buy me a coffee (beer) if this helped 🍺😁")
-
- gr.HTML(value="
")
-
- with gr.Box():
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(type="pil", label="Input")
- upscale_size = gr.Radio(["x4", "x2"], label="Upscale by:", value="x4")
- upscale_type = gr.Radio(["Manga", "Anime", "Photo", "General"], label="Select the type of picture you want to upscale:", value="Manga")
-
- with gr.Row():
- upscale_btn = gr.Button(value="Upscale", variant="primary")
-
- with gr.Column():
- output_image = gr.Image(type="filepath", interactive=False, label="Upscaled image", elem_id="preview_img")
-
- with gr.Row():
- out_file = gr.File(interactive=False, show_label=False, elem_id="preview_file")
-
- gr.HTML(value="")
-
- upscale_btn.click(process_image.inference, inputs=[input_image, upscale_size, upscale_type], outputs=[output_image, out_file])
-
-demo.queue()
-demo.launch(debug=is_colab, share=is_colab, inline=is_colab)
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Descargarfisiologiamedicaboronespanol Descubre la obra maestra de Boron en espaol.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Descargarfisiologiamedicaboronespanol Descubre la obra maestra de Boron en espaol.md
deleted file mode 100644
index 9356f5dc5f6682ac117fc3ea259d611e60571c4d..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Descargarfisiologiamedicaboronespanol Descubre la obra maestra de Boron en espaol.md
+++ /dev/null
@@ -1,92 +0,0 @@
-
-¿Te interesa aprender sobre la fisiología humana de una forma rigurosa y actualizada? ¿Quieres tener acceso a uno de los libros más recomendados y utilizados en el ámbito académico y profesional? ¿Te gustaría poder leerlo en tu idioma y en formato digital? Si tu respuesta es sí, entonces este artículo es para ti.
-En este artículo te vamos a explicar todo lo que necesitas saber sobre el libro Fisiología Médica de Boron, una obra de referencia en el mundo de la fisiología que te ayudará a comprender el funcionamiento del cuerpo humano desde un nivel molecular y celular hasta un nivel sistémico y clínico.
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También te vamos a mostrar las ventajas de descargar el libro en español y en formato digital, así como los pasos que debes seguir para hacerlo de forma gratuita y legal. Al final del artículo te daremos nuestra opinión sobre el libro y te invitaremos a dejar la tuya.
-Así que no esperes más y sigue leyendo para descubrir cómo descargar fisiología médica boron español gratis.
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Antes de explicarte cómo descargar el libro, vamos a repasar sus principales características para que sepas qué puedes esperar de él.
-El libro Fisiología Médica es una obra colectiva escrita por más de 40 expertos en diferentes áreas de la fisiología. Los editores principales son Walter F. Boron y Emile L. Boulpaep, dos reconocidos profesores e investigadores que han dedicado su carrera al estudio de la fisiología celular y molecular.
-Boron es profesor y director del Departamento de Fisiología y Biofísica de la Escuela de Medicina de la Universidad Case Western Reserve en Cleveland, Ohio. Boulpaep es profesor emérito del Departamento de Medicina Celular y Molecular de la Escuela de Medicina de la Universidad Yale en New Haven, Connecticut.
-Ambos editores cuentan con una amplia experiencia docente y han recibido numerosos premios y distinciones por su labor científica y educativa.
-El libro está organizado en 8 secciones que abarcan los principales aspectos de la fisiología humana. Cada sección contiene varios capítulos que tratan temas específicos relacionados con el funcionamiento de un órgano o un sistema.
-Las secciones son las siguientes:
-En total, el libro contiene 62 capítulos que suman más de 1300 páginas de contenido actualizado y riguroso.
-El libro tiene un enfoque único en la disciplina por su forma de explicar la fisiología partiendo de un nivel molecular y celular que sirve de base para entender el funcionamiento de un órgano o un sistema. Así mismo, a lo largo del texto se hace referencia constante a la correlación clínica y por tanto se estudia también las bases fisiológicas de la enfermedad, lo que le confiere un enfoque fisiopatológico.
-El libro combina la exposición teórica con ejemplos prácticos que ilustran los conceptos clave y facilitan su comprensión. Además, utiliza un lenguaje claro y preciso que evita las ambigüedades y los errores conceptuales.
-El libro cuenta con numerosos elementos didácticos que ayudan al lector a aprender y repasar los contenidos. Entre ellos se destacan:
-Ahora que ya conoc
0a6ba089ebIf you own an Epson WF 7011 or WF 7511 printer, you might have encountered some problems with your device, such as waste ink pad overflow, print head error, or printer initialization failure. These problems can affect the performance and quality of your printer, and sometimes even prevent it from working at all. Fortunately, there is a solution for these issues: Epson WF 7511 Adjustment Program.
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-If you are interested in tracing your roots and creating a family tree, you might have heard of Family Tree Maker, one of the most popular genealogy software in the market. But what if you have a Mac computer and you don't want to pay for the software? Is there a way to get it for free? The answer is yes, if you use a torrent file. In this article, we will explain what Family Tree Maker is, what a torrent file is, why you might want to use Family Tree Maker For Mac 2 Torrent, and how to download and use it. Read on to find out more.
-Family Tree Maker is a software that helps you create and share your family tree. It allows you to add information about your ancestors and relatives, such as names, dates, places, events, photos, documents, stories, and more. You can also import data from historical records and official archives, such as census records, birth certificates, marriage licenses, death certificates, military records, immigration records, etc. You can also collaborate with other family history enthusiasts online and compare notes and discover more together. Family Tree Maker also lets you share and print your family tree in various formats, such as charts, reports, books, slideshows, etc.
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A torrent file is a small file that contains information about a larger file or a group of files that you want to download from the internet. It does not contain the actual files themselves, but rather the metadata, such as the file names, sizes, locations, checksums, etc. A torrent file also contains information about the peers or sources that have the files or parts of the files that you want to download. To download a torrent file, you need a torrent client, which is a software that connects you to the peers and downloads the files for you.
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- | Name | Size | Type | Seeders | Leechers | Rating | Comments | | --- | --- | --- | --- | --- | --- | --- | Name | Size | Type | Seeders | Leechers | Rating | Comments | | --- | --- | --- | --- | --- | --- | --- | | Family Tree Maker for Mac 2 (2011) - Full Version [Mac OSX] | 1.2 GB | .dmg | 125 | 12 | 4.5/5 | "Works great, easy to install, no problems" | | Family Tree Maker for Mac 2 (2011) - Crack Only [Mac OSX] | 12 MB | .zip | 87 | 9 | 4/5 | "You need to disable your antivirus before running the crack, otherwise it won't work" | | Family Tree Maker for Mac 2 (2011) - Update Only [Mac OSX] | 150 MB | .dmg | 65 | 7 | 3.5/5 | "This update fixes some bugs and improves performance, but you need to have the full version installed first" |Choose the torrent file that suits your needs and preferences, and click on the download button or link to get it.
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-Wait until the download is complete. The time it takes to download depends on various factors, such as the size and quality of the files, the number and availability of peers or sources, the speed and stability of your internet connection, etc. It might take from a few minutes to a few hours or even days.
-Now that you have downloaded Family Tree Maker For Mac 2 Torrent, let's see how you can use it. Here are the steps you need to follow:
-The first thing you need to do is to install Family Tree Maker on your Mac. To do this, you need to locate the installation file that you downloaded via torrent. It should be in .dmg format, which is a disk image file that contains the software and its components. Double-click on the .dmg file to open it. You will see a window that shows the contents of the disk image file, such as an icon of Family Tree Maker and a shortcut to your Applications folder.
-Drag and drop the icon of Family Tree Maker into your Applications folder. This will copy the software into your Applications folder and create a shortcut for it. Alternatively, you can run the installer program that is included in the disk image file and follow the instructions on screen.
-If you downloaded a crack file or an update file via torrent, you might need to apply them before or after installing Family Tree Maker. A crack file is a file that modifies or bypasses the security features of a software, such as activation codes or serial numbers, allowing you to use the software for free or without limitations. An update file is a file that improves or fixes the software, such as adding new features or resolving bugs or errors. To apply a crack file or an update file, you need to locate them on your device and follow the instructions that are usually included in a text file or a readme file. You might need to copy and paste the crack file or the update file into the installation folder of Family Tree Maker, or run them as administrator, or restart your Mac, etc.
-After you have installed Family Tree Maker on your Mac, you can launch it from your Applications folder or from your Dock. You will see a welcome screen that gives you two options: create a new family tree or import an existing family tree.
-If you want to create a new family tree, you can click on the New button and enter a name for your family tree. You can also choose a template for your family tree, such as standard, extended, fan, bow tie, etc. You can also customize the appearance of your family tree, such as the color, font, style, etc.
-If you want to import an existing family tree, you can click on the Import button and browse for the file that contains your family tree data. Family Tree Maker supports various file formats, such as .ftm, .ftmb, .ftw, .ged, etc. You can also import data from online sources, such as Ancestry.com, FamilySearch.org, etc. You will need to sign in with your account and grant permission to Family Tree Maker to access your data.
-Once you have created or imported your family tree, you can start adding and editing information about your ancestors and relatives. You can do this by clicking on the People tab and selecting the person you want to work on. You will see a panel that shows the details of the person, such as name, birth date and place, death date and place, gender, occupation, etc. You can also see the relationships of the person with other people in your family tree, such as parents, spouse, children, siblings, etc.
-To add information about a person, you can click on the Add button and choose what kind of information you want to add. For example, you can add facts, events, notes, sources, media, etc. You can also add new people to your family tree by clicking on the Add button and choosing what kind of relationship you want to add. For example, you can add parents, spouse, children, siblings, etc.
-To edit information about a person, you can click on the Edit button and make changes to the existing information. For example, you can change the name, date, place, source, media, etc. of the person. You can also delete information or people from your family tree by clicking on the Delete button and confirming your action.
-One of the best features of Family Tree Maker is that it allows you to attach photos, documents, and stories to your family tree. This can help you enrich your family history and make it more personal and memorable. You can do this by clicking on the Media tab and selecting the person you want to work on. You will see a panel that shows the media items that are attached to the person, such as photos, documents, audio files, video files, etc.
