_t chan_impl<Flag>::recv_count(ipc::handle_t h) {
-    return detail_impl<policy_t<Flag>>::recv_count(h);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::wait_for_recv(h, r_count, tm);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::send(h, data, size, tm);
-}
-
-template <typename Flag>
-buff_t chan_impl<Flag>::recv(ipc::handle_t h, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::recv(h, tm);
-}
-
-template <typename Flag>
-bool chan_impl<Flag>::try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
-    return detail_impl<policy_t<Flag>>::try_send(h, data, size, tm);
-}
-
-template <typename Flag>
-buff_t chan_impl<Flag>::try_recv(ipc::handle_t h) {
-    return detail_impl<policy_t<Flag>>::try_recv(h);
-}
-
-template struct chan_impl<ipc::wr<relat::single, relat::single, trans::unicast  >>;
-// template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::unicast  >>; // TBD
-// template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::unicast  >>; // TBD
-template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::broadcast>>;
-template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::broadcast>>;
-
-} // namespace ipc

spaces/Amrrs/DragGan-Inversion/viz/capture_widget.py

@@ -1,96 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import re
-import numpy as np
-import imgui
-import PIL.Image
-from gui_utils import imgui_utils
-from . import renderer
-import torch
-import torchvision
-
-# ----------------------------------------------------------------------------
-
-
-class CaptureWidget:
-    def __init__(self, viz):
-        self.viz = viz
-        self.path = os.path.abspath(os.path.join(
-            os.path.dirname(__file__), '..', '_screenshots'))
-        self.dump_image = False
-        self.dump_gui = False
-        self.defer_frames = 0
-        self.disabled_time = 0
-
-    def dump_png(self, image):
-        viz = self.viz
-        try:
-            _height, _width, channels = image.shape
-            print(viz.result)
-            assert image.dtype == np.uint8
-            os.makedirs(self.path, exist_ok=True)
-            file_id = 0
-            for entry in os.scandir(self.path):
-                if entry.is_file():
-                    match = re.fullmatch(r'(\d+).*', entry.name)
-                    if match:
-                        file_id = max(file_id, int(match.group(1)) + 1)
-            if channels == 1:
-                pil_image = PIL.Image.fromarray(image[:, :, 0], 'L')
-            else:
-                pil_image = PIL.Image.fromarray(image[:, :, :3], 'RGB')
-            pil_image.save(os.path.join(self.path, f'{file_id:05d}.png'))
-            np.save(os.path.join(
-                self.path, f'{file_id:05d}.npy'), viz.result.w)
-        except:
-            viz.result.error = renderer.CapturedException()
-
-    @imgui_utils.scoped_by_object_id
-    def __call__(self, show=True):
-        viz = self.viz
-        if show:
-            with imgui_utils.grayed_out(self.disabled_time != 0):
-                imgui.text('Capture')
-                imgui.same_line(viz.label_w)
-
-                _changed, self.path = imgui_utils.input_text('##path', self.path, 1024,
-                                                             flags=(
-                                                                 imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE),
-                                                             width=(-1),
-                                                             help_text='PATH')
-                if imgui.is_item_hovered() and not imgui.is_item_active() and self.path != '':
-                    imgui.set_tooltip(self.path)
-                imgui.text(' ')
-                imgui.same_line(viz.label_w)
-                if imgui_utils.button('Save image', width=viz.button_w, enabled=(self.disabled_time == 0 and 'image' in viz.result)):
-                    self.dump_image = True
-                    self.defer_frames = 2
-                    self.disabled_time = 0.5
-                imgui.same_line()
-                if imgui_utils.button('Save GUI', width=viz.button_w, enabled=(self.disabled_time == 0)):
-                    self.dump_gui = True
-                    self.defer_frames = 2
-                    self.disabled_time = 0.5
-
-        self.disabled_time = max(self.disabled_time - viz.frame_delta, 0)
-        if self.defer_frames > 0:
-            self.defer_frames -= 1
-        elif self.dump_image:
-            if 'image' in viz.result:
-                self.dump_png(viz.result.image)
-            self.dump_image = False
-        elif self.dump_gui:
-            viz.capture_next_frame()
-            self.dump_gui = False
-        captured_frame = viz.pop_captured_frame()
-        if captured_frame is not None:
-            self.dump_png(captured_frame)
-
-# ----------------------------------------------------------------------------

spaces/Amrrs/github-star-tracking/README.md

@@ -1,37 +0,0 @@
----
-title: Github Star Tracking
-emoji: 📉
-colorFrom: green
-colorTo: red
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/optimization/coreml.md

@@ -1,167 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# How to run Stable Diffusion with Core ML
-
-[Core ML](https://developer.apple.com/documentation/coreml) is the model format and machine learning library supported by Apple frameworks. If you are interested in running Stable Diffusion models inside your macOS or iOS/iPadOS apps, this guide will show you how to convert existing PyTorch checkpoints into the Core ML format and use them for inference with Python or Swift.
-
-Core ML models can leverage all the compute engines available in Apple devices: the CPU, the GPU, and the Apple Neural Engine (or ANE, a tensor-optimized accelerator available in Apple Silicon Macs and modern iPhones/iPads). Depending on the model and the device it's running on, Core ML can mix and match compute engines too, so some portions of the model may run on the CPU while others run on GPU, for example.
-
-<Tip>
-
-You can also run the `diffusers` Python codebase on Apple Silicon Macs using the `mps` accelerator built into PyTorch. This approach is explained in depth in [the mps guide](mps), but it is not compatible with native apps.
-
-</Tip>
-
-## Stable Diffusion Core ML Checkpoints
-
-Stable Diffusion weights (or checkpoints) are stored in the PyTorch format, so you need to convert them to the Core ML format before we can use them inside native apps.
-
-Thankfully, Apple engineers developed [a conversion tool](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) based on `diffusers` to convert the PyTorch checkpoints to Core ML.
-
-Before you convert a model, though, take a moment to explore the Hugging Face Hub – chances are the model you're interested in is already available in Core ML format:
-
-- the [Apple](https://huggingface.co/apple) organization includes Stable Diffusion versions 1.4, 1.5, 2.0 base, and 2.1 base
-- [coreml](https://huggingface.co/coreml) organization includes custom DreamBoothed and finetuned models
-- use this [filter](https://huggingface.co/models?pipeline_tag=text-to-image&library=coreml&p=2&sort=likes) to return all available Core ML checkpoints
-
-If you can't find the model you're interested in, we recommend you follow the instructions for [Converting Models to Core ML](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) by Apple.
-
-## Selecting the Core ML Variant to Use
-
-Stable Diffusion models can be converted to different Core ML variants intended for different purposes:
-
-- The type of attention blocks used. The attention operation is used to "pay attention" to the relationship between different areas in the image representations and to understand how the image and text representations are related. Attention is compute- and memory-intensive, so different implementations exist that consider the hardware characteristics of different devices. For Core ML Stable Diffusion models, there are two attention variants:
-    * `split_einsum` ([introduced by Apple](https://machinelearning.apple.com/research/neural-engine-transformers)) is optimized for ANE devices, which is available in modern iPhones, iPads and M-series computers.
-    * The "original" attention (the base implementation used in `diffusers`) is only compatible with CPU/GPU and not ANE. It can be *faster* to run your model on CPU + GPU using `original` attention than ANE. See [this performance benchmark](https://huggingface.co/blog/fast-mac-diffusers#performance-benchmarks) as well as some [additional measures provided by the community](https://github.com/huggingface/swift-coreml-diffusers/issues/31) for additional details.
-
-- The supported inference framework.
-    * `packages` are suitable for Python inference. This can be used to test converted Core ML models before attempting to integrate them inside native apps, or if you want to explore Core ML performance but don't need to support native apps. For example, an application with a web UI could perfectly use a Python Core ML backend.
-    * `compiled` models are required for Swift code. The `compiled` models in the Hub split the large UNet model weights into several files for compatibility with iOS and iPadOS devices. This corresponds to the [`--chunk-unet` conversion option](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml). If you want to support native apps, then you need to select the `compiled` variant.
-
-The official Core ML Stable Diffusion [models](https://huggingface.co/apple/coreml-stable-diffusion-v1-4/tree/main) include these variants, but the community ones may vary:
-
-```
-coreml-stable-diffusion-v1-4
-├── README.md
-├── original
-│   ├── compiled
-│   └── packages
-└── split_einsum
-    ├── compiled
-    └── packages
-```
-
-You can download and use the variant you need as shown below.
-
-## Core ML Inference in Python
-
-Install the following libraries to run Core ML inference in Python:
-
-```bash
-pip install huggingface_hub
-pip install git+https://github.com/apple/ml-stable-diffusion
-```
-
-### Download the Model Checkpoints
-
-To run inference in Python, use one of the versions stored in the `packages` folders because the `compiled` ones are only compatible with Swift. You may choose whether you want to use `original` or `split_einsum` attention.
-
-This is how you'd download the `original` attention variant from the Hub to a directory called `models`:
-
-```Python
-from huggingface_hub import snapshot_download
-from pathlib import Path
-
-repo_id = "apple/coreml-stable-diffusion-v1-4"
-variant = "original/packages"
-
-model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_"))
-snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False)
-print(f"Model downloaded at {model_path}")
-```
-
-
-### Inference[[python-inference]]
-
-Once you have downloaded a snapshot of the model, you can test it using Apple's Python script.
-
-```shell
-python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i models/coreml-stable-diffusion-v1-4_original_packages -o </path/to/output/image> --compute-unit CPU_AND_GPU --seed 93
-```
-
-`<output-mlpackages-directory>` should point to the checkpoint you downloaded in the step above, and `--compute-unit` indicates the hardware you want to allow for inference. It must be one of the following options: `ALL`, `CPU_AND_GPU`, `CPU_ONLY`, `CPU_AND_NE`. You may also provide an optional output path, and a seed for reproducibility.
-
-The inference script assumes you're using the original version of the Stable Diffusion model, `CompVis/stable-diffusion-v1-4`. If you use another model, you *have* to specify its Hub id in the inference command line, using the `--model-version` option. This works for models already supported and custom models you trained or fine-tuned yourself.
-
-For example, if you want to use [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5):
-
-```shell
-python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" --compute-unit ALL -o output --seed 93 -i models/coreml-stable-diffusion-v1-5_original_packages --model-version runwayml/stable-diffusion-v1-5
-```
-
-
-## Core ML inference in Swift
-
-Running inference in Swift is slightly faster than in Python because the models are already compiled in the `mlmodelc` format. This is noticeable on app startup when the model is loaded but shouldn’t be noticeable if you run several generations afterward.
-
-### Download
-
-To run inference in Swift on your Mac, you need one of the `compiled` checkpoint versions. We recommend you download them locally using Python code similar to the previous example, but with one of the `compiled` variants:
-
-```Python
-from huggingface_hub import snapshot_download
-from pathlib import Path
-
-repo_id = "apple/coreml-stable-diffusion-v1-4"
-variant = "original/compiled"
-
-model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_"))
-snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False)
-print(f"Model downloaded at {model_path}")
-```
-
-### Inference[[swift-inference]]
-
-To run inference, please clone Apple's repo:
-
-```bash
-git clone https://github.com/apple/ml-stable-diffusion
-cd ml-stable-diffusion
-```
-
-And then use Apple's command line tool, [Swift Package Manager](https://www.swift.org/package-manager/#):
-
-```bash
-swift run StableDiffusionSample --resource-path models/coreml-stable-diffusion-v1-4_original_compiled --compute-units all "a photo of an astronaut riding a horse on mars"
-```
-
-You have to specify in `--resource-path` one of the checkpoints downloaded in the previous step, so please make sure it contains compiled Core ML bundles with the extension `.mlmodelc`. The `--compute-units` has to be one of these values: `all`, `cpuOnly`, `cpuAndGPU`, `cpuAndNeuralEngine`.
-
-For more details, please refer to the [instructions in Apple's repo](https://github.com/apple/ml-stable-diffusion).
-
-
-## Supported Diffusers Features
-
-The Core ML models and inference code don't support many of the features, options, and flexibility of 🧨 Diffusers. These are some of the limitations to keep in mind:
-
-- Core ML models are only suitable for inference. They can't be used for training or fine-tuning.
-- Only two schedulers have been ported to Swift, the default one used by Stable Diffusion and `DPMSolverMultistepScheduler`, which we ported to Swift from our `diffusers` implementation. We recommend you use `DPMSolverMultistepScheduler`, since it produces the same quality in about half the steps.
-- Negative prompts, classifier-free guidance scale, and image-to-image tasks are available in the inference code. Advanced features such as depth guidance, ControlNet, and latent upscalers are not available yet.
-
-Apple's [conversion and inference repo](https://github.com/apple/ml-stable-diffusion) and our own [swift-coreml-diffusers](https://github.com/huggingface/swift-coreml-diffusers) repos are intended as technology demonstrators to enable other developers to build upon. 
-
-If you feel strongly about any missing features, please feel free to open a feature request or, better yet, a contribution PR :)
-
-## Native Diffusers Swift app
-
-One easy way to run Stable Diffusion on your own Apple hardware is to use [our open-source Swift repo](https://github.com/huggingface/swift-coreml-diffusers), based on `diffusers` and Apple's conversion and inference repo. You can study the code, compile it with [Xcode](https://developer.apple.com/xcode/) and adapt it for your own needs. For your convenience, there's also a [standalone Mac app in the App Store](https://apps.apple.com/app/diffusers/id1666309574), so you can play with it without having to deal with the code or IDE. If you are a developer and have determined that Core ML is the best solution to build your Stable Diffusion app, then you can use the rest of this guide to get started with your project. We can't wait to see what you'll build :)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py

@@ -1,188 +0,0 @@
-import inspect
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import PIL
-import torch
-import torch.utils.checkpoint
-
-from ...models import UNet2DModel, VQModel
-from ...schedulers import (
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-)
-from ...utils import PIL_INTERPOLATION, randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-def preprocess(image):
-    w, h = image.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2.0 * image - 1.0
-
-
-class LDMSuperResolutionPipeline(DiffusionPipeline):
-    r"""
-    A pipeline for image super-resolution using latent diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Parameters:
-        vqvae ([`VQModel`]):
-            Vector-quantized (VQ) model to encode and decode images to and from latent representations.
-        unet ([`UNet2DModel`]):
-            A `UNet2DModel` to denoise the encoded image.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`],
-            [`EulerAncestralDiscreteScheduler`], [`DPMSolverMultistepScheduler`], or [`PNDMScheduler`].
-    """
-
-    def __init__(
-        self,
-        vqvae: VQModel,
-        unet: UNet2DModel,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-        ],
-    ):
-        super().__init__()
-        self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        image: Union[torch.Tensor, PIL.Image.Image] = None,
-        batch_size: Optional[int] = 1,
-        num_inference_steps: Optional[int] = 100,
-        eta: Optional[float] = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ) -> Union[Tuple, ImagePipelineOutput]:
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            image (`torch.Tensor` or `PIL.Image.Image`):
-                `Image` or tensor representing an image batch to be used as the starting point for the process.
-            batch_size (`int`, *optional*, defaults to 1):
-                Number of images to generate.
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
-
-        Example:
-
-        ```py
-        >>> import requests
-        >>> from PIL import Image
-        >>> from io import BytesIO
-        >>> from diffusers import LDMSuperResolutionPipeline
-        >>> import torch
-
-        >>> # load model and scheduler
-        >>> pipeline = LDMSuperResolutionPipeline.from_pretrained("CompVis/ldm-super-resolution-4x-openimages")
-        >>> pipeline = pipeline.to("cuda")
-
-        >>> # let's download an  image
-        >>> url = (
-        ...     "https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png"
-        ... )
-        >>> response = requests.get(url)
-        >>> low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
-        >>> low_res_img = low_res_img.resize((128, 128))
-
-        >>> # run pipeline in inference (sample random noise and denoise)
-        >>> upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]
-        >>> # save image
-        >>> upscaled_image.save("ldm_generated_image.png")
-        ```
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
-                returned where the first element is a list with the generated images
-        """
-        if isinstance(image, PIL.Image.Image):
-            batch_size = 1
-        elif isinstance(image, torch.Tensor):
-            batch_size = image.shape[0]
-        else:
-            raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(image)}")
-
-        if isinstance(image, PIL.Image.Image):
-            image = preprocess(image)
-
-        height, width = image.shape[-2:]
-
-        # in_channels should be 6: 3 for latents, 3 for low resolution image
-        latents_shape = (batch_size, self.unet.config.in_channels // 2, height, width)
-        latents_dtype = next(self.unet.parameters()).dtype
-
-        latents = randn_tensor(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
-
-        image = image.to(device=self.device, dtype=latents_dtype)
-
-        # set timesteps and move to the correct device
-        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
-        timesteps_tensor = self.scheduler.timesteps
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_kwargs = {}
-        if accepts_eta:
-            extra_kwargs["eta"] = eta
-
-        for t in self.progress_bar(timesteps_tensor):
-            # concat latents and low resolution image in the channel dimension.
-            latents_input = torch.cat([latents, image], dim=1)
-            latents_input = self.scheduler.scale_model_input(latents_input, t)
-            # predict the noise residual
-            noise_pred = self.unet(latents_input, t).sample
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample
-
-        # decode the image latents with the VQVAE
-        image = self.vqvae.decode(latents).sample
-        image = torch.clamp(image, -1.0, 1.0)
-        image = image / 2 + 0.5
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py

