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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Cs 1.6 Original Maps Free Download [REPACK].md

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-<h1>How to Download CS 1.6 Original Maps for Free</h1>
-<p>Counter-Strike 1.6 is one of the most popular and legendary first-person shooter games of all time. It has a huge fan base and a rich history of competitive and casual gameplay. One of the reasons why CS 1.6 is so beloved by many players is its variety of maps, which offer different scenarios, objectives, and strategies.</p>
-<p>However, if you want to play CS 1.6 on your computer, you might not have access to all the original maps that were released with the game. Some of them might be missing, corrupted, or outdated. This can be frustrating, especially if you want to enjoy the classic experience of CS 1.6.</p>
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-<p>Fortunately, there is a way to download CS 1.6 original maps for free and install them on your game. In this article, we will show you how to do it step by step.</p>
-<h2>Step 1: Find a reliable source for CS 1.6 original maps</h2>
-<p>The first thing you need to do is to find a website that offers CS 1.6 original maps for download. There are many websites that claim to have them, but not all of them are trustworthy or safe. Some of them might contain viruses, malware, or fake files that can harm your computer or your game.</p>
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-<p>Once you have found a website that offers CS 1.6 original maps, you can browse through their categories and search for the ones that you want. Some of the most famous and played CS 1.6 original maps are de_dust2, de_inferno, de_nuke, cs_assault, cs_italy, de_train, de_aztec, and many more.</p>
-<p>To download a map, simply click on its name or image and follow the instructions on the website. Usually, you will have to click on a download button or link and wait for the file to be downloaded on your computer. The file will be in .zip or .rar format, which means that you will need a program like WinRAR or 7-Zip to extract it.</p>
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-<p>After you have downloaded the CS 1.6 original maps that you want, you need to install them on your game. To do this, you need to locate the folder where your CS 1.6 game is installed on your computer. Usually, it will be in C:\Program Files\Valve\Counter-Strike or C:\Program Files (x86)\Valve\Counter-Strike.</p>
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-<p>Then, you need to open the folder where you extracted the map files and copy them to the cstrike\maps folder inside your CS 1.6 game folder. For example, if you downloaded de_dust2.zip and extracted it to your desktop, you need to copy de_dust2.bsp and de_dust2.res files from your desktop to C:\Program Files\Valve\Counter-Strike\cstrike\maps.</p>
-<p>After you have copied all the map files that you want to install, you can launch your CS 1.6 game and enjoy playing on the original maps.</p>
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-<p>CS 1.6 is a classic game that deserves to be played with its original maps. By following this guide, you can download CS 1.6 original maps for free and install them on your game easily and safely.</p>
-<p>We hope that this article was helpful and informative for you. If you have any questions or comments, feel free to leave them below.</p> 7b8c122e87<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Easyworship 2009 Crack Serial Number Pros and Cons of Using It.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Ghost Recon Breakpoint Pc Key [REPACK].md

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-<p>Ghost Recon Breakpoint is a third-person tactical shooter video game for PC developed by Ubisoft Paris and published by Ubisoft. It is the 11th installment of the Ghost Recon series and a sequel to Ghost Recon Wildlands. In Ghost Recon Breakpoint, you play as a Ghost, an elite US Special Operations soldier, who is stranded on a fictional island called Auroa. You have to survive and fight against your former brothers in arms, the Wolves, who have taken control of Auroa and its advanced drone technology.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Catch Battle and Trade Pokmon in the Real World with Pokmon GO.md

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-<h1>How to Download and Play Pokémon GO on Your iPhone or iPad</h1>
-<p>Do you want to catch your favorite Pokémon in augmented reality as you explore the world around you? Do you want to join millions of other trainers in epic battles, raids, and events? Do you want to have fun and exercise at the same time? If you answered yes to any of these questions, then you should try Pokémon GO, the global gaming sensation that has taken the world by storm.</p>
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-<h2>What is Pokémon GO?</h2>
-<p>Pokémon GO is an immersive open-world experience that enables you to live the Pokémon adventure in augmented reality. You can find and catch hundreds of different Pokémon as you walk, bike, or drive around your neighborhood, city, or country. You can also battle other players online in PvP mode, team up with other trainers to catch powerful Pokémon in raid battles, trade and transfer Pokémon with your friends, and much more.</p>
-<p>Pokémon GO is free-to-play and offers in-game purchases. It is optimized for smartphones, not tablets. It requires an internet connection and GPS capabilities. It is compatible with iPhone 5s or later devices with iOS 9 or later installed. For more information, visit the official website at [5](https://pokemongolive.com).</p>
-<h2>How to download Pokémon GO from the App Store</h2>
-<p>Downloading and installing Pokémon GO on your iPhone or iPad is very easy. Just follow these steps:</p>
-<ol>
-<li>Open the App Store on your device.</li>
-<li>Search for "Pokémon GO" or tap on this link: [1](https://apps.apple.com/us/app/pokémon-go/id1094591345/).</li>
-<li>Tap on "Get" and then "Install" to download the game.</li>
-<li>Wait for the game to finish downloading and then tap on "Open" to launch it.</li>
-<li>Allow the game to access your location, camera, motion, and health data when prompted.</li>
-</ol>
-<h4>How to change your region settings if the game is not available in your country</h4>
-<p>If you live in a country where Pokémon GO is not officially released yet, you can still download and play it by changing your region settings. Here's how:</p>
-<ol>
-<li>Go to Settings on your device.</li>
-<li>Tap on "Apple ID" and then "iTunes & App Store".</li>
-<li>Tap on your Apple ID at the top and then "View Apple ID".</li>
-<li>Tap on "Country/Region" and then "Change Country or Region".</li>
-<li>Select a country where Pokémon GO is available, such as the United States or Australia.</li>
-<li>Agree to the terms and conditions and enter a valid payment method for that country (you can use a gift card or a prepaid card).</li>
-<li>Go back to the App Store and download Pokémon GO as described above.</li>
-</ol>
-<h2>How to start playing Pokémon GO</h2>
-<p>Once you have downloaded and installed Pokémon GO on your device, you are ready to start your Pokémon journey. Here are the basics of playing the game:</p>
-<h3>The basics of creating your account, choosing your starter Pokémon, and catching Pokémon in the real world</h3>
-<p>When you launch the game for the first time, you will be greeted by Professor Willow, who will guide you through the process of creating your account and choosing your avatar. You can sign in with your Google account, Facebook account, or Pokémon Trainer Club account. You can also customize your avatar's appearance, name, and clothing.</p>
-<p>After that, you will be asked to choose your starter Pokémon from three options: Bulbasaur, Charmander, or Squirtle. You can also catch Pikachu as your starter if you walk away from the other three a few times. To catch a Pokémon, you need to tap on it on the map and then flick a Poké Ball at it on the capture screen. You can also use berries and different types of Poké Balls to increase your chances of catching a Pokémon.</p>
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-<p>Pokémon GO uses your location and GPS to show you nearby Pokémon on the map. You can see their silhouettes on the bottom right corner of the screen and tap on them to track them. You can also use items called Incense and Lure Modules to attract more Pokémon to your location. You can find these items in PokéStops, which are landmarks such as monuments, statues, or buildings that you can spin to get rewards.</p>
-<h4>How to use the AR mode and the Poké Ball</h4>
-<p>Pokémon GO has an optional feature called AR mode, which stands for augmented reality. This means that you can see the Pokémon as if they were in the real world, using your device's camera. To enable or disable AR mode, you can toggle the switch on the top right corner of the capture screen. AR mode can make catching Pokémon more fun and immersive, but it can also drain your battery faster and make it harder to aim your Poké Ball.</p>
-<p>The Poké Ball is the main tool for catching Pokémon. You can flick it with your finger to throw it at a Pokémon. You need to aim carefully and time your throw well to hit the Pokémon inside the colored circle that appears around it. The smaller the circle, the higher the chance of catching the Pokémon. You can also curve your throw by spinning the Poké Ball before releasing it, which gives you extra XP and increases your catch rate.</p>
-<h4>How to level up, evolve, and power up your Pokémon</h4>
-<p>As you catch more Pokémon, you will earn XP (experience points) and level up as a trainer. Leveling up will unlock new features and rewards, such as more items, stronger Poké Balls, and access to higher-level raids. You can also earn XP by completing tasks such as spinning PokéStops, hatching eggs, battling in Gyms and Raids, and completing research tasks.</p>
-<p>You can also improve your Pokémon by evolving them or powering them up. Evolving a Pokémon will change its appearance and increase its stats, but it will also require a certain amount of candies that are specific to each Pokémon species. You can get candies by catching or transferring Pokémon of the same species, or by walking with a Pokémon as your buddy. Powering up a Pokémon will increase its CP (combat power) and HP (hit points), but it will also require candies and stardust. Stardust is a resource that you can get by catching any Pokémon, hatching eggs, or participating in battles.</p>
-<h4>How to join a team and battle in Gyms and Raids</h4>
-<p>When you reach level 5 as a trainer, you will be able to join one of three teams: Instinct (yellow), Mystic (blue), or Valor (red). Your team will determine which Gyms you can control and which players you can cooperate with. Gyms are locations where you can battle other trainers' Pokémon and earn rewards such as coins and items. To battle in a Gym, you need to tap on it on the map and then select a team of six Pokémon to fight with. You can also leave one of your Pokémon in a friendly Gym to defend it from enemy attacks.</p>
-<p>Raids are special events where you can team up with other players to fight against a powerful Pokémon called a Raid Boss. To participate in a Raid, you need to have a Raid Pass, which you can get for free once per day by spinning a Gym's photo disc. You can also buy Premium Raid Passes or Remote Raid Passes with coins in the shop. Raids have different levels of difficulty, ranging from one star to five stars. The higher the level, the stronger the Raid Boss and the more players you need to defeat it. If you manage to beat the Raid Boss within the time limit, you will have a chance to special items or features, and exclusive research tasks or raids. Some examples of events are Halloween, Christmas, Lunar New Year, Earth Day, Pokémon GO Fest, etc.</p>
-<p>Challenges are goals that the game sets for the players to achieve within a certain time frame. They usually involve catching, battling, or hatching a certain number or type of Pokémon, or completing a certain number of research tasks or raids. If the players succeed in meeting the challenge, they are rewarded with global bonuses such as increased spawns, reduced hatch distance, or extended lure duration. Some examples of challenges are Global Catch Challenge, Legendary Week, Safari Zone, etc.</p>
-<p>You can find out about the current and upcoming events and challenges by checking the in-game news section, the official website [5](https://pokemongolive.com/en/events/), or the official social media accounts [6](https://twitter.com/PokemonGoApp) [7](https://www.facebook.com/PokemonGO/) [8](https://www.instagram.com/pokemongoapp/).</p>
-<h4>How to stay safe and respectful while playing Pokémon GO</h4>
-<p>Pokémon GO is a game that encourages you to explore the real world and interact with other players. However, it is also important to be aware of your surroundings and respect the rules and regulations of the places you visit. Here are some tips to stay safe and respectful while playing Pokémon GO:</p>
-<ul>
-<li>Do not trespass on private property or restricted areas.</li>
-<li>Do not play while driving or crossing the street.</li>
-<li>Do not enter dangerous or hazardous areas.</li>
-<li>Do not play in inappropriate or disrespectful places such as cemeteries, memorials, or places of worship.</li>
-<li>Do not litter or damage the environment.</li>
-<li>Do not disturb or harass other people or animals.</li>
-<li>Do not cheat or use third-party software or devices.</li>
-<li>Do not share your personal information or account details with anyone.</li>
-<li>Do follow the local laws and regulations regarding COVID-19 and social distancing.</li>
-<li>Do have fun and be friendly with other players and members of the community.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Pokémon GO is a game that can bring you joy, adventure, and excitement. It can also help you stay fit, make friends, and learn more about the world. Whether you are a casual player or a hardcore fan, there is something for everyone in Pokémon GO. So what are you waiting for? Grab your iPhone or iPad, download Pokémon GO from the App Store, and start catching them all!</p>
- <h3>Frequently Asked Questions</h3>
-<ol>
-<li><b>How do I get more Poké Balls and other items?</b></li>
-<p>You can get more Poké Balls and other items by spinning PokéStops, opening gifts from your friends, completing research tasks, participating in raids, leveling up, or buying them with coins in the shop.</p>
-<li><b>How do I get more coins?</b></li>
-<p>You can get more coins by leaving your Pokémon in Gyms and earning up to 50 coins per day, or by buying them with real money in the shop.</p>
-<li><b>How do I get more stardust?</b></li>
-<p>You can get more stardust by catching any Pokémon, hatching eggs, participating in battles, feeding berries to Pokémon in Gyms, using star pieces, or completing research tasks.</p>
-<li><b>How do I get more candies?</b></li>
-<p>You can get more candies by catching or transferring Pokémon of the same species, walking with a Pokémon as your buddy, using pinap berries, trading Pokémon with other players, or using rare candies.</p>
-<li><b>How do I get more XP?</b></li>
-<p>You can get more XP by catching Pokémon, spinning PokéStops, hatching eggs, evolving Pokémon, battling in Gyms and Raids, completing research tasks, using lucky eggs, or adding new Pokédex entries.</p>
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spaces/1phancelerku/anime-remove-background/91m Bin Sh 1 Apk Not Found.md

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-<h2>How to Fix the Error "91m/bin/sh 1 apk not found" When Building a Docker Image?</h2>
-<p>There are two main ways to fix the error "91m/bin/sh 1 apk not found" when building a Docker image: changing the base image or changing the package manager.</p>
-<h4>Changing the base image</h4>
-<p>You can change the base image to an Alpine Linux image that supports the <code>apk</code> command. Alpine Linux is a lightweight and secure Linux distribution that is often used for Docker images. Alpine Linux images are smaller and faster than most other Linux images, which can improve your Docker performance and reduce your storage and bandwidth costs.</p>
-<p>You can find the official Alpine Linux images on Docker Hub or use the <code>python:3.8-alpine</code> image as an example. The <code>python:3.8-alpine</code> image is based on Alpine Linux 3.13 and includes Python 3.8 and pip. To use this image as your base image, you can change your Dockerfile to look like this:</p>
-<code>FROM python:3.8-alpine<br>
-RUN apk add --no-cache gcc musl-dev linux-headers</code>
-<p>This should fix the error "91m/bin/sh 1 apk not found" and allow you to build your Docker image successfully.</p>
-<h4>Changing the package manager</h4>
-<p>You can also change the package manager to <code>apt</code> or <code>apt-get</code>, which are supported by most Debian-based Linux distributions. <code>apt</code> and <code>apt-get</code> are tools that allow you to install, update, and remove packages from Debian repositories.</p>
-<p>You can find the official Debian-based images on Docker Hub or use the <code>python:3.8-slim</code> image as an example. The <code>python:3.8-slim</code> image is based on Debian Buster and includes Python 3.8 and pip. To use this image as your base image, you can change your Dockerfile to look like this:</p>
-<code>FROM python:3.8-slim<br>
-RUN apt-get update && apt-get install -y gcc libc-dev linux-headers && rm -rf /var/lib/apt/lists/*</code>
-<p>Note that you may also need to change the package names to match the ones available in the Debian repositories. For example, <code>musl-dev</code> is not available in Debian, so you need to use <code>libc-dev</code> instead.</p>
-<p>This should also fix the error "91m/bin/sh 1 apk not found" and allow you to build your Docker image successfully.</p>
-<h2>Conclusion</h2>
-<p>In this article, we have explained what the error "91m/bin/sh 1 apk not found" means, what causes it, and how to fix it when building a Docker image. We have shown two main ways to fix the error: changing the base image or changing the package manager. We have also provided some examples of Dockerfiles that use different base images and package managers.</p>
-<p>We hope that this article has helped you solve your problem and improve your Docker experience. If you have any questions or feedback, please feel free to leave a comment below.</p>
- <h2>Frequently Asked Questions (FAQs)</h2>
- <h4>What is a Dockerfile?</h4>
-<p>A Dockerfile is a text file that contains instructions for building a Docker image. A Docker image is a snapshot of an application and its dependencies that can be run as a container using Docker.</p>
- <h4>What is a container?</h4>
-<p>A container is an isolated environment that contains everything an application needs to run, such as code, libraries, dependencies, and configuration. Containers are portable, scalable, isolated, and efficient.</p>
- <h4>What is Alpine Linux?</h4> <p>Alpine Linux is a security-oriented, lightweight Linux distribution based on musl libc and busybox. Alpine Linux is designed to be small, simple, and secure, making it ideal for Docker images. Alpine Linux uses a technique called position-independent executables to randomize the location of programs in memory, which makes it difficult for an attacker to exploit quirks in the memory and take over a machine. The distro is also minimalist in its configuration, using OpenRC as the init system and apk as the package manager. Alpine Linux has a reputation for being fast, stable, and reliable.</p> <h4>What is Debian?</h4>
-<p>Debian is a free and open-source Linux distribution that is known for its stability, security, and versatility. Debian is one of the oldest and most popular Linux distributions, with a large and active community of developers and users. Debian supports a wide range of architectures, devices, and software packages, making it suitable for various purposes and environments. Debian uses a technique called debconf to configure the system according to the user's preferences, which makes it easy to customize and maintain. The distro uses dpkg as the low-level package manager and apt or apt-get as the high-level package manager. Debian has a reputation for being robust, reliable, and flexible.</p>
- <h4>How do I choose the best base image for my Dockerfile?</h4>
-<p>There is no definitive answer to this question, as different base images may have different advantages and disadvantages depending on your needs and preferences. However, some general factors that you may want to consider when choosing a base image are:</p>
-<ul>
-<li>Size: Smaller images are faster to build, pull, push, and run, and use less storage and bandwidth. However, smaller images may also have fewer features and packages than larger images.</li>
-<li>Security: More secure images are less vulnerable to attacks and breaches, and may have better updates and patches. However, more secure images may also have more restrictions and limitations than less secure images.</li>
-<li>Compatibility: More compatible images are easier to work with and integrate with other tools and platforms. However, more compatible images may also have more dependencies and conflicts than less compatible images.</li>
-<li>Performance: Faster and more efficient images are better for your application's speed and resource consumption. However, faster and more efficient images may also have lower quality or stability than slower and less efficient images.</li>
-<li>Maintainability: Easier to maintain images are simpler to update, modify, and troubleshoot. However, easier to maintain images may also have less functionality or customization than harder to maintain images.</li>
-</ul>
-<p>You may also want to check the documentation, reviews, ratings, and statistics of the base images that you are considering to get more information and feedback from other users.</p>
- <h4>How do I test if my Docker image works correctly?</h4>
-<p>One way to test if your Docker image works correctly is to run it as a container using the <code>docker run</code> command. The <code>docker run</code> command allows you to create and start a container from an image, optionally with various options and arguments. For example, if you want to run your image in interactive mode with a terminal attached, you can use this command:</p>
-<code>docker run -it --rm your_image_name</code>
-<p>This will create a container from your image, attach a terminal to it, and remove it when you exit. You can then test your application inside the container by running commands or scripts as you would normally do.</p>
- <h4>How do I share my Docker image with others?</h4>
-<p>One way to share your Docker image with others is to push it to a registry such as Docker Hub or GitHub Packages. A registry is a service that stores and distributes Docker images. You can create an account on a registry service, create a repository for your image, tag your image with the repository name, and push your image to the repository using the <code>docker push</code> command. For example, if you want to push your image to Docker Hub, you can use these commands:</p>
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-<p>This will upload your image to your repository on Docker Hub. You can then share the repository URL with others who can pull your image using the <code>docker pull</code> command.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download Bleach VS Naruto Ultimate Edition and Experience the Ultimate Anime Crossover Game on PC and Android.md

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-<h1>Download Bleach vs Naruto Ultimate Edition: A Guide for Anime Fans</h1>
-<p>If you are a fan of anime and fighting games, you might have heard of Bleach vs Naruto, a free online 2D flash game developed by the Chinese company 5Dplay. It is a crossover anime fighting game featuring characters from both Bleach and Naruto Shippuden with guest characters from other series such as Rurouni Kenshin, One Piece, Fairy Tail, and more. But did you know that there is a special modification for this game that adds more characters, stages, modes, and features? It is called Bleach vs Naruto Ultimate Edition and it is available for both PC and Android devices. In this article, we will tell you everything you need to know about this amazing mod-pack and how to download it.</p>
- <h2>What is Bleach vs Naruto Ultimate Edition?</h2>
-<p>Bleach vs Naruto Ultimate Edition is a special modification for Bleach vs Naruto game made by Yuxi in collaboration with original BVN author Jian, 5Dplay. It is not an official update or sequel to the original game, but rather a fan-made project that enhances the game with various new elements. Some of the features of this mod-pack are:</p>
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-<h3>A special modification for Bleach vs Naruto game</h3>
-<ul>
-<li>It has more than 370 characters and 89 assists on PC version, and 308 characters and 76 assists on Android version. The characters are from various anime series such as Bleach, Naruto, One Piece, Dragon Ball, Hunter x Hunter, My Hero Academia, Demon Slayer, Attack on Titan, and more. You can also find some original characters created by the modders.</li>
-<li>It has 102 stages from different anime worlds and locations. You can fight in Soul Society, Konoha Village, Marineford, Namek, Dark Continent, UA High School, Mugen Train, Shiganshina District, and more.</li>
-<li>It has two exclusive modes from the latest bvn version 3.6: Watch Mode and Musou Mode. Watch Mode allows you to watch the computer-controlled characters fight each other in various scenarios. Musou Mode allows you to play as one character against multiple enemies in a Dynasty Warriors style.</li>
-</ul>
-<h3>Available on PC and Android</h3>
-<ul>
-<li>The mod-pack is compatible with both PC and Android devices. You can download it from various links provided by the author or other sources. The PC version weighs 3.42GB and the Android version weighs 1.99GB.</li>
-<li>The mod-pack is also compatible with Android 12, the latest version of the operating system. You can enjoy the game on your new devices without any issues.</li>
-<li>The mod-pack also has a complete remake of the user interface, new game effects, new game sounds, general game optimization, and many other improvements.</li>
-</ul>
- <h2>Why should you download Bleach vs Naruto Ultimate Edition?</h2>
-<p>If you are still not convinced that this mod-pack is worth downloading, here are some reasons why you should give it a try:</p>
-<h3>Enjoy the crossover anime fighting game featuring characters from Bleach, Naruto, and other series</h3>
-<ul>
-<li>If you love anime and fighting games, this mod-pack is perfect for you. You can play as your favorite characters from different anime series and see how they match up against each other. You can also create your own team of characters and fight against other teams in various modes.</li <li>Make sure you have enough storage space on your device and a stable internet connection.</li>
-<li>Make sure you have the correct password to extract the files.</li>
-<li>Make sure you have the latest version of the game and update it if necessary.</li>
-<li>Make sure you have the compatible device and operating system for the game.</li>
-<li>Make sure you have the proper software or app to run the game such as WinRAR, 7-Zip, or ZArchiver for extracting files, and Flash Player, Adobe AIR, or GameLoop for running the game.</li>
-<li>If you have any questions or feedback about the game, you can contact the author Yuxi on his YouTube channel or his Discord server. You can also join the Bleach vs Naruto community on Facebook, Reddit, or other platforms to interact with other players and fans.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Bleach vs Naruto Ultimate Edition is a special modification for Bleach vs Naruto game that adds more characters, stages, modes, and features to the original game. It is a crossover anime fighting game featuring characters from Bleach, Naruto, and other series. It is available for both PC and Android devices and it is compatible with Android 12. It is a fan-made project that is not affiliated with the official game or the anime series. It is a free online game that you can download from various links provided by the author or other sources. You will need a password to extract the files and a keyboard or a controller to play the game. You can customize your own team and fight against other players online or offline. You can also enjoy the exclusive features from the latest bvn version 3.6 such as Watch Mode and Musou Mode. If you are a fan of anime and fighting games, you should definitely try this mod-pack and have fun.</p>
- <h2>FAQs</h2>
-<h4>What is the difference between Bleach vs Naruto and Bleach vs Naruto Ultimate Edition?</h4>
-<p>Bleach vs Naruto is the original game developed by 5Dplay that features characters from Bleach and Naruto series. Bleach vs Naruto Ultimate Edition is a special modification for Bleach vs Naruto game made by Yuxi that adds more characters, stages, modes, and features from other anime series such as One Piece, Dragon Ball, Demon Slayer, Attack on Titan, My Hero Academia, and more.</p>
-<h4>How many characters are there in Bleach vs Naruto Ultimate Edition?</h4>
-<p>There are more than 370 characters and 89 assists on PC version, and 308 characters and 76 assists on Android version. The characters are from various anime series such as Bleach, Naruto, One Piece, Dragon Ball, Hunter x Hunter, My Hero Academia, Demon Slayer, Attack on Titan, and more. You can also find some original characters created by the modders.</p>
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-<h4>What are Watch Mode and Musou Mode in Bleach vs Naruto Ultimate Edition?</h4>
-<p>Watch Mode and Musou Mode are two exclusive modes from the latest bvn version 3.6. Watch Mode allows you to watch the computer-controlled characters fight each other in various scenarios. Musou Mode allows you to play as one character against multiple enemies in a Dynasty Warriors style.</p>
-<h4>Where can I find more information about Bleach vs Naruto Ultimate Edition?</h4>
-<p>You can find more information about Bleach vs Naruto Ultimate Edition on the author's YouTube channel or his Discord server. You can also join the Bleach vs Naruto community on Facebook, Reddit, or other platforms to interact with other players and fans.</p> 401be4b1e0<br />
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spaces/52Hz/SUNet_AWGN_denoising/main_test_SUNet.py