-To attach a photo or a document to a person, you can click on the Add button and choose what kind of media item you want to add. For example, you can add a photo from your computer, from your camera, from a scanner, from a web address, etc. You can also add a document from your computer, from a scanner, from a web address, etc. You can also drag and drop media items from your computer or other sources into the panel.
-To attach a story to a person, you can click on the Add button and choose Story. You will see a window where you can write or paste your story. You can also format your story using various tools, such as font, size, color, alignment, bullet points, etc. You can also add photos or documents to your story by clicking on the Insert button and choosing what kind of media item you want to add.
-To edit or delete a media item or a story that is attached to a person, you can click on the Edit or Delete button and make changes or confirm your action.
-After you have added and edited information and media items to your family tree, you might want to share and print it. Family Tree Maker offers various options for sharing and printing your family tree. You can do this by clicking on the Publish tab and selecting what kind of output you want to create. For example, you can create charts, reports, books, slideshows, etc.
-To create a chart of your family tree, you can click on the Chart button and choose what kind of chart you want to create. For example, you can create a pedigree chart, a descendant chart, a fan chart, a bow tie chart, etc. You can also customize the appearance of your chart, such as the size, shape, color, font, style, etc. You can also add or remove information and media items from your chart, such as names, dates, places, photos, etc.
-To create a report of your family tree, you can click on the Report button and choose what kind of report you want to create. For example, you can create a family group sheet, a kinship report, a timeline report, a source report, etc. You can also customize the content and format of your report, such as the fields, filters, sorting, layout, etc. You can also add or remove information and media items from your report, such as names, dates, places, photos, etc.
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The Legend of Zelda: Breath of the Wild lets you go anywhere and do anything. Throw a rock, find a new path, explore. The freedom the game offers is exhausting, and I don't know how many times I had to pause to catch my breath. Sure, collecting the Master Sword and fighting Ganon wasn't challenging, but a rich world full of secrets, opportunities, and hidden treasures is a lot more rewarding.
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This version comes with a brand new story and nine brand new modes to play. For an action game, it's not bad at all. None of the modes are replayable, so they're only there to give you something to do. As a matter of fact, I couldn't even finish the story because I needed to stop to check out the different modes and achievements. Unfortunately, the time I spent playing these modes couldn't be easily returned.
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My only complaint is that the game seems to repeat sections that got me far enough into the story. It's a minor issue, but I had to beat a bunch of story cutscenes twice because I couldn't make it through them the first time. Also, it seems that the game is never really "over" until I start a new game and try again, and that's a bit of a turn-off. All of that said, this game is a great game. The story, music, and visuals are top-notch, and the hack and slash action is even better than in the previous games.
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If you love playing solitaire games, you will love All-in-One Solitaire APK. This is an amazing app that lets you enjoy over 50 different solitaire games on your Android device. Whether you prefer classic solitaire games like Klondike, Spider, or FreeCell, or you want to try something new like Pyramid, Tri-Peaks, or Wasp, you will find them all in this app. You can also customize your cards and backgrounds, use unlimited undos and hints, and play offline or online. In this article, we will tell you more about All-in-One Solitaire APK, how to download and install it, how to play it, and why you should play it.
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Black Clover is a Japanese manga series written and illustrated by Yūki Tabata. It has been serialized in Shueisha's shōnen manga magazine Weekly Shōnen Jump since February 2015, with its chapters collected in 35 tankōbon volumes as of June 2023 . The manga has been adapted into an anime television series by Studio Pierrot, which aired from October 2017 to March 2021, with a total of 170 episodes . The anime is also available for streaming on platforms like Crunchyroll and Funimation.
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The protagonist of the series is Asta, a cheerful and energetic orphan who was abandoned at a church along with his rival and best friend, Yuno. Asta has no magic at all, while Yuno has immense magical talent and a four-leaf clover grimoire. Despite his disadvantage, Asta dreams of becoming the Wizard King, the strongest mage in the kingdom who protects the people from threats. To achieve his goal, he joins the Black Bulls, one of the nine Magic Knight squads that serve the Wizard King. Along with his teammates, he embarks on various missions and battles against enemies such as the Eye of the Midnight Sun, a terrorist group that seeks to destroy the kingdom; the Diamond Kingdom, a neighboring country that invades for resources; the Spade Kingdom, a militaristic nation that plans to conquer the world; and devils, evil beings from another dimension that manipulate humans for their own purposes.
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-The clover symbols in Black Clover represent the number of leaves on the grimoires that the characters use to cast magic. The more leaves a grimoire has, the more rare and powerful it is. The meanings of the leaves are as follows:
-This is a difficult question to answer as there are many factors that determine the strength of a character in Black Clover, such as magic type, magic amount, magic control, magic affinity, grimoire, spirit, devil, experience, skill, strategy, teamwork, and personality. However, based on the current events of the manga and anime, some of the candidates for the strongest character in Black Clover are: - Julius Novachrono: The former Wizard King and the leader of the Magic Knights. He has a time magic grimoire and can manipulate time to accelerate, decelerate, stop, or reverse it. He can also see the future and store time in his tattoos. He is a master of magic and combat, and has a vast knowledge of the world and its history. - Licht: The leader of the Elf Tribe and the original owner of the five-leaf clover grimoire. He has a sword magic grimoire and can create and wield various swords with different abilities. He can also use forbidden magic to summon a giant demon god. He is a prodigy of magic and swordsmanship, and has a strong connection to mana and nature. - Lumiere Silvamillion Clover: The first Wizard King and the savior of the Clover Kingdom. He has a light magic grimoire and can create and manipulate light to move at high speeds, attack with powerful beams, or heal with rays. He can also use sealing magic to seal away enemies or objects. He is a genius of magic and science, and has a noble and compassionate heart. - Zagred: The devil who orchestrated the massacre of the Elf Tribe and the reincarnation of the elves. He has a word magic grimoire and can create and manipulate anything he says with his mouth. He can also use other types of magic, such as fire, ice, wind, earth, water, plant, beast, spatial, healing, curse, ash, trap, mirror, dream, soul, and anti-magic. He is a cunning and ruthless being who seeks to destroy the world and create his own. - Asta: The main protagonist of the series and the current owner of the five-leaf clover grimoire. He has no magic but possesses a devil named Liebe who grants him anti-magic powers. He can use anti-magic to negate and repel any magic with his swords. He is also very physically strong and agile, and has a strong sense of justice and determination.
Black Clover anime has a total of 170 episodes that aired from October 2017 to March 2021 . The anime covers the first 270 chapters of the manga , which is divided into 11 arcs: Introduction Arc (episodes 1-3), Dungeon Exploration Arc (episodes 4-13), Royal Capital Arc (episodes 14-19), Eye of the Midnight Sun Arc (episodes 20-27), Seabed Temple Arc (episodes 28-49), Witches' Forest Arc (episodes 50-65), Hot Springs Training Camp Arc (episodes 66-68), Royal Knights Arc (episodes 69-101), Reincarnation Arc (episodes 102-120), Elf Reincarnation Arc (episodes 121-151), and Spade Kingdom Raid Arc (episodes 152-170).
-Yes, Black Clover manga is still ongoing as of June 2023 . The manga has currently released 35 tankōbon volumes that contain 298 chapters . The latest chapter is chapter 298 , which was released on June 18th, 2023 . The manga is still in the Spade Kingdom Raid Arc , which is the 12th arc of the story.
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-Once you are on the APKPure website or app, you can search for Call of Duty Mobile in the search bar. You will see the game icon and some information about it, such as the size, version, rating, and description. Tap on the game icon to go to the download page.
-On the download page, you will see two buttons: one for downloading the APK file and one for downloading the OBB file. The APK file is the application file that installs the game on your device, while the OBB file is the data file that contains the game content. You need both files to play Call of Duty Mobile. Tap on both buttons to start downloading them. You may need to enable unknown sources in your device settings to allow APK installation from third-party sources.
-After downloading both files, you need to install the APK file first. Locate the file in your device storage and tap on it to start the installation process. Follow the instructions on the screen and wait for it to finish. Then, you need to copy the OBB file to the game folder. The game folder is usually located in Android/obb/com.activision.callofduty.shooter. If you don't see this folder, you can create it manually. Paste the OBB file in this folder and make sure it has the same name as the original file.
-Now you are ready to play Call of Duty Mobile on your Android device. Launch the game from your app drawer or home screen and log in with your account or create a new one. You can also link your Facebook or Google account for easy access. Choose your preferred game mode and start shooting your enemies. Have fun!
-Call of Duty Mobile is a great way to enjoy a thrilling and immersive shooter game on your mobile device. You can play with millions of players around the world, customize your loadout, join clans, and participate in events and challenges. If you want to download Call of Duty Mobile from APKPure, you can follow our simple guide above and get the game in no time. APKPure offers fast, safe, and easy downloads for Call of Duty Mobile without any region restrictions or compatibility issues. Download Call of Duty Mobile from APKPure today and join the action!
-Here are some of the common questions that people ask about Call of Duty Mobile and APKPure:
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-Yes, Call of Duty Mobile is safe and secure to play. The game is developed by a reputable company (TiMi Studios) and published by a trusted company (Activision). The game also has anti-cheat measures and encryption systems to protect your data and privacy.
-Yes, APKPure is safe and reliable to use. APKPure is a well-known third-party app store that provides free and verified APK files for Android users. APKPure checks every APK file for viruses, malware, and other threats before uploading it to their website or app.
-No, you cannot play Call of Duty Mobile offline. The game requires an internet connection to run properly. You need to connect to a Wi-Fi or mobile data network to play online multiplayer modes or Battle Royale mode.
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401be4b1e0TikTok es una de las aplicaciones de redes sociales más populares del mundo, con más de mil millones de usuarios activos mensuales. Te permite crear y ver videos cortos que son divertidos, genuinos y creativos. ¿Pero sabías que también puedes ver TikTok en tu smart TV?
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Ver TikTok en tu smart TV puede darte una mejor experiencia de visualización y más contenido divertido. Puede disfrutar de los videos de TikTok en una pantalla más grande y clara, verlos con sus amigos y familiares y descubrir más categorías que se adapten a sus intereses.