@@ -1,395 +0,0 @@
-# Copyright 2023 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 inspect
-import warnings
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-import torch.utils.checkpoint
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-
-from ...image_processor import VaeImageProcessor
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import logging, randn_tensor
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for image variation using Versatile Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Parameters:
-        vqvae ([`VQModel`]):
-            Vector-quantized (VQ) model to encode and decode images to and from latent representations.
-        bert ([`LDMBertModel`]):
-            Text-encoder model based on [`~transformers.BERT`].
-        tokenizer ([`~transformers.BertTokenizer`]):
-            A `BertTokenizer` to tokenize text.
-        unet ([`UNet2DConditionModel`]):
-            A `UNet2DConditionModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-    """
-    image_feature_extractor: CLIPImageProcessor
-    image_encoder: CLIPVisionModelWithProjection
-    image_unet: UNet2DConditionModel
-    vae: AutoencoderKL
-    scheduler: KarrasDiffusionSchedulers
-
-    def __init__(
-        self,
-        image_feature_extractor: CLIPImageProcessor,
-        image_encoder: CLIPVisionModelWithProjection,
-        image_unet: UNet2DConditionModel,
-        vae: AutoencoderKL,
-        scheduler: KarrasDiffusionSchedulers,
-    ):
-        super().__init__()
-        self.register_modules(
-            image_feature_extractor=image_feature_extractor,
-            image_encoder=image_encoder,
-            image_unet=image_unet,
-            vae=vae,
-            scheduler=scheduler,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
-    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-        """
-
-        def normalize_embeddings(encoder_output):
-            embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state)
-            embeds = self.image_encoder.visual_projection(embeds)
-            embeds_pooled = embeds[:, 0:1]
-            embeds = embeds / torch.norm(embeds_pooled, dim=-1, keepdim=True)
-            return embeds
-
-        if isinstance(prompt, torch.Tensor) and len(prompt.shape) == 4:
-            prompt = list(prompt)
-
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        # get prompt text embeddings
-        image_input = self.image_feature_extractor(images=prompt, return_tensors="pt")
-        pixel_values = image_input.pixel_values.to(device).to(self.image_encoder.dtype)
-        image_embeddings = self.image_encoder(pixel_values)
-        image_embeddings = normalize_embeddings(image_embeddings)
-
-        # duplicate image embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = image_embeddings.shape
-        image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
-        image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_images: List[str]
-            if negative_prompt is None:
-                uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, PIL.Image.Image):
-                uncond_images = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_images = negative_prompt
-
-            uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors="pt")
-            pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype)
-            negative_prompt_embeds = self.image_encoder(pixel_values)
-            negative_prompt_embeds = normalize_embeddings(negative_prompt_embeds)
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and conditional embeddings into a single batch
-            # to avoid doing two forward passes
-            image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
-
-        return image_embeddings
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        warnings.warn(
-            "The decode_latents method is deprecated and will be removed in a future version. Please"
-            " use VaeImageProcessor instead",
-            FutureWarning,
-        )
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents, return_dict=False)[0]
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs
-    def check_inputs(self, image, height, width, callback_steps):
-        if (
-            not isinstance(image, torch.Tensor)
-            and not isinstance(image, PIL.Image.Image)
-            and not isinstance(image, list)
-        ):
-            raise ValueError(
-                "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
-                f" {type(image)}"
-            )
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        **kwargs,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):
-                The image prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
-                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
-            generator (`torch.Generator`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-
-        Examples:
-
-        ```py
-        >>> from diffusers import VersatileDiffusionImageVariationPipeline
-        >>> import torch
-        >>> import requests
-        >>> from io import BytesIO
-        >>> from PIL import Image
-
-        >>> # let's download an initial image
-        >>> url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg"
-
-        >>> response = requests.get(url)
-        >>> image = Image.open(BytesIO(response.content)).convert("RGB")
-
-        >>> pipe = VersatileDiffusionImageVariationPipeline.from_pretrained(
-        ...     "shi-labs/versatile-diffusion", torch_dtype=torch.float16
-        ... )
-        >>> pipe = pipe.to("cuda")
-
-        >>> generator = torch.Generator(device="cuda").manual_seed(0)
-        >>> image = pipe(image, generator=generator).images[0]
-        >>> image.save("./car_variation.png")
-        ```
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images.
-        """
-        # 0. Default height and width to unet
-        height = height or self.image_unet.config.sample_size * self.vae_scale_factor
-        width = width or self.image_unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(image, height, width, callback_steps)
-
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(image, PIL.Image.Image) else len(image)
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        image_embeddings = self._encode_prompt(
-            image, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.image_unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            image_embeddings.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs.
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Denoising loop
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            noise_pred = self.image_unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-        else:
-            image = latents
-
-        image = self.image_processor.postprocess(image, output_type=output_type)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/stable_diffusion_safe/test_safe_diffusion.py

@@ -1,436 +0,0 @@
-# coding=utf-8
-# Copyright 2023 HuggingFace Inc.
-#
-# 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 gc
-import random
-import tempfile
-import unittest
-
-import numpy as np
-import torch
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
-
-from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
-from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
-from diffusers.utils import floats_tensor, nightly, torch_device
-from diffusers.utils.testing_utils import require_torch_gpu
-
-
-class SafeDiffusionPipelineFastTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    @property
-    def dummy_image(self):
-        batch_size = 1
-        num_channels = 3
-        sizes = (32, 32)
-
-        image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
-        return image
-
-    @property
-    def dummy_cond_unet(self):
-        torch.manual_seed(0)
-        model = UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
-            sample_size=32,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=32,
-        )
-        return model
-
-    @property
-    def dummy_vae(self):
-        torch.manual_seed(0)
-        model = AutoencoderKL(
-            block_out_channels=[32, 64],
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-        )
-        return model
-
-    @property
-    def dummy_text_encoder(self):
-        torch.manual_seed(0)
-        config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=32,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        return CLIPTextModel(config)
-
-    @property
-    def dummy_extractor(self):
-        def extract(*args, **kwargs):
-            class Out:
-                def __init__(self):
-                    self.pixel_values = torch.ones([0])
-
-                def to(self, device):
-                    self.pixel_values.to(device)
-                    return self
-
-            return Out()
-
-        return extract
-
-    def test_safe_diffusion_ddim(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False,
-        )
-
-        vae = self.dummy_vae
-        bert = self.dummy_text_encoder
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionPipeline(
-            unet=unet,
-            scheduler=scheduler,
-            vae=vae,
-            text_encoder=bert,
-            tokenizer=tokenizer,
-            safety_checker=None,
-            feature_extractor=self.dummy_extractor,
-        )
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
-        image = output.images
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        image_from_tuple = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=6.0,
-            num_inference_steps=2,
-            output_type="np",
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_stable_diffusion_pndm(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        unet = self.dummy_cond_unet
-        scheduler = PNDMScheduler(skip_prk_steps=True)
-        vae = self.dummy_vae
-        bert = self.dummy_text_encoder
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionPipeline(
-            unet=unet,
-            scheduler=scheduler,
-            vae=vae,
-            text_encoder=bert,
-            tokenizer=tokenizer,
-            safety_checker=None,
-            feature_extractor=self.dummy_extractor,
-        )
-        sd_pipe = sd_pipe.to(device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.Generator(device=device).manual_seed(0)
-        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
-
-        image = output.images
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        image_from_tuple = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=6.0,
-            num_inference_steps=2,
-            output_type="np",
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993])
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_stable_diffusion_no_safety_checker(self):
-        pipe = StableDiffusionPipeline.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
-        )
-        assert isinstance(pipe, StableDiffusionPipeline)
-        assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
-        assert pipe.safety_checker is None
-
-        image = pipe("example prompt", num_inference_steps=2).images[0]
-        assert image is not None
-
-        # check that there's no error when saving a pipeline with one of the models being None
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            pipe.save_pretrained(tmpdirname)
-            pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)
-
-        # sanity check that the pipeline still works
-        assert pipe.safety_checker is None
-        image = pipe("example prompt", num_inference_steps=2).images[0]
-        assert image is not None
-
-    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
-    def test_stable_diffusion_fp16(self):
-        """Test that stable diffusion works with fp16"""
-        unet = self.dummy_cond_unet
-        scheduler = PNDMScheduler(skip_prk_steps=True)
-        vae = self.dummy_vae
-        bert = self.dummy_text_encoder
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        # put models in fp16
-        unet = unet.half()
-        vae = vae.half()
-        bert = bert.half()
-
-        # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionPipeline(
-            unet=unet,
-            scheduler=scheduler,
-            vae=vae,
-            text_encoder=bert,
-            tokenizer=tokenizer,
-            safety_checker=None,
-            feature_extractor=self.dummy_extractor,
-        )
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        image = sd_pipe([prompt], num_inference_steps=2, output_type="np").images
-
-        assert image.shape == (1, 64, 64, 3)
-
-
-@nightly
-@require_torch_gpu
-class SafeDiffusionPipelineIntegrationTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_harm_safe_stable_diffusion(self):
-        sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
-        sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = (
-            "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
-            " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
-            " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
-            " children from bahnhof zoo, detailed "
-        )
-        seed = 4003660346
-        guidance_scale = 7
-
-        # without safety guidance (sld_guidance_scale = 0)
-        generator = torch.manual_seed(seed)
-        output = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=guidance_scale,
-            num_inference_steps=50,
-            output_type="np",
-            width=512,
-            height=512,
-            sld_guidance_scale=0,
-        )
-
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-        expected_slice = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
-
-        assert image.shape == (1, 512, 512, 3)
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-        # without safety guidance (strong configuration)
-        generator = torch.manual_seed(seed)
-        output = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=guidance_scale,
-            num_inference_steps=50,
-            output_type="np",
-            width=512,
-            height=512,
-            sld_guidance_scale=2000,
-            sld_warmup_steps=7,
-            sld_threshold=0.025,
-            sld_momentum_scale=0.5,
-            sld_mom_beta=0.7,
-        )
-
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-        expected_slice = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
-
-        assert image.shape == (1, 512, 512, 3)
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_nudity_safe_stable_diffusion(self):
-        sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
-        sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = "padme amidala taking a bath artwork, safe for work, no nudity"
-        seed = 2734971755
-        guidance_scale = 7
-
-        generator = torch.manual_seed(seed)
-        output = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=guidance_scale,
-            num_inference_steps=50,
-            output_type="np",
-            width=512,
-            height=512,
-            sld_guidance_scale=0,
-        )
-
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-        expected_slice = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
-
-        assert image.shape == (1, 512, 512, 3)
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-        generator = torch.manual_seed(seed)
-        output = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=guidance_scale,
-            num_inference_steps=50,
-            output_type="np",
-            width=512,
-            height=512,
-            sld_guidance_scale=2000,
-            sld_warmup_steps=7,
-            sld_threshold=0.025,
-            sld_momentum_scale=0.5,
-            sld_mom_beta=0.7,
-        )
-
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-        expected_slice = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
-
-        assert image.shape == (1, 512, 512, 3)
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_nudity_safetychecker_safe_stable_diffusion(self):
-        sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-        sd_pipe = sd_pipe.to(torch_device)
-        sd_pipe.set_progress_bar_config(disable=None)
-
-        prompt = (
-            "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
-            " leyendecker"
-        )
-        seed = 1044355234
-        guidance_scale = 12
-
-        generator = torch.manual_seed(seed)
-        output = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=guidance_scale,
-            num_inference_steps=50,
-            output_type="np",
-            width=512,
-            height=512,
-            sld_guidance_scale=0,
-        )
-
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-        expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
-
-        assert image.shape == (1, 512, 512, 3)
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-7
-
-        generator = torch.manual_seed(seed)
-        output = sd_pipe(
-            [prompt],
-            generator=generator,
-            guidance_scale=guidance_scale,
-            num_inference_steps=50,
-            output_type="np",
-            width=512,
-            height=512,
-            sld_guidance_scale=2000,
-            sld_warmup_steps=7,
-            sld_threshold=0.025,
-            sld_momentum_scale=0.5,
-            sld_mom_beta=0.7,
-        )
-
-        image = output.images
-        image_slice = image[0, -3:, -3:, -1]
-        expected_slice = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561])
-        assert image.shape == (1, 512, 512, 3)
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

spaces/Andy1621/uniformer_image_detection/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py

@@ -1,13 +0,0 @@
-_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
-model = dict(
-    bbox_head=dict(transform_method='minmax', use_grid_points=True),
-    # training and testing settings
-    train_cfg=dict(
-        init=dict(
-            assigner=dict(
-                _delete_=True,
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.4,
-                min_pos_iou=0,
-                ignore_iof_thr=-1))))

spaces/Andy1621/uniformer_image_segmentation/configs/emanet/README.md

@@ -1,26 +0,0 @@
-# Expectation-Maximization Attention Networks for Semantic Segmentation
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@inproceedings{li2019expectation,
-  title={Expectation-maximization attention networks for semantic segmentation},
-  author={Li, Xia and Zhong, Zhisheng and Wu, Jianlong and Yang, Yibo and Lin, Zhouchen and Liu, Hong},
-  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
-  pages={9167--9176},
-  year={2019}
-}
-```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                      | download                                                                                                                                                                                                                                                                                                                                                     |
-| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| EMANet | R-50-D8  | 512x1024  |   80000 |      5.4 | 4.58           | 77.59 | 79.44         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes-20200901_100301.log.json)     |
-| EMANet | R-101-D8 | 512x1024  |   80000 |      6.2 | 2.87           | 79.10 | 81.21         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes-20200901_100301.log.json) |
-| EMANet | R-50-D8  | 769x769   |   80000 |      8.9 | 1.97           | 79.33 | 80.49         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes-20200901_100301.log.json)         |
-| EMANet | R-101-D8 | 769x769   |   80000 |     10.1 | 1.22           | 79.62 | 81.00         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes-20200901_100301.log.json)     |

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tenacity/before.py

@@ -1,46 +0,0 @@
-# Copyright 2016 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# 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 typing
-
-from pip._vendor.tenacity import _utils
-
-if typing.TYPE_CHECKING:
-    import logging
-
-    from pip._vendor.tenacity import RetryCallState
-
-
-def before_nothing(retry_state: "RetryCallState") -> None:
-    """Before call strategy that does nothing."""
-
-
-def before_log(logger: "logging.Logger", log_level: int) -> typing.Callable[["RetryCallState"], None]:
-    """Before call strategy that logs to some logger the attempt."""
-
-    def log_it(retry_state: "RetryCallState") -> None:
-        if retry_state.fn is None:
-            # NOTE(sileht): can't really happen, but we must please mypy
-            fn_name = "<unknown>"
-        else:
-            fn_name = _utils.get_callback_name(retry_state.fn)
-        logger.log(
-            log_level,
-            f"Starting call to '{fn_name}', "
-            f"this is the {_utils.to_ordinal(retry_state.attempt_number)} time calling it.",
-        )
-
-    return log_it

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/install_egg_info.py

@@ -1,91 +0,0 @@
-"""
-distutils.command.install_egg_info
-
-Implements the Distutils 'install_egg_info' command, for installing
-a package's PKG-INFO metadata.
-"""
-
-import os
-import sys
-import re
-
-from distutils.cmd import Command
-from distutils import log, dir_util
-
-
-class install_egg_info(Command):
-    """Install an .egg-info file for the package"""
-
-    description = "Install package's PKG-INFO metadata as an .egg-info file"
-    user_options = [
-        ('install-dir=', 'd', "directory to install to"),
-    ]
-
-    def initialize_options(self):
-        self.install_dir = None
-
-    @property
-    def basename(self):
-        """
-        Allow basename to be overridden by child class.
-        Ref pypa/distutils#2.
-        """
-        return "%s-%s-py%d.%d.egg-info" % (
-            to_filename(safe_name(self.distribution.get_name())),
-            to_filename(safe_version(self.distribution.get_version())),
-            *sys.version_info[:2],
-        )
-
-    def finalize_options(self):
-        self.set_undefined_options('install_lib', ('install_dir', 'install_dir'))
-        self.target = os.path.join(self.install_dir, self.basename)
-        self.outputs = [self.target]
-
-    def run(self):
-        target = self.target
-        if os.path.isdir(target) and not os.path.islink(target):
-            dir_util.remove_tree(target, dry_run=self.dry_run)
-        elif os.path.exists(target):
-            self.execute(os.unlink, (self.target,), "Removing " + target)
-        elif not os.path.isdir(self.install_dir):
-            self.execute(
-                os.makedirs, (self.install_dir,), "Creating " + self.install_dir
-            )
-        log.info("Writing %s", target)
-        if not self.dry_run:
-            with open(target, 'w', encoding='UTF-8') as f:
-                self.distribution.metadata.write_pkg_file(f)
-
-    def get_outputs(self):
-        return self.outputs
-
-
-# The following routines are taken from setuptools' pkg_resources module and
-# can be replaced by importing them from pkg_resources once it is included
-# in the stdlib.
-
-
-def safe_name(name):
-    """Convert an arbitrary string to a standard distribution name
-
-    Any runs of non-alphanumeric/. characters are replaced with a single '-'.
-    """
-    return re.sub('[^A-Za-z0-9.]+', '-', name)
-
-
-def safe_version(version):
-    """Convert an arbitrary string to a standard version string
-
-    Spaces become dots, and all other non-alphanumeric characters become
-    dashes, with runs of multiple dashes condensed to a single dash.
-    """
-    version = version.replace(' ', '.')
-    return re.sub('[^A-Za-z0-9.]+', '-', version)
-
-
-def to_filename(name):
-    """Convert a project or version name to its filename-escaped form
-
-    Any '-' characters are currently replaced with '_'.
-    """
-    return name.replace('-', '_')

