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\ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Baywatch (English) Movie Download Fixed 720p.md b/spaces/1gistliPinn/ChatGPT4/Baywatch (English) Movie Download Fixed 720p.md deleted file mode 100644 index 4bcb048fd698b7d9d564bc295d7102e457a9a130..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Baywatch (English) Movie Download Fixed 720p.md +++ /dev/null @@ -1,55 +0,0 @@ -## Baywatch (English) Movie Download 720p - - - -**Download File ===> [https://www.google.com/url?q=https%3A%2F%2Ftiurll.com%2F2twsAD&sa=D&sntz=1&usg=AOvVaw1R2vSPPaX4pvh7ddczSPN1](https://www.google.com/url?q=https%3A%2F%2Ftiurll.com%2F2twsAD&sa=D&sntz=1&usg=AOvVaw1R2vSPPaX4pvh7ddczSPN1)** - - - - Here is a possible title and article with HTML formatting for the keyword "Baywatch (English) Movie Download 720p": - -# How to Download Baywatch (English) Movie in 720p Quality - - - -If you are a fan of action, comedy and crime movies, you might be interested in downloading Baywatch (English) movie in 720p quality. Baywatch is a 2017 Hollywood movie based on the popular TV series of the same name. It stars Dwayne Johnson, Zac Efron, Alexandra Daddario and Priyanka Chopra as lifeguards who uncover a criminal plot that threatens their beach. The movie is full of humor, adventure and romance, and it has received mixed reviews from critics and audiences. - - - -There are several ways to download Baywatch (English) movie in 720p quality, but not all of them are safe and legal. Some websites may offer free downloads, but they may also contain viruses, malware or unwanted ads. Some websites may require you to sign up, pay or complete surveys before you can access the download links. Some websites may have low-quality or fake files that do not match the description. - - - -To avoid these risks and enjoy Baywatch (English) movie in 720p quality, you need to find a reliable and trustworthy source that offers high-quality and genuine files. One such source is PogoLinks, which is a website that provides direct Google Drive download links for Bollywood and Hollywood movies and web series. You can download Baywatch (English) movie in 720p quality from PogoLinks with the following steps: - - - -1. Go to [https://pogolinks.art/movies/baywatch-2017/](https://pogolinks.art/movies/baywatch-2017/) [^2^]. This is the page where you can find the download links for Baywatch (2017) movie in different languages, sizes and qualities. - -2. Scroll down to the section where it says "Download Links". You will see a table with various options for downloading Baywatch (2017) movie. Choose the one that says "Baywatch (2017) [Hindi-Eng] 720p .mkv" with a size of 1.24 GB and click on it. - -3. You will be redirected to a new page where you will see a button that says "Download Now". Click on it and wait for a few seconds until a pop-up window appears. - -4. In the pop-up window, you will see another button that says "Download". Click on it and you will be taken to Google Drive where you can download Baywatch (2017) movie in 720p quality. - -5. Click on the download icon on the top right corner of the Google Drive page and choose where you want to save the file on your device. - -6. Enjoy watching Baywatch (2017) movie in 720p quality! - - - -Note: You may need to turn off your ad-blocker or use a VPN when downloading torrents or movies from PogoLinks. You may also need to use Mx Player or VLC Player to switch audio or play the movie after downloading it. - -Here are a few more paragraphs with HTML formatting for the article: - -If you want to know more about Baywatch (English) movie, you can also check out some of the reviews from critics and audiences. The movie has a 17% rating on Rotten Tomatoes, based on 246 reviews, with an average score of 3.7/10. The critics consensus says: "Baywatch takes its source material's jiggle factor to R-rated levels, but lacks the original's campy charm -- and leaves its charming stars flailing in the shallows." [^1^] - - - -On IMDb, the movie has a 5.5/10 rating, based on 192,000 votes, with a metascore of 37 out of 100, based on 47 reviews. The movie has received mixed feedback from the users, with some praising the humor, action and chemistry of the cast, and others criticizing the plot, script and vulgarity of the movie. One user wrote: "Baywatch is a action comedy movie about a bunch of bad ass lifeguard at the beach saving people life while kicking drug dealer ass staring Dwayne Johnson A.K.A The Rock one of the most likable action hero of this generation with a cast full of young hot actors that always show up with close up slow mo boobs bouncing and muscle flexing plus a lot of dick jokes." [^3^] - - - -Whether you love it or hate it, Baywatch (English) movie is a movie that does not take itself too seriously and aims to entertain the fans of the original TV series and the new generation of viewers. It is a movie that you can watch with your friends and have a good laugh at the absurdity and fun of it. If you are looking for a movie that is light-hearted, action-packed and sexy, you can download Baywatch (English) movie in 720p quality from PogoLinks and enjoy it at your own convenience. - - dfd1c89656 \ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Dan Hartman Torrent Discography.md b/spaces/1gistliPinn/ChatGPT4/Examples/Dan Hartman Torrent Discography.md deleted file mode 100644 index 8b639d4744026369b20c33fb1e7ce045bc2c9af1..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Dan Hartman Torrent Discography.md +++ /dev/null @@ -1,63 +0,0 @@ -
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Dan Hartman Torrent Discography: How to Download and Enjoy His Music
-Dan Hartman was an American singer, songwriter and record producer who rose to fame in the 1970s and 1980s with his disco, pop and rock hits. He is best known for songs like "Relight My Fire", "I Can Dream About You", "Instant Replay" and "We Are the Young". He also collaborated with artists like Donna Summer, James Brown, Tina Turner and Edgar Winter.
-Dan Hartman Torrent Discography
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If you are a fan of Dan Hartman or want to discover his music, you might be interested in downloading his torrent discography. A torrent discography is a collection of all his albums and singles in digital format that you can download using a torrent client. Torrents are a way of sharing files over the internet that allow you to download large amounts of data quickly and efficiently.
-In this article, we will show you how to find and download Dan Hartman's torrent discography, as well as some tips on how to enjoy his music. Let's get started!
-How to Find Dan Hartman's Torrent Discography
-The first step to download Dan Hartman's torrent discography is to find a reliable source that offers it. There are many websites that host torrent files, but not all of them are safe and legal. Some of them might contain viruses, malware or fake files that can harm your computer or violate your privacy.
-To avoid these risks, you should use a reputable torrent site that has a large community of users and moderators who verify the quality and authenticity of the files. You should also use a VPN (virtual private network) service that encrypts your internet traffic and hides your IP address from prying eyes.
- -One of the most popular and trusted torrent sites that has Dan Hartman's torrent discography is RuTracker.org. This is a Russian website that has a huge collection of music torrents, including disco, pop, rock and other genres. You can find Dan Hartman's torrent discography by searching for his name or browsing through the categories.
-To access RuTracker.org, you need to register for a free account and use a VPN service that can bypass the geo-restrictions imposed by some countries. Once you are on the site, you can download the torrent file of Dan Hartman's collection, which contains 9 albums and 8 singles from 1976 to 2016, in MP3 format with different bitrates.
-How to Download Dan Hartman's Torrent Discography
-After you have downloaded the torrent file of Dan Hartman's collection, you need to open it with a torrent client. A torrent client is a software that connects you to other users who have the same file and allows you to download it from them in small pieces.
-There are many torrent clients available for different platforms, but some of the most popular ones are uTorrent, BitTorrent, qBittorrent and Transmission. You can download and install any of them on your computer and then open the torrent file with them.
-Once you have opened the torrent file with your torrent client, you will see a list of all the files included in the collection. You can choose which ones you want to download or download them all. You will also see information such as the size, progress, speed and peers of the download.
-The download time will depend on several factors, such as your internet connection, the number of seeders (users who have the complete file) and leechers (users who are downloading the file) and the settings of your torrent client. Generally, the more seeders and leechers there are, the faster the download will be.
-Once the download is complete, you will have all the files of Dan Hartman's collection on your computer. You can then play them with any media player that supports MP3 format or transfer them to your mobile device or external drive.
-How to Enjoy Dan Hartman's Music
-Now that you have downloaded Dan Hartman's torrent discography, you can enjoy his music anytime and anywhere. Here are some tips on how to make the most out of his music:
--
-
- Listen to his albums chronologically to appreciate his musical evolution and diversity. He started as a rock musician with his band The Edgar Winter Group, then switched to disco and pop in the late 1970s and early 1980s, and finally experimented with electronic and dance music in the late 1980s and early 1990s. -
- Pay attention to his lyrics and vocals. He was a talented songwriter who wrote catchy hooks and meaningful messages. He was also a versatile singer who could adapt his voice to different styles and moods. -
- Watch his videos and live performances. He was a charismatic performer who knew how to entertain his audience with his energy and charisma. He also had a great sense of fashion and style. -
- Learn more about his life and career. He was a prolific artist who worked with many other musicians as a producer, arranger or guest vocalist. He also faced many challenges in his personal life, such as his sexuality and his health issues. -
- Share his music with others. He was a beloved artist who influenced many other musicians and genres. He also had a loyal fan base who supported him throughout his career. You can introduce his music to your friends or family or join online communities of fans who appreciate his legacy. -
Conclusion
-Dan Hartman was one of the most talented and versatile artists of his generation. His music spanned across different genres and decades, offering something for everyone. His torrent discography is a great way to access his entire musical catalog in one place.
-To download Dan Hartman's torrent discography, you need to find a reliable torrent site that offers it, such as RuTracker.org; use a VPN service that protects your privacy; use a torrent client that downloads the file from other users; and enjoy his music with any media player or device.
-We hope this article has helped you learn how to download and enjoy Dan Hartman's music using torrents. If you have any questions or comments, feel free to leave them below.
-What are the Benefits of Dan Hartman's Torrent Discography
-Downloading Dan Hartman's torrent discography has many benefits for music lovers and fans. Here are some of them:
--
-
- You can access his entire musical catalog in one place. You don't have to buy or stream his albums and singles separately, which can be costly or inconvenient. You can also save space on your computer or device by storing them in a single folder. -
- You can enjoy his music in high quality. The torrent files of his collection have different bitrates, ranging from 128 to 320 kbps, which means you can choose the level of sound quality that suits your preference and device. You can also use a media player that supports MP3 format to adjust the volume, bass, treble and other settings. -
- You can discover new songs and albums that you might have missed or forgotten. Dan Hartman had a long and prolific career that spanned across different genres and decades. He released 9 albums and 8 singles from 1976 to 2016, some of which are rare or hard to find. By downloading his torrent discography, you can explore his musical diversity and evolution and find new favorites. -
- You can support his legacy and influence. Dan Hartman was a beloved artist who influenced many other musicians and genres. He also had a loyal fan base who supported him throughout his career. By downloading his torrent discography, you can show your appreciation and respect for his work and keep his music alive. -
How to Avoid Risks When Downloading Dan Hartman's Torrent Discography
-While downloading Dan Hartman's torrent discography has many benefits, it also has some risks that you should be aware of and avoid. Here are some of them:
--
-
- You might violate the copyright laws of your country or region. Torrenting is a legal activity, but downloading copyrighted material without permission is not. You might face legal consequences if you are caught by the authorities or reported by the rights holders. -
- You might expose your computer or device to viruses, malware or fake files that can harm your system or violate your privacy. Some torrent sites or files might contain malicious software or data that can infect your computer or device or steal your personal information. -
- You might have a slow or unstable download speed or experience interruptions or errors during the download process. Torrenting depends on several factors, such as your internet connection, the number of seeders and leechers, and the settings of your torrent client. Sometimes, these factors might cause problems that affect your download experience. -
To avoid these risks, you should follow these tips:
--
-
- Use a reputable torrent site that offers Dan Hartman's torrent discography, such as RuTracker.org. This site has a large community of users and moderators who verify the quality and authenticity of the files. It also has a lot of seeders and leechers who ensure a fast and stable download speed. -
- Use a VPN service that protects your privacy and security. A VPN service encrypts your internet traffic and hides your IP address from prying eyes. It also allows you to bypass the geo-restrictions imposed by some countries or regions that block access to certain torrent sites or files. -
- Use a torrent client that downloads the file from other users efficiently and safely. A torrent client is a software that connects you to other users who have the same file and allows you to download it from them in small pieces. Some of the most popular torrent clients are uTorrent, BitTorrent, qBittorrent and Transmission. -
- Use an antivirus software that scans your computer or device for viruses, malware or fake files that can harm your system or violate your privacy. An antivirus software is a software that detects and removes malicious software or data from your computer or device. Some of the most popular antivirus software are Avast, Norton, McAfee and Kaspersky. -
Conclusion
-Dan Hartman was one of the most talented and versatile artists of his generation. His music spanned across different genres and decades, offering something for everyone. His torrent discography is a great way to access his entire musical catalog in one place.
-To download Dan Hartman's torrent discography, you need to find a reliable torrent site that offers it, such as RuTracker.org; use a VPN service that protects your privacy; use a torrent client that downloads the file from other users; use an antivirus software that scans your computer or device; and enjoy his music with any media player or device.
-We hope this article has helped you learn how to download and enjoy Dan Hartman's music using torrents. If you have any questions or comments, feel free to leave them below.
-Conclusion
-Dan Hartman was one of the most talented and versatile artists of his generation. His music spanned across different genres and decades, offering something for everyone. His torrent discography is a great way to access his entire musical catalog in one place.
-To download Dan Hartman's torrent discography, you need to find a reliable torrent site that offers it, such as RuTracker.org; use a VPN service that protects your privacy; use a torrent client that downloads the file from other users; use an antivirus software that scans your computer or device; and enjoy his music with any media player or device.
-We hope this article has helped you learn how to download and enjoy Dan Hartman's music using torrents. If you have any questions or comments, feel free to leave them below.
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer MOD APK Drive Any Car You Want and Explore the Streets.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer MOD APK Drive Any Car You Want and Explore the Streets.md deleted file mode 100644 index 1eaa357b6c8266f50de564e067a111ca1006640e..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer MOD APK Drive Any Car You Want and Explore the Streets.md +++ /dev/null @@ -1,124 +0,0 @@ - -
Car Parking Multiplayer Mod APK Apkvision: A Review
-If you are looking for a realistic and fun driving and parking simulation game, you might want to check out Car Parking Multiplayer. This game offers more than just parking, as you can explore an open-world multiplayer mode, customize your car, chat with other players, and even play as a cop. And if you want to enhance your gaming experience, you can download the Car Parking Multiplayer Mod APK Apkvision, which gives you unlimited money and access to all cars and features. In this article, we will review the game and the mod apk, and show you how to download and install it on your device. We will also suggest some alternatives to Car Parking Multiplayer Mod APK Apkvision that you might like.
-What is Car Parking Multiplayer?
-Car Parking Multiplayer is a game that supports open-world multiplayer mode, car tuning, police mode, and free walking. Plus, you can decide to jump out of the car and walk around. There are several areas that you can explore in the game and you can choose to play either single-player mode or online mode if you want a more chaotic (fun) scene.
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Features of the game
-Some of the features of Car Parking Multiplayer are:
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- Free walking. You can get out of your car and walk around the city, interact with other players, or enter buildings. -
- Car exchange. You can exchange your cars with other players or join a team and share cars. -
- Multiplayer racing. You can compete against real players in different modes, such as drag racing, drifting, or parking. -
- Car customization. You can adjust the suspension, wheel angle, engine, turbo, gearbox, exhaust, and more. You can also change the appearance of your car with dynamic vinyls, body parts, and paint. -
- High-quality open world. The game has highly-detailed environments, 100 cars with real interiors, 16 player skins, and realistic physics. -
- Interesting gameplay. The game has 82 real-life parking and driving challenges, different vehicles such as tow trucks, pickups, trucks, sports cars, and classic cars. -
- Voice chat. You can communicate with other players using voice chat. -
- Police mode. You can play as a cop and chase criminals or join a gang and cause trouble. -
- Friend list. You can add other players as friends and join their games. -
Benefits of the mod apk
-With the Car Parking Multiplayer Mod APK Apkvision, you get unlimited money and a menu that gives you access to all of your favorite cars, upgrades and more. With the Car Parking Multiplayer Mod APK Apkvision, you can use your money to unlock cars and see a menu that gives you access to:
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- All cars unlocked -
- All colors unlocked -
- All vinyls unlocked -
- All wheels unlocked -
- All tuning parts unlocked -
- All levels unlocked -
- No ads -
How to download and install Car Parking Multiplayer Mod APK Apkvision?
-If you want to download and install Car Parking Multiplayer Mod APK Apkvision on your device, you need to follow these steps:
-Steps to follow
--
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- Go to [Apkvision](^8^) website and search for Car Parking Multiplayer Mod APK. -
- Select the latest version of the mod apk and click on the download button. -
- Wait for the download to finish and then locate the file on your device. -
- Before installing the mod apk, you need to enable the installation of apps from unknown sources on your device. To do this, go to your device settings, security, and toggle on the unknown sources option. -
- Now, tap on the mod apk file and follow the instructions to install it on your device. -
- Once the installation is done, you can launch the game and enjoy the mod features. -
Drawbacks of the mod apk
-While the Car Parking Multiplayer Mod APK Apkvision can give you a lot of advantages, it also has some drawbacks that you should be aware of. Some of the drawbacks are:
--
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- The mod apk is not official and may not be compatible with the latest version of the game or your device. -
- The mod apk may contain viruses or malware that can harm your device or compromise your data. -
- The mod apk may cause glitches or errors in the game or interfere with other apps on your device. -
- The mod apk may violate the terms and conditions of the game and result in a ban or suspension of your account. -
What are some alternatives to Car Parking Multiplayer Mod APK Apkvision?
-If you are looking for some other games that are similar to Car Parking Multiplayer, you might want to try these alternatives:
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Real Car Parking 2
-Real Car Parking 2 is a realistic and challenging parking simulation game that tests your driving skills. You can choose from a variety of cars, customize them, and park them in different scenarios. You can also enjoy the 3D graphics, realistic sounds, and multiplayer mode.
-Torque Burnout
-Torque Burnout is a game that lets you unleash your inner hoonigan and perform epic burnouts and stunts. You can drive and drift in various arenas, customize your car, and compete against other players. You can also enjoy the realistic physics, smoke effects, and engine sounds.
-Conclusion
-Car Parking Multiplayer is a game that offers more than just parking. It is a fun and realistic driving and parking simulation game that lets you explore an open-world multiplayer mode, customize your car, chat with other players, and even play as a cop. And if you want to get unlimited money and access to all cars and features, you can download the Car Parking Multiplayer Mod APK Apkvision. However, you should also be aware of the drawbacks of using the mod apk and consider some alternatives to Car Parking Multiplayer Mod APK Apkvision.
-FAQs
-Here are some frequently asked questions about Car Parking Multiplayer Mod APK Apkvision:
-Q: Is Car Parking Multiplayer Mod APK Apkvision safe to use?
-A: Car Parking Multiplayer Mod APK Apkvision is not an official app and may not be safe to use. It may contain viruses or malware that can harm your device or compromise your data. It may also cause glitches or errors in the game or interfere with other apps on your device. Therefore, you should use it at your own risk and discretion.
-Q: How do I update Car Parking Multiplayer Mod APK Apkvision?
-A: To update Car Parking Multiplayer Mod APK Apkvision, you need to visit [Apkvision] website and download the latest version of the mod apk. Then, you need to uninstall the previous version of the mod apk from your device and install the new one following the same steps as before.
-Q: Can I play Car Parking Multiplayer Mod APK Apkvision offline?
-A: Yes, you can play Car Parking Multiplayer Mod APK Apkvision offline. However, you will not be able to access some features such as multiplayer mode, voice chat, or car exchange. You will also not be able to save your progress online or sync it with other devices.
-Q: Can I play Car Parking Multiplayer Mod APK Apkvision with my friends?
-A: Yes, you can play Car Parking Multiplayer Mod APK Apkvision with your friends. You can join their games or invite them to yours using the friend list feature. You can also communicate with them using voice chat or text chat.
-Q: What are some tips and tricks for playing Car Parking Multiplayer Mod APK Apkvision?
-A: Some tips and tricks for playing Car Parking Multiplayer Mod APK Apkvision are:
--
-
- Use the map to find parking spots, gas stations, car washes, or other places of interest. -
- Use the camera angles to adjust your view and see the obstacles better. -
- Use the brake, handbrake, and clutch to control your speed and direction. -
- Use the indicators, horn, and headlights to signal your intentions and alert other drivers. -
- Use the tuning menu to customize your car and improve its performance. -
- Use the police mode to chase criminals or join a gang and cause trouble. -
-
-
\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/City Taxi Driving 3D Simulator The Ultimate Car Driving Game for Taxi Lovers.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/City Taxi Driving 3D Simulator The Ultimate Car Driving Game for Taxi Lovers.md deleted file mode 100644 index a54a66560b61a5ffd84e013a4e70d36b68eb96e2..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/City Taxi Driving 3D Simulator The Ultimate Car Driving Game for Taxi Lovers.md +++ /dev/null @@ -1,222 +0,0 @@ - -
City Taxi Driving 3D Simulator: A Fun and Realistic Game for Car Lovers
-Introduction
-What is city taxi driving 3d simulator and why should you play it?
-city taxi driving 3d simulator
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Features of the Game
-What are some of the exciting features of city taxi driving 3d simulator?
-100 Unique Levels
-How many levels are there and what are the challenges?
-50 Different Taxi Cars
-How many taxi cars can you choose from and how can you unlock them?
-Challenging Driving and Parking Missions
-What are some of the driving and parking missions you have to complete?
-Drop and Pick Up Interesting Passengers
-Who are some of the passengers you will meet and how do they affect your gameplay?
-Realistic and Lively City Environment
-How does the city environment look like and what are some of the landmarks you will see?
-Crazy Realistic Driving Physics
-How does the game simulate the real driving physics and what are some of the effects?
-How to Play the Game
-What are the controls and tips for playing city taxi driving 3d simulator?
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-What are some of the benefits of playing city taxi driving 3d simulator for your mental and physical health?
-The Drawbacks of Playing City Taxi Driving 3D Simulator
-What are some of the drawbacks of playing city taxi driving 3d simulator for your time and money?
-The Future of City Taxi Driving 3D Simulator
-What are some of the future updates and improvements that the developers are planning for city taxi driving 3d simulator?
-Conclusion
-A summary of the main points and a call to action for the readers.
-| City Taxi Driving 3D Simulator: A Fun and Realistic Game for Car Lovers | ||||
|---|---|---|---|---|
| Introduction | ||||
| Features of the Game | ||||
| 100 Unique Levels | 50 Different Taxi Cars | Challenging Driving and Parking Missions | Drop and Pick Up Interesting Passengers | |
| Realistic and Lively City Environment | ||||
| Crazy Realistic Driving Physics | ||||
| How to Play the Game | ||||
| Comparison with Other Similar Games | ||||
| City Taxi Simulator 3D Game | Taxi Life: A City Driving Simulator | |||
| The Benefits of Playing City Taxi Driving 3D Simulator | ||||
| The Drawbacks of Playing City Taxi Driving 3D Simulator | ||||
| The Future of City Taxi Driving 3D Simulator | ||||
| Conclusion | ||||
| FAQs | ||||
|---|---|---|---|---|
| Q: How can I download city taxi driving 3d simulator? | ||||
| A: You can download city taxi driving 3d simulator from the Google Play Store or the App Store for free. You can also visit their official website or social media pages for more information. | ||||
| Q: How can I earn more money in city taxi driving 3d simulator? | ||||
| Q: How can I unlock more cars in city taxi driving 3d simulator? | ||||
| A: You can unlock more cars in city taxi driving 3d simulator by earning more money, completing more levels, reaching higher ranks, and collecting car cards. | ||||
| Q: How can I customize my car in city taxi driving 3d simulator? | ||||
| A: You can customize your car in city taxi driving 3d simulator by going to the garage and selecting the paint, sticker, or accessory option. You can also change your driver's appearance and outfit. | ||||
| Q: How can I contact the developers of city taxi driving 3d simulator? | ||||
| A: You can contact the developers of city taxi driving 3d simulator by sending them an email at citytaxidriving3dsimulator@gmail.com or by following them on Facebook, Twitter, or Instagram. | ||||
| Option | -Description | -Advantages | -Disadvantages | -When to use | -
|---|---|---|---|---|
| Transcription-backed dictation (TBD) | -You dictate your clinical note using M*Modal Fluency Direct or M*Modal Fluency Mobile. Your dictation is sent to a human transcriptionist who edits and formats it according to your specifications. You receive a completed note within a specified turnaround time. | -You can dictate quickly and easily without worrying about editing or formatting. You can rely on the transcriptionist's expertise and quality assurance. You can save time and money compared to traditional transcription. | -You have to wait for the transcriptionist to finish your note before you can review and sign it. You have less control over the final output of your note. You may encounter delays or errors due to human factors. | -You have a complex or unusual case that requires a high level of accuracy and detail. You prefer to delegate the editing and formatting tasks to a professional. You are not in a hurry to complete your note. | -
| Transcription-less dictation (TLD) | -You dictate your clinical note using M*Modal Fluency Direct or M*Modal Fluency Mobile. Your dictation is converted into text by M*Modal's speech recognition engine. You receive a draft note that you can review, edit, and sign yourself. | -You can get your note done faster and more efficiently without waiting for a transcriptionist. You have more control over the content and style of your note. You can use CAPD functionality to improve your documentation quality and compliance. | -You have to spend some time and effort to review and edit your note yourself. You may encounter some errors or inaccuracies due to speech recognition limitations. You may need some training and practice to use voice commands and macros effectively. | -You have a simple or routine case that does not require a lot of detail or explanation. You prefer to do the editing and formatting tasks yourself. You are in a hurry to complete your note. | -
| Front-end speech recognition (FESR) | -You dictate your clinical note using M*Modal Fluency Direct or M*Modal Fluency Mobile. Your dictation is converted into text by M*Modal's speech recognition engine in real time. You can see the text on your screen as you speak and make corrections or changes on the fly. | -You can create your note in one step without having to review or edit it later. You can see the immediate results of your dictation and make adjustments as needed. You can use CAPD functionality to get real-time feedback and guidance. | -You have to pay attention to the text on your screen as you dictate and correct any errors or mistakes as you go. You may lose some of the natural flow and spontaneity of your dictation. You may need some training and practice to use voice commands and macros effectively. | -You have a moderate or standard case that requires some detail or explanation. You prefer to see the text as you dictate and make corrections or changes on the fly. You want to create your note in one step without having to review or edit it later. | -
How to use M*Modal Fluency for Transcription to create high quality electronic records with less time and cost?
-To use M*Modal Fluency for Transcription, you need to download M*Modal Fluency Direct from the official website and install it on your Windows PC. You will need a valid license key and a microphone to use the software. Once installed, you can launch M*Modal Fluency Direct from your desktop or from within your EHR system.
-To choose between TBD, TLD, or FESR options, you can either select them from the drop-down menu on the top left corner of the screen or say "switch mode" followed by the option name. For example, you can say "switch mode transcription-backed" or "switch mode front-end". You can also create custom voice commands for switching modes. For example, you can say "create command transcription mode" followed by the option name.
-To dictate your clinical note, you can either tap on the microphone icon on the bottom right corner of the screen or select a patient and a note type from your EHR system. You will see a red recording button on the bottom center of the screen indicating that the app is ready to capture your voice. You can speak naturally and conversationally as you would with a human transcriptionist.
-Depending on the mode you have chosen, you will see different results on your screen. If you have chosen TBD, you will see a message saying "Your dictation has been sent to transcription". If you have chosen TLD, you will see a draft note that you can review, edit, and sign yourself. If you have chosen FESR, you will see the text on your screen as you speak and make corrections or changes on the fly.
-In any mode, you can use voice commands, macros, and CAPD functionality to improve your documentation workflow and quality. You can also use touch gestures such as tapping, swiping, or pinching to edit your note.
-Conclusion
-M*Modal is a conversational AI platform that helps physicians and medical transcriptionists create high quality electronic records with less time and cost. It offers three products: M*Modal Fluency Direct, M*Modal Fluency Mobile, and M*Modal Fluency for Transcription. Each product has its own features and benefits that suit different needs and preferences.
-M*Modal Fluency Direct is a cloud-based speech recognition solution that enables physicians to conversationally create, review, edit, and sign clinical notes directly in their EHR system. It leverages 3M NLU technology for contextual understanding of the patient's narrative and helps to improve documentation accuracy from the first word.
-M*Modal Fluency Mobile is a robust mobile medical dictation app that enables physicians to dictate clinical notes using their iOS or Android devices. It works with M*Modal Fluency Direct to provide a consistent and seamless speech recognition experience across different platforms and devices. It also works offline, so you can dictate without an internet connection.
-M*Modal Fluency for Transcription is a flexible transcription solution that offers three options for creating electronic records: transcription-backed dictation (TBD), transcription-less dictation (TLD), and front-end speech recognition (FESR). It combines the best of both worlds: the speed and convenience of speech recognition and the accuracy and quality of human transcription.
-If you want to download M*Modal and try it for yourself, you can visit the official website and request a demo or a free trial. You can also contact M*Modal's sales team or customer support team for more information or assistance.
-We hope this article has helped you understand what M*Modal is and how it can help you with your medical transcription needs. Thank you for reading!
-FAQs
-What are the pricing options for M*Modal?
-M*Modal does not disclose its pricing options publicly on its website. However, according to some online sources, M*Modal charges per line or per minute of dictation, depending on the product and the option you choose. The average cost ranges from $0.05 to $0.15 per line or from $0.08 to $0.25 per minute. You may also have to pay additional fees for installation, training, support, or maintenance.
-What are the system requirements for M*Modal?
-M*Modal Fluency Direct requires a Windows PC with at least 4 GB of RAM, 1 GB of free disk space, and an internet connection. It also requires a microphone that meets M*Modal's specifications. You can use any headset or handheld microphone that has a noise-canceling feature and a USB connection.
-M*Modal Fluency Mobile requires an iOS or Android device with at least 1 GB of RAM, 100 MB of free disk space, and an internet connection. It also requires a microphone that meets M*Modal's specifications. You can use the built-in microphone of your device or an external microphone that has a noise-canceling feature and a Bluetooth connection.
-M*Modal Fluency for Transcription requires a Windows PC with at least 4 GB of RAM, 1 GB of free disk space, and an internet connection. It also requires a microphone that meets M*Modal's specifications. You can use any headset or handheld microphone that has a noise-canceling feature and a USB connection.
-What are some of the best practices for using M*Modal?
-Here are some tips and tricks to help you get the most out of M*Modal and improve your documentation quality and efficiency:
- - Speak clearly and naturally, as if you were talking to a colleague or a patient. Avoid mumbling, whispering, shouting, or speaking too fast or too slow. - Use proper grammar, punctuation, and spelling. Say "period", "comma", "question mark", or "new paragraph" to insert the corresponding symbols. Say "spell that" to spell a word letter by letter. - Use medical terminology and abbreviations that are appropriate for your specialty and your EHR system. Avoid using slang, jargon, or acronyms that may be ambiguous or confusing. - Provide enough detail and context for your clinical note. Include relevant information such as the patient's history, physical examination, diagnosis, treatment plan, and follow-up instructions. Avoid vague or incomplete statements that may raise questions or doubts. - Review your note carefully before signing it. Check for any errors, inaccuracies, or inconsistencies that may have occurred during the dictation or transcription process. Make any corrections or changes as needed using voice commands or touch gestures. - Follow the best practices and guidelines for clinical documentation in your organization and your industry. Comply with the standards and regulations for quality, compliance, and reimbursement purposes.How secure is M*Modal and how does it comply with HIPAA and other regulations?
-M*Modal is committed to ensuring the security and privacy of your data and your patients' data. It uses advanced encryption, authentication, and authorization technologies to protect your data from unauthorized access, use, or disclosure. It also complies with the Health Insurance Portability and Accountability Act (HIPAA) and other applicable laws and regulations regarding the handling of protected health information (PHI).
-M*Modal has obtained several certifications and accreditations that demonstrate its adherence to the highest standards of security and privacy. These include ISO 27001, SOC 2 Type II, HITRUST CSF, PCI DSS, GDPR, and CCPA.
-How can I get support and training for M*Modal?
-M*Modal provides various resources and channels for support and training for its customers. You can access the following options from the M*Modal website or the M*Modal Fluency Direct software:
- - User guides and manuals: You can download PDF files that contain detailed instructions and information on how to use M*Modal products. - Video tutorials: You can watch short videos that demonstrate how to perform common tasks or functions with M*Modal products. - Webinars: You can register for live or recorded webinars that cover various topics related to M*Modal products. - Knowledge base: You can search for answers to frequently asked questions or common issues with M*Modal products. - Support portal: You can submit a ticket or chat with a support agent online to get help with any technical or operational issues with M*Modal products. - Phone support: You can call a toll-free number to speak with a support agent over the phone to get help with any urgent or complex issues with M*Modal products. 401be4b1e0-
-
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Mortal Kombat Mod Apk: Everything You Need to Know
-Mortal Kombat is one of the most popular and iconic fighting game franchises of all time, with over 27 years of history and dozens of games across various platforms. The latest installment, Mortal Kombat 11, was released in 2019 for PC, PS4, Xbox One, Nintendo Switch, and Stadia, and received critical acclaim for its gameplay, story, graphics, and content.
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But what if you want to enjoy Mortal Kombat 11 with even more features and options than the official version offers? What if you want to
What if you want to unlock all the characters, skins, and gear without spending hours of grinding or real money? What if you want to experience the game in the highest quality possible, with 4K resolution, HDR support, and immersive sound? What if you want to play online with your friends and opponents across different platforms?
-If you answered yes to any of these questions, then you might be interested in Mortal Kombat mod apk. A mod apk is a modified version of an application that allows you to access features and functions that are not available in the original version. In this case, Mortal Kombat mod apk is a modified version of Mortal Kombat 11 that gives you unlimited access to all the content and options of the game, as well as some extra benefits that enhance your gaming experience.
-In this article, we will tell you everything you need to know about Mortal Kombat mod apk, including its features, how to download and install it, tips and tricks for playing it, and its pros and cons. By the end of this article, you will be able to decide whether Mortal Kombat mod apk is worth trying or not. So, let's get started!
-Features of Mortal Kombat Mod Apk
-Mortal Kombat mod apk has many features that make it stand out from the official version of the game. Here are some of the most notable ones:
-Unlimited Souls and Money
-One of the main advantages of Mortal Kombat mod apk is that it gives you unlimited souls and money, which are the two main currencies of the game. You can use souls and money to unlock characters, skins, gear, fatalities, brutalities, and other items from the in-game store or the krypt. You can also use them to upgrade your characters and their abilities. With Mortal Kombat mod apk, you don't have to worry about running out of resources or spending real money to get them. You can have everything you want in the game without any hassle.
-All Characters Unlocked
-Another great feature of Mortal Kombat mod apk is that it unlocks all the characters in the game, including the DLC characters that are normally only available through purchasing the season pass or individual packs. Mortal Kombat 11 has a total of 37 playable fighters, each with their own unique moves, combos, styles, and personalities. Some of the most popular characters are Scorpion, Sub-Zero, Raiden, Liu Kang, Sonya Blade, Johnny Cage, Kitana, Jade, Shao Kahn, Sindel, Spawn, Joker, Terminator, RoboCop, Rambo, Mileena, and Rain. With Mortal Kombat mod apk, you can access all these characters and more from the start of the game. You can also customize their appearance and equipment to suit your preferences.
-High-Quality Graphics and Sound
-Mortal Kombat mod apk also enhances the graphics and sound quality of the game to make it more realistic and immersive. The mod apk supports dynamic 4K resolution and HDR (high dynamic range) technology, which improve the clarity, contrast, and color of the images on your screen. The mod apk also supports Dolby Atmos and DTS:X audio formats, which create a surround sound effect that makes you feel like you are in the middle of the action. The graphics and sound of Mortal Kombat mod apk are so good that they will make you forget that you are playing on a mobile device.
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Cross-Play Support
-The last feature we will mention here is cross-play support. Cross-play is a feature that allows players to play online with other players who are using different platforms or devices. For example, if you are playing Mortal Kombat mod apk on your Android phone, you can play online with someone who is playing Mortal Kombat 11 on their PS4 or Xbox One. This feature expands your pool of potential opponents and friends online and makes the game more fun and social. Cross-play also allows you to sync your progress and data across different devices using your WB Games account.
-How to Download and Install Mortal Kombat Mod Apk
-Now that you know what Mortal Kombat mod apk can do for you, you might be wondering how to get it on your device. Well, it's not very difficult if you follow these simple steps:
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-The first thing you need to do is find a trustworthy website that offers the latest version of Mortal Kombat mod apk for free download. There are many sites that claim to provide this service but not all of them are safe or reliable. Some may contain viruses or malware
Some may contain viruses or malware that can harm your device or steal your personal information. Some may also have outdated or fake versions of the mod apk that do not work properly or at all. Therefore, you need to be careful and do some research before downloading anything from the internet. You can use online reviews, ratings, comments, and feedback from other users to help you find a reputable site. You can also use antivirus software and VPN services to protect your device and privacy.
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-Tips and Tricks for Playing Mortal Kombat Mod Apk
-Mortal Kombat mod apk is a fun and exciting way to play Mortal Kombat 11 with enhanced features and options. However, it can also be challenging and competitive, especially if you are new to the game or want to improve your skills and performance. Here are some tips and tricks that can help you play better and enjoy more:
-Tip 1: Use the tutorial mode to learn the basics and advanced techniques of the game.
-Mortal Kombat 11 has a comprehensive tutorial mode that teaches you everything you need to know about the game's mechanics, systems, modes, characters, moves, combos, styles, strategies, and more. The tutorial mode is divided into several sections that cover different aspects of the game, such as basic attacks, special moves, defense, offense, meter management, krushing blows, fatal blows, fatalities, brutalities, and more. The tutorial mode also has a practice mode that allows you to practice your moves and combos against a dummy opponent or a live opponent. The tutorial mode is a great way to learn the game and improve your skills, whether you are a beginner or a veteran. You can access the tutorial mode from the main menu of the game.
-Tip 2: Pin your favorite moves and combos on the screen for easy reference.
-Mortal Kombat 11 has a feature that allows you to pin your favorite moves and combos on the screen for easy reference. This is useful if you want to remember how to perform a certain move or combo, or if you want to try out new moves and combos that you have learned from the tutorial mode or elsewhere. To pin a move or combo on the screen, go to the pause menu of the game, then select "Move List", then select the move or combo you want to pin, then press the pin button. You can pin up to 10 moves or combos at a time, and you can unpin them anytime by pressing the same button. You can also customize the position and size of the pinned moves and combos on the screen from the settings menu of the game.
-Tip 3: Master your Krushing Blows and Fatal Blows to deal massive damage and turn the tide of battle.
-Krushing Blows and Fatal Blows are special moves that deal massive damage and have cinematic effects. They are also crucial for winning fights and turning the tide of battle. Krushing Blows are enhanced versions of certain moves that trigger when certain conditions are met, such as landing a counter hit, connecting a certain number of hits, or using a certain move as a punish. Krushing Blows can only be used once per match for each move, so use them wisely. Fatal Blows are powerful attacks that become available when your health drops below 30%. You can use them by pressing both triggers at the same time. Fatal Blows can only be used once per match, and they can be blocked or dodged, so use them strategically.
-Tip 4: Use the environment to your advantage by interacting with objects and stage hazards.
-Mortal Kombat 11 has interactive environments that allow you to use objects and stage hazards to your advantage. You can use objects such as barrels, chains, spears, swords, guns, and more to attack your opponent or escape from their attacks. You can also use stage hazards such as acid pools, spikes, fire pits, electric fences, and more to damage your opponent or change the position of the fight. To interact with an object or stage hazard, press the right bumper when you are near it. You can also throw your opponent into an object or stage hazard by pressing forward and right bumper when you are near it. Using the environment can give you an edge in combat and add some variety to your fights.
-Tip 5: Mix up your moves and strategies when fighting against the AI or other players.