-En este artículo, le mostraremos cómo descargar TikTok APK para Smart TV usando tres métodos diferentes. También explicaremos los beneficios de ver TikTok en su televisor inteligente y responderemos algunas preguntas frecuentes.
-TikTok es una aplicación que te permite crear y ver videos cortos que suelen durar entre 15 segundos y 3 minutos. Puedes usar varios filtros, efectos, música, pegatinas y hashtags para hacer tus videos más atractivos y entretenidos.
- -También puedes explorar videos de otros usuarios alrededor del mundo, basado en lo que te gusta, seguir o compartir. Puedes encontrar videos de varias categorías, como comedia, juegos, bricolaje, comida, deportes, memes, mascotas, ASMR, y más.
-Si bien TikTok está diseñado para dispositivos móviles, también puede verlo en su televisor inteligente. Esto puede darle varias ventajas, como:
-Esta opción funciona para cualquier televisor inteligente que admita el uso compartido de pantalla inalámbrica. Puede lanzar o reflejar TikTok desde su teléfono, tableta o computadora a su televisor utilizando una aplicación de reflejo de pantalla. Aquí está cómo hacerlo:
-Antes de que pueda lanzar o reflejar TikTok desde su dispositivo a su televisor, debe instalar una aplicación de reflejo de pantalla en ambos dispositivos. Una aplicación de reflejo de pantalla le permite compartir la pantalla de un dispositivo con otro dispositivo de forma inalámbrica. Puede encontrar muchas aplicaciones de reflejo de pantalla en la tienda de aplicaciones de su dispositivo.
-También necesitas conectar ambos dispositivos a la misma red Wi-Fi. Esto asegura que pueden comunicarse entre sí y transmitir el video sin problemas. Para hacer esto, siga estos pasos:
-Un cable HDMI es un tipo de cable que puede transmitir señales de vídeo y audio de alta definición entre dispositivos. Puede usarlo para conectar su dispositivo a su televisor y ver videos TikTok en una pantalla más grande. Puede comprar un cable HDMI en cualquier tienda de electrónica o en línea.
-También necesita cambiar la fuente de entrada de su TV a HDMI. Esto le dice a su televisor qué dispositivo mostrar en la pantalla. Para hacer esto, siga estos pasos:
-Ver TikTok en tu smart TV puede ofrecerte muchos beneficios, como:
-Otro beneficio de ver TikTok en tu smart TV es que puedes verlo con tus amigos y familiares y divertirte más. Puedes compartir los videos que te gustan, comentarlos o incluso crear tus propios videos juntos. También puede utilizar la aplicación de televisión inteligente para navegar por diferentes categorías y descubrir nuevo contenido que puede no encontrar en la aplicación móvil.
-Un tercer beneficio de ver TikTok en tu smart TV es que puedes descubrir más contenido y categorías que se adapten a tus intereses. Algunas categorías pueden no estar disponibles en la aplicación móvil, pero puedes encontrarlas en la aplicación de televisión inteligente. Por ejemplo, puedes ver videos de la categoría TikTok TV, que incluye contenido seleccionado de creadores y celebridades populares. También puede utilizar la función de búsqueda para encontrar vídeos por palabras clave, hashtags o nombres de usuario.
-TikTok es una popular aplicación de redes sociales que te permite crear y ver videos cortos que son divertidos, genuinos y creativos. También puede ver TikTok en su televisor inteligente utilizando tres métodos diferentes: instalar la aplicación TikTok TV desde la tienda de aplicaciones en su TV, emitir o duplicar TikTok desde su dispositivo a su TV, o conectar su dispositivo a su TV con un cable HDMI.
-Ver TikTok en tu smart TV puede darte una mejor experiencia de visualización y más contenido divertido. Puede disfrutar de los videos de TikTok en una pantalla más grande y clara, verlos con sus amigos y familiares y descubrir más categorías que se adapten a sus intereses.
-Entonces, ¿qué estás esperando? ¡Prueba a ver TikTok en tu smart TV hoy y comprueba por ti mismo lo divertido que puede ser!
-A2. Puedes controlar TikTok en tu smart TV usando diferentes métodos, dependiendo de cómo lo hayas instalado. Si ha instalado la aplicación TikTok TV desde la tienda de aplicaciones en su televisor, puede usar el control remoto de su televisor para navegar por la aplicación y reproducir los videos. Si lanzas o reflejas TikTok desde tu dispositivo a tu televisor, puedes usar el dispositivo como control remoto y reproducir los videos desde allí. Si conecta su dispositivo a su televisor con un cable HDMI, también puede usar el dispositivo como control remoto.
-A3. No, no puede crear vídeos TikTok en su televisor inteligente. Solo puede ver vídeos TikTok en su televisor inteligente. Para crear vídeos TikTok, necesitas usar la aplicación móvil en tu teléfono o tablet.
-A4. Puede ajustar la calidad de vídeo y el sonido de TikTok en su televisor inteligente utilizando el menú de configuración de su televisor o la aplicación de reflejo de pantalla. Puede cambiar la resolución, relación de aspecto, brillo, contraste, color, volumen y otras opciones para adaptarse a sus preferencias.
-A5. Algunas de las mejores categorías para ver en TikTok en mi smart TV son:
-Estas son solo algunas de las categorías que puedes ver en TikTok en tu smart TV. También puedes encontrar muchas otras categorías que coinciden con tus intereses y preferencias.
64aa2da5cf¿Te encanta jugar juegos de fiesta en línea con tus amigos? ¿Quieres experimentar la emoción de competir contra hasta 32 jugadores en caóticas carreras de obstáculos? ¿Quieres personalizar tu personaje con trajes y emotes impresionantes? Si respondiste sí a cualquiera de estas preguntas, entonces definitivamente deberías probar Stumble Guys, un juego multijugador gratuito para dispositivos Android e iOS. Y si quieres que tu juego sea aún más divertido y emocionante, entonces deberías descargar Stumble Guys Mod Apkmody, una versión modificada del juego que te da dinero ilimitado, gemas y atuendos. En este artículo, le mostraremos cómo descargar e instalar Stumble Guys Mod Apkmody en su dispositivo, y cómo jugar con algunos consejos y trucos. ¡Vamos a empezar!
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Stumble Guys es un juego multijugador masivo que se inspira en los populares Fall Guys. El juego consta de varios minijuegos que ponen a prueba tus habilidades, reflejos y suerte. Tienes que correr, saltar, correr, deslizarte y evitar obstáculos mientras compites con hasta 32 jugadores en línea. El último jugador en pie gana la corona y la gloria.
-Stumble Guys tiene muchas características que lo convierten en un juego divertido y adictivo. Algunas de ellas son:
-Con Stumble Guys Mod Apkmody, puedes disfrutar del juego sin limitaciones ni restricciones. Puedes personalizar a tu personaje con cualquier atuendo que te guste, desde superhéroes hasta animales y alimentos. También puedes comprar cualquier emote que quieras, desde bailes hasta burlas y gestos. También puedes usar tu dinero y gemas para comprar vidas extra o saltar niveles si te quedas atascado.
-Si desea descargar e instalar Stumble Guys Mod Apkmody en su dispositivo, debe seguir estos sencillos pasos:
-Aquí hay algunas capturas de pantalla del proceso de descarga e instalación:
-Sí, Stumble Guys Mod Apkmody es seguro para descargar e instalar en su dispositivo. No contiene ningún virus o malware que pueda dañar su dispositivo o comprometer su privacidad. Sin embargo, siempre debe descargarlo de una fuente confiable como , y escanearlo con un antivirus antes de instalarlo.
-Stumble Guys Mod Apkmody es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 5.0 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el mod o pueden experimentar algunos fallos o errores durante la reproducción. Si eso sucede, puede intentar desinstalar y reinstalar el mod, o ponerse en contacto con el desarrollador para obtener soporte.
-Sí, puedes jugar Stumble Guys Mod Apkmody con tus amigos en línea o fuera de línea. Puede unirse al modo multijugador en línea e invitar a sus amigos a unirse a su partido, o crear un partido privado con un código. También puedes jugar el modo fiesta y conectar con tus amigos a través de Bluetooth o Wi-Fi.
- -No, no te prohibirán el uso de Stumble Guys Mod Apkmody. El mod no interfiere con los servidores del juego ni con las cuentas de otros jugadores. Solo modifica tus propios datos y recursos de juego. Sin embargo, debes usar el mod de forma responsable y respetuosa, y no abusar de él o hacer trampa en el juego.