spaces/Audio-AGI/AudioSep/models/clap_encoder.py

@@ -1,117 +0,0 @@
-import random
-import torch
-import torch.nn as nn
-import torchaudio
-from models.CLAP.open_clip import create_model
-from models.CLAP.training.data import get_audio_features
-from transformers import RobertaTokenizer
-from utils import ignore_warnings; ignore_warnings()
-
-
-class CLAP_Encoder(nn.Module):
-    def __init__(
-        self,
-        pretrained_path='checkpoint/music_speech_audioset_epoch_15_esc_89.98.pt',
-        sampling_rate=32000,
-        amodel = "HTSAT-base",
-    ):
-        super().__init__()
-        self.device = "cpu"
-        self.precision = "fp32"
-        self.amodel = amodel  # or 'PANN-14'
-        self.tmodel = "roberta"  # the best text encoder in our training
-        self.enable_fusion = False  # False if you do not want to use the fusion model
-        self.fusion_type = "aff_2d"
-        self.pretrained = pretrained_path
-        self.sampling_rate = sampling_rate
-        self.tokenize = RobertaTokenizer.from_pretrained("roberta-base")
-        
-        self.model, self.model_cfg = create_model(
-            self.amodel,
-            self.tmodel,
-            self.pretrained,
-            precision=self.precision,
-            device=self.device,
-            enable_fusion=self.enable_fusion,
-            fusion_type=self.fusion_type,
-        )
-
-        for p in self.model.parameters():
-            p.requires_grad = False
-
-        self.model.eval()
-        self.encoder_type = 'CLAP'
-
-    def batch_to_list(self, batch):
-        ret = []
-        for i in range(batch.size(0)):
-            ret.append(batch[i])
-        return ret
-
-    def _get_audio_embed(self, batch):
-        # batch: [B, samples]
-        with torch.no_grad():
-            audio_dict_list = []
-            assert (
-                self.sampling_rate == 32000
-            ), "We only support 32000 sampling rate"
-                
-            # batch: [bs, 1, t-samples]
-            batch = torchaudio.functional.resample(
-                batch, orig_freq=self.sampling_rate, new_freq=48000
-            )
-            for waveform in self.batch_to_list(batch):
-                audio_dict = {}
-                audio_dict = get_audio_features(
-                    audio_dict,
-                    waveform,
-                    480000,
-                    data_truncating="fusion",
-                    data_filling="repeatpad",
-                    audio_cfg=self.model_cfg["audio_cfg"],
-                )
-                audio_dict_list.append(audio_dict)
-                # [bs, 512]
-                embed = self.model.get_audio_embedding(audio_dict_list)
-            
-                return embed.detach()
-
-    def _get_text_embed(self, batch):
-        double_batch = False
-        if len(batch) == 1:
-            batch = batch * 2
-            double_batch = True
-        with torch.no_grad():
-            # the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode
-            text_data = self.tokenizer(batch)
-            embed = self.model.get_text_embedding(text_data)
-        if double_batch:
-            embed = embed[0].unsqueeze(0)
-        
-        return embed.detach()
-
-
-    def get_query_embed(self, modality, audio=None, text=None, use_text_ratio=0.5, device=None):
-        if modality == 'audio':
-            embed = self._get_audio_embed(audio)
-        elif modality == 'text':
-            embed = self._get_text_embed(text)
-        elif modality == 'hybird':
-            if random.random() > use_text_ratio:
-                embed = self._get_audio_embed(audio)
-            else:
-                embed = self._get_text_embed(text)
-        else:
-            raise NotImplementedError("Please check flag 'training_modality'.")
-
-        return embed.float()
-
-    def tokenizer(self, text):
-        result = self.tokenize(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=512,
-            return_tensors="pt",
-        )
-        return {k: v.squeeze(0) for k, v in result.items()}

spaces/AutoLLM/AutoAgents/test.py

@@ -1,57 +0,0 @@
-import os
-import asyncio
-
-from pprint import pprint
-from ast import literal_eval
-from multiprocessing import Pool, TimeoutError
-
-from autoagents.agents.search import ActionRunner
-from langchain.callbacks import get_openai_callback
-from langchain.chat_models import ChatOpenAI
-
-
-async def work(user_input):
-    outputq = asyncio.Queue()
-    llm = ChatOpenAI(openai_api_key=os.getenv("OPENAI_API_KEY"),
-                     openai_organization=os.getenv("OPENAI_API_ORG"),
-                     temperature=0,
-                     model_name="gpt-3.5-turbo")
-    runner = ActionRunner(outputq, llm=llm)
-    task = asyncio.create_task(runner.run(user_input, outputq))
-
-    while True:
-        output = await outputq.get()
-        if isinstance(output, Exception):
-            print(output)
-            return
-        try:
-            pprint(literal_eval(output))
-        except:
-            print(output)
-        print("-----------------------------------------------------------")
-        if "Final Answer:" in output:
-            break
-    await task
-
-Q = [
-     "list 5 cities and their current populations where Paramore is playing this year.",
-     "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",
-     "How many watermelons can fit in a Tesla Model S?",
-     "Recommend me some laptops suitable for UI designers under $2000. Please include brand and price."
-     "Build me a vacation plan for Rome and Milan this summer for seven days. Include place to visit and hotels to stay. ",
-     "What is the sum of ages of the wives of Barack Obama and Donald Trump?",
-     "Who is the most recent NBA MVP? Which team does he play for? What is his season stats?",
-     "What were the scores for the last three games for the Los Angeles Lakers? Provide the dates and opposing teams.",
-     "Which team won in women's volleyball in the Summer Olympics that was held in London?",
-     "Provide a summary of the latest COVID-19 research paper published. Include the title, authors and abstract.",
-     "What is the top grossing movie in theatres this week? Provide the movie title, director, and a brief synopsis of the movie's plot. Attach a review for this movie.",
-     "Recommend a bagel shop near the Strip district in Pittsburgh that offer vegan food",
-     "Who are some top researchers in the field of machine learning systems nowadays?"
-     ]
-
-def main(q):
-    asyncio.run(work(q))
-
-if __name__ == "__main__":
-    with Pool(processes=10) as pool:
-        print(pool.map(main, Q))

spaces/Bambicita/rvc-models/infer_pack/models_onnx.py

@@ -1,849 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from infer_pack import modules
-from infer_pack import attentions
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from infer_pack.commons import init_weights
-import numpy as np
-from infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder256Sim(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        x = self.proj(x) * x_mask
-        return x, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class SynthesizerTrnMs256NSFsid_sim(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        # hop_length,
-        gin_channels=0,
-        use_sdp=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256Sim(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            is_half=kwargs["is_half"],
-        )
-
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
-    ):  # y是spec不需要了现在
-        g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        x = self.flow(x, x_mask, g=g, reverse=True)
-        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap

spaces/Benson/text-generation/Examples/Apk Hack Destruccin Total.md

@@ -1,99 +0,0 @@
-<br />
-<h1>Destrucción total Hack APK: Cómo descargar y jugar</h1>
-<p>¿Te encanta destruir cosas con armas y vehículos poderosos? ¿Quieres dar rienda suelta a tu experto en demolición interna y causar un caos total en un mundo de caja de arena? Si es así, entonces deberías probar Total Destruction, un divertido y adictivo juego que te permite destruir edificios, terrenos y enemigos usando ametralladoras, artillería, autocannon, cañones, bombas, cohetes y armas nucleares. Usted puede elegir entre helicópteros, aviones, tanques y varios otros tipos de vehículos de tierra! </p>
-<h2>apk hack destrucción total</h2><br /><p><b><b>Download File</b> &#9658; <a href="https://bltlly.com/2v6J7W">https://bltlly.com/2v6J7W</a></b></p><br /><br />
-¡Pero espera, hay más! También puede descargar e instalar Total Destruction Hack APK, una versión modificada del juego que le da dinero ilimitado, armas y vehículos desbloqueados, y el acceso a todos los niveles. En este artículo, le diremos todo lo que necesita saber sobre Total Destruction Hack APK, incluyendo lo que es, cómo descargarlo e instalarlo, por qué debe usarlo, cómo jugarlo, y algunas preguntas frecuentes. ¡Vamos a empezar! </p>
- <h2>¿Qué es la destrucción total? </h2>
-<p>Total Destruction es un juego de árcade desarrollado por GCenter, un estudio especializado en crear juegos con física y gráficos realistas. El juego fue lanzado en 2018 y desde entonces ha ganado más de 10 millones de descargas en Google Play Store. El juego está clasificado 4.1 de 5 estrellas por más de 100 mil usuarios. </p>
-<p>El juego se desarrolla en un mundo de caja de arena donde puedes destruir cualquier cosa que veas con varias armas y vehículos. También puedes personalizar tus armas y vehículos con diferentes pieles, colores y mejoras. El juego tiene dos modos: modo campaña y modo sandbox. En el modo campaña, tienes que completar misiones y objetivos en diferentes lugares del mundo. En el modo sandbox, puede explorar y destruir libremente el medio ambiente sin ninguna restricción. </p>
- <h3>Características de la destrucción total</h3>
-<p>Algunas de las características de Total Destruction son:</p>
-<ul>
-<li>Poderosas armas nucleares que pueden crear enormes explosiones y nubes de hongos</li>
-
-<li>Física realista y gráficos que simulan efectos de destrucción</li>
-<li>Múltiples ubicaciones con diferentes terrenos y edificios</li>
-<li>Diferentes tipos de vehículos como helicópteros, aviones, tanques, camiones, automóviles, motocicletas, barcos, etc.</li>
-<li>Diferentes tipos de armas como ametralladoras, artillería, autocannon, cañones, bombas, cohetes, misiles, granadas, etc.</li>
-<li>Controles fáciles e interfaz de usuario</li>
-<li>Juego sin conexión a Internet</li>
-</ul>
- <h3>Cómo descargar e instalar Total Destruction Hack APK</h3>
-<p>Si desea disfrutar de las características completas de Total Destruction sin gastar dinero o ver anuncios, puede descargar e instalar Total Destruction Hack APK. Esta es una versión modificada del juego que te da dinero ilimitado, armas y vehículos desbloqueados y acceso a todos los niveles. Aquí están los pasos para descargar e instalar Total Destruction Hack APK:</p>
-<p></p>
-<ol>
-<li>Ir a [Destrucción total MOD APK v2.9.3 (Dinero ilimitado) - Moddroid]( 1 ) o cualquier otro sitio web de confianza que proporciona el enlace para descargar Total Destruction Hack APK.</li>
-<li>Haga clic en el botón de descarga y espere a que el archivo se descargue en su dispositivo. </li>
-<li>Una vez descargado el archivo, vaya a la configuración del dispositivo y habilite la instalación desde fuentes desconocidas. </li>
-<li>Localice el archivo descargado en su administrador de archivos y toque en él para iniciar el proceso de instalación. </li>
-<li> Siga las instrucciones en la pantalla y espere a que se complete la instalación. </li>
-<li>Iniciar el juego desde el cajón de la aplicación o la pantalla de inicio y disfrutar! </li>
-</ol>
- <h2>¿Por qué utilizar Total Destruction Hack APK? </h2>
-<p>Es posible que se pregunte por qué debe utilizar Total Destruction Hack APK en lugar de la versión original del juego. Bueno, hay muchas razones por las que debe utilizar Total Destruction Hack APK, tales como:</p>
- <h3>Beneficios de la destrucción total Hack APK</h3>
-<p>Algunos de los beneficios de Total Destruction Hack APK son:</p>
-<ul>
-
-<li>Puedes desbloquear todas las armas y vehículos en el juego, incluyendo las armas nucleares y los vehículos más potentes. </li>
-<li>Puedes acceder a todos los niveles del juego, incluidos los secretos que están ocultos en la versión original. </li>
-<li>Puedes disfrutar del juego sin anuncios ni interrupciones. </li>
-<li>Puedes jugar el juego sin conexión a Internet. </li>
-</ul>
- <h3> Riesgos de destrucción total Hack APK</h3>
-<p>Sin embargo, también hay algunos riesgos de usar Total Destruction Hack APK, tales como:</p>
-<ul>
-<li>Es posible que tenga problemas de compatibilidad con su dispositivo o sistema operativo. </li>
-<li>Es posible que encuentre algunos errores o fallos en el juego que podrían afectar su experiencia de juego. </li>
-<li>Puedes perder tu progreso o datos si desinstalas el juego o lo actualizas a una versión más nueva. </li>
-<li>Puedes ser excluido del juego o enfrentarte a acciones legales si los desarrolladores detectan que estás usando una versión hackeada del juego. </li>
-<li>Puede exponer su dispositivo a malware o virus que podrían dañar su dispositivo o robar su información personal. </li>
-</ul>
- <h2>Cómo jugar Total Destruction Hack APK</h2>
-<p>Ahora que ha descargado e instalado Total Destruction Hack APK, es posible que se pregunte cómo jugarlo. Bueno, jugar Total Destruction Hack APK es muy fácil y divertido. Aquí hay algunos consejos y trucos para ayudarle a jugar Total Destruction Hack APK:</p>
- <h3> Consejos y trucos para la destrucción total Hack APK</h3>
-<p>Algunos de los consejos y trucos para Total Destruction Hack APK son:</p>
-<ul>
-<li>Experimenta con diferentes armas y vehículos para averiguar cuáles se adaptan a tu estilo y preferencia. </li>
-<li>Usa las armas nucleares con moderación, ya que pueden causar daños masivos a ti mismo y al medio ambiente. </li>
-<li>Usa el modo sandbox para practicar tus habilidades y probar tus armas y vehículos. </li>
-<li> Utilice la función de zoom para apuntar mejor y golpear sus objetivos con mayor precisión. </li>
-
-</ul>
- <h3>Las mejores armas y vehículos en Total Destruction Hack APK</h3>
-<p>Algunas de las mejores armas y vehículos en Total Destruction Hack APK son:</p>
- <borde de la tabla="1">
-<tr><th>Arma</th><th>Descripción</th></tr>
-<tr><td>Bomba nuclear</td><td>El arma más poderosa en el juego que puede crear una gran explosión y una nube de hongo. Puede destruir cualquier cosa dentro de un radio grande. </td></tr>
-<tr><td>Lanzador de cohetes</td><td>Un arma que puede disparar cohetes que pueden explotar en el impacto. Puede causar mucho daño a edificios y enemigos. </td></tr>
-<tr><td>Lanzador de granadas</td><td>Un arma que puede disparar granadas que pueden rebotar en superficies y explotar después de unos segundos. Se puede utilizar para golpear objetivos detrás de la cubierta o alrededor de las esquinas. </td></tr>
-<tr><td>Cannon</td><td>Un arma que puede disparar proyectiles grandes que pueden penetrar a través de paredes y objetos. Se puede utilizar para destruir estructuras gruesas y vehículos blindados. </td></tr>
-<tr><td>Ametralladora</td><td>Un arma que puede disparar balas a un ritmo rápido. Se puede utilizar para derribar enemigos y helicópteros. </td></tr>
-</tabla>
- <borde de la tabla="1">
-<tr><th>Vehículo</th><th>Descripción</th></tr>
-<tr><td>Tanque</td><td>El vehículo más duradero en el juego que puede soportar mucho daño. Tiene un cañón y una ametralladora como sus armas. Puede moverse rápido en cualquier terreno. </td></tr>
-<tr><td>Helicóptero</td><td>El vehículo más ágil del juego que puede volar en cualquier dirección. Tiene una ametralladora y un lanzacohetes como armas. Puede esquivar el fuego enemigo y alcanzar lugares altos. </td></tr>
-<tr><td>Plano</td><td>El vehículo más rápido en el juego que puede volar a alta velocidad. Tiene una ametralladora y una bomba como sus armas. Puede lanzar bombas sobre objetivos desde arriba. </td></tr>
-<tr><td>Camión</td><td>Un vehículo grande que puede llevar mucha carga. Tiene una ametralladora como su arma. Puede embestir a enemigos y edificios con su peso. </td></tr>
-
-</tabla>
- <h2>Conclusión</h2>
- <h <h3>Resumen del artículo</h3>
-<p>En este artículo, hemos discutido Total Destruction Hack APK, una versión modificada del juego Total Destruction que le da dinero ilimitado, armas y vehículos desbloqueados, y el acceso a todos los niveles. Hemos explicado lo que es Total Destruction, cómo descargar e instalar Total Destruction Hack APK, ¿por qué debe utilizar Total Destruction Hack APK, cómo jugar Total Destruction Hack APK, y algunas preguntas frecuentes. Esperamos que hayas disfrutado leyendo este artículo y hayas aprendido algo nuevo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí hay algunas preguntas frecuentes sobre Total Destruction Hack APK:</p>
-<ol>
-<li> Es la destrucción total Hack APK seguro de usar? </li>
-<p>Sí, Total Destruction Hack APK es seguro de usar, siempre y cuando se descarga desde un sitio web de confianza y escanear con un antivirus antes de instalarlo. Sin embargo, siempre debes tener cuidado al usar cualquier versión hackeada o modificada de un juego, ya que podría haber algunos riesgos involucrados. </p>
-<li> ¿Es Total Destruction Hack APK compatible con mi dispositivo? </li>
-<p>Total Destruction Hack APK es compatible con la mayoría de los dispositivos Android que tienen Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no soportar el juego o el hack debido a diferentes especificaciones o configuraciones. Puedes comprobar la compatibilidad de tu dispositivo visitando la p��gina de Google Play Store del juego original. </p>
-<li> ¿Cómo puedo actualizar Total Destruction Hack APK? </li>
-<p>Puede actualizar Total Destruction Hack APK visitando el sitio web donde lo descargó y comprobar si hay nuevas versiones disponibles. Sin embargo, debe tener en cuenta que la actualización del hack puede causar que pierda su progreso o los datos en el juego. Por lo tanto, debe hacer una copia de seguridad de sus datos antes de actualizar el hack. </p>
-<li> ¿Cómo puedo desinstalar Total Destruction Hack APK? </li>
-
-<li> ¿Dónde puedo encontrar más información sobre Total Destruction Hack APK? </li>
-<p>Usted puede encontrar más información acerca de Total Destruction Hack APK visitando el sitio web donde lo descargó o buscando en línea para comentarios, vídeos, o foros relacionados con el juego o el hack. También puede ponerse en contacto con los desarrolladores del juego o el hack si tiene alguna pregunta o problema. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Demon Hunter Premium Apk Mod.md