-Mortal Kombat 11 has a smart and adaptive AI that learns from your moves and strategies and adjusts accordingly. It also has a diverse and competitive online community that consists of players of different skill levels and play styles. Therefore, if you want to win against the AI or other players, you need to mix up your moves and strategies and avoid being predictable or repetitive. You can do this by using different attacks, blocks, throws, specials, combos, variations, stances, and more. You can also do this by changing your pace, timing, distance, angle, direction, and more. Mixing up your moves and strategies will keep your opponent guessing and off-balance, and give you an advantage in combat.
-Pros and Cons of Mortal Kombat Mod Apk
-Mortal Kombat mod apk has many pros that make it appealing and enjoyable for many players. However, it also has some cons that may deter some players from using it. Here are some of the pros and cons of Mortal Kombat mod apk:
-Pros
--
-
- More resources: You get unlimited souls and money that allow you to unlock everything in the game without any hassle or cost. -
- More characters: You get access to all 37 playable fighters in the game, including DLC characters that are normally only available through purchasing. -
- More fun: You get to enjoy the game in the highest quality possible, with 4K resolution, HDR support, Dolby Atmos sound, cross-play support, and more. -
- No ads: You don't have to deal with annoying ads that interrupt your gameplay or waste your time. -
- No root required: You don't have to root your device or modify its system settings to use Mortal Kombat mod apk.
- Possible security risks: You may expose your device or data to viruses or malware that can harm or steal them. Always download Mortal Kombat mod apk from trusted sources and scan the file for viruses before installing. -
- Possible compatibility issues: You may encounter some bugs or glitches that affect the performance or functionality of the game. Always check your device's compatibility and update the mod apk to the latest version before playing. -
- Possible legal issues: You may violate the terms of service of the game developer and publisher, and may face legal action or account suspension. Always use Mortal Kombat mod apk at your own risk and respect the intellectual property rights of the original creators. -
- Possible ban from online modes: You may be detected and banned by the game's anti-cheat system if you play online with Mortal Kombat mod apk. Always play online at your own risk and avoid using unfair or abusive tactics that ruin the game for others. -
- Unlimited money: You can buy anything you want in the game without worrying about your budget. -
- Unlocked players: You can choose from over 15,000 authentic soccer stars from over 600 teams, including world-class talent like Kylian Mbappé, Christian Pulisic, Vinicius Jr and Son Heung-min. -
- Unlocked kits, clubs, leagues: You can play with any team you want in any league you want, including Chelsea, Paris SG, Real Madrid, Liverpool and Juventus. -
- Unlocked World Cup mode: You can replay the official tournament brackets with any of the 32 qualified nations, with authentic kits, badges, stadiums, and commentary. -
- Menu mod: You can use a menu to adjust your gameplay settings, such as speed, difficulty, graphics, etc. -
- Android 5.0 or higher -
- At least 200 MB of free storage space -
- A stable internet connection -
- Enable unknown sources in your device settings to allow installation of apps from third-party sources. -
- Disable antivirus or firewall software that might interfere with the installation process. -
- Backup your data before installing the modded game in case something goes wrong. -
- Do not use your real account or personal information when playing the modded game to avoid getting banned or hacked. -
- Go to a reliable website that offers Nulls FIFA APK for download. For example, you can use [this link](^1^) to download the latest version of the game. -
- Tap on the download button and wait for the file to be downloaded on your device. -
- Locate the downloaded file in your file manager and tap on it to start the installation process. -
- Follow the instructions on the screen and wait for the installation to finish. You may need to grant some permissions to the app if asked. -
- Launch the game and enjoy playing FIFA Mobile with unlimited money and features. -
- You can enjoy FIFA Mobile with unlimited money and features. -
- You can play with any players, kits, clubs, and leagues you want. -
- You can relive the FIFA World Cup 2022 with authentic details. -
- You can compete in various modes and challenges. -
- You can customize your gameplay settings with the menu mod. -
- You may face some bugs or errors while playing the game. -
- You may get banned or hacked if you use your real account or personal information. -
- You may not be able to update the game or access some features that require an internet connection. -
- You may violate the terms and conditions of Electronic Arts by using a modded game. -
- What is Nulls FIFA APK?
-Nulls FIFA APK is a modded version of FIFA Mobile that gives you unlimited money and features.
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-You can download Nulls FIFA APK from third-party websites that offer it for download. For example, you can use [this link] to download the latest version of the game.
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-You need to enable unknown sources in your device settings, disable antivirus or firewall software, backup your data, and then follow the instructions on the screen to install Nulls FIFA APK on your device.
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-You can launch the game and enjoy playing FIFA Mobile with unlimited money and features. You can build your ultimate team, relive the FIFA World Cup 2022, and compete in various modes.
- - Is Nulls FIFA APK safe?
-Nulls FIFA APK is not safe as it may contain bugs, viruses, malware, or spyware. It may also get you banned or hacked if you use your real account or personal information. It may also violate the terms and conditions of Electronic Arts by using a modded game. Therefore, you should use it at your own discretion and responsibility.
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- Mod apk archivos no son oficiales o autorizados por los desarrolladores de juegos o editores. Son creados por desarrolladores de terceros o hackers que modifican los archivos originales del juego. -
- Los archivos apk mod pueden contener virus o malware que pueden dañar su dispositivo o robar su información personal. -
- Los archivos apk mod pueden no ser compatibles con su dispositivo o con la última versión del juego. Pueden causar errores o fallos en el juego o en tu dispositivo. -
- Los archivos apk mod pueden violar los términos de servicio o la política de privacidad de los desarrolladores o editores de juegos. Pueden resultar en la prohibición o suspensión de su cuenta del juego o de otros servicios en línea. Continuando con el artículo:
- Los archivos apk mod pueden no ser éticos o justos para los desarrolladores de juegos o editores que trabajan duro para crear y mantener el juego. También pueden arruinar la diversión y el desafío del juego para usted y otros jugadores. -
- Usa sabiamente tu dinero, energía y estrellas ilimitados. Puedes comprar cualquier artículo que quieras, jugar a cualquier nivel que quieras y desbloquear cualquier pista o evidencia que quieras. Pero no los desperdicies en cosas o niveles innecesarios. Guárdalos para los más difíciles o más importantes. -
- Usa tus pistas ilimitadas inteligentemente. Puedes usar tantas pistas como quieras para encontrar las pistas y evidencias en las escenas del crimen. Pero no confíes demasiado en ellos. Trata de encontrarlos por ti mismo primero, y usa pistas solo cuando estés atascado o tengas prisa. -
- Utilice su análisis instantáneo, informes y sugerencias de manera efectiva. Puede obtener resultados instantáneos para cualquier análisis o informe sin usar ninguna estrella o dinero en efectivo. También puede obtener sugerencias instantáneas para cualquier interrogatorio o arresto sin usar ninguna estrella o dinero en efectivo. Pero no las omita demasiado rápido. Léalas cuidadosamente y preste atención a los detalles. Pueden contener información útil o pistas para el caso. -
- Usa todos tus objetos, personajes y mapas de forma creativa. Puedes acceder a todos los objetos, personajes y mapas del juego sin desbloquearlos. Puedes personalizar tu personaje y tu hogar con cualquier elemento que te guste. Puedes interactuar con cualquier personaje que quieras. Puedes explorar cualquier mapa que quieras. Pero no olvides el objetivo principal del juego: resolver los casos. Usa tus objetos, personajes y mapas para mejorar tu jugabilidad y experiencia, no para distraerte de ella. -
Cons
--
-
Conclusion
-Mortal Kombat mod apk is a modified version of Mortal Kombat 11 that gives you unlimited access to all the content and options of the game, as well as some extra benefits that enhance your gaming experience. It is a great way to enjoy the best fighting game series with more resources, more characters, more fun, no ads, and no root required. However, it also comes with some drawbacks, such as possible security risks, possible compatibility issues, possible legal issues, and possible ban from online modes. Therefore, you need to weigh the pros and cons of Mortal Kombat mod apk before using it, and always use it responsibly and ethically.
-FAQs
-Here are some frequently asked questions about Mortal Kombat mod apk:
-Q1: Is Mortal Kombat mod apk safe to use?
-A1: It depends on the source of the mod apk file. Always download from trusted sites and scan the file for viruses before installing.
-Q2: Is Mortal Kombat mod apk legal to use?
-A2: It depends on your country's laws and regulations. Using a mod apk may violate the terms of service of the game developer and publisher, and may result in legal action or account suspension. Use at your own risk.
-Q3: Is Mortal Kombat mod apk compatible with my device?
-A3: It depends on your device's specifications and operating system. The mod apk requires Android 5.0 or higher and at least 4 GB of RAM to run smoothly. Check your device's compatibility before downloading and installing.
-Q4: Can I play online with Mortal Kombat mod apk?
-A4: Yes, but only with other players who are using the same mod apk version. You can also enable cross-play to play with players on other platforms, but be aware that you may be detected and banned by the game's anti-cheat system. Play online at your own risk.
-Q5: Can I update Mortal Kombat mod apk?
-A5: Yes, but only if there is a new version of the mod apk available from the same source. Do not update from the official app store or you will lose all the modded features.
197e85843d-
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy FIFA Mobile with Nulls FIFA APK The Best Mod for Soccer Fans.md b/spaces/1phancelerku/anime-remove-background/Enjoy FIFA Mobile with Nulls FIFA APK The Best Mod for Soccer Fans.md deleted file mode 100644 index 03ccf036267875037146a20b300e576a39b72f4b..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Enjoy FIFA Mobile with Nulls FIFA APK The Best Mod for Soccer Fans.md +++ /dev/null @@ -1,132 +0,0 @@ -
-
Nulls FIFA APK: How to Download and Play FIFA Mobile with Unlimited Money and Features
-If you are a fan of soccer games, you might have heard of FIFA Mobile, the official mobile game of the FIFA World Cup 2022. But did you know that there is a modified version of this game that gives you unlimited money, unlocked features, and more? It's called Nulls FIFA APK, and in this article, we will tell you everything you need to know about it.
-nulls fifa apk
Download ★ https://jinyurl.com/2uNQpO
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What is Nulls FIFA APK?
-A modified version of FIFA Mobile
-Nulls FIFA APK is a modded version of FIFA Mobile, the popular soccer game developed by Electronic Arts. It is not available on the Google Play Store or the App Store, but you can download it from third-party websites. The main difference between Nulls FIFA APK and the original game is that it gives you access to unlimited money, unlocked players, kits, clubs, leagues, and more. You can also use a menu to customize your gameplay settings, such as speed, difficulty, graphics, etc.
-Features of Nulls FIFA APK
-Some of the features that you can enjoy with Nulls FIFA APK are:
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How to Download and Install Nulls FIFA APK?
-Requirements and precautions
-Before you download and install Nulls FIFA APK, you need to make sure that your device meets the following requirements:
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You also need to take some precautions to avoid any problems or risks:
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Steps to download and install
-Once you have met the requirements and taken the precautions, you can follow these steps to download and install Nulls FIFA APK:
--
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How to Play Nulls FIFA APK?
-Build your ultimate team
-One of the main features of Nulls FIFA APK is that you can build your ultimate team with any players you want. You can use the unlimited money to buy packs, players, or items in the store. You can also use the menu mod to unlock all the players, kits, clubs, and leagues in the game. You can then create your own custom squad with your favorite stars and formations. You can also upgrade your players' skills and attributes to make them more powerful.
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Relive the FIFA World Cup 2022
-Another feature of Nulls FIFA APK is that you can relive the FIFA World Cup 2022 with the official tournament mode. You can choose any of the 32 qualified nations and play through the group stage, knockout stage, and final. You can also customize your team's kit, badge, stadium, and commentary to match the real event. You can experience the thrill and excitement of the world's biggest soccer competition on your mobile device.
-Compete in various modes
-Besides the World Cup mode, Nulls FIFA APK also offers other modes for you to compete in. You can play in the Season mode, where you can choose a league and a club and play a full season of matches. You can also play in the Events mode, where you can participate in various challenges and tournaments based on real-life soccer events. You can also play in the Versus mode, where you can challenge other players online and climb the leaderboards.
-Pros and Cons of Nulls FIFA APK
-Pros
-Some of the advantages of using Nulls FIFA APK are:
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-
Cons
-Some of the disadvantages of using Nulls FIFA APK are:
--
-
Conclusion
-Nulls FIFA APK is a modded version of FIFA Mobile that gives you unlimited money and features. It allows you to build your ultimate team, relive the FIFA World Cup 2022, and compete in various modes. However, it also has some drawbacks, such as bugs, risks, limitations, and violations. Therefore, you should use it at your own discretion and responsibility. If you want to try it out, you can follow the steps above to download and install it on your device.
-FAQs
-Here are some frequently asked questions about Nulls FIFA APK:
--
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-
-
\ No newline at end of file diff --git a/spaces/44ov41za8i/FreeVC/README.md b/spaces/44ov41za8i/FreeVC/README.md deleted file mode 100644 index 66ceea8c0c3a8abe147d899987aeddb88ed49a0e..0000000000000000000000000000000000000000 --- a/spaces/44ov41za8i/FreeVC/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: FreeVC -emoji: 🚀 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.13.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/52Hz/HWMNet_lowlight_enhancement/README.md b/spaces/52Hz/HWMNet_lowlight_enhancement/README.md deleted file mode 100644 index 5b72a41fb1eef0ca27dca45091fcb260e5859813..0000000000000000000000000000000000000000 --- a/spaces/52Hz/HWMNet_lowlight_enhancement/README.md +++ /dev/null @@ -1,45 +0,0 @@ ---- -title: HWMNet_low-light_enhancement -emoji: 🕶 -colorFrom: indigo -colorTo: black -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio`, `streamlit`, or `static` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code). -Path is relative to the root of the repository. - -`models`: _List[string]_ -HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space. -Will be parsed automatically from your code if not specified here. - -`datasets`: _List[string]_ -HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space. -Will be parsed automatically from your code if not specified here. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/7hao/bingo/tests/kblob.ts b/spaces/7hao/bingo/tests/kblob.ts deleted file mode 100644 index 9e15b41c1c94a690beb61b23cdb42fc78767ccd2..0000000000000000000000000000000000000000 --- a/spaces/7hao/bingo/tests/kblob.ts +++ /dev/null @@ -1,27 +0,0 @@ -import FormData from 'form-data' - -import { fetch } from '@/lib/isomorphic' - -const formData = new FormData() - -const knowledgeRequest = {"imageInfo":{"url":"https://www.baidu.com/img/PCfb_5bf082d29588c07f842ccde3f97243ea.png"},"knowledgeRequest":{"invokedSkills":["ImageById"],"subscriptionId":"Bing.Chat.Multimodal","invokedSkillsRequestData":{"enableFaceBlur":true},"convoData":{"convoid":"51D|BingProdUnAuthenticatedUsers|E3DCA904FF236C67C3450163BCEC64CFF3F618CC8A4AFD75FD518F5ED0ADA080","convotone":"Creative"}}} - -formData.append('knowledgeRequest', JSON.stringify(knowledgeRequest)) - - -fetch('https://bing.vcanbb.top/images/kblob', - { - method: 'POST', - body: formData.getBuffer(), - headers: { - "sec-ch-ua": "\"Not/A)Brand\";v=\"99\", \"Google Chrome\";v=\"115\", \"Chromium\";v=\"115\"", - "sec-ch-ua-mobile": "?0", - "sec-ch-ua-platform": "\"Windows\"", - "Referer": "https://bing.vcanbb.top/web/index.html", - "Referrer-Policy": "origin-when-cross-origin", - ...formData.getHeaders() - } - - } -).then(res => res.text()) -.then(res => console.log('res', res)) diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/layers/pqmf.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/layers/pqmf.py deleted file mode 100644 index bb31c430d2abe0219f58f153f69d836383e095ef..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/layers/pqmf.py +++ /dev/null @@ -1,132 +0,0 @@ -# -*- coding: utf-8 -*- - -# Copyright 2020 Tomoki Hayashi -# MIT License (https://opensource.org/licenses/MIT) - -"""Pseudo QMF modules.""" - -import numpy as np -import torch -import torch.nn.functional as F - -from scipy.signal import kaiser - - -def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0): - """Design prototype filter for PQMF. - - This method is based on `A Kaiser window approach for the design of prototype - filters of cosine modulated filterbanks`_. - - Args: - taps (int): The number of filter taps. - cutoff_ratio (float): Cut-off frequency ratio. - beta (float): Beta coefficient for kaiser window. - - Returns: - ndarray: Impluse response of prototype filter (taps + 1,). - - .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`: - https://ieeexplore.ieee.org/abstract/document/681427 - - """ - # check the arguments are valid - assert taps % 2 == 0, "The number of taps mush be even number." - assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0." - - # make initial filter - omega_c = np.pi * cutoff_ratio - with np.errstate(invalid='ignore'): - h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \ - / (np.pi * (np.arange(taps + 1) - 0.5 * taps)) - h_i[taps // 2] = np.cos(0) * cutoff_ratio # fix nan due to indeterminate form - - # apply kaiser window - w = kaiser(taps + 1, beta) - h = h_i * w - - return h - - -class PQMF(torch.nn.Module): - """PQMF module. - - This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_. - - .. _`Near-perfect-reconstruction pseudo-QMF banks`: - https://ieeexplore.ieee.org/document/258122 - - """ - - def __init__(self, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0): - """Initilize PQMF module. - - Args: - subbands (int): The number of subbands. - taps (int): The number of filter taps. - cutoff_ratio (float): Cut-off frequency ratio. - beta (float): Beta coefficient for kaiser window. - - """ - super(PQMF, self).__init__() - - # define filter coefficient - h_proto = design_prototype_filter(taps, cutoff_ratio, beta) - h_analysis = np.zeros((subbands, len(h_proto))) - h_synthesis = np.zeros((subbands, len(h_proto))) - for k in range(subbands): - h_analysis[k] = 2 * h_proto * np.cos( - (2 * k + 1) * (np.pi / (2 * subbands)) * - (np.arange(taps + 1) - ((taps - 1) / 2)) + - (-1) ** k * np.pi / 4) - h_synthesis[k] = 2 * h_proto * np.cos( - (2 * k + 1) * (np.pi / (2 * subbands)) * - (np.arange(taps + 1) - ((taps - 1) / 2)) - - (-1) ** k * np.pi / 4) - - # convert to tensor - analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1) - synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0) - - # register coefficients as beffer - self.register_buffer("analysis_filter", analysis_filter) - self.register_buffer("synthesis_filter", synthesis_filter) - - # filter for downsampling & upsampling - updown_filter = torch.zeros((subbands, subbands, subbands)).float() - for k in range(subbands): - updown_filter[k, k, 0] = 1.0 - self.register_buffer("updown_filter", updown_filter) - self.subbands = subbands - - # keep padding info - self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0) - - def analysis(self, x): - """Analysis with PQMF. - - Args: - x (Tensor): Input tensor (B, 1, T). - - Returns: - Tensor: Output tensor (B, subbands, T // subbands). - - """ - x = F.conv1d(self.pad_fn(x), self.analysis_filter) - return F.conv1d(x, self.updown_filter, stride=self.subbands) - - def synthesis(self, x): - """Synthesis with PQMF. - - Args: - x (Tensor): Input tensor (B, subbands, T // subbands). - - Returns: - Tensor: Output tensor (B, 1, T). - - """ - # NOTE(kan-bayashi): Power will be dreased so here multipy by # subbands. - # Not sure this is the correct way, it is better to check again. - # TODO(kan-bayashi): Understand the reconstruction procedure - x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands) - return F.conv1d(self.pad_fn(x), self.synthesis_filter) diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/diffspeech.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/diffspeech.py deleted file mode 100644 index db30b30b1c38d4bf624c950c9799e695ec2de8ba..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/diffspeech.py +++ /dev/null @@ -1,111 +0,0 @@ -import torch - -from text_to_speech.modules.tts.diffspeech.shallow_diffusion_tts import GaussianDiffusion -from tasks.tts.fs2_orig import FastSpeech2OrigTask - -import utils -from text_to_speech.utils.commons.hparams import hparams -from text_to_speech.utils.commons.ckpt_utils import load_ckpt -from text_to_speech.utils.audio.pitch.utils import denorm_f0 - - -class DiffSpeechTask(FastSpeech2OrigTask): - def build_tts_model(self): - # get min and max - # import torch - # from tqdm import tqdm - # v_min = torch.ones([80]) * 100 - # v_max = torch.ones([80]) * -100 - # for i, ds in enumerate(tqdm(self.dataset_cls('train'))): - # v_max = torch.max(torch.max(ds['mel'].reshape(-1, 80), 0)[0], v_max) - # v_min = torch.min(torch.min(ds['mel'].reshape(-1, 80), 0)[0], v_min) - # if i % 100 == 0: - # print(i, v_min, v_max) - # print('final', v_min, v_max) - dict_size = len(self.token_encoder) - self.model = GaussianDiffusion(dict_size, hparams) - if hparams['fs2_ckpt'] != '': - load_ckpt(self.model.fs2, hparams['fs2_ckpt'], 'model', strict=True) - # for k, v in self.model.fs2.named_parameters(): - # if 'predictor' not in k: - # v.requires_grad = False - # or - for k, v in self.model.fs2.named_parameters(): - v.requires_grad = False - - def build_optimizer(self, model): - self.optimizer = optimizer = torch.optim.AdamW( - filter(lambda p: p.requires_grad, model.parameters()), - lr=hparams['lr'], - betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']), - weight_decay=hparams['weight_decay']) - return optimizer - - def build_scheduler(self, optimizer): - return torch.optim.lr_scheduler.StepLR(optimizer, hparams['decay_steps'], gamma=0.5) - - def run_model(self, sample, infer=False, *args, **kwargs): - txt_tokens = sample['txt_tokens'] # [B, T_t] - spk_embed = sample.get('spk_embed') - spk_id = sample.get('spk_ids') - if not infer: - target = sample['mels'] # [B, T_s, 80] - mel2ph = sample['mel2ph'] # [B, T_s] - f0 = sample.get('f0') - uv = sample.get('uv') - output = self.model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, spk_id=spk_id, - ref_mels=target, f0=f0, uv=uv, infer=False) - losses = {} - if 'diff_loss' in output: - losses['mel'] = output['diff_loss'] - self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses) - if hparams['use_pitch_embed']: - self.add_pitch_loss(output, sample, losses) - return losses, output - else: - use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur']) - use_gt_f0 = kwargs.get('infer_use_gt_f0', hparams['use_gt_f0']) - mel2ph, uv, f0 = None, None, None - if use_gt_dur: - mel2ph = sample['mel2ph'] - if use_gt_f0: - f0 = sample['f0'] - uv = sample['uv'] - output = self.model(txt_tokens, mel2ph=mel2ph, spk_embed=spk_embed, spk_id=spk_id, - ref_mels=None, f0=f0, uv=uv, infer=True) - return output - - def save_valid_result(self, sample, batch_idx, model_out): - sr = hparams['audio_sample_rate'] - f0_gt = None - # mel_out = model_out['mel_out'] - if sample.get('f0') is not None: - f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu()) - # self.plot_mel(batch_idx, sample['mels'], mel_out, f0s=f0_gt) - if self.global_step > 0: - # wav_pred = self.vocoder.spec2wav(mel_out[0].cpu(), f0=f0_gt) - # self.logger.add_audio(f'wav_val_{batch_idx}', wav_pred, self.global_step, sr) - # with gt duration - model_out = self.run_model(sample, infer=True, infer_use_gt_dur=True) - dur_info = self.get_plot_dur_info(sample, model_out) - del dur_info['dur_pred'] - wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt) - self.logger.add_audio(f'wav_gdur_{batch_idx}', wav_pred, self.global_step, sr) - self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'diffmel_gdur_{batch_idx}', - dur_info=dur_info, f0s=f0_gt) - self.plot_mel(batch_idx, sample['mels'], model_out['fs2_mel'][0], f'fs2mel_gdur_{batch_idx}', - dur_info=dur_info, f0s=f0_gt) # gt mel vs. fs2 mel - - # with pred duration - if not hparams['use_gt_dur']: - model_out = self.run_model(sample, infer=True, infer_use_gt_dur=False) - dur_info = self.get_plot_dur_info(sample, model_out) - self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_pdur_{batch_idx}', - dur_info=dur_info, f0s=f0_gt) - wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt) - self.logger.add_audio(f'wav_pdur_{batch_idx}', wav_pred, self.global_step, sr) - # gt wav - if self.global_step <= hparams['valid_infer_interval']: - mel_gt = sample['mels'][0].cpu() - wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt) - self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, sr) diff --git a/spaces/AQaTaHaGoD/GoD/confing.py b/spaces/AQaTaHaGoD/GoD/confing.py deleted file mode 100644 index 3547e0af8029bcf1e7fca287de5dd8e0ee15966c..0000000000000000000000000000000000000000 --- a/spaces/AQaTaHaGoD/GoD/confing.py +++ /dev/null @@ -1,7 +0,0 @@ -class token: - - auth = "iyqmgydbhmsovgboojncfdpadzdmlmum" - # شناسه اکانت ربات را وارد کنید ! - - post_link = "https://rubika.ir/The_EnD_TaHa/DIEFFHBEDIGFJHA" - # لینک پست کانال را وارد کنید ! \ No newline at end of file diff --git a/spaces/Abhilashvj/planogram-compliance/utils/loggers/wandb/README.md b/spaces/Abhilashvj/planogram-compliance/utils/loggers/wandb/README.md deleted file mode 100644 index d78324b4c8e9405f388091310227d51d1ead5712..0000000000000000000000000000000000000000 --- a/spaces/Abhilashvj/planogram-compliance/utils/loggers/wandb/README.md +++ /dev/null @@ -1,162 +0,0 @@ -📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021. - -- [About Weights & Biases](#about-weights-&-biases) -- [First-Time Setup](#first-time-setup) -- [Viewing runs](#viewing-runs) -- [Disabling wandb](#disabling-wandb) -- [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage) -- [Reports: Share your work with the world!](#reports) - -## About Weights & Biases - -Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions. - -Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows: - -- [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time -- [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically -- [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization -- [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators -- [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently -- [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models - -## First-Time Setup - -
- 
-
-
-
-
-## Viewing Runs
-
-Toggle Details
-When you first train, W&B will prompt you to create a new account and will generate an **API key** for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device. - -W&B will create a cloud **project** (default is 'YOLOv5') for your training runs, and each new training run will be provided a unique run **name** within that project as project/name. You can also manually set your project and run name as: - -```shell -$ python train.py --project ... --name ... -``` - -YOLOv5 notebook example:
-
-
-
-
-
-## Disabling wandb
-
-- training after running `wandb disabled` inside that directory creates no wandb run
- 
-
-- To enable wandb again, run `wandb online`
- 
-
-## Advanced Usage
-
-You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.
-
-Toggle Details
-Run information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in realtime . All important information is logged: - -- Training & Validation losses -- Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95 -- Learning Rate over time -- A bounding box debugging panel, showing the training progress over time -- GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage** -- System: Disk I/0, CPU utilization, RAM memory usage -- Your trained model as W&B Artifact -- Environment: OS and Python types, Git repository and state, **training command** - -
-
-
-1: Train and Log Evaluation simultaneousy
- This is an extension of the previous section, but it'll also training after uploading the dataset. This also evaluation Table - Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets, - so no images will be uploaded from your system more than once. -
-
-
-Usage
- Code $ python train.py --upload_data val
-
-
-
-2. Visualize and Version Datasets
- Log, visualize, dynamically query, and understand your data with W&B Tables. You can use the following command to log your dataset as a W&B Table. This will generate a{dataset}_wandb.yaml file which can be used to train from dataset artifact.
-
-
-
-Usage
- Code $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data ..
-
-
-
-3: Train using dataset artifact
- When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that - can be used to train a model directly from the dataset artifact. This also logs evaluation -
-
-
-Usage
- Code $ python train.py --data {data}_wandb.yaml
-
-
-
-4: Save model checkpoints as artifacts
- To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval. - You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged - -
-
-
-Usage
- Code $ python train.py --save_period 1
-
-
-
-5: Resume runs from checkpoint artifacts.
-Any run can be resumed using artifacts if the--resume argument starts with wandb-artifact:// prefix followed by the run path, i.e, wandb-artifact://username/project/runid . This doesn't require the model checkpoint to be present on the local system.
-
-
-
-
-Usage
- Code $ python train.py --resume wandb-artifact://{run_path}
-
-
-
-6: Resume runs from dataset artifact & checkpoint artifacts.
- Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device - The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot--upload_dataset or
- train from _wandb.yaml file and set --save_period
-
-
-
-
-
-
-Usage
- Code $ python train.py --resume wandb-artifact://{run_path}
-
-
-
-Reports
-W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)). - -
-
-## Environments
-
-YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
-
-- **Google Colab and Kaggle** notebooks with free GPU:
-- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
-- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
-- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) 
-
-## Status
-
-
-
-If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Weuseing.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Weuseing.py
deleted file mode 100644
index ba79e8b9c2573418720495a20d4c1c8d5a6ca7e9..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Weuseing.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://api.gptplus.one'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
- headers = {
- 'Content-Type': 'application/json',
- 'Accept': '*/*',
- 'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7,ja;q=0.6,zh-TW;q=0.5,zh;q=0.4',
- }
- data = {
- 'messages': messages,
- 'model': model,
- }
- response = requests.post('https://api.gptplus.one/chat-process', json=data, stream=True)
- print(response)
-
- for token in response.iter_content(chunk_size=None):
- yield (token.decode('utf-8'))
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/AchyuthGamer/text-to-speech-client/style.css b/spaces/AchyuthGamer/text-to-speech-client/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/text-to-speech-client/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/tool.py b/spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/tool.py
deleted file mode 100644
index 6833a22a1a666bcd016accd37beb477487a8b259..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/agents/simulation_agent/tool.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import logging
-from string import Template
-from typing import List, NamedTuple, Optional, Union
-
-from langchain.tools import BaseTool
-from pydantic import Field
-
-
-from agentverse.memory import BaseMemory, ChatHistoryMemory
-from agentverse.message import Message
-from agentverse.utils import AgentAction, AgentFinish
-
-#from . import agent_registry
-#from .base import BaseAgent
-
-from agentverse.agents import agent_registry
-from agentverse.agents.base import BaseAgent
-
-class ToolNotExistError(BaseException):
- """Exception raised when parsing output from a command fails."""
-
- def __init__(self, tool_name=""):
- self.tool_name = tool_name
-
- def __str__(self):
- return f"Tool {self.tool_name} does not exist."
-
-
-@agent_registry.register("tool")
-class ToolAgent(BaseAgent):
- tools: List[BaseTool] = Field(default=[])
- tool_memory: BaseMemory = Field(default_factory=ChatHistoryMemory)
- verbose: bool = Field(default=False)
-
- def step(self, env_description: str = "") -> Message:
- parsed_response = None
- tool_observation = [self.tool_memory.to_string()]
- while True:
- prompt = self._fill_prompt_template(env_description, tool_observation)
-
- for i in range(self.max_retry):
- try:
- response = self.llm.generate_response(prompt)
- parsed_response = self.output_parser.parse(response)
- if isinstance(parsed_response, AgentAction):
- observation = self._call_tool(parsed_response)
- tool_observation.append(
- parsed_response.log.strip()
- + f"\nObservation: {observation.strip()}"
- )
- break
- except BaseException as e:
- logging.error(e)
- logging.warning("Retrying...")
- continue
- if parsed_response is None or isinstance(parsed_response, AgentFinish):
- break
-
- if parsed_response is None:
- logging.error(f"{self.name} failed to generate valid response.")
-
- self._update_tool_memory(tool_observation)
-
- message = Message(
- content=""
- if parsed_response is None
- else parsed_response.return_values["output"],
- sender=self.name,
- receiver=self.get_receiver(),
- )
- return message
-
- async def astep(self, env_description: str = "") -> Message:
- """Asynchronous version of step"""
- parsed_response = None
- # Initialize the tool_observation with tool_memory
- tool_observation = [self.tool_memory.to_string()]
- while True:
- prompt = self._fill_prompt_template(env_description, tool_observation)
-
- for i in range(self.max_retry):
- try:
- response = await self.llm.agenerate_response(prompt)
- parsed_response = self.output_parser.parse(response)
- if isinstance(parsed_response, AgentAction):
- # If the response is an action, call the tool
- # and append the observation to tool_observation
- observation = await self._acall_tool(parsed_response)
- tool_observation.append(
- parsed_response.log.strip()
- + f"\nObservation: {observation.strip()}"
- )
- break
- except BaseException as e:
- logging.error(e)
- logging.warning("Retrying...")
- continue
- if parsed_response is None or isinstance(parsed_response, AgentFinish):
- break
-
- if parsed_response is None:
- logging.error(f"{self.name} failed to generate valid response.")
-
- self._update_tool_memory(tool_observation)
-
- message = Message(
- content=""
- if parsed_response is None
- else parsed_response.return_values["output"],
- sender=self.name,
- receiver=self.get_receiver(),
- )
- return message
-
- def _call_tool(self, response: NamedTuple) -> str:
- """Call a tool and return the output"""
- name_to_tool = {tool.name: tool for tool in self.tools}
- if response.tool not in name_to_tool:
- raise ToolNotExistError(response.tool)
- tool = name_to_tool[response.tool]
- observation = tool.run(response.tool_input, verbose=self.verbose)
- return observation
-
- async def _acall_tool(self, response: NamedTuple) -> str:
- """Call a tool and return the output"""
- name_to_tool = {tool.name: tool for tool in self.tools}
- if response.tool not in name_to_tool:
- raise ToolNotExistError(response.tool)
- tool = name_to_tool[response.tool]
- observation = await tool.arun(response.tool_input, verbose=self.verbose)
- return observation
-
- def _update_tool_memory(self, tool_observation: List[str]):
- """Update the memory of the tool"""
- if len(tool_observation) == 1:
- # If no tool is called this turn, do nothing
- return
- messages = [
- Message(content=observation) for observation in tool_observation[1:]
- ]
- self.tool_memory.add_message(messages)
-
- def _fill_prompt_template(
- self, env_description: str = "", tool_observation: List[str] = []
- ) -> str:
- """Fill the placeholders in the prompt template
-
- In the tool agent, these placeholders are supported:
- - ${agent_name}: the name of the agent
- - ${env_description}: the description of the environment
- - ${role_description}: the description of the role of the agent
- - ${chat_history}: the chat history of the agent
- - ${tools}: the list of tools and their usage
- - ${tool_names}: the list of tool names
- - ${tool_observations}: the observation of the tool in this turn
- """
- tools = "\n".join([f"> {tool.name}: {tool.description}" for tool in self.tools])
- tools = tools.replace("{{", "{").replace("}}", "}")
- tool_names = ", ".join([tool.name for tool in self.tools])
- input_arguments = {
- "agent_name": self.name,
- "env_description": env_description,
- "role_description": self.role_description,
- "chat_history": self.memory.to_string(add_sender_prefix=True),
- "tools": tools,
- "tool_names": tool_names,
- "tool_observation": "\n".join(tool_observation),
- }
- return Template(self.prompt_template).safe_substitute(input_arguments)
-
- def add_message_to_memory(self, messages: List[Message]) -> None:
- self.memory.add_message(messages)
-
- def reset(self) -> None:
- """Reset the agent"""
- self.memory.reset()
- # TODO: reset receiver
diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/evaluator/base.py b/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/evaluator/base.py
deleted file mode 100644
index f3d72ad98d83c10ea259aa317ff0e778358c1764..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/environments/tasksolving_env/rules/evaluator/base.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from __future__ import annotations
-
-from abc import abstractmethod
-from typing import TYPE_CHECKING, List, Tuple
-
-from pydantic import BaseModel
-
-from agentverse.message import EvaluatorMessage
-
-if TYPE_CHECKING:
- from agentverse.agents import EvaluatorAgent
- from agentverse.message import EvaluatorMessage, SolverMessage, ExecutorMessage
-
-from . import evaluator_registry
-
-
-class BaseEvaluator(BaseModel):
- """
- The base class of execution.
- """
-
- @abstractmethod
- def step(
- self,
- agent: EvaluatorAgent,
- solution: List[SolverMessage],
- result: List[ExecutorMessage],
- task_description: str,
- all_role_description: List[str],
- *args,
- **kwargs,
- ) -> EvaluatorMessage:
- pass
-
- def reset(self):
- pass
-
-
-@evaluator_registry.register("none")
-class NoneEvaluator(BaseEvaluator):
- def step(
- self,
- agent: EvaluatorAgent,
- solution: List[SolverMessage],
- result: List[ExecutorMessage],
- task_description: str,
- all_role_description: List[str],
- *args,
- **kwargs,
- ) -> EvaluatorMessage:
- result = EvaluatorMessage(
- score=0, advice="\n".join([r.content for r in result])
- )
- return result
-
-
-@evaluator_registry.register("dummy")
-class DummyEvaluator(BaseEvaluator):
- def step(
- self,
- agent: EvaluatorAgent,
- solution: List[SolverMessage],
- result: List[ExecutorMessage],
- task_description: str,
- all_role_description: List[str],
- *args,
- **kwargs,
- ) -> EvaluatorMessage:
- result = EvaluatorMessage(score=1, advice="")
- return result
-
-
-@evaluator_registry.register("dummy")
-class DummyEvaluator(BaseEvaluator):
- def step(
- self,
- agent: EvaluatorAgent,
- solution: List[str] | str,
- result: List[str] | str,
- task_description: str,
- all_role_description: List[str],
- *args,
- **kwargs,
- ) -> EvaluatorMessage:
- result = EvaluatorMessage(
- score=0, advice="\n".join([r.content for r in result])
- )
- return result
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/canvas/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/canvas/Factory.d.ts
deleted file mode 100644
index b1aa1554226f621a8f667e4a67cc4a9ca5ca7f2c..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/canvas/Factory.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import Canvas from './Canvas';
-
-export default function (
- x?: number, y?: number,
- width?: number, height?: number
-): Canvas;
\ No newline at end of file
diff --git a/spaces/AlexWang/lama/saicinpainting/evaluation/losses/ssim.py b/spaces/AlexWang/lama/saicinpainting/evaluation/losses/ssim.py
deleted file mode 100644
index ee43a0095408eca98e253dea194db788446f9c0a..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/saicinpainting/evaluation/losses/ssim.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-
-class SSIM(torch.nn.Module):
- """SSIM. Modified from:
- https://github.com/Po-Hsun-Su/pytorch-ssim/blob/master/pytorch_ssim/__init__.py
- """
-
- def __init__(self, window_size=11, size_average=True):
- super().__init__()
- self.window_size = window_size
- self.size_average = size_average
- self.channel = 1
- self.register_buffer('window', self._create_window(window_size, self.channel))
-
- def forward(self, img1, img2):
- assert len(img1.shape) == 4
-
- channel = img1.size()[1]
-
- if channel == self.channel and self.window.data.type() == img1.data.type():
- window = self.window
- else:
- window = self._create_window(self.window_size, channel)
-
- # window = window.to(img1.get_device())
- window = window.type_as(img1)
-
- self.window = window
- self.channel = channel
-
- return self._ssim(img1, img2, window, self.window_size, channel, self.size_average)
-
- def _gaussian(self, window_size, sigma):
- gauss = torch.Tensor([
- np.exp(-(x - (window_size // 2)) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)
- ])
- return gauss / gauss.sum()
-
- def _create_window(self, window_size, channel):
- _1D_window = self._gaussian(window_size, 1.5).unsqueeze(1)
- _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
- return _2D_window.expand(channel, 1, window_size, window_size).contiguous()
-
- def _ssim(self, img1, img2, window, window_size, channel, size_average=True):
- mu1 = F.conv2d(img1, window, padding=(window_size // 2), groups=channel)
- mu2 = F.conv2d(img2, window, padding=(window_size // 2), groups=channel)
-
- mu1_sq = mu1.pow(2)
- mu2_sq = mu2.pow(2)
- mu1_mu2 = mu1 * mu2
-
- sigma1_sq = F.conv2d(
- img1 * img1, window, padding=(window_size // 2), groups=channel) - mu1_sq
- sigma2_sq = F.conv2d(
- img2 * img2, window, padding=(window_size // 2), groups=channel) - mu2_sq
- sigma12 = F.conv2d(
- img1 * img2, window, padding=(window_size // 2), groups=channel) - mu1_mu2
-
- C1 = 0.01 ** 2
- C2 = 0.03 ** 2
-
- ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / \
- ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
-
- if size_average:
- return ssim_map.mean()
-
- return ssim_map.mean(1).mean(1).mean(1)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
- return
diff --git a/spaces/Amon1/ChatGPTForAcadamic/request_llm/README.md b/spaces/Amon1/ChatGPTForAcadamic/request_llm/README.md
deleted file mode 100644
index c66cc15c0591613eda9ee8f3b6acb6ad66224bdb..0000000000000000000000000000000000000000
--- a/spaces/Amon1/ChatGPTForAcadamic/request_llm/README.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# 如何使用其他大语言模型（dev分支测试中）
-
-## 1. 先运行text-generation
-``` sh
-# 下载模型（ text-generation 这么牛的项目，别忘了给人家star ）
-git clone https://github.com/oobabooga/text-generation-webui.git
-
-# 安装text-generation的额外依赖
-pip install accelerate bitsandbytes flexgen gradio llamacpp markdown numpy peft requests rwkv safetensors sentencepiece tqdm datasets git+https://github.com/huggingface/transformers
-
-# 切换路径
-cd text-generation-webui
-
-# 下载模型
-python download-model.py facebook/galactica-1.3b
-# 其他可选如 facebook/opt-1.3b
-# facebook/galactica-6.7b
-# facebook/galactica-120b
-# facebook/pygmalion-1.3b 等
-# 详情见 https://github.com/oobabooga/text-generation-webui
-
-# 启动text-generation，注意把模型的斜杠改成下划线
-python server.py --cpu --listen --listen-port 7860 --model facebook_galactica-1.3b
-```
-
-## 2. 修改config.py
-``` sh
-# LLM_MODEL格式较复杂 TGUI:[模型]@[ws地址]:[ws端口] , 端口要和上面给定的端口一致
-LLM_MODEL = "TGUI:galactica-1.3b@localhost:7860"
-```
-
-## 3. 运行！
-``` sh
-cd chatgpt-academic
-python main.py
-```
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/spectrogram_diffusion/notes_encoder.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/spectrogram_diffusion/notes_encoder.py
deleted file mode 100644
index 94eaa176f3e5a15f4065e78b4b7714fa8c51ca83..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/spectrogram_diffusion/notes_encoder.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright 2022 The Music Spectrogram Diffusion Authors.