-出错了？试试把文件转换为MP3后再上传吧~
" - -examples=[['canon.flac'], ['download.wav']] - -gr.Interface( - inference, - gr.Audio(type="filepath", label="输入"), - outputs = gr.File(label="输出"), - title=title, - description=description, - article=article, - examples=examples - ).launch() \ No newline at end of file diff --git a/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/data/__init__.py b/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/data/__init__.py deleted file mode 100644 index 47bb6e24ddf25aa4fd5bf0fe9672f89099efb9b4..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/data/__init__.py +++ /dev/null @@ -1,11 +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. - -from .asr_dataset import AsrDataset - - -__all__ = [ - "AsrDataset", -] diff --git a/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/utils/wer_utils.py b/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/utils/wer_utils.py deleted file mode 100644 index cf6f3d09ba41a46ad4d7968fb3c286dd53d15c38..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/utils/wer_utils.py +++ /dev/null @@ -1,381 +0,0 @@ -#!/usr/bin/env python3 - -# 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. - -from __future__ import absolute_import, division, print_function, unicode_literals - -import re -from collections import deque -from enum import Enum - -import numpy as np - - -""" - Utility modules for computation of Word Error Rate, - Alignments, as well as more granular metrics like - deletion, insersion and substitutions. -""" - - -class Code(Enum): - match = 1 - substitution = 2 - insertion = 3 - deletion = 4 - - -class Token(object): - def __init__(self, lbl="", st=np.nan, en=np.nan): - if np.isnan(st): - self.label, self.start, self.end = "", 0.0, 0.0 - else: - self.label, self.start, self.end = lbl, st, en - - -class AlignmentResult(object): - def __init__(self, refs, hyps, codes, score): - self.refs = refs # std::deque
- self.score = score # float
-
-
-def coordinate_to_offset(row, col, ncols):
- return int(row * ncols + col)
-
-
-def offset_to_row(offset, ncols):
- return int(offset / ncols)
-
-
-def offset_to_col(offset, ncols):
- return int(offset % ncols)
-
-
-def trimWhitespace(str):
- return re.sub(" +", " ", re.sub(" *$", "", re.sub("^ *", "", str)))
-
-
-def str2toks(str):
- pieces = trimWhitespace(str).split(" ")
- toks = []
- for p in pieces:
- toks.append(Token(p, 0.0, 0.0))
- return toks
-
-
-class EditDistance(object):
- def __init__(self, time_mediated):
- self.time_mediated_ = time_mediated
- self.scores_ = np.nan # Eigen::Matrix
- self.backtraces_ = (
- np.nan
- ) # Eigen::Matrix backtraces_;
- self.confusion_pairs_ = {}
-
- def cost(self, ref, hyp, code):
- if self.time_mediated_:
- if code == Code.match:
- return abs(ref.start - hyp.start) + abs(ref.end - hyp.end)
- elif code == Code.insertion:
- return hyp.end - hyp.start
- elif code == Code.deletion:
- return ref.end - ref.start
- else: # substitution
- return abs(ref.start - hyp.start) + abs(ref.end - hyp.end) + 0.1
- else:
- if code == Code.match:
- return 0
- elif code == Code.insertion or code == Code.deletion:
- return 3
- else: # substitution
- return 4
-
- def get_result(self, refs, hyps):
- res = AlignmentResult(refs=deque(), hyps=deque(), codes=deque(), score=np.nan)
-
- num_rows, num_cols = self.scores_.shape
- res.score = self.scores_[num_rows - 1, num_cols - 1]
-
- curr_offset = coordinate_to_offset(num_rows - 1, num_cols - 1, num_cols)
-
- while curr_offset != 0:
- curr_row = offset_to_row(curr_offset, num_cols)
- curr_col = offset_to_col(curr_offset, num_cols)
-
- prev_offset = self.backtraces_[curr_row, curr_col]
-
- prev_row = offset_to_row(prev_offset, num_cols)
- prev_col = offset_to_col(prev_offset, num_cols)
-
- res.refs.appendleft(curr_row - 1) # Note: this was .push_front() in C++
- res.hyps.appendleft(curr_col - 1)
- if curr_row - 1 == prev_row and curr_col == prev_col:
- res.codes.appendleft(Code.deletion)
- elif curr_row == prev_row and curr_col - 1 == prev_col:
- res.codes.appendleft(Code.insertion)
- else:
- # assert(curr_row - 1 == prev_row and curr_col - 1 == prev_col)
- ref_str = refs[res.refs[0]].label
- hyp_str = hyps[res.hyps[0]].label
-
- if ref_str == hyp_str:
- res.codes.appendleft(Code.match)
- else:
- res.codes.appendleft(Code.substitution)
-
- confusion_pair = "%s -> %s" % (ref_str, hyp_str)
- if confusion_pair not in self.confusion_pairs_:
- self.confusion_pairs_[confusion_pair] = 1
- else:
- self.confusion_pairs_[confusion_pair] += 1
-
- curr_offset = prev_offset
-
- return res
-
- def align(self, refs, hyps):
- if len(refs) == 0 and len(hyps) == 0:
- return np.nan
-
- # NOTE: we're not resetting the values in these matrices because every value
- # will be overridden in the loop below. If this assumption doesn't hold,
- # be sure to set all entries in self.scores_ and self.backtraces_ to 0.
- self.scores_ = np.zeros((len(refs) + 1, len(hyps) + 1))
- self.backtraces_ = np.zeros((len(refs) + 1, len(hyps) + 1))
-
- num_rows, num_cols = self.scores_.shape
-
- for i in range(num_rows):
- for j in range(num_cols):
- if i == 0 and j == 0:
- self.scores_[i, j] = 0.0
- self.backtraces_[i, j] = 0
- continue
-
- if i == 0:
- self.scores_[i, j] = self.scores_[i, j - 1] + self.cost(
- None, hyps[j - 1], Code.insertion
- )
- self.backtraces_[i, j] = coordinate_to_offset(i, j - 1, num_cols)
- continue
-
- if j == 0:
- self.scores_[i, j] = self.scores_[i - 1, j] + self.cost(
- refs[i - 1], None, Code.deletion
- )
- self.backtraces_[i, j] = coordinate_to_offset(i - 1, j, num_cols)
- continue
-
- # Below here both i and j are greater than 0
- ref = refs[i - 1]
- hyp = hyps[j - 1]
- best_score = self.scores_[i - 1, j - 1] + (
- self.cost(ref, hyp, Code.match)
- if (ref.label == hyp.label)
- else self.cost(ref, hyp, Code.substitution)
- )
-
- prev_row = i - 1
- prev_col = j - 1
- ins = self.scores_[i, j - 1] + self.cost(None, hyp, Code.insertion)
- if ins < best_score:
- best_score = ins
- prev_row = i
- prev_col = j - 1
-
- delt = self.scores_[i - 1, j] + self.cost(ref, None, Code.deletion)
- if delt < best_score:
- best_score = delt
- prev_row = i - 1
- prev_col = j
-
- self.scores_[i, j] = best_score
- self.backtraces_[i, j] = coordinate_to_offset(
- prev_row, prev_col, num_cols
- )
-
- return self.get_result(refs, hyps)
-
-
-class WERTransformer(object):
- def __init__(self, hyp_str, ref_str, verbose=True):
- self.ed_ = EditDistance(False)
- self.id2oracle_errs_ = {}
- self.utts_ = 0
- self.words_ = 0
- self.insertions_ = 0
- self.deletions_ = 0
- self.substitutions_ = 0
-
- self.process(["dummy_str", hyp_str, ref_str])
-
- if verbose:
- print("'%s' vs '%s'" % (hyp_str, ref_str))
- self.report_result()
-
- def process(self, input): # std::vector&& input
- if len(input) < 3:
- print(
- "Input must be of the form ... , got ",
- len(input),
- " inputs:",
- )
- return None
-
- # Align
- # std::vector hyps;
- # std::vector refs;
-
- hyps = str2toks(input[-2])
- refs = str2toks(input[-1])
-
- alignment = self.ed_.align(refs, hyps)
- if alignment is None:
- print("Alignment is null")
- return np.nan
-
- # Tally errors
- ins = 0
- dels = 0
- subs = 0
- for code in alignment.codes:
- if code == Code.substitution:
- subs += 1
- elif code == Code.insertion:
- ins += 1
- elif code == Code.deletion:
- dels += 1
-
- # Output
- row = input
- row.append(str(len(refs)))
- row.append(str(ins))
- row.append(str(dels))
- row.append(str(subs))
- # print(row)
-
- # Accumulate
- kIdIndex = 0
- kNBestSep = "/"
-
- pieces = input[kIdIndex].split(kNBestSep)
-
- if len(pieces) == 0:
- print(
- "Error splitting ",
- input[kIdIndex],
- " on '",
- kNBestSep,
- "', got empty list",
- )
- return np.nan
-
- id = pieces[0]
- if id not in self.id2oracle_errs_:
- self.utts_ += 1
- self.words_ += len(refs)
- self.insertions_ += ins
- self.deletions_ += dels
- self.substitutions_ += subs
- self.id2oracle_errs_[id] = [ins, dels, subs]
- else:
- curr_err = ins + dels + subs
- prev_err = np.sum(self.id2oracle_errs_[id])
- if curr_err < prev_err:
- self.id2oracle_errs_[id] = [ins, dels, subs]
-
- return 0
-
- def report_result(self):
- # print("---------- Summary ---------------")
- if self.words_ == 0:
- print("No words counted")
- return
-
- # 1-best
- best_wer = (
- 100.0
- * (self.insertions_ + self.deletions_ + self.substitutions_)
- / self.words_
- )
-
- print(
- "\tWER = %0.2f%% (%i utts, %i words, %0.2f%% ins, "
- "%0.2f%% dels, %0.2f%% subs)"
- % (
- best_wer,
- self.utts_,
- self.words_,
- 100.0 * self.insertions_ / self.words_,
- 100.0 * self.deletions_ / self.words_,
- 100.0 * self.substitutions_ / self.words_,
- )
- )
-
- def wer(self):
- if self.words_ == 0:
- wer = np.nan
- else:
- wer = (
- 100.0
- * (self.insertions_ + self.deletions_ + self.substitutions_)
- / self.words_
- )
- return wer
-
- def stats(self):
- if self.words_ == 0:
- stats = {}
- else:
- wer = (
- 100.0
- * (self.insertions_ + self.deletions_ + self.substitutions_)
- / self.words_
- )
- stats = dict(
- {
- "wer": wer,
- "utts": self.utts_,
- "numwords": self.words_,
- "ins": self.insertions_,
- "dels": self.deletions_,
- "subs": self.substitutions_,
- "confusion_pairs": self.ed_.confusion_pairs_,
- }
- )
- return stats
-
-
-def calc_wer(hyp_str, ref_str):
- t = WERTransformer(hyp_str, ref_str, verbose=0)
- return t.wer()
-
-
-def calc_wer_stats(hyp_str, ref_str):
- t = WERTransformer(hyp_str, ref_str, verbose=0)
- return t.stats()
-
-
-def get_wer_alignment_codes(hyp_str, ref_str):
- """
- INPUT: hypothesis string, reference string
- OUTPUT: List of alignment codes (intermediate results from WER computation)
- """
- t = WERTransformer(hyp_str, ref_str, verbose=0)
- return t.ed_.align(str2toks(ref_str), str2toks(hyp_str)).codes
-
-
-def merge_counts(x, y):
- # Merge two hashes which have 'counts' as their values
- # This can be used for example to merge confusion pair counts
- # conf_pairs = merge_counts(conf_pairs, stats['confusion_pairs'])
- for k, v in y.items():
- if k not in x:
- x[k] = 0
- x[k] += v
- return x
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/distributed/distributed_timeout_wrapper.py b/spaces/ICML2022/OFA/fairseq/fairseq/distributed/distributed_timeout_wrapper.py
deleted file mode 100644
index 18107ef27ea837b8c72dcaa49db18fd8e64267b1..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/distributed/distributed_timeout_wrapper.py
+++ /dev/null
@@ -1,94 +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 logging
-import os
-import signal
-import threading
-
-from torch import nn
-
-
-logger = logging.getLogger(__name__)
-
-
-class DistributedTimeoutWrapper(nn.Module):
- """
- A wrapper that kills the process if no progress is made within a given
- *timeout*. The timer is reset every time :func:`forward` is called.