@@ -1,61 +0,0 @@
-
-<h1>Cazador de demonios Premium APK Mod: Una guía para los jugadores</h1>
-<p>Si usted está buscando un juego emocionante y desafiante que pondrá a prueba sus habilidades y reflejos, entonces usted debe probar Demon Hunter Premium APK Mod. Esta es una versión modificada del juego original de Demon Hunter que te da acceso a recursos ilimitados, funciones desbloqueadas y más. En este artículo, le diremos todo lo que necesita saber sobre Demon Hunter Premium APK Mod, incluyendo lo que es, cómo descargarlo e instalarlo, por qué debe jugar, y algunos consejos y trucos para ayudarle a tener éxito. </p>
- <h2>¿Qué es Demon Hunter Premium? </h2>
-<p>Demon Hunter Premium es un juego de acción y aventura en 3D que te pone en el papel de un cazador de demonios que tiene que luchar contra hordas de criaturas malvadas. Puedes elegir entre diferentes armas, habilidades y objetos para personalizar tu personaje y mejorar tus habilidades de combate. También puedes explorar varios lugares, como bosques, mazmorras, castillos y más, y enfrentarte a diferentes enemigos y jefes. El juego tiene gráficos impresionantes, efectos de sonido realistas y un juego suave que te mantendrá enganchado durante horas. </p>
-<h2>demon hunter premium apk mod</h2><br /><p><b><b>Download Zip</b> &#10084;&#10084;&#10084; <a href="https://bltlly.com/2v6Jzs">https://bltlly.com/2v6Jzs</a></b></p><br /><br />
- <h3>Características de Demon Hunter Premium</h3>
-<p>Algunas de las características que hacen que Demon Hunter Premium se destaque de otros juegos son:</p>
-<ul>
-<li>Versión Premium: Puedes disfrutar del juego completo sin anuncios ni compras en la aplicación. También obtienes 1000 gemas y 3000 de oro como bonus al iniciar el juego. </li>
-<li>Recursos ilimitados: Puedes usar el archivo APK modificado para obtener gemas y oro ilimitados que puedes usar para comprar armas, habilidades, artículos y más. También puedes actualizar tu personaje y equipo sin limitaciones. </li>
-<li>Características desbloqueadas: Puedes acceder a todas las características que normalmente están bloqueadas en el juego original, como el modo duro, el modo arena, el modo boss rush y más. También puedes desbloquear todas las armas, habilidades, objetos y disfraces disponibles en el juego. </li>
-
-</ul>
- <h3>Cómo descargar e instalar Demon Hunter Premium APK Mod</h3>
-<p>Para descargar e instalar Demon Hunter Premium APK Mod en tu dispositivo Android, debes seguir estos pasos:</p>
-<ol>
-<li>Descargar el archivo APK modded desde este enlace: [Demon Hunter Premium APK Mod]( 1 ). </li>
-<li>Habilite la instalación de aplicaciones de fuentes desconocidas en su dispositivo. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </li>
-<li>Busque el archivo APK descargado en su dispositivo y toque en él para iniciar el proceso de instalación. </li>
-<li>Siga las instrucciones en la pantalla y espere a que termine la instalación. </li>
-<li>Iniciar el juego y disfrutar de jugar Demon Hunter Premium APK Mod.</li>
-</ol>
- <h2>¿Por qué deberías jugar Demon Hunter Premium APK Mod? </h2>
-<p>Demonio Hunter Premium APK Mod no es solo otro juego de hack-and-slash. Es un juego que le ofrece un montón de diversión, desafío y satisfacción. Estas son algunas de las razones por las que debe jugar Demon Hunter Premium APK Mod:</p>
- <h3>Beneficios de jugar Demon Hunter Premium APK Mod</h3>
-<p>Algunos de los beneficios que se pueden obtener de jugar Demon Hunter Premium APK Mod son:</p>
-<ul>
-<li>Puedes ahorrar dinero: No tienes que gastar dinero en anuncios o compras en la aplicación. Puede obtener todo lo que necesita de forma gratuita con el archivo APK modded. </li>
-<li>Puedes ahorrar tiempo: No tienes que perder tiempo en moler o cultivar recursos. Puede obtener gemas ilimitadas y oro con el archivo APK modded. </li>
-<li>Puedes divertirte más: No tienes que preocuparte por quedarte sin recursos o quedarte atascado en un nivel. Usted puede disfrutar de jugar el juego a su propio ritmo y estilo con el archivo APK modded. </li>
-<li>Puedes mejorar tus habilidades: Puedes desafiarte con los diferentes modos y dificultades que se desbloquean con el archivo APK modded. También puedes aprender de los mejores jugadores viendo sus repeticiones y estrategias. </li>
-</ul>
- <h3> Consejos y trucos para jugar Demon Hunter Premium APK Mod</h3>
-
-<ul>
-<li>Elige tu arma sabiamente: Hay diferentes tipos de armas en el juego, como espadas, hachas, martillos, dagas y más. Cada arma tiene sus propias ventajas y desventajas, como velocidad, daño, rango y efectos especiales. Usted debe elegir un arma que se adapte a su estilo de juego y preferencia. </li>
-<li>Actualizar sus habilidades y artículos: Puede utilizar las gemas y el oro que se obtiene del archivo APK modded para actualizar sus habilidades y elementos. Mejorar tus habilidades puede aumentar su poder, duración, tiempo de reutilización y efectos. Actualizar tus artículos puede aumentar sus estadísticas, como ataque, defensa, salud y maná. </li>
-<li>Usa combos y dodges: Puedes realizar combos tocando el botón de ataque repetidamente o usando diferentes habilidades en sucesión. Los combos pueden causar más daño y aturdir a tus enemigos. También puedes esquivar los ataques enemigos deslizando la pantalla en cualquier dirección. Esquivar puede ayudarle a evitar daños y crear aperturas para contraataques. </li>
-<li>Recoger y usar pociones: Puedes encontrar pociones en cofres, barriles, o caer por los enemigos. Las pociones pueden restaurar tu salud, maná o resistencia, o darte beneficios temporales, como mayor velocidad, daño o defensa. Debes recolectar y usar pociones siempre que las necesites. </li>
-<li>Explora el mapa: Puedes encontrar secretos ocultos, tesoros y atajos explorando el mapa. También puedes descubrir nuevas áreas, enemigos y jefes saliéndote del camino trillado. Deberías explorar el mapa tanto como puedas para obtener más recompensas y diversión. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <h3>Preguntas frecuentes</h3>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Demon Hunter Premium APK Mod:</p>
-<p></p>
-<ol>
-<li><b>¿Es seguro usar Demon Hunter Premium APK Mod? </b><br>
-Sí, Demonio Hunter Premium APK Mod es seguro de usar. No contiene ningún virus o malware que puede dañar su dispositivo o datos. También es compatible con la mayoría de los dispositivos y versiones de Android. </li>
-<li><b>¿Es Demon Hunter Premium APK Mod legal de usar? </b><br>
-Sí, Demonio Hunter Premium APK Mod es legal de usar. No es una versión pirata o agrietada del juego original. Es una versión modificada que no viola los derechos de autor o marcas comerciales del juego original. </li>
-<li><b> ¿Cómo actualizo Demon Hunter Premium APK Mod? </b><br>
-Para actualizar Demon Hunter Premium APK Mod, es necesario descargar la última versión del archivo APK modded desde este enlace: [Demonio Hunter Premium APK Mod]. A continuación, debe desinstalar la versión anterior del juego desde su dispositivo e instalar la nueva versión siguiendo los mismos pasos que antes. </li>
-<li><b> ¿Cómo puedo desinstalar Demon Hunter Premium APK Mod? </b><br>
-Para desinstalar Demon Hunter Premium APK Mod, es necesario ir a Configuración > Aplicaciones > Demonio Hunter Premium > Desinstalar y confirmar su acción. También puede eliminar el archivo APK modificado de su dispositivo si lo desea. </li>
-<li><b>¿Dónde puedo obtener más información sobre Demon Hunter Premium APK Mod? </b><br>
-Puede obtener más información sobre Demon Hunter Premium APK Mod de este enlace: [Demonio Hunter Premium APK Mod]. También puedes visitar el sitio web oficial del juego original: [Demon Hunter]. </li>
-</ol></p> 64aa2da5cf<br />
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-<br />

spaces/Benson/text-generation/Examples/Descargar Gom Player.exe.md

@@ -1,122 +0,0 @@
-
-<h1>Cómo descargar vídeos de GoPro a tu PC</h1>
-<p>Las cámaras GoPro son dispositivos increíbles que te permiten capturar videos impresionantes de tus aventuras, pasatiempos y recuerdos. ¿Pero qué haces con esos videos después de filmarlos? ¿Cómo se transfieren desde la cámara al ordenador, donde se pueden almacenar, editar y compartir? </p>
-<p>En este artículo, te mostraremos cómo descargar videos GoPro a tu PC en unos pocos pasos fáciles. También explicaremos por qué es posible que desee hacer eso, qué desafíos puede enfrentar y qué software puede usar para editar sus videos GoPro en su PC. ¡Vamos a empezar! </p>
-<h2>descargar gom player.exe</h2><br /><p><b><b>Download File</b> &rarr;&rarr;&rarr; <a href="https://bltlly.com/2v6J6c">https://bltlly.com/2v6J6c</a></b></p><br /><br />
- <h2>¿Por qué descargar vídeos GoPro a su PC? </h2>
-<p>Hay muchas razones por las que es posible que desee descargar sus vídeos GoPro a su PC. Estos son algunos de los más comunes:</p>
-<h3>Beneficios de descargar vídeos GoPro a tu PC</h3>
-<ul>
-<li>Puede liberar espacio en la tarjeta SD de su cámara, que puede llenarse rápidamente si graba muchos videos de alta resolución o de 360 grados. </li>
-<li> Puede hacer copias de seguridad de sus vídeos en el disco duro de su computadora o en una unidad externa, lo que puede protegerlos de perderlos o dañarlos. </li>
-<li> Puede ver sus videos en una pantalla más grande, lo que puede ayudarlo a apreciar los detalles y la calidad de su metraje. </li>
-<li>Puede editar sus videos en su computadora usando varias herramientas de software, que pueden ayudarlo a mejorar, recortar, recortar, estabilizar, agregar efectos y más. </li>
-<li>Puede compartir sus videos con otros a través de correo electrónico, redes sociales, YouTube u otras plataformas, lo que puede ayudarlo a mostrar su creatividad e inspirar a otros. </li>
-</ul>
-<h3>Desafíos de descargar vídeos GoPro a tu PC</h3>
-<ul>
-<li>Es posible que necesite un cable USB compatible o un lector de tarjetas microSD para conectar la cámara al ordenador. </li>
-<li>Es posible que necesite instalar controladores o actualizaciones de software para su cámara o computadora para reconocerse. </li>
-
-<li>Es posible que necesite una computadora potente o una tarjeta gráfica dedicada para editar sus videos sin problemas, especialmente si están en formato de 360 grados. </li>
-</ul>
- <h2>¿Cómo conectar GoPro a su PC? </h2>
-<p>El primer paso para descargar tus vídeos GoPro a tu PC es conectar tu cámara al ordenador. Hay dos formas de hacerlo:</p>
-<h3>Método 1: Usando un cable USB</h3>
-<p>Esta es la forma más sencilla y cómoda de conectar tu GoPro a tu PC. Todo lo que necesitas es el cable USB que viene con tu cámara. Así es como:</p>
-<ol>
-<li>Apague su GoPro y conecte el extremo pequeño del cable USB en el puerto dentro del compartimiento de la batería. </li>
-<li>Conecte el otro extremo del cable USB en un puerto USB en su computadora. </li>
-<li>Encienda su GoPro y espere a que sea reconocido por su computadora. Una ventana emergente puede aparecer preguntándole qué quiere hacer con el dispositivo. Puede optar por importar fotos y vídeos con la aplicación Fotos (Windows) o Captura de imágenes (Mac), abrir la carpeta del dispositivo con Explorador de archivos (Windows) o Finder (Mac), o no tomar ninguna acción. </ <h3>Método 2: Uso de un lector de tarjetas microSD</h3>
-<p>Esta es otra forma de conectar tu GoPro a tu PC, especialmente si no tienes un cable USB o tu cámara no es reconocida por tu computadora. Todo lo que necesitas es un lector de tarjetas microSD que se ajuste a la tarjeta de memoria de tu cámara. Así es como:</p>
-<ol>
-<li>Apague su GoPro y retire la tarjeta microSD de la ranura dentro del compartimiento de la batería. </li>
-<li>Inserte la tarjeta microSD en el lector de tarjetas y conecte el lector de tarjetas en un puerto USB en su computadora. </li>
-<li>Espere a que su computadora detecte el lector de tarjetas como una unidad extraíble. Una ventana emergente puede aparecer preguntándole qué quiere hacer con el dispositivo. Puede optar por importar fotos y vídeos con la aplicación Fotos (Windows) o Captura de imágenes (Mac), abrir la carpeta del dispositivo con Explorador de archivos (Windows) o Finder (Mac), o no tomar ninguna acción. </li>
-</ol>
- <h2>¿Cómo transferir vídeos GoPro a su PC? </h2>
-
-<h3>¿Cómo transferir vídeos GoPro en Windows? </h3>
-<p>Si está usando un PC con Windows, puede usar la aplicación integrada Fotos para importar sus vídeos GoPro. Así es como:</p>
-<ol>
-<li>Abra la aplicación Fotos en su computadora y haga clic en el botón Importar en la esquina superior derecha. </li>
-<li>Seleccione Desde un dispositivo USB desde el menú desplegable y elija su tarjeta GoPro o microSD de la lista de dispositivos. </li>
-<li>Seleccione los vídeos que desea importar y haga clic en Continuar. También puede elegir dónde guardarlos y cómo organizarlos por fecha. </li>
-<li>Espera a que termine el proceso de importación y luego haz clic en Listo. Ahora puedes ver, editar y compartir tus videos GoPro en tu PC.</li>
-</ol>
-<h3> ¿Cómo transferir vídeos GoPro en Mac? </h3>
-<p>Si estás usando un Mac, puedes usar la aplicación integrada Image Capture para importar tus vídeos GoPro. Así es como:</p>
-<ol>
-<li>Abra la aplicación Image Capture en su computadora y seleccione su tarjeta GoPro o microSD de la lista de dispositivos en la barra lateral izquierda. </li>
-<li>Seleccione los vídeos que desea importar y haga clic en Importar o Importar todo en la esquina inferior derecha. También puede elegir dónde guardarlos y cómo eliminarlos después de importarlos. </li>
-<li>Espere a que termine el proceso de importación y luego cierre la aplicación Captura de imágenes. Ahora puede ver, editar y compartir sus videos GoPro en su Mac.</li>
-</ol> <h3>Cómo transferir videos en GoPro Quik para escritorio? </h3>
-<p>Si quieres usar el software oficial de GoPro para importar, editar y compartir tus videos GoPro, puedes descargar e instalar GoPro Quik para escritorio en tu PC. Esta es una aplicación gratuita que funciona tanto con ordenadores Windows y Mac. Así es como:</p>
-<ol>
-<li>Descargar GoPro Quik para escritorio desde el <a href="">GoPro web</a> y siga las instrucciones para instalarlo en su ordenador. </li>
-<li>Inicie la aplicación e inicie sesión con su cuenta GoPro o cree una si no tiene una. </li>
-<li>Conecte su GoPro a su PC usando un cable USB o un lector de tarjetas microSD. </li>
-
-<li>Una vez realizada la importación, puede ver, editar y compartir sus vídeos GoPro en la aplicación. También puede acceder a ellos desde la pestaña Medios en la barra lateral izquierda. </li>
-</ol>
- <h2>¿Cómo editar vídeos GoPro en tu PC? </h2>
-<p>Después de haber transferido sus vídeos GoPro a su PC, es posible que desee editarlos para que se vean mejor, más corto, o más interesante. Hay muchas herramientas de software que puedes usar para editar tus videos GoPro en tu PC, dependiendo de tu nivel de habilidad, presupuesto y preferencia. Estos son algunos de los mejores:</p>
-<p></p>
-<h3>El mejor software de edición de vídeo para GoPro</h3>
-<tabla>
-<tr>
-<th>Nombre</th>
-<th>Características</th>
-<th>Precio</th>
-</tr>
-<tr>
-<td>GoPro Quik para escritorio</td>
-<td>- Software oficial de GoPro<br>- Fácil de usar<br>- Creación automática de video<br>- Herramientas básicas de edición<br>- Música y pegatinas<br>- Almacenamiento en la nube y copia de seguridad</td>
-<td>Gratis</td>
-</tr>
-<tr>
-<td>Adobe Premiere Pro</td>
-<td>- Software profesional de edición de video<br>- Herramientas avanzadas de edición<br>- Corrección y clasificación de color<br>- Mezcla y edición de audio<br>- Gráficos y efectos de movimiento<br>- Integración con otras aplicaciones de Adobe</td>
-<td>$20.99/mes o $239.88/año</td>
-</tr>
-<tr>
-<td>Resolución de Davinci</td>
-<td>- Software profesional de edición de video<br>- Herramientas avanzadas de edición<br>- Corrección y clasificación de color<br>- Mezcla y edición de audio<br>- Efectos visuales y gráficos de movimiento<br>- Versión gratuita disponible</td>
-<td>$299 (compra única) o gratis</td>
-</tr>
-<tr>
-<td>Filmora X</td>
-<td>- Software de edición de video fácil de usar<br>- Herramientas de edición básicas e intermedias<br>- Filtros, transiciones y títulos<br>- Música y efectos de sonido<br>- Grabación de pantalla y captura de webcam<br>- Prueba gratuita disponible</td>
-<td>$69.99 (compra única) o prueba gratuita</td>
-</tr>
-<tr>
-<td>Editor de vídeo gratuito de VSDC</td>
-
-<td>Gratis</td>
-</tr> <h3>Consejos y trucos para editar vídeos de GoPro</h3>
-<p>Editar videos GoPro puede ser divertido y gratificante, pero también puede ser desafiante y consumir mucho tiempo. Aquí hay algunos consejos y trucos para ayudarte a editar tus vídeos GoPro como un pro:</p>
-<ul>
-<li>Recorta tus videos para eliminar partes no deseadas y mantener solo los mejores momentos. </li>
-<li>Recorte sus vídeos para ajustar el tamaño del marco y la relación de aspecto para adaptarse a la salida deseada. </li>
-<li>Estabilice sus vídeos para reducir las imágenes inestables o borrosas y hacerlos más suaves y claros. </li>
-<li> Ajuste el color, brillo, contraste, saturación y balance de blancos de sus vídeos para mejorar su apariencia y estado de ánimo. </li>
-<li>Añade transiciones, filtros, títulos, pegatinas, música y efectos de sonido a tus vídeos para hacerlos más atractivos y creativos. </li>
-<li>Exporta tus vídeos con la mayor calidad posible y elige el formato y la resolución adecuados para tu propósito. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Descargar vídeos GoPro a tu PC es una gran manera de almacenar, editar y compartir tus increíbles imágenes. Puede conectar su GoPro a su PC con un cable USB o un lector de tarjetas microSD, y luego transferir sus videos utilizando la aplicación Fotos (Windows), Captura de imágenes (Mac), o GoPro Quik para escritorio. También puede editar sus vídeos utilizando diversas herramientas de software, como Adobe Premiere Pro, Davinci Resolve, Filmora X o VSDC Free Video Editor. Esperamos que este artículo te haya ayudado a aprender a descargar vídeos GoPro a tu PC de forma fácil y rápida. ¡Ahora sigue adelante y disfruta de tus vídeos GoPro en tu PC! </p>
- <h3>Llamada a la acción</h3>
-<p>Si te gustó este artículo, por favor compártelo con tus amigos y familiares que podrían encontrarlo útil. Además, no te olvides de suscribirte a nuestro boletín para obtener más consejos y trucos sobre cómo usar tu cámara GoPro. ¡Gracias por leer! </p>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Cómo puedo descargar vídeos GoPro a mi PC sin Quik? </h3>
-
-<h3>¿Cómo puedo descargar vídeos GoPro a mi PC más rápido? </h3>
-<p>Puede descargar vídeos GoPro a su PC más rápido utilizando un cable USB de alta velocidad o un lector de tarjetas microSD que admite USB 3.0 o superior. También puede utilizar una tarjeta microSD rápida que tiene una alta velocidad de escritura y capacidad. Además, puede reducir el tamaño de sus vídeos reduciendo la resolución o la velocidad de fotogramas en la configuración de la cámara. </p>
-<h3>¿Cómo puedo descargar videos GoPro a mi PC de forma inalámbrica? </h3>
-<p>Puede descargar vídeos GoPro a su PC de forma inalámbrica mediante la aplicación GoPro en su teléfono inteligente o tableta. Puede conectar su cámara a su dispositivo móvil a través de Wi-Fi o Bluetooth, y luego transferir sus videos desde la aplicación a la nube o directamente a su PC. Sin embargo, este método puede ser más lento y menos confiable que usar un cable o un lector de tarjetas. </p>
-<h3>¿Cómo puedo reproducir vídeos GoPro en mi PC? </h3>
-<p>Puede reproducir vídeos GoPro en su PC utilizando cualquier reproductor multimedia que soporte formatos MP4 o HEVC, como VLC Media Player, Windows Media Player, QuickTime Player o GoPro Quik para escritorio. También puede utilizar un navegador web compatible con la reproducción de vídeo HTML5, como Chrome, Firefox, Safari o Edge.</p>
-<h3>¿Cómo puedo convertir vídeos GoPro en mi PC? </h3>
-<p>Puede convertir vídeos GoPro en su PC mediante el uso de cualquier software de conversión de vídeo que admite formatos MP4 o HEVC, como HandBrake, Freemake Video Converter, Any Video Converter o GoPro Quik para escritorio. También puede utilizar un servicio de conversión de vídeo en línea, como Online-Convert.com, CloudConvert.com o Zamzar.com.</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/tree.py