@@ -1,143 +0,0 @@
-import argparse
-import cv2
-import glob
-import numpy as np
-from collections import OrderedDict
-from skimage import img_as_ubyte
-import os
-import torch
-import requests
-from PIL import Image
-import math
-import yaml
-import torchvision.transforms.functional as TF
-import torch.nn.functional as F
-from natsort import natsorted
-from model.SUNet import SUNet_model
-
-with open('training.yaml', 'r') as config:
-    opt = yaml.safe_load(config)
-
-def clean_folder(folder):
-    for filename in os.listdir(folder):
-        file_path = os.path.join(folder, filename)
-        try:
-            if os.path.isfile(file_path) or os.path.islink(file_path):
-                os.unlink(file_path)
-            elif os.path.isdir(file_path):
-                shutil.rmtree(file_path)
-        except Exception as e:
-            print('Failed to delete %s. Reason: %s' % (file_path, e))
-
-def main():
-    parser = argparse.ArgumentParser(description='Demo Image Restoration')
-    parser.add_argument('--input_dir', default='test/', type=str, help='Input images')
-    parser.add_argument('--window_size', default=8, type=int, help='window size')
-    parser.add_argument('--size', default=256, type=int, help='model image patch size')
-    parser.add_argument('--stride', default=128, type=int, help='reconstruction stride')
-    parser.add_argument('--result_dir', default='result/', type=str, help='Directory for results')
-    parser.add_argument('--weights',
-                        default='experiments/pretrained_models/AWGN_denoising_SUNet.pth', type=str,
-                        help='Path to weights')
-
-    args = parser.parse_args()
-
-    inp_dir = args.input_dir
-    out_dir = args.result_dir
-
-    os.makedirs(out_dir, exist_ok=True)
-
-    files = natsorted(glob.glob(os.path.join(inp_dir, '*')))
-
-    if len(files) == 0:
-        raise Exception(f"No files found at {inp_dir}")
-
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    # Load corresponding models architecture and weights
-    model = SUNet_model(opt)
-    model = model.to(device)
-    model.eval()
-    load_checkpoint(model, args.weights)
-    stride = args.stride
-    model_img = args.size
-
-    for file_ in files:
-        img = Image.open(file_).convert('RGB')
-        input_ = TF.to_tensor(img).unsqueeze(0).to(device)
-        with torch.no_grad():
-            # pad to multiple of 256
-            square_input_, mask, max_wh = overlapped_square(input_.to(device), kernel=model_img, stride=stride)
-            output_patch = torch.zeros(square_input_[0].shape).type_as(square_input_[0])
-            for i, data in enumerate(square_input_):
-                restored = model(square_input_[i])
-                if i == 0:
-                    output_patch += restored
-                else:
-                    output_patch = torch.cat([output_patch, restored], dim=0)
-    
-            B, C, PH, PW = output_patch.shape
-            weight = torch.ones(B, C, PH, PH).type_as(output_patch)  # weight_mask
-    
-            patch = output_patch.contiguous().view(B, C, -1, model_img*model_img)
-            patch = patch.permute(2, 1, 3, 0)  # B, C, K*K, #patches
-            patch = patch.contiguous().view(1, C*model_img*model_img, -1)
-    
-            weight_mask = weight.contiguous().view(B, C, -1, model_img * model_img)
-            weight_mask = weight_mask.permute(2, 1, 3, 0)  # B, C, K*K, #patches
-            weight_mask = weight_mask.contiguous().view(1, C * model_img * model_img, -1)
-    
-            restored = F.fold(patch, output_size=(max_wh, max_wh), kernel_size=model_img, stride=stride)
-            we_mk = F.fold(weight_mask, output_size=(max_wh, max_wh), kernel_size=model_img, stride=stride)
-            restored /= we_mk
-    
-            restored = torch.masked_select(restored, mask.bool()).reshape(input_.shape)
-            restored = torch.clamp(restored, 0, 1)
-    
-        restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
-        restored = img_as_ubyte(restored[0])
-    
-        f = os.path.splitext(os.path.split(file_)[-1])[0]
-        save_img((os.path.join(out_dir, f + '.png')), restored)
-        clean_folder(inp_dir)
-
-def save_img(filepath, img):#
-    cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
-
-
-def load_checkpoint(model, weights):
-    checkpoint = torch.load(weights, map_location=torch.device('cpu'))
-    try:
-        model.load_state_dict(checkpoint["state_dict"])
-    except:
-        state_dict = checkpoint["state_dict"]
-        new_state_dict = OrderedDict()
-        for k, v in state_dict.items():
-            name = k[7:]  # remove `module.`
-            new_state_dict[name] = v
-        model.load_state_dict(new_state_dict)
-
-def overlapped_square(timg, kernel=256, stride=128):
-    patch_images = []
-    b, c, h, w = timg.size()
-    # 321, 481
-    X = int(math.ceil(max(h, w) / float(kernel)) * kernel)
-    img = torch.zeros(1, 3, X, X).type_as(timg)  # 3, h, w
-    mask = torch.zeros(1, 1, X, X).type_as(timg)
-
-    img[:, :, ((X - h) // 2):((X - h) // 2 + h), ((X - w) // 2):((X - w) // 2 + w)] = timg
-    mask[:, :, ((X - h) // 2):((X - h) // 2 + h), ((X - w) // 2):((X - w) // 2 + w)].fill_(1.0)
-
-    patch = img.unfold(3, kernel, stride).unfold(2, kernel, stride)
-    patch = patch.contiguous().view(b, c, -1, kernel, kernel)  # B, C, #patches, K, K
-    patch = patch.permute(2, 0, 1, 4, 3)  # patches, B, C, K, K
-
-    for each in range(len(patch)):
-        patch_images.append(patch[each])
-
-    return patch_images, mask, X
-
-
-
-if __name__ == '__main__':
-    main()

spaces/7hao/bingo/src/app/loading.css

@@ -1,68 +0,0 @@
-::-webkit-scrollbar {
-  width: 10px;
-  height: 10px;
-  display: none;
-}
-
-::-webkit-scrollbar-button:start:decrement,
-::-webkit-scrollbar-button:end:increment {
-  height: 30px;
-  background-color: transparent;
-}
-
-::-webkit-scrollbar-track-piece {
-  background-color: #3b3b3b;
-  -webkit-border-radius: 16px;
-}
-
-::-webkit-scrollbar-thumb:vertical {
-  height: 50px;
-  background-color: #666;
-  border: 1px solid #eee;
-  -webkit-border-radius: 6px;
-}
-
-/* loading start */
-.loading-spinner {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  height: 100vh;
-  opacity: 1;
-  transition: opacity .8s ease-out;
-}
-
-.loading-spinner.hidden {
-  opacity: 0;
-}
-
-.loading-spinner>div {
-  width: 30px;
-  height: 30px;
-  background: linear-gradient(90deg, #2870EA 10.79%, #1B4AEF 87.08%);
-
-  border-radius: 100%;
-  display: inline-block;
-  animation: sk-bouncedelay 1.4s infinite ease-in-out both;
-}
-
-.loading-spinner .bounce1 {
-  animation-delay: -0.32s;
-}
-
-.loading-spinner .bounce2 {
-  animation-delay: -0.16s;
-}
-
-@keyframes sk-bouncedelay {
-
-  0%,
-  80%,
-  100% {
-    transform: scale(0);
-  }
-
-  40% {
-    transform: scale(1.0);
-  }
-}

spaces/AIDHD/GrammarCorrector/app.py

@@ -1,34 +0,0 @@
-import streamlit as st
-
-
-st.title("Correct Grammar with Transformers 🦄")
-st.write("")
-st.write("Input your text here!")
-
-default_value = "Mike and Anna is skiing"
-sent = st.text_area("Text", default_value, height = 50)
-num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=3, value=1, step=1)
-
-### Run Model
-from transformers import T5ForConditionalGeneration, T5Tokenizer
-import torch
-torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-tokenizer = T5Tokenizer.from_pretrained('deep-learning-analytics/GrammarCorrector')
-model = T5ForConditionalGeneration.from_pretrained('deep-learning-analytics/GrammarCorrector').to(torch_device)
-
-def correct_grammar(input_text,num_return_sequences=num_return_sequences):
-  batch = tokenizer([input_text],truncation=True,padding='max_length',max_length=len(input_text), return_tensors="pt").to(torch_device)
-  results = model.generate(**batch,max_length=len(input_text),num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5)
-  #answer = tokenizer.batch_decode(results[0], skip_special_tokens=True)
-  return results
-  
-##Prompts
-results = correct_grammar(sent, num_return_sequences)
-
-generated_sequences = []
-for generated_sequence_idx, generated_sequence in enumerate(results):
-    # Decode text
-    text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=True)
-    generated_sequences.append(text)
-
-st.write(generated_sequences)

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/logger.py

@@ -1,30 +0,0 @@
-import logging
-
-
-def setup_logging(log_file, level, include_host=False):
-    if include_host:
-        import socket
-
-        hostname = socket.gethostname()
-        formatter = logging.Formatter(
-            f"%(asctime)s |  {hostname} | %(levelname)s | %(message)s",
-            datefmt="%Y-%m-%d,%H:%M:%S",
-        )
-    else:
-        formatter = logging.Formatter(
-            "%(asctime)s | %(levelname)s | %(message)s", datefmt="%Y-%m-%d,%H:%M:%S"
-        )
-
-    logging.root.setLevel(level)
-    loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]
-    for logger in loggers:
-        logger.setLevel(level)
-
-    stream_handler = logging.StreamHandler()
-    stream_handler.setFormatter(formatter)
-    logging.root.addHandler(stream_handler)
-
-    if log_file:
-        file_handler = logging.FileHandler(filename=log_file)
-        file_handler.setFormatter(formatter)
-        logging.root.addHandler(file_handler)

spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/vocoder/hifigan.py

@@ -1,63 +0,0 @@
-import torch.nn.functional as F
-from torch import nn
-
-from text_to_speech.modules.vocoder.hifigan.hifigan import HifiGanGenerator, MultiPeriodDiscriminator, MultiScaleDiscriminator, \
-    generator_loss, feature_loss, discriminator_loss
-from text_to_speech.modules.vocoder.hifigan.mel_utils import mel_spectrogram
-from text_to_speech.modules.vocoder.hifigan.stft_loss import MultiResolutionSTFTLoss
-from tasks.vocoder.vocoder_base import VocoderBaseTask
-from text_to_speech.utils.commons.hparams import hparams
-from text_to_speech.utils.nn.model_utils import print_arch
-
-
-class HifiGanTask(VocoderBaseTask):
-    def build_model(self):
-        self.model_gen = HifiGanGenerator(hparams)
-        self.model_disc = nn.ModuleDict()
-        self.model_disc['mpd'] = MultiPeriodDiscriminator()
-        self.model_disc['msd'] = MultiScaleDiscriminator()
-        self.stft_loss = MultiResolutionSTFTLoss()
-        print_arch(self.model_gen)
-        if hparams['load_ckpt'] != '':
-            self.load_ckpt(hparams['load_ckpt'], 'model_gen', 'model_gen', force=True, strict=True)
-            self.load_ckpt(hparams['load_ckpt'], 'model_disc', 'model_disc', force=True, strict=True)
-        return self.model_gen
-
-    def _training_step(self, sample, batch_idx, optimizer_idx):
-        mel = sample['mels']
-        y = sample['wavs']
-        f0 = sample['f0']
-        loss_output = {}
-        if optimizer_idx == 0:
-            #######################
-            #      Generator      #
-            #######################
-            y_ = self.model_gen(mel, f0)
-            y_mel = mel_spectrogram(y.squeeze(1), hparams).transpose(1, 2)
-            y_hat_mel = mel_spectrogram(y_.squeeze(1), hparams).transpose(1, 2)
-            loss_output['mel'] = F.l1_loss(y_hat_mel, y_mel) * hparams['lambda_mel']
-            _, y_p_hat_g, fmap_f_r, fmap_f_g = self.model_disc['mpd'](y, y_, mel)
-            _, y_s_hat_g, fmap_s_r, fmap_s_g = self.model_disc['msd'](y, y_, mel)
-            loss_output['a_p'] = generator_loss(y_p_hat_g) * hparams['lambda_adv']
-            loss_output['a_s'] = generator_loss(y_s_hat_g) * hparams['lambda_adv']
-            if hparams['use_fm_loss']:
-                loss_output['fm_f'] = feature_loss(fmap_f_r, fmap_f_g)
-                loss_output['fm_s'] = feature_loss(fmap_s_r, fmap_s_g)
-            if hparams['use_ms_stft']:
-                loss_output['sc'], loss_output['mag'] = self.stft_loss(y.squeeze(1), y_.squeeze(1))
-            self.y_ = y_.detach()
-            self.y_mel = y_mel.detach()
-            self.y_hat_mel = y_hat_mel.detach()
-        else:
-            #######################
-            #    Discriminator    #
-            #######################
-            y_ = self.y_
-            # MPD
-            y_p_hat_r, y_p_hat_g, _, _ = self.model_disc['mpd'](y, y_.detach(), mel)
-            loss_output['r_p'], loss_output['f_p'] = discriminator_loss(y_p_hat_r, y_p_hat_g)
-            # MSD
-            y_s_hat_r, y_s_hat_g, _, _ = self.model_disc['msd'](y, y_.detach(), mel)
-            loss_output['r_s'], loss_output['f_s'] = discriminator_loss(y_s_hat_r, y_s_hat_g)
-        total_loss = sum(loss_output.values())
-        return total_loss, loss_output

spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/nn/seq_utils.py

@@ -1,311 +0,0 @@
-from collections import defaultdict
-import torch
-import torch.nn.functional as F
-
-
-def make_positions(tensor, padding_idx):
-    """Replace non-padding symbols with their position numbers.
-
-    Position numbers begin at padding_idx+1. Padding symbols are ignored.
-    """
-    # The series of casts and type-conversions here are carefully
-    # balanced to both work with ONNX export and XLA. In particular XLA
-    # prefers ints, cumsum defaults to output longs, and ONNX doesn't know
-    # how to handle the dtype kwarg in cumsum.
-    mask = tensor.ne(padding_idx).int()
-    return (
-                   torch.cumsum(mask, dim=1).type_as(mask) * mask
-           ).long() + padding_idx
-
-
-def softmax(x, dim):
-    return F.softmax(x, dim=dim, dtype=torch.float32)
-
-
-def sequence_mask(lengths, maxlen, dtype=torch.bool):
-    if maxlen is None:
-        maxlen = lengths.max()
-    mask = ~(torch.ones((len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > lengths).t()
-    mask.type(dtype)
-    return mask
-
-
-def weights_nonzero_speech(target):
-    # target : B x T x mel
-    # Assign weight 1.0 to all labels except for padding (id=0).
-    dim = target.size(-1)
-    return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim)
-
-
-INCREMENTAL_STATE_INSTANCE_ID = defaultdict(lambda: 0)
-
-
-def _get_full_incremental_state_key(module_instance, key):
-    module_name = module_instance.__class__.__name__
-
-    # assign a unique ID to each module instance, so that incremental state is
-    # not shared across module instances
-    if not hasattr(module_instance, '_instance_id'):
-        INCREMENTAL_STATE_INSTANCE_ID[module_name] += 1
-        module_instance._instance_id = INCREMENTAL_STATE_INSTANCE_ID[module_name]
-
-    return '{}.{}.{}'.format(module_name, module_instance._instance_id, key)
-
-
-def get_incremental_state(module, incremental_state, key):
-    """Helper for getting incremental state for an nn.Module."""
-    full_key = _get_full_incremental_state_key(module, key)
-    if incremental_state is None or full_key not in incremental_state:
-        return None
-    return incremental_state[full_key]
-
-
-def set_incremental_state(module, incremental_state, key, value):
-    """Helper for setting incremental state for an nn.Module."""
-    if incremental_state is not None:
-        full_key = _get_full_incremental_state_key(module, key)
-        incremental_state[full_key] = value
-
-
-def fill_with_neg_inf(t):
-    """FP16-compatible function that fills a tensor with -inf."""
-    return t.float().fill_(float('-inf')).type_as(t)
-
-
-def fill_with_neg_inf2(t):
-    """FP16-compatible function that fills a tensor with -inf."""
-    return t.float().fill_(-1e8).type_as(t)
-
-
-def select_attn(attn_logits, type='best'):
-    """
-
-    :param attn_logits: [n_layers, B, n_head, T_sp, T_txt]
-    :return:
-    """
-    encdec_attn = torch.stack(attn_logits, 0).transpose(1, 2)
-    # [n_layers * n_head, B, T_sp, T_txt]
-    encdec_attn = (encdec_attn.reshape([-1, *encdec_attn.shape[2:]])).softmax(-1)
-    if type == 'best':
-        indices = encdec_attn.max(-1).values.sum(-1).argmax(0)
-        encdec_attn = encdec_attn.gather(
-            0, indices[None, :, None, None].repeat(1, 1, encdec_attn.size(-2), encdec_attn.size(-1)))[0]
-        return encdec_attn
-    elif type == 'mean':
-        return encdec_attn.mean(0)
-
-
-def make_pad_mask(lengths, xs=None, length_dim=-1):
-    """Make mask tensor containing indices of padded part.
-    Args:
-        lengths (LongTensor or List): Batch of lengths (B,).
-        xs (Tensor, optional): The reference tensor.
-            If set, masks will be the same shape as this tensor.
-        length_dim (int, optional): Dimension indicator of the above tensor.
-            See the example.
-    Returns:
-        Tensor: Mask tensor containing indices of padded part.
-                dtype=torch.uint8 in PyTorch 1.2-
-                dtype=torch.bool in PyTorch 1.2+ (including 1.2)
-    Examples:
-        With only lengths.
-        >>> lengths = [5, 3, 2]
-        >>> make_non_pad_mask(lengths)
-        masks = [[0, 0, 0, 0 ,0],
-                 [0, 0, 0, 1, 1],
-                 [0, 0, 1, 1, 1]]
-        With the reference tensor.
-        >>> xs = torch.zeros((3, 2, 4))
-        >>> make_pad_mask(lengths, xs)
-        tensor([[[0, 0, 0, 0],
-                 [0, 0, 0, 0]],
-                [[0, 0, 0, 1],
-                 [0, 0, 0, 1]],
-                [[0, 0, 1, 1],
-                 [0, 0, 1, 1]]], dtype=torch.uint8)
-        >>> xs = torch.zeros((3, 2, 6))
-        >>> make_pad_mask(lengths, xs)
-        tensor([[[0, 0, 0, 0, 0, 1],
-                 [0, 0, 0, 0, 0, 1]],
-                [[0, 0, 0, 1, 1, 1],
-                 [0, 0, 0, 1, 1, 1]],
-                [[0, 0, 1, 1, 1, 1],
-                 [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
-        With the reference tensor and dimension indicator.
-        >>> xs = torch.zeros((3, 6, 6))
-        >>> make_pad_mask(lengths, xs, 1)
-        tensor([[[0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [1, 1, 1, 1, 1, 1]],
-                [[0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1]],
-                [[0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1]]], dtype=torch.uint8)
-        >>> make_pad_mask(lengths, xs, 2)
-        tensor([[[0, 0, 0, 0, 0, 1],
-                 [0, 0, 0, 0, 0, 1],
-                 [0, 0, 0, 0, 0, 1],
-                 [0, 0, 0, 0, 0, 1],
-                 [0, 0, 0, 0, 0, 1],
-                 [0, 0, 0, 0, 0, 1]],
-                [[0, 0, 0, 1, 1, 1],
-                 [0, 0, 0, 1, 1, 1],
-                 [0, 0, 0, 1, 1, 1],
-                 [0, 0, 0, 1, 1, 1],
-                 [0, 0, 0, 1, 1, 1],
-                 [0, 0, 0, 1, 1, 1]],
-                [[0, 0, 1, 1, 1, 1],
-                 [0, 0, 1, 1, 1, 1],
-                 [0, 0, 1, 1, 1, 1],
-                 [0, 0, 1, 1, 1, 1],
-                 [0, 0, 1, 1, 1, 1],
-                 [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
-    """
-    if length_dim == 0:
-        raise ValueError("length_dim cannot be 0: {}".format(length_dim))
-
-    if not isinstance(lengths, list):
-        lengths = lengths.tolist()
-    bs = int(len(lengths))
-    if xs is None:
-        maxlen = int(max(lengths))
-    else:
-        maxlen = xs.size(length_dim)
-
-    seq_range = torch.arange(0, maxlen, dtype=torch.int64)
-    seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
-    seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
-    mask = seq_range_expand >= seq_length_expand
-
-    if xs is not None:
-        assert xs.size(0) == bs, (xs.size(0), bs)
-
-        if length_dim < 0:
-            length_dim = xs.dim() + length_dim
-        # ind = (:, None, ..., None, :, , None, ..., None)
-        ind = tuple(
-            slice(None) if i in (0, length_dim) else None for i in range(xs.dim())
-        )
-        mask = mask[ind].expand_as(xs).to(xs.device)
-    return mask
-
-
-def make_non_pad_mask(lengths, xs=None, length_dim=-1):
-    """Make mask tensor containing indices of non-padded part.
-    Args:
-        lengths (LongTensor or List): Batch of lengths (B,).
-        xs (Tensor, optional): The reference tensor.
-            If set, masks will be the same shape as this tensor.
-        length_dim (int, optional): Dimension indicator of the above tensor.
-            See the example.
-    Returns:
-        ByteTensor: mask tensor containing indices of padded part.
-                    dtype=torch.uint8 in PyTorch 1.2-
-                    dtype=torch.bool in PyTorch 1.2+ (including 1.2)
-    Examples:
-        With only lengths.
-        >>> lengths = [5, 3, 2]
-        >>> make_non_pad_mask(lengths)
-        masks = [[1, 1, 1, 1 ,1],
-                 [1, 1, 1, 0, 0],
-                 [1, 1, 0, 0, 0]]
-        With the reference tensor.
-        >>> xs = torch.zeros((3, 2, 4))
-        >>> make_non_pad_mask(lengths, xs)
-        tensor([[[1, 1, 1, 1],
-                 [1, 1, 1, 1]],
-                [[1, 1, 1, 0],
-                 [1, 1, 1, 0]],
-                [[1, 1, 0, 0],
-                 [1, 1, 0, 0]]], dtype=torch.uint8)
-        >>> xs = torch.zeros((3, 2, 6))
-        >>> make_non_pad_mask(lengths, xs)
-        tensor([[[1, 1, 1, 1, 1, 0],
-                 [1, 1, 1, 1, 1, 0]],
-                [[1, 1, 1, 0, 0, 0],
-                 [1, 1, 1, 0, 0, 0]],
-                [[1, 1, 0, 0, 0, 0],
-                 [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8)
-        With the reference tensor and dimension indicator.
-        >>> xs = torch.zeros((3, 6, 6))
-        >>> make_non_pad_mask(lengths, xs, 1)
-        tensor([[[1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [0, 0, 0, 0, 0, 0]],
-                [[1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0]],
-                [[1, 1, 1, 1, 1, 1],
-                 [1, 1, 1, 1, 1, 1],
-                 [0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0],
-                 [0, 0, 0, 0, 0, 0]]], dtype=torch.uint8)
-        >>> make_non_pad_mask(lengths, xs, 2)
-        tensor([[[1, 1, 1, 1, 1, 0],
-                 [1, 1, 1, 1, 1, 0],
-                 [1, 1, 1, 1, 1, 0],
-                 [1, 1, 1, 1, 1, 0],
-                 [1, 1, 1, 1, 1, 0],
-                 [1, 1, 1, 1, 1, 0]],
-                [[1, 1, 1, 0, 0, 0],
-                 [1, 1, 1, 0, 0, 0],
-                 [1, 1, 1, 0, 0, 0],
-                 [1, 1, 1, 0, 0, 0],
-                 [1, 1, 1, 0, 0, 0],
-                 [1, 1, 1, 0, 0, 0]],
-                [[1, 1, 0, 0, 0, 0],
-                 [1, 1, 0, 0, 0, 0],
-                 [1, 1, 0, 0, 0, 0],
-                 [1, 1, 0, 0, 0, 0],
-                 [1, 1, 0, 0, 0, 0],
-                 [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8)
-    """
-    return ~make_pad_mask(lengths, xs, length_dim)
-
-
-def get_mask_from_lengths(lengths):
-    max_len = torch.max(lengths).item()
-    ids = torch.arange(0, max_len).to(lengths.device)
-    mask = (ids < lengths.unsqueeze(1)).bool()
-    return mask
-
-
-def group_hidden_by_segs(h, seg_ids, max_len):
-    """
-
-    :param h: [B, T, H]
-    :param seg_ids: [B, T]
-    :return: h_ph: [B, T_ph, H]
-    """
-    B, T, H = h.shape
-    h_gby_segs = h.new_zeros([B, max_len + 1, H]).scatter_add_(1, seg_ids[:, :, None].repeat([1, 1, H]), h)
-    all_ones = h.new_ones(h.shape[:2])
-    cnt_gby_segs = h.new_zeros([B, max_len + 1]).scatter_add_(1, seg_ids, all_ones).contiguous()
-    h_gby_segs = h_gby_segs[:, 1:]
-    cnt_gby_segs = cnt_gby_segs[:, 1:]
-    h_gby_segs = h_gby_segs / torch.clamp(cnt_gby_segs[:, :, None], min=1)
-    return h_gby_segs, cnt_gby_segs
-
-def expand_word2ph(word_encoding, ph2word):
-    word_encoding = F.pad(word_encoding,[0,0,1,0])
-    ph2word_ = ph2word[:, :, None].repeat([1, 1, word_encoding.shape[-1]])
-    out = torch.gather(word_encoding, 1, ph2word_)  # [B, T, H]
-    return out