-# 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 torch
-import torch.nn as nn
-from transformers.modeling_utils import ModuleUtilsMixin
-from transformers.models.t5.modeling_t5 import T5Block, T5Config, T5LayerNorm
-
-from ...configuration_utils import ConfigMixin, register_to_config
-from ...models import ModelMixin
-
-
-class SpectrogramNotesEncoder(ModelMixin, ConfigMixin, ModuleUtilsMixin):
- @register_to_config
- def __init__(
- self,
- max_length: int,
- vocab_size: int,
- d_model: int,
- dropout_rate: float,
- num_layers: int,
- num_heads: int,
- d_kv: int,
- d_ff: int,
- feed_forward_proj: str,
- is_decoder: bool = False,
- ):
- super().__init__()
-
- self.token_embedder = nn.Embedding(vocab_size, d_model)
-
- self.position_encoding = nn.Embedding(max_length, d_model)
- self.position_encoding.weight.requires_grad = False
-
- self.dropout_pre = nn.Dropout(p=dropout_rate)
-
- t5config = T5Config(
- vocab_size=vocab_size,
- d_model=d_model,
- num_heads=num_heads,
- d_kv=d_kv,
- d_ff=d_ff,
- dropout_rate=dropout_rate,
- feed_forward_proj=feed_forward_proj,
- is_decoder=is_decoder,
- is_encoder_decoder=False,
- )
-
- self.encoders = nn.ModuleList()
- for lyr_num in range(num_layers):
- lyr = T5Block(t5config)
- self.encoders.append(lyr)
-
- self.layer_norm = T5LayerNorm(d_model)
- self.dropout_post = nn.Dropout(p=dropout_rate)
-
- def forward(self, encoder_input_tokens, encoder_inputs_mask):
- x = self.token_embedder(encoder_input_tokens)
-
- seq_length = encoder_input_tokens.shape[1]
- inputs_positions = torch.arange(seq_length, device=encoder_input_tokens.device)
- x += self.position_encoding(inputs_positions)
-
- x = self.dropout_pre(x)
-
- # inverted the attention mask
- input_shape = encoder_input_tokens.size()
- extended_attention_mask = self.get_extended_attention_mask(encoder_inputs_mask, input_shape)
-
- for lyr in self.encoders:
- x = lyr(x, extended_attention_mask)[0]
- x = self.layer_norm(x)
-
- return self.dropout_post(x), encoder_inputs_mask
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py
deleted file mode 100644
index 960c4369e45ab8792e90c8aaa253f52ac65cef5c..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py
+++ /dev/null
@@ -1,1259 +0,0 @@
-# Copyright 2023 Pix2Pix Zero Authors and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import warnings
-from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-import torch.nn.functional as F
-from transformers import (
- BlipForConditionalGeneration,
- BlipProcessor,
- CLIPImageProcessor,
- CLIPTextModel,
- CLIPTokenizer,
-)
-
-from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.attention_processor import Attention
-from ...schedulers import DDIMScheduler, DDPMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler
-from ...schedulers.scheduling_ddim_inverse import DDIMInverseScheduler
-from ...utils import (
- PIL_INTERPOLATION,
- BaseOutput,
- deprecate,
- is_accelerate_available,
- is_accelerate_version,
- logging,
- randn_tensor,
- replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-@dataclass
-class Pix2PixInversionPipelineOutput(BaseOutput, TextualInversionLoaderMixin):
- """
- Output class for Stable Diffusion pipelines.
-
- Args:
- latents (`torch.FloatTensor`)
- inverted latents tensor
- images (`List[PIL.Image.Image]` or `np.ndarray`)
- List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
- num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
- """
-
- latents: torch.FloatTensor
- images: Union[List[PIL.Image.Image], np.ndarray]
-
-
-EXAMPLE_DOC_STRING = """
- Examples:
- ```py
- >>> import requests
- >>> import torch
-
- >>> from diffusers import DDIMScheduler, StableDiffusionPix2PixZeroPipeline
-
-
- >>> def download(embedding_url, local_filepath):
- ... r = requests.get(embedding_url)
- ... with open(local_filepath, "wb") as f:
- ... f.write(r.content)
-
-
- >>> model_ckpt = "CompVis/stable-diffusion-v1-4"
- >>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16)
- >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
- >>> pipeline.to("cuda")
-
- >>> prompt = "a high resolution painting of a cat in the style of van gough"
- >>> source_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/cat.pt"
- >>> target_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/dog.pt"
-
- >>> for url in [source_emb_url, target_emb_url]:
- ... download(url, url.split("/")[-1])
-
- >>> src_embeds = torch.load(source_emb_url.split("/")[-1])
- >>> target_embeds = torch.load(target_emb_url.split("/")[-1])
- >>> images = pipeline(
- ... prompt,
- ... source_embeds=src_embeds,
- ... target_embeds=target_embeds,
- ... num_inference_steps=50,
- ... cross_attention_guidance_amount=0.15,
- ... ).images
-
- >>> images[0].save("edited_image_dog.png")
- ```
-"""
-
-EXAMPLE_INVERT_DOC_STRING = """
- Examples:
- ```py
- >>> import torch
- >>> from transformers import BlipForConditionalGeneration, BlipProcessor
- >>> from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline
-
- >>> import requests
- >>> from PIL import Image
-
- >>> captioner_id = "Salesforce/blip-image-captioning-base"
- >>> processor = BlipProcessor.from_pretrained(captioner_id)
- >>> model = BlipForConditionalGeneration.from_pretrained(
- ... captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True
- ... )
-
- >>> sd_model_ckpt = "CompVis/stable-diffusion-v1-4"
- >>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained(
- ... sd_model_ckpt,
- ... caption_generator=model,
- ... caption_processor=processor,
- ... torch_dtype=torch.float16,
- ... safety_checker=None,
- ... )
-
- >>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
- >>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
- >>> pipeline.enable_model_cpu_offload()
-
- >>> img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png"
-
- >>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512))
- >>> # generate caption
- >>> caption = pipeline.generate_caption(raw_image)
-
- >>> # "a photography of a cat with flowers and dai dai daie - daie - daie kasaii"
- >>> inv_latents = pipeline.invert(caption, image=raw_image).latents
- >>> # we need to generate source and target embeds
-
- >>> source_prompts = ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"]
-
- >>> target_prompts = ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"]
-
- >>> source_embeds = pipeline.get_embeds(source_prompts)
- >>> target_embeds = pipeline.get_embeds(target_prompts)
- >>> # the latents can then be used to edit a real image
- >>> # when using Stable Diffusion 2 or other models that use v-prediction
- >>> # set `cross_attention_guidance_amount` to 0.01 or less to avoid input latent gradient explosion
-
- >>> image = pipeline(
- ... caption,
- ... source_embeds=source_embeds,
- ... target_embeds=target_embeds,
- ... num_inference_steps=50,
- ... cross_attention_guidance_amount=0.15,
- ... generator=generator,
- ... latents=inv_latents,
- ... negative_prompt=caption,
- ... ).images[0]
- >>> image.save("edited_image.png")
- ```
-"""
-
-
-# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
-def preprocess(image):
- warnings.warn(
- "The preprocess method is deprecated and will be removed in a future version. Please"
- " use VaeImageProcessor.preprocess instead",
- FutureWarning,
- )
- if isinstance(image, torch.Tensor):
- return image
- elif isinstance(image, PIL.Image.Image):
- image = [image]
-
- if isinstance(image[0], PIL.Image.Image):
- w, h = image[0].size
- w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
-
- image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
- image = np.concatenate(image, axis=0)
- image = np.array(image).astype(np.float32) / 255.0
- image = image.transpose(0, 3, 1, 2)
- image = 2.0 * image - 1.0
- image = torch.from_numpy(image)
- elif isinstance(image[0], torch.Tensor):
- image = torch.cat(image, dim=0)
- return image
-
-
-def prepare_unet(unet: UNet2DConditionModel):
- """Modifies the UNet (`unet`) to perform Pix2Pix Zero optimizations."""
- pix2pix_zero_attn_procs = {}
- for name in unet.attn_processors.keys():
- module_name = name.replace(".processor", "")
- module = unet.get_submodule(module_name)
- if "attn2" in name:
- pix2pix_zero_attn_procs[name] = Pix2PixZeroAttnProcessor(is_pix2pix_zero=True)
- module.requires_grad_(True)
- else:
- pix2pix_zero_attn_procs[name] = Pix2PixZeroAttnProcessor(is_pix2pix_zero=False)
- module.requires_grad_(False)
-
- unet.set_attn_processor(pix2pix_zero_attn_procs)
- return unet
-
-
-class Pix2PixZeroL2Loss:
- def __init__(self):
- self.loss = 0.0
-
- def compute_loss(self, predictions, targets):
- self.loss += ((predictions - targets) ** 2).sum((1, 2)).mean(0)
-
-
-class Pix2PixZeroAttnProcessor:
- """An attention processor class to store the attention weights.
- In Pix2Pix Zero, it happens during computations in the cross-attention blocks."""
-
- def __init__(self, is_pix2pix_zero=False):
- self.is_pix2pix_zero = is_pix2pix_zero
- if self.is_pix2pix_zero:
- self.reference_cross_attn_map = {}
-
- def __call__(
- self,
- attn: Attention,
- hidden_states,
- encoder_hidden_states=None,
- attention_mask=None,
- timestep=None,
- loss=None,
- ):
- batch_size, sequence_length, _ = hidden_states.shape
- attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
- query = attn.to_q(hidden_states)
-
- if encoder_hidden_states is None:
- encoder_hidden_states = hidden_states
- elif attn.norm_cross:
- encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
- key = attn.to_k(encoder_hidden_states)
- value = attn.to_v(encoder_hidden_states)
-
- query = attn.head_to_batch_dim(query)
- key = attn.head_to_batch_dim(key)
- value = attn.head_to_batch_dim(value)
-
- attention_probs = attn.get_attention_scores(query, key, attention_mask)
- if self.is_pix2pix_zero and timestep is not None:
- # new bookkeeping to save the attention weights.
- if loss is None:
- self.reference_cross_attn_map[timestep.item()] = attention_probs.detach().cpu()
- # compute loss
- elif loss is not None:
- prev_attn_probs = self.reference_cross_attn_map.pop(timestep.item())
- loss.compute_loss(attention_probs, prev_attn_probs.to(attention_probs.device))
-
- hidden_states = torch.bmm(attention_probs, value)
- hidden_states = attn.batch_to_head_dim(hidden_states)
-
- # linear proj
- hidden_states = attn.to_out[0](hidden_states)
- # dropout
- hidden_states = attn.to_out[1](hidden_states)
-
- return hidden_states
-
-
-class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline):
- r"""
- Pipeline for pixel-levl image editing using Pix2Pix Zero. Based on Stable Diffusion.
-
- 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.)
-
- 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 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.
- tokenizer (`CLIPTokenizer`):
- 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`], [`EulerAncestralDiscreteScheduler`], or [`DDPMScheduler`].
- safety_checker ([`StableDiffusionSafetyChecker`]):
- Classification module that estimates whether generated images could be considered offensive or harmful.
- Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
- feature_extractor ([`CLIPImageProcessor`]):
- Model that extracts features from generated images to be used as inputs for the `safety_checker`.
- requires_safety_checker (bool):
- Whether the pipeline requires a safety checker. We recommend setting it to True if you're using the
- pipeline publicly.
- """
- _optional_components = [
- "safety_checker",
- "feature_extractor",
- "caption_generator",
- "caption_processor",
- "inverse_scheduler",
- ]
-
- def __init__(
- self,
- vae: AutoencoderKL,
- text_encoder: CLIPTextModel,
- tokenizer: CLIPTokenizer,
- unet: UNet2DConditionModel,
- scheduler: Union[DDPMScheduler, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler],
- feature_extractor: CLIPImageProcessor,
- safety_checker: StableDiffusionSafetyChecker,
- inverse_scheduler: DDIMInverseScheduler,
- caption_generator: BlipForConditionalGeneration,
- caption_processor: BlipProcessor,
- requires_safety_checker: bool = True,
- ):
- super().__init__()
-
- if safety_checker is None and requires_safety_checker:
- logger.warning(
- f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
- " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
- " results in services or applications open to the public. Both the diffusers team and Hugging Face"
- " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
- " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
- " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
- )
-
- if safety_checker is not None and feature_extractor is None:
- raise ValueError(
- "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
- " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
- )
-
- self.register_modules(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
- caption_processor=caption_processor,
- caption_generator=caption_generator,
- inverse_scheduler=inverse_scheduler,
- )
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
- self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
- self.register_to_config(requires_safety_checker=requires_safety_checker)
-
- 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}")
-
- hook = None
- for cpu_offloaded_model in [self.vae, self.text_encoder, self.unet, self.vae]:
- _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
- if self.safety_checker is not None:
- _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
- # We'll offload the last model manually.
- self.final_offload_hook = hook
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
- def _encode_prompt(
- self,
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt=None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_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
- 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`).
- 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.
- lora_scale (`float`, *optional*):
- A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
- """
- # 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]
-
- if prompt_embeds is None:
- # textual inversion: procecss multi-vector tokens if necessary
- if isinstance(self, TextualInversionLoaderMixin):
- prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
- text_inputs = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_input_ids = text_inputs.input_ids
- untruncated_ids = self.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 = self.tokenizer.batch_decode(
- untruncated_ids[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = text_inputs.attention_mask.to(device)
- else:
- attention_mask = None
-
- prompt_embeds = self.text_encoder(
- text_input_ids.to(device),
- attention_mask=attention_mask,
- )
- prompt_embeds = prompt_embeds[0]
-
- prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.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)
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance and negative_prompt_embeds is None:
- uncond_tokens: List[str]
- if negative_prompt is None:
- uncond_tokens = [""] * batch_size
- elif 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]
- 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
-
- # textual inversion: procecss multi-vector tokens if necessary
- if isinstance(self, TextualInversionLoaderMixin):
- uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
- max_length = prompt_embeds.shape[1]
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="pt",
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = uncond_input.attention_mask.to(device)
- else:
- attention_mask = None
-
- negative_prompt_embeds = self.text_encoder(
- uncond_input.input_ids.to(device),
- attention_mask=attention_mask,
- )
- negative_prompt_embeds = negative_prompt_embeds[0]
-
- 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.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)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
- return prompt_embeds
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
- def run_safety_checker(self, image, device, dtype):
- if self.safety_checker is None:
- has_nsfw_concept = None
- else:
- if torch.is_tensor(image):
- feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
- else:
- feature_extractor_input = self.image_processor.numpy_to_pil(image)
- safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
- image, has_nsfw_concept = self.safety_checker(
- images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
- )
- return image, has_nsfw_concept
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
- def decode_latents(self, latents):
- warnings.warn(
- "The decode_latents method is deprecated and will be removed in a future version. Please"
- " use VaeImageProcessor instead",
- FutureWarning,
- )
- latents = 1 / self.vae.config.scaling_factor * latents
- image = self.vae.decode(latents, return_dict=False)[0]
- image = (image / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
- image = image.cpu().permute(0, 2, 3, 1).float().numpy()
- return image
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- def check_inputs(
- self,
- prompt,
- source_embeds,
- target_embeds,
- callback_steps,
- prompt_embeds=None,
- ):
- 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 source_embeds is None and target_embeds is None:
- raise ValueError("`source_embeds` and `target_embeds` cannot be undefined.")
-
- 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 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)}")
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
- def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
- shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
-
- if latents is None:
- latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
- else:
- latents = latents.to(device)
-
- # scale the initial noise by the standard deviation required by the scheduler
- latents = latents * self.scheduler.init_noise_sigma
- return latents
-
- @torch.no_grad()
- def generate_caption(self, images):
- """Generates caption for a given image."""
- text = "a photography of"
-
- prev_device = self.caption_generator.device
-
- device = self._execution_device
- inputs = self.caption_processor(images, text, return_tensors="pt").to(
- device=device, dtype=self.caption_generator.dtype
- )
- self.caption_generator.to(device)
- outputs = self.caption_generator.generate(**inputs, max_new_tokens=128)
-
- # offload caption generator
- self.caption_generator.to(prev_device)
-
- caption = self.caption_processor.batch_decode(outputs, skip_special_tokens=True)[0]
- return caption
-
- def construct_direction(self, embs_source: torch.Tensor, embs_target: torch.Tensor):
- """Constructs the edit direction to steer the image generation process semantically."""
- return (embs_target.mean(0) - embs_source.mean(0)).unsqueeze(0)
-
- @torch.no_grad()
- def get_embeds(self, prompt: List[str], batch_size: int = 16) -> torch.FloatTensor:
- num_prompts = len(prompt)
- embeds = []
- for i in range(0, num_prompts, batch_size):
- prompt_slice = prompt[i : i + batch_size]
-
- input_ids = self.tokenizer(
- prompt_slice,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- ).input_ids
-
- input_ids = input_ids.to(self.text_encoder.device)
- embeds.append(self.text_encoder(input_ids)[0])
-
- return torch.cat(embeds, dim=0).mean(0)[None]
-
- def prepare_image_latents(self, image, batch_size, dtype, device, generator=None):
- 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)}"
- )
-
- image = image.to(device=device, dtype=dtype)
-
- if image.shape[1] == 4:
- latents = image
-
- else:
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
-
- if isinstance(generator, list):
- latents = [
- self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
- ]
- latents = torch.cat(latents, dim=0)
- else:
- latents = self.vae.encode(image).latent_dist.sample(generator)
-
- latents = self.vae.config.scaling_factor * latents
-
- if batch_size != latents.shape[0]:
- if batch_size % latents.shape[0] == 0:
- # expand image_latents for batch_size
- deprecation_message = (
- f"You have passed {batch_size} text prompts (`prompt`), but only {latents.shape[0]} initial"
- " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
- " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
- " your script to pass as many initial images as text prompts to suppress this warning."
- )
- deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
- additional_latents_per_image = batch_size // latents.shape[0]
- latents = torch.cat([latents] * additional_latents_per_image, dim=0)
- else:
- raise ValueError(
- f"Cannot duplicate `image` of batch size {latents.shape[0]} to {batch_size} text prompts."
- )
- else:
- latents = torch.cat([latents], dim=0)
-
- return latents
-
- def get_epsilon(self, model_output: torch.Tensor, sample: torch.Tensor, timestep: int):
- pred_type = self.inverse_scheduler.config.prediction_type
- alpha_prod_t = self.inverse_scheduler.alphas_cumprod[timestep]
-
- beta_prod_t = 1 - alpha_prod_t
-
- if pred_type == "epsilon":
- return model_output
- elif pred_type == "sample":
- return (sample - alpha_prod_t ** (0.5) * model_output) / beta_prod_t ** (0.5)
- elif pred_type == "v_prediction":
- return (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
- else:
- raise ValueError(
- f"prediction_type given as {pred_type} must be one of `epsilon`, `sample`, or `v_prediction`"
- )
-
- def auto_corr_loss(self, hidden_states, generator=None):
- reg_loss = 0.0
- for i in range(hidden_states.shape[0]):
- for j in range(hidden_states.shape[1]):
- noise = hidden_states[i : i + 1, j : j + 1, :, :]
- while True:
- roll_amount = torch.randint(noise.shape[2] // 2, (1,), generator=generator).item()
- reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=2)).mean() ** 2
- reg_loss += (noise * torch.roll(noise, shifts=roll_amount, dims=3)).mean() ** 2
-
- if noise.shape[2] <= 8:
- break
- noise = F.avg_pool2d(noise, kernel_size=2)
- return reg_loss
-
- def kl_divergence(self, hidden_states):
- mean = hidden_states.mean()
- var = hidden_states.var()
- return var + mean**2 - 1 - torch.log(var + 1e-7)
-
- @torch.no_grad()
- @replace_example_docstring(EXAMPLE_DOC_STRING)
- def __call__(
- self,
- prompt: Optional[Union[str, List[str]]] = None,
- source_embeds: torch.Tensor = None,
- target_embeds: torch.Tensor = None,
- height: Optional[int] = None,
- width: Optional[int] = None,
- num_inference_steps: int = 50,
- guidance_scale: float = 7.5,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- num_images_per_prompt: Optional[int] = 1,
- eta: float = 0.0,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- latents: Optional[torch.FloatTensor] = None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- negative_prompt_embeds: Optional[torch.FloatTensor] = None,
- cross_attention_guidance_amount: float = 0.1,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: Optional[int] = 1,
- cross_attention_kwargs: Optional[Dict[str, Any]] = None,
- ):
- 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.
- source_embeds (`torch.Tensor`):
- Source concept embeddings. Generation of the embeddings as per the [original
- paper](https://arxiv.org/abs/2302.03027). Used in discovering the edit direction.
- target_embeds (`torch.Tensor`):
- Target concept embeddings. Generation of the embeddings as per the [original
- paper](https://arxiv.org/abs/2302.03027). Used in discovering the edit direction.
- height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The width in pixels of the generated image.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- 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`).
- 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.
- cross_attention_guidance_amount (`float`, defaults to 0.1):
- Amount of guidance needed from the reference cross-attention maps.
- 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.StableDiffusionPipelineOutput`] 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.
-
- Examples:
-
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
- """
- # 0. Define the spatial resolutions.
- height = height or self.unet.config.sample_size * self.vae_scale_factor
- width = width or self.unet.config.sample_size * self.vae_scale_factor
-
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(
- prompt,
- source_embeds,
- target_embeds,
- callback_steps,
- prompt_embeds,
- )
-
- # 3. 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]
- if cross_attention_kwargs is None:
- cross_attention_kwargs = {}
-
- 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
- prompt_embeds = self._encode_prompt(
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt,
- prompt_embeds=prompt_embeds,
- negative_prompt_embeds=negative_prompt_embeds,
- )
-
- # 4. Prepare timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps = self.scheduler.timesteps
-
- # 5. Generate the inverted noise from the input image or any other image
- # generated from the input prompt.
- num_channels_latents = self.unet.config.in_channels
- latents = self.prepare_latents(
- batch_size * num_images_per_prompt,
- num_channels_latents,
- height,
- width,
- prompt_embeds.dtype,
- device,
- generator,
- latents,
- )
- latents_init = latents.clone()
-
- # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- # 8. Rejig the UNet so that we can obtain the cross-attenion maps and
- # use them for guiding the subsequent image generation.
- self.unet = prepare_unet(self.unet)
-
- # 7. Denoising loop where we obtain the cross-attention maps.
- num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
- 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
- noise_pred = self.unet(
- latent_model_input,
- t,
- encoder_hidden_states=prompt_embeds,
- cross_attention_kwargs={"timestep": t},
- ).sample
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
- # call the callback, if provided
- if 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)
-
- # 8. Compute the edit directions.
- edit_direction = self.construct_direction(source_embeds, target_embeds).to(prompt_embeds.device)
-
- # 9. Edit the prompt embeddings as per the edit directions discovered.
- prompt_embeds_edit = prompt_embeds.clone()
- prompt_embeds_edit[1:2] += edit_direction
-
- # 10. Second denoising loop to generate the edited image.
- latents = latents_init
- num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
- 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)
-
- # we want to learn the latent such that it steers the generation
- # process towards the edited direction, so make the make initial
- # noise learnable
- x_in = latent_model_input.detach().clone()
- x_in.requires_grad = True
-
- # optimizer
- opt = torch.optim.SGD([x_in], lr=cross_attention_guidance_amount)
-
- with torch.enable_grad():
- # initialize loss
- loss = Pix2PixZeroL2Loss()
-
- # predict the noise residual
- noise_pred = self.unet(
- x_in,
- t,
- encoder_hidden_states=prompt_embeds_edit.detach(),
- cross_attention_kwargs={"timestep": t, "loss": loss},
- ).sample
-
- loss.loss.backward(retain_graph=False)
- opt.step()
-
- # recompute the noise
- noise_pred = self.unet(
- x_in.detach(),
- t,
- encoder_hidden_states=prompt_embeds_edit,
- cross_attention_kwargs={"timestep": None},
- ).sample
-
- latents = x_in.detach().chunk(2)[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)
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
- # call the callback, if provided
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
-
- if not output_type == "latent":
- image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
- image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
- else:
- image = latents
- has_nsfw_concept = None
-
- if has_nsfw_concept is None:
- do_denormalize = [True] * image.shape[0]
- else:
- do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
- image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-
- # 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, has_nsfw_concept)
-
- return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
-
- @torch.no_grad()
- @replace_example_docstring(EXAMPLE_INVERT_DOC_STRING)
- def invert(
- self,
- prompt: Optional[str] = None,
- image: Union[
- torch.FloatTensor,
- PIL.Image.Image,
- np.ndarray,
- List[torch.FloatTensor],
- List[PIL.Image.Image],
- List[np.ndarray],
- ] = None,
- num_inference_steps: int = 50,
- guidance_scale: float = 1,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- latents: Optional[torch.FloatTensor] = None,
- prompt_embeds: Optional[torch.FloatTensor] = None,
- cross_attention_guidance_amount: float = 0.1,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: Optional[int] = 1,
- cross_attention_kwargs: Optional[Dict[str, Any]] = None,
- lambda_auto_corr: float = 20.0,
- lambda_kl: float = 20.0,
- num_reg_steps: int = 5,
- num_auto_corr_rolls: int = 5,
- ):
- r"""
- Function used to generate inverted latents given a prompt and image.
-
- 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.
- image (`torch.FloatTensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
- `Image`, or tensor representing an image batch which will be used for conditioning. Can also accpet
- image latents as `image`, if passing latents directly, it will not be encoded again.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 1):
- 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.
- 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.
- cross_attention_guidance_amount (`float`, defaults to 0.1):
- Amount of guidance needed from the reference cross-attention maps.
- 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.StableDiffusionPipelineOutput`] 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.
- lambda_auto_corr (`float`, *optional*, defaults to 20.0):
- Lambda parameter to control auto correction
- lambda_kl (`float`, *optional*, defaults to 20.0):
- Lambda parameter to control Kullback–Leibler divergence output
- num_reg_steps (`int`, *optional*, defaults to 5):
- Number of regularization loss steps
- num_auto_corr_rolls (`int`, *optional*, defaults to 5):
- Number of auto correction roll steps
-
- Examples:
-
- Returns:
- [`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] or
- `tuple`:
- [`~pipelines.stable_diffusion.pipeline_stable_diffusion_pix2pix_zero.Pix2PixInversionPipelineOutput`] if
- `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is the inverted
- latents tensor and then second is the corresponding decoded image.
- """
- # 1. 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]
- if cross_attention_kwargs is None:
- cross_attention_kwargs = {}
-
- 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. Preprocess image
- image = self.image_processor.preprocess(image)
-
- # 4. Prepare latent variables
- latents = self.prepare_image_latents(image, batch_size, self.vae.dtype, device, generator)
-
- # 5. Encode input prompt
- num_images_per_prompt = 1
- prompt_embeds = self._encode_prompt(
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- prompt_embeds=prompt_embeds,
- )
-
- # 4. Prepare timesteps
- self.inverse_scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps = self.inverse_scheduler.timesteps
-
- # 6. Rejig the UNet so that we can obtain the cross-attenion maps and
- # use them for guiding the subsequent image generation.
- self.unet = prepare_unet(self.unet)
-
- # 7. Denoising loop where we obtain the cross-attention maps.
- num_warmup_steps = len(timesteps) - num_inference_steps * self.inverse_scheduler.order
- 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.inverse_scheduler.scale_model_input(latent_model_input, t)
-
- # predict the noise residual
- noise_pred = self.unet(
- latent_model_input,
- t,
- encoder_hidden_states=prompt_embeds,
- cross_attention_kwargs={"timestep": t},
- ).sample
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
- # regularization of the noise prediction
- with torch.enable_grad():
- for _ in range(num_reg_steps):
- if lambda_auto_corr > 0:
- for _ in range(num_auto_corr_rolls):
- var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)
-
- # Derive epsilon from model output before regularizing to IID standard normal
- var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)
-
- l_ac = self.auto_corr_loss(var_epsilon, generator=generator)
- l_ac.backward()
-
- grad = var.grad.detach() / num_auto_corr_rolls
- noise_pred = noise_pred - lambda_auto_corr * grad
-
- if lambda_kl > 0:
- var = torch.autograd.Variable(noise_pred.detach().clone(), requires_grad=True)
-
- # Derive epsilon from model output before regularizing to IID standard normal
- var_epsilon = self.get_epsilon(var, latent_model_input.detach(), t)
-
- l_kld = self.kl_divergence(var_epsilon)
- l_kld.backward()
-
- grad = var.grad.detach()
- noise_pred = noise_pred - lambda_kl * grad
-
- noise_pred = noise_pred.detach()
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample
-
- # call the callback, if provided
- if i == len(timesteps) - 1 or (
- (i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0
- ):
- progress_bar.update()
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- inverted_latents = latents.detach().clone()
-
- # 8. Post-processing
- image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
- 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 (inverted_latents, image)
-
- return Pix2PixInversionPipelineOutput(latents=inverted_latents, images=image)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion_xl/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion_xl/__init__.py
deleted file mode 100644
index 02bd96cfc23cc5508056a9ecacf5956f30d472f7..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion_xl/__init__.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from dataclasses import dataclass
-from typing import List, Optional, Union
-
-import numpy as np
-import PIL
-
-from ...utils import (
- BaseOutput,
- OptionalDependencyNotAvailable,
- is_torch_available,
- is_transformers_available,
-)
-
-
-@dataclass
-class StableDiffusionXLPipelineOutput(BaseOutput):
- """
- Output class for Stable Diffusion pipelines.