@@ -1,251 +0,0 @@
-from typing import Iterator, List, Optional, Tuple
-
-from ._loop import loop_first, loop_last
-from .console import Console, ConsoleOptions, RenderableType, RenderResult
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment
-from .style import Style, StyleStack, StyleType
-from .styled import Styled
-
-
-class Tree(JupyterMixin):
-    """A renderable for a tree structure.
-
-    Args:
-        label (RenderableType): The renderable or str for the tree label.
-        style (StyleType, optional): Style of this tree. Defaults to "tree".
-        guide_style (StyleType, optional): Style of the guide lines. Defaults to "tree.line".
-        expanded (bool, optional): Also display children. Defaults to True.
-        highlight (bool, optional): Highlight renderable (if str). Defaults to False.
-    """
-
-    def __init__(
-        self,
-        label: RenderableType,
-        *,
-        style: StyleType = "tree",
-        guide_style: StyleType = "tree.line",
-        expanded: bool = True,
-        highlight: bool = False,
-        hide_root: bool = False,
-    ) -> None:
-        self.label = label
-        self.style = style
-        self.guide_style = guide_style
-        self.children: List[Tree] = []
-        self.expanded = expanded
-        self.highlight = highlight
-        self.hide_root = hide_root
-
-    def add(
-        self,
-        label: RenderableType,
-        *,
-        style: Optional[StyleType] = None,
-        guide_style: Optional[StyleType] = None,
-        expanded: bool = True,
-        highlight: Optional[bool] = False,
-    ) -> "Tree":
-        """Add a child tree.
-
-        Args:
-            label (RenderableType): The renderable or str for the tree label.
-            style (StyleType, optional): Style of this tree. Defaults to "tree".
-            guide_style (StyleType, optional): Style of the guide lines. Defaults to "tree.line".
-            expanded (bool, optional): Also display children. Defaults to True.
-            highlight (Optional[bool], optional): Highlight renderable (if str). Defaults to False.
-
-        Returns:
-            Tree: A new child Tree, which may be further modified.
-        """
-        node = Tree(
-            label,
-            style=self.style if style is None else style,
-            guide_style=self.guide_style if guide_style is None else guide_style,
-            expanded=expanded,
-            highlight=self.highlight if highlight is None else highlight,
-        )
-        self.children.append(node)
-        return node
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-
-        stack: List[Iterator[Tuple[bool, Tree]]] = []
-        pop = stack.pop
-        push = stack.append
-        new_line = Segment.line()
-
-        get_style = console.get_style
-        null_style = Style.null()
-        guide_style = get_style(self.guide_style, default="") or null_style
-        SPACE, CONTINUE, FORK, END = range(4)
-
-        ASCII_GUIDES = ("    ", "|   ", "+-- ", "`-- ")
-        TREE_GUIDES = [
-            ("    ", "│   ", "├── ", "└── "),
-            ("    ", "┃   ", "┣━━ ", "┗━━ "),
-            ("    ", "║   ", "╠══ ", "╚══ "),
-        ]
-        _Segment = Segment
-
-        def make_guide(index: int, style: Style) -> Segment:
-            """Make a Segment for a level of the guide lines."""
-            if options.ascii_only:
-                line = ASCII_GUIDES[index]
-            else:
-                guide = 1 if style.bold else (2 if style.underline2 else 0)
-                line = TREE_GUIDES[0 if options.legacy_windows else guide][index]
-            return _Segment(line, style)
-
-        levels: List[Segment] = [make_guide(CONTINUE, guide_style)]
-        push(iter(loop_last([self])))
-
-        guide_style_stack = StyleStack(get_style(self.guide_style))
-        style_stack = StyleStack(get_style(self.style))
-        remove_guide_styles = Style(bold=False, underline2=False)
-
-        depth = 0
-
-        while stack:
-            stack_node = pop()
-            try:
-                last, node = next(stack_node)
-            except StopIteration:
-                levels.pop()
-                if levels:
-                    guide_style = levels[-1].style or null_style
-                    levels[-1] = make_guide(FORK, guide_style)
-                    guide_style_stack.pop()
-                    style_stack.pop()
-                continue
-            push(stack_node)
-            if last:
-                levels[-1] = make_guide(END, levels[-1].style or null_style)
-
-            guide_style = guide_style_stack.current + get_style(node.guide_style)
-            style = style_stack.current + get_style(node.style)
-            prefix = levels[(2 if self.hide_root else 1) :]
-            renderable_lines = console.render_lines(
-                Styled(node.label, style),
-                options.update(
-                    width=options.max_width
-                    - sum(level.cell_length for level in prefix),
-                    highlight=self.highlight,
-                    height=None,
-                ),
-                pad=options.justify is not None,
-            )
-
-            if not (depth == 0 and self.hide_root):
-                for first, line in loop_first(renderable_lines):
-                    if prefix:
-                        yield from _Segment.apply_style(
-                            prefix,
-                            style.background_style,
-                            post_style=remove_guide_styles,
-                        )
-                    yield from line
-                    yield new_line
-                    if first and prefix:
-                        prefix[-1] = make_guide(
-                            SPACE if last else CONTINUE, prefix[-1].style or null_style
-                        )
-
-            if node.expanded and node.children:
-                levels[-1] = make_guide(
-                    SPACE if last else CONTINUE, levels[-1].style or null_style
-                )
-                levels.append(
-                    make_guide(END if len(node.children) == 1 else FORK, guide_style)
-                )
-                style_stack.push(get_style(node.style))
-                guide_style_stack.push(get_style(node.guide_style))
-                push(iter(loop_last(node.children)))
-                depth += 1
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        stack: List[Iterator[Tree]] = [iter([self])]
-        pop = stack.pop
-        push = stack.append
-        minimum = 0
-        maximum = 0
-        measure = Measurement.get
-        level = 0
-        while stack:
-            iter_tree = pop()
-            try:
-                tree = next(iter_tree)
-            except StopIteration:
-                level -= 1
-                continue
-            push(iter_tree)
-            min_measure, max_measure = measure(console, options, tree.label)
-            indent = level * 4
-            minimum = max(min_measure + indent, minimum)
-            maximum = max(max_measure + indent, maximum)
-            if tree.expanded and tree.children:
-                push(iter(tree.children))
-                level += 1
-        return Measurement(minimum, maximum)
-
-
-if __name__ == "__main__":  # pragma: no cover
-
-    from pip._vendor.rich.console import Group
-    from pip._vendor.rich.markdown import Markdown
-    from pip._vendor.rich.panel import Panel
-    from pip._vendor.rich.syntax import Syntax
-    from pip._vendor.rich.table import Table
-
-    table = Table(row_styles=["", "dim"])
-
-    table.add_column("Released", style="cyan", no_wrap=True)
-    table.add_column("Title", style="magenta")
-    table.add_column("Box Office", justify="right", style="green")
-
-    table.add_row("Dec 20, 2019", "Star Wars: The Rise of Skywalker", "$952,110,690")
-    table.add_row("May 25, 2018", "Solo: A Star Wars Story", "$393,151,347")
-    table.add_row("Dec 15, 2017", "Star Wars Ep. V111: The Last Jedi", "$1,332,539,889")
-    table.add_row("Dec 16, 2016", "Rogue One: A Star Wars Story", "$1,332,439,889")
-
-    code = """\
-class Segment(NamedTuple):
-    text: str = ""
-    style: Optional[Style] = None
-    is_control: bool = False
-"""
-    syntax = Syntax(code, "python", theme="monokai", line_numbers=True)
-
-    markdown = Markdown(
-        """\
-### example.md
-> Hello, World!
->
-> Markdown _all_ the things
-"""
-    )
-
-    root = Tree("🌲 [b green]Rich Tree", highlight=True, hide_root=True)
-
-    node = root.add(":file_folder: Renderables", guide_style="red")
-    simple_node = node.add(":file_folder: [bold yellow]Atomic", guide_style="uu green")
-    simple_node.add(Group("📄 Syntax", syntax))
-    simple_node.add(Group("📄 Markdown", Panel(markdown, border_style="green")))
-
-    containers_node = node.add(
-        ":file_folder: [bold magenta]Containers", guide_style="bold magenta"
-    )
-    containers_node.expanded = True
-    panel = Panel.fit("Just a panel", border_style="red")
-    containers_node.add(Group("📄 Panels", panel))
-
-    containers_node.add(Group("📄 [b magenta]Table", table))
-
-    console = Console()
-
-    console.print(root)

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/util/ssltransport.py

@@ -1,221 +0,0 @@
-import io
-import socket
-import ssl
-
-from ..exceptions import ProxySchemeUnsupported
-from ..packages import six
-
-SSL_BLOCKSIZE = 16384
-
-
-class SSLTransport:
-    """
-    The SSLTransport wraps an existing socket and establishes an SSL connection.
-
-    Contrary to Python's implementation of SSLSocket, it allows you to chain
-    multiple TLS connections together. It's particularly useful if you need to
-    implement TLS within TLS.
-
-    The class supports most of the socket API operations.
-    """
-
-    @staticmethod
-    def _validate_ssl_context_for_tls_in_tls(ssl_context):
-        """
-        Raises a ProxySchemeUnsupported if the provided ssl_context can't be used
-        for TLS in TLS.
-
-        The only requirement is that the ssl_context provides the 'wrap_bio'
-        methods.
-        """
-
-        if not hasattr(ssl_context, "wrap_bio"):
-            if six.PY2:
-                raise ProxySchemeUnsupported(
-                    "TLS in TLS requires SSLContext.wrap_bio() which isn't "
-                    "supported on Python 2"
-                )
-            else:
-                raise ProxySchemeUnsupported(
-                    "TLS in TLS requires SSLContext.wrap_bio() which isn't "
-                    "available on non-native SSLContext"
-                )
-
-    def __init__(
-        self, socket, ssl_context, server_hostname=None, suppress_ragged_eofs=True
-    ):
-        """
-        Create an SSLTransport around socket using the provided ssl_context.
-        """
-        self.incoming = ssl.MemoryBIO()
-        self.outgoing = ssl.MemoryBIO()
-
-        self.suppress_ragged_eofs = suppress_ragged_eofs
-        self.socket = socket
-
-        self.sslobj = ssl_context.wrap_bio(
-            self.incoming, self.outgoing, server_hostname=server_hostname
-        )
-
-        # Perform initial handshake.
-        self._ssl_io_loop(self.sslobj.do_handshake)
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *_):
-        self.close()
-
-    def fileno(self):
-        return self.socket.fileno()
-
-    def read(self, len=1024, buffer=None):
-        return self._wrap_ssl_read(len, buffer)
-
-    def recv(self, len=1024, flags=0):
-        if flags != 0:
-            raise ValueError("non-zero flags not allowed in calls to recv")
-        return self._wrap_ssl_read(len)
-
-    def recv_into(self, buffer, nbytes=None, flags=0):
-        if flags != 0:
-            raise ValueError("non-zero flags not allowed in calls to recv_into")
-        if buffer and (nbytes is None):
-            nbytes = len(buffer)
-        elif nbytes is None:
-            nbytes = 1024
-        return self.read(nbytes, buffer)
-
-    def sendall(self, data, flags=0):
-        if flags != 0:
-            raise ValueError("non-zero flags not allowed in calls to sendall")
-        count = 0
-        with memoryview(data) as view, view.cast("B") as byte_view:
-            amount = len(byte_view)
-            while count < amount:
-                v = self.send(byte_view[count:])
-                count += v
-
-    def send(self, data, flags=0):
-        if flags != 0:
-            raise ValueError("non-zero flags not allowed in calls to send")
-        response = self._ssl_io_loop(self.sslobj.write, data)
-        return response
-
-    def makefile(
-        self, mode="r", buffering=None, encoding=None, errors=None, newline=None
-    ):
-        """
-        Python's httpclient uses makefile and buffered io when reading HTTP
-        messages and we need to support it.
-
-        This is unfortunately a copy and paste of socket.py makefile with small
-        changes to point to the socket directly.
-        """
-        if not set(mode) <= {"r", "w", "b"}:
-            raise ValueError("invalid mode %r (only r, w, b allowed)" % (mode,))
-
-        writing = "w" in mode
-        reading = "r" in mode or not writing
-        assert reading or writing
-        binary = "b" in mode
-        rawmode = ""
-        if reading:
-            rawmode += "r"
-        if writing:
-            rawmode += "w"
-        raw = socket.SocketIO(self, rawmode)
-        self.socket._io_refs += 1
-        if buffering is None:
-            buffering = -1
-        if buffering < 0:
-            buffering = io.DEFAULT_BUFFER_SIZE
-        if buffering == 0:
-            if not binary:
-                raise ValueError("unbuffered streams must be binary")
-            return raw
-        if reading and writing:
-            buffer = io.BufferedRWPair(raw, raw, buffering)
-        elif reading:
-            buffer = io.BufferedReader(raw, buffering)
-        else:
-            assert writing
-            buffer = io.BufferedWriter(raw, buffering)
-        if binary:
-            return buffer
-        text = io.TextIOWrapper(buffer, encoding, errors, newline)
-        text.mode = mode
-        return text
-
-    def unwrap(self):
-        self._ssl_io_loop(self.sslobj.unwrap)
-
-    def close(self):
-        self.socket.close()
-
-    def getpeercert(self, binary_form=False):
-        return self.sslobj.getpeercert(binary_form)
-
-    def version(self):
-        return self.sslobj.version()
-
-    def cipher(self):
-        return self.sslobj.cipher()
-
-    def selected_alpn_protocol(self):
-        return self.sslobj.selected_alpn_protocol()
-
-    def selected_npn_protocol(self):
-        return self.sslobj.selected_npn_protocol()
-
-    def shared_ciphers(self):
-        return self.sslobj.shared_ciphers()
-
-    def compression(self):
-        return self.sslobj.compression()
-
-    def settimeout(self, value):
-        self.socket.settimeout(value)
-
-    def gettimeout(self):
-        return self.socket.gettimeout()
-
-    def _decref_socketios(self):
-        self.socket._decref_socketios()
-
-    def _wrap_ssl_read(self, len, buffer=None):
-        try:
-            return self._ssl_io_loop(self.sslobj.read, len, buffer)
-        except ssl.SSLError as e:
-            if e.errno == ssl.SSL_ERROR_EOF and self.suppress_ragged_eofs:
-                return 0  # eof, return 0.
-            else:
-                raise
-
-    def _ssl_io_loop(self, func, *args):
-        """Performs an I/O loop between incoming/outgoing and the socket."""
-        should_loop = True
-        ret = None
-
-        while should_loop:
-            errno = None
-            try:
-                ret = func(*args)
-            except ssl.SSLError as e:
-                if e.errno not in (ssl.SSL_ERROR_WANT_READ, ssl.SSL_ERROR_WANT_WRITE):
-                    # WANT_READ, and WANT_WRITE are expected, others are not.
-                    raise e
-                errno = e.errno
-
-            buf = self.outgoing.read()
-            self.socket.sendall(buf)
-
-            if errno is None:
-                should_loop = False
-            elif errno == ssl.SSL_ERROR_WANT_READ:
-                buf = self.socket.recv(SSL_BLOCKSIZE)
-                if buf:
-                    self.incoming.write(buf)
-                else:
-                    self.incoming.write_eof()
-        return ret