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/app.py

@@ -1,160 +0,0 @@
-import os
-import numpy as np
-import torch
-from torch import no_grad, LongTensor
-import argparse
-import commons
-from mel_processing import spectrogram_torch
-import utils
-from models import SynthesizerTrn
-import gradio as gr
-import librosa
-import webbrowser
-
-from text import text_to_sequence, _clean_text
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-language_marks = {
-    "English": "[EN]",
-    "Japanese": "",
-    "日本語": "[JA]",
-    "简体中文": "[ZH]",
-    "Mix": "",
-}
-lang = ['English','日本語', '简体中文','Mix']
-def get_text(text, hps, is_symbol):
-    text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm
-
-def create_tts_fn(model, hps, speaker_ids):
-    def tts_fn(text, speaker, language, speed):
-        if language is not None:
-            text = language_marks[language] + text + language_marks[language]
-        speaker_id = speaker_ids[speaker]
-        stn_tst = get_text(text, hps, False)
-        with no_grad():
-            x_tst = stn_tst.unsqueeze(0).to(device)
-            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
-            sid = LongTensor([speaker_id]).to(device)
-            audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8,
-                                length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
-        del stn_tst, x_tst, x_tst_lengths, sid
-        return "Success", (hps.data.sampling_rate, audio)
-
-    return tts_fn
-
-def create_vc_fn(model, hps, speaker_ids):
-    def vc_fn(original_speaker, target_speaker, record_audio, upload_audio):
-        input_audio = record_audio if record_audio is not None else upload_audio
-        if input_audio is None:
-            return "You need to record or upload an audio", None
-        sampling_rate, audio = input_audio
-        original_speaker_id = speaker_ids[original_speaker]
-        target_speaker_id = speaker_ids[target_speaker]
-
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        if sampling_rate != hps.data.sampling_rate:
-            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
-        with no_grad():
-            y = torch.FloatTensor(audio)
-            y = y / max(-y.min(), y.max()) / 0.99
-            y = y.to(device)
-            y = y.unsqueeze(0)
-            spec = spectrogram_torch(y, hps.data.filter_length,
-                                     hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
-                                     center=False).to(device)
-            spec_lengths = LongTensor([spec.size(-1)]).to(device)
-            sid_src = LongTensor([original_speaker_id]).to(device)
-            sid_tgt = LongTensor([target_speaker_id]).to(device)
-            audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
-                0, 0].data.cpu().float().numpy()
-        del y, spec, spec_lengths, sid_src, sid_tgt
-        return "Success", (hps.data.sampling_rate, audio)
-
-    return vc_fn
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_dir", default="./inference/G_latest.pth", help="directory to your fine-tuned model")
-    parser.add_argument("--config_dir", default="./inference/finetune_speaker.json", help="directory to your model config file")
-    parser.add_argument("--share", default=False, help="make link public (used in colab)")
-
-    args = parser.parse_args()
-    hps = utils.get_hparams_from_file(args.config_dir)
-
-
-    net_g = SynthesizerTrn(
-        len(hps.symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model).to(device)
-    _ = net_g.eval()
-
-    _ = utils.load_checkpoint(args.model_dir, net_g, None)
-    speaker_ids = hps.speakers
-    speakers = list(hps.speakers.keys())
-    tts_fn = create_tts_fn(net_g, hps, speaker_ids)
-    vc_fn = create_vc_fn(net_g, hps, speaker_ids)
-    app = gr.Blocks()
-
-    with app:
-
-        gr.Markdown(
-            """# League of Legends Yuumi Text to Speech Demo 魔法猫咪  悠米 TTS
-
-League of Legends Yuumi Text to Speech model trained with Yuumi's English in-game audio. 
-
-魔法猫咪  悠米 TTS模型训练数据为游戏内英文语音
-
-## 👍Give original author stars & likes if you liked the project 如果喜欢给原作者一个星星和赞吧！
-
-https://github.com/Plachtaa/VITS-fast-fine-tuning 
-
-https://huggingface.co/spaces/Plachta/VITS-Umamusume-voice-synthesizer
-
-https://huggingface.co/spaces/zomehwh/vits-uma-genshin-honkai
-
-## ❓How to fine-tune your own model 如何调试自己的模型
-
-Follow the directions in this repo: https://github.com/Plachtaa/VITS-fast-fine-tuning
-
-按照 https://github.com/Plachtaa/VITS-fast-fine-tuning 操作
-
-## ⚠
-Use of the model should respect https://www.riotgames.com/en/legal
-用该模型请遵守 https://www.riotgames.com/en/legal
-
-Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.
-请不要生成会对个人以及组织造成侵害的内容
-
-⚠Disclaimer: Not legally responsible for anything the model generates
-⚠免责声明: 不对该模型任何输出负责"""
-
-        )
-        
-        with gr.Tab("Text-to-Speech"):
-            with gr.Row():
-                with gr.Column():
-                    textbox = gr.TextArea(label="Text",
-                                          placeholder="Type your sentence here",
-                                          value="Hello...... I am Yuumi... Please play me next game! Thank you!", elem_id=f"tts-input")
-                    # select character
-                    char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character')
-                    language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language')
-                    duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1,
-                                                label='速度 Speed')
-                with gr.Column():
-                    text_output = gr.Textbox(label="Message")
-                    audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
-                    btn = gr.Button("Generate!")
-                    btn.click(tts_fn,
-                              inputs=[textbox, char_dropdown, language_dropdown, duration_slider,],
-                              outputs=[text_output, audio_output])
-
-    app.queue(concurrency_count=1, api_open=False).launch(share=args.share)
-    
-

spaces/AlexKorGKLT/webui-cpua/app.py

@@ -1,155 +0,0 @@
-import os
-from sys import executable as pyexecutable
-import subprocess
-import pathlib
-import gc
-
-def Gitclone(URI:str,ClonePath:str = "") -> int :
-  if(ClonePath == "") :
-    while True:
-      i=subprocess.run([r"git",r"clone",URI])
-      if(i.returncode == 0 ): 
-       del i
-       gc.collect()
-       return 0
-      else :
-       del i
-  else: 
-    while True:
-      i=subprocess.run([r"git",r"clone",URI,ClonePath])
-      if(i.returncode == 0 ): 
-       del i
-       gc.collect()
-       return 0
-      else :
-       del i
-def DownLoad(URI:str,DownloadPath:str,DownLoadFileName:str ) -> int:
-  while (True):
-    i=subprocess.run([r"aria2c",r"-c",r"-x" ,r"16", r"-s",r"16", r"-k" ,r"1M" ,r"-m",r"0",r"--enable-mmap=false",r"--console-log-level=error",r"-d",DownloadPath,r"-o",DownLoadFileName,URI]);
-    if(i.returncode == 0 ): 
-      del i
-      gc.collect()
-      return 0
-    else :
-      del i
-user_home =pathlib.Path.home().resolve()
-os.chdir(str(user_home))
-#clone stable-diffusion-webui repo
-print("cloning stable-diffusion-webui repo")
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui.git",str(user_home / r"stable-diffusion-webui"))
-os.chdir(str(user_home / r"stable-diffusion-webui"))
-os.system("git reset --hard 89f9faa63388756314e8a1d96cf86bf5e0663045")
-#
-
-#install extensions
-print("installing extensions")
-Gitclone(r"https://huggingface.co/embed/negative",str(user_home / r"stable-diffusion-webui" / r"embeddings"  / r"negative"))
-Gitclone(r"https://huggingface.co/embed/lora",str(user_home / r"stable-diffusion-webui" / r"models" / r"Lora" / r"positive"))
-DownLoad(r"https://huggingface.co/embed/upscale/resolve/main/4x-UltraSharp.pth",str(user_home / r"stable-diffusion-webui" / r"models" / r"ESRGAN") ,r"4x-UltraSharp.pth")
-while True: 
-  if(subprocess.run([r"wget",r"https://raw.githubusercontent.com/camenduru/stable-diffusion-webui-scripts/main/run_n_times.py",r"-O",str(user_home / r"stable-diffusion-webui" / r"scripts" / r"run_n_times.py")]).returncode == 0):
-    break
-Gitclone(r"https://github.com/deforum-art/deforum-for-automatic1111-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"deforum-for-automatic1111-webui" ))
-Gitclone(r"https://github.com/AlUlkesh/stable-diffusion-webui-images-browser",str(user_home / r"stable-diffusion-webui" / r"extensions"/ r"stable-diffusion-webui-images-browser"))
-Gitclone(r"https://github.com/camenduru/stable-diffusion-webui-huggingface",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-huggingface"))
-Gitclone(r"https://github.com/camenduru/sd-civitai-browser",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-civitai-browser"))
-Gitclone(r"https://github.com/kohya-ss/sd-webui-additional-networks",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks"))
-Gitclone(r"https://github.com/Mikubill/sd-webui-controlnet",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-controlnet"))
-Gitclone(r"https://github.com/fkunn1326/openpose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"openpose-editor"))
-Gitclone(r"https://github.com/jexom/sd-webui-depth-lib",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-depth-lib"))
-Gitclone(r"https://github.com/hnmr293/posex",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"posex"))
-Gitclone(r"https://github.com/nonnonstop/sd-webui-3d-open-pose-editor",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-3d-open-pose-editor"))
-#中文本地化的请解除下一行的注释
-#Gitclone(r"https://github.com/dtlnor/stable-diffusion-webui-localization-zh_CN.git",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-localization-zh_CN"))
-Gitclone(r"https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git" , str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-tagcomplete"))
-Gitclone(r"https://github.com/camenduru/sd-webui-tunnels",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-tunnels"))
-Gitclone(r"https://github.com/etherealxx/batchlinks-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"batchlinks-webui"))
-Gitclone(r"https://github.com/catppuccin/stable-diffusion-webui",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-catppuccin"))
-
-#Gitclone(r"https://github.com/KohakuBueleaf/a1111-sd-webui-locon",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"a1111-sd-webui-locon" ))
-Gitclone(r"https://github.com/AUTOMATIC1111/stable-diffusion-webui-rembg",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-rembg"))
-Gitclone(r"https://github.com/ashen-sensored/stable-diffusion-webui-two-shot",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"stable-diffusion-webui-two-shot"))
-Gitclone(r"https://github.com/camenduru/sd_webui_stealth_pnginfo",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd_webui_stealth_pnginfo"))
-
-os.chdir(user_home / r"stable-diffusion-webui")
-
-#download ControlNet models
-print("extensions dolwnload done .\ndownloading ControlNet models")
-dList =[r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_scribble_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_seg_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile_fp16.safetensors",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_ip2p_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11e_sd15_shuffle_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_canny_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1p_sd15_depth_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_inpaint_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_lineart_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_mlsd_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_normalbae_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_openpose_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_scribble_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_seg_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15_softedge_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11p_sd15s2_lineart_anime_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/raw/main/control_v11f1e_sd15_tile_fp16.yaml",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_style_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_seg_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_openpose_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_keypose_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd14v1.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_canny_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_depth_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_sketch_sd15v2.pth",
-               r"https://huggingface.co/ckpt/ControlNet-v1-1/resolve/main/t2iadapter_zoedepth_sd15v1.pth"]
-for i in range(0,len(dList)): DownLoad(dList[i],str(user_home / "stable-diffusion-webui" / "extensions" / "sd-webui-controlnet" / "models"),pathlib.Path(dList[i]).name)
-del dList
-
-#download model    
-#you can change model download address here
-print("ControlNet models download done.\ndownloading model")
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.5-pruned.ckpt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.5-pruned.ckpt")
-DownLoad(r"https://huggingface.co/ckpt/anything-v4.0/resolve/main/anything-v4.0.vae.pt",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anything-v4.0.vae.pt")
-DownLoad(r"https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/Counterfeit-V3.0_fp16.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"Counterfeit-V3.0_fp16.safetensors")
-DownLoad(r"https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1B_orangemixs.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"AOM3A1B_orangemixs.safetensors")
-DownLoad(r"https://huggingface.co/Meina/MeinaPastel/resolve/main/MeinaPastelV5%20-%20Without%20VAE.safetensors",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"MeinaPastelV5_WithoutVAE.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/9474",str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"chilloutmix_NiPrunedFp16.safetensors")
-
-#My customly added models
-DownLoad(r"https://civitai.com/api/download/models/105674?", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"realisticVisionV30_v30VAE.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/94640", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"majicmixRealistic_v6.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/109123", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"dreamshaper_7.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/27392", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"openjourney_V4.ckpt")
-DownLoad(r"https://civitai.com/api/download/models/95489", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"anyloraCheckpoint_bakedvaeBlessedFp16.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/90854", str(user_home / r"stable-diffusion-webui" / r"models" / r"Stable-diffusion"),r"AnythingV5Ink_ink.safetensors")
-
-#LoRa ?
-DownLoad(r"https://civitai.com/api/download/models/39885",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"Better_light.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/39164",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"backlighting.safetensors")
-DownLoad(r"https://civitai.com/api/download/models/62833",str(user_home / r"stable-diffusion-webui" / r"extensions" / r"sd-webui-additional-networks" / r"models"/ r"lora"),r"add_detail.safetensors")
-
-#strt webui
-
-print("Done\nStarting Webui...")
-os.chdir(user_home / r"stable-diffusion-webui")
-while True:
-  ret=subprocess.run([r"python3" ,r"launch.py",r"--precision",r"full",r"--no-half",r"--no-half-vae",r"--enable-insecure-extension-access",r"--medvram",r"--skip-torch-cuda-test",r"--enable-console-prompts",r"--ui-settings-file="+str(pathlib.Path(__file__).parent /r"config.json")])
-  if(ret.returncode == 0 ): 
-    del ret
-    gc.collect()
-  else :
-    del ret
-
-del os ,user_home ,pyexecutable ,subprocess

spaces/AlexWang/lama/fetch_data/sampler.py

@@ -1,39 +0,0 @@
-import os
-import random
-
-test_files_path = os.path.abspath('.') + '/places_standard_dataset/original/test/'
-test_files = [test_files_path + image for image in os.listdir(test_files_path)]
-print(f'found {len(test_files)} images in {test_files_path}')
-
-random.shuffle(test_files)
-test_files_random = test_files[0:2000]
-#print(test_files_random[0:10])
-
-list_of_random_test_files = os.path.abspath('.') \
-+ '/places_standard_dataset/original/test_random_files.txt'
-
-print(f'copying 100 random images to {list_of_random_test_files}')
-with open(list_of_random_test_files, 'w') as fw:
-    for filename in test_files_random:
-        fw.write(filename+'\n')
-print('...done')
-
-# ----------------------------------------------------------------------------------
-
-
-val_files_path = os.path.abspath('.') + '/places_standard_dataset/original/val/'
-val_files = [val_files_path + image for image in os.listdir(val_files_path)]
-print(f'found {len(val_files)} images in {val_files_path}')
-
-random.shuffle(val_files)
-val_files_random = val_files[0:100]
-
-list_of_random_val_files = os.path.abspath('.') \
-+ '/places_standard_dataset/original/val_random_files.txt'
-
-print(f'copying 100 random images to {list_of_random_val_files}')
-with open(list_of_random_val_files, 'w') as fw:
-    for filename in val_files_random:
-        fw.write(filename+'\n')
-print('...done')      
-

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/cm_stochastic_iterative.md

@@ -1,11 +0,0 @@
-# Consistency Model Multistep Scheduler
-
-## Overview
-
-Multistep and onestep scheduler (Algorithm 1) introduced alongside consistency models in the paper [Consistency Models](https://arxiv.org/abs/2303.01469) by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever.
-Based on the [original consistency models implementation](https://github.com/openai/consistency_models).
-Should generate good samples from [`ConsistencyModelPipeline`] in one or a small number of steps.
-
-## CMStochasticIterativeScheduler
-[[autodoc]] CMStochasticIterativeScheduler
-

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/onnxruntime/text_to_image/README.md