-
- Args:
- images (`List[PIL.Image.Image]` or `np.ndarray`)
- List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
- num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
- """
-
- images: Union[List[PIL.Image.Image], np.ndarray]
-
-
-try:
- if not (is_transformers_available() and is_torch_available()):
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
-else:
- from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
- from .pipeline_stable_diffusion_xl_img2img import StableDiffusionXLImg2ImgPipeline
- from .pipeline_stable_diffusion_xl_inpaint import StableDiffusionXLInpaintPipeline
- from .pipeline_stable_diffusion_xl_instruct_pix2pix import StableDiffusionXLInstructPix2PixPipeline
diff --git a/spaces/Andy1621/uniformer_image_demo/imagenet_class_index.py b/spaces/Andy1621/uniformer_image_demo/imagenet_class_index.py
deleted file mode 100644
index 5407d1471197fd9bfa466c47d8dfb6683cb9f551..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_demo/imagenet_class_index.py
+++ /dev/null
@@ -1,1002 +0,0 @@
-imagenet_classnames = {
- "0": ["n01440764", "tench"],
- "1": ["n01443537", "goldfish"],
- "2": ["n01484850", "great_white_shark"],
- "3": ["n01491361", "tiger_shark"],
- "4": ["n01494475", "hammerhead"],
- "5": ["n01496331", "electric_ray"],
- "6": ["n01498041", "stingray"],
- "7": ["n01514668", "cock"],
- "8": ["n01514859", "hen"],
- "9": ["n01518878", "ostrich"],
- "10": ["n01530575", "brambling"],
- "11": ["n01531178", "goldfinch"],
- "12": ["n01532829", "house_finch"],
- "13": ["n01534433", "junco"],
- "14": ["n01537544", "indigo_bunting"],
- "15": ["n01558993", "robin"],
- "16": ["n01560419", "bulbul"],
- "17": ["n01580077", "jay"],
- "18": ["n01582220", "magpie"],
- "19": ["n01592084", "chickadee"],
- "20": ["n01601694", "water_ouzel"],
- "21": ["n01608432", "kite"],
- "22": ["n01614925", "bald_eagle"],
- "23": ["n01616318", "vulture"],
- "24": ["n01622779", "great_grey_owl"],
- "25": ["n01629819", "European_fire_salamander"],
- "26": ["n01630670", "common_newt"],
- "27": ["n01631663", "eft"],
- "28": ["n01632458", "spotted_salamander"],
- "29": ["n01632777", "axolotl"],
- "30": ["n01641577", "bullfrog"],
- "31": ["n01644373", "tree_frog"],
- "32": ["n01644900", "tailed_frog"],
- "33": ["n01664065", "loggerhead"],
- "34": ["n01665541", "leatherback_turtle"],
- "35": ["n01667114", "mud_turtle"],
- "36": ["n01667778", "terrapin"],
- "37": ["n01669191", "box_turtle"],
- "38": ["n01675722", "banded_gecko"],
- "39": ["n01677366", "common_iguana"],
- "40": ["n01682714", "American_chameleon"],
- "41": ["n01685808", "whiptail"],
- "42": ["n01687978", "agama"],
- "43": ["n01688243", "frilled_lizard"],
- "44": ["n01689811", "alligator_lizard"],
- "45": ["n01692333", "Gila_monster"],
- "46": ["n01693334", "green_lizard"],
- "47": ["n01694178", "African_chameleon"],
- "48": ["n01695060", "Komodo_dragon"],
- "49": ["n01697457", "African_crocodile"],
- "50": ["n01698640", "American_alligator"],
- "51": ["n01704323", "triceratops"],
- "52": ["n01728572", "thunder_snake"],
- "53": ["n01728920", "ringneck_snake"],
- "54": ["n01729322", "hognose_snake"],
- "55": ["n01729977", "green_snake"],
- "56": ["n01734418", "king_snake"],
- "57": ["n01735189", "garter_snake"],
- "58": ["n01737021", "water_snake"],
- "59": ["n01739381", "vine_snake"],
- "60": ["n01740131", "night_snake"],
- "61": ["n01742172", "boa_constrictor"],
- "62": ["n01744401", "rock_python"],
- "63": ["n01748264", "Indian_cobra"],
- "64": ["n01749939", "green_mamba"],
- "65": ["n01751748", "sea_snake"],
- "66": ["n01753488", "horned_viper"],
- "67": ["n01755581", "diamondback"],
- "68": ["n01756291", "sidewinder"],
- "69": ["n01768244", "trilobite"],
- "70": ["n01770081", "harvestman"],
- "71": ["n01770393", "scorpion"],
- "72": ["n01773157", "black_and_gold_garden_spider"],
- "73": ["n01773549", "barn_spider"],
- "74": ["n01773797", "garden_spider"],
- "75": ["n01774384", "black_widow"],
- "76": ["n01774750", "tarantula"],
- "77": ["n01775062", "wolf_spider"],
- "78": ["n01776313", "tick"],
- "79": ["n01784675", "centipede"],
- "80": ["n01795545", "black_grouse"],
- "81": ["n01796340", "ptarmigan"],
- "82": ["n01797886", "ruffed_grouse"],
- "83": ["n01798484", "prairie_chicken"],
- "84": ["n01806143", "peacock"],
- "85": ["n01806567", "quail"],
- "86": ["n01807496", "partridge"],
- "87": ["n01817953", "African_grey"],
- "88": ["n01818515", "macaw"],
- "89": ["n01819313", "sulphur-crested_cockatoo"],
- "90": ["n01820546", "lorikeet"],
- "91": ["n01824575", "coucal"],
- "92": ["n01828970", "bee_eater"],
- "93": ["n01829413", "hornbill"],
- "94": ["n01833805", "hummingbird"],
- "95": ["n01843065", "jacamar"],
- "96": ["n01843383", "toucan"],
- "97": ["n01847000", "drake"],
- "98": ["n01855032", "red-breasted_merganser"],
- "99": ["n01855672", "goose"],
- "100": ["n01860187", "black_swan"],
- "101": ["n01871265", "tusker"],
- "102": ["n01872401", "echidna"],
- "103": ["n01873310", "platypus"],
- "104": ["n01877812", "wallaby"],
- "105": ["n01882714", "koala"],
- "106": ["n01883070", "wombat"],
- "107": ["n01910747", "jellyfish"],
- "108": ["n01914609", "sea_anemone"],
- "109": ["n01917289", "brain_coral"],
- "110": ["n01924916", "flatworm"],
- "111": ["n01930112", "nematode"],
- "112": ["n01943899", "conch"],
- "113": ["n01944390", "snail"],
- "114": ["n01945685", "slug"],
- "115": ["n01950731", "sea_slug"],
- "116": ["n01955084", "chiton"],
- "117": ["n01968897", "chambered_nautilus"],
- "118": ["n01978287", "Dungeness_crab"],
- "119": ["n01978455", "rock_crab"],
- "120": ["n01980166", "fiddler_crab"],
- "121": ["n01981276", "king_crab"],
- "122": ["n01983481", "American_lobster"],
- "123": ["n01984695", "spiny_lobster"],
- "124": ["n01985128", "crayfish"],
- "125": ["n01986214", "hermit_crab"],
- "126": ["n01990800", "isopod"],
- "127": ["n02002556", "white_stork"],
- "128": ["n02002724", "black_stork"],
- "129": ["n02006656", "spoonbill"],
- "130": ["n02007558", "flamingo"],
- "131": ["n02009229", "little_blue_heron"],
- "132": ["n02009912", "American_egret"],
- "133": ["n02011460", "bittern"],
- "134": ["n02012849", "crane"],
- "135": ["n02013706", "limpkin"],
- "136": ["n02017213", "European_gallinule"],
- "137": ["n02018207", "American_coot"],
- "138": ["n02018795", "bustard"],
- "139": ["n02025239", "ruddy_turnstone"],
- "140": ["n02027492", "red-backed_sandpiper"],
- "141": ["n02028035", "redshank"],
- "142": ["n02033041", "dowitcher"],
- "143": ["n02037110", "oystercatcher"],
- "144": ["n02051845", "pelican"],
- "145": ["n02056570", "king_penguin"],
- "146": ["n02058221", "albatross"],
- "147": ["n02066245", "grey_whale"],
- "148": ["n02071294", "killer_whale"],
- "149": ["n02074367", "dugong"],
- "150": ["n02077923", "sea_lion"],
- "151": ["n02085620", "Chihuahua"],
- "152": ["n02085782", "Japanese_spaniel"],
- "153": ["n02085936", "Maltese_dog"],
- "154": ["n02086079", "Pekinese"],
- "155": ["n02086240", "Shih-Tzu"],
- "156": ["n02086646", "Blenheim_spaniel"],
- "157": ["n02086910", "papillon"],
- "158": ["n02087046", "toy_terrier"],
- "159": ["n02087394", "Rhodesian_ridgeback"],
- "160": ["n02088094", "Afghan_hound"],
- "161": ["n02088238", "basset"],
- "162": ["n02088364", "beagle"],
- "163": ["n02088466", "bloodhound"],
- "164": ["n02088632", "bluetick"],
- "165": ["n02089078", "black-and-tan_coonhound"],
- "166": ["n02089867", "Walker_hound"],
- "167": ["n02089973", "English_foxhound"],
- "168": ["n02090379", "redbone"],
- "169": ["n02090622", "borzoi"],
- "170": ["n02090721", "Irish_wolfhound"],
- "171": ["n02091032", "Italian_greyhound"],
- "172": ["n02091134", "whippet"],
- "173": ["n02091244", "Ibizan_hound"],
- "174": ["n02091467", "Norwegian_elkhound"],
- "175": ["n02091635", "otterhound"],
- "176": ["n02091831", "Saluki"],
- "177": ["n02092002", "Scottish_deerhound"],
- "178": ["n02092339", "Weimaraner"],
- "179": ["n02093256", "Staffordshire_bullterrier"],
- "180": ["n02093428", "American_Staffordshire_terrier"],
- "181": ["n02093647", "Bedlington_terrier"],
- "182": ["n02093754", "Border_terrier"],
- "183": ["n02093859", "Kerry_blue_terrier"],
- "184": ["n02093991", "Irish_terrier"],
- "185": ["n02094114", "Norfolk_terrier"],
- "186": ["n02094258", "Norwich_terrier"],
- "187": ["n02094433", "Yorkshire_terrier"],
- "188": ["n02095314", "wire-haired_fox_terrier"],
- "189": ["n02095570", "Lakeland_terrier"],
- "190": ["n02095889", "Sealyham_terrier"],
- "191": ["n02096051", "Airedale"],
- "192": ["n02096177", "cairn"],
- "193": ["n02096294", "Australian_terrier"],
- "194": ["n02096437", "Dandie_Dinmont"],
- "195": ["n02096585", "Boston_bull"],
- "196": ["n02097047", "miniature_schnauzer"],
- "197": ["n02097130", "giant_schnauzer"],
- "198": ["n02097209", "standard_schnauzer"],
- "199": ["n02097298", "Scotch_terrier"],
- "200": ["n02097474", "Tibetan_terrier"],
- "201": ["n02097658", "silky_terrier"],
- "202": ["n02098105", "soft-coated_wheaten_terrier"],
- "203": ["n02098286", "West_Highland_white_terrier"],
- "204": ["n02098413", "Lhasa"],
- "205": ["n02099267", "flat-coated_retriever"],
- "206": ["n02099429", "curly-coated_retriever"],
- "207": ["n02099601", "golden_retriever"],
- "208": ["n02099712", "Labrador_retriever"],
- "209": ["n02099849", "Chesapeake_Bay_retriever"],
- "210": ["n02100236", "German_short-haired_pointer"],
- "211": ["n02100583", "vizsla"],
- "212": ["n02100735", "English_setter"],
- "213": ["n02100877", "Irish_setter"],
- "214": ["n02101006", "Gordon_setter"],
- "215": ["n02101388", "Brittany_spaniel"],
- "216": ["n02101556", "clumber"],
- "217": ["n02102040", "English_springer"],
- "218": ["n02102177", "Welsh_springer_spaniel"],
- "219": ["n02102318", "cocker_spaniel"],
- "220": ["n02102480", "Sussex_spaniel"],
- "221": ["n02102973", "Irish_water_spaniel"],
- "222": ["n02104029", "kuvasz"],
- "223": ["n02104365", "schipperke"],
- "224": ["n02105056", "groenendael"],
- "225": ["n02105162", "malinois"],
- "226": ["n02105251", "briard"],
- "227": ["n02105412", "kelpie"],
- "228": ["n02105505", "komondor"],
- "229": ["n02105641", "Old_English_sheepdog"],
- "230": ["n02105855", "Shetland_sheepdog"],
- "231": ["n02106030", "collie"],
- "232": ["n02106166", "Border_collie"],
- "233": ["n02106382", "Bouvier_des_Flandres"],
- "234": ["n02106550", "Rottweiler"],
- "235": ["n02106662", "German_shepherd"],
- "236": ["n02107142", "Doberman"],
- "237": ["n02107312", "miniature_pinscher"],
- "238": ["n02107574", "Greater_Swiss_Mountain_dog"],
- "239": ["n02107683", "Bernese_mountain_dog"],
- "240": ["n02107908", "Appenzeller"],
- "241": ["n02108000", "EntleBucher"],
- "242": ["n02108089", "boxer"],
- "243": ["n02108422", "bull_mastiff"],
- "244": ["n02108551", "Tibetan_mastiff"],
- "245": ["n02108915", "French_bulldog"],
- "246": ["n02109047", "Great_Dane"],
- "247": ["n02109525", "Saint_Bernard"],
- "248": ["n02109961", "Eskimo_dog"],
- "249": ["n02110063", "malamute"],
- "250": ["n02110185", "Siberian_husky"],
- "251": ["n02110341", "dalmatian"],
- "252": ["n02110627", "affenpinscher"],
- "253": ["n02110806", "basenji"],
- "254": ["n02110958", "pug"],
- "255": ["n02111129", "Leonberg"],
- "256": ["n02111277", "Newfoundland"],
- "257": ["n02111500", "Great_Pyrenees"],
- "258": ["n02111889", "Samoyed"],
- "259": ["n02112018", "Pomeranian"],
- "260": ["n02112137", "chow"],
- "261": ["n02112350", "keeshond"],
- "262": ["n02112706", "Brabancon_griffon"],
- "263": ["n02113023", "Pembroke"],
- "264": ["n02113186", "Cardigan"],
- "265": ["n02113624", "toy_poodle"],
- "266": ["n02113712", "miniature_poodle"],
- "267": ["n02113799", "standard_poodle"],
- "268": ["n02113978", "Mexican_hairless"],
- "269": ["n02114367", "timber_wolf"],
- "270": ["n02114548", "white_wolf"],
- "271": ["n02114712", "red_wolf"],
- "272": ["n02114855", "coyote"],
- "273": ["n02115641", "dingo"],
- "274": ["n02115913", "dhole"],
- "275": ["n02116738", "African_hunting_dog"],
- "276": ["n02117135", "hyena"],
- "277": ["n02119022", "red_fox"],
- "278": ["n02119789", "kit_fox"],
- "279": ["n02120079", "Arctic_fox"],
- "280": ["n02120505", "grey_fox"],
- "281": ["n02123045", "tabby"],
- "282": ["n02123159", "tiger_cat"],
- "283": ["n02123394", "Persian_cat"],
- "284": ["n02123597", "Siamese_cat"],
- "285": ["n02124075", "Egyptian_cat"],
- "286": ["n02125311", "cougar"],
- "287": ["n02127052", "lynx"],
- "288": ["n02128385", "leopard"],
- "289": ["n02128757", "snow_leopard"],
- "290": ["n02128925", "jaguar"],
- "291": ["n02129165", "lion"],
- "292": ["n02129604", "tiger"],
- "293": ["n02130308", "cheetah"],
- "294": ["n02132136", "brown_bear"],
- "295": ["n02133161", "American_black_bear"],
- "296": ["n02134084", "ice_bear"],
- "297": ["n02134418", "sloth_bear"],
- "298": ["n02137549", "mongoose"],
- "299": ["n02138441", "meerkat"],
- "300": ["n02165105", "tiger_beetle"],
- "301": ["n02165456", "ladybug"],
- "302": ["n02167151", "ground_beetle"],
- "303": ["n02168699", "long-horned_beetle"],
- "304": ["n02169497", "leaf_beetle"],
- "305": ["n02172182", "dung_beetle"],
- "306": ["n02174001", "rhinoceros_beetle"],
- "307": ["n02177972", "weevil"],
- "308": ["n02190166", "fly"],
- "309": ["n02206856", "bee"],
- "310": ["n02219486", "ant"],
- "311": ["n02226429", "grasshopper"],
- "312": ["n02229544", "cricket"],
- "313": ["n02231487", "walking_stick"],
- "314": ["n02233338", "cockroach"],
- "315": ["n02236044", "mantis"],
- "316": ["n02256656", "cicada"],
- "317": ["n02259212", "leafhopper"],
- "318": ["n02264363", "lacewing"],
- "319": ["n02268443", "dragonfly"],
- "320": ["n02268853", "damselfly"],
- "321": ["n02276258", "admiral"],
- "322": ["n02277742", "ringlet"],
- "323": ["n02279972", "monarch"],
- "324": ["n02280649", "cabbage_butterfly"],
- "325": ["n02281406", "sulphur_butterfly"],
- "326": ["n02281787", "lycaenid"],
- "327": ["n02317335", "starfish"],
- "328": ["n02319095", "sea_urchin"],
- "329": ["n02321529", "sea_cucumber"],
- "330": ["n02325366", "wood_rabbit"],
- "331": ["n02326432", "hare"],
- "332": ["n02328150", "Angora"],
- "333": ["n02342885", "hamster"],
- "334": ["n02346627", "porcupine"],
- "335": ["n02356798", "fox_squirrel"],
- "336": ["n02361337", "marmot"],
- "337": ["n02363005", "beaver"],
- "338": ["n02364673", "guinea_pig"],
- "339": ["n02389026", "sorrel"],
- "340": ["n02391049", "zebra"],
- "341": ["n02395406", "hog"],
- "342": ["n02396427", "wild_boar"],
- "343": ["n02397096", "warthog"],
- "344": ["n02398521", "hippopotamus"],
- "345": ["n02403003", "ox"],
- "346": ["n02408429", "water_buffalo"],
- "347": ["n02410509", "bison"],
- "348": ["n02412080", "ram"],
- "349": ["n02415577", "bighorn"],
- "350": ["n02417914", "ibex"],
- "351": ["n02422106", "hartebeest"],
- "352": ["n02422699", "impala"],
- "353": ["n02423022", "gazelle"],
- "354": ["n02437312", "Arabian_camel"],
- "355": ["n02437616", "llama"],
- "356": ["n02441942", "weasel"],
- "357": ["n02442845", "mink"],
- "358": ["n02443114", "polecat"],
- "359": ["n02443484", "black-footed_ferret"],
- "360": ["n02444819", "otter"],
- "361": ["n02445715", "skunk"],
- "362": ["n02447366", "badger"],
- "363": ["n02454379", "armadillo"],
- "364": ["n02457408", "three-toed_sloth"],
- "365": ["n02480495", "orangutan"],
- "366": ["n02480855", "gorilla"],
- "367": ["n02481823", "chimpanzee"],
- "368": ["n02483362", "gibbon"],
- "369": ["n02483708", "siamang"],
- "370": ["n02484975", "guenon"],
- "371": ["n02486261", "patas"],
- "372": ["n02486410", "baboon"],
- "373": ["n02487347", "macaque"],
- "374": ["n02488291", "langur"],
- "375": ["n02488702", "colobus"],
- "376": ["n02489166", "proboscis_monkey"],
- "377": ["n02490219", "marmoset"],
- "378": ["n02492035", "capuchin"],
- "379": ["n02492660", "howler_monkey"],
- "380": ["n02493509", "titi"],
- "381": ["n02493793", "spider_monkey"],
- "382": ["n02494079", "squirrel_monkey"],
- "383": ["n02497673", "Madagascar_cat"],
- "384": ["n02500267", "indri"],
- "385": ["n02504013", "Indian_elephant"],
- "386": ["n02504458", "African_elephant"],
- "387": ["n02509815", "lesser_panda"],
- "388": ["n02510455", "giant_panda"],
- "389": ["n02514041", "barracouta"],
- "390": ["n02526121", "eel"],
- "391": ["n02536864", "coho"],
- "392": ["n02606052", "rock_beauty"],
- "393": ["n02607072", "anemone_fish"],
- "394": ["n02640242", "sturgeon"],
- "395": ["n02641379", "gar"],
- "396": ["n02643566", "lionfish"],
- "397": ["n02655020", "puffer"],
- "398": ["n02666196", "abacus"],
- "399": ["n02667093", "abaya"],
- "400": ["n02669723", "academic_gown"],
- "401": ["n02672831", "accordion"],
- "402": ["n02676566", "acoustic_guitar"],
- "403": ["n02687172", "aircraft_carrier"],
- "404": ["n02690373", "airliner"],
- "405": ["n02692877", "airship"],
- "406": ["n02699494", "altar"],
- "407": ["n02701002", "ambulance"],
- "408": ["n02704792", "amphibian"],
- "409": ["n02708093", "analog_clock"],
- "410": ["n02727426", "apiary"],
- "411": ["n02730930", "apron"],
- "412": ["n02747177", "ashcan"],
- "413": ["n02749479", "assault_rifle"],
- "414": ["n02769748", "backpack"],
- "415": ["n02776631", "bakery"],
- "416": ["n02777292", "balance_beam"],
- "417": ["n02782093", "balloon"],
- "418": ["n02783161", "ballpoint"],
- "419": ["n02786058", "Band_Aid"],
- "420": ["n02787622", "banjo"],
- "421": ["n02788148", "bannister"],
- "422": ["n02790996", "barbell"],
- "423": ["n02791124", "barber_chair"],
- "424": ["n02791270", "barbershop"],
- "425": ["n02793495", "barn"],
- "426": ["n02794156", "barometer"],
- "427": ["n02795169", "barrel"],
- "428": ["n02797295", "barrow"],
- "429": ["n02799071", "baseball"],
- "430": ["n02802426", "basketball"],
- "431": ["n02804414", "bassinet"],
- "432": ["n02804610", "bassoon"],
- "433": ["n02807133", "bathing_cap"],
- "434": ["n02808304", "bath_towel"],
- "435": ["n02808440", "bathtub"],
- "436": ["n02814533", "beach_wagon"],
- "437": ["n02814860", "beacon"],
- "438": ["n02815834", "beaker"],
- "439": ["n02817516", "bearskin"],
- "440": ["n02823428", "beer_bottle"],
- "441": ["n02823750", "beer_glass"],
- "442": ["n02825657", "bell_cote"],
- "443": ["n02834397", "bib"],
- "444": ["n02835271", "bicycle-built-for-two"],
- "445": ["n02837789", "bikini"],
- "446": ["n02840245", "binder"],
- "447": ["n02841315", "binoculars"],
- "448": ["n02843684", "birdhouse"],
- "449": ["n02859443", "boathouse"],
- "450": ["n02860847", "bobsled"],
- "451": ["n02865351", "bolo_tie"],
- "452": ["n02869837", "bonnet"],
- "453": ["n02870880", "bookcase"],
- "454": ["n02871525", "bookshop"],
- "455": ["n02877765", "bottlecap"],
- "456": ["n02879718", "bow"],
- "457": ["n02883205", "bow_tie"],
- "458": ["n02892201", "brass"],
- "459": ["n02892767", "brassiere"],
- "460": ["n02894605", "breakwater"],
- "461": ["n02895154", "breastplate"],
- "462": ["n02906734", "broom"],
- "463": ["n02909870", "bucket"],
- "464": ["n02910353", "buckle"],
- "465": ["n02916936", "bulletproof_vest"],
- "466": ["n02917067", "bullet_train"],
- "467": ["n02927161", "butcher_shop"],
- "468": ["n02930766", "cab"],
- "469": ["n02939185", "caldron"],
- "470": ["n02948072", "candle"],
- "471": ["n02950826", "cannon"],
- "472": ["n02951358", "canoe"],
- "473": ["n02951585", "can_opener"],
- "474": ["n02963159", "cardigan"],
- "475": ["n02965783", "car_mirror"],
- "476": ["n02966193", "carousel"],
- "477": ["n02966687", "carpenter's_kit"],
- "478": ["n02971356", "carton"],
- "479": ["n02974003", "car_wheel"],
- "480": ["n02977058", "cash_machine"],
- "481": ["n02978881", "cassette"],
- "482": ["n02979186", "cassette_player"],
- "483": ["n02980441", "castle"],
- "484": ["n02981792", "catamaran"],
- "485": ["n02988304", "CD_player"],
- "486": ["n02992211", "cello"],
- "487": ["n02992529", "cellular_telephone"],
- "488": ["n02999410", "chain"],
- "489": ["n03000134", "chainlink_fence"],
- "490": ["n03000247", "chain_mail"],
- "491": ["n03000684", "chain_saw"],
- "492": ["n03014705", "chest"],
- "493": ["n03016953", "chiffonier"],
- "494": ["n03017168", "chime"],
- "495": ["n03018349", "china_cabinet"],
- "496": ["n03026506", "Christmas_stocking"],
- "497": ["n03028079", "church"],
- "498": ["n03032252", "cinema"],
- "499": ["n03041632", "cleaver"],
- "500": ["n03042490", "cliff_dwelling"],
- "501": ["n03045698", "cloak"],
- "502": ["n03047690", "clog"],
- "503": ["n03062245", "cocktail_shaker"],
- "504": ["n03063599", "coffee_mug"],
- "505": ["n03063689", "coffeepot"],
- "506": ["n03065424", "coil"],
- "507": ["n03075370", "combination_lock"],
- "508": ["n03085013", "computer_keyboard"],
- "509": ["n03089624", "confectionery"],
- "510": ["n03095699", "container_ship"],
- "511": ["n03100240", "convertible"],
- "512": ["n03109150", "corkscrew"],
- "513": ["n03110669", "cornet"],
- "514": ["n03124043", "cowboy_boot"],
- "515": ["n03124170", "cowboy_hat"],
- "516": ["n03125729", "cradle"],
- "517": ["n03126707", "crane"],
- "518": ["n03127747", "crash_helmet"],
- "519": ["n03127925", "crate"],
- "520": ["n03131574", "crib"],
- "521": ["n03133878", "Crock_Pot"],
- "522": ["n03134739", "croquet_ball"],
- "523": ["n03141823", "crutch"],
- "524": ["n03146219", "cuirass"],
- "525": ["n03160309", "dam"],
- "526": ["n03179701", "desk"],
- "527": ["n03180011", "desktop_computer"],
- "528": ["n03187595", "dial_telephone"],
- "529": ["n03188531", "diaper"],
- "530": ["n03196217", "digital_clock"],
- "531": ["n03197337", "digital_watch"],
- "532": ["n03201208", "dining_table"],
- "533": ["n03207743", "dishrag"],
- "534": ["n03207941", "dishwasher"],
- "535": ["n03208938", "disk_brake"],
- "536": ["n03216828", "dock"],
- "537": ["n03218198", "dogsled"],
- "538": ["n03220513", "dome"],
- "539": ["n03223299", "doormat"],
- "540": ["n03240683", "drilling_platform"],
- "541": ["n03249569", "drum"],
- "542": ["n03250847", "drumstick"],
- "543": ["n03255030", "dumbbell"],
- "544": ["n03259280", "Dutch_oven"],
- "545": ["n03271574", "electric_fan"],
- "546": ["n03272010", "electric_guitar"],
- "547": ["n03272562", "electric_locomotive"],
- "548": ["n03290653", "entertainment_center"],
- "549": ["n03291819", "envelope"],
- "550": ["n03297495", "espresso_maker"],
- "551": ["n03314780", "face_powder"],
- "552": ["n03325584", "feather_boa"],
- "553": ["n03337140", "file"],
- "554": ["n03344393", "fireboat"],
- "555": ["n03345487", "fire_engine"],
- "556": ["n03347037", "fire_screen"],
- "557": ["n03355925", "flagpole"],
- "558": ["n03372029", "flute"],
- "559": ["n03376595", "folding_chair"],
- "560": ["n03379051", "football_helmet"],
- "561": ["n03384352", "forklift"],
- "562": ["n03388043", "fountain"],
- "563": ["n03388183", "fountain_pen"],
- "564": ["n03388549", "four-poster"],
- "565": ["n03393912", "freight_car"],
- "566": ["n03394916", "French_horn"],
- "567": ["n03400231", "frying_pan"],
- "568": ["n03404251", "fur_coat"],
- "569": ["n03417042", "garbage_truck"],
- "570": ["n03424325", "gasmask"],
- "571": ["n03425413", "gas_pump"],
- "572": ["n03443371", "goblet"],
- "573": ["n03444034", "go-kart"],
- "574": ["n03445777", "golf_ball"],
- "575": ["n03445924", "golfcart"],
- "576": ["n03447447", "gondola"],
- "577": ["n03447721", "gong"],
- "578": ["n03450230", "gown"],
- "579": ["n03452741", "grand_piano"],
- "580": ["n03457902", "greenhouse"],
- "581": ["n03459775", "grille"],
- "582": ["n03461385", "grocery_store"],
- "583": ["n03467068", "guillotine"],
- "584": ["n03476684", "hair_slide"],
- "585": ["n03476991", "hair_spray"],
- "586": ["n03478589", "half_track"],
- "587": ["n03481172", "hammer"],
- "588": ["n03482405", "hamper"],
- "589": ["n03483316", "hand_blower"],
- "590": ["n03485407", "hand-held_computer"],
- "591": ["n03485794", "handkerchief"],
- "592": ["n03492542", "hard_disc"],
- "593": ["n03494278", "harmonica"],
- "594": ["n03495258", "harp"],
- "595": ["n03496892", "harvester"],
- "596": ["n03498962", "hatchet"],
- "597": ["n03527444", "holster"],
- "598": ["n03529860", "home_theater"],
- "599": ["n03530642", "honeycomb"],
- "600": ["n03532672", "hook"],
- "601": ["n03534580", "hoopskirt"],
- "602": ["n03535780", "horizontal_bar"],
- "603": ["n03538406", "horse_cart"],
- "604": ["n03544143", "hourglass"],
- "605": ["n03584254", "iPod"],
- "606": ["n03584829", "iron"],
- "607": ["n03590841", "jack-o'-lantern"],
- "608": ["n03594734", "jean"],
- "609": ["n03594945", "jeep"],
- "610": ["n03595614", "jersey"],
- "611": ["n03598930", "jigsaw_puzzle"],
- "612": ["n03599486", "jinrikisha"],
- "613": ["n03602883", "joystick"],
- "614": ["n03617480", "kimono"],
- "615": ["n03623198", "knee_pad"],
- "616": ["n03627232", "knot"],
- "617": ["n03630383", "lab_coat"],
- "618": ["n03633091", "ladle"],
- "619": ["n03637318", "lampshade"],
- "620": ["n03642806", "laptop"],
- "621": ["n03649909", "lawn_mower"],
- "622": ["n03657121", "lens_cap"],
- "623": ["n03658185", "letter_opener"],
- "624": ["n03661043", "library"],
- "625": ["n03662601", "lifeboat"],
- "626": ["n03666591", "lighter"],
- "627": ["n03670208", "limousine"],
- "628": ["n03673027", "liner"],
- "629": ["n03676483", "lipstick"],
- "630": ["n03680355", "Loafer"],
- "631": ["n03690938", "lotion"],
- "632": ["n03691459", "loudspeaker"],
- "633": ["n03692522", "loupe"],
- "634": ["n03697007", "lumbermill"],
- "635": ["n03706229", "magnetic_compass"],
- "636": ["n03709823", "mailbag"],
- "637": ["n03710193", "mailbox"],
- "638": ["n03710637", "maillot"],
- "639": ["n03710721", "maillot"],
- "640": ["n03717622", "manhole_cover"],
- "641": ["n03720891", "maraca"],
- "642": ["n03721384", "marimba"],
- "643": ["n03724870", "mask"],
- "644": ["n03729826", "matchstick"],
- "645": ["n03733131", "maypole"],
- "646": ["n03733281", "maze"],
- "647": ["n03733805", "measuring_cup"],
- "648": ["n03742115", "medicine_chest"],
- "649": ["n03743016", "megalith"],
- "650": ["n03759954", "microphone"],
- "651": ["n03761084", "microwave"],
- "652": ["n03763968", "military_uniform"],
- "653": ["n03764736", "milk_can"],
- "654": ["n03769881", "minibus"],
- "655": ["n03770439", "miniskirt"],
- "656": ["n03770679", "minivan"],
- "657": ["n03773504", "missile"],
- "658": ["n03775071", "mitten"],
- "659": ["n03775546", "mixing_bowl"],
- "660": ["n03776460", "mobile_home"],
- "661": ["n03777568", "Model_T"],
- "662": ["n03777754", "modem"],
- "663": ["n03781244", "monastery"],
- "664": ["n03782006", "monitor"],
- "665": ["n03785016", "moped"],
- "666": ["n03786901", "mortar"],
- "667": ["n03787032", "mortarboard"],
- "668": ["n03788195", "mosque"],
- "669": ["n03788365", "mosquito_net"],
- "670": ["n03791053", "motor_scooter"],
- "671": ["n03792782", "mountain_bike"],
- "672": ["n03792972", "mountain_tent"],
- "673": ["n03793489", "mouse"],
- "674": ["n03794056", "mousetrap"],
- "675": ["n03796401", "moving_van"],
- "676": ["n03803284", "muzzle"],
- "677": ["n03804744", "nail"],
- "678": ["n03814639", "neck_brace"],
- "679": ["n03814906", "necklace"],
- "680": ["n03825788", "nipple"],
- "681": ["n03832673", "notebook"],
- "682": ["n03837869", "obelisk"],
- "683": ["n03838899", "oboe"],
- "684": ["n03840681", "ocarina"],
- "685": ["n03841143", "odometer"],
- "686": ["n03843555", "oil_filter"],
- "687": ["n03854065", "organ"],
- "688": ["n03857828", "oscilloscope"],
- "689": ["n03866082", "overskirt"],
- "690": ["n03868242", "oxcart"],
- "691": ["n03868863", "oxygen_mask"],
- "692": ["n03871628", "packet"],
- "693": ["n03873416", "paddle"],
- "694": ["n03874293", "paddlewheel"],
- "695": ["n03874599", "padlock"],
- "696": ["n03876231", "paintbrush"],
- "697": ["n03877472", "pajama"],
- "698": ["n03877845", "palace"],
- "699": ["n03884397", "panpipe"],
- "700": ["n03887697", "paper_towel"],
- "701": ["n03888257", "parachute"],
- "702": ["n03888605", "parallel_bars"],
- "703": ["n03891251", "park_bench"],
- "704": ["n03891332", "parking_meter"],
- "705": ["n03895866", "passenger_car"],
- "706": ["n03899768", "patio"],
- "707": ["n03902125", "pay-phone"],
- "708": ["n03903868", "pedestal"],
- "709": ["n03908618", "pencil_box"],
- "710": ["n03908714", "pencil_sharpener"],
- "711": ["n03916031", "perfume"],
- "712": ["n03920288", "Petri_dish"],
- "713": ["n03924679", "photocopier"],
- "714": ["n03929660", "pick"],
- "715": ["n03929855", "pickelhaube"],
- "716": ["n03930313", "picket_fence"],
- "717": ["n03930630", "pickup"],
- "718": ["n03933933", "pier"],
- "719": ["n03935335", "piggy_bank"],
- "720": ["n03937543", "pill_bottle"],
- "721": ["n03938244", "pillow"],
- "722": ["n03942813", "ping-pong_ball"],
- "723": ["n03944341", "pinwheel"],
- "724": ["n03947888", "pirate"],
- "725": ["n03950228", "pitcher"],
- "726": ["n03954731", "plane"],
- "727": ["n03956157", "planetarium"],
- "728": ["n03958227", "plastic_bag"],
- "729": ["n03961711", "plate_rack"],
- "730": ["n03967562", "plow"],
- "731": ["n03970156", "plunger"],
- "732": ["n03976467", "Polaroid_camera"],
- "733": ["n03976657", "pole"],
- "734": ["n03977966", "police_van"],
- "735": ["n03980874", "poncho"],
- "736": ["n03982430", "pool_table"],
- "737": ["n03983396", "pop_bottle"],
- "738": ["n03991062", "pot"],
- "739": ["n03992509", "potter's_wheel"],
- "740": ["n03995372", "power_drill"],
- "741": ["n03998194", "prayer_rug"],
- "742": ["n04004767", "printer"],
- "743": ["n04005630", "prison"],
- "744": ["n04008634", "projectile"],
- "745": ["n04009552", "projector"],
- "746": ["n04019541", "puck"],
- "747": ["n04023962", "punching_bag"],
- "748": ["n04026417", "purse"],
- "749": ["n04033901", "quill"],
- "750": ["n04033995", "quilt"],
- "751": ["n04037443", "racer"],
- "752": ["n04039381", "racket"],
- "753": ["n04040759", "radiator"],
- "754": ["n04041544", "radio"],
- "755": ["n04044716", "radio_telescope"],
- "756": ["n04049303", "rain_barrel"],
- "757": ["n04065272", "recreational_vehicle"],
- "758": ["n04067472", "reel"],
- "759": ["n04069434", "reflex_camera"],
- "760": ["n04070727", "refrigerator"],
- "761": ["n04074963", "remote_control"],
- "762": ["n04081281", "restaurant"],
- "763": ["n04086273", "revolver"],
- "764": ["n04090263", "rifle"],
- "765": ["n04099969", "rocking_chair"],
- "766": ["n04111531", "rotisserie"],
- "767": ["n04116512", "rubber_eraser"],
- "768": ["n04118538", "rugby_ball"],
- "769": ["n04118776", "rule"],
- "770": ["n04120489", "running_shoe"],
- "771": ["n04125021", "safe"],
- "772": ["n04127249", "safety_pin"],
- "773": ["n04131690", "saltshaker"],
- "774": ["n04133789", "sandal"],
- "775": ["n04136333", "sarong"],
- "776": ["n04141076", "sax"],
- "777": ["n04141327", "scabbard"],
- "778": ["n04141975", "scale"],
- "779": ["n04146614", "school_bus"],
- "780": ["n04147183", "schooner"],
- "781": ["n04149813", "scoreboard"],
- "782": ["n04152593", "screen"],
- "783": ["n04153751", "screw"],
- "784": ["n04154565", "screwdriver"],
- "785": ["n04162706", "seat_belt"],
- "786": ["n04179913", "sewing_machine"],
- "787": ["n04192698", "shield"],
- "788": ["n04200800", "shoe_shop"],
- "789": ["n04201297", "shoji"],
- "790": ["n04204238", "shopping_basket"],
- "791": ["n04204347", "shopping_cart"],
- "792": ["n04208210", "shovel"],
- "793": ["n04209133", "shower_cap"],
- "794": ["n04209239", "shower_curtain"],
- "795": ["n04228054", "ski"],
- "796": ["n04229816", "ski_mask"],
- "797": ["n04235860", "sleeping_bag"],
- "798": ["n04238763", "slide_rule"],
- "799": ["n04239074", "sliding_door"],
- "800": ["n04243546", "slot"],
- "801": ["n04251144", "snorkel"],
- "802": ["n04252077", "snowmobile"],
- "803": ["n04252225", "snowplow"],
- "804": ["n04254120", "soap_dispenser"],
- "805": ["n04254680", "soccer_ball"],
- "806": ["n04254777", "sock"],
- "807": ["n04258138", "solar_dish"],
- "808": ["n04259630", "sombrero"],
- "809": ["n04263257", "soup_bowl"],
- "810": ["n04264628", "space_bar"],
- "811": ["n04265275", "space_heater"],
- "812": ["n04266014", "space_shuttle"],
- "813": ["n04270147", "spatula"],
- "814": ["n04273569", "speedboat"],
- "815": ["n04275548", "spider_web"],
- "816": ["n04277352", "spindle"],
- "817": ["n04285008", "sports_car"],
- "818": ["n04286575", "spotlight"],
- "819": ["n04296562", "stage"],
- "820": ["n04310018", "steam_locomotive"],
- "821": ["n04311004", "steel_arch_bridge"],
- "822": ["n04311174", "steel_drum"],
- "823": ["n04317175", "stethoscope"],
- "824": ["n04325704", "stole"],
- "825": ["n04326547", "stone_wall"],
- "826": ["n04328186", "stopwatch"],
- "827": ["n04330267", "stove"],
- "828": ["n04332243", "strainer"],
- "829": ["n04335435", "streetcar"],
- "830": ["n04336792", "stretcher"],
- "831": ["n04344873", "studio_couch"],
- "832": ["n04346328", "stupa"],
- "833": ["n04347754", "submarine"],
- "834": ["n04350905", "suit"],
- "835": ["n04355338", "sundial"],
- "836": ["n04355933", "sunglass"],
- "837": ["n04356056", "sunglasses"],
- "838": ["n04357314", "sunscreen"],
- "839": ["n04366367", "suspension_bridge"],
- "840": ["n04367480", "swab"],
- "841": ["n04370456", "sweatshirt"],
- "842": ["n04371430", "swimming_trunks"],
- "843": ["n04371774", "swing"],
- "844": ["n04372370", "switch"],
- "845": ["n04376876", "syringe"],
- "846": ["n04380533", "table_lamp"],
- "847": ["n04389033", "tank"],
- "848": ["n04392985", "tape_player"],
- "849": ["n04398044", "teapot"],
- "850": ["n04399382", "teddy"],
- "851": ["n04404412", "television"],
- "852": ["n04409515", "tennis_ball"],
- "853": ["n04417672", "thatch"],
- "854": ["n04418357", "theater_curtain"],
- "855": ["n04423845", "thimble"],
- "856": ["n04428191", "thresher"],
- "857": ["n04429376", "throne"],
- "858": ["n04435653", "tile_roof"],
- "859": ["n04442312", "toaster"],
- "860": ["n04443257", "tobacco_shop"],
- "861": ["n04447861", "toilet_seat"],
- "862": ["n04456115", "torch"],
- "863": ["n04458633", "totem_pole"],
- "864": ["n04461696", "tow_truck"],
- "865": ["n04462240", "toyshop"],
- "866": ["n04465501", "tractor"],
- "867": ["n04467665", "trailer_truck"],
- "868": ["n04476259", "tray"],
- "869": ["n04479046", "trench_coat"],
- "870": ["n04482393", "tricycle"],
- "871": ["n04483307", "trimaran"],
- "872": ["n04485082", "tripod"],
- "873": ["n04486054", "triumphal_arch"],
- "874": ["n04487081", "trolleybus"],
- "875": ["n04487394", "trombone"],
- "876": ["n04493381", "tub"],
- "877": ["n04501370", "turnstile"],
- "878": ["n04505470", "typewriter_keyboard"],
- "879": ["n04507155", "umbrella"],
- "880": ["n04509417", "unicycle"],
- "881": ["n04515003", "upright"],
- "882": ["n04517823", "vacuum"],
- "883": ["n04522168", "vase"],
- "884": ["n04523525", "vault"],
- "885": ["n04525038", "velvet"],
- "886": ["n04525305", "vending_machine"],
- "887": ["n04532106", "vestment"],
- "888": ["n04532670", "viaduct"],
- "889": ["n04536866", "violin"],
- "890": ["n04540053", "volleyball"],
- "891": ["n04542943", "waffle_iron"],
- "892": ["n04548280", "wall_clock"],
- "893": ["n04548362", "wallet"],
- "894": ["n04550184", "wardrobe"],
- "895": ["n04552348", "warplane"],
- "896": ["n04553703", "washbasin"],
- "897": ["n04554684", "washer"],
- "898": ["n04557648", "water_bottle"],
- "899": ["n04560804", "water_jug"],
- "900": ["n04562935", "water_tower"],
- "901": ["n04579145", "whiskey_jug"],
- "902": ["n04579432", "whistle"],
- "903": ["n04584207", "wig"],
- "904": ["n04589890", "window_screen"],
- "905": ["n04590129", "window_shade"],
- "906": ["n04591157", "Windsor_tie"],
- "907": ["n04591713", "wine_bottle"],
- "908": ["n04592741", "wing"],
- "909": ["n04596742", "wok"],
- "910": ["n04597913", "wooden_spoon"],
- "911": ["n04599235", "wool"],
- "912": ["n04604644", "worm_fence"],
- "913": ["n04606251", "wreck"],
- "914": ["n04612504", "yawl"],
- "915": ["n04613696", "yurt"],
- "916": ["n06359193", "web_site"],
- "917": ["n06596364", "comic_book"],
- "918": ["n06785654", "crossword_puzzle"],
- "919": ["n06794110", "street_sign"],
- "920": ["n06874185", "traffic_light"],
- "921": ["n07248320", "book_jacket"],
- "922": ["n07565083", "menu"],
- "923": ["n07579787", "plate"],
- "924": ["n07583066", "guacamole"],
- "925": ["n07584110", "consomme"],
- "926": ["n07590611", "hot_pot"],
- "927": ["n07613480", "trifle"],
- "928": ["n07614500", "ice_cream"],
- "929": ["n07615774", "ice_lolly"],
- "930": ["n07684084", "French_loaf"],
- "931": ["n07693725", "bagel"],
- "932": ["n07695742", "pretzel"],
- "933": ["n07697313", "cheeseburger"],
- "934": ["n07697537", "hotdog"],
- "935": ["n07711569", "mashed_potato"],
- "936": ["n07714571", "head_cabbage"],
- "937": ["n07714990", "broccoli"],
- "938": ["n07715103", "cauliflower"],
- "939": ["n07716358", "zucchini"],
- "940": ["n07716906", "spaghetti_squash"],
- "941": ["n07717410", "acorn_squash"],
- "942": ["n07717556", "butternut_squash"],
- "943": ["n07718472", "cucumber"],
- "944": ["n07718747", "artichoke"],
- "945": ["n07720875", "bell_pepper"],
- "946": ["n07730033", "cardoon"],
- "947": ["n07734744", "mushroom"],
- "948": ["n07742313", "Granny_Smith"],
- "949": ["n07745940", "strawberry"],
- "950": ["n07747607", "orange"],
- "951": ["n07749582", "lemon"],
- "952": ["n07753113", "fig"],
- "953": ["n07753275", "pineapple"],
- "954": ["n07753592", "banana"],
- "955": ["n07754684", "jackfruit"],
- "956": ["n07760859", "custard_apple"],
- "957": ["n07768694", "pomegranate"],
- "958": ["n07802026", "hay"],
- "959": ["n07831146", "carbonara"],
- "960": ["n07836838", "chocolate_sauce"],
- "961": ["n07860988", "dough"],
- "962": ["n07871810", "meat_loaf"],
- "963": ["n07873807", "pizza"],
- "964": ["n07875152", "potpie"],
- "965": ["n07880968", "burrito"],
- "966": ["n07892512", "red_wine"],
- "967": ["n07920052", "espresso"],
- "968": ["n07930864", "cup"],
- "969": ["n07932039", "eggnog"],
- "970": ["n09193705", "alp"],
- "971": ["n09229709", "bubble"],
- "972": ["n09246464", "cliff"],
- "973": ["n09256479", "coral_reef"],
- "974": ["n09288635", "geyser"],
- "975": ["n09332890", "lakeside"],
- "976": ["n09399592", "promontory"],
- "977": ["n09421951", "sandbar"],
- "978": ["n09428293", "seashore"],
- "979": ["n09468604", "valley"],
- "980": ["n09472597", "volcano"],
- "981": ["n09835506", "ballplayer"],
- "982": ["n10148035", "groom"],
- "983": ["n10565667", "scuba_diver"],
- "984": ["n11879895", "rapeseed"],
- "985": ["n11939491", "daisy"],
- "986": ["n12057211", "yellow_lady's_slipper"],
- "987": ["n12144580", "corn"],
- "988": ["n12267677", "acorn"],
- "989": ["n12620546", "hip"],
- "990": ["n12768682", "buckeye"],
- "991": ["n12985857", "coral_fungus"],
- "992": ["n12998815", "agaric"],
- "993": ["n13037406", "gyromitra"],
- "994": ["n13040303", "stinkhorn"],
- "995": ["n13044778", "earthstar"],
- "996": ["n13052670", "hen-of-the-woods"],
- "997": ["n13054560", "bolete"],
- "998": ["n13133613", "ear"],
- "999": ["n15075141", "toilet_tissue"]
-}
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/app.py b/spaces/Andy1621/uniformer_image_detection/app.py
deleted file mode 100644
index 7428aaa14f8874fe96596b59da8e4a42fa56048f..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-
-import torch
-import torch.nn.functional as F
-import torchvision.transforms as T
-from mmdet.apis import init_detector, inference_detector, show_result_pyplot
-import mmcv
-
-import gradio as gr
-from huggingface_hub import hf_hub_download
-
-# Device on which to run the model
-# Set to cuda to load on GPU
-device = "cpu"
-checkpoint_file = hf_hub_download(repo_id="Andy1621/uniformer", filename="mask_rcnn_3x_ms_hybrid_small.pth")
-config_file = './exp/mask_rcnn_3x_ms_hybrid_small/config.py'
-# init detector
-# build the model from a config file and a checkpoint file
-model = init_detector(config_file, checkpoint_file, device='cpu')
-
-
-def set_example_image(example: list) -> dict:
- return gr.Image.update(value=example[0])
-
-
-def inference(img):
- result = inference_detector(model, img)
- res_img = show_result_pyplot(model, img, result)
- return res_img
-
-
-demo = gr.Blocks()
-with demo:
- gr.Markdown(
- """
- # UniFormer-S
- Gradio demo for UniFormer: To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.