-
- Usage::
-
- module = DistributedTimeoutWrapper(module, timeout=30)
- x = module(input)
- time.sleep(20) # safe
- x = module(input)
- time.sleep(45) # job will be killed before this returns
-
- Args:
- module (nn.Module): module to wrap
- timeout (int): number of seconds before killing the process
- (set to a value <= 0 to disable the timeout)
- signal (Optional): signal to send once timeout is triggered
- """
- def __init__(self, module: nn.Module, timeout: int, signal=signal.SIGINT):
- super().__init__()
- self.module = module
- self.timeout = timeout
- self.signal = signal
-
- if timeout > 0:
- self._heartbeat = threading.Event()
- self._heartbeat_thread = threading.Thread(
- target=self._check_heartbeat,
- args=(os.getpid(),),
- daemon=True,
- )
- self._heartbeat_thread.start()
- self._terminated = False
- else:
- self._heartbeat = None
- self._heartbeat_thread = None
-
- def __del__(self):
- self.stop_timeout()
-
- def __getattr__(self, name):
- """Forward missing attributes to wrapped module."""
- try:
- return super().__getattr__(name) # defer to nn.Module's logic
- except AttributeError:
- return getattr(self.module, name)
-
- def stop_timeout(self):
- if self._heartbeat_thread is not None:
- self._terminated = True
- self._heartbeat_thread.join()
-
- def state_dict(self, *args, **kwargs):
- return self.module.state_dict(*args, **kwargs)
-
- def load_state_dict(self, *args, **kwargs):
- return self.module.load_state_dict(*args, **kwargs)
-
- def forward(self, *args, **kwargs):
- if self._heartbeat is not None:
- self._heartbeat.set()
- return self.module(*args, **kwargs)
-
- def _check_heartbeat(self, parent_pid):
- self._heartbeat.wait() # wait for the first forward pass
- while True:
- self._heartbeat.clear()
- success = self._heartbeat.wait(timeout=self.timeout)
- if self._terminated:
- break
- elif not success:
- logger.error((
- "Killing job for not making progress in {} seconds. "
- "Set --heartbeat-timeout=-1 to disable this timeout."
- ).format(int(self.timeout)))
- os.kill(parent_pid, self.signal)
- return
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/pix2pixhd.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/pix2pixhd.py
deleted file mode 100644
index 08c6afd777a88cd232592acbbf0ef25db8d43217..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/modules/pix2pixhd.py
+++ /dev/null
@@ -1,669 +0,0 @@
-# original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py
-import collections
-from functools import partial
-import functools
-import logging
-from collections import defaultdict
-
-import numpy as np
-import torch.nn as nn
-
-from saicinpainting.training.modules.base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation
-from saicinpainting.training.modules.ffc import FFCResnetBlock
-from saicinpainting.training.modules.multidilated_conv import MultidilatedConv
-
-class DotDict(defaultdict):
- # https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary
- """dot.notation access to dictionary attributes"""
- __getattr__ = defaultdict.get
- __setattr__ = defaultdict.__setitem__
- __delattr__ = defaultdict.__delitem__
-
-class Identity(nn.Module):
- def __init__(self):
- super().__init__()
-
- def forward(self, x):
- return x
-
-
-class ResnetBlock(nn.Module):
- def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
- dilation=1, in_dim=None, groups=1, second_dilation=None):
- super(ResnetBlock, self).__init__()
- self.in_dim = in_dim
- self.dim = dim
- if second_dilation is None:
- second_dilation = dilation
- self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
- conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
- second_dilation=second_dilation)
-
- if self.in_dim is not None:
- self.input_conv = nn.Conv2d(in_dim, dim, 1)
-
- self.out_channnels = dim
-
- def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
- dilation=1, in_dim=None, groups=1, second_dilation=1):
- conv_layer = get_conv_block_ctor(conv_kind)
-
- conv_block = []
- p = 0
- if padding_type == 'reflect':
- conv_block += [nn.ReflectionPad2d(dilation)]
- elif padding_type == 'replicate':
- conv_block += [nn.ReplicationPad2d(dilation)]
- elif padding_type == 'zero':
- p = dilation
- else:
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-
- if in_dim is None:
- in_dim = dim
-
- conv_block += [conv_layer(in_dim, dim, kernel_size=3, padding=p, dilation=dilation),
- norm_layer(dim),
- activation]
- if use_dropout:
- conv_block += [nn.Dropout(0.5)]
-
- p = 0
- if padding_type == 'reflect':
- conv_block += [nn.ReflectionPad2d(second_dilation)]
- elif padding_type == 'replicate':
- conv_block += [nn.ReplicationPad2d(second_dilation)]
- elif padding_type == 'zero':
- p = second_dilation
- else:
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
- conv_block += [conv_layer(dim, dim, kernel_size=3, padding=p, dilation=second_dilation, groups=groups),
- norm_layer(dim)]
-
- return nn.Sequential(*conv_block)
-
- def forward(self, x):
- x_before = x
- if self.in_dim is not None:
- x = self.input_conv(x)
- out = x + self.conv_block(x_before)
- return out
-
-class ResnetBlock5x5(nn.Module):
- def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
- dilation=1, in_dim=None, groups=1, second_dilation=None):
- super(ResnetBlock5x5, self).__init__()
- self.in_dim = in_dim
- self.dim = dim
- if second_dilation is None:
- second_dilation = dilation
- self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
- conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
- second_dilation=second_dilation)
-
- if self.in_dim is not None:
- self.input_conv = nn.Conv2d(in_dim, dim, 1)
-
- self.out_channnels = dim
-
- def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
- dilation=1, in_dim=None, groups=1, second_dilation=1):
- conv_layer = get_conv_block_ctor(conv_kind)
-
- conv_block = []
- p = 0
- if padding_type == 'reflect':
- conv_block += [nn.ReflectionPad2d(dilation * 2)]
- elif padding_type == 'replicate':
- conv_block += [nn.ReplicationPad2d(dilation * 2)]
- elif padding_type == 'zero':
- p = dilation * 2
- else:
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-
- if in_dim is None:
- in_dim = dim
-
- conv_block += [conv_layer(in_dim, dim, kernel_size=5, padding=p, dilation=dilation),
- norm_layer(dim),
- activation]
- if use_dropout:
- conv_block += [nn.Dropout(0.5)]
-
- p = 0
- if padding_type == 'reflect':
- conv_block += [nn.ReflectionPad2d(second_dilation * 2)]
- elif padding_type == 'replicate':
- conv_block += [nn.ReplicationPad2d(second_dilation * 2)]
- elif padding_type == 'zero':
- p = second_dilation * 2
- else:
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
- conv_block += [conv_layer(dim, dim, kernel_size=5, padding=p, dilation=second_dilation, groups=groups),
- norm_layer(dim)]
-
- return nn.Sequential(*conv_block)
-
- def forward(self, x):
- x_before = x
- if self.in_dim is not None:
- x = self.input_conv(x)
- out = x + self.conv_block(x_before)
- return out
-
-
-class MultidilatedResnetBlock(nn.Module):
- def __init__(self, dim, padding_type, conv_layer, norm_layer, activation=nn.ReLU(True), use_dropout=False):
- super().__init__()
- self.conv_block = self.build_conv_block(dim, padding_type, conv_layer, norm_layer, activation, use_dropout)
-
- def build_conv_block(self, dim, padding_type, conv_layer, norm_layer, activation, use_dropout, dilation=1):
- conv_block = []
- conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
- norm_layer(dim),
- activation]
- if use_dropout:
- conv_block += [nn.Dropout(0.5)]
-
- conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
- norm_layer(dim)]
-
- return nn.Sequential(*conv_block)
-
- def forward(self, x):
- out = x + self.conv_block(x)
- return out
-
-
-class MultiDilatedGlobalGenerator(nn.Module):
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
- n_blocks=3, norm_layer=nn.BatchNorm2d,
- padding_type='reflect', conv_kind='default',
- deconv_kind='convtranspose', activation=nn.ReLU(True),
- up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
- add_out_act=True, max_features=1024, multidilation_kwargs={},
- ffc_positions=None, ffc_kwargs={}):
- assert (n_blocks >= 0)
- super().__init__()
-
- conv_layer = get_conv_block_ctor(conv_kind)
- resnet_conv_layer = functools.partial(get_conv_block_ctor('multidilated'), **multidilation_kwargs)
- norm_layer = get_norm_layer(norm_layer)
- if affine is not None:
- norm_layer = partial(norm_layer, affine=affine)
- up_norm_layer = get_norm_layer(up_norm_layer)
- if affine is not None:
- up_norm_layer = partial(up_norm_layer, affine=affine)
-
- model = [nn.ReflectionPad2d(3),
- conv_layer(input_nc, ngf, kernel_size=7, padding=0),
- norm_layer(ngf),
- activation]
-
- identity = Identity()
- ### downsample
- for i in range(n_downsampling):
- mult = 2 ** i
-
- model += [conv_layer(min(max_features, ngf * mult),
- min(max_features, ngf * mult * 2),
- kernel_size=3, stride=2, padding=1),
- norm_layer(min(max_features, ngf * mult * 2)),
- activation]
-
- mult = 2 ** n_downsampling
- feats_num_bottleneck = min(max_features, ngf * mult)
-
- ### resnet blocks
- for i in range(n_blocks):
- if ffc_positions is not None and i in ffc_positions:
- model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
- inline=True, **ffc_kwargs)]
- model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
- conv_layer=resnet_conv_layer, activation=activation,
- norm_layer=norm_layer)]
-
- ### upsample
- for i in range(n_downsampling):
- mult = 2 ** (n_downsampling - i)
- model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
- model += [nn.ReflectionPad2d(3),
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
- if add_out_act:
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
- self.model = nn.Sequential(*model)
-
- def forward(self, input):
- return self.model(input)
-
-class ConfigGlobalGenerator(nn.Module):
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
- n_blocks=3, norm_layer=nn.BatchNorm2d,
- padding_type='reflect', conv_kind='default',
- deconv_kind='convtranspose', activation=nn.ReLU(True),
- up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
- add_out_act=True, max_features=1024,
- manual_block_spec=[],
- resnet_block_kind='multidilatedresnetblock',
- resnet_conv_kind='multidilated',
- resnet_dilation=1,
- multidilation_kwargs={}):
- assert (n_blocks >= 0)
- super().__init__()
-
- conv_layer = get_conv_block_ctor(conv_kind)