spaces/BreadBytes1/SB-Dashboard/app.py

@@ -1,730 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.14.2
-#   kernelspec:
-#     display_name: Python [conda env:bbytes] *
-#     language: python
-#     name: conda-env-bbytes-py
-# ---
-
-# +
-import csv 
-import pandas as pd
-from datetime import datetime, timedelta
-import numpy as np
-import datetime as dt
-import matplotlib.pyplot as plt
-from pathlib import Path
-import time
-import plotly.graph_objects as go
-import plotly.io as pio
-from PIL import Image
-
-import streamlit as st
-import plotly.express as px
-import altair as alt
-import dateutil.parser
-from  matplotlib.colors import LinearSegmentedColormap
-
-
-# +
-class color:
-    PURPLE = '\033[95m'
-    CYAN = '\033[96m'
-    DARKCYAN = '\033[36m'
-    BLUE = '\033[94m'
-    GREEN = '\033[92m'
-    YELLOW = '\033[93m'
-    RED = '\033[91m'
-    BOLD = '\033[1m'
-    UNDERLINE = '\033[4m'
-    END = '\033[0m'
-    
-@st.experimental_memo    
-def print_PL(amnt, thresh, extras = "" ):
-    if amnt > 0:
-        return color.BOLD + color.GREEN + str(amnt) + extras + color.END
-    elif amnt < 0:
-        return color.BOLD + color.RED + str(amnt)+ extras + color.END
-    elif np.isnan(amnt):
-        return str(np.nan)
-    else:
-        return str(amnt + extras)
-    
-@st.experimental_memo      
-def get_headers(logtype):
-    otimeheader = ""
-    cheader = ""
-    plheader = ""
-    fmat = '%Y-%m-%d %H:%M:%S'
-    
-    if logtype == "ByBit":
-        otimeheader = 'Create Time'
-        cheader = 'Contracts'
-        plheader = 'Closed P&L'
-        fmat = '%Y-%m-%d %H:%M:%S'
-        
-    if logtype == "BitGet":
-        otimeheader = 'Date'
-        cheader = 'Futures'
-        plheader = 'Realized P/L'
-        fmat = '%Y-%m-%d %H:%M:%S'  
-        
-    if logtype == "MEXC":
-        otimeheader = 'Trade time'
-        cheader = 'Futures'
-        plheader = 'closing position'
-        fmat = '%Y/%m/%d %H:%M'
-        
-    if logtype == "Binance":
-        otimeheader = 'Date'
-        cheader = 'Symbol'
-        plheader = 'Realized Profit'
-        fmat = '%Y-%m-%d %H:%M:%S'
-        
-    #if logtype == "Kucoin":
-    #    otimeheader = 'Time'
-    #    cheader = 'Contract'
-    #    plheader = ''
-    #    fmat = '%Y/%m/%d %H:%M:%S' 
-
-        
-    if logtype == "Kraken":
-        otimeheader = 'time'
-        cheader = 'asset'
-        plheader = 'amount'
-        fmat = '%Y-%m-%d %H:%M:%S.%f'  
-        
-    if logtype == "OkX":
-        otimeheader = '\ufeffOrder Time'
-        cheader = '\ufeffInstrument'
-        plheader = '\ufeffPL'
-        fmat = '%Y-%m-%d %H:%M:%S'        
-    
-    return otimeheader.lower(), cheader.lower(), plheader.lower(), fmat
-    
-@st.experimental_memo    
-def get_coin_info(df_coin, principal_balance,plheader):
-    numtrades = int(len(df_coin))
-    numwin = int(sum(df_coin[plheader] > 0))
-    numloss = int(sum(df_coin[plheader] < 0))
-    winrate = np.round(100*numwin/numtrades,2)
-    
-    grosswin = sum(df_coin[df_coin[plheader] > 0][plheader])
-    grossloss = sum(df_coin[df_coin[plheader] < 0][plheader])
-    if grossloss != 0:
-        pfactor = -1*np.round(grosswin/grossloss,2)
-    else: 
-        pfactor = np.nan
-    
-    cum_PL = np.round(sum(df_coin[plheader].values),2)
-    cum_PL_perc = np.round(100*cum_PL/principal_balance,2)
-    mean_PL = np.round(sum(df_coin[plheader].values/len(df_coin)),2)
-    mean_PL_perc = np.round(100*mean_PL/principal_balance,2)
-    
-    return numtrades, numwin, numloss, winrate, pfactor, cum_PL, cum_PL_perc, mean_PL, mean_PL_perc
-
-@st.experimental_memo
-def get_hist_info(df_coin, principal_balance,plheader):
-    numtrades = int(len(df_coin))
-    numwin = int(sum(df_coin[plheader] > 0))
-    numloss = int(sum(df_coin[plheader] < 0))
-    if numtrades != 0:
-        winrate = int(np.round(100*numwin/numtrades,2))
-    else: 
-        winrate = np.nan
-    
-    grosswin = sum(df_coin[df_coin[plheader] > 0][plheader])
-    grossloss = sum(df_coin[df_coin[plheader] < 0][plheader])
-    if grossloss != 0:
-        pfactor = -1*np.round(grosswin/grossloss,2)
-    else: 
-        pfactor = np.nan
-    return numtrades, numwin, numloss, winrate, pfactor
-
-@st.experimental_memo
-def get_rolling_stats(df, lev, otimeheader, days):
-    max_roll = (df[otimeheader].max() - df[otimeheader].min()).days
-    
-    if max_roll >= days:
-        rollend = df[otimeheader].max()-timedelta(days=days)
-        rolling_df = df[df[otimeheader] >= rollend]
-
-        if len(rolling_df) > 0:
-            rolling_perc = rolling_df['Return Per Trade'].dropna().cumprod().values[-1]-1
-        else: 
-            rolling_perc = np.nan
-    else:
-        rolling_perc = np.nan
-    return 100*rolling_perc
-@st.experimental_memo
-def cc_coding(row):
-    return ['background-color: lightgrey'] * len(row) if row['Exit Date'] <= datetime.strptime('2022-12-16 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
-def ctt_coding(row):
-    return ['background-color: lightgrey'] * len(row) if row['Exit Date'] <= datetime.strptime('2023-01-02 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
-
-@st.experimental_memo
-def my_style(v, props=''):
-    props = 'color:red' if v < 0 else 'color:green'
-    return props
-
-def filt_df(df, cheader, symbol_selections):
-    
-    df = df.copy()
-    df = df[df[cheader].isin(symbol_selections)]
-
-    return df
-
-def tv_reformat(close50filename):
-    try:
-        data = pd.read_csv(open(close50filename,'r'), sep='[,|\t]', engine='python')
-    except: 
-        data = pd.DataFrame([])
-    
-    if data.empty:
-        return data 
-    else:
-        entry_df = data[data['Type'].str.contains("Entry")]
-        exit_df = data[data['Type'].str.contains("Exit")]
-
-        entry_df.index = range(len(entry_df))
-        exit_df.index = range(len(exit_df))
-
-        df = pd.DataFrame([], columns=['Trade','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %'])
-        
-        df['Signal'] = [string.split(' ')[1] for string in entry_df['Type']]
-        df['Trade'] = entry_df.index
-        df['Entry Date'] = entry_df['Date/Time']
-        df['Buy Price'] = entry_df['Price USDT']
-
-        df['Sell Price'] = exit_df['Price USDT']
-        df['Exit Date'] = exit_df['Date/Time']
-        df['P/L per token'] = df['Sell Price'] - df['Buy Price']
-        df['P/L %'] = exit_df['Profit %']
-        df['Drawdown %'] = exit_df['Drawdown %']
-        df['Close 50'] = [int(i == "Close 50% of Position") for i in exit_df['Signal']]
-        df = df.sort_values(['Entry Date','Close 50'], ascending = [False, True])
-        df.index = range(len(df))
-        
-        df.loc[df['Close 50'] == 1, 'Exit Date'] = np.copy(df.loc[df[df['Close 50'] == 1].index.values -1]['Exit Date'])
-
-        grouped_df = df.groupby('Entry Date').agg({'Signal' : 'first', 'Entry Date': 'min', 'Buy Price':'mean',
-                                 'Sell Price' : 'mean',
-                                 'Exit Date': 'max',
-                                 'P/L per token': 'mean', 
-                                 'P/L %' : 'mean'})
-
-        grouped_df.insert(0,'Trade', range(len(grouped_df)))
-        grouped_df.index = range(len(grouped_df))
-        return grouped_df
-
-def load_data(filename, otimeheader, fmat):
-    df = pd.read_csv(open(filename,'r'), sep='\t') # so as not to mutate cached value
-    close50filename = filename.split('.')[0] + '-50.' + filename.split('.')[1]
-    df2 = tv_reformat(close50filename)
-    
-    if filename == "CT-Trade-Log.csv":
-        df.columns = ['Trade','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %']
-        df.insert(1, 'Signal', ['Long']*len(df)) 
-    elif filename == "CC-Trade-Log.csv":
-        df.columns = ['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %']
-    else: 
-        df.columns = ['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %']
-        
-    if filename != "CT-Toasted-Trade-Log.csv":
-        df['Signal'] = df['Signal'].str.replace(' ', '', regex=True)
-        df['Buy Price'] = df['Buy Price'].str.replace('$', '', regex=True)
-        df['Sell Price'] = df['Sell Price'].str.replace('$', '', regex=True)
-        df['Buy Price'] = df['Buy Price'].str.replace(',', '', regex=True)
-        df['Sell Price'] = df['Sell Price'].str.replace(',', '', regex=True)
-        df['P/L per token'] = df['P/L per token'].str.replace('$', '', regex=True)
-        df['P/L per token'] = df['P/L per token'].str.replace(',', '', regex=True)
-        df['P/L %'] = df['P/L %'].str.replace('%', '', regex=True)
-
-        df['Buy Price'] = pd.to_numeric(df['Buy Price'])
-        df['Sell Price'] = pd.to_numeric(df['Sell Price'])
-        df['P/L per token'] = pd.to_numeric(df['P/L per token'])
-        df['P/L %'] = pd.to_numeric(df['P/L %'])
-    
-    if df2.empty: 
-        df = df
-    else:
-        df = pd.concat([df,df2], axis=0, ignore_index=True)
-    
-    if filename == "CT-Trade-Log.csv":
-        df['Signal'] = ['Long']*len(df)
-    
-    dateheader = 'Date'
-    theader = 'Time'
-    
-    df[dateheader] = [tradetimes.split(" ")[0] for tradetimes in df[otimeheader].values]
-    df[theader] = [tradetimes.split(" ")[1] for tradetimes in df[otimeheader].values]
-
-    df[otimeheader]= [dateutil.parser.parse(date+' '+time)
-                                  for date,time in zip(df[dateheader],df[theader])]
-    df[otimeheader] = pd.to_datetime(df[otimeheader])
-    df['Exit Date'] = pd.to_datetime(df['Exit Date'])
-    df.sort_values(by=otimeheader, inplace=True)
-    
-    df[dateheader] = [dateutil.parser.parse(date).date() for date in df[dateheader]]
-    df[theader] = [dateutil.parser.parse(time).time() for time in df[theader]]
-    df['Trade'] = df.index + 1 #reindex
-    
-    if filename == "CT-Trade-Log.csv":
-        df['DCA'] = np.nan
-
-        for exit in pd.unique(df['Exit Date']):
-            df_exit = df[df['Exit Date']==exit]
-            if dateutil.parser.parse(str(exit)) < dateutil.parser.parse('2023-02-07 13:00:00'):
-                for i in range(len(df_exit)):
-                    ind = df_exit.index[i]
-                    df.loc[ind,'DCA'] = i+1
-                    
-            else: 
-                for i in range(len(df_exit)):
-                    ind = df_exit.index[i]
-                    df.loc[ind,'DCA'] = i+1.1
-    return df 
-
-
-def get_sd_df(sd_df, sd, bot_selections, dca1, dca2, dca3, dca4, dca5, dca6, fees, lev, dollar_cap, principal_balance):
-    sd = 2*.00026
-    # ------ Standard Dev. Calculations. 
-    if bot_selections == "Cinnamon Toast":
-        dca_map = {1: dca1/100, 2: dca2/100, 3: dca3/100, 4: dca4/100, 1.1: dca5/100, 2.1: dca6/100}
-        sd_df['DCA %'] = sd_df['DCA'].map(dca_map)
-        sd_df['Calculated Return % (+)'] = df['Signal'].map(signal_map)*(df['DCA %'])*(1-fees)*((df['Sell Price']*(1+df['Signal'].map(signal_map)*sd) - df['Buy Price']*(1-df['Signal'].map(signal_map)*sd))/df['Buy Price']*(1-df['Signal'].map(signal_map)*sd) - fees) #accounts for fees on open and close of trade 
-        sd_df['Calculated Return % (-)'] = df['Signal'].map(signal_map)*(df['DCA %'])*(1-fees)*((df['Sell Price']*(1-df['Signal'].map(signal_map)*sd)-df['Buy Price']*(1+df['Signal'].map(signal_map)*sd))/df['Buy Price']*(1+df['Signal'].map(signal_map)*sd) - fees) #accounts for fees on open and close of trade 
-        sd_df['DCA'] = np.floor(sd_df['DCA'].values)
-
-        sd_df['Return Per Trade (+)'] = np.nan
-        sd_df['Return Per Trade (-)'] = np.nan
-        sd_df['Balance used in Trade (+)'] = np.nan
-        sd_df['Balance used in Trade (-)'] = np.nan
-        sd_df['New Balance (+)'] = np.nan
-        sd_df['New Balance (-)'] = np.nan
-
-        g1 = sd_df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return % (+)'].reset_index(name='Return Per Trade (+)')
-        g2 = sd_df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return % (-)'].reset_index(name='Return Per Trade (-)')
-        sd_df.loc[sd_df['DCA']==1.0,'Return Per Trade (+)'] = 1+lev*g1['Return Per Trade (+)'].values
-        sd_df.loc[sd_df['DCA']==1.0,'Return Per Trade (-)'] = 1+lev*g2['Return Per Trade (-)'].values
-
-        sd_df['Compounded Return (+)'] = sd_df['Return Per Trade (+)'].cumprod()
-        sd_df['Compounded Return (-)'] = sd_df['Return Per Trade (-)'].cumprod()
-        sd_df.loc[sd_df['DCA']==1.0,'New Balance (+)'] = [min(dollar_cap/lev, bal*principal_balance) for bal in sd_df.loc[sd_df['DCA']==1.0,'Compounded Return (+)']]
-        sd_df.loc[sd_df['DCA']==1.0,'Balance used in Trade (+)'] = np.concatenate([[principal_balance], sd_df.loc[sd_df['DCA']==1.0,'New Balance (+)'].values[:-1]])
-
-        sd_df.loc[sd_df['DCA']==1.0,'New Balance (-)'] = [min(dollar_cap/lev, bal*principal_balance) for bal in sd_df.loc[sd_df['DCA']==1.0,'Compounded Return (-)']]
-        sd_df.loc[sd_df['DCA']==1.0,'Balance used in Trade (-)'] = np.concatenate([[principal_balance], sd_df.loc[sd_df['DCA']==1.0,'New Balance (-)'].values[:-1]])
-    else: 
-        sd_df['Calculated Return % (+)'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']*(1+df['Signal'].map(signal_map)*sd) - df['Buy Price']*(1-df['Signal'].map(signal_map)*sd))/df['Buy Price']*(1-df['Signal'].map(signal_map)*sd) - fees) #accounts for fees on open and close of trade 
-        sd_df['Calculated Return % (-)'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']*(1-df['Signal'].map(signal_map)*sd)-df['Buy Price']*(1+df['Signal'].map(signal_map)*sd))/df['Buy Price']*(1+df['Signal'].map(signal_map)*sd) - fees) #accounts for fees on open and close of trade 
-        sd_df['Return Per Trade (+)'] = np.nan
-        sd_df['Return Per Trade (-)'] = np.nan
-
-        g1 = sd_df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return % (+)'].reset_index(name='Return Per Trade (+)')
-        g2 = sd_df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return % (-)'].reset_index(name='Return Per Trade (-)')
-        sd_df['Return Per Trade (+)'] = 1+lev*g1['Return Per Trade (+)'].values
-        sd_df['Return Per Trade (-)'] = 1+lev*g2['Return Per Trade (-)'].values
-
-        sd_df['Compounded Return (+)'] = sd_df['Return Per Trade (+)'].cumprod()
-        sd_df['Compounded Return (-)'] = sd_df['Return Per Trade (-)'].cumprod()
-        sd_df['New Balance (+)'] = [min(dollar_cap/lev, bal*principal_balance) for bal in sd_df['Compounded Return (+)']]
-        sd_df['Balance used in Trade (+)'] = np.concatenate([[principal_balance], sd_df['New Balance (+)'].values[:-1]])
-
-        sd_df['New Balance (-)'] = [min(dollar_cap/lev, bal*principal_balance) for bal in sd_df['Compounded Return (-)']]
-        sd_df['Balance used in Trade (-)'] = np.concatenate([[principal_balance], sd_df['New Balance (-)'].values[:-1]])
-
-    sd_df['Net P/L Per Trade (+)'] = (sd_df['Return Per Trade (+)']-1)*sd_df['Balance used in Trade (+)']
-    sd_df['Cumulative P/L (+)'] = sd_df['Net P/L Per Trade (+)'].cumsum()
-
-    sd_df['Net P/L Per Trade (-)'] = (sd_df['Return Per Trade (-)']-1)*sd_df['Balance used in Trade (-)']
-    sd_df['Cumulative P/L (-)'] = sd_df['Net P/L Per Trade (-)'].cumsum()
-    return sd_df
-
-def runapp() -> None:
-            bot_selections = "Short Bread"
-            otimeheader = 'Exit Date'
-            fmat = '%Y-%m-%d %H:%M:%S'
-            fees = .075/100
-    
-            st.header(f"{bot_selections} Performance Dashboard :bread: :moneybag:")
-            no_errors = True
-            st.write("Welcome to the Trading Bot Dashboard by BreadBytes! You can use this dashboard to track " +
-                 "the performance of our trading bots.")
-
-            if bot_selections == "Cinnamon Toast":
-                lev_cap = 5
-                dollar_cap = 1000000000.00
-                data = load_data("CT-Trade-Log.csv",otimeheader, fmat)
-            if bot_selections == "French Toast":
-                lev_cap = 3
-                dollar_cap = 10000000000.00
-                data = load_data("FT-Trade-Log.csv",otimeheader, fmat)
-            if bot_selections == "Short Bread":
-                lev_cap = 5
-                dollar_cap = 1000000000.00
-                data = load_data("SB-Trade-Log.csv",otimeheader, fmat)
-            if bot_selections == "Cosmic Cupcake":
-                lev_cap = 3
-                dollar_cap = 1000000000.00
-                data = load_data("CC-Trade-Log.csv",otimeheader, fmat)
-            if bot_selections == "CT Toasted":
-                lev_cap = 5
-                dollar_cap = 1000000000.00
-                data = load_data("CT-Toasted-Trade-Log.csv",otimeheader, fmat)
-            
-            df = data.copy(deep=True)
-
-            dateheader = 'Date'
-            theader = 'Time'
-            
-            st.subheader("Choose your settings:")
-            with st.form("user input", ):
-                if no_errors:
-                    with st.container():
-                        col1, col2 = st.columns(2)
-                        with col1:
-                            try:
-                                startdate = st.date_input("Start Date", value=pd.to_datetime(df[otimeheader]).min())
-                            except:
-                                st.error("Please select your exchange or upload a supported trade log file.")
-                                no_errors = False 
-                        with col2:
-                            try:
-                                enddate = st.date_input("End Date", value=datetime.today())
-                            except:
-                                st.error("Please select your exchange or upload a supported trade log file.")
-                                no_errors = False 
-                        #st.sidebar.subheader("Customize your Dashboard")
-
-                        if no_errors and (enddate < startdate): 
-                            st.error("End Date must be later than Start date. Please try again.")
-                            no_errors = False 
-                    with st.container(): 
-                        col1,col2 = st.columns(2) 
-                        with col2:
-                            lev = st.number_input('Leverage', min_value=1, value=1, max_value= lev_cap, step=1)
-                        with col1:
-                            principal_balance = st.number_input('Starting Balance', min_value=0.00, value=1000.00, max_value= dollar_cap, step=.01)
-                
-                if bot_selections == "Cinnamon Toast":
-                    st.write("Choose your DCA setup (for trades before 02/07/2023)")
-                    with st.container():
-                        col1, col2, col3, col4 = st.columns(4)
-                        with col1: 
-                            dca1 = st.number_input('DCA 1 Allocation', min_value=0, value=25, max_value= 100, step=1)
-                        with col2: 
-                            dca2 = st.number_input('DCA 2 Allocation', min_value=0, value=25, max_value= 100, step=1)
-                        with col3: 