- """
- )
-
- with gr.Box():
- with gr.Row():
- with gr.Column():
- with gr.Row():
- input_image = gr.Image(label='Input Image', type='numpy')
- with gr.Row():
- submit_button = gr.Button('Submit')
- with gr.Column():
- res_image = gr.Image(type='numpy', label='Detection Resutls')
- with gr.Row():
- example_images = gr.Dataset(components=[input_image], samples=[['demo.jpg']])
-
- gr.Markdown(
- """
- UniFormer: Unifying Convolution and Self-attention for Visual Recognition | Github Repo
- """ - ) - - submit_button.click(fn=inference, inputs=input_image, outputs=res_image) - example_images.click(fn=set_example_image, inputs=example_images, outputs=example_images.components) - -demo.launch(enable_queue=True) \ No newline at end of file diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py b/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py deleted file mode 100644 index fda9110603d71e14cab6e537949be191f2adf6db..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r101-d8_480x480_80k_pascal_context.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './pspnet_r50-d8_480x480_80k_pascal_context.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/canny/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/canny/__init__.py deleted file mode 100644 index cb0da951dc838ec9dec2131007e036113281800b..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/canny/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -import cv2 - - -class CannyDetector: - def __call__(self, img, low_threshold, high_threshold): - return cv2.Canny(img, low_threshold, high_threshold) diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/arraymisc/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/arraymisc/__init__.py deleted file mode 100644 index 4b4700d6139ae3d604ff6e542468cce4200c020c..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/arraymisc/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .quantization import dequantize, quantize - -__all__ = ['quantize', 'dequantize'] diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/inpaint_zoom/utils/__init__.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/inpaint_zoom/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/transformer_vanilla.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/transformer_vanilla.py deleted file mode 100644 index 10c0920c1a217af5bb3e1b13077568035ab3b7b5..0000000000000000000000000000000000000000 --- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/models/GroundingDINO/transformer_vanilla.py +++ /dev/null @@ -1,123 +0,0 @@ -# ------------------------------------------------------------------------ -# Grounding DINO -# url: https://github.com/IDEA-Research/GroundingDINO -# Copyright (c) 2023 IDEA. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------ -# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -DETR Transformer class. - -Copy-paste from torch.nn.Transformer with modifications: - * positional encodings are passed in MHattention - * extra LN at the end of encoder is removed - * decoder returns a stack of activations from all decoding layers -""" -from typing import Optional - -import torch -import torch.nn.functional as F -from torch import Tensor, nn - -from .utils import ( - MLP, - _get_activation_fn, - _get_clones, - gen_encoder_output_proposals, - gen_sineembed_for_position, - sigmoid_focal_loss, -) - - -class TextTransformer(nn.Module): - def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1): - super().__init__() - self.num_layers = num_layers - self.d_model = d_model - self.nheads = nheads - self.dim_feedforward = dim_feedforward - self.norm = None - - single_encoder_layer = TransformerEncoderLayer( - d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout - ) - self.layers = _get_clones(single_encoder_layer, num_layers) - - def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor): - """ - - Args: - text_attention_mask: bs, num_token - memory_text: bs, num_token, d_model - - Raises: - RuntimeError: _description_ - - Returns: - output: bs, num_token, d_model - """ - - output = memory_text.transpose(0, 1) - - for layer in self.layers: - output = layer(output, src_key_padding_mask=text_attention_mask) - - if self.norm is not None: - output = self.norm(output) - - return output.transpose(0, 1) - - -class TransformerEncoderLayer(nn.Module): - def __init__( - self, - d_model, - nhead, - dim_feedforward=2048, - dropout=0.1, - activation="relu", - normalize_before=False, - ): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - self.nhead = nhead - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward( - self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - ): - # repeat attn mask - if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]: - # bs, num_q, num_k - src_mask = src_mask.repeat(self.nhead, 1, 1) - - q = k = self.with_pos_embed(src, pos) - - src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0] - - # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - src = self.norm2(src) - return src diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/__init__.py deleted file mode 100644 index 4ad3b2acb9a6a0bf02394a480ba0aa6fca2a4af6..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from typing import List, Optional - -__version__ = "23.1.2" - - -def main(args: Optional[List[str]] = None) -> int: - """This is an internal API only meant for use by pip's own console scripts. - - For additional details, see https://github.com/pypa/pip/issues/7498. - """ - from pip._internal.utils.entrypoints import _wrapper - - return _wrapper(args) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/vcs/subversion.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/vcs/subversion.py deleted file mode 100644 index 16d93a67b7b6feed66f2cc432f6250ca3ad34914..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/vcs/subversion.py +++ /dev/null @@ -1,324 +0,0 @@ -import logging -import os -import re -from typing import List, Optional, Tuple - -from pip._internal.utils.misc import ( - HiddenText, - display_path, - is_console_interactive, - is_installable_dir, - split_auth_from_netloc, -) -from pip._internal.utils.subprocess import CommandArgs, make_command -from pip._internal.vcs.versioncontrol import ( - AuthInfo, - RemoteNotFoundError, - RevOptions, - VersionControl, - vcs, -) - -logger = logging.getLogger(__name__) - -_svn_xml_url_re = re.compile('url="([^"]+)"') -_svn_rev_re = re.compile(r'committed-rev="(\d+)"') -_svn_info_xml_rev_re = re.compile(r'\s*revision="(\d+)"') -_svn_info_xml_url_re = re.compile(r"-
Caso Penal Mod Apk desbloquear todos los niveles: Una revisión
-Si eres un fan de las historias de detectives y los juegos de objetos ocultos, es posible que hayas oído hablar de Criminal Case, un juego popular y adictivo que te permite resolver casos de asesinato en una ciudad sombría y corrupta. Pero ¿sabías que hay una manera de desbloquear todos los niveles y características del juego sin gastar dinero o esperar horas? En este artículo, vamos a revisar Criminal Case Mod Apk, una versión modificada del juego que le da acceso ilimitado a todo lo que necesita para atrapar a los asesinos. También te mostraremos cómo descargarlo e instalarlo, cómo jugarlo y cuáles son los pros y los contras de usarlo.
-caso penal mod apk desbloquear todos los niveles
DOWNLOAD ⇒ https://bltlly.com/2v6MCP
-
¿Qué es un caso penal?
-Un juego de objetos ocultos con misterios de asesinato
-Criminal Case es un juego gratuito que fue lanzado en 2012 por Pretty Simple Games. Está disponible en Facebook y dispositivos móviles (Android, iOS, Windows). El juego es un juego de objetos ocultos, lo que significa que tienes que encontrar pistas en diferentes escenas del crimen tocando o haciendo clic en ellos. También tienes que examinar pistas, interrogar sospechosos, analizar pruebas y llevar al asesino ante la justicia. El juego tiene cientos de casos para resolver, cada uno con su propia historia, personajes y ubicaciones. El juego se divide en varias temporadas, cada una en una ciudad o región diferente.
-Un juego popular y adictivo en Facebook y dispositivos móviles
- -¿Qué es Caso Penal Mod Apk?
-Una versión modificada del juego con funciones ilimitadas
-Criminal Case Mod Apk es una versión modificada del juego original que le da acceso ilimitado a todas las características y contenido del juego. Esto significa que puedes jugar a cualquier nivel que quieras, usar cualquier elemento que necesites y disfrutar de cualquier personaje o mapa que te guste. Usted no tiene que pagar ningún dinero o esperar a que cualquier temporizador para rellenar su energía o obtener sus informes. También puede obtener pistas y estrellas ilimitadas, que se utilizan para desbloquear más pistas y pruebas. Con Criminal Case Mod Apk, puede resolver cualquier caso en ningún momento y convertirse en el mejor detective nunca.
-Una forma de desbloquear todos los niveles y elementos sin pagar o esperar
-Caso Penal Mod Apk es una manera de evitar las limitaciones y restricciones del juego original. Como sabrás, Criminal Case es un juego gratuito, pero también tiene algunas compras en la aplicación que te permiten comprar más energía, estrellas, monedas, dinero en efectivo u objetos. Estas compras pueden variar desde $ Continuar el artículo: 0.99 a $99.99. Además, el juego tiene una cantidad limitada de energía que se puede utilizar para jugar los niveles. Cada nivel cuesta 20 puntos de energía, y solo se puede tener un máximo de 110 puntos de energía a la vez. Las recargas de energía a razón de un punto cada tres minutos, lo que significa que tienes que esperar horas para jugar más niveles. El juego también tiene un tiempo de espera para el análisis y los informes de las pistas, que pueden tardar de 30 minutos a 24 horas. Estos tiempos de espera se pueden saltarse usando estrellas o dinero en efectivo, que también son limitados y difíciles de ganar. Con Caso Penal Mod Apk, usted no tiene que preocuparse por cualquiera de estos problemas. Puedes jugar todo lo que quieras, cuando quieras, y obtener resultados instantáneos.
-¿Cuáles son las características de Caso Penal Mod Apk?
-Dinero, energía y estrellas ilimitados
- -Todos los elementos, caracteres y mapas desbloqueados
-Otra característica de Criminal Case Mod Apk es que desbloquea todos los elementos, personajes y mapas en el juego. Los elementos se utilizan para personalizar tu personaje y tu hogar, así como para mejorar tu rendimiento en el juego. Los personajes son las personas que conoces en el juego, como tus compañeros, sospechosos, testigos o asesinos. Los mapas son los lugares donde ocurren los casos, como Grimsborough, Pacific Bay, Misterios del pasado, La conspiración o Viajar en el tiempo. Con Criminal Case Mod Apk, se puede acceder a todos los elementos, personajes y mapas en el juego. Puedes vestir a tu personaje como quieras, interactuar con quien quieras y explorar cualquier lugar que desees.
- -Análisis instantáneo, informes y sugerencias
-Una tercera característica de Criminal Case Mod Apk es que proporciona análisis instantáneo, informes, y pistas en el juego. El análisis es el proceso de examinar las pistas y pruebas que se encuentran en las escenas del crimen. Los informes son los resultados del análisis que le dan más información sobre las pistas y las pruebas. Las pistas son los consejos que le ayudan a encontrar las pistas y la evidencia más rápido y más fácil. Con Caso Penal Mod Apk, usted no tiene que esperar a que se complete cualquier análisis o informe. Puedes conseguirlos al instante sin usar ninguna estrella ni dinero. Tampoco tienes que luchar para encontrar las pistas o pruebas en las escenas del crimen. Puedes obtener pistas ilimitadas que te muestran dónde están.
-Cómo descargar e instalar Penal Case Mod Apk?
-Los pasos para descargar e instalar el archivo apk mod
-Si desea descargar e instalar Criminal Case Mod Apk en su dispositivo, es necesario seguir estos pasos:
--
-
Las precauciones y riesgos de usar archivos apk mod
-Antes de descargar e instalar Criminal Case Mod Apk en su dispositivo, usted necesita ser consciente de algunas precauciones y riesgos de usar archivos apk mod:
--
-
Por lo tanto, usted debe utilizar Caso Penal Mod Apk a su propio riesgo y discreción. También debes respetar los derechos y esfuerzos de los desarrolladores y editores de juegos, y apoyarlos jugando el juego original o haciendo compras legítimas.
-¿Cómo se juega Caso Penal Mod Apk?
-La jugabilidad básica y los controles del juego
- -Los consejos y trucos para resolver los casos más rápido y fácil
-Con Criminal Case Mod Apk, puede resolver cualquier caso más rápido y más fácil que nunca. Aquí hay algunos consejos y trucos para ayudarle a jugar mejor:
--
-
Conclusión
-Un resumen de los puntos principales del artículo
- -Una recomendación y un llamado a la acción para los lectores
-Si usted está interesado en tratar Criminal Case Mod Apk, se puede descargar desde un sitio web de confianza que proporciona archivos mod apk para juegos de Android. También puede seguir los pasos que proporcionamos anteriormente para instalarlo en su dispositivo. Sin embargo, también debe ser consciente de las precauciones y riesgos de usar archivos mod apk, tales como virus, errores, prohibiciones o cuestiones éticas. Usted debe utilizar Caso Penal Mod Apk a su propio riesgo y discreción. También debes respetar los derechos y esfuerzos de los desarrolladores y editores de juegos, y apoyarlos jugando el juego original o haciendo compras legítimas.
-Preguntas frecuentes
-¿Qué es un caso penal?
-Criminal Case es un juego de objetos ocultos gratuito que te permite resolver casos de asesinato en una ciudad sombría y corrupta.
-¿Qué es Caso Penal Mod Apk?
-Criminal Case Mod Apk es una versión modificada de Criminal Case que le da acceso ilimitado a todas las características y contenido del juego.
Cómo descargar e instalar Criminal Case Mod Apk? -Puede descargar Criminal Case Mod Apk desde un sitio web de confianza que proporciona archivos mod apk para juegos de Android. También puede seguir los pasos que proporcionamos anteriormente para instalarlo en su dispositivo.
-¿Cuáles son las características de Caso Penal Mod Apk?
-Caso Penal Mod Apk tiene muchas características, tales como dinero ilimitado, energía y estrellas, todos los elementos, personajes, y mapas desbloqueados, y análisis instantáneo, informes, y sugerencias.
-¿Cuáles son las precauciones y riesgos de usar Caso Penal Mod Apk?
-Caso Penal Mod Apk puede tener algunas precauciones y riesgos, tales como virus, errores, prohibiciones, o cuestiones éticas. Debe usarlo bajo su propio riesgo y discreción. También debe respetar los derechos y esfuerzos de los desarrolladores y editores de juegos.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cache.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cache.py deleted file mode 100644 index 05f0a9acb240fec7113b8b7e147d39edd151d149..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cache.py +++ /dev/null @@ -1,272 +0,0 @@ -"""Cache Management -""" - -import hashlib -import json -import logging -import os -from pathlib import Path -from typing import Any, Dict, List, Optional - -from pip._vendor.packaging.tags import Tag, interpreter_name, interpreter_version -from pip._vendor.packaging.utils import canonicalize_name - -from pip._internal.exceptions import InvalidWheelFilename -from pip._internal.models.direct_url import DirectUrl -from pip._internal.models.link import Link -from pip._internal.models.wheel import Wheel -from pip._internal.utils.temp_dir import TempDirectory, tempdir_kinds -from pip._internal.utils.urls import path_to_url - -logger = logging.getLogger(__name__) - -ORIGIN_JSON_NAME = "origin.json" - - -def _hash_dict(d: Dict[str, str]) -> str: - """Return a stable sha224 of a dictionary.""" - s = json.dumps(d, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - return hashlib.sha224(s.encode("ascii")).hexdigest() - - -class Cache: - """An abstract class - provides cache directories for data from links - - :param cache_dir: The root of the cache. - """ - - def __init__(self, cache_dir: str) -> None: - super().__init__() - assert not cache_dir or os.path.isabs(cache_dir) - self.cache_dir = cache_dir or None - - def _get_cache_path_parts(self, link: Link) -> List[str]: - """Get parts of part that must be os.path.joined with cache_dir""" - - # We want to generate an url to use as our cache key, we don't want to - # just re-use the URL because it might have other items in the fragment - # and we don't care about those. - key_parts = {"url": link.url_without_fragment} - if link.hash_name is not None and link.hash is not None: - key_parts[link.hash_name] = link.hash - if link.subdirectory_fragment: - key_parts["subdirectory"] = link.subdirectory_fragment - - # Include interpreter name, major and minor version in cache key - # to cope with ill-behaved sdists that build a different wheel - # depending on the python version their setup.py is being run on, - # and don't encode the difference in compatibility tags. - # https://github.com/pypa/pip/issues/7296 - key_parts["interpreter_name"] = interpreter_name() - key_parts["interpreter_version"] = interpreter_version() - - # Encode our key url with sha224, we'll use this because it has similar - # security properties to sha256, but with a shorter total output (and - # thus less secure). However the differences don't make a lot of - # difference for our use case here. - hashed = _hash_dict(key_parts) - - # We want to nest the directories some to prevent having a ton of top - # level directories where we might run out of sub directories on some - # FS. - parts = [hashed[:2], hashed[2:4], hashed[4:6], hashed[6:]] - - return parts - - def _get_candidates(self, link: Link, canonical_package_name: str) -> List[Any]: - can_not_cache = not self.cache_dir or not canonical_package_name or not link - if can_not_cache: - return [] - - candidates = [] - path = self.get_path_for_link(link) - if os.path.isdir(path): - for candidate in os.listdir(path): - candidates.append((candidate, path)) - return candidates - - def get_path_for_link(self, link: Link) -> str: - """Return a directory to store cached items in for link.""" - raise NotImplementedError() - - def get( - self, - link: Link, - package_name: Optional[str], - supported_tags: List[Tag], - ) -> Link: - """Returns a link to a cached item if it exists, otherwise returns the - passed link. - """ - raise NotImplementedError() - - -class SimpleWheelCache(Cache): - """A cache of wheels for future installs.""" - - def __init__(self, cache_dir: str) -> None: - super().__init__(cache_dir) - - def get_path_for_link(self, link: Link) -> str: - """Return a directory to store cached wheels for link - - Because there are M wheels for any one sdist, we provide a directory - to cache them in, and then consult that directory when looking up - cache hits. - - We only insert things into the cache if they have plausible version - numbers, so that we don't contaminate the cache with things that were - not unique. E.g. ./package might have dozens of installs done for it - and build a version of 0.0...and if we built and cached a wheel, we'd - end up using the same wheel even if the source has been edited. - - :param link: The link of the sdist for which this will cache wheels. - """ - parts = self._get_cache_path_parts(link) - assert self.cache_dir - # Store wheels within the root cache_dir - return os.path.join(self.cache_dir, "wheels", *parts) - - def get( - self, - link: Link, - package_name: Optional[str], - supported_tags: List[Tag], - ) -> Link: - candidates = [] - - if not package_name: - return link - - canonical_package_name = canonicalize_name(package_name) - for wheel_name, wheel_dir in self._get_candidates(link, canonical_package_name): - try: - wheel = Wheel(wheel_name) - except InvalidWheelFilename: - continue - if canonicalize_name(wheel.name) != canonical_package_name: - logger.debug( - "Ignoring cached wheel %s for %s as it " - "does not match the expected distribution name %s.", - wheel_name, - link, - package_name, - ) - continue - if not wheel.supported(supported_tags): - # Built for a different python/arch/etc - continue - candidates.append( - ( - wheel.support_index_min(supported_tags), - wheel_name, - wheel_dir, - ) - ) - - if not candidates: - return link - - _, wheel_name, wheel_dir = min(candidates) - return Link(path_to_url(os.path.join(wheel_dir, wheel_name))) - - -class EphemWheelCache(SimpleWheelCache): - """A SimpleWheelCache that creates it's own temporary cache directory""" - - def __init__(self) -> None: - self._temp_dir = TempDirectory( - kind=tempdir_kinds.EPHEM_WHEEL_CACHE, - globally_managed=True, - ) - - super().__init__(self._temp_dir.path) - - -class CacheEntry: - def __init__( - self, - link: Link, - persistent: bool, - ): - self.link = link - self.persistent = persistent - self.origin: Optional[DirectUrl] = None - origin_direct_url_path = Path(self.link.file_path).parent / ORIGIN_JSON_NAME - if origin_direct_url_path.exists(): - self.origin = DirectUrl.from_json(origin_direct_url_path.read_text()) - - -class WheelCache(Cache): - """Wraps EphemWheelCache and SimpleWheelCache into a single Cache - - This Cache allows for gracefully degradation, using the ephem wheel cache - when a certain link is not found in the simple wheel cache first. - """ - - def __init__(self, cache_dir: str) -> None: - super().__init__(cache_dir) - self._wheel_cache = SimpleWheelCache(cache_dir) - self._ephem_cache = EphemWheelCache() - - def get_path_for_link(self, link: Link) -> str: - return self._wheel_cache.get_path_for_link(link) - - def get_ephem_path_for_link(self, link: Link) -> str: - return self._ephem_cache.get_path_for_link(link) - - def get( - self, - link: Link, - package_name: Optional[str], - supported_tags: List[Tag], - ) -> Link: - cache_entry = self.get_cache_entry(link, package_name, supported_tags) - if cache_entry is None: - return link - return cache_entry.link - - def get_cache_entry( - self, - link: Link, - package_name: Optional[str], - supported_tags: List[Tag], - ) -> Optional[CacheEntry]: - """Returns a CacheEntry with a link to a cached item if it exists or - None. The cache entry indicates if the item was found in the persistent - or ephemeral cache. - """ - retval = self._wheel_cache.get( - link=link, - package_name=package_name, - supported_tags=supported_tags, - ) - if retval is not link: - return CacheEntry(retval, persistent=True) - - retval = self._ephem_cache.get( - link=link, - package_name=package_name, - supported_tags=supported_tags, - ) - if retval is not link: - return CacheEntry(retval, persistent=False) - - return None - - @staticmethod - def record_download_origin(cache_dir: str, download_info: DirectUrl) -> None: - origin_path = Path(cache_dir) / ORIGIN_JSON_NAME - if origin_path.is_file(): - origin = DirectUrl.from_json(origin_path.read_text()) - # TODO: use DirectUrl.equivalent when https://github.com/pypa/pip/pull/10564 - # is merged. - if origin.url != download_info.url: - logger.warning( - "Origin URL %s in cache entry %s does not match download URL %s. " - "This is likely a pip bug or a cache corruption issue.", - origin.url, - cache_dir, - download_info.url, - ) - origin_path.write_text(download_info.to_json(), encoding="utf-8") diff --git a/spaces/BucketHeadP65/confusion_matrix/app.py b/spaces/BucketHeadP65/confusion_matrix/app.py deleted file mode 100644 index 5cb5c389d73a72a841115ce2260f055c04c42385..0000000000000000000000000000000000000000 --- a/spaces/BucketHeadP65/confusion_matrix/app.py +++ /dev/null @@ -1,6 +0,0 @@ -import evaluate -from evaluate.utils import launch_gradio_widget - - -module = evaluate.load("BucketHeadP65/confusion_matrix") -launch_gradio_widget(module) diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/datasets/clevr/clevr_loader.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/datasets/clevr/clevr_loader.py deleted file mode 100644 index 898cc6bac70f379eea6347a4e2705895ac7562bf..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/datasets/clevr/clevr_loader.py +++ /dev/null @@ -1,200 +0,0 @@ -# -------------------------------------------------------- -# OpenVQA -# Written by Yuhao Cui https://github.com/cuiyuhao1996 -# -------------------------------------------------------- - -import numpy as np -import glob, json, re, en_vectors_web_lg -from openvqa.core.base_dataset import BaseDataSet -from openvqa.utils.ans_punct import prep_ans - - -class DataSet(BaseDataSet): - def __init__(self, __C): - super(DataSet, self).__init__() - self.__C = __C - - # -------------------------- - # ---- Raw data loading ---- - # -------------------------- - - # Loading all image paths - # grid_feat_path_list = \ - # glob.glob(__C.FEATS_PATH[__C.DATASET]['train'] + '/*.npz') + \ - # glob.glob(__C.FEATS_PATH[__C.DATASET]['val'] + '/*.npz') + \ - # glob.glob(__C.FEATS_PATH[__C.DATASET]['test'] + '/*.npz') - - # Loading question word list - stat_ques_list = \ - json.load(open(__C.RAW_PATH[__C.DATASET]['train'], 'r'))['questions'] + \ - json.load(open(__C.RAW_PATH[__C.DATASET]['val'], 'r'))['questions'] + \ - json.load(open(__C.RAW_PATH[__C.DATASET]['test'], 'r'))['questions'] - - # Loading answer word list - stat_ans_list = \ - json.load(open(__C.RAW_PATH[__C.DATASET]['train'], 'r'))['questions'] + \ - json.load(open(__C.RAW_PATH[__C.DATASET]['val'], 'r'))['questions'] - - # Loading question and answer list - self.ques_list = [] - grid_feat_path_list = [] - - split_list = __C.SPLIT[__C.RUN_MODE].split('+') - for split in split_list: - self.ques_list += json.load(open(__C.RAW_PATH[__C.DATASET][split], 'r'))['questions'] - grid_feat_path_list += glob.glob(__C.FEATS_PATH[__C.DATASET][split] + '/*.npz') - - # Define run data size - self.data_size = self.ques_list.__len__() - - print(' ========== Dataset size:', self.data_size) - - - # ------------------------ - # ---- Data statistic ---- - # ------------------------ - - # {image id} -> {image feature absolutely path} - self.iid_to_grid_feat_path = self.img_feat_path_load(grid_feat_path_list) - - # Tokenize - self.token_to_ix, self.pretrained_emb, max_token = self.tokenize(stat_ques_list, __C.USE_GLOVE) - self.token_size = self.token_to_ix.__len__() - print(' ========== Question token vocab size:', self.token_size) - - self.max_token = -1 - if self.max_token == -1: - self.max_token = max_token - print('Max token length:', max_token, 'Trimmed to:', self.max_token) - - # Answers statistic - self.ans_to_ix, self.ix_to_ans = self.ans_stat(stat_ans_list) - self.ans_size = self.ans_to_ix.__len__() - print(' ========== Answer token vocab size:', self.ans_size) - print('Finished!') - print('') - - - - def img_feat_path_load(self, path_list): - iid_to_path = {} - - for ix, path in enumerate(path_list): - iid = path.split('/')[-1].split('.')[0] - iid_to_path[iid] = path - - return iid_to_path - - - def tokenize(self, stat_ques_list, use_glove): - token_to_ix = { - 'PAD': 0, - 'UNK': 1, - 'CLS': 2, - } - - spacy_tool = None - pretrained_emb = [] - if use_glove: - spacy_tool = en_vectors_web_lg.load() - pretrained_emb.append(spacy_tool('PAD').vector) - pretrained_emb.append(spacy_tool('UNK').vector) - pretrained_emb.append(spacy_tool('CLS').vector) - - max_token = 0 - for ques in stat_ques_list: - words = re.sub( - r"([.,'!?\"()*#:;])", - '', - ques['question'].lower() - ).replace('-', ' ').replace('/', ' ').split() - - if len(words) > max_token: - max_token = len(words) - - for word in words: - if word not in token_to_ix: - token_to_ix[word] = len(token_to_ix) - if use_glove: - pretrained_emb.append(spacy_tool(word).vector) - - pretrained_emb = np.array(pretrained_emb) - - return token_to_ix, pretrained_emb, max_token - - - def ans_stat(self, stat_ans_list): - ans_to_ix = {} - ix_to_ans = {} - - for ans_stat in stat_ans_list: - ans = ans_stat['answer'] - - if ans not in ans_to_ix: - ix_to_ans[ans_to_ix.__len__()] = ans - ans_to_ix[ans] = ans_to_ix.__len__() - - return ans_to_ix, ix_to_ans - - - - # ---------------------------------------------- - # ---- Real-Time Processing Implementations ---- - # ---------------------------------------------- - - def load_ques_ans(self, idx): - # if self.__C.RUN_MODE in ['train']: - ques = self.ques_list[idx] - iid = str(ques['image_index']) - - # Process question - ques_ix_iter = self.proc_ques(ques, self.token_to_ix, max_token=self.max_token) - ans_iter = np.zeros(1) - - if self.__C.RUN_MODE in ['train']: - # process answers - ans = ques['answer'] - ans_iter = self.proc_ans(ans, self.ans_to_ix) - - return ques_ix_iter, ans_iter, iid - - - def load_img_feats(self, idx, iid): - grid_feat = np.load(self.iid_to_grid_feat_path[iid]) - grid_feat_iter = grid_feat['x'] - - return np.zeros(1), grid_feat_iter, np.zeros(1) - - - - # ------------------------------------ - # ---- Real-Time Processing Utils ---- - # ------------------------------------ - - def proc_ques(self, ques, token_to_ix, max_token): - ques_ix = np.zeros(max_token, np.int64) - - words = re.sub( - r"([.,'!?\"()*#:;])", - '', - ques['question'].lower() - ).replace('-', ' ').replace('/', ' ').split() - - for ix, word in enumerate(words): - if word in token_to_ix: - ques_ix[ix] = token_to_ix[word] - else: - ques_ix[ix] = token_to_ix['UNK'] - - if ix + 1 == max_token: - break - - return ques_ix - - - def proc_ans(self, ans, ans_to_ix): - ans_ix = np.zeros(1, np.int64) - ans_ix[0] = ans_to_ix[ans] - - return ans_ix - diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/type_traits/is_call_possible.h b/spaces/CVPR/LIVE/thrust/thrust/detail/type_traits/is_call_possible.h deleted file mode 100644 index bff0493772ba7b1dfe8f3846ab3270a6c2e8a368..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/detail/type_traits/is_call_possible.h +++ /dev/null @@ -1,161 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include
🍏 Applio (Mangio-RVC-Fork HF)
") - gr.HTML("The current space only uses CPU, so it's only for inference. If you have issues with the queue, I recommend duplicating the space.