@@ -1,74 +0,0 @@
-# Stable Diffusion text-to-image fine-tuning
-
-The `train_text_to_image.py` script shows how to fine-tune stable diffusion model on your own dataset.
-
-___Note___:
-
-___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.___
-
-
-## Running locally with PyTorch
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install .
-```
-
-Then cd in the example folder  and run
-```bash
-pip install -r requirements.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-### Pokemon example
-
-You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-4`, so you'll need to visit [its card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree. 
-
-You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
-
-Run the following command to authenticate your token
-
-```bash
-huggingface-cli login
-```
-
-If you have already cloned the repo, then you won't need to go through these steps.
-
-<br>
-
-## Use ONNXRuntime to accelerate training
-In order to leverage onnxruntime to accelerate training, please use train_text_to_image.py
-
-The command to train a DDPM UNetCondition model on the Pokemon dataset with onnxruntime:
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export dataset_name="lambdalabs/pokemon-blip-captions"
-accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME \
-  --dataset_name=$dataset_name \
-  --use_ema \
-  --resolution=512 --center_crop --random_flip \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=4 \
-  --gradient_checkpointing \
-  --max_train_steps=15000 \
-  --learning_rate=1e-05 \
-  --max_grad_norm=1 \
-  --lr_scheduler="constant" --lr_warmup_steps=0 \
-  --output_dir="sd-pokemon-model" 
-```
-
-Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -1,1016 +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
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import PIL.Image
-import torch
-from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
-
-from ...image_processor import VaeImageProcessor
-from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    LoRAAttnProcessor2_0,
-    LoRAXFormersAttnProcessor,
-    XFormersAttnProcessor,
-)
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    is_invisible_watermark_available,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionXLPipelineOutput
-
-
-if is_invisible_watermark_available():
-    from .watermark import StableDiffusionXLWatermarker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import StableDiffusionXLImg2ImgPipeline
-        >>> from diffusers.utils import load_image
-
-        >>> pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
-        ...     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16
-        ... )
-        >>> pipe = pipe.to("cuda")
-        >>> url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
-
-        >>> init_image = load_image(url).convert("RGB")
-        >>> prompt = "a photo of an astronaut riding a horse on mars"
-        >>> image = pipe(prompt, image=init_image).images[0]
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
-def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
-    """
-    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
-    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
-    """
-    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
-    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
-    # rescale the results from guidance (fixes overexposure)
-    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
-    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
-    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
-    return noise_cfg
-
-
-class StableDiffusionXLImg2ImgPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
-    r"""
-    Pipeline for text-to-image generation using Stable Diffusion XL.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    In addition the pipeline inherits the following loading methods:
-        - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
-        - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
-        - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
-
-    as well as the following saving methods:
-        - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion XL uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        text_encoder_2 ([` CLIPTextModelWithProjection`]):
-            Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
-            specifically the
-            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
-            variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        tokenizer_2 (`CLIPTokenizer`):
-            Second Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture 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`].
-    """
-    _optional_components = ["tokenizer", "text_encoder"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        text_encoder_2: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        tokenizer_2: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        requires_aesthetics_score: bool = False,
-        force_zeros_for_empty_prompt: bool = True,
-        add_watermarker: Optional[bool] = None,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            text_encoder_2=text_encoder_2,
-            tokenizer=tokenizer,
-            tokenizer_2=tokenizer_2,
-            unet=unet,
-            scheduler=scheduler,
-        )
-        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
-        self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
-        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
-
-        if add_watermarker:
-            self.watermark = StableDiffusionXLWatermarker()
-        else:
-            self.watermark = None
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
-    def enable_vae_tiling(self):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.vae.enable_tiling()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
-    def disable_vae_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_tiling()
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        model_sequence = (
-            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
-        )
-        model_sequence.extend([self.unet, self.vae])
-
-        hook = None
-        for cpu_offloaded_model in model_sequence:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
-    def encode_prompt(
-        self,
-        prompt: str,
-        prompt_2: Optional[str] = None,
-        device: Optional[torch.device] = None,
-        num_images_per_prompt: int = 1,
-        do_classifier_free_guidance: bool = True,
-        negative_prompt: Optional[str] = None,
-        negative_prompt_2: Optional[str] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
-                used in both text-encoders
-            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]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
-                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
-                If not provided, pooled text embeddings will be generated from `prompt` input argument.
-            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
-                input argument.
-            lora_scale (`float`, *optional*):
-                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-        """
-        device = device or self._execution_device
-
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        # Define tokenizers and text encoders
-        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
-        text_encoders = (
-            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
-        )
-
-        if prompt_embeds is None:
-            prompt_2 = prompt_2 or prompt
-            # textual inversion: procecss multi-vector tokens if necessary
-            prompt_embeds_list = []
-            prompts = [prompt, prompt_2]
-            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
-                if isinstance(self, TextualInversionLoaderMixin):
-                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
-
-                text_inputs = tokenizer(
-                    prompt,
-                    padding="max_length",
-                    max_length=tokenizer.model_max_length,
-                    truncation=True,
-                    return_tensors="pt",
-                )
-
-                text_input_ids = text_inputs.input_ids
-                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                    text_input_ids, untruncated_ids
-                ):
-                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
-                    logger.warning(
-                        "The following part of your input was truncated because CLIP can only handle sequences up to"
-                        f" {tokenizer.model_max_length} tokens: {removed_text}"
-                    )
-
-                prompt_embeds = text_encoder(
-                    text_input_ids.to(device),
-                    output_hidden_states=True,
-                )
-
-                # We are only ALWAYS interested in the pooled output of the final text encoder
-                pooled_prompt_embeds = prompt_embeds[0]
-                prompt_embeds = prompt_embeds.hidden_states[-2]
-
-                prompt_embeds_list.append(prompt_embeds)
-
-            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
-
-        # get unconditional embeddings for classifier free guidance
-        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
-        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
-            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
-            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
-        elif do_classifier_free_guidance and negative_prompt_embeds is None:
-            negative_prompt = negative_prompt or ""
-            negative_prompt_2 = negative_prompt_2 or negative_prompt
-
-            uncond_tokens: List[str]
-            if prompt is not None and 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, str):
-                uncond_tokens = [negative_prompt, negative_prompt_2]
-            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_tokens = [negative_prompt, negative_prompt_2]
-
-            negative_prompt_embeds_list = []
-            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
-                if isinstance(self, TextualInversionLoaderMixin):
-                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
-
-                max_length = prompt_embeds.shape[1]
-                uncond_input = tokenizer(
-                    negative_prompt,
-                    padding="max_length",
-                    max_length=max_length,
-                    truncation=True,
-                    return_tensors="pt",
-                )
-
-                negative_prompt_embeds = text_encoder(
-                    uncond_input.input_ids.to(device),
-                    output_hidden_states=True,
-                )
-                # We are only ALWAYS interested in the pooled output of the final text encoder
-                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
-                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
-
-                negative_prompt_embeds_list.append(negative_prompt_embeds)
-
-            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        if do_classifier_free_guidance:
-            # 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.to(dtype=self.text_encoder_2.dtype, device=device)
-            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)
-
-        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
-            bs_embed * num_images_per_prompt, -1
-        )
-        if do_classifier_free_guidance:
-            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
-                bs_embed * num_images_per_prompt, -1
-            )
-
-        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
-
-    # 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
-
-    def check_inputs(
-        self,
-        prompt,
-        prompt_2,
-        strength,
-        num_inference_steps,
-        callback_steps,
-        negative_prompt=None,
-        negative_prompt_2=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if strength < 0 or strength > 1:
-            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
-        if num_inference_steps is None:
-            raise ValueError("`num_inference_steps` cannot be None.")
-        elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:
-            raise ValueError(
-                f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
-                f" {type(num_inference_steps)}."
-            )
-        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)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt_2 is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
-            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
-        # get the original timestep using init_timestep
-        if denoising_start is None:
-            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-            t_start = max(num_inference_steps - init_timestep, 0)
-        else:
-            t_start = 0
-
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
-
-        # Strength is irrelevant if we directly request a timestep to start at;
-        # that is, strength is determined by the denoising_start instead.
-        if denoising_start is not None:
-            discrete_timestep_cutoff = int(
-                round(
-                    self.scheduler.config.num_train_timesteps
-                    - (denoising_start * self.scheduler.config.num_train_timesteps)
-                )
-            )
-            timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps))
-            return torch.tensor(timesteps), len(timesteps)
-
-        return timesteps, num_inference_steps - t_start
-
-    def prepare_latents(
-        self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True
-    ):
-        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
-            raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
-            )
-
-        # Offload text encoder if `enable_model_cpu_offload` was enabled
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.text_encoder_2.to("cpu")
-            torch.cuda.empty_cache()
-
-        image = image.to(device=device, dtype=dtype)
-
-        batch_size = batch_size * num_images_per_prompt
-
-        if image.shape[1] == 4:
-            init_latents = image
-
-        else:
-            # make sure the VAE is in float32 mode, as it overflows in float16
-            if self.vae.config.force_upcast:
-                image = image.float()
-                self.vae.to(dtype=torch.float32)
-
-            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."
-                )
-
-            elif isinstance(generator, list):
-                init_latents = [
-                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
-                ]
-                init_latents = torch.cat(init_latents, dim=0)
-            else:
-                init_latents = self.vae.encode(image).latent_dist.sample(generator)
-
-            if self.vae.config.force_upcast:
-                self.vae.to(dtype)
-
-            init_latents = init_latents.to(dtype)
-            init_latents = self.vae.config.scaling_factor * init_latents
-
-        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
-            # expand init_latents for batch_size
-            additional_image_per_prompt = batch_size // init_latents.shape[0]
-            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
-        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
-            raise ValueError(
-                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
-            )
-        else:
-            init_latents = torch.cat([init_latents], dim=0)
-
-        if add_noise:
-            shape = init_latents.shape
-            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            # get latents
-            init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-
-        latents = init_latents
-
-        return latents
-
-    def _get_add_time_ids(
-        self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype
-    ):
-        if self.config.requires_aesthetics_score:
-            add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
-            add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,))
-        else:
-            add_time_ids = list(original_size + crops_coords_top_left + target_size)
-            add_neg_time_ids = list(original_size + crops_coords_top_left + target_size)
-
-        passed_add_embed_dim = (
-            self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
-        )
-        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
-
-        if (
-            expected_add_embed_dim > passed_add_embed_dim
-            and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim
-        ):
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model."
-            )
-        elif (
-            expected_add_embed_dim < passed_add_embed_dim
-            and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim
-        ):
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model."
-            )
-        elif expected_add_embed_dim != passed_add_embed_dim:
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
-            )
-
-        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
-        add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
-
-        return add_time_ids, add_neg_time_ids
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
-    def upcast_vae(self):
-        dtype = self.vae.dtype
-        self.vae.to(dtype=torch.float32)
-        use_torch_2_0_or_xformers = isinstance(
-            self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-                LoRAXFormersAttnProcessor,
-                LoRAAttnProcessor2_0,
-            ),
-        )
-        # if xformers or torch_2_0 is used attention block does not need
-        # to be in float32 which can save lots of memory
-        if use_torch_2_0_or_xformers:
-            self.vae.post_quant_conv.to(dtype)
-            self.vae.decoder.conv_in.to(dtype)
-            self.vae.decoder.mid_block.to(dtype)
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        prompt_2: Optional[Union[str, List[str]]] = None,
-        image: Union[
-            torch.FloatTensor,
-            PIL.Image.Image,
-            np.ndarray,
-            List[torch.FloatTensor],
-            List[PIL.Image.Image],
-            List[np.ndarray],
-        ] = None,
-        strength: float = 0.3,
-        num_inference_steps: int = 50,
-        denoising_start: Optional[float] = None,
-        denoising_end: Optional[float] = None,
-        guidance_scale: float = 5.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt_2: 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,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: 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,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        guidance_rescale: float = 0.0,
-        original_size: Tuple[int, int] = None,
-        crops_coords_top_left: Tuple[int, int] = (0, 0),
-        target_size: Tuple[int, int] = None,
-        aesthetic_score: float = 6.0,
-        negative_aesthetic_score: float = 2.5,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
-                used in both text-encoders
-            image (`torch.FloatTensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.FloatTensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`):
-                The image(s) to modify with the pipeline.
-            strength (`float`, *optional*, defaults to 0.3):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of
-                `denoising_start` being declared as an integer, the value of `strength` will be ignored.
-            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.
-            denoising_start (`float`, *optional*):
-                When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
-                bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
-                it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
-                strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
-                is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image
-                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
-            denoising_end (`float`, *optional*):
-                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
-                completed before it is intentionally prematurely terminated. As a result, the returned sample will
-                still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
-                denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
-                final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
-                forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
-                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
-                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
-            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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](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 will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
-                If not provided, pooled text embeddings will be generated from `prompt` input argument.
-            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
-                input argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                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 will be called. If not specified, the callback will be
-                called at every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-            guidance_rescale (`float`, *optional*, defaults to 0.7):
-                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
-                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
-                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
-                Guidance rescale factor should fix overexposure when using zero terminal SNR.
-            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
-                `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
-                explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
-                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
-                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
-                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                For most cases, `target_size` should be set to the desired height and width of the generated image. If
-                not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
-                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            aesthetic_score (`float`, *optional*, defaults to 6.0):
-                Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
-                Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            negative_aesthetic_score (`float`, *optional*, defaults to 2.5):
-                Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
-                simulate an aesthetic score of the generated image by influencing the negative text condition.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple. When returning a tuple, the first element is a list with the generated images.
-        """
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            prompt_2,
-            strength,
-            num_inference_steps,
-            callback_steps,
-            negative_prompt,
-            negative_prompt_2,
-            prompt_embeds,
-            negative_prompt_embeds,
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        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
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = self.encode_prompt(
-            prompt=prompt,
-            prompt_2=prompt_2,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            negative_prompt=negative_prompt,
-            negative_prompt_2=negative_prompt_2,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        # 4. Preprocess image
-        image = self.image_processor.preprocess(image)
-
-        # 5. Prepare timesteps
-        def denoising_value_valid(dnv):
-            return type(denoising_end) == float and 0 < dnv < 1
-
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, num_inference_steps = self.get_timesteps(
-            num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None
-        )
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-
-        add_noise = True if denoising_start is None else False
-        # 6. Prepare latent variables
-        latents = self.prepare_latents(
-            image,
-            latent_timestep,
-            batch_size,
-            num_images_per_prompt,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            add_noise,
-        )
-        # 7. Prepare extra step kwargs.
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        height, width = latents.shape[-2:]
-        height = height * self.vae_scale_factor
-        width = width * self.vae_scale_factor
-
-        original_size = original_size or (height, width)
-        target_size = target_size or (height, width)
-
-        # 8. Prepare added time ids & embeddings
-        add_text_embeds = pooled_prompt_embeds
-        add_time_ids, add_neg_time_ids = self._get_add_time_ids(
-            original_size,
-            crops_coords_top_left,
-            target_size,
-            aesthetic_score,
-            negative_aesthetic_score,
-            dtype=prompt_embeds.dtype,
-        )
-        add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
-
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
-            add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)
-            add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
-
-        prompt_embeds = prompt_embeds.to(device)
-        add_text_embeds = add_text_embeds.to(device)
-        add_time_ids = add_time_ids.to(device)
-
-        # 9. Denoising loop
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-
-        # 9.1 Apply denoising_end
-        if (
-            denoising_end is not None
-            and denoising_start is not None
-            and denoising_value_valid(denoising_end)
-            and denoising_value_valid(denoising_start)
-            and denoising_start >= denoising_end
-        ):
-            raise ValueError(
-                f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: "
-                + f" {denoising_end} when using type float."
-            )
-        elif denoising_end is not None and denoising_value_valid(denoising_end):
-            discrete_timestep_cutoff = int(
-                round(
-                    self.scheduler.config.num_train_timesteps
-                    - (denoising_end * self.scheduler.config.num_train_timesteps)
-                )
-            )
-            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
-            timesteps = timesteps[:num_inference_steps]
-
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(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
-                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    added_cond_kwargs=added_cond_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # 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)
-
-                if do_classifier_free_guidance and guidance_rescale > 0.0:
-                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # make sure the VAE is in float32 mode, as it overflows in float16
-        if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
-            self.upcast_vae()
-            latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
-
-        if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-        else:
-            image = latents
-            return StableDiffusionXLPipelineOutput(images=image)
-
-        # apply watermark if available
-        if self.watermark is not None:
-            image = self.watermark.apply_watermark(image)
-
-        image = self.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image,)
-
-        return StableDiffusionXLPipelineOutput(images=image)

spaces/Andy1621/uniformer_image_detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py

@@ -1,4 +0,0 @@
-_base_ = '../dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py'
-model = dict(
-    backbone=dict(
-        norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))

spaces/Andy1621/uniformer_image_detection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py

@@ -1,4 +0,0 @@
-_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)

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

@@ -1,47 +0,0 @@
-# Dual Attention Network for Scene Segmentation
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@article{fu2018dual,
-  title={Dual Attention Network for Scene Segmentation},
-  author={Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu},
-  booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-  year={2019}
-}
-```
-
-## Results and models
-
-### Cityscapes
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                    | download                                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DANet  | R-50-D8  | 512x1024  |   40000 | 7.4      | 2.66           | 78.74 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json)     |
-| DANet  | R-101-D8 | 512x1024  |   40000 | 10.9     | 1.99           | 80.52 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json) |
-| DANet  | R-50-D8  | 769x769   |   40000 | 8.8      | 1.56           | 78.88 | 80.62         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_769x769_40k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json)         |
-| DANet  | R-101-D8 | 769x769   |   40000 | 12.8     | 1.07           | 79.88 | 81.47         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_769x769_40k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json)     |
-| DANet  | R-50-D8  | 512x1024  |   80000 | -        | -              | 79.34 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json)     |
-| DANet  | R-101-D8 | 512x1024  |   80000 | -        | -              | 80.41 | -             | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json) |
-| DANet  | R-50-D8  | 769x769   |   80000 | -        | -              | 79.27 | 80.96         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_769x769_80k_cityscapes.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json)         |
-| DANet  | R-101-D8 | 769x769   |   80000 | -        | -              | 80.47 | 82.02         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_769x769_80k_cityscapes.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json)     |
-
-### ADE20K
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                | download                                                                                                                                                                                                                                                                                                                               |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | --------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| DANet  | R-50-D8  | 512x512   |   80000 | 11.5     | 21.20          | 41.66 |         42.90 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_80k_ade20k.py)   | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json)         |
-| DANet  | R-101-D8 | 512x512   |   80000 | 15       | 14.18          | 43.64 |         45.19 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_80k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json)     |
-| DANet  | R-50-D8  | 512x512   |  160000 | -        | -              | 42.45 |         43.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_160k_ade20k.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json)     |
-| DANet  | R-101-D8 | 512x512   |  160000 | -        | -              | 44.17 |         45.02 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json) |
-
-### Pascal VOC 2012 + Aug
-
-| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                   |
-| ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| DANet  | R-50-D8  | 512x512   |   20000 | 6.5      | 20.94          | 74.45 |         75.69 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_20k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json)     |
-| DANet  | R-101-D8 | 512x512   |   20000 | 9.9      | 13.76          | 76.02 |         77.23 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_20k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json) |
-| DANet  | R-50-D8  | 512x512   |   40000 | -        | -              | 76.37 |         77.29 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py)  | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json)     |
-| DANet  | R-101-D8 | 512x512   |   40000 | -        | -              | 76.51 |         77.32 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet/danet_r101-d8_512x512_40k_voc12aug.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth) &#124; [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json) |

spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_769x769_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/danet_r50-d8.py',
-    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(align_corners=True),
-    auxiliary_head=dict(align_corners=True),
-    test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/visualization/optflow.py

@@ -1,112 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from __future__ import division
-
-import numpy as np
-
-from annotator.uniformer.mmcv.image import rgb2bgr
-from annotator.uniformer.mmcv.video import flowread
-from .image import imshow
-
-
-def flowshow(flow, win_name='', wait_time=0):
-    """Show optical flow.
-
-    Args:
-        flow (ndarray or str): The optical flow to be displayed.
-        win_name (str): The window name.
-        wait_time (int): Value of waitKey param.
-    """
-    flow = flowread(flow)
-    flow_img = flow2rgb(flow)
-    imshow(rgb2bgr(flow_img), win_name, wait_time)
-
-
-def flow2rgb(flow, color_wheel=None, unknown_thr=1e6):
-    """Convert flow map to RGB image.
-
-    Args:
-        flow (ndarray): Array of optical flow.
-        color_wheel (ndarray or None): Color wheel used to map flow field to
-            RGB colorspace. Default color wheel will be used if not specified.
-        unknown_thr (str): Values above this threshold will be marked as
-            unknown and thus ignored.
-
-    Returns:
-        ndarray: RGB image that can be visualized.
-    """
-    assert flow.ndim == 3 and flow.shape[-1] == 2
-    if color_wheel is None:
-        color_wheel = make_color_wheel()
-    assert color_wheel.ndim == 2 and color_wheel.shape[1] == 3
-    num_bins = color_wheel.shape[0]
-
-    dx = flow[:, :, 0].copy()
-    dy = flow[:, :, 1].copy()
-
-    ignore_inds = (
-        np.isnan(dx) | np.isnan(dy) | (np.abs(dx) > unknown_thr) |
-        (np.abs(dy) > unknown_thr))
-    dx[ignore_inds] = 0
-    dy[ignore_inds] = 0
-
-    rad = np.sqrt(dx**2 + dy**2)
-    if np.any(rad > np.finfo(float).eps):
-        max_rad = np.max(rad)
-        dx /= max_rad
-        dy /= max_rad
-
-    rad = np.sqrt(dx**2 + dy**2)
-    angle = np.arctan2(-dy, -dx) / np.pi
-
-    bin_real = (angle + 1) / 2 * (num_bins - 1)
-    bin_left = np.floor(bin_real).astype(int)
-    bin_right = (bin_left + 1) % num_bins
-    w = (bin_real - bin_left.astype(np.float32))[..., None]
-    flow_img = (1 -
-                w) * color_wheel[bin_left, :] + w * color_wheel[bin_right, :]
-    small_ind = rad <= 1
-    flow_img[small_ind] = 1 - rad[small_ind, None] * (1 - flow_img[small_ind])
-    flow_img[np.logical_not(small_ind)] *= 0.75
-
-    flow_img[ignore_inds, :] = 0
-
-    return flow_img
-
-
-def make_color_wheel(bins=None):
-    """Build a color wheel.
-
-    Args:
-        bins(list or tuple, optional): Specify the number of bins for each
-            color range, corresponding to six ranges: red -> yellow,
-            yellow -> green, green -> cyan, cyan -> blue, blue -> magenta,
-            magenta -> red. [15, 6, 4, 11, 13, 6] is used for default
-            (see Middlebury).
-
-    Returns:
-        ndarray: Color wheel of shape (total_bins, 3).
-    """
-    if bins is None:
-        bins = [15, 6, 4, 11, 13, 6]
-    assert len(bins) == 6
-
-    RY, YG, GC, CB, BM, MR = tuple(bins)
-
-    ry = [1, np.arange(RY) / RY, 0]
-    yg = [1 - np.arange(YG) / YG, 1, 0]
-    gc = [0, 1, np.arange(GC) / GC]
-    cb = [0, 1 - np.arange(CB) / CB, 1]
-    bm = [np.arange(BM) / BM, 0, 1]
-    mr = [1, 0, 1 - np.arange(MR) / MR]
-
-    num_bins = RY + YG + GC + CB + BM + MR
-
-    color_wheel = np.zeros((3, num_bins), dtype=np.float32)
-
-    col = 0
-    for i, color in enumerate([ry, yg, gc, cb, bm, mr]):
-        for j in range(3):
-            color_wheel[j, col:col + bins[i]] = color[j]
-        col += bins[i]
-
-    return color_wheel.T

spaces/ArkanDash/rvc-models/infer_pack/modules.py

@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        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.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        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.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x

spaces/Axolotlily/Interpolate/app.py

@@ -1,63 +0,0 @@
-import os
-os.system("git clone https://github.com/google-research/frame-interpolation")
-import sys
-sys.path.append("frame-interpolation")
-import numpy as np
-import tensorflow as tf
-import mediapy
-from PIL import Image
-from eval import interpolator, util
-import gradio as gr
-
-from huggingface_hub import snapshot_download
-
-from image_tools.sizes import resize_and_crop
-
-
-model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style")
-
-interpolator = interpolator.Interpolator(model, None)
-
-ffmpeg_path = util.get_ffmpeg_path()
-mediapy.set_ffmpeg(ffmpeg_path)
-
-def resize(width,img):
-  basewidth = width
-  img = Image.open(img)
-  wpercent = (basewidth/float(img.size[0]))
-  hsize = int((float(img.size[1])*float(wpercent)))
-  img = img.resize((basewidth,hsize), Image.ANTIALIAS)
-  return img
-  
-
-def resize_img(img1,img2):
- img_target_size = Image.open(img1)
- img_to_resize = resize_and_crop(
-     img2, 
-     (img_target_size.size[0],img_target_size.size[1]), #set width and height to match img1
-     crop_origin="middle"
-     )
- img_to_resize.save('resized_img2.png')
-    
-def predict(frame1, frame2, times_to_interpolate):
-   
-    frame1 = resize(512,frame1)
-    frame2 = resize(512,frame2)
-
-    frame1.save("test1.png")
-    frame2.save("test2.png")
-
-    resize_img("test1.png","test2.png")
-    input_frames = ["test1.png", "resized_img2.png"]
-
-    frames = list(
-        util.interpolate_recursively_from_files(
-            input_frames, times_to_interpolate, interpolator))
-
-    mediapy.write_video("out.mp4", frames, fps=15)
-    return "out.mp4"
-article=""
-description="Using AI to guess the frames between two separate images."
-title="Frame Interpolation"
-examples=[['cat3.jpeg','cat4.jpeg',2]]
-gr.Interface(predict,[gr.inputs.Image(type='filepath'),gr.inputs.Image(type='filepath'),gr.inputs.Slider(minimum=2,maximum=8,step=1)],"playable_video",title=title,description=description,article=article,examples=examples).launch(enable_queue=True)

spaces/BRICS/README/README.md

@@ -1,10 +0,0 @@
----
-title: README
-emoji: 💻
-colorFrom: yellow
-colorTo: blue
-sdk: static
-pinned: false
----
-
-Edit this `README.md` markdown file to author your organization card.

spaces/BasToTheMax/tensor/README.md

@@ -1,13 +0,0 @@
----
-title: Tensor
-emoji: 🐨
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Benson/text-generation/Examples/Coche De Playa Carreras Ruedas Calientes Apk.md