-                            dca3 = st.number_input('DCA 3 Allocation', min_value=0, value=25, max_value= 100, step=1)
-                        with col4: 
-                            dca4 = st.number_input('DCA 4 Allocation', min_value=0, value=25, max_value= 100, step=1)
-                    st.write("Choose your DCA setup (for trades on or after 02/07/2023)")
-                    with st.container():
-                        col1, col2 = st.columns(2)
-                        with col1: 
-                            dca5 = st.number_input('DCA 1 Allocation', min_value=0, value=50, max_value= 100, step=1)
-                        with col2: 
-                            dca6 = st.number_input('DCA 2 Allocation', min_value=0, value=50, max_value= 100, step=1)
-
-                #hack way to get button centered 
-                c = st.columns(9)
-                with c[4]: 
-                    submitted = st.form_submit_button("Get Cookin'!")           
-
-            if submitted and principal_balance * lev > dollar_cap:
-                lev = np.floor(dollar_cap/principal_balance)
-                st.error(f"WARNING: (Starting Balance)*(Leverage) exceeds the ${dollar_cap} limit. Using maximum available leverage of {lev}")
-
-            if submitted and no_errors:
-                df = df[(df[dateheader] >= startdate) & (df[dateheader] <= enddate)]
-                signal_map = {'Long': 1, 'Short':-1}  
-            
-                
-                if len(df) == 0:
-                        st.error("There are no available trades matching your selections. Please try again!")
-                        no_errors = False
-                        
-                if no_errors:
-                    if bot_selections == "Cinnamon Toast":
-                        dca_map = {1: dca1/100, 2: dca2/100, 3: dca3/100, 4: dca4/100, 1.1: dca5/100, 2.1: dca6/100}
-                        df['DCA %'] = df['DCA'].map(dca_map)
-                        df['Calculated Return %'] = df['Signal'].map(signal_map)*(df['DCA %'])*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
-                        df['DCA'] = np.floor(df['DCA'].values)
-                        
-                        df['Return Per Trade'] = np.nan
-                        df['Balance used in Trade'] = np.nan
-                        df['New Balance'] = np.nan
-
-                        g = df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return %'].reset_index(name='Return Per Trade')
-                        df.loc[df['DCA']==1.0,'Return Per Trade'] = 1+lev*g['Return Per Trade'].values
-                        
-                        df['Compounded Return'] = df['Return Per Trade'].cumprod()
-                        df.loc[df['DCA']==1.0,'New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df.loc[df['DCA']==1.0,'Compounded Return']]
-                        df.loc[df['DCA']==1.0,'Balance used in Trade'] = np.concatenate([[principal_balance], df.loc[df['DCA']==1.0,'New Balance'].values[:-1]])
-                    else: 
-                        df['Calculated Return %'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
-                        df['Return Per Trade'] = np.nan
-                        g = df.groupby('Exit Date').sum(numeric_only=True)['Calculated Return %'].reset_index(name='Return Per Trade')
-                        df['Return Per Trade'] = 1+lev*g['Return Per Trade'].values
-                        
-                        df['Compounded Return'] = df['Return Per Trade'].cumprod()
-                        df['New Balance'] = [min(dollar_cap/lev, bal*principal_balance) for bal in df['Compounded Return']]
-                        df['Balance used in Trade'] = np.concatenate([[principal_balance], df['New Balance'].values[:-1]])
-                    df['Net P/L Per Trade'] = (df['Return Per Trade']-1)*df['Balance used in Trade']
-                    df['Cumulative P/L'] = df['Net P/L Per Trade'].cumsum()
-                    
-                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake":
-                        cum_pl = df.loc[df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L'] + principal_balance
-                        #cum_sdp = sd_df.loc[sd_df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L (+)'] + principal_balance
-                        #cum_sdm = sd_df.loc[sd_df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L (-)'] + principal_balance
-                    else: 
-                        cum_pl = df.loc[df.dropna().index[-1],'Cumulative P/L'] + principal_balance
-                        #cum_sdp = sd_df.loc[sd_df.dropna().index[-1],'Cumulative P/L (+)'] + principal_balance
-                        #cum_sdm = sd_df.loc[sd_df.dropna().index[-1],'Cumulative P/L (-)'] + principal_balance
-                    #sd = 2*.00026
-                    #sd_df = get_sd_df(get_sd_df(df.copy(), sd, bot_selections, dca1, dca2, dca3, dca4, dca5, dca6, fees, lev, dollar_cap, principal_balance)
-                    
-                    effective_return = 100*((cum_pl - principal_balance)/principal_balance)
-
-                    st.header(f"{bot_selections} Results")
-                    with st.container(): 
-                        
-                        if len(bot_selections) > 1:
-                            col1, col2 = st.columns(2)
-                            with col1: 
-                                st.metric(
-                                    "Total Account Balance",
-                                    f"${cum_pl:.2f}",
-                                    f"{100*(cum_pl-principal_balance)/(principal_balance):.2f} %",
-                                )
-
-#                             with col2:
-#                                 st.write("95% of trades should fall within this 2 std. dev. range.")
-#                                 st.metric(
-#                                     "High Range (+ 2 std. dev.)",
-#                                     f"", #${cum_sdp:.2f}
-#                                     f"{100*(cum_sdp-principal_balance)/(principal_balance):.2f} %",
-#                                 )
-#                                 st.metric(
-#                                     "Low Range (- 2 std. dev.)",
-#                                     f"" ,#${cum_sdm:.2f}"
-#                                     f"{100*(cum_sdm-principal_balance)/(principal_balance):.2f} %",
-#                                 )
-                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake":
-                        #st.line_chart(data=df.drop('Drawdown %', axis=1).dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
-                        dfdata = df.drop('Drawdown %', axis=1).dropna()
-                        #sd_df = sd_df.drop('Drawdown %', axis=1).dropna()
-                    else: 
-                        #st.line_chart(data=df.dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
-                        dfdata = df.dropna()
-                        #sd_df = sd_df.dropna()
-                        
-                    # Create figure
-                    fig = go.Figure()
-
-                    pyLogo = Image.open("logo.png")
-                    
-#                     fig.add_traces(go.Scatter(x=sd_df['Exit Date'], y = sd_df['Cumulative P/L (+)'],line_shape='spline',
-#                              line = dict(smoothing = 1.3, color='rgba(31, 119, 200,0)'),  showlegend = False)
-#                                   )
-                    
-#                     fig.add_traces(go.Scatter(x=sd_df['Exit Date'], y = sd_df['Cumulative P/L (-)'],
-#                              line = dict(smoothing = 1.3, color='rgba(31, 119, 200,0)'), line_shape='spline',
-#                              fill='tonexty', 
-#                              fillcolor = 'rgba(31, 119, 200,.2)', name = '+/- Standard Deviation')
-#                                  )
-
-                    # Add trace
-                    fig.add_trace(
-                        go.Scatter(x=dfdata['Exit Date'], y=np.round(dfdata['Cumulative P/L'].values,2), line_shape='spline', 
-                                   line = {'smoothing': 1.0, 'color' : 'rgba(31, 119, 200,.8)'},
-                                   name='Cumulative P/L')
-                    )
-                    buyhold  = (principal_balance/dfdata['Buy Price'][dfdata.index[0]])*(dfdata['Buy Price']-dfdata['Buy Price'][dfdata.index[0]])
-                    fig.add_trace(go.Scatter(x=dfdata['Exit Date'], y=np.round(buyhold.values,2), line_shape='spline', 
-                                             line = {'smoothing': 1.0, 'color' :'red'}, name = 'Buy & Hold Return')
-                    )
-
-                    fig.add_layout_image(
-                            dict(
-                                source=pyLogo,
-                                xref="paper",
-                                yref="paper",
-                                x = 0.05, #dfdata['Exit Date'].astype('int64').min() // 10**9, 
-                                y = .85, #dfdata['Cumulative P/L'].max(),
-                                sizex= .9, #(dfdata['Exit Date'].astype('int64').max() - dfdata['Exit Date'].astype('int64').min()) // 10**9,
-                                sizey= .9, #(dfdata['Cumulative P/L'].max() - dfdata['Cumulative P/L'].min()),
-                                sizing="contain",
-                                opacity=0.2, 
-                            layer = "below")
-                    )
-                    
-                    #style layout 
-                    fig.update_layout(
-                        height = 600,
-                        xaxis=dict(
-                            title="Exit Date", 
-                            tickmode='array',
-                        ),
-                        yaxis=dict(
-                            title="Cumulative P/L"
-                        ) ) 
-                    
-                    st.plotly_chart(fig, theme=None, use_container_width=True,height=600)
-                    st.write()
-                    df['Per Trade Return Rate'] = df['Return Per Trade']-1
-
-                    totals = pd.DataFrame([], columns = ['# of Trades', 'Wins', 'Losses', 'Win Rate', 'Profit Factor'])
-                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake":
-                        data = get_hist_info(df.drop('Drawdown %', axis=1).dropna(), principal_balance,'Per Trade Return Rate')
-                    else: 
-                        data = get_hist_info(df.dropna(), principal_balance,'Per Trade Return Rate')
-                    totals.loc[len(totals)] = list(i for i in data)
-
-                    totals['Cum. P/L'] = cum_pl-principal_balance
-                    totals['Cum. P/L (%)'] = 100*(cum_pl-principal_balance)/principal_balance
-                    
-                    if df.empty:
-                        st.error("Oops! None of the data provided matches your selection(s). Please try again.")
-                    else:
-                        with st.container():
-                            for row in totals.itertuples():
-                                col1, col2, col3, col4= st.columns(4)
-                                c1, c2, c3, c4 = st.columns(4)
-                                with col1:
-                                    st.metric(
-                                        "Total Trades",
-                                        f"{row._1:.0f}",
-                                    )
-                                with c1:
-                                    st.metric(
-                                        "Profit Factor",
-                                        f"{row._5:.2f}",
-                                    )
-                                with col2: 
-                                    st.metric(
-                                        "Wins",
-                                        f"{row.Wins:.0f}",
-                                    )
-                                with c2:
-                                    st.metric(
-                                        "Cumulative P/L",
-                                        f"${row._6:.2f}",
-                                        f"{row._7:.2f} %",
-                                    )
-                                with col3: 
-                                    st.metric(
-                                        "Losses",
-                                        f"{row.Losses:.0f}",
-                                    )
-                                with c3:
-                                    st.metric(
-                                    "Rolling 7 Days",
-                                        "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
-                                        f"{get_rolling_stats(df,lev, otimeheader, 7):.2f}%",
-                                    )
-                                    st.metric(
-                                    "Rolling 30 Days",
-                                        "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
-                                        f"{get_rolling_stats(df,lev, otimeheader, 30):.2f}%",
-                                    )
-
-                                with col4: 
-                                    st.metric(
-                                        "Win Rate",
-                                        f"{row._4:.1f}%",
-                                    )
-                                with c4:
-                                    st.metric(
-                                    "Rolling 90 Days",
-                                        "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
-                                        f"{get_rolling_stats(df,lev, otimeheader, 90):.2f}%",
-                                    )
-                                    st.metric(
-                                    "Rolling 180 Days",
-                                        "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
-                                        f"{get_rolling_stats(df,lev, otimeheader, 180):.2f}%",
-                                    )
-            
-            if bot_selections == "Cinnamon Toast":
-                if submitted:                     
-                    grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
-                                             'Sell Price' : 'max',
-                                             'Net P/L Per Trade': 'mean', 
-                                             'Calculated Return %' : lambda x: np.round(100*lev*x.sum(),2), 
-                                             'DCA': lambda x: int(np.floor(x.max()))})
-                    grouped_df.index = range(1, len(grouped_df)+1)
-                    grouped_df.rename(columns={'DCA' : '# of DCAs', 'Buy Price':'Avg. Buy Price',
-                                               'Net P/L Per Trade':'Net P/L', 
-                                               'Calculated Return %':'P/L %'}, inplace=True)
-                else:
-                    dca_map = {1: 25/100, 2: 25/100, 3: 25/100, 4: 25/100, 1.1: 50/100, 2.1: 50/100}
-                    df['DCA %'] = df['DCA'].map(dca_map)
-                    df['Calculated Return %'] = (df['DCA %'])*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade
-                        
-                    grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
-                                             'Sell Price' : 'max',
-                                             'P/L per token': 'mean', 
-                                             'Calculated Return %' : lambda x: np.round(100*x.sum(),2), 
-                                             'DCA': lambda x: int(np.floor(x.max()))})
-                    grouped_df.index = range(1, len(grouped_df)+1)
-                    grouped_df.rename(columns={'DCA' : '# of DCAs', 'Buy Price':'Avg. Buy Price',
-                                               'Calculated Return %':'P/L %',
-                                               'P/L per token':'Net P/L'}, inplace=True)
-                
-            else: 
-                if submitted: 
-                    grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
-                                             'Sell Price' : 'max',
-                                             'Net P/L Per Trade': 'mean', 
-                                             'Calculated Return %' : lambda x: np.round(100*lev*x.sum(),2)})
-                    grouped_df.index = range(1, len(grouped_df)+1)
-                    grouped_df.rename(columns={'Buy Price':'Avg. Buy Price',
-                                               'Net P/L Per Trade':'Net P/L', 
-                                               'Calculated Return %':'P/L %'}, inplace=True)        
-                else: 
-                    grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
-                                             'Sell Price' : 'max',
-                                             'P/L per token': 'mean', 
-                                             'P/L %':'mean'})
-                    grouped_df.index = range(1, len(grouped_df)+1)
-                    grouped_df.rename(columns={'Buy Price':'Avg. Buy Price',
-                                               'P/L per token':'Net P/L'}, inplace=True)
-            st.subheader("Trade Logs")
-            grouped_df['Entry Date'] = pd.to_datetime(grouped_df['Entry Date'])
-            grouped_df['Exit Date'] = pd.to_datetime(grouped_df['Exit Date'])
-            if bot_selections == "Cosmic Cupcake" or bot_selections == "CT Toasted":
-                coding = cc_coding if bot_selections == "Cosmic Cupcake" else ctt_coding
-                st.dataframe(grouped_df.style.format({'Entry Date':'{:%m-%d-%Y %H:%M:%S}','Exit Date':'{:%m-%d-%Y %H:%M:%S}','Avg. Buy Price': '${:.2f}', 'Sell Price': '${:.2f}', 'Net P/L':'${:.2f}', 'P/L %':'{:.2f}%'})\
-                .apply(coding, axis=1)\
-                .applymap(my_style,subset=['Net P/L'])\
-                .applymap(my_style,subset=['P/L %']), use_container_width=True)
-                new_title = '<div style="text-align: right;"><span style="background-color:lightgrey;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span> Not Live Traded</div>'
-                st.markdown(new_title, unsafe_allow_html=True)
-            else: 
-                st.dataframe(grouped_df.style.format({'Entry Date':'{:%m-%d-%Y %H:%M:%S}','Exit Date':'{:%m-%d-%Y %H:%M:%S}','Avg. Buy Price': '${:.2f}', 'Sell Price': '${:.2f}', 'Net P/L':'${:.2f}', 'P/L %':'{:.2f}%'})\
-                .applymap(my_style,subset=['Net P/L'])\
-                .applymap(my_style,subset=['P/L %']), use_container_width=True)
-                
-#             st.subheader("Checking Status")
-#             if submitted:
-#                 st.dataframe(sd_df)
-                                
-if __name__ == "__main__":
-    st.set_page_config(
-        "Trading Bot Dashboard",
-        layout="wide",
-    )
-    runapp()
-# -
-
-
-
-