") - gr.Markdown( - "[](https://huggingface.co/spaces/r3gm/RVC_HF?duplicate=true)\n\n" - ) - with gr.Tabs(): - with gr.TabItem(i18n("Model Inference")): - with gr.Row(): - sid0 = gr.Dropdown(label=i18n("Inferencing voice:"), choices=sorted(names), value=default_weight) - refresh_button = gr.Button(i18n("Refresh"), variant="primary") - clean_button = gr.Button(i18n("Unload voice to save GPU memory"), variant="primary") - clean_button.click(fn=lambda: ({"value": "", "__type__": "update"}), inputs=[], outputs=[sid0]) - - - with gr.TabItem(i18n("Single")): - with gr.Row(): - spk_item = gr.Slider( - minimum=0, - maximum=2333, - step=1, - label=i18n("Select Speaker/Singer ID:"), - value=0, - visible=False, - interactive=True, - ) - - - with gr.Group(): - with gr.Row(): - with gr.Column(): # First column for audio-related inputs - dropbox = gr.File(label=i18n("Drag your audio here:")) - record_button=gr.Audio(source="microphone", label=i18n("Or record an audio:"), type="filepath") - input_audio0 = gr.Textbox( - label=i18n("Manual path to the audio file to be processed"), - value=os.path.join(now_dir, "audios", "someguy.mp3"), - visible=False - ) - input_audio1 = gr.Dropdown( - label=i18n("Auto detect audio path and select from the dropdown:"), - choices=sorted(audio_paths), - value='', - interactive=True, - ) - - input_audio1.select(fn=lambda:'',inputs=[],outputs=[input_audio0]) - input_audio0.input(fn=lambda:'',inputs=[],outputs=[input_audio1]) - - dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0]) - dropbox.upload(fn=easy_infer.change_choices2, inputs=[], outputs=[input_audio1]) - record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0]) - record_button.change(fn=easy_infer.change_choices2, inputs=[], outputs=[input_audio1]) - - best_match_index_path1 = match_index(sid0.value) # Get initial index from default sid0 (first voice model in list) - - with gr.Column(): # Second column for pitch shift and other options - file_index2 = gr.Dropdown( - label=i18n("Auto-detect index path and select from the dropdown:"), - choices=get_indexes(), - value=best_match_index_path1, - interactive=True, - allow_custom_value=True, - ) - index_rate1 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("Search feature ratio:"), - value=0.75, - interactive=True, - ) - refresh_button.click( - fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio1] - ) - with gr.Column(): - vc_transform0 = gr.Number( - label=i18n("Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12):"), value=0 - ) - - # Create a checkbox for advanced settings - advanced_settings_checkbox = gr.Checkbox( - value=False, - label=i18n("Advanced Settings"), - interactive=True, - ) - - # Advanced settings container - with gr.Column(visible=False) as advanced_settings: # Initially hidden - with gr.Row(label = i18n("Advanced Settings"), open = False): - with gr.Column(): - f0method0 = gr.Radio( - label=i18n( - "Select the pitch extraction algorithm:" - ), - choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe", "rmvpe+"], - value="rmvpe+", - interactive=True, - ) - f0_autotune = gr.Checkbox( - label="Enable autotune", - interactive=True - ) - crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label=i18n("Mangio-Crepe Hop Length (Only applies to mangio-crepe): Hop length refers to the time it takes for the speaker to jump to a dramatic pitch. Lower hop lengths take more time to infer but are more pitch accurate."), - value=120, - interactive=True, - visible=False, - ) - filter_radius0 = gr.Slider( - minimum=0, - maximum=7, - label=i18n("If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness."), - value=3, - step=1, - interactive=True, - ) - - minpitch_slider = gr.Slider( - label = i18n("Min pitch:"), - info = i18n("Specify minimal pitch for inference [HZ]"), - step = 0.1, - minimum = 1, - scale = 0, - value = 50, - maximum = 16000, - interactive = True, - visible = (not rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'), - ) - minpitch_txtbox = gr.Textbox( - label = i18n("Min pitch:"), - info = i18n("Specify minimal pitch for inference [NOTE][OCTAVE]"), - placeholder = "C5", - visible = (rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'), - interactive = True, - ) - - maxpitch_slider = gr.Slider( - label = i18n("Max pitch:"), - info = i18n("Specify max pitch for inference [HZ]"), - step = 0.1, - minimum = 1, - scale = 0, - value = 1100, - maximum = 16000, - interactive = True, - visible = (not rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'), - ) - maxpitch_txtbox = gr.Textbox( - label = i18n("Max pitch:"), - info = i18n("Specify max pitch for inference [NOTE][OCTAVE]"), - placeholder = "C6", - visible = (rvc_globals.NotesOrHertz) and (f0method0.value != 'rmvpe'), - interactive = True, - ) - - with gr.Column(): - file_index1 = gr.Textbox( - label=i18n("Feature search database file path:"), - value="", - interactive=True, - ) - - with gr.Accordion(label = i18n("Custom f0 [Root pitch] File"), open = False): - f0_file = gr.File(label=i18n("F0 curve file (optional). One pitch per line. Replaces the default F0 and pitch modulation:")) - - f0method0.change( - fn=lambda radio: ( - { - "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], - "__type__": "update" - } - ), - inputs=[f0method0], - outputs=[crepe_hop_length] - ) - - f0method0.change( - fn=switch_pitch_controls, - inputs=[f0method0], - outputs=[minpitch_slider, minpitch_txtbox, - maxpitch_slider, maxpitch_txtbox] - ) - - with gr.Column(): - resample_sr0 = gr.Slider( - minimum=0, - maximum=48000, - label=i18n("Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling:"), - value=0, - step=1, - interactive=True, - ) - rms_mix_rate0 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used:"), - value=0.25, - interactive=True, - ) - protect0 = gr.Slider( - minimum=0, - maximum=0.5, - label=i18n( - "Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy:" - ), - value=0.33, - step=0.01, - interactive=True, - ) - formanting = gr.Checkbox( - value=bool(DoFormant), - label=i18n("Formant shift inference audio"), - info=i18n("Used for male to female and vice-versa conversions"), - interactive=True, - visible=True, - ) - - formant_preset = gr.Dropdown( - value='', - choices=get_fshift_presets(), - label=i18n("Browse presets for formanting"), - info=i18n("Presets are located in formantshiftcfg/ folder"), - visible=bool(DoFormant), - ) - - formant_refresh_button = gr.Button( - value='\U0001f504', - visible=bool(DoFormant), - variant='primary', - ) - - qfrency = gr.Slider( - value=Quefrency, - info=i18n("Default value is 1.0"), - label=i18n("Quefrency for formant shifting"), - minimum=0.0, - maximum=16.0, - step=0.1, - visible=bool(DoFormant), - interactive=True, - ) - - tmbre = gr.Slider( - value=Timbre, - info=i18n("Default value is 1.0"), - label=i18n("Timbre for formant shifting"), - minimum=0.0, - maximum=16.0, - step=0.1, - visible=bool(DoFormant), - interactive=True, - ) - frmntbut = gr.Button( - "Apply", variant="primary", visible=bool(DoFormant) - ) - - formant_preset.change( - fn=preset_apply, - inputs=[formant_preset, qfrency, tmbre], - outputs=[qfrency, tmbre], - ) - formanting.change( - fn=formant_enabled, - inputs=[ - formanting, - qfrency, - tmbre, - frmntbut, - formant_preset, - formant_refresh_button, - ], - outputs=[ - formanting, - qfrency, - tmbre, - frmntbut, - formant_preset, - formant_refresh_button, - ], - ) - frmntbut.click( - fn=formant_apply, - inputs=[qfrency, tmbre], - outputs=[qfrency, tmbre], - ) - formant_refresh_button.click( - fn=update_fshift_presets, - inputs=[formant_preset, qfrency, tmbre], - outputs=[formant_preset, qfrency, tmbre], - ) - - # Function to toggle advanced settings - def toggle_advanced_settings(checkbox): - return {"visible": checkbox, "__type__": "update"} - - # Attach the change event - advanced_settings_checkbox.change( - fn=toggle_advanced_settings, - inputs=[advanced_settings_checkbox], - outputs=[advanced_settings] - ) - - - but0 = gr.Button(i18n("Convert"), variant="primary").style(full_width=True) - - with gr.Row(): # Defines output info + output audio download after conversion - vc_output1 = gr.Textbox(label=i18n("Output information:")) - vc_output2 = gr.Audio(label=i18n("Export audio (click on the three dots in the lower right corner to download)")) - - with gr.Group(): # I think this defines the big convert button - with gr.Row(): - but0.click( - vc.vc_single, - [ - spk_item, - input_audio0, - input_audio1, - vc_transform0, - f0_file, - f0method0, - file_index1, - file_index2, - index_rate1, - filter_radius0, - resample_sr0, - rms_mix_rate0, - protect0, - crepe_hop_length, - minpitch_slider, minpitch_txtbox, - maxpitch_slider, maxpitch_txtbox, - f0_autotune - ], - [vc_output1, vc_output2], - ) - - - with gr.TabItem(i18n("Batch")): # Dont Change - with gr.Group(): # Markdown explanation of batch inference - gr.Markdown( - value=i18n("Batch conversion. Enter the folder containing the audio files to be converted or upload multiple audio files. The converted audio will be output in the specified folder (default: 'opt').") - ) - with gr.Row(): - with gr.Column(): - vc_transform1 = gr.Number( - label=i18n("Transpose (integer, number of semitones, raise by an octave: 12, lower by an octave: -12):"), value=0 - ) - opt_input = gr.Textbox(label=i18n("Specify output folder:"), value="opt") - with gr.Column(): - file_index4 = gr.Dropdown( - label=i18n("Auto-detect index path and select from the dropdown:"), - choices=get_indexes(), - value=best_match_index_path1, - interactive=True, - ) - sid0.select(fn=match_index, inputs=[sid0], outputs=[file_index2, file_index4]) - - refresh_button.click( - fn=lambda: change_choices()[1], - inputs=[], - outputs=file_index4, - ) - index_rate2 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("Search feature ratio:"), - value=0.75, - interactive=True, - ) - with gr.Row(): - dir_input = gr.Textbox( - label=i18n("Enter the path of the audio folder to be processed (copy it from the address bar of the file manager):"), - value=os.path.join(now_dir, "audios"), - ) - inputs = gr.File( - file_count="multiple", label=i18n("You can also input audio files in batches. Choose one of the two options. Priority is given to reading from the folder.") - ) - - with gr.Row(): - with gr.Column(): - # Create a checkbox for advanced batch settings - advanced_settings_batch_checkbox = gr.Checkbox( - value=False, - label=i18n("Advanced Settings"), - interactive=True, - ) - - # Advanced batch settings container - with gr.Row(visible=False) as advanced_settings_batch: # Initially hidden - with gr.Row(label = i18n("Advanced Settings"), open = False): - with gr.Column(): - file_index3 = gr.Textbox( - label=i18n("Feature search database file path:"), - value="", - interactive=True, - ) - - f0method1 = gr.Radio( - label=i18n( - "Select the pitch extraction algorithm:" - ), - choices=["pm", "harvest", "crepe", "rmvpe"], - value="rmvpe", - interactive=True, - ) - f0_autotune = gr.Checkbox( - label="Enable autotune", - interactive=True - ) - filter_radius1 = gr.Slider( - minimum=0, - maximum=7, - label=i18n("If >=3: apply median filtering to the harvested pitch results. The value represents the filter radius and can reduce breathiness."), - value=3, - step=1, - interactive=True, - ) - - with gr.Row(): - format1 = gr.Radio( - label=i18n("Export file format"), - choices=["wav", "flac", "mp3", "m4a"], - value="wav", - interactive=True, - ) - - - with gr.Column(): - resample_sr1 = gr.Slider( - minimum=0, - maximum=48000, - label=i18n("Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling:"), - value=0, - step=1, - interactive=True, - ) - rms_mix_rate1 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used:"), - value=1, - interactive=True, - ) - protect1 = gr.Slider( - minimum=0, - maximum=0.5, - label=i18n( - "Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy:" - ), - value=0.33, - step=0.01, - interactive=True, - ) - vc_output3 = gr.Textbox(label=i18n("Output information:")) - but1 = gr.Button(i18n("Convert"), variant="primary") - but1.click( - vc.vc_multi, - [ - spk_item, - dir_input, - opt_input, - inputs, - vc_transform1, - f0method1, - file_index3, - file_index4, - index_rate2, - filter_radius1, - resample_sr1, - rms_mix_rate1, - protect1, - format1, - crepe_hop_length, - minpitch_slider if (not rvc_globals.NotesOrHertz) else minpitch_txtbox, - maxpitch_slider if (not rvc_globals.NotesOrHertz) else maxpitch_txtbox, - f0_autotune - ], - [vc_output3], - ) - - sid0.change( - fn=vc.get_vc, - inputs=[sid0, protect0, protect1], - outputs=[spk_item, protect0, protect1], - ) - if not sid0.value == '': - spk_item, protect0, protect1 = vc.get_vc(sid0.value, protect0, protect1) - - #spk_item, protect0, protect1 = vc.get_vc(sid0.value, protect0, protect1) - - # Function to toggle advanced settings - def toggle_advanced_settings_batch(checkbox): - return {"visible": checkbox, "__type__": "update"} - - # Attach the change event - advanced_settings_batch_checkbox.change( - fn=toggle_advanced_settings_batch, - inputs=[advanced_settings_batch_checkbox], - outputs=[advanced_settings_batch] - ) - - - with gr.TabItem(i18n("Train")): - with gr.Accordion(label=i18n("Step 1: Processing data")): - with gr.Row(): - exp_dir1 = gr.Textbox(label=i18n("Enter the model name:"), value=i18n("Model_Name")) - sr2 = gr.Radio( - label=i18n("Target sample rate:"), - choices=["40k", "48k", "32k"], - value="40k", - interactive=True, - ) - if_f0_3 = gr.Checkbox( - label=i18n("Whether the model has pitch guidance."), - value=True, - interactive=True, - ) - version19 = gr.Radio( - label=i18n("Version:"), - choices=["v1", "v2"], - value="v2", - interactive=True, - visible=True, - ) - np7 = gr.Slider( - minimum=0, - maximum=config.n_cpu, - step=1, - label=i18n("Number of CPU processes:"), - value=int(np.ceil(config.n_cpu / 1.5)), - interactive=True, - ) - with gr.Group(): - with gr.Accordion(label=i18n("Step 2: Skipping pitch extraction")): - - with gr.Row(): - # trainset_dir4 = gr.Textbox( - # label=i18n("Enter the path of the training folder:"), value=os.path.join(now_dir, datasets_root) - # ) - with gr.Column(): - trainset_dir4 = gr.Dropdown(choices=sorted(datasets), label=i18n("Select your dataset:"), value=get_dataset()) - btn_update_dataset_list = gr.Button(i18n("Update list"), variant="primary") - spk_id5 = gr.Slider( - minimum=0, - maximum=4, - step=1, - label=i18n("Specify the model ID:"), - value=0, - interactive=True, - ) - btn_update_dataset_list.click( - easy_infer.update_dataset_list, [spk_id5], trainset_dir4 - ) - but1 = gr.Button(i18n("Process data"), variant="primary") - info1 = gr.Textbox(label=i18n("Output information:"), value="") - but1.click( - preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] - ) - with gr.Group(): - with gr.Accordion(label=i18n("Step 3: Extracting features")): - with gr.Row(): - with gr.Column(): - gpus6 = gr.Textbox( - label=i18n("Provide the GPU index(es) separated by '-', like 0-1-2 for using GPUs 0, 1, and 2:"), - value=gpus, - interactive=True, - ) - gpu_info9 = gr.Textbox( - label=i18n("GPU Information:"), value=gpu_info, visible=F0GPUVisible - ) - with gr.Column(): - f0method8 = gr.Radio( - label=i18n( - "Select the pitch extraction algorithm:" - ), - choices=["pm", "harvest", "dio", "crepe", "mangio-crepe", "rmvpe", "rmvpe_gpu"], - # [ MANGIO ]: Fork feature: Crepe on f0 extraction for training. - value="rmvpe", - interactive=True, - ) - gpus_rmvpe = gr.Textbox( - label=i18n( - "rmvpe卡号配置：以-分隔输入使用的不同进程卡号,例如0-0-1使用在卡0上跑2个进程并在卡1上跑1个进程" - ), - value="%s-%s" % (gpus, gpus), - interactive=True, - visible=F0GPUVisible, - ) - - extraction_crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label=i18n("Mangio-Crepe Hop Length (Only applies to mangio-crepe): Hop length refers to the time it takes for the speaker to jump to a dramatic pitch. Lower hop lengths take more time to infer but are more pitch accurate."), - value=64, - interactive=True, - visible=False, - ) - - f0method8.change( - fn=lambda radio: ( - { - "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], - "__type__": "update" - } - ), - inputs=[f0method8], - outputs=[extraction_crepe_hop_length] - ) - f0method8.change( - fn=change_f0_method, - inputs=[f0method8], - outputs=[gpus_rmvpe], - ) - but2 = gr.Button(i18n("Feature extraction"), variant="primary") - info2 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8, interactive=False) - but2.click( - extract_f0_feature, - [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length, gpus_rmvpe,], - [info2], - ) - with gr.Group(): - with gr.Row(): - with gr.Accordion(label=i18n("Step 4: Model training started")): - with gr.Row(): - save_epoch10 = gr.Slider( - minimum=1, - maximum=100, - step=1, - label=i18n("Save frequency:"), - value=10, - interactive=True, - visible=True, - ) - total_epoch11 = gr.Slider( - minimum=1, - maximum=10000, - step=2, - label=i18n("Training epochs:"), - value=750, - interactive=True, - ) - batch_size12 = gr.Slider( - minimum=1, - maximum=50, - step=1, - label=i18n("Batch size per GPU:"), - value=default_batch_size, - #value=20, - interactive=True, - ) - - with gr.Row(): - if_save_latest13 = gr.Checkbox( - label=i18n("Whether to save only the latest .ckpt file to save hard drive space"), - value=True, - interactive=True, - ) - if_cache_gpu17 = gr.Checkbox( - label=i18n("Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training"), - value=False, - interactive=True, - ) - if_save_every_weights18 = gr.Checkbox( - label=i18n("Save a small final model to the 'weights' folder at each save point"), - value=True, - interactive=True, - ) - - with gr.Row(): - pretrained_G14 = gr.Textbox( - lines=4, - label=i18n("Load pre-trained base model G path:"), - value="assets/pretrained_v2/f0G40k.pth", - interactive=True, - ) - pretrained_D15 = gr.Textbox( - lines=4, - label=i18n("Load pre-trained base model D path:"), - value="assets/pretrained_v2/f0D40k.pth", - interactive=True, - ) - gpus16 = gr.Textbox( - label=i18n("Provide the GPU index(es) separated by '-', like 0-1-2 for using GPUs 0, 1, and 2:"), - value=gpus, - interactive=True, - ) - sr2.change( - change_sr2, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15], - ) - version19.change( - change_version19, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15, sr2], - ) - if_f0_3.change( - fn=change_f0, - inputs=[if_f0_3, sr2, version19], - outputs=[f0method8, pretrained_G14, pretrained_D15], - ) - if_f0_3.change(fn=lambda radio: ( - { - "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], - "__type__": "update" - } - ), inputs=[f0method8], outputs=[extraction_crepe_hop_length]) - - butstop = gr.Button(i18n("Stop training"), - variant='primary', - visible=False, - ) - but3 = gr.Button(i18n("Train model"), variant="primary", visible=True) - but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop]) - butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[but3, butstop]) - - - with gr.Column(): - info3 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=4) - save_action = gr.Dropdown(label=i18n("Save type"), choices=[i18n("Save all"),i18n("Save D and G"),i18n("Save voice")], value=i18n("Choose the method"), interactive=True) - - but7 = gr.Button(i18n("Save model"), variant="primary") - but4 = gr.Button(i18n("Train feature index"), variant="primary") - - - - if_save_every_weights18.change( - fn=lambda if_save_every_weights: ( - { - "visible": if_save_every_weights, - "__type__": "update" - } - ), - inputs=[if_save_every_weights18], - outputs=[save_epoch10] - ) - - but3.click( - click_train, - [ - exp_dir1, - sr2, - if_f0_3, - spk_id5, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, - ], - [info3, butstop, but3], - ) - - but4.click(train_index, [exp_dir1, version19], info3) - but7.click(easy_infer.save_model, [exp_dir1, save_action], info3) - with gr.Group(): - with gr.Row(): - with gr.Accordion(label=i18n("Step 5: Export lowest points on a graph of the model")): - - lowestval_weight_dir = gr.Textbox(visible=False) - ds = gr.Textbox(visible=False) - weights_dir1 = gr.Textbox(visible=False, value=weights_dir) - - - with gr.Row(): - amntlastmdls = gr.Slider( - minimum=1, - maximum=25, - label=i18n('How many lowest points to save:'), - value=3, - step=1, - interactive=True, - ) - lpexport = gr.Button( - value=i18n('Export lowest points of a model'), - variant='primary', - ) - lw_mdls = gr.File( - file_count="multiple", - label=i18n("Output models:"), - interactive=False, - ) ##### - - with gr.Row(): - infolpex = gr.Textbox(label=i18n("Output information:"), value="", max_lines=10) - mdlbl = gr.Dataframe(label=i18n('Stats of selected models:'), datatype='number', type='pandas') - - lpexport.click( - lambda model_name: os.path.join("logs", model_name, "lowestvals"), - inputs=[exp_dir1], - outputs=[lowestval_weight_dir] - ) - - lpexport.click(fn=tensorlowest.main, inputs=[exp_dir1, save_epoch10, amntlastmdls], outputs=[ds]) - - ds.change( - fn=tensorlowest.selectweights, - inputs=[exp_dir1, ds, weights_dir1, lowestval_weight_dir], - outputs=[infolpex, lw_mdls, mdlbl], - ) - with gr.TabItem(i18n("UVR5")): # UVR section - with gr.Group(): - with gr.Row(): - with gr.Column(): - model_select = gr.Radio( - label=i18n("Model Architecture:"), - choices=["VR", "MDX"], - value="VR", - interactive=True, - ) - dir_wav_input = gr.Textbox( - label=i18n("Enter the path of the audio folder to be processed:"), - value=os.path.join(now_dir, "audios") - ) - wav_inputs = gr.File( - file_count="multiple", label=i18n("You can also input audio files in batches. Choose one of the two options. Priority is given to reading from the folder.") - ) - - with gr.Column(): - model_choose = gr.Dropdown(label=i18n("Model:"), choices=uvr5_names) - agg = gr.Slider( - minimum=0, - maximum=20, - step=1, - label="Vocal Extraction Aggressive", - value=10, - interactive=True, - visible=False, - ) - opt_vocal_root = gr.Textbox( - label=i18n("Specify the output folder for vocals:"), value="opt" - ) - opt_ins_root = gr.Textbox( - label=i18n("Specify the output folder for accompaniment:"), value="opt" - ) - format0 = gr.Radio( - label=i18n("Export file format:"), - choices=["wav", "flac", "mp3", "m4a"], - value="flac", - interactive=True, - ) - model_select.change( - fn=update_model_choices, - inputs=model_select, - outputs=model_choose, - ) - but2 = gr.Button(i18n("Convert"), variant="primary") - vc_output4 = gr.Textbox(label=i18n("Output information:")) - #wav_inputs.upload(fn=save_to_wav2_edited, inputs=[wav_inputs], outputs=[]) - but2.click( - uvr, - [ - model_choose, - dir_wav_input, - opt_vocal_root, - wav_inputs, - opt_ins_root, - agg, - format0, - model_select - ], - [vc_output4], - ) - with gr.TabItem(i18n("TTS")): - with gr.Group(): - with gr.Column(): - text_test = gr.Textbox(label=i18n("Text:"), placeholder=i18n("Enter the text you want to convert to voice..."), lines=6) - - with gr.Group(): - with gr.Row(): - with gr.Column(): - tts_methods_voice = ["Edge-tts", "Bark-tts"] - ttsmethod_test = gr.Dropdown(tts_methods_voice, value='Edge-tts', label = i18n('TTS Method:'), visible=True) - tts_test = gr.Dropdown(set_edge_voice, label = i18n('TTS Model:'), visible=True) - ttsmethod_test.change( - fn=update_tts_methods_voice, - inputs=ttsmethod_test, - outputs=tts_test, - ) - - with gr.Column(): - model_voice_path07 = gr.Dropdown(label=i18n('RVC Model:'), choices=sorted(names), value=default_weight) - best_match_index_path1 = match_index(model_voice_path07.value) - - file_index2_07 = gr.Dropdown( - label=i18n('Select the .index file:'), - choices=get_indexes(), - value=best_match_index_path1, - interactive=True, - allow_custom_value=True, - ) - #transpose_test = gr.Number(label = i18n('Transpose (integer, number Fof semitones, raise by an octave: 12, lower by an octave: -12):'), value=0, visible=True, interactive= True) - - - - - with gr.Row(): - refresh_button_ = gr.Button(i18n("Refresh"), variant="primary") - refresh_button_.click(fn=change_choices2, inputs=[], outputs=[model_voice_path07, file_index2_07]) - with gr.Row(): - original_ttsvoice = gr.Audio(label=i18n('Audio TTS:')) - ttsvoice = gr.Audio(label=i18n('Audio RVC:')) - - with gr.Row(): - button_test = gr.Button(i18n("Convert"), variant="primary") - - - button_test.click(make_test, inputs=[ - text_test, - tts_test, - model_voice_path07, - file_index2_07, - #transpose_test, - vc_transform0, - f0method8, - index_rate1, - crepe_hop_length, - f0_autotune, - ttsmethod_test - ], outputs=[ttsvoice, original_ttsvoice]) - - with gr.TabItem(i18n("Resources")): - easy_infer.download_model() - easy_infer.download_backup() - easy_infer.download_dataset(trainset_dir4) - easy_infer.download_audio() - easy_infer.youtube_separator() - with gr.TabItem(i18n("Extra")): - gr.Markdown( - value=i18n("This section contains some extra utilities that often may be in experimental phases") - ) - with gr.TabItem(i18n("Merge Audios")): - with gr.Group(): - gr.Markdown( - value="## " + i18n("Merge your generated audios with the instrumental") - ) - gr.Markdown(value=".",visible=True) - gr.Markdown(value=".",visible=True) - with gr.Row(): - with gr.Column(): - dropbox = gr.File(label=i18n("Drag your audio here:")) - gr.Markdown(value=i18n("### Instrumental settings:")) - input_audio1 = gr.Dropdown( - label=i18n("Choose your instrumental:"), - choices=sorted(audio_others_paths), - value='', - interactive=True, - ) - input_audio1_scale = gr.Slider( - minimum=0, - maximum=10, - label=i18n("Volume of the instrumental audio:"), - value=1.00, - interactive=True, - ) - gr.Markdown(value=i18n("### Audio settings:")) - input_audio3 = gr.Dropdown( - label=i18n("Select the generated audio"), - choices=sorted(audio_paths), - value='', - interactive=True, - ) - with gr.Row(): - input_audio3_scale = gr.Slider( - minimum=0, - maximum=10, - label=i18n("Volume of the generated audio:"), - value=1.00, - interactive=True, - ) - - gr.Markdown(value=i18n("### Add the effects:")) - reverb_ = gr.Checkbox( - label=i18n("Reverb"), - value=False, - interactive=True, - ) - compressor_ = gr.Checkbox( - label=i18n("Compressor"), - value=False, - interactive=True, - ) - noise_gate_ = gr.Checkbox( - label=i18n("Noise Gate"), - value=False, - interactive=True, - ) - - butnone = gr.Button(i18n("Merge"), variant="primary").style(full_width=True) - - vc_output1 = gr.Textbox(label=i18n("Output information:")) - vc_output2 = gr.Audio(label=i18n("Export audio (click on the three dots in the lower right corner to download)"), type='filepath') - - dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio1]) - dropbox.upload(fn=easy_infer.change_choices2, inputs=[], outputs=[input_audio1]) - - refresh_button.click( - fn=lambda: change_choices3(), - inputs=[], - outputs=[input_audio1, input_audio3], - ) - - butnone.click( - fn=audio_combined, - inputs=[input_audio1, input_audio3,input_audio1_scale,input_audio3_scale,reverb_,compressor_,noise_gate_], - outputs=[vc_output1, vc_output2] - ) - - - with gr.TabItem(i18n("Processing")): - with gr.Group(): - - with gr.Accordion(label=i18n("Model fusion, can be used to test timbre fusion")): - with gr.Row(): - with gr.Column(): - name_to_save0 = gr.Textbox( - label=i18n("Name:"), - value="", - max_lines=1, - interactive=True, - placeholder=i18n("Name for saving") - ) - alpha_a = gr.Slider( - minimum=0, - maximum=1, - label=i18n("Weight for Model A:"), - value=0.5, - interactive=True, - ) - if_f0_ = gr.Checkbox( - label=i18n("Whether the model has pitch guidance."), - value=True, - interactive=True, - ) - version_2 = gr.Radio( - label=i18n("Model architecture version:"), - choices=["v1", "v2"], - value="v2", - interactive=True, - ) - sr_ = gr.Radio( - label=i18n("Target sample rate:"), - choices=["40k", "48k"], - value="40k", - interactive=True, - ) - - - with gr.Column(): - ckpt_a = gr.Textbox(label=i18n("Path to Model A:"), value="", interactive=True, placeholder=i18n("Path to model")) - - ckpt_b = gr.Textbox(label=i18n("Path to Model B:"), value="", interactive=True, placeholder=i18n("Path to model")) - - info__ = gr.Textbox( - label=i18n("Model information to be placed:"), value="", max_lines=8, interactive=True, placeholder=i18n("Model information to be placed") - ) - info4 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8) - - - but6 = gr.Button(i18n("Fusion"), variant="primary") - - but6.click( - merge, - [ - ckpt_a, - ckpt_b, - alpha_a, - sr_, - if_f0_, - info__, - name_to_save0, - version_2, - ], - info4, - ) # def merge(path1,path2,alpha1,sr,f0,info): - with gr.Group(): - with gr.Accordion(label=i18n("Modify model information")): - with gr.Row(): ###### - with gr.Column(): - ckpt_path0 = gr.Textbox( - label=i18n("Path to Model:"), value="", interactive=True, placeholder=i18n("Path to model") - ) - info_ = gr.Textbox( - label=i18n("Model information to be modified:"), value="", max_lines=8, interactive=True, placeholder=i18n("Model information to be placed") - ) - - with gr.Column(): - name_to_save1 = gr.Textbox( - label=i18n("Save file name:"), - placeholder=i18n("Name for saving"), - value="", - max_lines=8, - interactive=True, - - ) - - info5 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8) - but7 = gr.Button(i18n("Modify"), variant="primary") - but7.click(change_info, [ckpt_path0, info_, name_to_save1], info5) - with gr.Group(): - with gr.Accordion(label=i18n("View model information")): - with gr.Row(): - with gr.Column(): - ckpt_path1 = gr.Textbox( - label=i18n("Path to Model:"), value="", interactive=True, placeholder=i18n("Path to model") - ) - - info6 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8) - but8 = gr.Button(i18n("View"), variant="primary") - but8.click(show_info, [ckpt_path1], info6) - with gr.Group(): - with gr.Accordion(label=i18n("Model extraction")): - with gr.Row(): - with gr.Column(): - save_name = gr.Textbox( - label=i18n("Name:"), value="", interactive=True, placeholder=i18n("Name for saving") - ) - if_f0__ = gr.Checkbox( - label=i18n("Whether the model has pitch guidance."), - value=True, - interactive=True, - ) - version_1 = gr.Radio( - label=i18n("Model architecture version:"), - choices=["v1", "v2"], - value="v2", - interactive=True, - ) - sr__ = gr.Radio( - label=i18n("Target sample rate:"), - choices=["32k", "40k", "48k"], - value="40k", - interactive=True, - ) - - with gr.Column(): - ckpt_path2 = gr.Textbox( - - label=i18n("Path to Model:"), - placeholder=i18n("Path to model"), - interactive=True, - ) - info___ = gr.Textbox( - label=i18n("Model information to be placed:"), value="", max_lines=8, interactive=True, placeholder=i18n("Model information to be placed") - ) - info7 = gr.Textbox(label=i18n("Output information:"), value="", max_lines=8) - - with gr.Row(): - - but9 = gr.Button(i18n("Extract"), variant="primary") - ckpt_path2.change( - change_info_, [ckpt_path2], [sr__, if_f0__, version_1] - ) - but9.click( - extract_small_model, - [ckpt_path2, save_name, sr__, if_f0__, info___, version_1], - info7, - ) - - - - - with gr.TabItem(i18n("Settings")): - with gr.Row(): - gr.Markdown(value= - i18n("Pitch settings") - ) - noteshertz = gr.Checkbox( - label = i18n("Whether to use note names instead of their hertz value. E.G. [C5, D6] instead of [523.25, 1174.66]Hz"), - value = rvc_globals.NotesOrHertz, - interactive = True, - ) - - noteshertz.change(fn=lambda nhertz: rvc_globals.__setattr__('NotesOrHertz', nhertz), inputs=[noteshertz], outputs=[]) - - noteshertz.change( - fn=switch_pitch_controls, - inputs=[f0method0], - outputs=[ - minpitch_slider, minpitch_txtbox, - maxpitch_slider, maxpitch_txtbox,] - ) - return app - -def GradioRun(app): - share_gradio_link = config.iscolab or config.paperspace - concurrency_count = 511 - max_size = 1022 - - if ( - config.iscolab or config.paperspace - ): - app.queue(concurrency_count=concurrency_count, max_size=max_size).launch( - favicon_path="./images/icon.png", - ) - else: - app.queue(concurrency_count=concurrency_count, max_size=max_size).launch( - favicon_path=".\images\icon.png", - ) - -if __name__ == "__main__": - if os.name == 'nt': - print(i18n("Any ConnectionResetErrors post-conversion are irrelevant and purely visual; they can be ignored.\n")) - app = GradioSetup(UTheme=config.grtheme) - GradioRun(app) \ No newline at end of file diff --git a/spaces/Fernando22/freegpt-webui/server/bp.py b/spaces/Fernando22/freegpt-webui/server/bp.py deleted file mode 100644 index 61d416797039dababd9e8222b4fc910ef65c40b9..0000000000000000000000000000000000000000 --- a/spaces/Fernando22/freegpt-webui/server/bp.py +++ /dev/null @@ -1,6 +0,0 @@ -from flask import Blueprint - -bp = Blueprint('bp', __name__, - template_folder='./../client/html', - static_folder='./../client', - static_url_path='assets') diff --git a/spaces/FooYou/marvel/app.py b/spaces/FooYou/marvel/app.py deleted file mode 100644 index 15046d3e441d70460a4acd732aabe72abdbb28de..0000000000000000000000000000000000000000 --- a/spaces/FooYou/marvel/app.py +++ /dev/null @@ -1,43 +0,0 @@ -import gradio - -class Model: - def __init__(self, name, path="", prefix=""): - self.name = name - self.path = path - self.prefix = prefix - -models = [ - Model("Anime","models/DucHaiten/DucHaitenAnime","DucHaiten style"), - Model("dreamlike","models/dreamlike-art/dreamlike-diffusion-1.0","dreamlike style"), - #Model("Illuminati_Diffusion","models/IlluminatiAI/Illuminati_Diffusion_v1.0","photo realistic style"), - Model("CF25","models/gsdf/Counterfeit-V2.5","best quality"), - Model("Midjourney","models/prompthero/midjourney-v4-diffusion","midjourney style"), - Model("PhotoReal","models/dreamlike-art/dreamlike-photoreal-2.0","photoreal style"), - Model("MF Base","models/MyneFactory/MF-Base","booru style"), - Model("Anything","models/Linaqruf/anything-v3.0", "Anything style") -] - -model1=[] -model2=[] -model3=[] - -for i in range(len(models)): - model3.append(models[i].name) - model1.append(gradio.Interface.load(models[i].path)) - model2.append(models[i].prefix) - -def ProcessP(prompt, modelSelected): - print(prompt) - print(modelSelected) - if (modelSelected==''): - modelSelected = "Anything" - model_idx=model3.index(modelSelected) - prompt+=", in "+model2[model_idx] - image_return = model1[model_idx](prompt) - return image_return - -sandbox = gradio.Interface(fn=ProcessP, - inputs=[gradio.Textbox(label="Enter Prompt:"), gradio.Dropdown(model3)], - outputs=[gradio.Image(label="Produced Image")], - title='AlStable Text to Image') -sandbox.queue(concurrency_count=20).launch(debug=True) \ No newline at end of file diff --git a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/train/process_ckpt.py b/spaces/FridaZuley/RVC_HFKawaii/infer/lib/train/process_ckpt.py deleted file mode 100644 index 36d359d5f853452da4e1a696a84b8457b8386c29..0000000000000000000000000000000000000000 --- a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/train/process_ckpt.py +++ /dev/null @@ -1,261 +0,0 @@ -import os -import sys -import traceback -from collections import OrderedDict - -import torch - -from i18n import I18nAuto - -i18n = I18nAuto() - - -def savee(ckpt, sr, if_f0, name, epoch, version, hps): - try: - opt = OrderedDict() - opt["weight"] = {} - for key in ckpt.keys(): - if "enc_q" in key: - continue - opt["weight"][key] = ckpt[key].half() - opt["config"] = [ - hps.data.filter_length // 2 + 1, - 32, - hps.model.inter_channels, - hps.model.hidden_channels, - hps.model.filter_channels, - hps.model.n_heads, - hps.model.n_layers, - hps.model.kernel_size, - hps.model.p_dropout, - hps.model.resblock, - hps.model.resblock_kernel_sizes, - hps.model.resblock_dilation_sizes, - hps.model.upsample_rates, - hps.model.upsample_initial_channel, - hps.model.upsample_kernel_sizes, - hps.model.spk_embed_dim, - hps.model.gin_channels, - hps.data.sampling_rate, - ] - opt["info"] = "%sepoch" % epoch - opt["sr"] = sr - opt["f0"] = if_f0 - opt["version"] = version - torch.save(opt, "weights/%s.pth" % name) - return "Success." - except: - return traceback.format_exc() - - -def show_info(path): - try: - a = torch.load(path, map_location="cpu") - return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % ( - a.get("info", "None"), - a.get("sr", "None"), - a.get("f0", "None"), - a.get("version", "None"), - ) - except: - return traceback.format_exc() - - -def extract_small_model(path, name, sr, if_f0, info, version): - try: - ckpt = torch.load(path, map_location="cpu") - if "model" in ckpt: - ckpt = ckpt["model"] - opt = OrderedDict() - opt["weight"] = {} - for key in ckpt.keys(): - if "enc_q" in key: - continue - opt["weight"][key] = ckpt[key].half() - if sr == "40k": - opt["config"] = [ - 1025, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 10, 2, 2], - 512, - [16, 16, 4, 4], - 109, - 256, - 40000, - ] - elif sr == "48k": - if version == "v1": - opt["config"] = [ - 1025, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 6, 2, 2, 2], - 512, - [16, 16, 4, 4, 4], - 109, - 256, - 48000, - ] - else: - opt["config"] = [ - 1025, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [12, 10, 2, 2], - 512, - [24, 20, 4, 4], - 109, - 256, - 48000, - ] - elif sr == "32k": - if version == "v1": - opt["config"] = [ - 513, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 4, 2, 2, 2], - 512, - [16, 16, 4, 4, 4], - 109, - 256, - 32000, - ] - else: - opt["config"] = [ - 513, - 32, - 192, - 192, - 768, - 2, - 6, - 3, - 0, - "1", - [3, 7, 11], - [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - [10, 8, 2, 2], - 512, - [20, 16, 4, 4], - 109, - 256, - 32000, - ] - if info == "": - info = "Extracted model." - opt["info"] = info - opt["version"] = version - opt["sr"] = sr - opt["f0"] = int(if_f0) - torch.save(opt, "weights/%s.pth" % name) - return "Success." - except: - return traceback.format_exc() - - -def change_info(path, info, name): - try: - ckpt = torch.load(path, map_location="cpu") - ckpt["info"] = info - if name == "": - name = os.path.basename(path) - torch.save(ckpt, "weights/%s" % name) - return "Success." - except: - return traceback.format_exc() - - -def merge(path1, path2, alpha1, sr, f0, info, name, version): - try: - - def extract(ckpt): - a = ckpt["model"] - opt = OrderedDict() - opt["weight"] = {} - for key in a.keys(): - if "enc_q" in key: - continue - opt["weight"][key] = a[key] - return opt - - ckpt1 = torch.load(path1, map_location="cpu") - ckpt2 = torch.load(path2, map_location="cpu") - cfg = ckpt1["config"] - if "model" in ckpt1: - ckpt1 = extract(ckpt1) - else: - ckpt1 = ckpt1["weight"] - if "model" in ckpt2: - ckpt2 = extract(ckpt2) - else: - ckpt2 = ckpt2["weight"] - if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())): - return "Fail to merge the models. The model architectures are not the same." - opt = OrderedDict() - opt["weight"] = {} - for key in ckpt1.keys(): - # try: - if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape: - min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0]) - opt["weight"][key] = ( - alpha1 * (ckpt1[key][:min_shape0].float()) - + (1 - alpha1) * (ckpt2[key][:min_shape0].float()) - ).half() - else: - opt["weight"][key] = ( - alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float()) - ).half() - # except: - # pdb.set_trace() - opt["config"] = cfg - """ - if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000] - elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000] - elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000] - """ - opt["sr"] = sr - opt["f0"] = 1 if f0 == i18n("是") else 0 - opt["version"] = version - opt["info"] = info - torch.save(opt, "weights/%s.pth" % name) - return "Success." - except: - return traceback.format_exc() diff --git a/spaces/GeorgeOrville/bingo/src/components/chat-header.tsx b/spaces/GeorgeOrville/bingo/src/components/chat-header.tsx deleted file mode 100644 index c6664b8dee61179f844d45c5bd650518fc2cb4c2..0000000000000000000000000000000000000000 --- a/spaces/GeorgeOrville/bingo/src/components/chat-header.tsx +++ /dev/null @@ -1,12 +0,0 @@ -import LogoIcon from '@/assets/images/logo.svg' -import Image from 'next/image' - -export function ChatHeader() { - return ( -
-
-
- )
-}
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
deleted file mode 100644
index 636f3f67c7c246a60512e2b70d333320fbb85feb..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
+++ /dev/null
@@ -1,22 +0,0 @@
-_base_ = [
- '../_base_/models/faster_rcnn_r50_fpn.py',
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
-model = dict(
- pretrained=None,
- backbone=dict(
- frozen_stages=-1, zero_init_residual=False, norm_cfg=norm_cfg),
- neck=dict(norm_cfg=norm_cfg),
- roi_head=dict(
- bbox_head=dict(
- type='Shared4Conv1FCBBoxHead',
- conv_out_channels=256,
- norm_cfg=norm_cfg)))
-# optimizer
-optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0))
-optimizer_config = dict(_delete_=True, grad_clip=None)
-# learning policy
-lr_config = dict(warmup_ratio=0.1, step=[65, 71])
-runner = dict(type='EpochBasedRunner', max_epochs=73)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/datasets/chase_db1.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/datasets/chase_db1.py
deleted file mode 100644
index 298594ea925f87f22b37094a2ec50e370aec96a0..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/datasets/chase_db1.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# dataset settings
-dataset_type = 'ChaseDB1Dataset'
-data_root = 'data/CHASE_DB1'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-img_scale = (960, 999)
-crop_size = (128, 128)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations'),
- dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg'])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=img_scale,
- # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
-]
-
-data = dict(
- samples_per_gpu=4,
- workers_per_gpu=4,
- train=dict(
- type='RepeatDataset',
- times=40000,
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/training',
- ann_dir='annotations/training',
- pipeline=train_pipeline)),
- val=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/validation',
- ann_dir='annotations/validation',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/validation',
- ann_dir='annotations/validation',
- pipeline=test_pipeline))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/__init__.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/__init__.py
deleted file mode 100644
index 96a8ca14fee923d613c1cd65c82b25545b6d1fc2..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/__init__.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import mmcv
-
-from .version import __version__, version_info
-
-MMCV_MIN = '1.3.1'
-MMCV_MAX = '1.4.0'
-
-
-def digit_version(version_str):
- digit_version = []
- for x in version_str.split('.'):
- if x.isdigit():
- digit_version.append(int(x))
- elif x.find('rc') != -1:
- patch_version = x.split('rc')
- digit_version.append(int(patch_version[0]) - 1)
- digit_version.append(int(patch_version[1]))
- return digit_version
-
-
-mmcv_min_version = digit_version(MMCV_MIN)
-mmcv_max_version = digit_version(MMCV_MAX)
-mmcv_version = digit_version(mmcv.__version__)
-
-
-assert (mmcv_min_version <= mmcv_version <= mmcv_max_version), \
- f'MMCV=={mmcv.__version__} is used but incompatible. ' \
- f'Please install mmcv>={mmcv_min_version}, <={mmcv_max_version}.'
-
-__all__ = ['__version__', 'version_info']
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/adversarial/discriminators/base.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/adversarial/discriminators/base.py
deleted file mode 100644
index a9d517e9f5bf0f4e18252c45c8db3a35a7255f69..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/adversarial/discriminators/base.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from abc import ABC, abstractmethod
-import typing as tp
-
-import torch
-import torch.nn as nn
-
-
-FeatureMapType = tp.List[torch.Tensor]
-LogitsType = torch.Tensor
-MultiDiscriminatorOutputType = tp.Tuple[tp.List[LogitsType], tp.List[FeatureMapType]]
-
-
-class MultiDiscriminator(ABC, nn.Module):
- """Base implementation for discriminators composed of sub-discriminators acting at different scales.
- """
- def __init__(self):
- super().__init__()
-
- @abstractmethod
- def forward(self, x: torch.Tensor) -> MultiDiscriminatorOutputType:
- ...
-
- @property
- @abstractmethod
- def num_discriminators(self) -> int:
- """Number of discriminators.
- """
- ...
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/utils/universal_checkpoint.py b/spaces/HaloMaster/chinesesummary/fengshen/utils/universal_checkpoint.py
deleted file mode 100644
index 3e62f6993f22fa01dad5cabc4d832e36478c5deb..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/utils/universal_checkpoint.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from pytorch_lightning.callbacks import ModelCheckpoint
-
-
-class UniversalCheckpoint(ModelCheckpoint):
- @staticmethod
- def add_argparse_args(parent_args):
- parser = parent_args.add_argument_group('universal checkpoint callback')
-
- parser.add_argument('--monitor', default='train_loss', type=str)
- parser.add_argument('--mode', default='min', type=str)
- parser.add_argument('--save_ckpt_path', default='./ckpt/', type=str)
- parser.add_argument('--load_ckpt_path', default='./ckpt/', type=str)
- parser.add_argument(
- '--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str)
- parser.add_argument('--save_last', action='store_true', default=False)
- parser.add_argument('--save_top_k', default=3, type=float)
- parser.add_argument('--every_n_train_steps', default=None, type=float)
- parser.add_argument('--save_weights_only', action='store_true', default=False)
- parser.add_argument('--every_n_epochs', default=None, type=int)
- parser.add_argument('--save_on_train_epoch_end', action='store_true', default=None)
-
- return parent_args
-
- def __init__(self, args):
- super().__init__(monitor=args.monitor,
- save_top_k=args.save_top_k,
- mode=args.mode,
- every_n_train_steps=args.every_n_train_steps,
- save_weights_only=args.save_weights_only,
- dirpath=args.save_ckpt_path,
- filename=args.filename,
- save_last=args.save_last,
- every_n_epochs=args.every_n_epochs,
- save_on_train_epoch_end=args.save_on_train_epoch_end)
diff --git a/spaces/Haokko/AronaTTS/monotonic_align/__init__.py b/spaces/Haokko/AronaTTS/monotonic_align/__init__.py
deleted file mode 100644
index 40b6f64aa116c74cac2f6a33444c9eeea2fdb38c..0000000000000000000000000000000000000000
--- a/spaces/Haokko/AronaTTS/monotonic_align/__init__.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
- """ numba optimized version.
- neg_cent: [b, t_t, t_s]
- mask: [b, t_t, t_s]
- """
- device = neg_cent.device
- dtype = neg_cent.dtype
- neg_cent = neg_cent.data.cpu().numpy().astype(float32)
- path = zeros(neg_cent.shape, dtype=int32)
-
- t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
- t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
- maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
- return from_numpy(path).to(device=device, dtype=dtype)
-
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/roberta/wsc/README.md b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/roberta/wsc/README.md
deleted file mode 100644
index 21a045d999739836a17574593292e42131315ae9..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/roberta/wsc/README.md
+++ /dev/null
@@ -1,125 +0,0 @@
-# Finetuning RoBERTa on Winograd Schema Challenge (WSC) data
-
-The following instructions can be used to finetune RoBERTa on the WSC training
-data provided by [SuperGLUE](https://super.gluebenchmark.com/).