- resnet_conv_layer = functools.partial(get_conv_block_ctor(resnet_conv_kind), **multidilation_kwargs)
- norm_layer = get_norm_layer(norm_layer)
- if affine is not None:
- norm_layer = partial(norm_layer, affine=affine)
- up_norm_layer = get_norm_layer(up_norm_layer)
- if affine is not None:
- up_norm_layer = partial(up_norm_layer, affine=affine)
-
- model = [nn.ReflectionPad2d(3),
- conv_layer(input_nc, ngf, kernel_size=7, padding=0),
- norm_layer(ngf),
- activation]
-
- identity = Identity()
-
- ### downsample
- for i in range(n_downsampling):
- mult = 2 ** i
- model += [conv_layer(min(max_features, ngf * mult),
- min(max_features, ngf * mult * 2),
- kernel_size=3, stride=2, padding=1),
- norm_layer(min(max_features, ngf * mult * 2)),
- activation]
-
- mult = 2 ** n_downsampling
- feats_num_bottleneck = min(max_features, ngf * mult)
-
- if len(manual_block_spec) == 0:
- manual_block_spec = [
- DotDict(lambda : None, {
- 'n_blocks': n_blocks,
- 'use_default': True})
- ]
-
- ### resnet blocks
- for block_spec in manual_block_spec:
- def make_and_add_blocks(model, block_spec):
- block_spec = DotDict(lambda : None, block_spec)
- if not block_spec.use_default:
- resnet_conv_layer = functools.partial(get_conv_block_ctor(block_spec.resnet_conv_kind), **block_spec.multidilation_kwargs)
- resnet_conv_kind = block_spec.resnet_conv_kind
- resnet_block_kind = block_spec.resnet_block_kind
- if block_spec.resnet_dilation is not None:
- resnet_dilation = block_spec.resnet_dilation
- for i in range(block_spec.n_blocks):
- if resnet_block_kind == "multidilatedresnetblock":
- model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
- conv_layer=resnet_conv_layer, activation=activation,
- norm_layer=norm_layer)]
- if resnet_block_kind == "resnetblock":
- model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
- conv_kind=resnet_conv_kind)]
- if resnet_block_kind == "resnetblock5x5":
- model += [ResnetBlock5x5(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
- conv_kind=resnet_conv_kind)]
- if resnet_block_kind == "resnetblockdwdil":
- model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
- conv_kind=resnet_conv_kind, dilation=resnet_dilation, second_dilation=resnet_dilation)]
- make_and_add_blocks(model, block_spec)
-
- ### upsample
- for i in range(n_downsampling):
- mult = 2 ** (n_downsampling - i)
- model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
- model += [nn.ReflectionPad2d(3),
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
- if add_out_act:
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
- self.model = nn.Sequential(*model)
-
- def forward(self, input):
- return self.model(input)
-
-
-def make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs):
- blocks = []
- for i in range(dilated_blocks_n):
- if dilation_block_kind == 'simple':
- blocks.append(ResnetBlock(**dilated_block_kwargs, dilation=2 ** (i + 1)))
- elif dilation_block_kind == 'multi':
- blocks.append(MultidilatedResnetBlock(**dilated_block_kwargs))
- else:
- raise ValueError(f'dilation_block_kind could not be "{dilation_block_kind}"')
- return blocks
-
-
-class GlobalGenerator(nn.Module):
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
- padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
- up_norm_layer=nn.BatchNorm2d, affine=None,
- up_activation=nn.ReLU(True), dilated_blocks_n=0, dilated_blocks_n_start=0,
- dilated_blocks_n_middle=0,
- add_out_act=True,
- max_features=1024, is_resblock_depthwise=False,
- ffc_positions=None, ffc_kwargs={}, dilation=1, second_dilation=None,
- dilation_block_kind='simple', multidilation_kwargs={}):
- assert (n_blocks >= 0)
- super().__init__()
-
- conv_layer = get_conv_block_ctor(conv_kind)
- norm_layer = get_norm_layer(norm_layer)
- if affine is not None:
- norm_layer = partial(norm_layer, affine=affine)
- up_norm_layer = get_norm_layer(up_norm_layer)
- if affine is not None:
- up_norm_layer = partial(up_norm_layer, affine=affine)
-
- if ffc_positions is not None:
- ffc_positions = collections.Counter(ffc_positions)
-
- model = [nn.ReflectionPad2d(3),
- conv_layer(input_nc, ngf, kernel_size=7, padding=0),
- norm_layer(ngf),
- activation]
-
- identity = Identity()
- ### downsample
- for i in range(n_downsampling):
- mult = 2 ** i
-
- model += [conv_layer(min(max_features, ngf * mult),
- min(max_features, ngf * mult * 2),
- kernel_size=3, stride=2, padding=1),
- norm_layer(min(max_features, ngf * mult * 2)),
- activation]
-
- mult = 2 ** n_downsampling
- feats_num_bottleneck = min(max_features, ngf * mult)
-
- dilated_block_kwargs = dict(dim=feats_num_bottleneck, padding_type=padding_type,
- activation=activation, norm_layer=norm_layer)
- if dilation_block_kind == 'simple':
- dilated_block_kwargs['conv_kind'] = conv_kind
- elif dilation_block_kind == 'multi':
- dilated_block_kwargs['conv_layer'] = functools.partial(
- get_conv_block_ctor('multidilated'), **multidilation_kwargs)
-
- # dilated blocks at the start of the bottleneck sausage
- if dilated_blocks_n_start is not None and dilated_blocks_n_start > 0:
- model += make_dil_blocks(dilated_blocks_n_start, dilation_block_kind, dilated_block_kwargs)
-
- # resnet blocks
- for i in range(n_blocks):
- # dilated blocks at the middle of the bottleneck sausage
- if i == n_blocks // 2 and dilated_blocks_n_middle is not None and dilated_blocks_n_middle > 0:
- model += make_dil_blocks(dilated_blocks_n_middle, dilation_block_kind, dilated_block_kwargs)
-
- if ffc_positions is not None and i in ffc_positions:
- for _ in range(ffc_positions[i]): # same position can occur more than once
- model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
- inline=True, **ffc_kwargs)]
-
- if is_resblock_depthwise:
- resblock_groups = feats_num_bottleneck
- else:
- resblock_groups = 1
-
- model += [ResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation=activation,
- norm_layer=norm_layer, conv_kind=conv_kind, groups=resblock_groups,
- dilation=dilation, second_dilation=second_dilation)]
-
-
- # dilated blocks at the end of the bottleneck sausage
- if dilated_blocks_n is not None and dilated_blocks_n > 0:
- model += make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs)
-
- # upsample
- for i in range(n_downsampling):
- mult = 2 ** (n_downsampling - i)
- model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
- min(max_features, int(ngf * mult / 2)),
- kernel_size=3, stride=2, padding=1, output_padding=1),
- up_norm_layer(min(max_features, int(ngf * mult / 2))),
- up_activation]
- model += [nn.ReflectionPad2d(3),
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
- if add_out_act:
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
- self.model = nn.Sequential(*model)
-
- def forward(self, input):
- return self.model(input)
-
-
-class GlobalGeneratorGated(GlobalGenerator):
- def __init__(self, *args, **kwargs):
- real_kwargs=dict(
- conv_kind='gated_bn_relu',
- activation=nn.Identity(),
- norm_layer=nn.Identity
- )
- real_kwargs.update(kwargs)
- super().__init__(*args, **real_kwargs)
-
-
-class GlobalGeneratorFromSuperChannels(nn.Module):
- def __init__(self, input_nc, output_nc, n_downsampling, n_blocks, super_channels, norm_layer="bn", padding_type='reflect', add_out_act=True):
- super().__init__()
- self.n_downsampling = n_downsampling
- norm_layer = get_norm_layer(norm_layer)
- if type(norm_layer) == functools.partial:
- use_bias = (norm_layer.func == nn.InstanceNorm2d)
- else:
- use_bias = (norm_layer == nn.InstanceNorm2d)
-
- channels = self.convert_super_channels(super_channels)
- self.channels = channels
-
- model = [nn.ReflectionPad2d(3),
- nn.Conv2d(input_nc, channels[0], kernel_size=7, padding=0, bias=use_bias),
- norm_layer(channels[0]),
- nn.ReLU(True)]
-
- for i in range(n_downsampling): # add downsampling layers
- mult = 2 ** i
- model += [nn.Conv2d(channels[0+i], channels[1+i], kernel_size=3, stride=2, padding=1, bias=use_bias),
- norm_layer(channels[1+i]),
- nn.ReLU(True)]
-
- mult = 2 ** n_downsampling
-
- n_blocks1 = n_blocks // 3
- n_blocks2 = n_blocks1
- n_blocks3 = n_blocks - n_blocks1 - n_blocks2
-
- for i in range(n_blocks1):
- c = n_downsampling
- dim = channels[c]
- model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer)]
-
- for i in range(n_blocks2):
- c = n_downsampling+1
- dim = channels[c]
- kwargs = {}
- if i == 0:
- kwargs = {"in_dim": channels[c-1]}
- model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
-
- for i in range(n_blocks3):
- c = n_downsampling+2
- dim = channels[c]
- kwargs = {}
- if i == 0:
- kwargs = {"in_dim": channels[c-1]}
- model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
-
- for i in range(n_downsampling): # add upsampling layers
- mult = 2 ** (n_downsampling - i)
- model += [nn.ConvTranspose2d(channels[n_downsampling+3+i],
- channels[n_downsampling+3+i+1],
- kernel_size=3, stride=2,
- padding=1, output_padding=1,
- bias=use_bias),
- norm_layer(channels[n_downsampling+3+i+1]),
- nn.ReLU(True)]
- model += [nn.ReflectionPad2d(3)]
- model += [nn.Conv2d(channels[2*n_downsampling+3], output_nc, kernel_size=7, padding=0)]
-
- if add_out_act:
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
- self.model = nn.Sequential(*model)
-
- def convert_super_channels(self, super_channels):
- n_downsampling = self.n_downsampling
- result = []
- cnt = 0
-
- if n_downsampling == 2:
- N1 = 10
- elif n_downsampling == 3:
- N1 = 13
- else:
- raise NotImplementedError
-
- for i in range(0, N1):
- if i in [1,4,7,10]:
- channel = super_channels[cnt] * (2 ** cnt)
- config = {'channel': channel}
- result.append(channel)
- logging.info(f"Downsample channels {result[-1]}")
- cnt += 1
-
- for i in range(3):
- for counter, j in enumerate(range(N1 + i * 3, N1 + 3 + i * 3)):
- if len(super_channels) == 6:
- channel = super_channels[3] * 4
- else:
- channel = super_channels[i + 3] * 4
- config = {'channel': channel}
- if counter == 0:
- result.append(channel)
- logging.info(f"Bottleneck channels {result[-1]}")
- cnt = 2
-
- for i in range(N1+9, N1+21):
- if i in [22, 25,28]:
- cnt -= 1
- if len(super_channels) == 6:
- channel = super_channels[5 - cnt] * (2 ** cnt)
- else:
- channel = super_channels[7 - cnt] * (2 ** cnt)
- result.append(int(channel))
- logging.info(f"Upsample channels {result[-1]}")
- return result
-
- def forward(self, input):
- return self.model(input)
-
-
-# Defines the PatchGAN discriminator with the specified arguments.