@@ -1,50 +0,0 @@
-
-<h1>Beach Buggy Racing Hot Wheels APK Descargar: Un divertido y rápido juego de carreras de karts</h1>
-<p>Si usted está buscando un juego de carreras de karts divertido y de ritmo rápido que se puede jugar en su dispositivo Android, entonces usted debe comprobar Beach Buggy Racing Hot Wheels APK. Este es un juego gratuito que te permite conducir en un mundo lleno de acción y lleno de sorpresas de caos de carreras de karts todoterreno. Puedes competir contra un campo de pilotos rivales, cada uno con personalidades únicas y habilidades especiales. También puedes crear una colección de poderes locos, como Dodgeball Frenzy, Fireball y oíl Slick. También puede desbloquear y actualizar una variedad de coches, desde buggies de dunas a camiones monstruo a rovers lunares. También puedes poner a prueba tus habilidades en 6 modos de juego diferentes en 15 imaginativas pistas de carreras en 3D, contra un grupo de rivales amantes de lo tropical con un caso serio de furia en el camino. Esta es la secuela oficial de Beach Buggy Blitz, el juego de conducción gratuita con más de 30 millones de jugadores en todo el mundo. Rápido, furioso, divertido y gratis, Beach Buggy Racing Hot Wheels APK es una aventura de kart-racing isla para todas las edades. </p>
-<h2>coche de playa carreras ruedas calientes apk</h2><br /><p><b><b>Download Zip</b> &hArr; <a href="https://bltlly.com/2v6IAu">https://bltlly.com/2v6IAu</a></b></p><br /><br />
- <h2>Características de Beach Buggy Racing Hot Wheels APK</h2>
-<p>Beach Buggy Racing Hot Wheels APK tiene muchas características que lo convierten en un juego emocionante y agradable. Estos son algunos de ellos:</p>
-<ul>
-<li><b>Emocionante acción de carreras de karts con potenciadores creativos y jugabilidad basada en la física</b>: Puedes utilizar tus habilidades de conducción y una colección de potenciadores creativos para luchar hasta la meta. No es solo un gran juego de carreras en 3D, es una batalla épica con un juego espectacular basado en la física. </li>
-<li><b>Coches geniales para personalizar, desde buggies de dunas a camiones monstruosos a rovers lunares</b>: Puedes usar tus ganancias para recoger y actualizar un garaje lleno de coches únicos, desde camiones monstruosos hasta coches musculosos y rovers lunares. </li>
-
-<li><b>15 espectaculares pistas de carreras, desde selvas infestadas de dinosaurios a volcanes que arrojan lava a hermosas playas</b>: Puedes explorar selvas, volcanes, playas y más en un mundo lleno de acción de sorpresas. Verás cangrejos gigantes, yetis enojados, dragones voladores y más. </li>
-<li><b>Reúne un equipo de corredores, cada uno con un poder especial único</b>: Puedes reclutar un equipo de conductores para jugar, cada uno con un poder especial único como la teletransportación, pistas de fuego en llamas y hechizos de confusión. </li>
-<li><b>Modo multijugador de pantalla dividida para hasta 4 amigos</b>: Puedes retar a tus amigos en emocionantes carreras multijugador de pantalla dividida. Puedes jugar con hasta 4 amigos en un dispositivo. </li>
-<li><b>Integración de servicios de juego de Google Play para tablas de clasificación, logros, almacenamiento en la nube y sincronización</b>: Puedes competir con tus amigos en tablas de clasificación, obtener logros, hacer copias de seguridad de tu juego en la nube y mantener varios dispositivos sincronizados con tu cuenta de Google. </li>
-<li><b>Juega de la manera que quieras con la dirección de inclinación, la pantalla táctil o los controles del gamepad</b>: Puedes elegir entre varias opciones de control y personalizar la configuración de gráficos 3D para optimizar tu experiencia de juego. </li>
-<li><b>Personalice la configuración de gráficos 3D para optimizar su experiencia de juego</b>: Puede ajustar la calidad de los gráficos y la configuración de rendimiento para adaptarse a su dispositivo y preferencia. </li>
-</ul>
- <h2>Cómo descargar e instalar Beach Buggy Racing Hot Wheels APK</h2>
-<p>Si desea descargar e instalar Beach Buggy Racing Hot Wheels APK en su dispositivo Android, puede seguir estos sencillos pasos:</p>
-<ol>
-<li><b>Ir a la página web oficial de Beach Buggy Racing Hot Wheels APK y haga clic en el botón de descarga</b>: Esto iniciará el proceso de descarga del archivo APK en su dispositivo. Asegúrese de tener suficiente espacio de almacenamiento y una conexión a Internet estable. </li>
-
-<li><b>Si ves un mensaje de advertencia sobre la instalación de aplicaciones desde fuentes desconocidas, ve a la configuración de tu dispositivo y habilita la opción para permitirlo</b>: Algunos dispositivos pueden bloquear la instalación de aplicaciones desde fuentes distintas de Google Play Store por razones de seguridad. Si ves tal mensaje, necesitas ir a la configuración de tu dispositivo, encontrar la opción de seguridad o privacidad y habilitar la opción para permitir la instalación de aplicaciones desde fuentes desconocidas. Esto le permitirá instalar Beach Buggy Racing Hot Wheels APK sin ningún problema. </li>
-<li><b>Siga las instrucciones en pantalla para instalar el juego y disfrutar</b>: Después de habilitar la opción de instalar aplicaciones de fuentes desconocidas, puede seguir las instrucciones en pantalla para instalar Beach Buggy Racing Hot Wheels APK en su dispositivo. Tardará unos segundos o minutos dependiendo de la velocidad de tu dispositivo. Una vez finalizada la instalación, puedes iniciar el juego y disfrutarlo. </li>
-</ol>
- <h2> Consejos y trucos para jugar Beach Buggy Racing Hot Wheels APK</h2>
-<p>Beach Buggy Racing Hot Wheels APK es un juego divertido y adictivo que te mantendrá entretenido durante horas. Sin embargo, si quieres mejorar tus habilidades y rendimiento en el juego, puedes utilizar estos consejos y trucos:</p>
-<ul>
-<li><b>Utilice sus powerups sabiamente y estratégicamente para ganar una ventaja sobre sus oponentes</b>: Powerups son uno de los aspectos más importantes de Beach Buggy Racing Hot Wheels APK. Pueden ayudarte a acelerar, ralentizar, atacar o defenderte de otros corredores. Sin embargo, necesitas usarlos sabiamente y estratégicamente. Por ejemplo, puedes usar una bola de fuego para disparar a un oponente delante de ti, o una mancha de aceite para que se deslice detrás de ti. También puedes usar un escudo para protegerte de ataques entrantes, o un turbo para acercarte más allá de todos. Usted necesita saber cuándo y cómo utilizar cada powerup con eficacia. </li>
-
-<li><b>Pruebe diferentes modos de juego y pistas de carreras para desafiarse y divertirse</b>: Beach Buggy Racing Hot Wheels APK tiene seis modos de juego diferentes y 15 pistas de carreras 3D imaginativas para desafiarse y divertirse. Puedes probar el modo Campeonato, donde compites en una serie de carreras para convertirte en el campeón definitivo. También puedes probar el modo Carrera rápida, donde puedes elegir cualquier pista y cualquier coche y carrera por diversión. También puedes probar el modo Desafío diario, donde puedes ganar recompensas adicionales completando una tarea específica cada día. También puedes probar el modo Eventos especiales, donde puedes participar en eventos de tiempo limitado con reglas y premios especiales. También puedes probar el modo Hot Wheels, donde puedes competir con coches y pistas de Hot Wheels. También puedes probar el modo Boss Battle, donde puedes enfrentarte a los jefes de carreras y sus poderes especiales. Cada modo de juego y pista de carreras tiene sus propios desafíos, sorpresas y diversión. </li>
-<li><b>Reclutar nuevos corredores y utilizar sus poderes especiales para su ventaja</b>: Beach Buggy Racing Hot Wheels APK tiene una lista de 12 corredores, cada uno con un poder especial único. Puedes reclutarlos ganando carreras o comprándolas con gemas. También puedes cambiar entre ellas antes de cada carrera. Cada corredor tiene una personalidad, estilo y poder diferentes que pueden ayudarte en diferentes situaciones. Por ejemplo, Rez tiene el poder de teletransportación, lo que le permite avanzar por delante de la manada. Beach Bro tiene el poder de las pistas de fuego en llamas, que incendia el suelo detrás de él. Tiki tiene el poder de los hechizos de confusión, lo que hace que otros corredores pierdan el control de sus coches. Necesitas experimentar con diferentes corredores y sus poderes para encontrar la mejor combinación para cada carrera. </li>
-
-</ul>
- <h2>Conclusión</h2>
-<p>Beach Buggy Racing Hot Wheels APK es un divertido y rápido juego de carreras de karts que se puede descargar de forma gratuita y disfrutar en su dispositivo Android. Tiene muchas características que lo convierten en un juego emocionante y agradable, como powerups creativos, coches frescos, pistas de carreras espectaculares, corredores únicos y diferentes modos de juego. También tiene un proceso de instalación simple y fácil, así como consejos y trucos para ayudarle a mejorar sus habilidades y rendimiento en el juego. También tiene un modo multijugador de pantalla dividida y un modo online que te permite jugar con tus amigos y divertirte más. </p>
-<p></p>
-<p>Si usted está buscando una aventura isla de carreras de karts para todas las edades, entonces usted debe descargar Beach Buggy Racing Hot Wheels APK hoy y unirse al caos de carreras. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Beach Buggy Racing Hot Wheels APK:</p>
-<ol>
-<li><b> ¿Es seguro descargar e instalar Beach Buggy Racing Hot Wheels APK? </b>: Sí, Beach Buggy Racing Hot Wheels APK es seguro para descargar e instalar en su dispositivo Android. Es desarrollado por Vector Unit, un desarrollador de juegos de buena reputación que ha creado muchos juegos populares como Riptide GP, Shine Runner, MouseBot, etc. También es verificado por Google Play Protect, que escanea aplicaciones de malware y otras amenazas antes de instalarlos en su dispositivo. </li>
-<li><b>Es Beach Buggy Racing Hot Wheels APK libre para jugar? </b>: Sí, Beach Buggy Racing Hot Wheels APK es gratis para jugar en su dispositivo Android. Sin embargo, contiene algunas compras opcionales en la aplicación que pueden mejorar su experiencia de juego. Por ejemplo, puedes comprar gemas para desbloquear nuevos coches y corredores más rápido, o comprar monedas para mejorar tus coches más fácilmente. También puedes ver anuncios para ganar monedas y gemas extra gratis. </li>
-
-<li><b> ¿Cuáles son los requisitos mínimos del sistema para Beach Buggy Racing Hot Wheels APK? </b>: Beach Buggy Racing Hot Wheels APK requiere Android 4.1 o superior para funcionar sin problemas en su dispositivo. También requiere al menos 1 GB de RAM y un procesador decente para manejar los gráficos 3D y el juego basado en la física. </li>
-<li><b> ¿Cómo puedo contactar con el desarrollador de Beach Buggy Racing Hot Wheels APK? </b>: Si usted tiene alguna pregunta, comentarios, o problemas con respecto a Beach Buggy Racing Hot Wheels APK, puede ponerse en contacto con el desarrollador del juego por correo electrónico a [email protected]. También puede visitar su sitio web o seguirlos en Facebook o Twitter para obtener más información y actualizaciones sobre el juego. </li>
-</ol>
- : https://beach-buggy-racing-hotwheels.en.uptodown.com/android. : https://www.vectorunit.com/ : https://www.facebook.com/VectorUnit : https:/twitter.com/VectorUnit</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_distutils/command/install_headers.py

@@ -1,45 +0,0 @@
-"""distutils.command.install_headers
-
-Implements the Distutils 'install_headers' command, to install C/C++ header
-files to the Python include directory."""
-
-from distutils.core import Command
-
-
-# XXX force is never used
-class install_headers(Command):
-
-    description = "install C/C++ header files"
-
-    user_options = [
-        ('install-dir=', 'd', "directory to install header files to"),
-        ('force', 'f', "force installation (overwrite existing files)"),
-    ]
-
-    boolean_options = ['force']
-
-    def initialize_options(self):
-        self.install_dir = None
-        self.force = 0
-        self.outfiles = []
-
-    def finalize_options(self):
-        self.set_undefined_options(
-            'install', ('install_headers', 'install_dir'), ('force', 'force')
-        )
-
-    def run(self):
-        headers = self.distribution.headers
-        if not headers:
-            return
-
-        self.mkpath(self.install_dir)
-        for header in headers:
-            (out, _) = self.copy_file(header, self.install_dir)
-            self.outfiles.append(out)
-
-    def get_inputs(self):
-        return self.distribution.headers or []
-
-    def get_outputs(self):
-        return self.outfiles

spaces/BilalSardar/karlo-cpu-api/README.md

@@ -1,12 +0,0 @@
----
-title: Karlo Cpu Api
-emoji: 🦀
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/roi_heads/fast_rcnn.py

@@ -1,498 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-import torch
-from fvcore.nn import smooth_l1_loss
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import Linear, ShapeSpec, batched_nms, cat
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.structures import Boxes, Instances
-from detectron2.utils.events import get_event_storage
-
-logger = logging.getLogger(__name__)
-
-"""
-Shape shorthand in this module:
-
-    N: number of images in the minibatch
-    R: number of ROIs, combined over all images, in the minibatch
-    Ri: number of ROIs in image i
-    K: number of foreground classes. E.g.,there are 80 foreground classes in COCO.
-
-Naming convention:
-
-    deltas: refers to the 4-d (dx, dy, dw, dh) deltas that parameterize the box2box
-    transform (see :class:`box_regression.Box2BoxTransform`).
-
-    pred_class_logits: predicted class scores in [-inf, +inf]; use
-        softmax(pred_class_logits) to estimate P(class).
-
-    gt_classes: ground-truth classification labels in [0, K], where [0, K) represent
-        foreground object classes and K represents the background class.
-
-    pred_proposal_deltas: predicted box2box transform deltas for transforming proposals
-        to detection box predictions.
-
-    gt_proposal_deltas: ground-truth box2box transform deltas
-"""
-
-
-def fast_rcnn_inference(boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image):
-    """
-    Call `fast_rcnn_inference_single_image` for all images.
-
-    Args:
-        boxes (list[Tensor]): A list of Tensors of predicted class-specific or class-agnostic
-            boxes for each image. Element i has shape (Ri, K * 4) if doing
-            class-specific regression, or (Ri, 4) if doing class-agnostic
-            regression, where Ri is the number of predicted objects for image i.
-            This is compatible with the output of :meth:`FastRCNNOutputs.predict_boxes`.
-        scores (list[Tensor]): A list of Tensors of predicted class scores for each image.
-            Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
-            for image i. Compatible with the output of :meth:`FastRCNNOutputs.predict_probs`.
-        image_shapes (list[tuple]): A list of (width, height) tuples for each image in the batch.
-        score_thresh (float): Only return detections with a confidence score exceeding this
-            threshold.
-        nms_thresh (float):  The threshold to use for box non-maximum suppression. Value in [0, 1].
-        topk_per_image (int): The number of top scoring detections to return. Set < 0 to return
-            all detections.
-
-    Returns:
-        instances: (list[Instances]): A list of N instances, one for each image in the batch,
-            that stores the topk most confidence detections.
-        kept_indices: (list[Tensor]): A list of 1D tensor of length of N, each element indicates
-            the corresponding boxes/scores index in [0, Ri) from the input, for image i.
-    """
-    result_per_image = [
-        fast_rcnn_inference_single_image(
-            boxes_per_image, scores_per_image, image_shape, score_thresh, nms_thresh, topk_per_image
-        )
-        for scores_per_image, boxes_per_image, image_shape in zip(scores, boxes, image_shapes)
-    ]
-    return [x[0] for x in result_per_image], [x[1] for x in result_per_image]
-
-
-def fast_rcnn_inference_single_image(
-    boxes, scores, image_shape, score_thresh, nms_thresh, topk_per_image
-):
-    """
-    Single-image inference. Return bounding-box detection results by thresholding
-    on scores and applying non-maximum suppression (NMS).
-
-    Args:
-        Same as `fast_rcnn_inference`, but with boxes, scores, and image shapes
-        per image.
-
-    Returns:
-        Same as `fast_rcnn_inference`, but for only one image.
-    """
-    valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
-    if not valid_mask.all():
-        boxes = boxes[valid_mask]
-        scores = scores[valid_mask]
-
-    scores = scores[:, :-1]
-    num_bbox_reg_classes = boxes.shape[1] // 4
-    # Convert to Boxes to use the `clip` function ...
-    boxes = Boxes(boxes.reshape(-1, 4))
-    boxes.clip(image_shape)
-    boxes = boxes.tensor.view(-1, num_bbox_reg_classes, 4)  # R x C x 4
-
-    # Filter results based on detection scores
-    filter_mask = scores > score_thresh  # R x K
-    # R' x 2. First column contains indices of the R predictions;
-    # Second column contains indices of classes.
-    filter_inds = filter_mask.nonzero()
-    if num_bbox_reg_classes == 1:
-        boxes = boxes[filter_inds[:, 0], 0]
-    else:
-        boxes = boxes[filter_mask]
-    scores = scores[filter_mask]
-
-    # Apply per-class NMS
-    keep = batched_nms(boxes, scores, filter_inds[:, 1], nms_thresh)
-    if topk_per_image >= 0:
-        keep = keep[:topk_per_image]
-    boxes, scores, filter_inds = boxes[keep], scores[keep], filter_inds[keep]
-
-    result = Instances(image_shape)
-    result.pred_boxes = Boxes(boxes)
-    result.scores = scores
-    result.pred_classes = filter_inds[:, 1]
-    return result, filter_inds[:, 0]
-
-
-class FastRCNNOutputs(object):
-    """
-    A class that stores information about outputs of a Fast R-CNN head.
-    It provides methods that are used to decode the outputs of a Fast R-CNN head.
-    """
-
-    def __init__(
-        self,
-        box2box_transform,
-        pred_class_logits,
-        pred_proposal_deltas,
-        proposals,
-        smooth_l1_beta=0,
-    ):
-        """
-        Args:
-            box2box_transform (Box2BoxTransform/Box2BoxTransformRotated):
-                box2box transform instance for proposal-to-detection transformations.
-            pred_class_logits (Tensor): A tensor of shape (R, K + 1) storing the predicted class
-                logits for all R predicted object instances.
-                Each row corresponds to a predicted object instance.
-            pred_proposal_deltas (Tensor): A tensor of shape (R, K * B) or (R, B) for
-                class-specific or class-agnostic regression. It stores the predicted deltas that
-                transform proposals into final box detections.
-                B is the box dimension (4 or 5).
-                When B is 4, each row is [dx, dy, dw, dh (, ....)].
-                When B is 5, each row is [dx, dy, dw, dh, da (, ....)].
-            proposals (list[Instances]): A list of N Instances, where Instances i stores the
-                proposals for image i, in the field "proposal_boxes".
-                When training, each Instances must have ground-truth labels
-                stored in the field "gt_classes" and "gt_boxes".
-                The total number of all instances must be equal to R.
-            smooth_l1_beta (float): The transition point between L1 and L2 loss in
-                the smooth L1 loss function. When set to 0, the loss becomes L1. When
-                set to +inf, the loss becomes constant 0.
-        """
-        self.box2box_transform = box2box_transform
-        self.num_preds_per_image = [len(p) for p in proposals]
-        self.pred_class_logits = pred_class_logits
-        self.pred_proposal_deltas = pred_proposal_deltas
-        self.smooth_l1_beta = smooth_l1_beta
-        self.image_shapes = [x.image_size for x in proposals]
-
-        if len(proposals):
-            box_type = type(proposals[0].proposal_boxes)
-            # cat(..., dim=0) concatenates over all images in the batch
-            self.proposals = box_type.cat([p.proposal_boxes for p in proposals])
-            assert (
-                not self.proposals.tensor.requires_grad
-            ), "Proposals should not require gradients!"
-
-            # The following fields should exist only when training.
-            if proposals[0].has("gt_boxes"):
-                self.gt_boxes = box_type.cat([p.gt_boxes for p in proposals])
-                assert proposals[0].has("gt_classes")
-                self.gt_classes = cat([p.gt_classes for p in proposals], dim=0)
-        else:
-            self.proposals = Boxes(torch.zeros(0, 4, device=self.pred_proposal_deltas.device))
-        self._no_instances = len(proposals) == 0  # no instances found
-
-    def _log_accuracy(self):
-        """
-        Log the accuracy metrics to EventStorage.
-        """
-        num_instances = self.gt_classes.numel()
-        pred_classes = self.pred_class_logits.argmax(dim=1)
-        bg_class_ind = self.pred_class_logits.shape[1] - 1
-
-        fg_inds = (self.gt_classes >= 0) & (self.gt_classes < bg_class_ind)
-        num_fg = fg_inds.nonzero().numel()
-        fg_gt_classes = self.gt_classes[fg_inds]
-        fg_pred_classes = pred_classes[fg_inds]
-
-        num_false_negative = (fg_pred_classes == bg_class_ind).nonzero().numel()
-        num_accurate = (pred_classes == self.gt_classes).nonzero().numel()
-        fg_num_accurate = (fg_pred_classes == fg_gt_classes).nonzero().numel()
-
-        storage = get_event_storage()
-        if num_instances > 0:
-            storage.put_scalar("fast_rcnn/cls_accuracy", num_accurate / num_instances)
-            if num_fg > 0:
-                storage.put_scalar("fast_rcnn/fg_cls_accuracy", fg_num_accurate / num_fg)
-                storage.put_scalar("fast_rcnn/false_negative", num_false_negative / num_fg)
-
-    def softmax_cross_entropy_loss(self):
-        """
-        Compute the softmax cross entropy loss for box classification.
-
-        Returns:
-            scalar Tensor
-        """
-        if self._no_instances:
-            return 0.0 * F.cross_entropy(
-                self.pred_class_logits,
-                torch.zeros(0, dtype=torch.long, device=self.pred_class_logits.device),
-                reduction="sum",
-            )
-        else:
-            self._log_accuracy()
-            return F.cross_entropy(self.pred_class_logits, self.gt_classes, reduction="mean")
-
-    def smooth_l1_loss(self):
-        """
-        Compute the smooth L1 loss for box regression.
-
-        Returns:
-            scalar Tensor
-        """
-        if self._no_instances:
-            return 0.0 * smooth_l1_loss(
-                self.pred_proposal_deltas,
-                torch.zeros_like(self.pred_proposal_deltas),
-                0.0,
-                reduction="sum",
-            )
-        gt_proposal_deltas = self.box2box_transform.get_deltas(
-            self.proposals.tensor, self.gt_boxes.tensor
-        )
-        box_dim = gt_proposal_deltas.size(1)  # 4 or 5
-        cls_agnostic_bbox_reg = self.pred_proposal_deltas.size(1) == box_dim
-        device = self.pred_proposal_deltas.device
-
-        bg_class_ind = self.pred_class_logits.shape[1] - 1
-
-        # Box delta loss is only computed between the prediction for the gt class k
-        # (if 0 <= k < bg_class_ind) and the target; there is no loss defined on predictions
-        # for non-gt classes and background.
-        # Empty fg_inds produces a valid loss of zero as long as the size_average
-        # arg to smooth_l1_loss is False (otherwise it uses torch.mean internally
-        # and would produce a nan loss).
-        fg_inds = torch.nonzero((self.gt_classes >= 0) & (self.gt_classes < bg_class_ind)).squeeze(
-            1
-        )
-        if cls_agnostic_bbox_reg:
-            # pred_proposal_deltas only corresponds to foreground class for agnostic
-            gt_class_cols = torch.arange(box_dim, device=device)
-        else:
-            fg_gt_classes = self.gt_classes[fg_inds]
-            # pred_proposal_deltas for class k are located in columns [b * k : b * k + b],
-            # where b is the dimension of box representation (4 or 5)
-            # Note that compared to Detectron1,
-            # we do not perform bounding box regression for background classes.
-            gt_class_cols = box_dim * fg_gt_classes[:, None] + torch.arange(box_dim, device=device)
-
-        loss_box_reg = smooth_l1_loss(
-            self.pred_proposal_deltas[fg_inds[:, None], gt_class_cols],
-            gt_proposal_deltas[fg_inds],
-            self.smooth_l1_beta,
-            reduction="sum",
-        )
-        # The loss is normalized using the total number of regions (R), not the number
-        # of foreground regions even though the box regression loss is only defined on
-        # foreground regions. Why? Because doing so gives equal training influence to
-        # each foreground example. To see how, consider two different minibatches:
-        #  (1) Contains a single foreground region
-        #  (2) Contains 100 foreground regions
-        # If we normalize by the number of foreground regions, the single example in
-        # minibatch (1) will be given 100 times as much influence as each foreground
-        # example in minibatch (2). Normalizing by the total number of regions, R,
-        # means that the single example in minibatch (1) and each of the 100 examples
-        # in minibatch (2) are given equal influence.
-        loss_box_reg = loss_box_reg / self.gt_classes.numel()
-        return loss_box_reg
-
-    def _predict_boxes(self):
-        """
-        Returns:
-            Tensor: A Tensors of predicted class-specific or class-agnostic boxes
-                for all images in a batch. Element i has shape (Ri, K * B) or (Ri, B), where Ri is
-                the number of predicted objects for image i and B is the box dimension (4 or 5)
-        """
-        num_pred = len(self.proposals)
-        B = self.proposals.tensor.shape[1]
-        K = self.pred_proposal_deltas.shape[1] // B
-        boxes = self.box2box_transform.apply_deltas(
-            self.pred_proposal_deltas.view(num_pred * K, B),
-            self.proposals.tensor.unsqueeze(1).expand(num_pred, K, B).reshape(-1, B),
-        )
-        return boxes.view(num_pred, K * B)
-
-    """
-    A subclass is expected to have the following methods because
-    they are used to query information about the head predictions.0
-    """
-
-    def losses(self):
-        """
-        Compute the default losses for box head in Fast(er) R-CNN,
-        with softmax cross entropy loss and smooth L1 loss.
-
-        Returns:
-            A dict of losses (scalar tensors) containing keys "loss_cls" and "loss_box_reg".
-        """
-        return {
-            "loss_cls": self.softmax_cross_entropy_loss(),
-            "loss_box_reg": self.smooth_l1_loss(),
-        }
-
-    def predict_boxes(self):
-        """
-        Returns:
-            list[Tensor]: A list of Tensors of predicted class-specific or class-agnostic boxes
-                for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is
-                the number of predicted objects for image i and B is the box dimension (4 or 5)
-        """
-        return self._predict_boxes().split(self.num_preds_per_image, dim=0)
-
-    def predict_boxes_for_gt_classes(self):
-        """
-        Returns:
-            list[Tensor]: A list of Tensors of predicted boxes for GT classes in case of
-                class-specific box head. Element i of the list has shape (Ri, B), where Ri is
-                the number of predicted objects for image i and B is the box dimension (4 or 5)
-        """
-        predicted_boxes = self._predict_boxes()
-        B = self.proposals.tensor.shape[1]
-        # If the box head is class-agnostic, then the method is equivalent to `predicted_boxes`.
-        if predicted_boxes.shape[1] > B:
-            num_pred = len(self.proposals)
-            num_classes = predicted_boxes.shape[1] // B
-            # Some proposals are ignored or have a background class. Their gt_classes
-            # cannot be used as index.
-            gt_classes = torch.clamp(self.gt_classes, 0, num_classes - 1)
-            predicted_boxes = predicted_boxes.view(num_pred, num_classes, B)[
-                torch.arange(num_pred, dtype=torch.long, device=predicted_boxes.device), gt_classes
-            ]
-        return predicted_boxes.split(self.num_preds_per_image, dim=0)
-
-    def predict_probs(self):
-        """
-        Returns:
-            list[Tensor]: A list of Tensors of predicted class probabilities for each image.
-                Element i has shape (Ri, K + 1), where Ri is the number of predicted objects
-                for image i.
-        """
-        probs = F.softmax(self.pred_class_logits, dim=-1)
-        return probs.split(self.num_preds_per_image, dim=0)
-
-    def inference(self, score_thresh, nms_thresh, topk_per_image):
-        """
-        Args:
-            score_thresh (float): same as fast_rcnn_inference.
-            nms_thresh (float): same as fast_rcnn_inference.
-            topk_per_image (int): same as fast_rcnn_inference.
-        Returns:
-            list[Instances]: same as fast_rcnn_inference.
-            list[Tensor]: same as fast_rcnn_inference.
-        """
-        boxes = self.predict_boxes()
-        scores = self.predict_probs()
-        image_shapes = self.image_shapes
-
-        return fast_rcnn_inference(
-            boxes, scores, image_shapes, score_thresh, nms_thresh, topk_per_image
-        )
-
-
-class FastRCNNOutputLayers(nn.Module):
-    """
-    Two linear layers for predicting Fast R-CNN outputs:
-      (1) proposal-to-detection box regression deltas
-      (2) classification scores
-    """
-
-    @configurable
-    def __init__(
-        self,
-        input_shape,
-        box2box_transform,
-        num_classes,
-        cls_agnostic_bbox_reg=False,
-        smooth_l1_beta=0.0,
-        test_score_thresh=0.0,
-        test_nms_thresh=0.5,
-        test_topk_per_image=100,
-    ):
-        """
-        Args:
-            input_shape (ShapeSpec): shape of the input feature to this module
-            box2box_transform (Box2BoxTransform or Box2BoxTransformRotated):
-            num_classes (int): number of foreground classes
-            cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression
-            smooth_l1_beta (float): transition point from L1 to L2 loss.
-            test_score_thresh (float): threshold to filter predictions results.
-            test_nms_thresh (float): NMS threshold for prediction results.
-            test_topk_per_image (int): number of top predictions to produce per image.
-        """
-        super().__init__()
-        if isinstance(input_shape, int):  # some backward compatbility
-            input_shape = ShapeSpec(channels=input_shape)
-        input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1)
-        # The prediction layer for num_classes foreground classes and one background class
-        # (hence + 1)
-        self.cls_score = Linear(input_size, num_classes + 1)
-        num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes
-        box_dim = len(box2box_transform.weights)
-        self.bbox_pred = Linear(input_size, num_bbox_reg_classes * box_dim)
-
-        nn.init.normal_(self.cls_score.weight, std=0.01)
-        nn.init.normal_(self.bbox_pred.weight, std=0.001)
-        for l in [self.cls_score, self.bbox_pred]:
-            nn.init.constant_(l.bias, 0)
-
-        self.box2box_transform = box2box_transform
-        self.smooth_l1_beta = smooth_l1_beta
-        self.test_score_thresh = test_score_thresh
-        self.test_nms_thresh = test_nms_thresh
-        self.test_topk_per_image = test_topk_per_image
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        return {
-            "input_shape": input_shape,
-            "box2box_transform": Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS),
-            # fmt: off
-            "num_classes"           : cfg.MODEL.ROI_HEADS.NUM_CLASSES,
-            "cls_agnostic_bbox_reg" : cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG,
-            "smooth_l1_beta"        : cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA,
-            "test_score_thresh"     : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST,
-            "test_nms_thresh"       : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,
-            "test_topk_per_image"   : cfg.TEST.DETECTIONS_PER_IMAGE
-            # fmt: on
-        }
-
-    def forward(self, x):
-        """
-        Returns:
-            Tensor: Nx(K+1) scores for each box
-            Tensor: Nx4 or Nx(Kx4) bounding box regression deltas.
-        """
-        if x.dim() > 2:
-            x = torch.flatten(x, start_dim=1)
-        scores = self.cls_score(x)
-        proposal_deltas = self.bbox_pred(x)
-        return scores, proposal_deltas
-
-    # TODO: move the implementation to this class.
-    def losses(self, predictions, proposals):
-        """
-        Args:
-            predictions: return values of :meth:`forward()`.
-            proposals (list[Instances]): proposals that match the features
-                that were used to compute predictions.
-        """
-        scores, proposal_deltas = predictions
-        return FastRCNNOutputs(
-            self.box2box_transform, scores, proposal_deltas, proposals, self.smooth_l1_beta
-        ).losses()
-
-    def inference(self, predictions, proposals):
-        scores, proposal_deltas = predictions
-        return FastRCNNOutputs(
-            self.box2box_transform, scores, proposal_deltas, proposals, self.smooth_l1_beta
-        ).inference(self.test_score_thresh, self.test_nms_thresh, self.test_topk_per_image)
-
-    def predict_boxes_for_gt_classes(self, predictions, proposals):
-        scores, proposal_deltas = predictions
-        return FastRCNNOutputs(
-            self.box2box_transform, scores, proposal_deltas, proposals, self.smooth_l1_beta
-        ).predict_boxes_for_gt_classes()
-
-    def predict_boxes(self, predictions, proposals):
-        scores, proposal_deltas = predictions
-        return FastRCNNOutputs(
-            self.box2box_transform, scores, proposal_deltas, proposals, self.smooth_l1_beta
-        ).predict_boxes()
-
-    def predict_probs(self, predictions, proposals):
-        scores, proposal_deltas = predictions
-        return FastRCNNOutputs(
-            self.box2box_transform, scores, proposal_deltas, proposals, self.smooth_l1_beta
-        ).predict_probs()