-
-Note that there is high variance in the results. For our GLUE/SuperGLUE
-submission we swept over the learning rate (1e-5, 2e-5, 3e-5), batch size (16,
-32, 64) and total number of updates (500, 1000, 2000, 3000), as well as the
-random seed. Out of ~100 runs we chose the best 7 models and ensembled them.
-
-**Approach:** The instructions below use a slightly different loss function than
-what's described in the original RoBERTa arXiv paper. In particular,
-[Kocijan et al. (2019)](https://arxiv.org/abs/1905.06290) introduce a margin
-ranking loss between `(query, candidate)` pairs with tunable hyperparameters
-alpha and beta. This is supported in our code as well with the `--wsc-alpha` and
-`--wsc-beta` arguments. However, we achieved slightly better (and more robust)
-results on the development set by instead using a single cross entropy loss term
-over the log-probabilities for the query and all mined candidates. **The
-candidates are mined using spaCy from each input sentence in isolation, so the
-approach remains strictly pointwise.** This reduces the number of
-hyperparameters and our best model achieved 92.3% development set accuracy,
-compared to ~90% accuracy for the margin loss. Later versions of the RoBERTa
-arXiv paper will describe this updated formulation.
-
-### 1) Download the WSC data from the SuperGLUE website:
-```bash
-wget https://dl.fbaipublicfiles.com/glue/superglue/data/v2/WSC.zip
-unzip WSC.zip
-
-# we also need to copy the RoBERTa dictionary into the same directory
-wget -O WSC/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt
-```
-
-### 2) Finetune over the provided training data:
-```bash
-TOTAL_NUM_UPDATES=2000 # Total number of training steps.
-WARMUP_UPDATES=250 # Linearly increase LR over this many steps.
-LR=2e-05 # Peak LR for polynomial LR scheduler.
-MAX_SENTENCES=16 # Batch size per GPU.
-SEED=1 # Random seed.
-ROBERTA_PATH=/path/to/roberta/model.pt
-
-# we use the --user-dir option to load the task and criterion
-# from the examples/roberta/wsc directory:
-FAIRSEQ_PATH=/path/to/fairseq
-FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/wsc
-
-CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train WSC/ \
- --restore-file $ROBERTA_PATH \
- --reset-optimizer --reset-dataloader --reset-meters \
- --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \
- --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \
- --valid-subset val \
- --fp16 --ddp-backend legacy_ddp \
- --user-dir $FAIRSEQ_USER_DIR \
- --task wsc --criterion wsc --wsc-cross-entropy \
- --arch roberta_large --bpe gpt2 --max-positions 512 \
- --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \
- --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \
- --lr-scheduler polynomial_decay --lr $LR \
- --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \
- --batch-size $MAX_SENTENCES \
- --max-update $TOTAL_NUM_UPDATES \
- --log-format simple --log-interval 100 \
- --seed $SEED
-```
-
-The above command assumes training on 4 GPUs, but you can achieve the same
-results on a single GPU by adding `--update-freq=4`.
-
-### 3) Evaluate
-```python
-from fairseq.models.roberta import RobertaModel
-from examples.roberta.wsc import wsc_utils # also loads WSC task and criterion
-roberta = RobertaModel.from_pretrained('checkpoints', 'checkpoint_best.pt', 'WSC/')
-roberta.cuda()
-nsamples, ncorrect = 0, 0
-for sentence, label in wsc_utils.jsonl_iterator('WSC/val.jsonl', eval=True):
- pred = roberta.disambiguate_pronoun(sentence)
- nsamples += 1
- if pred == label:
- ncorrect += 1
-print('Accuracy: ' + str(ncorrect / float(nsamples)))
-# Accuracy: 0.9230769230769231
-```
-
-## RoBERTa training on WinoGrande dataset
-We have also provided `winogrande` task and criterion for finetuning on the
-[WinoGrande](https://mosaic.allenai.org/projects/winogrande) like datasets
-where there are always two candidates and one is correct.
-It's more efficient implementation for such subcases.
-
-```bash
-TOTAL_NUM_UPDATES=23750 # Total number of training steps.
-WARMUP_UPDATES=2375 # Linearly increase LR over this many steps.
-LR=1e-05 # Peak LR for polynomial LR scheduler.
-MAX_SENTENCES=32 # Batch size per GPU.
-SEED=1 # Random seed.
-ROBERTA_PATH=/path/to/roberta/model.pt
-
-# we use the --user-dir option to load the task and criterion
-# from the examples/roberta/wsc directory:
-FAIRSEQ_PATH=/path/to/fairseq
-FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/wsc
-
-cd fairseq
-CUDA_VISIBLE_DEVICES=0 fairseq-train winogrande_1.0/ \
- --restore-file $ROBERTA_PATH \
- --reset-optimizer --reset-dataloader --reset-meters \
- --no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \
- --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \
- --valid-subset val \
- --fp16 --ddp-backend legacy_ddp \
- --user-dir $FAIRSEQ_USER_DIR \
- --task winogrande --criterion winogrande \
- --wsc-margin-alpha 5.0 --wsc-margin-beta 0.4 \
- --arch roberta_large --bpe gpt2 --max-positions 512 \
- --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \
- --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \
- --lr-scheduler polynomial_decay --lr $LR \
- --warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \
- --batch-size $MAX_SENTENCES \
- --max-update $TOTAL_NUM_UPDATES \
- --log-format simple --log-interval 100
-```
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/dictionary.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/dictionary.py
deleted file mode 100644
index d6495389f0102156f0b2dc6f892946d572911bbe..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/dictionary.py
+++ /dev/null
@@ -1,401 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-from collections import Counter
-from multiprocessing import Pool
-
-import torch
-from fairseq import utils
-from fairseq.data import data_utils
-from fairseq.file_chunker_utils import Chunker, find_offsets
-from fairseq.file_io import PathManager
-from fairseq.tokenizer import tokenize_line
-
-
-class Dictionary:
- """A mapping from symbols to consecutive integers"""
-
- def __init__(
- self,
- *, # begin keyword-only arguments
- bos="欢迎使用新必应
- 由 AI 支持的网页版 Copilot
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/KPCGD/bingo/src/components/ui/alert-dialog.tsx b/spaces/KPCGD/bingo/src/components/ui/alert-dialog.tsx
deleted file mode 100644
index 17fec4d16510328deacc1416569173c97761ef72..0000000000000000000000000000000000000000
--- a/spaces/KPCGD/bingo/src/components/ui/alert-dialog.tsx
+++ /dev/null
@@ -1,150 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as AlertDialogPrimitive from '@radix-ui/react-alert-dialog'
-
-import { cn } from '@/lib/utils'
-import { buttonVariants } from '@/components/ui/button'
-
-const AlertDialog = AlertDialogPrimitive.Root
-
-const AlertDialogTrigger = AlertDialogPrimitive.Trigger
-
-const AlertDialogPortal = ({
- className,
- children,
- ...props
-}: AlertDialogPrimitive.AlertDialogPortalProps) => (
- Relation
-with other
-SD models
-Facts speak louder than words
-Calculate the cosine similarity to compare
-the similarity with other models
-SimilarityCalculatior made by
-Nyanko Lepsoni and RcINS.
-Danke schön!
-SimilarityCalculatior SimilarityCalculatior SimilarityCalculatior
-100%
-Base NovelAI
-71.5% 83.8%
-SD1.4 SD1.2
-83.8% 67.9% 100%
-80.9% 74.7% 90.3%
-80.5% 74.7% 89.9%
-71.5% 100% 67.9%
-80.9% 74.7% 90.3%
-84.1% 67.7% 98.3%
-87.8% 68.9% 95.5%
-SD1.4
-SD1.2
-NovelAI
-CModel
-CThing
-AnythingV3
-83.9% 68% 99.9%
-WD1.3.5
-Emis1.0
-Certinty
-
-
-
-
- {children}
-
-
-
-
-
-## Dataset
-
-### Train Dataset
-
-| trainset | instance_num | repeat_num | source |
-| :--------: | :----------: | :--------: | :------------------------: |
-| icdar_2011 | 3567 | 20 | real |
-| icdar_2013 | 848 | 20 | real |
-| icdar2015 | 4468 | 20 | real |
-| coco_text | 42142 | 20 | real |
-| IIIT5K | 2000 | 20 | real |
-| SynthText | 2400000 | 1 | synth |
-| SynthAdd | 1216889 | 1 | synth, 1.6m in [\[1\]](#1) |
-| Syn90k | 2400000 | 1 | synth |
-
-### Test Dataset
-
-| testset | instance_num | type |
-| :-----: | :----------: | :---------------------------: |
-| IIIT5K | 3000 | regular |
-| SVT | 647 | regular |
-| IC13 | 1015 | regular |
-| IC15 | 2077 | irregular |
-| SVTP | 645 | irregular, 639 in [\[1\]](#1) |
-| CT80 | 288 | irregular |
-
-## Results and Models
-
-| Methods | Backbone | Decoder | | Regular Text | | | | Irregular Text | | download |
-| :----------------------------------------------------------: | :---------: | :------------------: | :----: | :----------: | :--: | :-: | :--: | :------------: | :--: | :------------------------------------------------------------: |
-| | | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
-| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | 95.0 | 89.6 | 93.7 | | 79.0 | 82.2 | 88.9 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_r31_parallel_decoder_academic-dba3a4a3.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/20210327_154129.log.json) |
-| [SAR](configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | 95.2 | 88.7 | 92.4 | | 78.2 | 81.9 | 89.6 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_r31_sequential_decoder_academic-d06c9a8e.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/20210330_105728.log.json) |
-
-## Chinese Dataset
-
-## Results and Models
-
-| Methods | Backbone | Decoder | | download |
-| :---------------------------------------------------------------: | :---------: | :----------------: | :-: | :-----------------------------------------------------------------------------------------------------: |
-| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_chinese.py) | R31-1/8-1/4 | ParallelSARDecoder | | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_r31_parallel_decoder_chineseocr_20210507-b4be8214.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/20210506_225557.log.json) \| [dict](https://download.openmmlab.com/mmocr/textrecog/sar/dict_printed_chinese_english_digits.txt) |
-
-```{note}
-
-- `R31-1/8-1/4` means the height of feature from backbone is 1/8 of input image, where 1/4 for width.
-- We did not use beam search during decoding.
-- We implemented two kinds of decoder. Namely, `ParallelSARDecoder` and `SequentialSARDecoder`.
- - `ParallelSARDecoder`: Parallel decoding during training with `LSTM` layer. It would be faster.
- - `SequentialSARDecoder`: Sequential Decoding during training with `LSTMCell`. It would be easier to understand.
-- For train dataset.
- - We did not construct distinct data groups (20 groups in [[1]](#1)) to train the model group-by-group since it would render model training too complicated.
- - Instead, we randomly selected `2.4m` patches from `Syn90k`, `2.4m` from `SynthText` and `1.2m` from `SynthAdd`, and grouped all data together. See [config](https://download.openmmlab.com/mmocr/textrecog/sar/sar_r31_academic.py) for details.
-- We used 48 GPUs with `total_batch_size = 64 * 48` in the experiment above to speedup training, while keeping the `initial lr = 1e-3` unchanged.
-```
-
-## Citation
-
-```bibtex
-@inproceedings{li2019show,
- title={Show, attend and read: A simple and strong baseline for irregular text recognition},
- author={Li, Hui and Wang, Peng and Shen, Chunhua and Zhang, Guyu},
- booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
- volume={33},
- number={01},
- pages={8610--8617},
- year={2019}
-}
-```
diff --git a/spaces/MMMMQZ/MQZGPT/locale/extract_locale.py b/spaces/MMMMQZ/MQZGPT/locale/extract_locale.py
deleted file mode 100644
index 32b0924bd6dffe150cb3e481ddadef836b91b83c..0000000000000000000000000000000000000000
--- a/spaces/MMMMQZ/MQZGPT/locale/extract_locale.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import json
-import re
-
-# Define regular expression patterns
-pattern = r'i18n\((\"{3}.*?\"{3}|\".*?\")\)'
-
-# Load the .py file
-with open('ChuanhuChatbot.py', 'r', encoding='utf-8') as f:
- contents = f.read()
-
-# Load the .py files in the modules folder
-for filename in os.listdir("modules"):
- if filename.endswith(".py"):
- with open(os.path.join("modules", filename), "r", encoding="utf-8") as f:
- contents += f.read()
-
-# Matching with regular expressions
-matches = re.findall(pattern, contents, re.DOTALL)
-
-# Convert to key/value pairs
-data = {match.strip('()"'): '' for match in matches}
-
-# Save as a JSON file
-with open('labels.json', 'w', encoding='utf-8') as f:
- json.dump(data, f, ensure_ascii=False, indent=4)
\ No newline at end of file
diff --git a/spaces/MWilinski/bot/tests/api/question_answering/__init__.py b/spaces/MWilinski/bot/tests/api/question_answering/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/data_utils.py b/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/data_utils.py
deleted file mode 100644
index 1d10e95398af4e76ae61de15fca8e0e80d85282d..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/lovelive-ShojoKageki-vits/data_utils.py
+++ /dev/null
@@ -1,307 +0,0 @@
-import os
-import random
-
-import torch
-import torchaudio
-import torch.utils.data
-
-import commons
-from mel_processing import spectrogram_torch
-from utils import load_filepaths_and_text
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, speaker_id, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
- def __init__(self, audiopaths_sid_text, hparams):
- self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
- # self.text_cleaners = hparams.text_cleaners
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
- self.src_sampling_rate = getattr(hparams, "src_sampling_rate",
- self.sampling_rate)
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 190)
-
- phone_file = getattr(hparams, "phone_table", None)
- self.phone_dict = None
- if phone_file is not None:
- self.phone_dict = {}
- with open(phone_file) as fin:
- for line in fin:
- arr = line.strip().split()
- self.phone_dict[arr[0]] = int(arr[1])
-
- speaker_file = getattr(hparams, "speaker_table", None)
- self.speaker_dict = None
- if speaker_file is not None:
- self.speaker_dict = {}
- with open(speaker_file) as fin:
- for line in fin:
- arr = line.strip().split()
- self.speaker_dict[arr[0]] = int(arr[1])
-
- random.seed(1234)
- random.shuffle(self.audiopaths_sid_text)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_sid_text_new = []
- lengths = []
- for item in self.audiopaths_sid_text:
- audiopath = item[0]
- # filename|text or filename|speaker|text
- text = item[1] if len(item) == 2 else item[2]
- if self.min_text_len <= len(text) and len(
- text) <= self.max_text_len:
- audiopaths_sid_text_new.append(item)
- lengths.append(
- int(
- os.path.getsize(audiopath) * self.sampling_rate /
- self.src_sampling_rate) // (2 * self.hop_length))
- self.audiopaths_sid_text = audiopaths_sid_text_new
- self.lengths = lengths
-
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
- audiopath = audiopath_sid_text[0]
- if len(audiopath_sid_text) == 2: # filename|text
- sid = 0
- text = audiopath_sid_text[1]
- else: # filename|speaker|text
- sid = self.speaker_dict[audiopath_sid_text[1]]
- text = audiopath_sid_text[2]
- text = self.get_text(text)
- spec, wav = self.get_audio(audiopath)
- sid = self.get_sid(sid)
- return (text, spec, wav, sid)
-
- def get_audio(self, filename):
- audio, sampling_rate = torchaudio.load(filename, normalize=False)
- if sampling_rate != self.sampling_rate:
- audio = audio.to(torch.float)
- audio = torchaudio.transforms.Resample(sampling_rate,
- self.sampling_rate)(audio)
- audio = audio.to(torch.int16)
- audio = audio[0] # Get the first channel
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec = spectrogram_torch(audio_norm,
- self.filter_length,
- self.sampling_rate,
- self.hop_length,
- self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- return spec, audio_norm
-
- def get_text(self, text):
- text_norm = [self.phone_dict[phone] for phone in text.split()]
- if self.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = torch.LongTensor(text_norm)
- return text_norm
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def __getitem__(self, index):
- return self.get_audio_text_speaker_pair(
- self.audiopaths_sid_text[index])
-
- def __len__(self):
- return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
- """ Zero-pads model inputs and targets
- """
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text, audio and speaker identities
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized, sid]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(torch.LongTensor(
- [x[1].size(1) for x in batch]),
- dim=0,
- descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
- sid = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0),
- max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- sid[i] = row[3]
-
- if self.return_ids:
- return (text_padded, text_lengths, spec_padded, spec_lengths,
- wav_padded, wav_lengths, sid, ids_sorted_decreasing)
- return (text_padded, text_lengths, spec_padded, spec_lengths,
- wav_padded, wav_lengths, sid)
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler
- ):
- """
- Maintain similar input lengths in a batch.
- Length groups are specified by boundaries.
- Ex) boundaries = [b1, b2, b3] -> any batch is included either
- {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
- It removes samples which are not included in the boundaries.
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1
- or length(x) > b3 are discarded.
- """
- def __init__(self,
- dataset,
- batch_size,
- boundaries,
- num_replicas=None,
- rank=None,
- shuffle=True):
- super().__init__(dataset,
- num_replicas=num_replicas,
- rank=rank,
- shuffle=shuffle)
- self.lengths = dataset.lengths
- self.batch_size = batch_size
- self.boundaries = boundaries
-
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
- self.total_size = sum(self.num_samples_per_bucket)
- self.num_samples = self.total_size // self.num_replicas
-
- def _create_buckets(self):
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
- for i in range(len(self.lengths)):
- length = self.lengths[i]
- idx_bucket = self._bisect(length)
- if idx_bucket != -1:
- buckets[idx_bucket].append(i)
-
- for i in range(len(buckets) - 1, 0, -1):
- if len(buckets[i]) == 0:
- buckets.pop(i)
- self.boundaries.pop(i + 1)
-
- num_samples_per_bucket = []
- for i in range(len(buckets)):
- len_bucket = len(buckets[i])
- total_batch_size = self.num_replicas * self.batch_size
- rem = (total_batch_size -
- (len_bucket % total_batch_size)) % total_batch_size
- num_samples_per_bucket.append(len_bucket + rem)
- return buckets, num_samples_per_bucket
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
-
- indices = []
- if self.shuffle:
- for bucket in self.buckets:
- indices.append(
- torch.randperm(len(bucket), generator=g).tolist())
- else:
- for bucket in self.buckets:
- indices.append(list(range(len(bucket))))
-
- batches = []
- for i in range(len(self.buckets)):
- bucket = self.buckets[i]
- len_bucket = len(bucket)
- ids_bucket = indices[i]
- num_samples_bucket = self.num_samples_per_bucket[i]
-
- # add extra samples to make it evenly divisible
- rem = num_samples_bucket - len_bucket
- ids_bucket = ids_bucket + ids_bucket * (
- rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
- # subsample
- ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
- # batching
- for j in range(len(ids_bucket) // self.batch_size):
- batch = [
- bucket[idx]
- for idx in ids_bucket[j * self.batch_size:(j + 1) *
- self.batch_size]
- ]
- batches.append(batch)
-
- if self.shuffle:
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
- batches = [batches[i] for i in batch_ids]
- self.batches = batches
-
- assert len(self.batches) * self.batch_size == self.num_samples
- return iter(self.batches)
-
- def _bisect(self, x, lo=0, hi=None):
- if hi is None:
- hi = len(self.boundaries) - 1
-
- if hi > lo:
- mid = (hi + lo) // 2
- if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
- return mid
- elif x <= self.boundaries[mid]:
- return self._bisect(x, lo, mid)
- else:
- return self._bisect(x, mid + 1, hi)
- else:
- return -1
-
- def __len__(self):
- return self.num_samples // self.batch_size
diff --git a/spaces/Manjushri/MusicGen/audiocraft/modules/transformer.py b/spaces/Manjushri/MusicGen/audiocraft/modules/transformer.py
deleted file mode 100644
index e69cca829d774d0b8b36c0de9b7924373da81b43..0000000000000000000000000000000000000000
--- a/spaces/Manjushri/MusicGen/audiocraft/modules/transformer.py
+++ /dev/null
@@ -1,747 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Transformer model, with streaming support, xformer attention support
-and easy causal attention with a potentially finite receptive field.
-
-See `StreamingTransformer` for more information.
-
-Unlike regular PyTorch Transformer, we make the hard choice that batches are first.
-"""
-
-import typing as tp
-
-from einops import rearrange
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-from torch.utils.checkpoint import checkpoint as torch_checkpoint
-from xformers import ops
-
-from .rope import RotaryEmbedding
-from .streaming import StreamingModule
-
-_efficient_attention_backend: str = 'torch'
-
-
-def set_efficient_attention_backend(backend: str = 'torch'):
- # Using torch by default, it seems a bit faster on older P100 GPUs (~20% faster).
- global _efficient_attention_backend
- assert _efficient_attention_backend in ['xformers', 'torch']
- _efficient_attention_backend = backend
-
-
-def _get_attention_time_dimension() -> int:
- if _efficient_attention_backend == 'torch':
- return 2
- else:
- return 1
-
-
-def _is_profiled() -> bool:
- # Return true if we are currently running with a xformers profiler activated.
- try:
- from xformers.profiler import profiler
- except ImportError:
- return False
- return profiler._Profiler._CURRENT_PROFILER is not None
-
-
-def create_norm_fn(norm_type: str, dim: int, **kwargs) -> nn.Module:
- """Create normalization module for transformer encoder layer.
-
- Args:
- norm_type (str): Normalization method.
- dim (int): Dimension of the normalized layer.
- **kwargs (dict): Additional parameters for normalization layer.
- Returns:
- nn.Module: Normalization module.
- """
- if norm_type == 'layer_norm':
- return nn.LayerNorm(dim, eps=1e-5, **kwargs)
- else:
- raise ValueError(f"Unknown norm type: {norm_type}")
-
-
-def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000,
- dtype: torch.dtype = torch.float32) -> torch.Tensor:
- """Create sinusoidal positional embedding, with shape `[B, T, C]`.
-
- Args:
- positions (torch.Tensor): LongTensor of positions.
- dim (int): Dimension of the embedding.
- max_period (float): Maximum period of the cosine/sine functions.
- dtype (torch.dtype or str): dtype to use to generate the embedding.
- Returns:
- torch.Tensor: Sinusoidal positional embedding.
- """
- # We aim for BTC format
- assert dim % 2 == 0
- half_dim = dim // 2
- positions = positions.to(dtype)
- adim = torch.arange(half_dim, device=positions.device, dtype=dtype).view(1, 1, -1)
- max_period_tensor = torch.full([], max_period, device=positions.device, dtype=dtype) # avoid sync point
- phase = positions / (max_period_tensor ** (adim / (half_dim - 1)))
- return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)
-
-
-def expand_repeated_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
- """torch.repeat_interleave(x, dim=2, repeats=n_rep) from xlformers"""
- if n_rep == 1:
- return x
- if _efficient_attention_backend == 'torch':
- bs, n_kv_heads, slen, head_dim = x.shape
- return (
- x[:, :, None, :, :]
- .expand(bs, n_kv_heads, n_rep, slen, head_dim)
- .reshape(bs, n_kv_heads * n_rep, slen, head_dim)
- )
- else:
- bs, slen, n_kv_heads, head_dim = x.shape
- return (
- x[:, :, :, None, :]
- .expand(bs, slen, n_kv_heads, n_rep, head_dim)
- .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
- )
-
-
-class LayerScale(nn.Module):
- """Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
- This rescales diagonaly the residual outputs close to 0, with a learnt scale.
-
- Args:
- channels (int): Number of channels.
- init (float): Initial scale.
- channel_last (bool): If True, expect `[*, C]` shaped tensors, otherwise, `[*, C, T]`.
- device (torch.device or None): Device on which to initialize the module.
- dtype (torch.dtype or None): dtype to use to initialize the module.
- """
- def __init__(self, channels: int, init: float = 1e-4, channel_last: bool = True,
- device=None, dtype=None):
- super().__init__()
- self.channel_last = channel_last
- self.scale = nn.Parameter(
- torch.full((channels,), init,
- requires_grad=True, device=device, dtype=dtype))
-
- def forward(self, x: torch.Tensor):
- if self.channel_last:
- return self.scale * x
- else:
- return self.scale[:, None] * x
-
-
-class StreamingMultiheadAttention(StreamingModule):
- """Similar to `nn.MultiheadAttention` but with support for streaming, causal evaluation.
-
- Args:
- embed_dim (int): Dimension to project to.
- num_heads (int): Number of heads.
- dropout (float): Dropout level.
- bias (bool): Use bias in projections.
- causal (bool): Causal mask applied automatically.
- past_context (int or None): Receptive field for the causal mask, infinite if None.
- custom (bool): Use custom MHA implementation, for testing / benchmarking.
- memory_efficient (bool): Use xformers based memory efficient attention.
- attention_as_float32 (bool): Perform the attention as float32
- (especially important with memory_efficient as autocast won't do this automatically).
- rope (`RotaryEmbedding` or None): Rope embedding to use.
- cross_attention: Should be true when used as a cross attention.
- All keys and values must be available at once, streaming is only for the queries.
- Cannot be used with `causal` or `rope` (as it wouldn't make sens to
- intepret the time steps in the keys relative to those in the queries).
- safe_streaming (bool): Bug fix, will go away with xformers update.
- qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product.
- kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
- This will lead to faster decoding time on A100 or other GPUs with tensorcore.
- device (torch.device or None): Sevice on which to initialize.
- dtype (torch.dtype or None): dtype to use.
- """
- def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, bias: bool = True,
- causal: bool = False, past_context: tp.Optional[int] = None, custom: bool = False,
- memory_efficient: bool = False, attention_as_float32: bool = False,
- rope: tp.Optional[RotaryEmbedding] = None, cross_attention: bool = False,
- safe_streaming: bool = True, qk_layer_norm: bool = False, kv_repeat: int = 1,
- device=None, dtype=None):
- super().__init__()
- factory_kwargs = {'device': device, 'dtype': dtype}
- if past_context is not None:
- assert causal
-
- self.embed_dim = embed_dim
- self.causal = causal
- self.past_context = past_context
- self.memory_efficient = memory_efficient
- self.attention_as_float32 = attention_as_float32
- self.rope = rope
- self.cross_attention = cross_attention
- self.safe_streaming = safe_streaming
- self.num_heads = num_heads
- self.dropout = dropout
- self.kv_repeat = kv_repeat
- if cross_attention:
- assert not causal, "Causal cannot work with cross attention."
- assert rope is None, "Rope cannot work with cross attention."
-
- if memory_efficient:
- _verify_xformers_memory_efficient_compat()
-
- self.custom = _is_custom(custom, memory_efficient)
- if self.custom:
- out_dim = embed_dim
- assert num_heads % kv_repeat == 0
- assert not cross_attention or kv_repeat == 1
- num_kv = num_heads // kv_repeat
- kv_dim = (embed_dim // num_heads) * num_kv
- out_dim += 2 * kv_dim
- in_proj = nn.Linear(embed_dim, out_dim, bias=bias, **factory_kwargs)
- # We try to follow the default PyTorch MHA convention, to easily compare results.
- self.in_proj_weight = in_proj.weight
- self.in_proj_bias = in_proj.bias
- if bias:
- self.in_proj_bias.data.zero_() # Following Pytorch convention
- self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
- if bias:
- self.out_proj.bias.data.zero_()
- else:
- assert not qk_layer_norm
- assert kv_repeat == 1
- self.mha = nn.MultiheadAttention(
- embed_dim, num_heads, dropout=dropout, bias=bias, batch_first=True,
- **factory_kwargs)
- self.qk_layer_norm = qk_layer_norm
- if qk_layer_norm:
- assert self.custom
- assert kv_repeat == 1
- ln_dim = embed_dim
- self.q_layer_norm = nn.LayerNorm(ln_dim)
- self.k_layer_norm = nn.LayerNorm(ln_dim)
-
- def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
- if not self.custom:
- # Support compat with regular MHA
- keys = [n for n, _ in self.mha.named_parameters()]
- for key in keys:
- if prefix + key in state_dict:
- state_dict[prefix + "mha." + key] = state_dict.pop(prefix + key)
- super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
- def _get_mask(self, current_steps: int, device: torch.device, dtype: torch.dtype):
- # Return a causal mask, accounting for potentially stored past keys/values
- # We actually return a bias for the attention score, as this has the same
- # convention both in the builtin MHA in Pytorch, and Xformers functions.
- time_dim = _get_attention_time_dimension()
- if self.memory_efficient:
- from xformers.ops import LowerTriangularMask
- if current_steps == 1:
- # If we only have one step, then we do not need a mask.
- return None
- elif 'past_keys' in self._streaming_state:
- raise RuntimeError('Not supported at the moment')
- else:
- # Then we can safely use a lower triangular mask
- return LowerTriangularMask()
- if self._streaming_state:
- past_keys = self._streaming_state['past_keys']
- past_steps = past_keys.shape[time_dim]
- else:
- past_steps = 0
-
- queries_pos = torch.arange(
- past_steps, current_steps + past_steps, device=device).view(-1, 1)
- keys_pos = torch.arange(past_steps + current_steps, device=device).view(1, -1)
- delta = queries_pos - keys_pos
- valid = delta >= 0
- if self.past_context is not None:
- valid &= (delta <= self.past_context)
- return torch.where(
- valid,
- torch.zeros([], device=device, dtype=dtype),
- torch.full([], float('-inf'), device=device, dtype=dtype))
-
- def _complete_kv(self, k, v):
- time_dim = _get_attention_time_dimension()
- if self.cross_attention:
- # With cross attention we assume all keys and values
- # are already available, and streaming is with respect
- # to the queries only.
- return k, v
- # Complete the key/value pair using the streaming state.
- if self._streaming_state:
- pk = self._streaming_state['past_keys']
- nk = torch.cat([pk, k], dim=time_dim)
- if v is k:
- nv = nk
- else:
- pv = self._streaming_state['past_values']
- nv = torch.cat([pv, v], dim=time_dim)
- else:
- nk = k
- nv = v
-
- assert nk.shape[time_dim] == nv.shape[time_dim]
- offset = 0
- if self.past_context is not None:
- offset = max(0, nk.shape[time_dim] - self.past_context)
- if self._is_streaming:
- self._streaming_state['past_keys'] = nk[:, offset:]
- if v is not k:
- self._streaming_state['past_values'] = nv[:, offset:]
- if 'offset' in self._streaming_state:
- self._streaming_state['offset'] += offset
- else:
- self._streaming_state['offset'] = torch.tensor(0)
- return nk, nv
-
- def _apply_rope(self, query: torch.Tensor, key: torch.Tensor):
- # TODO: fix and verify layout.
- assert _efficient_attention_backend == 'xformers', 'Rope not supported with torch attn.'
- # Apply rope embeddings to query and key tensors.
- assert self.rope is not None
- if 'past_keys' in self._streaming_state:
- past_keys_offset = self._streaming_state['past_keys'].shape[1]
- else:
- past_keys_offset = 0
- if 'offset' in self._streaming_state:
- past_context_offset = int(self._streaming_state['offset'].item())
- else:
- past_context_offset = 0
- streaming_offset = past_context_offset + past_keys_offset
- return self.rope.rotate_qk(query, key, start=streaming_offset)
-
- def forward(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
- key_padding_mask=None, need_weights=False, attn_mask=None,
- average_attn_weights=True, is_causal=False):
- assert attn_mask is None
- assert not is_causal, ("new param added in torch 2.0.1 not supported, "
- "use the causal args in the constructor.")
-
- time_dim = _get_attention_time_dimension()
- if time_dim == 2:
- layout = "b h t d"
- else:
- layout = "b t h d"
- dtype = query.dtype
- if self._is_streaming:
- assert self.causal or self.cross_attention, \
- "Streaming only available for causal or cross attention"
-
- if self.causal:
- # At the moment we specialize only for the self-attention case.
- assert query.shape[1] == key.shape[1], "Causal only for same length query / key / value"
- assert value.shape[1] == key.shape[1], "Causal only for same length query / key / value"
- attn_mask = self._get_mask(query.shape[1], query.device, query.dtype)
-
- if self.custom:
- # custom implementation
- assert need_weights is False
- assert key_padding_mask is None
- if self.cross_attention:
- # Different queries, keys, values, we have to spit manually the weights
- # before applying the linear.
- dim = self.in_proj_weight.shape[0] // 3
- if self.in_proj_bias is None:
- bias_q, bias_k, bias_v = None, None, None
- else:
- bias_q = self.in_proj_bias[:dim]
- bias_k = self.in_proj_bias[dim: 2 * dim]
- bias_v = self.in_proj_bias[2 * dim:]
- q = nn.functional.linear(query, self.in_proj_weight[:dim], bias_q)
- # todo: when streaming, we could actually save k, v and check the shape actually match.
- k = nn.functional.linear(key, self.in_proj_weight[dim: 2 * dim], bias_k)
- v = nn.functional.linear(value, self.in_proj_weight[2 * dim:], bias_v)
- if self.qk_layer_norm is True:
- q = self.q_layer_norm(q)
- k = self.k_layer_norm(k)
- q, k, v = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k, v]]
- else:
- if not _is_profiled():
- # profiling breaks that propertysomehow.
- assert query is key, "specialized implementation"
- assert value is key, "specialized implementation"
- projected = nn.functional.linear(query, self.in_proj_weight, self.in_proj_bias)
- if self.kv_repeat == 1:
- if time_dim == 2:
- bound_layout = "b h p t d"
- else:
- bound_layout = "b t p h d"
- packed = rearrange(projected, f"b t (p h d) -> {bound_layout}", p=3, h=self.num_heads)
- q, k, v = ops.unbind(packed, dim=2)
- else:
- embed_dim = self.embed_dim
- per_head_dim = (embed_dim // self.num_heads)
- kv_heads = self.num_heads // self.kv_repeat
- q = projected[:, :, :embed_dim]
- start = embed_dim
- end = start + per_head_dim * kv_heads
- k = projected[:, :, start: end]
- v = projected[:, :, end:]
- q = rearrange(q, f"b t (h d) -> {layout}", h=self.num_heads)
- k = rearrange(k, f"b t (h d) -> {layout}", h=kv_heads)
- v = rearrange(v, f"b t (h d) -> {layout}", h=kv_heads)
-
- if self.qk_layer_norm is True:
- assert self.kv_repeat == 1
- q, k = [rearrange(x, f"{layout} -> b t (h d)") for x in [q, k]]
- q = self.q_layer_norm(q)
- k = self.k_layer_norm(k)
- q, k = [rearrange(x, f"b t (h d) -> {layout}", h=self.num_heads) for x in [q, k]]
- if self.rope:
- q, k = self._apply_rope(q, k)
- k, v = self._complete_kv(k, v)
- if self.kv_repeat > 1:
- k = expand_repeated_kv(k, self.kv_repeat)
- v = expand_repeated_kv(v, self.kv_repeat)
- if self.attention_as_float32:
- q, k, v = [x.float() for x in [q, k, v]]
- if self.memory_efficient:
- p = self.dropout if self.training else 0
- if _efficient_attention_backend == 'torch':
- x = torch.nn.functional.scaled_dot_product_attention(
- q, k, v, is_causal=attn_mask is not None, dropout_p=p)
- else:
- x = ops.memory_efficient_attention(q, k, v, attn_mask, p=p)
- else:
- # We include the dot product as float32, for consistency
- # with the other implementations that include that step
- # as part of the attention. Note that when using `autocast`,
- # the einsums would be done as bfloat16, but the softmax
- # would be done as bfloat16, so `attention_as_float32` will
- # extend a bit the range of operations done in float32,
- # although this should make no difference.
- q = q / q.shape[-1] ** 0.5
- key_layout = layout.replace('t', 'k')
- query_layout = layout
- if self._is_streaming and self.safe_streaming and q.device.type == 'cuda':
- with torch.autocast(device_type=q.device.type, dtype=torch.float32):
- pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
- else:
- pre_w = torch.einsum(f"{query_layout},{key_layout}-> b h t k", q, k)
- if attn_mask is not None:
- pre_w = pre_w + attn_mask
- w = torch.softmax(pre_w, dim=-1)
- w = F.dropout(w, self.dropout, training=self.training).to(v)
- # Key and value have the same format.
- x = torch.einsum(f"b h t k, {key_layout} -> {layout}", w, v)
- x = x.to(dtype)
- x = rearrange(x, f"{layout} -> b t (h d)", h=self.num_heads)
- x = self.out_proj(x)
- else:
- key, value = self._complete_kv(key, value)
- if self.attention_as_float32:
- query, key, value = [x.float() for x in [query, key, value]]
- x, _ = self.mha(
- query, key, value, key_padding_mask,
- need_weights, attn_mask, average_attn_weights)
- x = x.to(dtype)
-
- return x, None
-
-
-class StreamingTransformerLayer(nn.TransformerEncoderLayer):
- """TransformerLayer with Streaming / Causal support.
- This also integrates cross_attention, when passing `cross_attention=True`,
- rather than having two separate classes like in PyTorch.
-
- Args:
- d_model (int): Dimension of the data.
- num_heads (int): Number of heads.
- dim_feedforward (int): Intermediate dimension of FF module.
- dropout (float): Dropout both for MHA and FF.
- bias_ff (bool): Use bias for FF.
- bias_attn (bool): Use bias for MHA.
- causal (bool): Causal mask applied automatically.
- past_context (int or None): Receptive field for the causal mask, infinite if None.
- custom (bool): Use custom MHA implementation, for testing / benchmarking.
- memory_efficient (bool): Use xformers based memory efficient attention.
- attention_as_float32 (bool): Perform the attention as float32
- (especially important with memory_efficient as autocast won't do this automatically).
- qk_layer_norm (bool): Layer normalization applied to queries and keys before dot product in attention.
- qk_layer_norm_cross (bool): Same for the cross attention.
- cross_attention (bool): If True, expect to get secondary input for cross-attention.
- Cross attention will use the default MHA, as it typically won't require
- special treatment.
- layer_scale (float or None): If not None, LayerScale will be used with
- the given value as initial scale.
- rope (`RotaryEmbedding` or None): Rope embedding to use.
- attention_dropout (float or None): If not None, separate the value of the dimension dropout
- in FFN and of the attention dropout.
- kv_repeat (int): If > 1, will repeat keys and queries multiple times (need to divide num_heads).
- This will lead to faster decoding time on A100 or other GPUs with tensorcore.
- device (torch.device or None): Device on which to initialize.
- dtype (torch.dtype or None): dtype to use.
- **kwargs: See `nn.TransformerEncoderLayer`.
- """
- def __init__(self, d_model: int, num_heads: int, dim_feedforward: int = 2048, dropout: float = 0.1,
- bias_ff: bool = True, bias_attn: bool = True, causal: bool = False,
- past_context: tp.Optional[int] = None, custom: bool = False,
- memory_efficient: bool = False, attention_as_float32: bool = False,
- qk_layer_norm: bool = False, qk_layer_norm_cross: bool = False,
- cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
- rope: tp.Optional[RotaryEmbedding] = None, attention_dropout: tp.Optional[float] = None,
- kv_repeat: int = 1, norm: str = 'layer_norm', device=None, dtype=None, **kwargs):
- super().__init__(d_model, num_heads, dim_feedforward, dropout,
- device=device, dtype=dtype, batch_first=True, **kwargs)
- factory_kwargs = {'device': device, 'dtype': dtype}
- # Redefine self_attn to our streaming multi-head attention
- attn_kwargs: tp.Dict[str, tp.Any] = {
- 'embed_dim': d_model,
- 'num_heads': num_heads,
- 'dropout': dropout if attention_dropout is None else attention_dropout,
- 'bias': bias_attn,
- 'custom': custom,
- 'memory_efficient': memory_efficient,
- 'attention_as_float32': attention_as_float32,
- }
- self.self_attn: StreamingMultiheadAttention = StreamingMultiheadAttention(
- causal=causal, past_context=past_context, rope=rope, qk_layer_norm=qk_layer_norm,
- kv_repeat=kv_repeat, **attn_kwargs, **factory_kwargs) # type: ignore
- # Redefine feedforward layers to expose bias parameter
- self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias_ff, **factory_kwargs)
- self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias_ff, **factory_kwargs)
-
- self.layer_scale_1: nn.Module
- self.layer_scale_2: nn.Module
- if layer_scale is None:
- self.layer_scale_1 = nn.Identity()
- self.layer_scale_2 = nn.Identity()
- else:
- self.layer_scale_1 = LayerScale(d_model, layer_scale, **factory_kwargs)
- self.layer_scale_2 = LayerScale(d_model, layer_scale, **factory_kwargs)
-
- self.cross_attention: tp.Optional[nn.Module] = None
- if cross_attention:
- self.cross_attention = StreamingMultiheadAttention(
- cross_attention=True, qk_layer_norm=qk_layer_norm_cross,
- **attn_kwargs, **factory_kwargs)
- # Norm and dropout
- self.dropout_cross = nn.Dropout(dropout)
- # eps value matching that used in PyTorch reference implementation.