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/tests/test_data_transform.py

@@ -1,80 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import logging
-import numpy as np
-import unittest
-
-from detectron2.config import get_cfg
-from detectron2.data import detection_utils
-from detectron2.data import transforms as T
-from detectron2.utils.logger import setup_logger
-
-logger = logging.getLogger(__name__)
-
-
-class TestTransforms(unittest.TestCase):
-    def setUp(self):
-        setup_logger()
-
-    def test_apply_rotated_boxes(self):
-        np.random.seed(125)
-        cfg = get_cfg()
-        is_train = True
-        transform_gen = detection_utils.build_transform_gen(cfg, is_train)
-        image = np.random.rand(200, 300)
-        image, transforms = T.apply_transform_gens(transform_gen, image)
-        image_shape = image.shape[:2]  # h, w
-        assert image_shape == (800, 1200)
-        annotation = {"bbox": [179, 97, 62, 40, -56]}
-
-        boxes = np.array([annotation["bbox"]], dtype=np.float64)  # boxes.shape = (1, 5)
-        transformed_bbox = transforms.apply_rotated_box(boxes)[0]
-
-        expected_bbox = np.array([484, 388, 248, 160, 56], dtype=np.float64)
-        err_msg = "transformed_bbox = {}, expected {}".format(transformed_bbox, expected_bbox)
-        assert np.allclose(transformed_bbox, expected_bbox), err_msg
-
-    def test_apply_rotated_boxes_unequal_scaling_factor(self):
-        np.random.seed(125)
-        h, w = 400, 200
-        newh, neww = 800, 800
-        image = np.random.rand(h, w)
-        transform_gen = []
-        transform_gen.append(T.Resize(shape=(newh, neww)))
-        image, transforms = T.apply_transform_gens(transform_gen, image)
-        image_shape = image.shape[:2]  # h, w
-        assert image_shape == (newh, neww)
-
-        boxes = np.array(
-            [
-                [150, 100, 40, 20, 0],
-                [150, 100, 40, 20, 30],
-                [150, 100, 40, 20, 90],
-                [150, 100, 40, 20, -90],
-            ],
-            dtype=np.float64,
-        )
-        transformed_boxes = transforms.apply_rotated_box(boxes)
-
-        expected_bboxes = np.array(
-            [
-                [600, 200, 160, 40, 0],
-                [600, 200, 144.22205102, 52.91502622, 49.10660535],
-                [600, 200, 80, 80, 90],
-                [600, 200, 80, 80, -90],
-            ],
-            dtype=np.float64,
-        )
-        err_msg = "transformed_boxes = {}, expected {}".format(transformed_boxes, expected_bboxes)
-        assert np.allclose(transformed_boxes, expected_bboxes), err_msg
-
-    def test_print_transform_gen(self):
-        t = T.RandomCrop("relative", (100, 100))
-        self.assertTrue(str(t) == "RandomCrop(crop_type='relative', crop_size=(100, 100))")
-
-        t = T.RandomFlip(prob=0.5)
-        self.assertTrue(str(t) == "RandomFlip(prob=0.5)")
-
-        t = T.RandomFlip()
-        self.assertTrue(str(t) == "RandomFlip()")

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/inner_product.h

@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  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.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special inner_product functions
-

spaces/Caoyunkang/Segment-Any-Anomaly/SAA/prompts/mvtec_parameters.py

@@ -1,92 +0,0 @@
-manual_prompts = {
-    'carpet': [
-        # prompts, filtered phrase
-        ['black hole', 'carpet'],
-        ['thread', 'carpet'],
-        ['defect.', 'carpet'],
-
-    ],
-
-    'grid': [
-        # prompts, filtered phrase
-        ['irregular pattern', 'grid'],
-        ['defect.', 'grid'],
-    ],
-
-    'leather': [
-        ['defect.', 'leather'],
-    ],
-
-    'tile': [
-        ['defect.', 'tile'],
-    ],
-
-    'wood': [
-        ['defect.', 'wood'],
-    ],
-
-    'bottle': [
-        # prompts, filtered phrase
-        ['broken part. contamination. white broken.', 'bottle'],
-    ],
-
-    'cable': [
-        # prompts, filtered phrase
-        ['crack. flawed golden wire. black hole.', 'cable'],
-    ],
-
-    'capsule': [
-        ['white crack. hole.', 'capsule'],
-        # ['hole on capsule', 'capsule']
-
-    ],
-
-    'hazelnut': [
-        # prompts, filtered phrase
-        ['white print. crack. thread.', 'hazelnut'],
-    ],
-
-    'metal_nut': [
-        # prompts, filtered phrase
-        ['blue defect. black defect. red defect. scratch.', 'nut'],
-    ],
-
-    'pill': [
-        # prompts, filtered phrase
-        ['red defect. yellow defect. blue defect. crack. scratch.', 'pill'],
-    ],
-
-    'screw': [
-        ['defect.', 'screw'],
-    ],
-
-    'toothbrush': [
-        ['defect.', 'toothbrush'],
-    ],
-
-    'transistor': [
-        ['defect.', 'transistor'],
-    ],
-
-    'zipper': [
-        ['crack. broken leather.', 'zipper']
-    ]
-}
-
-property_prompts = {
-    'carpet': 'the image of carpet have 1 dissimilar carpet, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-    'grid': 'the image of grid have 1 dissimilar grid, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-    'leather': 'the image of leather have 1 dissimilar leather, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-    'tile': 'the image of tile have 1 dissimilar tile, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-    'wood': 'the image of wood have 1 dissimilar wood, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-    'bottle': 'the image of bottle have 1 dissimilar bottle, with a maximum of 5 anomaly. The anomaly would not exceed 0.3 object area. ',
-    'cable': 'the image of cable have 1 dissimilar cable, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-    'capsule': 'the image of capsule have 1 dissimilar capsule, with a maximum of 5 anomaly. The anomaly would not exceed 0.6 object area. ',
-    'hazelnut': 'the image of hazelnut have 1 dissimilar hazelnut, with a maximum of 5 anomaly. The anomaly would not exceed 0.9 object area. ',
-    'metal_nut': 'the image of metal_nut have 1 dissimilar metal_nut, with a maximum of 5 anomaly. The anomaly would not exceed 1. object area. ',
-    'pill': 'the image of pill have 1 dissimilar pill, with a maximum of 5 anomaly. The anomaly would not exceed 1. object area. ',
-    'screw': 'the image of screw have 1 dissimilar screw, with a maximum of 5 anomaly. The anomaly would not exceed 0.1 object area. ',
-    'toothbrush': 'the image of toothbrush have 1 dissimilar toothbrush, with a maximum of 5 anomaly. The anomaly would not exceed 0.5 object area. ',
-    'transistor': 'the image of transistor have 1 dissimilar transistor, with a maximum of 5 anomaly. The anomaly would not exceed 1. object area. ',
-    'zipper': 'the image of zipper have 1 dissimilar zipper, with a maximum of 5 anomaly. The anomaly would not exceed 0.5 object area. ',
-}

spaces/CyStorm/instruct-pix2pix/README.md

@@ -1,217 +0,0 @@
----
-title: InstructPix2Pix
-sdk: gradio
-sdk_version: 3.16.2
-app_file: edit_app.py
-pinned: true
-duplicated_from: timbrooks/instruct-pix2pix
----
-
-# InstructPix2Pix: Learning to Follow Image Editing Instructions
-### [Project Page](https://www.timothybrooks.com/instruct-pix2pix/) | [Paper](https://arxiv.org/abs/2211.09800) | [Data](http://instruct-pix2pix.eecs.berkeley.edu/)
-PyTorch implementation of InstructPix2Pix, an instruction-based image editing model, based on the original [CompVis/stable_diffusion](https://github.com/CompVis/stable-diffusion) repo. <br>
-
-[InstructPix2Pix: Learning to Follow Image Editing Instructions](https://www.timothybrooks.com/instruct-pix2pix/)  
- [Tim Brooks](https://www.timothybrooks.com/)\*,
- [Aleksander Holynski](https://holynski.org/)\*,
- [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros/) <br>
- UC Berkeley <br>
-  \*denotes equal contribution  
-  
-  <img src='https://instruct-pix2pix.timothybrooks.com/teaser.jpg'/>
-
-## TL;DR: quickstart 
-
-Set up a conda environment, and download a pretrained model:
-```
-conda env create -f environment.yaml
-conda activate ip2p
-bash scripts/download_checkpoints.sh
-```
-
-Edit a single image:
-```
-python edit_cli.py --input imgs/example.jpg --output imgs/output.jpg --edit "turn him into a cyborg"
-
-# Optionally, you can specify parameters to tune your result:
-# python edit_cli.py --steps 100 --resolution 512 --seed 1371 --cfg-text 7.5 --cfg-image 1.2 --input imgs/example.jpg --output imgs/output.jpg --edit "turn him into a cyborg"
-```
-
-Or launch your own interactive editing Gradio app:
-```
-python edit_app.py 
-```
-![Edit app](https://github.com/timothybrooks/instruct-pix2pix/blob/main/imgs/edit_app.jpg?raw=true)
-
-_(For advice on how to get the best results by tuning parameters, see the [Tips](https://github.com/timothybrooks/instruct-pix2pix#tips) section)._
-
-## Setup
-
-Install all dependencies with:
-```
-conda env create -f environment.yaml
-```
-
-Download the pretrained models by running:
-```
-bash scripts/download_checkpoints.sh
-```
-
-## Generated Dataset
-
-Our image editing model is trained on a generated dataset consisting of 454,445 examples. Each example contains (1) an input image, (2) an editing instruction, and (3) an output edited image. We provide two versions of the dataset, one in which each pair of edited images is generated 100 times, and the best examples are chosen based on CLIP metrics (Section 3.1.2 in the paper) (`clip-filtered-dataset`), and one in which examples are randomly chosen (`random-sample-dataset`).
-
-For the released version of this dataset, we've additionally filtered prompts and images for NSFW content. After NSFW filtering, the GPT-3 generated dataset contains 451,990 examples. The final image-pair datasets contain:
-
-|  | # of image editing examples | Dataset size |
-|--|-----------------------|----------------------- |
-| `random-sample-dataset` |451990|727GB|
-|  `clip-filtered-dataset` |313010|436GB|
-
-To download one of these datasets, along with the entire NSFW-filtered text data, run the following command with the appropriate dataset name:
-
-```
-bash scripts/download_data.sh clip-filtered-dataset
-```
-
-
-## Training InstructPix2Pix
-
-InstructPix2Pix is trained by fine-tuning from an initial StableDiffusion checkpoint. The first step is to download a Stable Diffusion checkpoint. For our trained models, we used the v1.5 checkpoint as the starting point. To download the same ones we used, you can run the following script:
-```
-bash scripts/download_pretrained_sd.sh
-```
-If you'd like to use a different checkpoint, point to it in the config file `configs/train.yaml`, on line 8, after `ckpt_path:`. 
-
-Next, we need to change the config to point to our downloaded (or generated) dataset. If you're using the `clip-filtered-dataset` from above, you can skip this. Otherwise, you may need to edit lines 85 and 94 of the config (`data.params.train.params.path`, `data.params.validation.params.path`). 
-
-Finally, start a training job with the following command:
-
-```
-python main.py --name default --base configs/train.yaml --train --gpus 0,1,2,3,4,5,6,7
-```
-
-
-## Creating your own dataset
-
-Our generated dataset of paired images and editing instructions is made in two phases: First, we use GPT-3 to generate text triplets: (a) a caption describing an image, (b) an edit instruction, (c) a caption describing the image after the edit. Then, we turn pairs of captions (before/after the edit) into pairs of images using Stable Diffusion and Prompt-to-Prompt.
-
-### (1) Generate a dataset of captions and instructions
-
-We provide our generated dataset of captions and edit instructions [here](https://instruct-pix2pix.eecs.berkeley.edu/gpt-generated-prompts.jsonl). If you plan to use our captions+instructions, skip to step (2). Otherwise, if you would like to create your own text dataset, please follow steps (1.1-1.3) below. Note that generating very large datasets using GPT-3 can be expensive.
-
-#### (1.1) Manually write a dataset of instructions and captions
-
-The first step of the process is fine-tuning GPT-3. To do this, we made a dataset of 700 examples broadly covering of edits that we might want our model to be able to perform. Our examples are available [here](https://instruct-pix2pix.eecs.berkeley.edu/human-written-prompts.jsonl). These should be diverse and cover a wide range of possible captions and types of edits. Ideally, they should avoid duplication or significant overlap of captions and instructions. It is also important to be mindful of limitations of Stable Diffusion and Prompt-to-Prompt in writing these examples, such as inability to perform large spatial transformations (e.g., moving the camera, zooming in, swapping object locations). 
-
-Input prompts should closely match the distribution of input prompts used to generate the larger dataset. We sampled the 700 input prompts from the _LAION Improved Aesthetics 6.5+_ dataset and also use this dataset for generating examples. We found this dataset is quite noisy (many of the captions are overly long and contain irrelevant text). For this reason, we also considered MSCOCO and LAION-COCO datasets, but ultimately chose _LAION Improved Aesthetics 6.5+_ due to its diversity of content, proper nouns, and artistic mediums. If you choose to use another dataset or combination of datasets as input to GPT-3 when generating examples, we recommend you sample the input prompts from the same distribution when manually writing training examples.
-
-#### (1.2) Finetune GPT-3
-
-The next step is to finetune a large language model on the manually written instructions/outputs to generate edit instructions and edited caption from a new input caption. For this, we finetune GPT-3's Davinci model via the OpenAI API, although other language models could be used.
-
-To prepare training data for GPT-3, one must first create an OpenAI developer account to access the needed APIs, and [set up the API keys on your local device](https://beta.openai.com/docs/api-reference/introduction). Also, run the `prompts/prepare_for_gpt.py` script, which forms the prompts into the correct format by concatenating instructions and captions and adding delimiters and stop sequences.
-
-```bash
-python dataset_creation/prepare_for_gpt.py --input-path data/human-written-prompts.jsonl --output-path data/human-written-prompts-for-gpt.jsonl
-```
-
-Next, finetune GPT-3 via the OpenAI CLI. We provide an example below, although please refer to OpenAI's official documentation for this, as best practices may change. We trained the Davinci model for a single epoch. You can experiment with smaller less expensive GPT-3 variants or with open source language models, although this may negatively affect performance.
-
-```bash
-openai api fine_tunes.create -t data/human-written-prompts-for-gpt.jsonl -m davinci --n_epochs 1 --suffix "instruct-pix2pix"
-```
-
-You can test out the finetuned GPT-3 model by launching the provided Gradio app:
-
-```bash
-python prompt_app.py --openai-api-key OPENAI_KEY --openai-model OPENAI_MODEL_NAME
-```
-
-![Prompt app](https://github.com/timothybrooks/instruct-pix2pix/blob/main/imgs/prompt_app.jpg?raw=true)
-
-#### (1.3) Generate a large dataset of captions and instructions
-
-We now use the finetuned GPT-3 model to generate a large dataset. Our dataset cost thousands of dollars to create. See `prompts/gen_instructions_and_captions.py` for the script which generates these examples. We recommend first generating a small number of examples (by setting a low value of `--num-samples`) and gradually increasing the scale to ensure the results are working as desired before increasing scale.
-
-```bash
-python dataset_creation/generate_txt_dataset.py --openai-api-key OPENAI_KEY --openai-model OPENAI_MODEL_NAME
-```
-
-If you are generating at a very large scale (e.g., 100K+), it will be noteably faster to generate the dataset with multiple processes running in parallel. This can be accomplished by setting `--partitions=N` to a higher number and running multiple processes, setting each `--partition` to the corresponding value.
-
-```bash
-python dataset_creation/generate_txt_dataset.py --openai-api-key OPENAI_KEY --openai-model OPENAI_MODEL_NAME --partitions=10 --partition=0
-```
-
-### (2) Turn paired captions into paired images
-
-The next step is to turn pairs of text captions into pairs of images. For this, we need to copy some pre-trained Stable Diffusion checkpoints to `stable_diffusion/models/ldm/stable-diffusion-v1/`. You may have already done this if you followed the instructions above for training with our provided data, but if not, you can do this by running:
-
-```bash
-bash scripts/download_pretrained_sd.sh
-```
-
-For our model, we used [checkpoint v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.ckpt), and the [new autoencoder](https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt), but other models may work as well. If you choose to use other models, make sure to change point to the corresponding checkpoints by passing in the `--ckpt` and `--vae-ckpt` arguments. Once all checkpoints have been downloaded, we can generate the dataset with the following command:
-
-```
-python dataset_creation/generate_img_dataset.py --out_dir data/instruct-pix2pix-dataset-000 --prompts_file path/to/generated_prompts.jsonl
-```
-
-This command operates on a single GPU (typically a V100 or A100). To parallelize over many GPUs/machines, set `--n-partitions` to the total number of parallel jobs and `--partition` to the index of each job.
-
-```
-python dataset_creation/generate_img_dataset.py --out_dir data/instruct-pix2pix-dataset-000 --prompts_file path/to/generated_prompts.jsonl --n-partitions 100 --partition 0
-```
-
-The default parameters match that of our dataset, although in practice you can use a smaller number of steps (e.g., `--steps=25`) to generate high quality data faster. By default, we generate 100 samples per prompt and use CLIP filtering to keep a max of 4 per prompt. You can experiment with fewer samples by setting `--n-samples`. The command below turns off CLIP filtering entirely and is therefore faster:
-
-```
-python dataset_creation/generate_img_dataset.py --out_dir data/instruct-pix2pix-dataset-000 --prompts_file path/to/generated_prompts.jsonl --n-samples 4 --clip-threshold 0 --clip-dir-threshold 0 --clip-img-threshold 0 --n-partitions 100 --partition 0
-```
-
-After generating all of the dataset examples, run the following command below to create a list of the examples. This is needed for the dataset onject to efficiently be able to sample examples without needing to iterate over the entire dataset directory at the start of each training run.
-
-```
-python dataset_creation/prepare_dataset.py data/instruct-pix2pix-dataset-000
-```
-
-## Evaluation
-
-To generate plots like the ones in Figures 8 and 10 in the paper, run the following command:
-
-```
-python metrics/compute_metrics.py --ckpt /path/to/your/model.ckpt
-```
-
-## Tips
-
-If you're not getting the quality result you want, there may be a few reasons:
-1. **Is the image not changing enough?** Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
-    * Decreasing the Image CFG weight, or
-    * Incerasing the Text CFG weight, or
-2. Conversely, **is the image changing too much**, such that the details in the original image aren't preserved? Try:
-    * Increasing the Image CFG weight, or
-    * Decreasing the Text CFG weight
-3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
-4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
-5. Increasing the number of steps sometimes improves results.
-6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try cropping the image so the face takes up a larger portion of the frame.
-
-## Comments
-
-- Our codebase is based on the [Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion).
-
-## BibTeX
-
-```
-@article{brooks2022instructpix2pix,
-  title={InstructPix2Pix: Learning to Follow Image Editing Instructions},
-  author={Brooks, Tim and Holynski, Aleksander and Efros, Alexei A},
-  journal={arXiv preprint arXiv:2211.09800},
-  year={2022}
-}
-```
-
-
-

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-8997c120.js

@@ -1,2 +0,0 @@
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