-class NLayerDiscriminator(BaseDiscriminator):
- def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,):
- super().__init__()
- self.n_layers = n_layers
-
- kw = 4
- padw = int(np.ceil((kw-1.0)/2))
- sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
- nn.LeakyReLU(0.2, True)]]
-
- nf = ndf
- for n in range(1, n_layers):
- nf_prev = nf
- nf = min(nf * 2, 512)
-
- cur_model = []
- cur_model += [
- nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
- norm_layer(nf),
- nn.LeakyReLU(0.2, True)
- ]
- sequence.append(cur_model)
-
- nf_prev = nf
- nf = min(nf * 2, 512)
-
- cur_model = []
- cur_model += [
- nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
- norm_layer(nf),
- nn.LeakyReLU(0.2, True)
- ]
- sequence.append(cur_model)
-
- sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
- for n in range(len(sequence)):
- setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
-
- def get_all_activations(self, x):
- res = [x]
- for n in range(self.n_layers + 2):
- model = getattr(self, 'model' + str(n))
- res.append(model(res[-1]))
- return res[1:]
-
- def forward(self, x):
- act = self.get_all_activations(x)
- return act[-1], act[:-1]
-
-
-class MultidilatedNLayerDiscriminator(BaseDiscriminator):
- def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, multidilation_kwargs={}):
- super().__init__()
- self.n_layers = n_layers
-
- kw = 4
- padw = int(np.ceil((kw-1.0)/2))
- sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
- nn.LeakyReLU(0.2, True)]]
-
- nf = ndf
- for n in range(1, n_layers):
- nf_prev = nf
- nf = min(nf * 2, 512)
-
- cur_model = []
- cur_model += [
- MultidilatedConv(nf_prev, nf, kernel_size=kw, stride=2, padding=[2, 3], **multidilation_kwargs),
- norm_layer(nf),
- nn.LeakyReLU(0.2, True)
- ]
- sequence.append(cur_model)
-
- nf_prev = nf
- nf = min(nf * 2, 512)
-
- cur_model = []
- cur_model += [
- nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
- norm_layer(nf),
- nn.LeakyReLU(0.2, True)
- ]
- sequence.append(cur_model)
-
- sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
- for n in range(len(sequence)):
- setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
-
- def get_all_activations(self, x):
- res = [x]
- for n in range(self.n_layers + 2):
- model = getattr(self, 'model' + str(n))
- res.append(model(res[-1]))
- return res[1:]
-
- def forward(self, x):
- act = self.get_all_activations(x)
- return act[-1], act[:-1]
-
-
-class NLayerDiscriminatorAsGen(NLayerDiscriminator):
- def forward(self, x):
- return super().forward(x)[0]
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/js/overlay.js b/spaces/Jamkonams/AutoGPT/autogpt/js/overlay.js
deleted file mode 100644
index 1c99c72673330b8ea8cf037ef889233f2d4326be..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/js/overlay.js
+++ /dev/null
@@ -1,29 +0,0 @@
-const overlay = document.createElement('div');
-Object.assign(overlay.style, {
- position: 'fixed',
- zIndex: 999999,
- top: 0,
- left: 0,
- width: '100%',
- height: '100%',
- background: 'rgba(0, 0, 0, 0.7)',
- color: '#fff',
- fontSize: '24px',
- fontWeight: 'bold',
- display: 'flex',
- justifyContent: 'center',
- alignItems: 'center',
-});
-const textContent = document.createElement('div');
-Object.assign(textContent.style, {
- textAlign: 'center',
-});
-textContent.textContent = 'AutoGPT Analyzing Page';
-overlay.appendChild(textContent);
-document.body.append(overlay);
-document.body.style.overflow = 'hidden';
-let dotCount = 0;
-setInterval(() => {
- textContent.textContent = 'AutoGPT Analyzing Page' + '.'.repeat(dotCount);
- dotCount = (dotCount + 1) % 4;
-}, 1000);
diff --git a/spaces/Jojelf/dreamlike-photoreal-2.0/app.py b/spaces/Jojelf/dreamlike-photoreal-2.0/app.py
deleted file mode 100644
index 0d0ae718eacc495efdbb94276323b93eb3321f76..0000000000000000000000000000000000000000
--- a/spaces/Jojelf/dreamlike-photoreal-2.0/app.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import gradio as gr
-
-API_KEY=os.environ.get('HUGGING_FACE_HUB_TOKEN', None)
-
-article = """---
-This space was created using [SD Space Creator](https://huggingface.co/spaces/anzorq/sd-space-creator)."""
-
-gr.Interface.load(
- name="models/dreamlike-art/dreamlike-photoreal-2.0",
- title="""Dreamlike Photoreal 2.0""",
- description="""Demo for Dreamlike Photoreal 2.0 Stable Diffusion model.""",
- article=article,
- api_key=API_KEY,
- ).queue(concurrency_count=20).launch()
diff --git a/spaces/KennyUTC/BotChat/README.md b/spaces/KennyUTC/BotChat/README.md
deleted file mode 100644
index 62ca406fa76e9cd7afe17bb41398b4ebe69439ac..0000000000000000000000000000000000000000
--- a/spaces/KennyUTC/BotChat/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: BotChat
-emoji: 🌖
-colorFrom: blue
-colorTo: pink
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/hparams.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/hparams.py
deleted file mode 100644
index 8bcdb635a90a7700d4e133410268a897d3fd4a8c..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/hparams.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import ast
-import pprint
-import json
-
-class HParams(object):
- def __init__(self, **kwargs): self.__dict__.update(kwargs)
- def __setitem__(self, key, value): setattr(self, key, value)
- def __getitem__(self, key): return getattr(self, key)
- def __repr__(self): return pprint.pformat(self.__dict__)
-
- def parse(self, string):
- # Overrides hparams from a comma-separated string of name=value pairs
- if len(string) > 0:
- overrides = [s.split("=") for s in string.split(",")]
- keys, values = zip(*overrides)
- keys = list(map(str.strip, keys))
- values = list(map(str.strip, values))
- for k in keys:
- self.__dict__[k] = ast.literal_eval(values[keys.index(k)])
- return self
-
- def loadJson(self, dict):
- print("\Loading the json with %s\n", dict)
- for k in dict.keys():
- if k not in ["tts_schedule", "tts_finetune_layers"]:
- self.__dict__[k] = dict[k]
- return self
-
- def dumpJson(self, fp):
- print("\Saving the json with %s\n", fp)
- with fp.open("w", encoding="utf-8") as f:
- json.dump(self.__dict__, f)
- return self
-
-hparams = HParams(
- ### Signal Processing (used in both synthesizer and vocoder)
- sample_rate = 16000,
- n_fft = 800,
- num_mels = 80,
- hop_size = 200, # Tacotron uses 12.5 ms frame shift (set to sample_rate * 0.0125)
- win_size = 800, # Tacotron uses 50 ms frame length (set to sample_rate * 0.050)
- fmin = 55,
- min_level_db = -100,
- ref_level_db = 20,
- max_abs_value = 4., # Gradient explodes if too big, premature convergence if too small.
- preemphasis = 0.97, # Filter coefficient to use if preemphasize is True
- preemphasize = True,
-
- ### Tacotron Text-to-Speech (TTS)
- tts_embed_dims = 512, # Embedding dimension for the graphemes/phoneme inputs
- tts_encoder_dims = 256,
- tts_decoder_dims = 128,
- tts_postnet_dims = 512,
- tts_encoder_K = 5,
- tts_lstm_dims = 1024,
- tts_postnet_K = 5,
- tts_num_highways = 4,
- tts_dropout = 0.5,
- tts_cleaner_names = ["basic_cleaners"],
- tts_stop_threshold = -3.4, # Value below which audio generation ends.
- # For example, for a range of [-4, 4], this
- # will terminate the sequence at the first
- # frame that has all values < -3.4
-
- ### Tacotron Training
- tts_schedule = [(2, 1e-3, 10_000, 12), # Progressive training schedule
- (2, 5e-4, 15_000, 12), # (r, lr, step, batch_size)
- (2, 2e-4, 20_000, 12), # (r, lr, step, batch_size)
- (2, 1e-4, 30_000, 12), #
- (2, 5e-5, 40_000, 12), #
- (2, 1e-5, 60_000, 12), #
- (2, 5e-6, 160_000, 12), # r = reduction factor (# of mel frames
- (2, 3e-6, 320_000, 12), # synthesized for each decoder iteration)
- (2, 1e-6, 640_000, 12)], # lr = learning rate
-
- tts_clip_grad_norm = 1.0, # clips the gradient norm to prevent explosion - set to None if not needed
- tts_eval_interval = 500, # Number of steps between model evaluation (sample generation)
- # Set to -1 to generate after completing epoch, or 0 to disable
- tts_eval_num_samples = 1, # Makes this number of samples
-
- ## For finetune usage, if set, only selected layers will be trained, available: encoder,encoder_proj,gst,decoder,postnet,post_proj
- tts_finetune_layers = [],
-
- ### Data Preprocessing
- max_mel_frames = 900,
- rescale = True,
- rescaling_max = 0.9,
- synthesis_batch_size = 16, # For vocoder preprocessing and inference.