- self.norm_cross = nn.LayerNorm(d_model, eps=1e-5, **factory_kwargs)
- self.layer_scale_cross: nn.Module
- if layer_scale is None:
- self.layer_scale_cross = nn.Identity()
- else:
- self.layer_scale_cross = LayerScale(d_model, layer_scale, **factory_kwargs)
- self.norm1 = create_norm_fn(norm, d_model, **factory_kwargs) # type: ignore
- self.norm2 = create_norm_fn(norm, d_model, **factory_kwargs) # type: ignore
-
- def _cross_attention_block(self, src: torch.Tensor,
- cross_attention_src: torch.Tensor) -> torch.Tensor:
- assert self.cross_attention is not None
- # queries are from src, keys and values from cross_attention_src.
- x = self.cross_attention(
- src, cross_attention_src, cross_attention_src, need_weights=False)[0]
- return self.dropout_cross(x) # type: ignore
-
- def forward(self, src: torch.Tensor, src_mask: tp.Optional[torch.Tensor] = None, # type: ignore
- src_key_padding_mask: tp.Optional[torch.Tensor] = None,
- cross_attention_src: tp.Optional[torch.Tensor] = None):
- if self.cross_attention is None:
- assert cross_attention_src is None
- else:
- assert cross_attention_src is not None
- x = src
- if self.norm_first:
- x = x + self.layer_scale_1(
- self._sa_block(self.norm1(x), src_mask, src_key_padding_mask))
- if cross_attention_src is not None:
- x = x + self.layer_scale_cross(
- self._cross_attention_block(
- self.norm_cross(x), cross_attention_src))
- x = x + self.layer_scale_2(self._ff_block(self.norm2(x)))
- else:
- x = self.norm1(x + self.layer_scale_1(
- self._sa_block(x, src_mask, src_key_padding_mask)))
- if cross_attention_src is not None:
- x = self.norm_cross(
- x + self.layer_scale_cross(
- self._cross_attention_block(src, cross_attention_src)))
- x = self.norm2(x + self.layer_scale_2(self._ff_block(x)))
- return x
-
-
-class StreamingTransformer(StreamingModule):
- """Transformer with Streaming / Causal support.
-
- Args:
- d_model (int): Dimension of the data.
- num_heads (int): Number of heads.
- dim_feedforward (int): Intermediate dimension of FF module.
- dropout (float): Dropout both for MHA and FF.
- bias_ff (bool): Use bias for FF.
- bias_attn (bool): Use bias for MHA.
- causal (bool): Causal mask applied automatically.
- past_context (int or None): Receptive field for the causal mask, infinite if None.
- custom (bool): Use custom MHA implementation, for testing / benchmarking.
- memory_efficient (bool): Use xformers based memory efficient attention.
- attention_as_float32 (bool): Perform the attention as float32
- (especially important with memory_efficient as autocast won't do this automatically).
- cross_attention (bool): If True, expect to get secondary input for cross-attention.
- layer_scale (float or None): If not None, LayerScale will be used
- with the given value as initial scale.
- positional_embedding (str): Positional embedding strategy (sin, rope, or sin_rope).
- max_period (float): Maximum period of the time embedding.
- positional_scale (float): Scale of positional embedding, set to 0 to deactivate.
- xpos (bool): Apply xpos exponential decay to positional embedding (rope only).
- lr (float or None): learning rate override through the `make_optim_group` API.
- weight_decay (float or None): Weight_decay override through the `make_optim_group` API.
- layer_class: (subclass of `StreamingTransformerLayer): class to use
- to initialize the layers, allowing further customization outside of Audiocraft.
- checkpointing (str): Checkpointing strategy to reduce memory usage.
- No checkpointing if set to 'none'. Per layer checkpointing using PyTorch
- if set to 'torch' (entire layer checkpointed, i.e. linears are evaluated twice,
- minimal memory usage, but maximal runtime). Finally, `xformers_default` provide
- a policy for opting-out some operations of the checkpointing like
- linear layers and attention, providing a middle ground between speed and memory.
- device (torch.device or None): Device on which to initialize.
- dtype (torch.dtype or None): dtype to use.
- **kwargs: See `nn.TransformerEncoderLayer`.
- """
- def __init__(self, d_model: int, num_heads: int, num_layers: int, dim_feedforward: int = 2048,
- dropout: float = 0.1, bias_ff: bool = True, bias_attn: bool = True,
- causal: bool = False, past_context: tp.Optional[int] = None,
- custom: bool = False, memory_efficient: bool = False, attention_as_float32: bool = False,
- cross_attention: bool = False, layer_scale: tp.Optional[float] = None,
- positional_embedding: str = 'sin', max_period: float = 10_000, positional_scale: float = 1.,
- xpos: bool = False, lr: tp.Optional[float] = None, weight_decay: tp.Optional[float] = None,
- layer_class: tp.Type[StreamingTransformerLayer] = StreamingTransformerLayer,
- checkpointing: str = 'none', device=None, dtype=None, **kwargs):
- super().__init__()
- assert d_model % num_heads == 0
-
- self.positional_embedding = positional_embedding
- self.max_period = max_period
- self.positional_scale = positional_scale
- self.weight_decay = weight_decay
- self.lr = lr
-
- assert positional_embedding in ['sin', 'rope', 'sin_rope']
- self.rope: tp.Optional[RotaryEmbedding] = None
- if self.positional_embedding in ['rope', 'sin_rope']:
- assert _is_custom(custom, memory_efficient)
- self.rope = RotaryEmbedding(d_model // num_heads, max_period=max_period,
- xpos=xpos, scale=positional_scale, device=device)
-
- self.checkpointing = checkpointing
-
- assert checkpointing in ['none', 'torch', 'xformers_default', 'xformers_mm']
- if self.checkpointing.startswith('xformers'):
- _verify_xformers_internal_compat()
-
- self.layers = nn.ModuleList()
- for idx in range(num_layers):
- self.layers.append(
- layer_class(
- d_model=d_model, num_heads=num_heads, dim_feedforward=dim_feedforward,
- dropout=dropout, bias_ff=bias_ff, bias_attn=bias_attn,
- causal=causal, past_context=past_context, custom=custom,
- memory_efficient=memory_efficient, attention_as_float32=attention_as_float32,
- cross_attention=cross_attention, layer_scale=layer_scale, rope=self.rope,
- device=device, dtype=dtype, **kwargs))
-
- if self.checkpointing != 'none':
- for layer in self.layers:
- # see audiocraft/optim/fsdp.py, magic signal to indicate this requires fixing the
- # backward hook inside of FSDP...
- layer._magma_checkpointed = True # type: ignore
- assert layer.layer_drop == 0., "Need further checking" # type: ignore
-
- def _apply_layer(self, layer, *args, **kwargs):
- method = self.checkpointing
- if method == 'none':
- return layer(*args, **kwargs)
- elif method == 'torch':
- return torch_checkpoint(layer, *args, use_reentrant=False, **kwargs)
- elif method.startswith('xformers'):
- from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy
- if method == 'xformers_default':
- # those operations will be saved, and not recomputed.
- # According to Francisco we can get smarter policies but this is a good start.
- allow_list = [
- "xformers.efficient_attention_forward_cutlass.default",
- "xformers_flash.flash_fwd.default",
- "aten.addmm.default",
- "aten.mm.default",
- ]
- elif method == 'xformers_mm':
- # those operations will be saved, and not recomputed.
- # According to Francisco we can get smarter policies but this is a good start.
- allow_list = [
- "aten.addmm.default",
- "aten.mm.default",
- ]
- else:
- raise ValueError(f"xformers checkpointing xformers policy {method} is not known.")
- policy_fn = _get_default_policy(allow_list)
- return checkpoint(layer, *args, policy_fn=policy_fn, **kwargs)
- else:
- raise ValueError(f"Checkpointing method {method} is unknown.")
-
- def forward(self, x: torch.Tensor, *args, **kwargs):
- B, T, C = x.shape
-
- if 'offsets' in self._streaming_state:
- offsets = self._streaming_state['offsets']
- else:
- offsets = torch.zeros(B, dtype=torch.long, device=x.device)
-
- if self.positional_embedding in ['sin', 'sin_rope']:
- positions = torch.arange(T, device=x.device).view(1, -1, 1)
- positions = positions + offsets.view(-1, 1, 1)
- pos_emb = create_sin_embedding(positions, C, max_period=self.max_period, dtype=x.dtype)
- x = x + self.positional_scale * pos_emb
-
- for layer in self.layers:
- x = self._apply_layer(layer, x, *args, **kwargs)
-
- if self._is_streaming:
- self._streaming_state['offsets'] = offsets + T
-
- return x
-
- def make_optim_group(self):
- group = {"params": list(self.parameters())}
- if self.lr is not None:
- group["lr"] = self.lr
- if self.weight_decay is not None:
- group["weight_decay"] = self.weight_decay
- return group
-
-
-# special attention attention related function
-
-def _verify_xformers_memory_efficient_compat():
- try:
- from xformers.ops import memory_efficient_attention, LowerTriangularMask # noqa
- except ImportError:
- raise ImportError(
- "xformers is not installed. Please install it and try again.\n"
- "To install on AWS and Azure, run \n"
- "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
- "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
- "To install on FAIR Cluster, run \n"
- "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
- "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _verify_xformers_internal_compat():
- try:
- from xformers.checkpoint_fairinternal import checkpoint, _get_default_policy # noqa
- except ImportError:
- raise ImportError(
- "Francisco's fairinternal xformers is not installed. Please install it and try again.\n"
- "To install on AWS and Azure, run \n"
- "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='8.0'\\\n"
- "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n"
- "To install on FAIR Cluster, run \n"
- "FORCE_CUDA=1 TORCH_CUDA_ARCH_LIST='6.0;7.0'\\\n"
- "pip install -U git+https://git@github.com/fairinternal/xformers.git#egg=xformers\n")
-
-
-def _is_custom(custom: bool, memory_efficient: bool):
- return custom or memory_efficient
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/bricks/conv_ws.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/bricks/conv_ws.py
deleted file mode 100644
index a3941e27874993418b3b5708d5a7485f175ff9c8..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/cnn/bricks/conv_ws.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .registry import CONV_LAYERS
-
-
-def conv_ws_2d(input,
- weight,
- bias=None,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- eps=1e-5):
- c_in = weight.size(0)
- weight_flat = weight.view(c_in, -1)
- mean = weight_flat.mean(dim=1, keepdim=True).view(c_in, 1, 1, 1)
- std = weight_flat.std(dim=1, keepdim=True).view(c_in, 1, 1, 1)
- weight = (weight - mean) / (std + eps)
- return F.conv2d(input, weight, bias, stride, padding, dilation, groups)
-
-
-@CONV_LAYERS.register_module('ConvWS')
-class ConvWS2d(nn.Conv2d):
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias=True,
- eps=1e-5):
- super(ConvWS2d, self).__init__(
- in_channels,
- out_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- bias=bias)
- self.eps = eps
-
- def forward(self, x):
- return conv_ws_2d(x, self.weight, self.bias, self.stride, self.padding,
- self.dilation, self.groups, self.eps)
-
-
-@CONV_LAYERS.register_module(name='ConvAWS')
-class ConvAWS2d(nn.Conv2d):
- """AWS (Adaptive Weight Standardization)
-
- This is a variant of Weight Standardization
- (https://arxiv.org/pdf/1903.10520.pdf)
- It is used in DetectoRS to avoid NaN
- (https://arxiv.org/pdf/2006.02334.pdf)
-
- Args:
- in_channels (int): Number of channels in the input image
- out_channels (int): Number of channels produced by the convolution
- kernel_size (int or tuple): Size of the conv kernel
- stride (int or tuple, optional): Stride of the convolution. Default: 1
- padding (int or tuple, optional): Zero-padding added to both sides of
- the input. Default: 0
- dilation (int or tuple, optional): Spacing between kernel elements.
- Default: 1
- groups (int, optional): Number of blocked connections from input
- channels to output channels. Default: 1
- bias (bool, optional): If set True, adds a learnable bias to the
- output. Default: True
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias=True):
- super().__init__(
- in_channels,
- out_channels,
- kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- bias=bias)
- self.register_buffer('weight_gamma',
- torch.ones(self.out_channels, 1, 1, 1))
- self.register_buffer('weight_beta',
- torch.zeros(self.out_channels, 1, 1, 1))
-
- def _get_weight(self, weight):
- weight_flat = weight.view(weight.size(0), -1)
- mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1)
- std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1)
- weight = (weight - mean) / std
- weight = self.weight_gamma * weight + self.weight_beta
- return weight
-
- def forward(self, x):
- weight = self._get_weight(self.weight)
- return F.conv2d(x, weight, self.bias, self.stride, self.padding,
- self.dilation, self.groups)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- """Override default load function.
-
- AWS overrides the function _load_from_state_dict to recover
- weight_gamma and weight_beta if they are missing. If weight_gamma and
- weight_beta are found in the checkpoint, this function will return
- after super()._load_from_state_dict. Otherwise, it will compute the
- mean and std of the pretrained weights and store them in weight_beta
- and weight_gamma.
- """
-
- self.weight_gamma.data.fill_(-1)
- local_missing_keys = []
- super()._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, local_missing_keys,
- unexpected_keys, error_msgs)
- if self.weight_gamma.data.mean() > 0:
- for k in local_missing_keys:
- missing_keys.append(k)
- return
- weight = self.weight.data
- weight_flat = weight.view(weight.size(0), -1)
- mean = weight_flat.mean(dim=1).view(-1, 1, 1, 1)
- std = torch.sqrt(weight_flat.var(dim=1) + 1e-5).view(-1, 1, 1, 1)
- self.weight_beta.data.copy_(mean)
- self.weight_gamma.data.copy_(std)
- missing_gamma_beta = [
- k for k in local_missing_keys
- if k.endswith('weight_gamma') or k.endswith('weight_beta')
- ]
- for k in missing_gamma_beta:
- local_missing_keys.remove(k)
- for k in local_missing_keys:
- missing_keys.append(k)
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/datasets/pipelines/test_time_aug.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/datasets/pipelines/test_time_aug.py
deleted file mode 100644
index 6a1611a04d9d927223c9afbe5bf68af04d62937a..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmseg/datasets/pipelines/test_time_aug.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import warnings
-
-import annotator.uniformer.mmcv as mmcv
-
-from ..builder import PIPELINES
-from .compose import Compose
-
-
-@PIPELINES.register_module()
-class MultiScaleFlipAug(object):
- """Test-time augmentation with multiple scales and flipping.
-
- An example configuration is as followed:
-
- .. code-block::
-
- img_scale=(2048, 1024),
- img_ratios=[0.5, 1.0],
- flip=True,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ]
-
- After MultiScaleFLipAug with above configuration, the results are wrapped
- into lists of the same length as followed:
-
- .. code-block::
-
- dict(
- img=[...],
- img_shape=[...],
- scale=[(1024, 512), (1024, 512), (2048, 1024), (2048, 1024)]
- flip=[False, True, False, True]
- ...
- )
-
- Args:
- transforms (list[dict]): Transforms to apply in each augmentation.
- img_scale (None | tuple | list[tuple]): Images scales for resizing.
- img_ratios (float | list[float]): Image ratios for resizing
- flip (bool): Whether apply flip augmentation. Default: False.
- flip_direction (str | list[str]): Flip augmentation directions,
- options are "horizontal" and "vertical". If flip_direction is list,
- multiple flip augmentations will be applied.
- It has no effect when flip == False. Default: "horizontal".
- """
-
- def __init__(self,
- transforms,
- img_scale,
- img_ratios=None,
- flip=False,
- flip_direction='horizontal'):
- self.transforms = Compose(transforms)
- if img_ratios is not None:
- img_ratios = img_ratios if isinstance(img_ratios,
- list) else [img_ratios]
- assert mmcv.is_list_of(img_ratios, float)
- if img_scale is None:
- # mode 1: given img_scale=None and a range of image ratio
- self.img_scale = None
- assert mmcv.is_list_of(img_ratios, float)
- elif isinstance(img_scale, tuple) and mmcv.is_list_of(
- img_ratios, float):
- assert len(img_scale) == 2
- # mode 2: given a scale and a range of image ratio
- self.img_scale = [(int(img_scale[0] * ratio),
- int(img_scale[1] * ratio))
- for ratio in img_ratios]
- else:
- # mode 3: given multiple scales
- self.img_scale = img_scale if isinstance(img_scale,
- list) else [img_scale]
- assert mmcv.is_list_of(self.img_scale, tuple) or self.img_scale is None
- self.flip = flip
- self.img_ratios = img_ratios
- self.flip_direction = flip_direction if isinstance(
- flip_direction, list) else [flip_direction]
- assert mmcv.is_list_of(self.flip_direction, str)
- if not self.flip and self.flip_direction != ['horizontal']:
- warnings.warn(
- 'flip_direction has no effect when flip is set to False')
- if (self.flip
- and not any([t['type'] == 'RandomFlip' for t in transforms])):
- warnings.warn(
- 'flip has no effect when RandomFlip is not in transforms')
-
- def __call__(self, results):
- """Call function to apply test time augment transforms on results.
-
- Args:
- results (dict): Result dict contains the data to transform.
-
- Returns:
- dict[str: list]: The augmented data, where each value is wrapped
- into a list.
- """
-
- aug_data = []
- if self.img_scale is None and mmcv.is_list_of(self.img_ratios, float):
- h, w = results['img'].shape[:2]
- img_scale = [(int(w * ratio), int(h * ratio))
- for ratio in self.img_ratios]
- else:
- img_scale = self.img_scale
- flip_aug = [False, True] if self.flip else [False]
- for scale in img_scale:
- for flip in flip_aug:
- for direction in self.flip_direction:
- _results = results.copy()
- _results['scale'] = scale
- _results['flip'] = flip
- _results['flip_direction'] = direction
- data = self.transforms(_results)
- aug_data.append(data)
- # list of dict to dict of list
- aug_data_dict = {key: [] for key in aug_data[0]}
- for data in aug_data:
- for key, val in data.items():
- aug_data_dict[key].append(val)
- return aug_data_dict
-
- def __repr__(self):
- repr_str = self.__class__.__name__
- repr_str += f'(transforms={self.transforms}, '
- repr_str += f'img_scale={self.img_scale}, flip={self.flip})'
- repr_str += f'flip_direction={self.flip_direction}'
- return repr_str
diff --git a/spaces/MirageML/sjc/sd1/ldm/modules/diffusionmodules/model.py b/spaces/MirageML/sjc/sd1/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index 2fa615e4e76ee0df6ad2e0341cb486f93ea1b36d..0000000000000000000000000000000000000000
--- a/spaces/MirageML/sjc/sd1/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,843 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-
-from ldm.util import instantiate_from_config
-from ldm.modules.attention import LinearAttention
-import torch.nn.functional as F
-
-def get_timestep_embedding(timesteps, embedding_dim):
- """
- This matches the implementation in Denoising Diffusion Probabilistic Models:
- From Fairseq.
- Build sinusoidal embeddings.
- This matches the implementation in tensor2tensor, but differs slightly
- from the description in Section 3.5 of "Attention Is All You Need".
- """
- assert len(timesteps.shape) == 1
-
- half_dim = embedding_dim // 2
- emb = math.log(10000) / (half_dim - 1)
- emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
- emb = emb.to(device=timesteps.device)
- emb = timesteps.float()[:, None] * emb[None, :]
- emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
- if embedding_dim % 2 == 1: # zero pad
- emb = torch.nn.functional.pad(emb, (0,1,0,0))
- return emb
-
-
-def nonlinearity(x):
- # swish
- return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
- return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
- if self.with_conv:
- x = self.conv(x)
- return x
-
-
-class Downsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- # no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=2,
- padding=0)
-
- def forward(self, x):
- if self.with_conv:
- pad = (0,1,0,1)
- x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
- x = self.conv(x)
- else:
- x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
- return x
-
-
-class ResnetBlock(nn.Module):
- def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
- dropout, temb_channels=512):
- super().__init__()
- self.in_channels = in_channels
- out_channels = in_channels if out_channels is None else out_channels
- self.out_channels = out_channels
- self.use_conv_shortcut = conv_shortcut
-
- self.norm1 = Normalize(in_channels)
- self.conv1 = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if temb_channels > 0:
- self.temb_proj = torch.nn.Linear(temb_channels,
- out_channels)
- self.norm2 = Normalize(out_channels)
- self.dropout = torch.nn.Dropout(dropout)
- self.conv2 = torch.nn.Conv2d(out_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- self.conv_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- else:
- self.nin_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
- def forward(self, x, temb):
- h = x
- h = self.norm1(h)
- h = nonlinearity(h)
- h = self.conv1(h)
-
- if temb is not None:
- h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
- h = self.norm2(h)
- h = nonlinearity(h)
- h = self.dropout(h)
- h = self.conv2(h)
-
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- x = self.conv_shortcut(x)
- else:
- x = self.nin_shortcut(x)
-
- return x+h
-
-
-class LinAttnBlock(LinearAttention):
- """to match AttnBlock usage"""
- def __init__(self, in_channels):
- super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
-
-
-class AttnBlock(nn.Module):
- def __init__(self, in_channels):
- super().__init__()
- self.in_channels = in_channels
-
- self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
-
- def forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
-
- # compute attention
- b,c,h,w = q.shape
- q = q.reshape(b,c,h*w)
- q = q.permute(0,2,1) # b,hw,c
- k = k.reshape(b,c,h*w) # b,c,hw
- w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
- w_ = w_ * (int(c)**(-0.5))
- w_ = torch.nn.functional.softmax(w_, dim=2)
-
- # attend to values
- v = v.reshape(b,c,h*w)
- w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
- h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
- h_ = h_.reshape(b,c,h,w)
-
- h_ = self.proj_out(h_)
-
- return x+h_
-
-
-def make_attn(in_channels, attn_type="vanilla"):
- assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
- print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
- if attn_type == "vanilla":
- return AttnBlock(in_channels)
- elif attn_type == "none":
- return nn.Identity(in_channels)
- else:
- return LinAttnBlock(in_channels)
-
-
-class Model(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = self.ch*4
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
-
- self.use_timestep = use_timestep
- if self.use_timestep:
- # timestep embedding
- self.temb = nn.Module()
- self.temb.dense = nn.ModuleList([
- torch.nn.Linear(self.ch,
- self.temb_ch),
- torch.nn.Linear(self.temb_ch,
- self.temb_ch),
- ])
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # upsampling
- self.up = nn.ModuleList()
- for i_level in reversed(range(self.num_resolutions)):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- skip_in = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- if i_block == self.num_res_blocks:
- skip_in = ch*in_ch_mult[i_level]
- block.append(ResnetBlock(in_channels=block_in+skip_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- up = nn.Module()
- up.block = block
- up.attn = attn
- if i_level != 0:
- up.upsample = Upsample(block_in, resamp_with_conv)
- curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x, t=None, context=None):
- #assert x.shape[2] == x.shape[3] == self.resolution
- if context is not None:
- # assume aligned context, cat along channel axis
- x = torch.cat((x, context), dim=1)
- if self.use_timestep:
- # timestep embedding
- assert t is not None
- temb = get_timestep_embedding(t, self.ch)
- temb = self.temb.dense[0](temb)
- temb = nonlinearity(temb)
- temb = self.temb.dense[1](temb)
- else:
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # upsampling
- for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
- h = self.up[i_level].block[i_block](
- torch.cat([h, hs.pop()], dim=1), temb)
- if len(self.up[i_level].attn) > 0:
- h = self.up[i_level].attn[i_block](h)
- if i_level != 0:
- h = self.up[i_level].upsample(h)
-
- # end
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
- def get_last_layer(self):
- return self.conv_out.weight
-
-
-class Encoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
- **ignore_kwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.in_ch_mult = in_ch_mult
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- 2*z_channels if double_z else z_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- # timestep embedding
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # end
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
-
-class Decoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
- attn_type="vanilla", **ignorekwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
- self.give_pre_end = give_pre_end
- self.tanh_out = tanh_out
-
- # compute in_ch_mult, block_in and curr_res at lowest res
- in_ch_mult = (1,)+tuple(ch_mult)
- block_in = ch*ch_mult[self.num_resolutions-1]
- curr_res = resolution // 2**(self.num_resolutions-1)
- self.z_shape = (1,z_channels,curr_res,curr_res)
- print("Working with z of shape {} = {} dimensions.".format(
- self.z_shape, np.prod(self.z_shape)))
-
- # z to block_in
- self.conv_in = torch.nn.Conv2d(z_channels,
- block_in,
- kernel_size=3,
- stride=1,
- padding=1)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # upsampling
- self.up = nn.ModuleList()
- for i_level in reversed(range(self.num_resolutions)):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- up = nn.Module()
- up.block = block
- up.attn = attn
- if i_level != 0:
- up.upsample = Upsample(block_in, resamp_with_conv)
- curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, z):
- #assert z.shape[1:] == self.z_shape[1:]
- self.last_z_shape = z.shape
-
- # timestep embedding
- temb = None
-
- # z to block_in
- h = self.conv_in(z)
-
- # middle
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # upsampling
- for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
- h = self.up[i_level].block[i_block](h, temb)
- if len(self.up[i_level].attn) > 0:
- h = self.up[i_level].attn[i_block](h)
- if i_level != 0:
- h = self.up[i_level].upsample(h)
-
- # end
- if self.give_pre_end:
- return h
-
-
- h_fake = self.norm_out(h).type(torch.float16)
- #h_fake = F.group_norm(h.float(),32,eps=1e-1).detach()
- h = (h - h.mean([2,3],keepdims=True)) / h.std([2,3],keepdims=True)
- #std_val = h_fake.std([2,3],keepdims=True)
- #h = h*(0.5+h_fake.std([2,3],keepdims=True).type(torch.float16))+h_fake.mean([2,3],keepdims=True).type(torch.float16)
- #h = h + h_fake.mean([2,3],keepdims=True).type(torch.float16)
- h = h + (h_fake-h).detach()
- h = nonlinearity(h)
- h = self.conv_out(h)
- if self.tanh_out:
- h = torch.tanh(h)
-
- return h
-
-
-class SimpleDecoder(nn.Module):
- def __init__(self, in_channels, out_channels, *args, **kwargs):
- super().__init__()
- self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
- ResnetBlock(in_channels=in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=2 * in_channels,
- out_channels=4 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=4 * in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- nn.Conv2d(2*in_channels, in_channels, 1),
- Upsample(in_channels, with_conv=True)])
- # end
- self.norm_out = Normalize(in_channels)
- self.conv_out = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- for i, layer in enumerate(self.model):
- if i in [1,2,3]:
- x = layer(x, None)
- else:
- x = layer(x)
-
- h = self.norm_out(x)
- h = nonlinearity(h)
- x = self.conv_out(h)
- return x
-
-
-class UpsampleDecoder(nn.Module):
- def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
- ch_mult=(2,2), dropout=0.0):
- super().__init__()
- # upsampling
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- block_in = in_channels
- curr_res = resolution // 2 ** (self.num_resolutions - 1)
- self.res_blocks = nn.ModuleList()
- self.upsample_blocks = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- res_block = []
- block_out = ch * ch_mult[i_level]
- for i_block in range(self.num_res_blocks + 1):
- res_block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- self.res_blocks.append(nn.ModuleList(res_block))
- if i_level != self.num_resolutions - 1:
- self.upsample_blocks.append(Upsample(block_in, True))
- curr_res = curr_res * 2
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- # upsampling
- h = x
- for k, i_level in enumerate(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks + 1):
- h = self.res_blocks[i_level][i_block](h, None)
- if i_level != self.num_resolutions - 1:
- h = self.upsample_blocks[k](h)
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
-
-class LatentRescaler(nn.Module):
- def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
- super().__init__()
- # residual block, interpolate, residual block
- self.factor = factor
- self.conv_in = nn.Conv2d(in_channels,
- mid_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
- self.attn = AttnBlock(mid_channels)
- self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
-
- self.conv_out = nn.Conv2d(mid_channels,
- out_channels,
- kernel_size=1,
- )
-
- def forward(self, x):
- x = self.conv_in(x)
- for block in self.res_block1:
- x = block(x, None)
- x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
- x = self.attn(x)
- for block in self.res_block2:
- x = block(x, None)
- x = self.conv_out(x)
- return x
-
-
-class MergedRescaleEncoder(nn.Module):
- def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True,
- ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
- super().__init__()
- intermediate_chn = ch * ch_mult[-1]
- self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
- z_channels=intermediate_chn, double_z=False, resolution=resolution,
- attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
- out_ch=None)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
- mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
- def forward(self, x):
- x = self.encoder(x)
- x = self.rescaler(x)
- return x
-
-
-class MergedRescaleDecoder(nn.Module):
- def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
- dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
- super().__init__()
- tmp_chn = z_channels*ch_mult[-1]
- self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
- resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
- ch_mult=ch_mult, resolution=resolution, ch=ch)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
- out_channels=tmp_chn, depth=rescale_module_depth)
-
- def forward(self, x):
- x = self.rescaler(x)
- x = self.decoder(x)
- return x
-
-
-class Upsampler(nn.Module):
- def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
- super().__init__()
- assert out_size >= in_size
- num_blocks = int(np.log2(out_size//in_size))+1
- factor_up = 1.+ (out_size % in_size)
- print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
- self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
- out_channels=in_channels)
- self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
- attn_resolutions=[], in_channels=None, ch=in_channels,
- ch_mult=[ch_mult for _ in range(num_blocks)])
-
- def forward(self, x):
- x = self.rescaler(x)
- x = self.decoder(x)
- return x
-
-
-class Resize(nn.Module):
- def __init__(self, in_channels=None, learned=False, mode="bilinear"):
- super().__init__()
- self.with_conv = learned
- self.mode = mode
- if self.with_conv:
- print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
- raise NotImplementedError()
- assert in_channels is not None
- # no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=4,
- stride=2,
- padding=1)
-
- def forward(self, x, scale_factor=1.0):
- if scale_factor==1.0:
- return x
- else:
- x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
- return x
-
-class FirstStagePostProcessor(nn.Module):
-
- def __init__(self, ch_mult:list, in_channels,
- pretrained_model:nn.Module=None,
- reshape=False,
- n_channels=None,
- dropout=0.,
- pretrained_config=None):
- super().__init__()
- if pretrained_config is None:
- assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
- self.pretrained_model = pretrained_model
- else:
- assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
- self.instantiate_pretrained(pretrained_config)
-
- self.do_reshape = reshape
-
- if n_channels is None:
- n_channels = self.pretrained_model.encoder.ch
-
- self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
- self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
- stride=1,padding=1)
-
- blocks = []
- downs = []
- ch_in = n_channels
- for m in ch_mult:
- blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
- ch_in = m * n_channels
- downs.append(Downsample(ch_in, with_conv=False))
-
- self.model = nn.ModuleList(blocks)
- self.downsampler = nn.ModuleList(downs)
-
-
- def instantiate_pretrained(self, config):
- model = instantiate_from_config(config)
- self.pretrained_model = model.eval()
- # self.pretrained_model.train = False
- for param in self.pretrained_model.parameters():
- param.requires_grad = False
-
-
- @torch.no_grad()
- def encode_with_pretrained(self,x):
- c = self.pretrained_model.encode(x)
- if isinstance(c, DiagonalGaussianDistribution):
- c = c.mode()
- return c
-
- def forward(self,x):
- z_fs = self.encode_with_pretrained(x)
- z = self.proj_norm(z_fs)
- z = self.proj(z)
- z = nonlinearity(z)
-
- for submodel, downmodel in zip(self.model,self.downsampler):
- z = submodel(z,temb=None)
- z = downmodel(z)
-
- if self.do_reshape:
- z = rearrange(z,'b c h w -> b (h w) c')
- return z
-
diff --git a/spaces/MisterZee/PIFu-Clothed-Human-Digitization/PIFu/lib/data/__init__.py b/spaces/MisterZee/PIFu-Clothed-Human-Digitization/PIFu/lib/data/__init__.py
deleted file mode 100644
index f87dc45d179d82778d6187ae1ffe9a18371296e8..0000000000000000000000000000000000000000
--- a/spaces/MisterZee/PIFu-Clothed-Human-Digitization/PIFu/lib/data/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .EvalDataset import EvalDataset
-from .TrainDataset import TrainDataset
\ No newline at end of file
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/svt_parser.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/svt_parser.py
deleted file mode 100644
index 553f46fb0f83c6b0b8d65479de6c2f6d597c64a3..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/parsers/svt_parser.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import xml.etree.ElementTree as ET
-from typing import List, Tuple
-
-from mmocr.registry import DATA_PARSERS
-from .base import BaseParser
-
-
-@DATA_PARSERS.register_module()
-class SVTTextDetAnnParser(BaseParser):
- """SVT Text Detection Parser.
-
- Args:
- data_root (str): The root of the dataset. Defaults to None.
- nproc (int): The number of processes to parse the annotation. Defaults
- to 1.
- """
-
- def parse_files(self, img_dir: str, ann_path: str) -> List:
- """Parse annotations."""
- assert isinstance(ann_path, str)
- samples = list()
- for img_name, instance in self.loader(ann_path):
- samples.append((osp.join(img_dir,
- osp.basename(img_name)), instance))
-
- return samples
-
- def loader(self, file_path: str) -> Tuple[str, List]:
- """Load annotation from SVT xml format file. See annotation example in
- dataset_zoo/svt/sample_anno.md.
-
- Args:
- file_path (str): The path of the annotation file.
-
- Returns:
- Tuple[str, List]: The image name and the annotation list.
-
- Yields:
- Iterator[Tuple[str, List]]: The image name and the annotation list.
- """
- tree = ET.parse(file_path)
- root = tree.getroot()
- for image in root.findall('image'):
- image_name = image.find('imageName').text
- instances = list()
- for rectangle in image.find('taggedRectangles'):
- x = int(rectangle.get('x'))
- y = int(rectangle.get('y'))
- w = int(rectangle.get('width'))
- h = int(rectangle.get('height'))
- # The text annotation of this dataset is not case sensitive.
- # All of the texts were labeled as upper case. We convert them
- # to lower case for convenience.
- text = rectangle.find('tag').text.lower()
- instances.append(
- dict(
- poly=[x, y, x + w, y, x + w, y + h, x, y + h],
- text=text,
- ignore=False))
- yield image_name, instances
diff --git a/spaces/NCTCMumbai/NCTC/models/official/pip_package/setup.py b/spaces/NCTCMumbai/NCTC/models/official/pip_package/setup.py
deleted file mode 100644
index 8433f25321c6f28c1c0c51797154633aa1a3ec71..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/pip_package/setup.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. 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.
-# ==============================================================================
-"""Sets up TensorFlow Official Models."""
-import datetime
-import os
-import sys
-
-from setuptools import find_packages
-from setuptools import setup
-
-version = '2.3.0'
-
-project_name = 'tf-models-official'
-
-long_description = """The TensorFlow official models are a collection of
-models that use TensorFlow's high-level APIs.
-They are intended to be well-maintained, tested, and kept up to date with the
-latest TensorFlow API. They should also be reasonably optimized for fast
-performance while still being easy to read."""
-
-if '--project_name' in sys.argv:
- project_name_idx = sys.argv.index('--project_name')
- project_name = sys.argv[project_name_idx + 1]
- sys.argv.remove('--project_name')
- sys.argv.pop(project_name_idx)
-
-
-def _get_requirements():
- """Parses requirements.txt file."""
- install_requires_tmp = []
- dependency_links_tmp = []
- with open(
- os.path.join(os.path.dirname(__file__), '../requirements.txt'), 'r') as f:
- for line in f:
- package_name = line.strip()
- if package_name.startswith('-e '):
- dependency_links_tmp.append(package_name[3:].strip())
- else:
- install_requires_tmp.append(package_name)
- return install_requires_tmp, dependency_links_tmp
-
-install_requires, dependency_links = _get_requirements()
-
-if project_name == 'tf-models-nightly':
- version += '.dev' + datetime.datetime.now().strftime('%Y%m%d')
- install_requires.append('tf-nightly')
-else:
- install_requires.append('tensorflow>=2.3.0')
-
-print('install_requires: ', install_requires)
-print('dependency_links: ', dependency_links)
-
-setup(
- name=project_name,
- version=version,
- description='TensorFlow Official Models',
- long_description=long_description,
- author='Google Inc.',
- author_email='no-reply@google.com',
- url='https://github.com/tensorflow/models',
- license='Apache 2.0',
- packages=find_packages(exclude=[
- 'research*',
- 'tutorials*',
- 'samples*',
- 'official.r1*',
- 'official.pip_package*',
- 'official.benchmark*',
- 'official.colab*',
- ]),
- exclude_package_data={
- '': ['*_test.py',],
- },
- install_requires=install_requires,
- dependency_links=dependency_links,
- python_requires='>=3.6',
-)
diff --git a/spaces/NN520/AI/src/components/tailwind-indicator.tsx b/spaces/NN520/AI/src/components/tailwind-indicator.tsx
deleted file mode 100644
index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000
--- a/spaces/NN520/AI/src/components/tailwind-indicator.tsx
+++ /dev/null
@@ -1,14 +0,0 @@
-export function TailwindIndicator() {
- if (process.env.NODE_ENV === 'production') return null
-
- return (
-
-
-
- )
-}
diff --git a/spaces/Narsil/gradiofold/README.md b/spaces/Narsil/gradiofold/README.md
deleted file mode 100644
index 0128615d268eb0ec448d0655b8ec1ac8677137df..0000000000000000000000000000000000000000
--- a/spaces/Narsil/gradiofold/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Gradiofold
-emoji: 📊
-colorFrom: red
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Nee001/bing0/src/components/chat-panel.tsx b/spaces/Nee001/bing0/src/components/chat-panel.tsx
deleted file mode 100644
index 56b2112bd75ba08134383871177851fa2e3f43a4..0000000000000000000000000000000000000000
--- a/spaces/Nee001/bing0/src/components/chat-panel.tsx
+++ /dev/null
@@ -1,153 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import Image from 'next/image'
-import Textarea from 'react-textarea-autosize'
-import { useAtomValue } from 'jotai'
-import { useEnterSubmit } from '@/lib/hooks/use-enter-submit'
-import { cn } from '@/lib/utils'
-
-import BrushIcon from '@/assets/images/brush.svg'
-import ChatIcon from '@/assets/images/chat.svg'
-import VisualSearchIcon from '@/assets/images/visual-search.svg'
-import SendIcon from '@/assets/images/send.svg'
-import PinIcon from '@/assets/images/pin.svg'
-import PinFillIcon from '@/assets/images/pin-fill.svg'
-
-import { useBing } from '@/lib/hooks/use-bing'
-import { voiceListenAtom } from '@/state'
-import Voice from './voice'
-import { ChatImage } from './chat-image'
-import { ChatAttachments } from './chat-attachments'
-
-export interface ChatPanelProps
- extends Pick<
- ReturnTypexs
- sm
- md
- lg
- xl
- 2xl
-