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

@@ -1,240 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-
-import os
-import tempfile
-import unittest
-import torch
-
-from detectron2.config import configurable, downgrade_config, get_cfg, upgrade_config
-from detectron2.layers import ShapeSpec
-
-_V0_CFG = """
-MODEL:
-  RPN_HEAD:
-    NAME: "TEST"
-VERSION: 0
-"""
-
-_V1_CFG = """
-MODEL:
-  WEIGHT: "/path/to/weight"
-"""
-
-
-class TestConfigVersioning(unittest.TestCase):
-    def test_upgrade_downgrade_consistency(self):
-        cfg = get_cfg()
-        # check that custom is preserved
-        cfg.USER_CUSTOM = 1
-
-        down = downgrade_config(cfg, to_version=0)
-        up = upgrade_config(down)
-        self.assertTrue(up == cfg)
-
-    def _merge_cfg_str(self, cfg, merge_str):
-        f = tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False)
-        try:
-            f.write(merge_str)
-            f.close()
-            cfg.merge_from_file(f.name)
-        finally:
-            os.remove(f.name)
-        return cfg
-
-    def test_auto_upgrade(self):
-        cfg = get_cfg()
-        latest_ver = cfg.VERSION
-        cfg.USER_CUSTOM = 1
-
-        self._merge_cfg_str(cfg, _V0_CFG)
-
-        self.assertEqual(cfg.MODEL.RPN.HEAD_NAME, "TEST")
-        self.assertEqual(cfg.VERSION, latest_ver)
-
-    def test_guess_v1(self):
-        cfg = get_cfg()
-        latest_ver = cfg.VERSION
-        self._merge_cfg_str(cfg, _V1_CFG)
-        self.assertEqual(cfg.VERSION, latest_ver)
-
-
-class _TestClassA(torch.nn.Module):
-    @configurable
-    def __init__(self, arg1, arg2, arg3=3):
-        super().__init__()
-        self.arg1 = arg1
-        self.arg2 = arg2
-        self.arg3 = arg3
-        assert arg1 == 1
-        assert arg2 == 2
-        assert arg3 == 3
-
-    @classmethod
-    def from_config(cls, cfg):
-        args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2}
-        return args
-
-
-class _TestClassB(_TestClassA):
-    @configurable
-    def __init__(self, input_shape, arg1, arg2, arg3=3):
-        """
-        Doc of _TestClassB
-        """
-        assert input_shape == "shape"
-        super().__init__(arg1, arg2, arg3)
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):  # test extra positional arg in from_config
-        args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2}
-        args["input_shape"] = input_shape
-        return args
-
-
-class _LegacySubClass(_TestClassB):
-    # an old subclass written in cfg style
-    def __init__(self, cfg, input_shape, arg4=4):
-        super().__init__(cfg, input_shape)
-        assert self.arg1 == 1
-        assert self.arg2 == 2
-        assert self.arg3 == 3
-
-
-class _NewSubClassNewInit(_TestClassB):
-    # test new subclass with a new __init__
-    @configurable
-    def __init__(self, input_shape, arg4=4, **kwargs):
-        super().__init__(input_shape, **kwargs)
-        assert self.arg1 == 1
-        assert self.arg2 == 2
-        assert self.arg3 == 3
-
-
-class _LegacySubClassNotCfg(_TestClassB):
-    # an old subclass written in cfg style, but argument is not called "cfg"
-    def __init__(self, config, input_shape):
-        super().__init__(config, input_shape)
-        assert self.arg1 == 1
-        assert self.arg2 == 2
-        assert self.arg3 == 3
-
-
-class _TestClassC(_TestClassB):
-    @classmethod
-    def from_config(cls, cfg, input_shape, **kwargs):  # test extra kwarg overwrite
-        args = {"arg1": cfg.ARG1, "arg2": cfg.ARG2}
-        args["input_shape"] = input_shape
-        args.update(kwargs)
-        return args
-
-
-class _TestClassD(_TestClassA):
-    @configurable
-    def __init__(self, input_shape: ShapeSpec, arg1: int, arg2, arg3=3):
-        assert input_shape == "shape"
-        super().__init__(arg1, arg2, arg3)
-
-    # _TestClassA.from_config does not have input_shape args.
-    # Test whether input_shape will be forwarded to __init__
-
-
-class TestConfigurable(unittest.TestCase):
-    def testInitWithArgs(self):
-        _ = _TestClassA(arg1=1, arg2=2, arg3=3)
-        _ = _TestClassB("shape", arg1=1, arg2=2)
-        _ = _TestClassC("shape", arg1=1, arg2=2)
-        _ = _TestClassD("shape", arg1=1, arg2=2, arg3=3)
-
-    def testPatchedAttr(self):
-        self.assertTrue("Doc" in _TestClassB.__init__.__doc__)
-        self.assertEqual(_TestClassD.__init__.__annotations__["arg1"], int)
-
-    def testInitWithCfg(self):
-        cfg = get_cfg()
-        cfg.ARG1 = 1
-        cfg.ARG2 = 2
-        cfg.ARG3 = 3
-        _ = _TestClassA(cfg)
-        _ = _TestClassB(cfg, input_shape="shape")
-        _ = _TestClassC(cfg, input_shape="shape")
-        _ = _TestClassD(cfg, input_shape="shape")
-        _ = _LegacySubClass(cfg, input_shape="shape")
-        _ = _NewSubClassNewInit(cfg, input_shape="shape")
-        _ = _LegacySubClassNotCfg(cfg, input_shape="shape")
-        with self.assertRaises(TypeError):
-            # disallow forwarding positional args to __init__ since it's prone to errors
-            _ = _TestClassD(cfg, "shape")
-
-        # call with kwargs instead
-        _ = _TestClassA(cfg=cfg)
-        _ = _TestClassB(cfg=cfg, input_shape="shape")
-        _ = _TestClassC(cfg=cfg, input_shape="shape")
-        _ = _TestClassD(cfg=cfg, input_shape="shape")
-        _ = _LegacySubClass(cfg=cfg, input_shape="shape")
-        _ = _NewSubClassNewInit(cfg=cfg, input_shape="shape")
-        _ = _LegacySubClassNotCfg(config=cfg, input_shape="shape")
-
-    def testInitWithCfgOverwrite(self):
-        cfg = get_cfg()
-        cfg.ARG1 = 1
-        cfg.ARG2 = 999  # wrong config
-        with self.assertRaises(AssertionError):
-            _ = _TestClassA(cfg, arg3=3)
-
-        # overwrite arg2 with correct config later:
-        _ = _TestClassA(cfg, arg2=2, arg3=3)
-        _ = _TestClassB(cfg, input_shape="shape", arg2=2, arg3=3)
-        _ = _TestClassC(cfg, input_shape="shape", arg2=2, arg3=3)
-        _ = _TestClassD(cfg, input_shape="shape", arg2=2, arg3=3)
-
-        # call with kwargs cfg=cfg instead
-        _ = _TestClassA(cfg=cfg, arg2=2, arg3=3)
-        _ = _TestClassB(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
-        _ = _TestClassC(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
-        _ = _TestClassD(cfg=cfg, input_shape="shape", arg2=2, arg3=3)
-
-    def testInitWithCfgWrongArgs(self):
-        cfg = get_cfg()
-        cfg.ARG1 = 1
-        cfg.ARG2 = 2
-        with self.assertRaises(TypeError):
-            _ = _TestClassB(cfg, "shape", not_exist=1)
-        with self.assertRaises(TypeError):
-            _ = _TestClassC(cfg, "shape", not_exist=1)
-        with self.assertRaises(TypeError):
-            _ = _TestClassD(cfg, "shape", not_exist=1)
-
-    def testBadClass(self):
-        class _BadClass1:
-            @configurable
-            def __init__(self, a=1, b=2):
-                pass
-
-        class _BadClass2:
-            @configurable
-            def __init__(self, a=1, b=2):
-                pass
-
-            def from_config(self, cfg):  # noqa
-                pass
-
-        class _BadClass3:
-            @configurable
-            def __init__(self, a=1, b=2):
-                pass
-
-            # bad name: must be cfg
-            @classmethod
-            def from_config(cls, config):  # noqa
-                pass
-
-        with self.assertRaises(AttributeError):
-            _ = _BadClass1(a=1)
-
-        with self.assertRaises(TypeError):
-            _ = _BadClass2(a=1)
-
-        with self.assertRaises(TypeError):
-            _ = _BadClass3(get_cfg())

spaces/CVPR/LIVE/pybind11/tests/test_operator_overloading.cpp

@@ -1,226 +0,0 @@
-/*
-    tests/test_operator_overloading.cpp -- operator overloading
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-#include "constructor_stats.h"
-#include <pybind11/operators.h>
-#include <functional>
-
-class Vector2 {
-public:
-    Vector2(float x, float y) : x(x), y(y) { print_created(this, toString()); }
-    Vector2(const Vector2 &v) : x(v.x), y(v.y) { print_copy_created(this); }
-    Vector2(Vector2 &&v) : x(v.x), y(v.y) { print_move_created(this); v.x = v.y = 0; }
-    Vector2 &operator=(const Vector2 &v) { x = v.x; y = v.y; print_copy_assigned(this); return *this; }
-    Vector2 &operator=(Vector2 &&v) { x = v.x; y = v.y; v.x = v.y = 0; print_move_assigned(this); return *this; }
-    ~Vector2() { print_destroyed(this); }
-
-    std::string toString() const { return "[" + std::to_string(x) + ", " + std::to_string(y) + "]"; }
-
-    Vector2 operator-() const { return Vector2(-x, -y); }
-    Vector2 operator+(const Vector2 &v) const { return Vector2(x + v.x, y + v.y); }
-    Vector2 operator-(const Vector2 &v) const { return Vector2(x - v.x, y - v.y); }
-    Vector2 operator-(float value) const { return Vector2(x - value, y - value); }
-    Vector2 operator+(float value) const { return Vector2(x + value, y + value); }
-    Vector2 operator*(float value) const { return Vector2(x * value, y * value); }
-    Vector2 operator/(float value) const { return Vector2(x / value, y / value); }
-    Vector2 operator*(const Vector2 &v) const { return Vector2(x * v.x, y * v.y); }
-    Vector2 operator/(const Vector2 &v) const { return Vector2(x / v.x, y / v.y); }
-    Vector2& operator+=(const Vector2 &v) { x += v.x; y += v.y; return *this; }
-    Vector2& operator-=(const Vector2 &v) { x -= v.x; y -= v.y; return *this; }
-    Vector2& operator*=(float v) { x *= v; y *= v; return *this; }
-    Vector2& operator/=(float v) { x /= v; y /= v; return *this; }
-    Vector2& operator*=(const Vector2 &v) { x *= v.x; y *= v.y; return *this; }
-    Vector2& operator/=(const Vector2 &v) { x /= v.x; y /= v.y; return *this; }
-
-    friend Vector2 operator+(float f, const Vector2 &v) { return Vector2(f + v.x, f + v.y); }
-    friend Vector2 operator-(float f, const Vector2 &v) { return Vector2(f - v.x, f - v.y); }
-    friend Vector2 operator*(float f, const Vector2 &v) { return Vector2(f * v.x, f * v.y); }
-    friend Vector2 operator/(float f, const Vector2 &v) { return Vector2(f / v.x, f / v.y); }
-
-    bool operator==(const Vector2 &v) const {
-        return x == v.x && y == v.y;
-    }
-    bool operator!=(const Vector2 &v) const {
-        return x != v.x || y != v.y;
-    }
-private:
-    float x, y;
-};
-
-class C1 { };
-class C2 { };
-
-int operator+(const C1 &, const C1 &) { return 11; }
-int operator+(const C2 &, const C2 &) { return 22; }
-int operator+(const C2 &, const C1 &) { return 21; }
-int operator+(const C1 &, const C2 &) { return 12; }
-
-// Note: Specializing explicit within `namespace std { ... }` is done due to a
-// bug in GCC<7. If you are supporting compilers later than this, consider
-// specializing `using template<> struct std::hash<...>` in the global
-// namespace instead, per this recommendation:
-// https://en.cppreference.com/w/cpp/language/extending_std#Adding_template_specializations
-namespace std {
-    template<>
-    struct hash<Vector2> {
-        // Not a good hash function, but easy to test
-        size_t operator()(const Vector2 &) { return 4; }
-    };
-}
-
-// Not a good abs function, but easy to test.
-std::string abs(const Vector2&) {
-    return "abs(Vector2)";
-}
-
-// MSVC warns about unknown pragmas, and warnings are errors.
-#ifndef _MSC_VER
-  #pragma GCC diagnostic push
-  // clang 7.0.0 and Apple LLVM 10.0.1 introduce `-Wself-assign-overloaded` to
-  // `-Wall`, which is used here for overloading (e.g. `py::self += py::self `).
-  // Here, we suppress the warning using `#pragma diagnostic`.
-  // Taken from: https://github.com/RobotLocomotion/drake/commit/aaf84b46
-  // TODO(eric): This could be resolved using a function / functor (e.g. `py::self()`).
-  #if (__APPLE__) && (__clang__)
-    #if (__clang_major__ >= 10) && (__clang_minor__ >= 0) && (__clang_patchlevel__ >= 1)
-      #pragma GCC diagnostic ignored "-Wself-assign-overloaded"
-    #endif
-  #elif (__clang__)
-    #if (__clang_major__ >= 7)
-      #pragma GCC diagnostic ignored "-Wself-assign-overloaded"
-    #endif
-  #endif
-#endif
-
-TEST_SUBMODULE(operators, m) {
-
-    // test_operator_overloading
-    py::class_<Vector2>(m, "Vector2")
-        .def(py::init<float, float>())
-        .def(py::self + py::self)
-        .def(py::self + float())
-        .def(py::self - py::self)
-        .def(py::self - float())
-        .def(py::self * float())
-        .def(py::self / float())
-        .def(py::self * py::self)
-        .def(py::self / py::self)
-        .def(py::self += py::self)
-        .def(py::self -= py::self)
-        .def(py::self *= float())
-        .def(py::self /= float())
-        .def(py::self *= py::self)
-        .def(py::self /= py::self)
-        .def(float() + py::self)
-        .def(float() - py::self)
-        .def(float() * py::self)
-        .def(float() / py::self)
-        .def(-py::self)
-        .def("__str__", &Vector2::toString)
-        .def("__repr__", &Vector2::toString)
-        .def(py::self == py::self)
-        .def(py::self != py::self)
-        .def(py::hash(py::self))
-        // N.B. See warning about usage of `py::detail::abs(py::self)` in
-        // `operators.h`.
-        .def("__abs__", [](const Vector2& v) { return abs(v); })
-        ;
-
-    m.attr("Vector") = m.attr("Vector2");
-
-    // test_operators_notimplemented
-    // #393: need to return NotSupported to ensure correct arithmetic operator behavior
-    py::class_<C1>(m, "C1")
-        .def(py::init<>())
-        .def(py::self + py::self);
-
-    py::class_<C2>(m, "C2")
-        .def(py::init<>())
-        .def(py::self + py::self)
-        .def("__add__", [](const C2& c2, const C1& c1) { return c2 + c1; })
-        .def("__radd__", [](const C2& c2, const C1& c1) { return c1 + c2; });
-
-    // test_nested
-    // #328: first member in a class can't be used in operators
-    struct NestABase { int value = -2; };
-    py::class_<NestABase>(m, "NestABase")
-        .def(py::init<>())
-        .def_readwrite("value", &NestABase::value);
-
-    struct NestA : NestABase {
-        int value = 3;
-        NestA& operator+=(int i) { value += i; return *this; }
-    };
-    py::class_<NestA>(m, "NestA")
-        .def(py::init<>())
-        .def(py::self += int())
-        .def("as_base", [](NestA &a) -> NestABase& {
-            return (NestABase&) a;
-        }, py::return_value_policy::reference_internal);
-    m.def("get_NestA", [](const NestA &a) { return a.value; });
-
-    struct NestB {
-        NestA a;
-        int value = 4;
-        NestB& operator-=(int i) { value -= i; return *this; }
-    };
-    py::class_<NestB>(m, "NestB")
-        .def(py::init<>())
-        .def(py::self -= int())
-        .def_readwrite("a", &NestB::a);
-    m.def("get_NestB", [](const NestB &b) { return b.value; });
-
-    struct NestC {
-        NestB b;
-        int value = 5;
-        NestC& operator*=(int i) { value *= i; return *this; }
-    };
-    py::class_<NestC>(m, "NestC")
-        .def(py::init<>())
-        .def(py::self *= int())
-        .def_readwrite("b", &NestC::b);
-    m.def("get_NestC", [](const NestC &c) { return c.value; });
-
-
-    // test_overriding_eq_reset_hash
-    // #2191 Overriding __eq__ should set __hash__ to None
-    struct Comparable {
-        int value;
-        bool operator==(const Comparable& rhs) const {return value == rhs.value;}
-    };
-
-    struct Hashable : Comparable {
-        explicit Hashable(int value): Comparable{value}{};
-        size_t hash() const { return static_cast<size_t>(value); }
-    };
-
-    struct Hashable2 : Hashable {
-        using Hashable::Hashable;
-    };
-
-    py::class_<Comparable>(m, "Comparable")
-        .def(py::init<int>())
-        .def(py::self == py::self);
-
-    py::class_<Hashable>(m, "Hashable")
-        .def(py::init<int>())
-        .def(py::self == py::self)
-        .def("__hash__", &Hashable::hash);
-
-    // define __hash__ before __eq__
-    py::class_<Hashable2>(m, "Hashable2")
-        .def("__hash__", &Hashable::hash)
-        .def(py::init<int>())
-        .def(py::self == py::self);
-}
-
-#ifndef _MSC_VER
-  #pragma GCC diagnostic pop
-#endif