-
- ### Mel Visualization and Griffin-Lim
- signal_normalization = True,
- power = 1.5,
- griffin_lim_iters = 60,
-
- ### Audio processing options
- fmax = 7600, # Should not exceed (sample_rate // 2)
- allow_clipping_in_normalization = True, # Used when signal_normalization = True
- clip_mels_length = True, # If true, discards samples exceeding max_mel_frames
- use_lws = False, # "Fast spectrogram phase recovery using local weighted sums"
- symmetric_mels = True, # Sets mel range to [-max_abs_value, max_abs_value] if True,
- # and [0, max_abs_value] if False
- trim_silence = True, # Use with sample_rate of 16000 for best results
-
- ### SV2TTS
- speaker_embedding_size = 256, # Dimension for the speaker embedding
- silence_min_duration_split = 0.4, # Duration in seconds of a silence for an utterance to be split
- utterance_min_duration = 1.6, # Duration in seconds below which utterances are discarded
- use_gst = True, # Whether to use global style token
- use_ser_for_gst = True, # Whether to use speaker embedding referenced for global style token
- )
diff --git a/spaces/KyanChen/RSPrompter/mmdet/engine/hooks/memory_profiler_hook.py b/spaces/KyanChen/RSPrompter/mmdet/engine/hooks/memory_profiler_hook.py
deleted file mode 100644
index 3dcdcae0b669ade46026d28c46b35f35d90b504b..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/engine/hooks/memory_profiler_hook.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional, Sequence
-
-from mmengine.hooks import Hook
-from mmengine.runner import Runner
-
-from mmdet.registry import HOOKS
-from mmdet.structures import DetDataSample
-
-
-@HOOKS.register_module()
-class MemoryProfilerHook(Hook):
- """Memory profiler hook recording memory information including virtual
- memory, swap memory, and the memory of the current process.
-
- Args:
- interval (int): Checking interval (every k iterations).
- Default: 50.
- """
-
- def __init__(self, interval: int = 50) -> None:
- try:
- from psutil import swap_memory, virtual_memory
- self._swap_memory = swap_memory
- self._virtual_memory = virtual_memory
- except ImportError:
- raise ImportError('psutil is not installed, please install it by: '
- 'pip install psutil')
-
- try:
- from memory_profiler import memory_usage
- self._memory_usage = memory_usage
- except ImportError:
- raise ImportError(
- 'memory_profiler is not installed, please install it by: '
- 'pip install memory_profiler')
-
- self.interval = interval
-
- def _record_memory_information(self, runner: Runner) -> None:
- """Regularly record memory information.
-
- Args:
- runner (:obj:`Runner`): The runner of the training or evaluation
- process.
- """
- # in Byte
- virtual_memory = self._virtual_memory()
- swap_memory = self._swap_memory()
- # in MB
- process_memory = self._memory_usage()[0]
- factor = 1024 * 1024
- runner.logger.info(
- 'Memory information '
- 'available_memory: '
- f'{round(virtual_memory.available / factor)} MB, '
- 'used_memory: '
- f'{round(virtual_memory.used / factor)} MB, '
- f'memory_utilization: {virtual_memory.percent} %, '
- 'available_swap_memory: '
- f'{round((swap_memory.total - swap_memory.used) / factor)}'
- ' MB, '
- f'used_swap_memory: {round(swap_memory.used / factor)} MB, '
- f'swap_memory_utilization: {swap_memory.percent} %, '
- 'current_process_memory: '
- f'{round(process_memory)} MB')
-
- def after_train_iter(self,
- runner: Runner,
- batch_idx: int,
- data_batch: Optional[dict] = None,
- outputs: Optional[dict] = None) -> None:
- """Regularly record memory information.
-
- Args:
- runner (:obj:`Runner`): The runner of the training process.
- batch_idx (int): The index of the current batch in the train loop.
- data_batch (dict, optional): Data from dataloader.
- Defaults to None.
- outputs (dict, optional): Outputs from model. Defaults to None.
- """
- if self.every_n_inner_iters(batch_idx, self.interval):
- self._record_memory_information(runner)
-
- def after_val_iter(
- self,
- runner: Runner,
- batch_idx: int,
- data_batch: Optional[dict] = None,
- outputs: Optional[Sequence[DetDataSample]] = None) -> None:
- """Regularly record memory information.
-
- Args:
- runner (:obj:`Runner`): The runner of the validation process.
- batch_idx (int): The index of the current batch in the val loop.
- data_batch (dict, optional): Data from dataloader.
- Defaults to None.
- outputs (Sequence[:obj:`DetDataSample`], optional):
- Outputs from model. Defaults to None.
- """
- if self.every_n_inner_iters(batch_idx, self.interval):
- self._record_memory_information(runner)
-
- def after_test_iter(
- self,
- runner: Runner,
- batch_idx: int,
- data_batch: Optional[dict] = None,
- outputs: Optional[Sequence[DetDataSample]] = None) -> None:
- """Regularly record memory information.
-
- Args:
- runner (:obj:`Runner`): The runner of the testing process.
- batch_idx (int): The index of the current batch in the test loop.
- data_batch (dict, optional): Data from dataloader.
- Defaults to None.
- outputs (Sequence[:obj:`DetDataSample`], optional):
- Outputs from model. Defaults to None.
- """
- if self.every_n_inner_iters(batch_idx, self.interval):
- self._record_memory_information(runner)
diff --git a/spaces/LaynzKunz/Advanced-RVC-Inference/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py b/spaces/LaynzKunz/Advanced-RVC-Inference/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
deleted file mode 100644
index b2c592527a5966e6f8e79e8c52dc5b414246dcc6..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Advanced-RVC-Inference/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import parselmouth
-import numpy as np
-
-
-class PMF0Predictor(F0Predictor):
- def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
- self.hop_length = hop_length
- self.f0_min = f0_min
- self.f0_max = f0_max
- self.sampling_rate = sampling_rate
-
- def interpolate_f0(self, f0):
- """
- 对F0进行插值处理
- """
-
- data = np.reshape(f0, (f0.size, 1))
-
- vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
- vuv_vector[data > 0.0] = 1.0
- vuv_vector[data <= 0.0] = 0.0
-
- ip_data = data
-
- frame_number = data.size
- last_value = 0.0
- for i in range(frame_number):
- if data[i] <= 0.0:
- j = i + 1
- for j in range(i + 1, frame_number):
- if data[j] > 0.0:
- break
- if j < frame_number - 1:
- if last_value > 0.0:
- step = (data[j] - data[i - 1]) / float(j - i)
- for k in range(i, j):
- ip_data[k] = data[i - 1] + step * (k - i + 1)
- else:
- for k in range(i, j):
- ip_data[k] = data[j]
- else:
- for k in range(i, frame_number):
- ip_data[k] = last_value
- else:
- ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝
- last_value = data[i]
-
- return ip_data[:, 0], vuv_vector[:, 0]
-
- def compute_f0(self, wav, p_len=None):
- x = wav
- if p_len is None:
- p_len = x.shape[0] // self.hop_length
- else:
- assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
- time_step = self.hop_length / self.sampling_rate * 1000
- f0 = (
- parselmouth.Sound(x, self.sampling_rate)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=self.f0_min,
- pitch_ceiling=self.f0_max,
- )
- .selected_array["frequency"]
- )
-
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
- f0, uv = self.interpolate_f0(f0)
- return f0
-
- def compute_f0_uv(self, wav, p_len=None):
- x = wav
- if p_len is None:
- p_len = x.shape[0] // self.hop_length
- else:
- assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
- time_step = self.hop_length / self.sampling_rate * 1000
- f0 = (
- parselmouth.Sound(x, self.sampling_rate)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=self.f0_min,
- pitch_ceiling=self.f0_max,
- )
- .selected_array["frequency"]
- )
-
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
- f0, uv = self.interpolate_f0(f0)
- return f0, uv
diff --git a/spaces/Lbin123/Lbingo/src/components/chat-panel.tsx b/spaces/Lbin123/Lbingo/src/components/chat-panel.tsx
deleted file mode 100644
index 1fbc3c2bf05b914e0c229661832fbb560745f488..0000000000000000000000000000000000000000
--- a/spaces/Lbin123/Lbingo/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<
- ReturnType,
- | 'generating'
- | 'input'
- | 'setInput'
- | 'sendMessage'
- | 'resetConversation'
- | 'isSpeaking'
- | 'attachmentList'
- | 'uploadImage'
- | 'setAttachmentList'
- > {
- id?: string
- className?: string
-}
-
-export function ChatPanel({
- isSpeaking,
- generating,
- input,
- setInput,
- className,
- sendMessage,
- resetConversation,
- attachmentList,
- uploadImage,
- setAttachmentList
-}: ChatPanelProps) {
- const inputRef = React.useRef(null)
- const {formRef, onKeyDown} = useEnterSubmit()
- const [focused, setFocused] = React.useState(false)
- const [active, setActive] = React.useState(false)
- const [pin, setPin] = React.useState(false)
- const [tid, setTid] = React.useState()
- const voiceListening = useAtomValue(voiceListenAtom)
-
- const setBlur = React.useCallback(() => {
- clearTimeout(tid)
- setActive(false)
- const _tid = setTimeout(() => setFocused(false), 2000);
- setTid(_tid)
- }, [tid])
-
- const setFocus = React.useCallback(() => {
- setFocused(true)
- setActive(true)
- clearTimeout(tid)
- inputRef.current?.focus()
- }, [tid])
-
- React.useEffect(() => {
- if (input) {
- setFocus()
- }
- }, [input])
-
- return (
-