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

@@ -1,202 +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.
- */
-
-
-/*! \file remove.h
- *  \brief Sequential implementations of remove functions.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/function.h>
-#include <thrust/system/detail/sequential/execution_policy.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace sequential
-{
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename Predicate>
-__host__ __device__
-  ForwardIterator remove_if(sequential::execution_policy<DerivedPolicy> &,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            Predicate pred)
-{
-  // wrap pred
-  thrust::detail::wrapped_function<
-    Predicate,
-    bool
-  > wrapped_pred(pred);
-
-  // advance iterators until wrapped_pred(*first) is true or we reach the end of input
-  while(first != last && !wrapped_pred(*first))
-    ++first;
-
-  if(first == last)
-    return first;
-
-  // result always trails first 
-  ForwardIterator result = first;
-
-  ++first;
-
-  while(first != last)
-  {
-    if(!wrapped_pred(*first))
-    {
-      *result = *first;
-      ++result;
-    }
-    ++first;
-  }
-
-  return result;
-}
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy,
-         typename ForwardIterator,
-         typename InputIterator,
-         typename Predicate>
-__host__ __device__
-  ForwardIterator remove_if(sequential::execution_policy<DerivedPolicy> &,
-                            ForwardIterator first,
-                            ForwardIterator last,
-                            InputIterator stencil,
-                            Predicate pred)
-{
-  // wrap pred
-  thrust::detail::wrapped_function<
-    Predicate,
-    bool
-  > wrapped_pred(pred);
-
-  // advance iterators until wrapped_pred(*stencil) is true or we reach the end of input
-  while(first != last && !wrapped_pred(*stencil))
-  {
-    ++first;
-    ++stencil;
-  }
-
-  if(first == last)
-    return first;
-
-  // result always trails first 
-  ForwardIterator result = first;
-
-  ++first;
-  ++stencil;
-
-  while(first != last)
-  {
-    if(!wrapped_pred(*stencil))
-    {
-      *result = *first;
-      ++result;
-    }
-    ++first;
-    ++stencil;
-  }
-
-  return result;
-}
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename OutputIterator,
-         typename Predicate>
-__host__ __device__
-  OutputIterator remove_copy_if(sequential::execution_policy<DerivedPolicy> &,
-                                InputIterator first,
-                                InputIterator last,
-                                OutputIterator result,
-                                Predicate pred)
-{
-  // wrap pred
-  thrust::detail::wrapped_function<
-    Predicate,
-    bool
-  > wrapped_pred(pred);
-
-  while (first != last)
-  {
-    if (!wrapped_pred(*first))
-    {
-      *result = *first;
-      ++result;
-    }
-
-    ++first;
-  }
-
-  return result;
-}
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator,
-         typename Predicate>
-__host__ __device__
-  OutputIterator remove_copy_if(sequential::execution_policy<DerivedPolicy> &,
-                                InputIterator1 first,
-                                InputIterator1 last,
-                                InputIterator2 stencil,
-                                OutputIterator result,
-                                Predicate pred)
-{
-  // wrap pred
-  thrust::detail::wrapped_function<
-    Predicate,
-    bool
-  > wrapped_pred(pred);
-
-  while (first != last)
-  {
-    if (!wrapped_pred(*stencil))
-    {
-      *result = *first;
-      ++result;
-    }
-
-    ++first;
-    ++stencil;
-  }
-
-  return result;
-}
-
-
-} // end namespace sequential
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-

spaces/Cecil8352/vits-models/text/__init__.py

@@ -1,57 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence, clean_text
-
-
-def cleaned_text_to_sequence(cleaned_text):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text

spaces/Chomkwoy/Nilkessye/cpool_new/src/right_pool.cpp

@@ -1,91 +0,0 @@
-// #include <torch/torch.h>
-#include <torch/extension.h>
-
-#include <vector>
-
-std::vector<torch::Tensor> pool_forward(
-    torch::Tensor input
-) {
-    // Initialize output
-    torch::Tensor output = torch::zeros_like(input);
-
-    // Get width
-    int64_t width = input.size(3);
-
-    // Copy the last column
-    torch::Tensor input_temp  = input.select(3, 0);
-    torch::Tensor output_temp = output.select(3, 0);
-    output_temp.copy_(input_temp);
-
-    torch::Tensor max_temp;
-    for (int64_t ind = 0; ind < width - 1; ++ind) {
-        input_temp  = input.select(3, ind + 1);
-        output_temp = output.select(3, ind);
-        max_temp    = output.select(3, ind + 1);
-
-        torch::max_out(max_temp, input_temp, output_temp);
-    }
-
-    return { 
-        output
-    };
-}
-
-std::vector<torch::Tensor> pool_backward(
-    torch::Tensor input,
-    torch::Tensor grad_output
-) {
-    torch::Tensor output = torch::zeros_like(input);
-
-    int32_t batch   = input.size(0);
-    int32_t channel = input.size(1);
-    int32_t height  = input.size(2);
-    int32_t width   = input.size(3);
-
-    // auto max_val = torch::zeros(torch::CUDA(torch::kFloat), {batch, channel, height});
-    // auto max_ind = torch::zeros(torch::CUDA(torch::kLong),  {batch, channel, height});
-    auto max_val = torch::zeros({batch, channel, height}, torch::TensorOptions().dtype(torch::kFloat).device(torch::kCUDA));
-    auto max_ind = torch::zeros({batch, channel, height}, torch::TensorOptions().dtype(torch::kLong).device(torch::kCUDA));
-
-    auto input_temp = input.select(3, 0);
-    max_val.copy_(input_temp);
-
-    max_ind.fill_(0);
-
-    auto output_temp      = output.select(3, 0);
-    auto grad_output_temp = grad_output.select(3, 0);
-    output_temp.copy_(grad_output_temp);
-
-    auto un_max_ind = max_ind.unsqueeze(3);
-    // auto gt_mask    = torch::zeros(torch::CUDA(torch::kByte),  {batch, channel, height});
-    // auto max_temp   = torch::zeros(torch::CUDA(torch::kFloat), {batch, channel, height});
-	auto gt_mask    = torch::zeros({batch, channel, height}, torch::TensorOptions().dtype(torch::kByte).device(torch::kCUDA));
-    auto max_temp   = torch::zeros({batch, channel, height}, torch::TensorOptions().dtype(torch::kFloat).device(torch::kCUDA));
-
-    for (int32_t ind = 0; ind < width - 1; ++ind) {
-        input_temp = input.select(3, ind + 1);
-        torch::gt_out(gt_mask, input_temp, max_val);
-
-        torch::masked_select_out(max_temp, input_temp, gt_mask);
-        max_val.masked_scatter_(gt_mask, max_temp);
-        max_ind.masked_fill_(gt_mask, ind + 1);
-
-        grad_output_temp = grad_output.select(3, ind + 1).unsqueeze(3);
-        output.scatter_add_(3, un_max_ind, grad_output_temp);
-    }
-
-    return {
-        output
-    };
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def(
-        "forward", &pool_forward, "Right Pool Forward",
-        py::call_guard<py::gil_scoped_release>()
-    );
-    m.def(
-        "backward", &pool_backward, "Right Pool Backward",
-        py::call_guard<py::gil_scoped_release>()     
-    );
-}

spaces/CikeyQI/Yunzai/Yunzai/plugins/adapter/GSUIDCore.js

@@ -1,249 +0,0 @@
-import { randomUUID } from "crypto"
-import path from "node:path"
-import fs from "node:fs"
-
-Bot.adapter.push(new class GSUIDCoreAdapter {
-  constructor() {
-    this.id = "GSUIDCore"
-    this.name = "早柚核心"
-    this.path = this.id
-  }
-
-  toStr(data) {
-    switch (typeof data) {
-      case "string":
-        return data
-      case "number":
-        return String(data)
-      case "object":
-        if (Buffer.isBuffer(data))
-          return Buffer.from(data, "utf8").toString()
-        else
-          return JSON.stringify(data)
-    }
-    return data
-  }
-
-  makeLog(msg) {
-    return this.toStr(msg).replace(/base64:\/\/.*?"/g, "base64://...\"")
-  }
-
-  makeMsg(msg) {
-    if (!Array.isArray(msg))
-      msg = [msg]
-    const msgs = []
-    for (let i of msg) {
-      if (typeof i != "object")
-        i = { type: "text", text: i }
-
-      switch (i.type) {
-        case "text":
-          i = { type: "text", data: i.text }
-          break
-        case "image":
-          i = { type: "image", data: i.file }
-          break
-        case "record":
-          i = { type: "file", data: i.file }
-          break
-        case "video":
-          i = { type: "file", data: i.file }
-          break
-        case "file":
-          i = { type: "file", data: i.file }
-          break
-        case "at":
-          i = { type: "at", data: i.qq }
-          break
-        case "reply":
-          i = { type: "reply", data: i.id }
-          break
-        case "node": {
-          const array = []
-          for (const { message } of i.data)
-            array.push(...this.makeMsg(message))
-          i.data = array
-          break
-        } default:
-          i = { type: "text", data: JSON.stringify(i) }
-      }
-      msgs.push(i)
-    }
-    return msgs
-  }
-
-  sendFriendMsg(data, msg) {
-    const content = this.makeMsg(msg)
-    logger.info(`${logger.blue(`[${data.self_id} => ${data.user_id}]`)} 发送好友消息：${this.makeLog(content)}`)
-    data.bot.sendApi({
-      bot_id: data.bot.bot_id,
-      bot_self_id: data.bot.bot_self_id,
-      target_type: "direct",
-      target_id: data.user_id,
-      content,
-    })
-    return { message_id: Date.now() }
-  }
-
-  sendGroupMsg(data, msg) {
-    const target = data.group_id.split("-")
-    const content = this.makeMsg(msg)
-    logger.info(`${logger.blue(`[${data.self_id} => ${data.group_id}]`)} 发送群消息：${this.makeLog(content)}`)
-    data.bot.sendApi({
-      bot_id: data.bot.bot_id,
-      bot_self_id: data.bot.bot_self_id,
-      target_type: target[0],
-      target_id: target[1],
-      content,
-    })
-    return { message_id: Date.now() }
-  }
-
-  pickFriend(id, user_id) {
-    const i = {
-      ...Bot[id].fl.get(user_id),
-      self_id: id,
-      bot: Bot[id],
-      user_id: user_id,
-    }
-    return {
-      ...i,
-      sendMsg: msg => this.sendFriendMsg(i, msg),
-    }
-  }
-
-  pickMember(id, group_id, user_id) {
-    const i = {
-      ...Bot[id].fl.get(user_id),
-      self_id: id,
-      bot: Bot[id],
-      group_id: group_id,
-      user_id: user_id,
-    }
-    return {
-      ...this.pickFriend(id, user_id),
-      ...i,
-    }
-  }
-
-  pickGroup(id, group_id) {
-    const i = {
-      ...Bot[id].gl.get(group_id),
-      self_id: id,
-      bot: Bot[id],
-      group_id: group_id,
-    }
-    return {
-      ...i,
-      sendMsg: msg => this.sendGroupMsg(i, msg),
-      pickMember: user_id => this.pickMember(id, group_id, user_id),
-    }
-  }
-
-  makeBot(data, ws) {
-    Bot[data.self_id] = {
-      adapter: this,
-      ws: ws,
-      get sendApi() { return this.ws.sendMsg },
-      uin: data.self_id,
-      bot_id: data.bot_id,
-      bot_self_id: data.bot_self_id,
-      stat: { start_time: Date.now()/1000 },
-      version: {
-        id: this.id,
-        name: this.name,
-      },
-      pickFriend: user_id => this.pickFriend(data.self_id, user_id),
-      get pickUser() { return this.pickFriend },
-      pickMember: (group_id, user_id) => this.pickMember(data.self_id, group_id, user_id),
-      pickGroup: group_id => this.pickGroup(data.self_id, group_id),
-      fl: new Map,
-      gl: new Map,
-      gml: new Map,
-    }
-
-    logger.mark(`${logger.blue(`[${data.self_id}]`)} ${this.name}(${this.id}) 已连接`)
-    Bot.em(`connect.${data.self_id}`, data)
-  }
-
-  message(data, ws) {
-    try {
-      data = JSON.parse(data)
-    } catch (err) {
-      return logger.error(`解码数据失败：${logger.red(err)}`)
-    }
-
-    data.self_id = data.bot_self_id
-    if (Bot[data.self_id]) {
-      data.bot = Bot[data.self_id]
-      data.bot.ws = ws
-    } else {
-      this.makeBot(data, ws)
-    }
-
-    data.post_type = "message"
-    data.message_id = data.msg_id
-    data.user_id = data.user_id
-    data.sender = {
-      user_id: data.user_id,
-      user_pm: data.user_pm,
-    }
-    if (!data.bot.fl.has(data.user_id))
-      data.bot.fl.set(data.user_id, data.sender)
-
-    data.message = []
-    data.raw_message = ""
-    for (const i of data.content) {
-      switch (i.type) {
-        case "text":
-          data.message.push({ type: "text", text: i.data })
-          data.raw_message += i.data
-          break
-        case "image":
-          data.message.push({ type: "image", url: i.data })
-          data.raw_message += `[图片：${i.data}]`
-          break
-        case "file":
-          data.message.push({ type: "file", url: i.data })
-          data.raw_message += `[文件：${i.data}]`
-          break
-        case "at":
-          data.message.push({ type: "at", qq: i.data })
-          data.raw_message += `[提及：${i.data}]`
-          break
-        case "reply":
-          data.message.push({ type: "reply", id: i.data })
-          data.raw_message += `[回复：${i.data}]`
-          break
-        case "node":
-          data.message.push({ type: "node", data: i.data })
-          data.raw_message += `[合并转发：${JSON.stringify(i.data)}]`
-          break
-        default:
-          data.message.push(i)
-          data.raw_message += JSON.stringify(i)
-      }
-    }
-
-    if (data.user_type == "direct") {
-      data.message_type = "private"
-      logger.info(`${logger.blue(`[${data.self_id}]`)} 好友消息：[${data.user_id}] ${data.raw_message}`)
-    } else {
-      data.message_type = "group"
-      data.group_id = `${data.user_type}-${data.group_id}`
-      if (!data.bot.gl.has(data.group_id))
-        data.bot.gl.set(data.group_id, { group_id: data.group_id })
-      logger.info(`${logger.blue(`[${data.self_id}]`)} 群消息：[${data.group_id}, ${data.user_id}] ${data.raw_message}`)
-    }
-
-    Bot.em(`${data.post_type}.${data.message_type}`, data)
-  }
-
-  load() {
-    if (!Array.isArray(Bot.wsf[this.path]))
-      Bot.wsf[this.path] = []
-    Bot.wsf[this.path].push((ws, ...args) =>
-      ws.on("message", data => this.message(data, ws, ...args))
-    )
-  }
-})

spaces/CikeyQI/meme-api/meme_generator/memes/call_110/__init__.py

@@ -1,20 +0,0 @@
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-
-
-def call_110(images: List[BuildImage], texts, args):
-    img1 = images[0].convert("RGBA").square().resize((250, 250))
-    img0 = images[1].convert("RGBA").square().resize((250, 250))
-
-    frame = BuildImage.new("RGB", (900, 500), "white")
-    frame.draw_text((0, 0, 900, 200), "遇到困难请拨打", max_fontsize=100)
-    frame.paste(img1, (50, 200), alpha=True)
-    frame.paste(img1, (325, 200), alpha=True)
-    frame.paste(img0, (600, 200), alpha=True)
-    return frame.save_jpg()
-
-
-add_meme("call_110", call_110, min_images=2, max_images=2, keywords=["遇到困难请拨打"])

spaces/CognitiveLabs/Research-Assistant/config/singleton.py

@@ -1,24 +0,0 @@
-"""The singleton metaclass for ensuring only one instance of a class."""
-import abc
-
-
-class Singleton(abc.ABCMeta, type):
-    """
-    Singleton metaclass for ensuring only one instance of a class.
-    """
-
-    _instances = {}
-
-    def __call__(cls, *args, **kwargs):
-        """Call method for the singleton metaclass."""
-        if cls not in cls._instances:
-            cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
-        return cls._instances[cls]
-
-
-class AbstractSingleton(abc.ABC, metaclass=Singleton):
-    """
-    Abstract singleton class for ensuring only one instance of a class.
-    """
-
-    pass

spaces/Cpp4App/Cpp4App/CDM/detect_classify/classification.py

@@ -1,380 +0,0 @@
-from CDM.detect_merge.Element import Element
-import CDM.detect_compo.lib_ip.ip_preprocessing as pre
-import time
-import cv2
-import torch
-import numpy as np
-from torchvision import models
-from torch import nn
-import pandas as pd
-import re
-import openai
-import random
-import os
-from CDM.detect_merge.merge import reassign_ids
-import CDM.detect_merge.merge as merge
-from os.path import join as pjoin, exists
-
-label_dic ={'72':'Location', '42':'Photos', '77':'Social media', '91':'Voices', '6':'Email', '89':'Social media', '40':'Location', '43':'Phone', '82':'Photos',
-                                                                        '3':'Contacts', '68':'Contacts', '49':'Profile', '56':'Photos'}
-
-keyword_list = {'Name':['name', 'first name', 'last name', 'full name', 'real name', 'surname', 'family name', 'given name'],
-                        'Birthday':['birthday', 'date of birth', 'birth date', 'DOB', 'dob full birthday', 'birth year'],
-                        'Address':['mailing address', 'physical address', 'postal address', 'billing address', 'shipping address', 'delivery address', 'residence', 'collect address', 'personal address', 'residential address'],
-                        'Phone':['phone', 'phone number', 'mobile', 'mobile phone', 'mobile number', 'telephone', 'telephone number', 'call'],
-                        'Email':['email', 'e-mail', 'email address', 'e-mail address'],
-                        'Contacts':['contacts', 'phone-book', 'phone book', 'phonebook', 'contact list', 'phone contacts', 'address book'],
-                        'Location':['location', 'locate', 'geography', 'geo', 'geo-location', 'precision location', 'nearby'],
-                        'Photos':['camera', 'photo', 'scan', 'album', 'picture', 'gallery', 'photo library', 'storage', 'image', 'video', 'scanner', 'photograph'],
-                        'Voices':['microphone', 'voice', 'mic', 'speech', 'talk'],
-                        'Financial info':['credit card', 'pay', 'payment', 'debit card', 'mastercard', 'wallet'],
-                        'IP':['IP', 'Internet Protocol', 'IP address', 'internet protocol address'],
-                        'Cookies':['cookies', 'cookie'],
-                        'Social media':['facebook', 'twitter', 'socialmedia', 'social media'],
-                        'Profile':['profile', 'account'],
-                        'Gender':['gender']}
-
-def get_data_type(sentence, keywords, use_gpt=True):
-
-    sent_data_type = "others"
-
-    if use_gpt:
-        openai.api_key = os.environ["OPENAI_API_KEY"]
-
-        prompt = f"Is this piece of texts \"{sentence}\" related to any following privacy information data types? Or not relevant to any of them? ONLY answer the data type or \"not relevant\". ONLY use following data type list. Data types and their Description:\n" \
-                 f"Name: How a user refers to themselves," \
-                 f" Birthday: A user’s birthday," \
-                 f" Address: A user’s address," \
-                 f" Phone: A user’s phone number," \
-                 f" Email: A user’s email address," \
-                 f" Contacts: A user’s contact information, or the access to the contact permission," \
-                 f" Location: A user’s location information, or the access to the location permission," \
-                 f" Photos: A user’s photos, videos, or the access to the camera permission," \
-                 f" Voices: A user’s voices, recordings, or the access to the microphone permission," \
-                 f" Financial Info: Information about a user’s financial accounts, purchases, or transactions," \
-                 f" Profile: A user’s account information," \
-                 f"Social Media: A user's social media information, or the access to social media accounts"
-
-        response = openai.ChatCompletion.create(
-            # engine="text-davinci-002",
-            model="gpt-3.5-turbo",
-            messages=[
-                # {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": prompt}
-            ],
-            max_tokens=100,
-            n=1,
-            stop=None,
-            temperature=0,
-        )
-
-        # response_full_text = response.choices[0].text.strip()
-        response_full_text = response.choices[0].message['content']
-        for k in keywords.keys():
-            if k == "Financial info" or k == "Social media":
-                if k.lower() in response_full_text.lower():
-                    sent_data_type = k
-                    break
-            else:
-                words = re.split(r'\W+', response_full_text.lower())
-                if k.lower() in words:
-                    sent_data_type = k
-                    break
-
-        # print("----------------------")
-        # print("sentence: ", sentence)
-        # print("prompt: ", prompt)
-        # print("response: ", response_full_text)
-        # print("sent_data_type: ", sent_data_type)
-
-    else:
-        for k in keywords.keys():
-            for w in keywords[k]:
-                words = re.split(r'\W+', sentence.lower())
-                if w.lower() in words:
-                    sent_data_type = k
-                    break
-            if sent_data_type != "others":
-                break
-
-    return sent_data_type
-
-# def get_clf_model(use_resnet18=True, use_gpu=False):
-#
-#     device = 'cpu'
-#     if use_gpu:
-#         device = 'cuda:0'
-#
-#     if use_resnet18:
-#         model = models.resnet18().to(device)
-#         in_feature_num = model.fc.in_features
-#         model.fc = nn.Linear(in_feature_num, 99)
-#         model.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=(5, 5), padding=(3, 3), stride=(2, 2),
-#                                 bias=False)
-#
-#         PATH = "./CDM/model/model-99-resnet18.pkl"
-#         model.load_state_dict(torch.load(PATH, map_location=torch.device(device)))
-#
-#         model.eval()
-#     else:
-#         # replace with your own model
-#         None
-#
-#     return model
-
-def get_clf_model(clf_model="ResNet18", use_gpu=False):
-
-    device = 'cpu'
-    if use_gpu:
-        device = 'cuda:0'
-
-    if clf_model == "ResNet18":
-        model = models.resnet18().to(device)
-        in_feature_num = model.fc.in_features
-        model.fc = nn.Linear(in_feature_num, 99)
-        model.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=(5, 5), padding=(3, 3), stride=(2, 2),
-                                bias=False)
-
-        PATH = "./CDM/model/model-99-resnet18.pkl"
-        model.load_state_dict(torch.load(PATH, map_location=torch.device(device)))
-
-        model.eval()
-    elif clf_model == "ViT":
-        model = torch.load('./CDM/model/model-99-ViT-entire.pkl', map_location=torch.device(device))
-        model = model.to(device)
-        model.eval()
-
-    else:
-        # replace with your own model
-        None
-
-    return model
-
-def compo_classification(input_img, output_root, segment_root, merge_json, output_data, resize_by_height=800, clf_model="ResNet18"):
-    # load text and non-text compo
-    ele_id = 0
-    compos = []
-    texts = []
-    elements = []
-
-    for compo in merge_json['compos']:
-        if compo['class'] == 'Text':
-            element = Element(ele_id,
-                              (compo["position"]['column_min'], compo["position"]['row_min'],
-                               compo["position"]['column_max'], compo["position"]['row_max']),
-                              'Text', text_content=compo['text_content'])
-            texts.append(element)
-            ele_id += 1
-        else:
-            element = Element(ele_id,
-                              (compo["position"]['column_min'], compo["position"]['row_min'],
-                               compo["position"]['column_max'], compo["position"]['row_max']),
-                              compo['class'])
-            compos.append(element)
-            ele_id += 1
-
-    org, grey = pre.read_img(input_img, resize_by_height)
-
-    grey = grey.astype('float32')
-    grey = grey / 255
-
-    # grey = (grey - grey.mean()) / grey.std()
-
-    # --------- classification ----------
-
-    classification_start_time = time.process_time()
-
-    for compo in compos:
-
-        # comp_grey = grey[compo.row_min:compo.row_max, compo.col_min:compo.col_max]
-        #
-        # comp_crop = cv2.resize(comp_grey, (32, 32))
-        #
-        # comp_crop = comp_crop.reshape(1, 1, 32, 32)
-        #
-        # comp_tensor = torch.tensor(comp_crop)
-        # comp_tensor = comp_tensor.permute(0, 1, 3, 2)
-        #
-        # model = get_clf_model()
-        # pred_label = model(comp_tensor)
-        #
-        # if str(np.argmax(pred_label.cpu().data.numpy(), axis=1)[0]) in label_dic.keys():
-        #     compo.label = label_dic[str(np.argmax(pred_label.cpu().data.numpy(), axis=1)[0])]
-        #     elements.append(compo)
-        # else:
-        #     compo.label = str(np.argmax(pred_label.cpu().data.numpy(), axis=1)[0])
-
-        if clf_model == "ResNet18":
-
-            comp_grey = grey[compo.row_min:compo.row_max, compo.col_min:compo.col_max]
-
-            comp_crop = cv2.resize(comp_grey, (32, 32))
-
-            comp_crop = comp_crop.reshape(1, 1, 32, 32)
-
-            comp_tensor = torch.tensor(comp_crop)
-            comp_tensor = comp_tensor.permute(0, 1, 3, 2)
-
-            model = get_clf_model(clf_model)
-            pred_label = model(comp_tensor)
-
-            if str(np.argmax(pred_label.cpu().data.numpy(), axis=1)[0]) in label_dic.keys():
-                compo.label = label_dic[str(np.argmax(pred_label.cpu().data.numpy(), axis=1)[0])]
-                elements.append(compo)
-            else:
-                compo.label = str(np.argmax(pred_label.cpu().data.numpy(), axis=1)[0])
-
-        elif clf_model == "ViT":
-
-            comp_grey = grey[compo.row_min:compo.row_max, compo.col_min:compo.col_max]
-
-            comp_crop = cv2.resize(comp_grey, (224, 224))
-
-            # Convert the image to tensor
-            comp_tensor = torch.from_numpy(comp_crop)
-
-            # Reshape and repeat along the channel dimension to convert to RGB
-            comp_tensor = comp_tensor.view(1, 224, 224).repeat(3, 1, 1)
-
-            # comp_tensor = comp_tensor.permute(0, 2, 1)
-
-            comp_tensor = comp_tensor.unsqueeze(0)  # add a batch dimension
-
-            model = get_clf_model(clf_model)
-            # pred_label = model(comp_tensor)
-
-            # Forward pass through the model
-            with torch.no_grad():
-                output = model(comp_tensor)
-
-            # Get the predicted label
-            _, predicted = torch.max(output.logits, 1)
-
-            # print("predicted_label: ", predicted.cpu().numpy())
-
-            if str(predicted.cpu().numpy()[0]) in label_dic.keys():
-                compo.label = label_dic[str(predicted.cpu().numpy()[0])]
-                elements.append(compo)
-            else:
-                compo.label = str(predicted.cpu().numpy()[0])
-
-        else:
-            print("clf_model has to be ResNet18 or ViT")
-
-    time_cost_ic = time.process_time() - classification_start_time
-    print("time cost for icon classification: %2.2f s" % time_cost_ic)
-    # ic_time_cost_all.append(time_cost_ic)
-
-    # --------- end classification ----------
-
-    text_selection_time = time.process_time()
-
-    for this_text in texts:
-        # found_flag = 0
-        #
-        # for key in keyword_list:
-        #     for w in keyword_list[key]:
-        #         words = re.split(r'\W+', this_text.text_content.lower())
-        #         if w.lower() in words:
-        #             this_text.label = key
-        #             elements.append(this_text)
-        #             found_flag = 1
-        #             break
-        #
-        # if found_flag == 0:
-        #     this_text.label = 'others'
-
-        retries = 10
-        for i in range(retries):
-            try:
-                text_label = get_data_type(this_text.text_content.lower(), keyword_list, use_gpt=False)
-                break
-            except openai.error.RateLimitError as e:
-                if "overloaded" in str(e):
-                    # Exponential backoff with jitter
-                    sleep_time = 2 * (2 ** i) + random.uniform(0, 0.1)
-                    time.sleep(sleep_time)
-                else:
-                    raise
-            except Exception as e:
-                raise
-
-        this_text.label = text_label
-
-        if this_text.label != "others":
-            elements.append(this_text)
-
-    time_cost_ts = time.process_time() - text_selection_time
-    print("time cost for text selection: %2.2f s" % time_cost_ts)
-    # ts_time_cost_all.append(time_cost_ts)
-
-    # ---------- end -------------------------------
-
-    full_size_org, full_size_grey = pre.read_img(input_img)
-    ratio = full_size_org.shape[0]/org.shape[0]
-
-    show = False
-    wait_key = 0
-
-    reassign_ids(elements)
-    board = merge.show_elements(full_size_org, elements, ratio, show=show, win_name='elements after merging', wait_key=wait_key, line=3)
-    board_one_element = merge.show_one_element(full_size_org, elements, ratio, show=show, win_name='elements after merging', wait_key=wait_key, line=3)
-
-    classification_root = pjoin(output_root, 'classification')
-
-    # save all merged elements, clips and blank background
-    name = input_img.replace('\\', '/').split('/')[-1][:-4]
-    components = merge.save_elements(pjoin(classification_root, name + '.json'), elements, full_size_org.shape, ratio)
-    cv2.imwrite(pjoin(classification_root, name + '.jpg'), board)
-
-    print("len(board_one_element): ", len(board_one_element))
-
-    for i in range(len(elements)):
-        e_name = str(int(elements[i].id) + 1)
-        cv2.imwrite(pjoin(classification_root + '/GUI', name + '-' + e_name + '.jpg'), board_one_element[i])
-
-    print('[Classification Completed] Input: %s Output: %s' % (input_img, pjoin(classification_root, name + '.jpg')))
-
-    # ---------- matching result -----------
-
-    index = input_img.split('/')[-1][:-4]
-    app_id = str(index).split('-')[0]
-
-    index_path = pjoin(segment_root, app_id, 'classified_sentences/keyword_index.txt')
-    dict_index = {}
-    if exists(index_path):
-        with open(index_path, 'r') as g:
-            for line in g:
-                key, value = line.strip().split(':', 1)
-                dict_index[key] = value
-
-    for item in elements:
-        complete_path = pjoin(segment_root, app_id, 'classified_sentences', item.label + '.txt')
-        print("complete_path: ", complete_path)
-
-        if exists(complete_path):
-
-            with open(complete_path, 'r', encoding='utf-8') as file:
-                content = file.read()
-
-            # Replace line breaks with spaces and strip any extra whitespace
-            this_text = ' '.join(content.splitlines()).strip()
-
-            lines = content.splitlines()
-            non_empty_lines = [line for line in lines if line.strip() != ""]
-            for i in range(len(non_empty_lines)):
-                if non_empty_lines[i][0].isalpha():
-                    non_empty_lines[i] = non_empty_lines[i][0].upper() + non_empty_lines[i][1:]
-
-            # output_data = output_data.append({'screenshot': 's' + str(index), 'id': item.id + 1, 'label': item.label, 'index': dict_index[item.label], 'text': this_text, 'sentences': non_empty_lines}, ignore_index=True)
-            output_data = pd.concat([output_data, pd.DataFrame([{'screenshot': 's' + str(index), 'id': item.id + 1,
-                                                                 'label': item.label, 'index': dict_index[item.label],
-                                                                 'text': this_text, 'sentences': non_empty_lines}])])
-
-        else:
-            # output_data = output_data.append({'screenshot': 's' + str(index), 'id': item.id + 1, 'label': item.label, 'index': "None", 'text': "No information!", 'sentences': "None"},
-            #                                  ignore_index=True)
-            output_data = pd.concat([output_data, pd.DataFrame([{'screenshot': 's' + str(index), 'id': item.id + 1,
-                                                                 'label': item.label, 'index': "None",
-                                                                 'text': "No information!", 'sentences': "None"}])])
-    return time_cost_ic, time_cost_ts, output_data, board

spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/common/optims.py

@@ -1,119 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import math
-
-from video_llama.common.registry import registry
-
-
-@registry.register_lr_scheduler("linear_warmup_step_lr")
-class LinearWarmupStepLRScheduler:
-    def __init__(
-        self,
-        optimizer,
-        max_epoch,
-        min_lr,
-        init_lr,
-        decay_rate=1,
-        warmup_start_lr=-1,
-        warmup_steps=0,
-        **kwargs
-    ):
-        self.optimizer = optimizer
-
-        self.max_epoch = max_epoch
-        self.min_lr = min_lr
-
-        self.decay_rate = decay_rate
-
-        self.init_lr = init_lr
-        self.warmup_steps = warmup_steps
-        self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr
-
-    def step(self, cur_epoch, cur_step):
-        if cur_epoch == 0:
-            warmup_lr_schedule(
-                step=cur_step,
-                optimizer=self.optimizer,
-                max_step=self.warmup_steps,
-                init_lr=self.warmup_start_lr,
-                max_lr=self.init_lr,
-            )
-        else:
-            step_lr_schedule(
-                epoch=cur_epoch,
-                optimizer=self.optimizer,
-                init_lr=self.init_lr,
-                min_lr=self.min_lr,
-                decay_rate=self.decay_rate,
-            )
-
-
-@registry.register_lr_scheduler("linear_warmup_cosine_lr")
-class LinearWarmupCosineLRScheduler:
-    def __init__(
-        self,
-        optimizer,
-        max_epoch,
-        iters_per_epoch,
-        min_lr,
-        init_lr,
-        warmup_steps=0,
-        warmup_start_lr=-1,
-        **kwargs
-    ):
-        self.optimizer = optimizer
-
-        self.max_epoch = max_epoch
-        self.iters_per_epoch = iters_per_epoch
-        self.min_lr = min_lr
-
-        self.init_lr = init_lr
-        self.warmup_steps = warmup_steps
-        self.warmup_start_lr = warmup_start_lr if warmup_start_lr >= 0 else init_lr
-
-    def step(self, cur_epoch, cur_step):
-        total_cur_step = cur_epoch * self.iters_per_epoch + cur_step
-        if total_cur_step < self.warmup_steps:
-            warmup_lr_schedule(
-                step=cur_step,
-                optimizer=self.optimizer,
-                max_step=self.warmup_steps,
-                init_lr=self.warmup_start_lr,
-                max_lr=self.init_lr,
-            )
-        else:
-            cosine_lr_schedule(
-                epoch=total_cur_step,
-                optimizer=self.optimizer,
-                max_epoch=self.max_epoch * self.iters_per_epoch,
-                init_lr=self.init_lr,
-                min_lr=self.min_lr,
-            )
-
-
-def cosine_lr_schedule(optimizer, epoch, max_epoch, init_lr, min_lr):
-    """Decay the learning rate"""
-    lr = (init_lr - min_lr) * 0.5 * (
-        1.0 + math.cos(math.pi * epoch / max_epoch)
-    ) + min_lr
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
-
-
-def warmup_lr_schedule(optimizer, step, max_step, init_lr, max_lr):
-    """Warmup the learning rate"""
-    lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max(max_step, 1))
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr
-
-
-def step_lr_schedule(optimizer, epoch, init_lr, min_lr, decay_rate):
-    """Decay the learning rate"""
-    lr = max(min_lr, init_lr * (decay_rate**epoch))
-    for param_group in optimizer.param_groups:
-        param_group["lr"] = lr

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attr/__init__.py

@@ -1,132 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-"""
-Classes Without Boilerplate
-"""
-
-from functools import partial
-from typing import Callable
-
-from . import converters, exceptions, filters, setters, validators
-from ._cmp import cmp_using
-from ._config import get_run_validators, set_run_validators
-from ._funcs import asdict, assoc, astuple, evolve, has, resolve_types
-from ._make import (
-    NOTHING,
-    Attribute,
-    Factory,
-    attrib,
-    attrs,
-    fields,
-    fields_dict,
-    make_class,
-    validate,
-)
-from ._next_gen import define, field, frozen, mutable
-from ._version_info import VersionInfo
-
-
-s = attributes = attrs
-ib = attr = attrib
-dataclass = partial(attrs, auto_attribs=True)  # happy Easter ;)
-
-
-class AttrsInstance:
-    pass
-
-
-__all__ = [
-    "Attribute",
-    "AttrsInstance",
-    "Factory",
-    "NOTHING",
-    "asdict",
-    "assoc",
-    "astuple",
-    "attr",
-    "attrib",
-    "attributes",
-    "attrs",
-    "cmp_using",
-    "converters",
-    "define",
-    "evolve",
-    "exceptions",
-    "field",
-    "fields",
-    "fields_dict",
-    "filters",
-    "frozen",
-    "get_run_validators",
-    "has",
-    "ib",
-    "make_class",
-    "mutable",
-    "resolve_types",
-    "s",
-    "set_run_validators",
-    "setters",
-    "validate",
-    "validators",
-]
-
-
-def _make_getattr(mod_name: str) -> Callable:
-    """
-    Create a metadata proxy for packaging information that uses *mod_name* in
-    its warnings and errors.
-    """
-
-    def __getattr__(name: str) -> str:
-        dunder_to_metadata = {
-            "__title__": "Name",
-            "__copyright__": "",
-            "__version__": "version",
-            "__version_info__": "version",
-            "__description__": "summary",
-            "__uri__": "",
-            "__url__": "",
-            "__author__": "",
-            "__email__": "",
-            "__license__": "license",
-        }
-        if name not in dunder_to_metadata.keys():
-            raise AttributeError(f"module {mod_name} has no attribute {name}")
-
-        import sys
-        import warnings
-
-        if sys.version_info < (3, 8):
-            from importlib_metadata import metadata
-        else:
-            from importlib.metadata import metadata
-
-        if name != "__version_info__":
-            warnings.warn(
-                f"Accessing {mod_name}.{name} is deprecated and will be "
-                "removed in a future release. Use importlib.metadata directly "
-                "to query for attrs's packaging metadata.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-
-        meta = metadata("attrs")
-        if name == "__license__":
-            return "MIT"
-        elif name == "__copyright__":
-            return "Copyright (c) 2015 Hynek Schlawack"
-        elif name in ("__uri__", "__url__"):
-            return meta["Project-URL"].split(" ", 1)[-1]
-        elif name == "__version_info__":
-            return VersionInfo._from_version_string(meta["version"])
-        elif name == "__author__":
-            return meta["Author-email"].rsplit(" ", 1)[0]
-        elif name == "__email__":
-            return meta["Author-email"].rsplit("<", 1)[1][:-1]
-
-        return meta[dunder_to_metadata[name]]
-
-    return __getattr__
-
-
-__getattr__ = _make_getattr(__name__)

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/svgLib/__init__.py

@@ -1,3 +0,0 @@
-from .path import SVGPath, parse_path
-
-__all__ = ["SVGPath", "parse_path"]

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/reload.py

@@ -1,91 +0,0 @@
-"""
-
-Contains the functions that run when `gradio` is called from the command line. Specifically, allows
-
-$ gradio app.py, to run app.py in reload mode where any changes in the app.py file or Gradio library reloads the demo.
-$ gradio app.py my_demo.app, to use variable names other than "demo"
-"""
-import inspect
-import os
-import sys
-from pathlib import Path
-
-from uvicorn import Config
-from uvicorn.supervisors import ChangeReload
-
-import gradio
-from gradio import networking, utils
-
-
-def _setup_config():
-    args = sys.argv[1:]
-    if len(args) == 0:
-        raise ValueError("No file specified.")
-    if len(args) == 1 or args[1].startswith("--"):
-        demo_name = "demo.app"
-    else:
-        demo_name = args[1]
-        if "." not in demo_name:
-            print(
-                "\nWARNING: As of Gradio 3.31, the parameter after the file path must be the name of the FastAPI app, not the Gradio demo. In most cases, this just means you should add '.app' after the name of your demo, e.g. 'demo' -> 'demo.app'."
-            )
-
-    original_path = args[0]
-    abs_original_path = utils.abspath(original_path)
-    path = os.path.normpath(original_path)
-    path = path.replace("/", ".")
-    path = path.replace("\\", ".")
-    filename = os.path.splitext(path)[0]
-
-    gradio_folder = Path(inspect.getfile(gradio)).parent
-
-    port = networking.get_first_available_port(
-        networking.INITIAL_PORT_VALUE,
-        networking.INITIAL_PORT_VALUE + networking.TRY_NUM_PORTS,
-    )
-    print(
-        f"\nLaunching in *reload mode* on: http://{networking.LOCALHOST_NAME}:{port} (Press CTRL+C to quit)\n"
-    )
-
-    gradio_app = f"{filename}:{demo_name}"
-    message = "Watching:"
-    message_change_count = 0
-
-    watching_dirs = []
-    if str(gradio_folder).strip():
-        watching_dirs.append(gradio_folder)
-        message += f" '{gradio_folder}'"
-        message_change_count += 1
-
-    abs_parent = abs_original_path.parent
-    if str(abs_parent).strip():
-        watching_dirs.append(abs_parent)
-        if message_change_count == 1:
-            message += ","
-        message += f" '{abs_parent}'"
-
-    print(message + "\n")
-
-    # guaranty access to the module of an app
-    sys.path.insert(0, os.getcwd())
-
-    # uvicorn.run blocks the execution (looping) which makes it hard to test
-    return Config(
-        gradio_app,
-        reload=True,
-        port=port,
-        log_level="warning",
-        reload_dirs=watching_dirs,
-    )
-
-
-def main():
-    # default execution pattern to start the server and watch changes
-    config = _setup_config()
-    server = networking.Server(config)
-    sock = config.bind_socket()
-    ChangeReload(config, target=server.run, sockets=[sock]).run()
-
-
-if __name__ == "__main__":
-    main()

spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/visualization/grad.py

@@ -1,117 +0,0 @@
-""" 
-@Date: 2021/11/06
-@description:
-"""
-import cv2
-import numpy as np
-import torch
-import matplotlib.pyplot as plt
-
-from utils.conversion import depth2xyz
-
-
-def convert_img(value, h, need_nor=True, cmap=None):
-    value = value.clone().detach().cpu().numpy()[None]
-    if need_nor:
-        value -= value.min()
-        value /= value.max() - value.min()
-    grad_img = value.repeat(int(h), axis=0)
-
-    if cmap is None:
-        grad_img = grad_img[..., np.newaxis].repeat(3, axis=-1)
-    elif cmap == cv2.COLORMAP_PLASMA:
-        grad_img = cv2.applyColorMap((grad_img * 255).astype(np.uint8), colormap=cmap)
-        grad_img = grad_img[..., ::-1]
-        grad_img = grad_img.astype(np.float) / 255.0
-    elif cmap == 'HSV':
-        grad_img = np.round(grad_img * 1000) / 1000.0
-        grad_img = grad_img[..., np.newaxis].repeat(3, axis=-1)
-        grad_img[..., 0] = grad_img[..., 0] * 180
-        grad_img[..., 1] = 255
-        grad_img[..., 2] = 255
-        grad_img = grad_img.astype(np.uint8)
-        grad_img = cv2.cvtColor(grad_img, cv2.COLOR_HSV2RGB)
-        grad_img = grad_img.astype(np.float) / 255.0
-    return grad_img
-
-
-def show_grad(depth, grad_conv, h=5, show=False):
-    """
-    :param h:
-    :param depth: [patch_num]
-    :param grad_conv:
-    :param show:
-    :return:
-    """
-
-    direction, angle, grad = get_all(depth[None], grad_conv)
-
-    # depth_img = convert_img(depth, h)
-    # angle_img = convert_img(angle[0], h)
-    # grad_img = convert_img(grad[0], depth.shape[-1] // 4 - h * 2)
-    depth_img = convert_img(depth, h, cmap=cv2.COLORMAP_PLASMA)
-    angle_img = convert_img(angle[0], h, cmap='HSV')
-
-    # vis_grad = grad[0] / grad[0].max() / 2 + 0.5
-    grad_img = convert_img(grad[0], h)
-    img = np.concatenate([depth_img, angle_img, grad_img], axis=0)
-    if show:
-        plt.imshow(img)
-        plt.show()
-    return img
-
-
-def get_grad(direction):
-    """
-    :param direction: [b patch_num]
-    :return:[b patch_num]
-    """
-    a = torch.roll(direction, -1, dims=1)  # xz[i+1]
-    b = torch.roll(direction, 1, dims=1)  # xz[i-1]
-    grad = torch.acos(torch.clip(a[..., 0] * b[..., 0] + a[..., 1] * b[..., 1], -1+1e-6, 1-1e-6))
-    return grad
-
-
-def get_grad2(angle, grad_conv):
-    """
-    :param angle: [b patch_num]
-    :param grad_conv:
-    :return:[b patch_num]
-    """
-    angle = torch.sin(angle)
-    angle = angle + 1
-
-    angle = torch.cat([angle[..., -1:], angle, angle[..., :1]], dim=-1)
-    grad = grad_conv(angle[:, None])  # [b, patch_num] -> [b, 1, patch_num]
-    # grad = torch.abs(grad)
-    return grad.reshape(angle.shape[0], -1)
-
-
-def get_edge_angle(direction):
-    """
-    :param direction: [b patch_num 2]
-    :return:
-    """
-    angle = torch.atan2(direction[..., 1], direction[..., 0])
-    return angle
-
-
-def get_edge_direction(depth):
-    xz = depth2xyz(depth)[..., ::2]
-    direction = torch.roll(xz, -1, dims=1) - xz  # direct[i] = xz[i+1] - xz[i]
-    direction = direction / direction.norm(p=2, dim=-1)[..., None]
-    return direction
-
-
-def get_all(depth, grad_conv):
-    """
-
-    :param grad_conv:
-    :param depth: [b patch_num]
-    :return:
-    """
-    direction = get_edge_direction(depth)
-    angle = get_edge_angle(direction)
-    # angle_grad = get_grad(direction)
-    angle_grad = get_grad2(angle, grad_conv)  # signed gradient
-    return direction, angle, angle_grad

spaces/Detomo/ai-comic-generation/src/app/store/index.ts

@@ -1,203 +0,0 @@
-"use client"
-
-import { create } from "zustand"
-
-import { FontName } from "@/lib/fonts"
-import { Preset, PresetName, defaultPreset, getPreset, getRandomPreset } from "@/app/engine/presets"
-import { LayoutName, defaultLayout, getRandomLayoutName, getRandomLayoutNames } from "../layouts"
-import html2canvas from "html2canvas"
-import { RenderedScene } from "@/types"
-
-export const useStore = create<{
-  prompt: string
-  font: FontName
-  preset: Preset
-  nbFrames: number
-  panels: string[]
-  captions: string[]
-  upscaleQueue: Record<string, RenderedScene>
-  showCaptions: boolean
-  renderedScenes: Record<string, RenderedScene>
-  layout: LayoutName
-  layouts: LayoutName[]
-  zoomLevel: number
-  page: HTMLDivElement
-  isGeneratingStory: boolean
-  panelGenerationStatus: Record<number, boolean>
-  isGeneratingText: boolean
-  atLeastOnePanelIsBusy: boolean
-  setRendered: (panelId: string, renderedScene: RenderedScene) => void
-  addToUpscaleQueue: (panelId: string, renderedScene: RenderedScene) => void
-  removeFromUpscaleQueue: (panelId: string) => void
-  setPrompt: (prompt: string) => void
-  setFont: (font: FontName) => void
-  setPreset: (preset: Preset) => void
-  setPanels: (panels: string[]) => void
-  setShowCaptions: (showCaptions: boolean) => void
-  setLayout: (layout: LayoutName) => void
-  setLayouts: (layouts: LayoutName[]) => void
-  setCaptions: (captions: string[]) => void
-  setZoomLevel: (zoomLevel: number) => void
-  setPage: (page: HTMLDivElement) => void
-  setGeneratingStory: (isGeneratingStory: boolean) => void
-  setGeneratingImages: (panelId: string, value: boolean) => void
-  setGeneratingText: (isGeneratingText: boolean) => void
-  pageToImage: () => Promise<string>
-  download: () => Promise<void>
-  generate: (prompt: string, presetName: PresetName, layoutName: LayoutName) => void
-}>((set, get) => ({
-  prompt: "",
-  font: "actionman",
-  preset: getPreset(defaultPreset),
-  nbFrames: 1,
-  panels: [],
-  captions: [],
-  upscaleQueue: {} as Record<string, RenderedScene>,
-  renderedScenes: {} as Record<string, RenderedScene>,
-  showCaptions: false,
-  layout: defaultLayout,
-  layouts: [defaultLayout, defaultLayout],
-  zoomLevel: 60,
-  page: undefined as unknown as HTMLDivElement,
-  isGeneratingStory: false,
-  panelGenerationStatus: {},
-  isGeneratingText: false,
-  atLeastOnePanelIsBusy: false,
-  setRendered: (panelId: string, renderedScene: RenderedScene) => {
-    const { renderedScenes } = get()
-    set({
-      renderedScenes: {
-        ...renderedScenes,
-        [panelId]: renderedScene
-      }
-    })
-  },
-  addToUpscaleQueue: (panelId: string, renderedScene: RenderedScene) => {
-    const { upscaleQueue } = get()
-    set({
-      upscaleQueue: {
-        ...upscaleQueue,
-        [panelId]: renderedScene
-      },
-    })
-  },
-  removeFromUpscaleQueue: (panelId: string) => {
-    const upscaleQueue = { ...get().upscaleQueue }
-    delete upscaleQueue[panelId]
-    set({
-      upscaleQueue,
-    })
-  },
-  setPrompt: (prompt: string) => {
-    const existingPrompt = get().prompt
-    if (prompt === existingPrompt) { return }
-    set({
-      prompt,
-    })
-  },
-  setFont: (font: FontName) => {
-    const existingFont = get().font
-    if (font === existingFont) { return }
-    set({
-      font,
-    })
-  },
-  setPreset: (preset: Preset) => {
-    const existingPreset = get().preset
-    if (preset.label === existingPreset.label) { return }
-    set({
-      preset,
-    })
-  },
-  setNbFrames: (nbFrames: number) => {
-    const existingNbFrames = get().nbFrames
-    if (nbFrames === existingNbFrames) { return }
-    set({
-      nbFrames,
-    })
-  },
-  setPanels: (panels: string[]) => set({ panels }),
-  setCaptions: (captions: string[]) => {
-    set({
-      captions,
-    })
-  },
-  setShowCaptions: (showCaptions: boolean) => {
-    set({
-      showCaptions,
-    })
-  },
-  setLayout: (layoutName: LayoutName) => {
-    const layout = layoutName === "random"
-      ? getRandomLayoutName()
-      : layoutName
-
-    set({
-      layout,
-      layouts: [layout, layout]
-    })
-  },
-  setLayouts: (layouts: LayoutName[]) => set({ layouts }),
-  setZoomLevel: (zoomLevel: number) =>  set({ zoomLevel }),
-  setPage: (page: HTMLDivElement) => {
-    if (!page) { return }
-    set({ page })
-  },
-  setGeneratingStory: (isGeneratingStory: boolean) => set({ isGeneratingStory }),
-  setGeneratingImages: (panelId: string, value: boolean) => {
-    const panelGenerationStatus: Record<string, boolean> = {
-      ...get().panelGenerationStatus,
-      [panelId]: value
-    }
-
-    const atLeastOnePanelIsBusy = Object.values(panelGenerationStatus).includes(true)
-    
-    set({
-      panelGenerationStatus,
-      atLeastOnePanelIsBusy
-    })
-  },
-  setGeneratingText: (isGeneratingText: boolean) => set({ isGeneratingText }),
-  pageToImage: async () => {
-    const { page } = get()
-    if (!page) { return "" }
-    
-    
-    const canvas = await html2canvas(page)
-    console.log("canvas:", canvas)
-
-    const data = canvas.toDataURL('image/jpeg', 0.5)
-    return data
-  },
-  download: async () => {
-    const { pageToImage } = get()
-    const data = await pageToImage()
-   
-    const link = document.createElement('a')
-
-    if (typeof link.download === 'string') {
-      link.href = data
-      link.download = 'comic.jpg'
-      document.body.appendChild(link)
-      link.click()
-      document.body.removeChild(link)
-    } else {
-      window.open(data)
-    }
-  },
-  generate: (prompt: string, presetName: PresetName, layoutName: LayoutName) => {
-    const layout = layoutName === "random"
-      ? getRandomLayoutName()
-      : layoutName
-    set({
-      prompt,
-      panels: [],
-      captions: [],
-      preset: presetName === "random"
-        ? getRandomPreset()
-        : getPreset(presetName),
-      layout,
-      layouts: [layout, layout],
-    })
-  }
-}))

spaces/DiViorg/categories_error_analysis/README.md

@@ -1,12 +0,0 @@
----
-title: Categories Error Analysis
-emoji: 🐛
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference